69265




LCSAR – The World Bank



Climate Change and
Agriculture in Latin
America, 2020-2050
Projected Impacts and Response to Adaptation Strategies




            Erick C.M. Fernandes, Ayat Soliman, Roberto
            Confalonieri, Marcello Donatelli, Francesco
            Tubiello,
Contents
Figures .............................................................................................................................................. 3
Tables ............................................................................................................................................... 6
Executive Summary ..................................................................................................................... 11
The AZS biophysical results .......................................................................................................... 11
   Wheat........................................................................................................................................ 12
   Soybean .................................................................................................................................... 12
   Maize ........................................................................................................................................ 13
   Rice ........................................................................................................................................... 13
   Economic impacts .................................................................................................................... 14
Major findings and conclusions ..................................................................................................... 15
Chapter 1. Introduction ............................................................................................................... 18
Chapter 2. The Knowledge Base on Climate Change Projections and Assessments of
Agricultural Impacts and Economic Implications .................................................................... 21
Climate change and agriculture ...................................................................................................... 21
Chapter 3. An Open-Access Climate-Crop Impact Agroecological Zone Simulation
Modeling Platform for Latin America and the Caribbean ...................................................... 23
AZS conceptual framework............................................................................................................ 23
The AZS-BioMA and applications................................................................................................. 26
AZS-BioMA inputs ........................................................................................................................ 27
   Climate data and weather generation ....................................................................................... 27
   Soils .......................................................................................................................................... 30
The AZS-BioMA modeling platform ............................................................................................. 33
Chapter 4. Estimates of Climate Change Impacts for Wheat, Soybean, Maize, and Rice in
Latin America (2020 and 2050) ................................................................................................... 35
Limitations...................................................................................................................................... 36
   Weather data ............................................................................................................................. 37
   Model calibration ..................................................................................................................... 37
   Soils .......................................................................................................................................... 37
   Effects of elevated carbon dioxide ........................................................................................... 37
   Production systems ................................................................................................................... 38
   Adaptation strategies ................................................................................................................ 38
Results ............................................................................................................................................ 38
   Wheat........................................................................................................................................ 39
   Soybean .................................................................................................................................... 41
   Maize ........................................................................................................................................ 44
   Rice ........................................................................................................................................... 47
   Agromanagement adaptation .................................................................................................... 50
Chapter 5. Conclusions and Policy Implications ....................................................................... 56
Helping policy makers assess the best options ............................................................................... 56
Next steps for World Bank and other donor agency investments, operations, and research ......... 57
Bibliography.................................................................................................................................. 60
Annex I. The Agroecological Zone Simulator and Applications.............................................. 66
Annex II. Crop Yield Simulation Results ................................................................................. 135

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Figures
Figure 3.1. Flow of information (green=inputs; orange=outputs) linking agroclimatic datasets to
   assessments of yield categories depending on land utilization type through dynamic
   biophysical simulations ............................................................................................................ 26
Figure 3.2. Projected mean surface temperature change as a function of IPCC SRES scenario,
   indicating mean projected values (solid colors) from a range of GCM simulations
   corresponding to each family of greenhouse gas emission scenario (grey bars) ..................... 28
Figure 3.3. Summer (December, January, February) temperature anomalies obtained by
   generating A1B scenarios (2020 on the left, 2050 on the right) with the Hadley GCM .......... 30
Figure 3.4. Drainage rate (whole profile) and saturated hydraulic conductivity (top soil). Crops,
   distributions (masks), and calendars ........................................................................................ 31
Figure 3.5. Crop masks (areas where crops are currently grown) used within the project ............ 32
Figure 4.1. Aggregate impacts on crop yields, with adaptation, computed by the AZS-BioMA
   platform under 2020 and 2050 NCAR GCM for the A1B scenario ......................................... 39
Figure 4.2. Wheat productivity shocks (Hadley A1B) to 2020 ...................................................... 40
Figure 4.3. Wheat productivity shocks (Hadley A1B) to 2050 ...................................................... 41
Figure 4.4. Soybean productivity shocks (Hadley A1B) to 2020 .................................................. 43
Figure 4.5. Soybean productivity shocks (Hadley A1B) to 2050 .................................................. 44
Figure 4.6. Maize productivity shocks (Hadley A1B) to 2020 ...................................................... 46
Figure 4.7. Maize productivity shocks (Hadley A1B) to 2050 ...................................................... 47
Figure 4.8. Rice productivity impacts (Hadley A1B) to 2020 ....................................................... 49
Figure 4.9. Rice productivity impacts (Hadley A1B) to 2050. ...................................................... 50
Figure 4.10. Maize productivity impacts (Hadley A1B) 2020 and 2050 ....................................... 51
Figure 4.11. Soybean productivity impacts (Hadley A1B) 2020 and 2050 ................................... 52
Figure I1. Geographical distribution of (a) maize Gray leaf spots (Cercospora zeae-maydis), (b)
   wheat leaf rust (Puccinia recondita), (c) soybean rust (Phakopsora pachyrizi), and (d) rice
   blast disease (Pyricularia oryzae) across Latin America ......................................................... 68
Figure I2. Possible use of environmental outputs for selecting specific production systems ........ 70
Figure I3. Level of prediction and level of empiricism in process-based models (redrawn from
   Acock and Acock 1991) ........................................................................................................... 72
Figure I4. The biophysical models implemented in the APES-BioMA and in the WARM-BioMA
   modeling solutions ................................................................................................................... 74
Figure I5. Model parameter editor interface showing the CropSyst parameters for
   Wheat_SouthAmerica_3 crop key (variety with medium crop cycle length) .......................... 75
Figure I6. Graphic data display interface showing leaf area index (m2 m-2) of wheat (potential,
   disease limited, water limited, frost limited, and with all limitations) simulated in a sample
   cell of Brazil with the APES-BioMA modeling solution ......................................................... 76
Figure I7. Graphic data display interface showing aboveground biomass (kg ha-1) of wheat
   (potential, disease limited, water limited, frost limited, and with all limitations) simulated in a
   sample cell of Brazil with the APES-BioMA modeling solution ............................................ 77
Figure I8. Model parameter editor interface showing the CropSyst parameters for
   Maize_SouthAmerica_4 crop key (variety with long crop cycle) ........................................... 78



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Figure I9. Graphic data display interface showing leaf area index (m2 m-2) curves of maize
   (potential, disease limited, water limited, frost limited, and with all limitations) simulated in a
   sample cell of Brazil with the APES-BioMA modeling solution ............................................ 79
Figure I10. Graphic data display interface showing aboveground biomass (kg ha-1) curves of
   maize (potential, disease limited, water limited, frost limited, and with all limitations)
   simulated in a sample cell of Brazil with the APES-BioMA modeling solution ..................... 80
Figure I11. Model parameter editor interface showing the CropSyst parameters for
   Soybean_SouthAmerica_2 crop key (variety with short crop cycle) ....................................... 81
Figure I12. Graphic data display interface showing leaf area index (m2 m-2) curves of soybean
   (potential, disease limited, water limited, frost limited and with all limitations) simulated in a
   sample cell of Brazil with the APES-BioMA modeling solution. Frost damages do not affect
   productivity in this simulation. ................................................................................................. 82
Figure I13. Graphic data display interface showing aboveground biomass (kg ha-1) curves of
   soybean (potential, disease limited, water limited, frost limited, and with all limitations)
   simulated in a sample cell of Brazil with the APES-BioMA modeling solution ..................... 83
Figure I14. Model parameter editor interface showing the WARM parameters for
   Rice_SouthAmerica_2 crop key (variety with short crop cycle) ............................................. 84
Figure I15. Graphic data display interface showing leaf area index (m2 m-2) curves of rice
   (potential, disease limited, water limited, and with all limitations) simulated in a sample cell
   of Argentina with the WARM-BioMA modeling solution ...................................................... 85
Figure I16. Graphic data display interface showing storage organs (kg ha-1) curves of rice
   (potential, disease limited, water limited, sterility cold limited, and with all limitations)
   simulated in a sample cell of Brazil with the APES-BioMA modeling solution ..................... 86
Figure I17. Graphic data display interface showing soil water content simulated in a sample cell
   of Brazil with the APES-BioMA modeling solution................................................................ 87
Figure I18. Graphic data display interface showing soil temperature simulated in a sample cell of
   Brazil with the APES-BioMA modeling solution .................................................................... 88
Figure I19. Model parameter editor interface showing the options for simulating the three
   pathogens: Cercospora Zeae-maydis (maize), Phakospora pachirizi (soybean), and Puccinia
   recondita (wheat) accounted for by the APES-BioMA modeling solution in this study ......... 89
Figure I20. Flow chart of the logic behind the UNIMI.Suitability feature for automatically
   selecting the most suitable approach according to the availability of data under specific
   conditions ................................................................................................................................. 91
Figure I21. Strategy diagram of the AZS-BioMA UNIMI.Suitability component ........................ 92
Figure I22. FAO EcoCrop response function for temperature suitability, using parameter values
   for maize ................................................................................................................................... 92
Figure I23. FAO EcoCrop response function for rainfall suitability using parameter values for
   maize ........................................................................................................................................ 93
Figure I24. Flow chart of the FAO EcoCrop suitability criterion .................................................. 93
Figure I25. Flow chart of the less favored areas suitability criterion (Eliasson criterion) ............. 95
Figure I26. Flow chart of the direct crop suitability discriminant criterion ................................... 97
Figure I27. Flow chart of the district criterion (to be coupled with one of the other methods) ..... 99
Figure I28. Screen-shot of the model parameter editor showing a configuration where all the
   Boolean parameters are set to true ......................................................................................... 100
Figure I29. Screenshot of the model parameter editor showing a configuration where
   UseWaterBalanceInformation is set to false .......................................................................... 100

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Figure I30. Screen-shot of the model parameter editor showing a configuration where
   UseSoilPhysicsInformation and UseSoilChemistryInformation are set to ―false‖ ................ 101
Figure I31. Screenshot of the model parameter editor showing a configuration where
   UseClimateInformation, UseSoilPhysicsInformation and UseSoilChemistryInformation are
   set to ―false‖ ........................................................................................................................... 101
Figure I32. Test results of the application of the multiple regression approach based on the
   Eliasson and others (2010) regressors, mapping percentage of crop presence: (A) all
   categories of regressors (climate, soil physics, soil chemistry, water balance) used; (B) water
   balance regressor not used; (C) soil physical and chemical information not used; (D) only
   water balance regressor used .................................................................................................. 102
Figure I33. Screenshot of the model parameter editor showing a configuration where
   PercentageNeighbouringThresholdForDistrictEffect parameter is set to 75% and
   RelativePercentageIncreaseDueToNeighbouring is set to 10% and the district criterion is
   activated.................................................................................................................................. 103
Figure I34. Test results of the application of the multiple regression approach based on the
   Eliasson and others (2010) regressors, mapping percentage of crop presence ...................... 103
Figure I35. RuleAirTemp object model diagram showing the parameters needed by this rule ... 104
Figure I36. RuleDate object model diagram showing the parameters needed by this rule .......... 105
Figure I37. RuleDatesWindow object model diagram showing the parameters needed by this rule
    ................................................................................................................................................ 105
Figure I38. RuleDatesWindowNoRain object model diagram showing the parameters needed by
   this rule ................................................................................................................................... 105
Figure I39. RuleSoilTempLayer object model diagram showing the parameters needed by this
   rule .......................................................................................................................................... 106
Figure I40. CropPlanting object model diagram showing the parameters needed by this impact 106
Figure I41. RuleIrrigatePAW object model diagram showing the parameters needed by this rule.
    ................................................................................................................................................ 106
Figure I42. RuleIrrigatePAWOnce object model diagram showing the parameters needed by this
   rule .......................................................................................................................................... 107
Figure I43. IrrigationSimple object model diagram showing the parameters needed by this impact
    ................................................................................................................................................ 107
Figure I44. Agro Management configuration generator interface showing how to set an agro
   management rule (a rule based on a relative date in this example) ........................................ 108
Figure I45. Agro Management configuration generator interface showing how to set an agro
   management impact (crop planting in this example) ............................................................. 108
Figure I46. Agro Management configuration generator interface showing how to plan an
   irrigation set of rules based on soil water content .................................................................. 109
Figure I47. Available options for setting an irrigation event ....................................................... 109
Figure I48. Agro Management configuration generator interface showing the diagram of the
   sample configuration built ...................................................................................................... 110
Figure I49. Crop distribution of the four simulated crops, with circles indicating areas selected for
   the calibration of AZS-Bioma modeling solutions................................................................. 111
Table I3. Reference data used for the calibration of crop models parameters describing
   morphological and physiological features of three groups of varieties for each crop ........... 112
Figure I50. Representation of meteorological data for 1990–99 for Mexico, Argentina, and Chile
   for wheat calibration ............................................................................................................... 114

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Figure I51. Reproducing the reference data about the wheat crop cycle, leaf area index, and yield
   for Mexico, Argentina, and Chile for wheat calibration ........................................................ 115
Figure I52. Representation of meteorological data for 1990–99 for Argentina, Brazil, and Mexico
   for maize calibration ............................................................................................................... 117
Figure I53. Reproducing the reference data about the maize crop cycle, leaf area index, and yield
   for Mexico, Argentina, and Brazil for maize calibration ....................................................... 118
Figure I54. Representation of meteorological data for 1990–99 for Argentina and Brazil for
   soybean calibration ................................................................................................................. 119
Figure I55. Reproducing the reference data about the soybean crop cycle, leaf area index, and
   yield for Argentina and Brazil for soybean calibration .......................................................... 120
Figure I56. Representation of meteorological data for 1990–99 for Argentina and Brazil for rice
   calibration ............................................................................................................................... 121
Figure I57. Reproducing the reference data about the rice crop cycle, leaf area index, and yield for
   Argentina and Brazil for rice calibration ................................................................................ 122
Figure I58. South American countries in which Cercospora zeae-maydis is present .................. 124
Figure I59. South American countries in which Phakopsora phachyrizi is present .................... 126
Figure I60. South American countries in which Puccinia recondita is present ........................... 127
Figure I61. South American countries in which Pyricularia oryzae is present ........................... 129
Figure I62. Occurrence, in percentage, of the attributes in the SOTERLAC 2.0......................... 130
Figure I63. Spatial distribution of the soil profiles of SOTERLAC 2.0 ...................................... 131


Tables
Table 3.1. Tonnage and economic productivity for the four crops analysed in the project ........... 33
Table I1. Soil and climate criteria for classifying land according to its suitability for generic
   agricultural activity................................................................................................................... 95
Table I2. Direct crop suitability discriminant for classifying an area according to its suitability for
   a particular crop ........................................................................................................................ 97
Table I4. CropSyst parameters used to describe crop morphological and physiological features 113
Table I5. WARM parameters describing rice morphological and physiological features ........... 113
Table I5. Parameterizations adopted for wheat ............................................................................ 115
Table I6. Parameterizations adopted for maize ............................................................................ 117
Table I7. Parameterizations adopted for soybean ........................................................................ 120
Table I8. Parameterizations adopted for rice ................................................................................ 122
Table I9. List of the main information enclosed in SOTERLAC 2.0 related to layers, profiles, and
   soil components ...................................................................................................................... 131
Table II1. Percentage impact (no adaptation) of climate change on wheat yields for potential,
   water limited, abiotic damaged, diseased, and actual production levels; projections for 2020
   ................................................................................................................................................ 135
Table II2. Percentage impact (no adaptation) of climate change on maize yields for potential,
   water limited, abiotic damaged, diseased, and actual production levels; projections for 2020
   ................................................................................................................................................ 136
Table II3. Percentage impact (no adaptation) of climate change on soybean yields for potential,
   water limited, abiotic damaged, diseased, and actual production levels; projections for 2020
   ................................................................................................................................................ 137
Table II4. Percentage impact (no adaptation) of climate change on rice yields for potential, water
   limited, abiotic damaged, diseased, and actual production levels; projections for 2020 ....... 138

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Table II5. Percentage impact (no adaptation) of climate change on wheat yields for potential,
   water limited, abiotic damaged, diseased, and actual production levels; projections for 2050
   ................................................................................................................................................ 139
Table II6. Percentage impact (no adaptation) of climate change on maize yields for potential,
   water limited, abiotic damaged, diseased, and actual production levels; projections for 2050
   ................................................................................................................................................ 140
Table II7. Percentage impact (no adaptation) of climate change on soybean yields for potential,
   water limited, abiotic damaged, diseased, and actual production levels; projections for 2050
   ................................................................................................................................................ 141
Table II8. Percentage impact (no adaptation) of climate change on rice yields for potential, water
   limited, abiotic damaged, diseased, and actual production levels; projections for 2050 ....... 142
Table II9. Percentage impact (with adaptation) of climate change on wheat yields for potential,
   water limited, abiotic damaged, diseased, and actual production levels; projections for 2020
   ................................................................................................................................................ 143
Table II10. Percentage impact (with adaptation) of climate change on maize yields for potential,
   water limited, abiotic damaged, diseased, and actual production levels; projections for 2020
   ................................................................................................................................................ 144
Table II11. Percentage impact (with adaptation) of climate change on soybean yields for potential,
   water limited, abiotic damaged, diseased, and actual production levels; projections for 2020
   ................................................................................................................................................ 145
Table II12. Percentage impact (with adaptation) of climate change on rice yields for potential,
   water limited, abiotic damaged, diseased, and actual production levels; projections for 2020
   ................................................................................................................................................ 146
Table II13. Percentage impact (with adaptation) of climate change on wheat yields for potential,
   water limited, abiotic damaged, diseased, and actual production levels; projections for 2050
   ................................................................................................................................................ 147
Table II14. Percentage impact (with adaptation) of climate change on maize yields for potential,
   water limited, abiotic damaged, diseased, and actual production levels; projections for 2050
   ................................................................................................................................................ 148
Table II15. Percentage impact (with adaptation) of climate change on soybean yields for potential,
   water limited, abiotic damaged, diseased, and actual production levels; projections for 2050
   ................................................................................................................................................ 149
Table II16. Percentage impact (with adaptation) of climate change on rice yields for potential,
   water limited, abiotic damaged, diseased, and actual production levels; projections for 2050
   ................................................................................................................................................ 150
Table II17. Application of coupled AZS-ENVISAGE platform: change in agricultural value
   added from climate–induced damages in the four focus crops (%) ....................................... 151




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Glossary of terms and abbreviations
Abbreviations       Definition                       Description (if needed)
and acronyms

Adaptation          Adjustment in natural or
                    human systems—in response to
                    actual or expected climatic
                    stimuli or their effects—to
                    moderate harm or exploit
                    beneficial opportunities (IPCC
                    2007)
Adaptive capacity   The ability of a system to
                    adjust to climate change
                    (including     variability    and
                    extremes),      to      moderate
                    potential damages, to take
                    advantage of opportunities, or
                    to cope with the consequences
                    (IPCC 2007)
AEZ                 Agroecological zone
AR4                 Fourth Assessment Report of Published in 2007
                    the Intergovernmental Panel on
                    Climate Change
AZS                 Agroecological zone simulator The agroecological zone simulator provides
                                                       biophysical representations of crop growth as a
                                                       function of agroclimatology and crop-field
                                                       management.
BioMA               Biophysical model applications
CGE                 Computable                 general
                    equilibrium model
CH4                 Methane                            A greenhouse gas
Climate change      Climate change refers to a Climate change may be due to natural internal
                    change in the state of the processes or external forcing, or to persistent
                    climate that can be identified anthropogenic changes in the composition of the
                    by changes in the mean or in atmosphere or in land use (IPCC 2007).
                    the variability of its properties
                    and that persists for an
                    extended       period—typically
                    decades or longer
CO2                 Carbon dioxide                     A greenhouse gas
CO2-e               Carbon dioxide equivalent          A measure used to compare non-CO2 gases with
                                                       CO2 based on their global warming potential
                                                       (GWP—see below)
ECMWF               European Centre for Medium-
                    Range Weather Forecasts
ENSO                El Niño Southern Oscillation
ENVISAGE            Environmental impacts and See CGE

                                                                                                    8
                sustainability applied general
                equilibrium model
GCM             General circulation models     A general circulation model is a mathematical
                                               model of the general circulation of a planetary
                                               atmosphere or ocean and based on equations for
                                               a rotating sphere with thermodynamic terms for
                                               various energy sources (radiation, latent heat).
                                               These equations are the basis for simulating the
                                               atmosphere or ocean of the Earth. Atmospheric
                                               and Oceanic GCMs (AGCM and OGCM) are
                                               key components of global climate models along
                                               with sea ice and land-surface components.
                                               GCMs and global climate models are widely
                                               applied for weather forecasting, understanding
                                               the climate, and projecting climate change.

GHG             Greenhouse gas                     Greenhouse gases are gaseous constituents of the
                                                   atmosphere, both natural and anthropogenic, that
                                                   absorb and emit radiation at specific
                                                   wavelengths within the spectrum of thermal
                                                   infrared radiation emitted by the Earth‘s surface,
                                                   atmosphere, and clouds. This property causes the
                                                   greenhouse effect (IPCC 2007).
GWP             Global warming potential           An estimate of the effectiveness of a gas in
                                                   trapping heat in the atmosphere relative to CO2
                                                   over a specific time horizon. As per IPCC, over
                                                   100 years, methane‘s GWP is 21 and nitrous
                                                   oxide is 310.
IPCC            Intergovernmental Panel on
                Climate Change
LUT             Land utilization types
N2O             Nitrous oxide                      A greenhouse gas
NCAR            National        Center       for
                Atmospheric Research
Probabilistic   The production of large            Specific probabilities may be assigned to
scenarios       ensembles of climate change        individual events or climate change impacts by
                scenarios enables the              incorporating model uncertainties within a large
                production of probability          model ensemble
                density functions to represent
                the range of projected change
                in a specific event
R&D             Research and development
RCMs            Regional climate models
SFLAC           Spanish Fund for Latin             The Fund is meant to provide resources to
                America and the Caribbean          enhance the impact of the Bank Group‘s
                                                   development activities
SRES            Special Report on Emissions
                Scenarios
UNFCCC          United Nations Framework     The UN body charged with implementing
                Convention on Climate Change climate change action globally

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Acknowledgements
This study was made possible by the generous contribution of the Spanish Ministry of Economy
and Finance, through the Spanish Fund for Latin America and the Caribbean (SFLAC).

The project was implemented by a Consortium led by the Consorzio Milano Ricerche, with the
participation of the European Commission Joint Research Center (EC-JRC).




   Consortium leader: Prof: Roberto Confalonieri ; Simone Bregaglio, Co-coordinator of the
   project "Development of an Adapter to run the APES Modelling Solutions into the BioMA
   Framework"

The project was made possible by accessing data and resources of the project
EUROCLIMA, granted by DG AIDCO, under the action of the JRC‘s Institute of Environmental
Sustainability (JRC-IES-MARS).




   Scientific leader: Dr. Marcello Donatelli



Special thanks are also due to Dominique van der Mensbrugghe from the World Bank‘s
Development Prospects Group (DECPG) for his inputs on coupling the climate-crop impact
modeling outputs to the Bank‘s CGE model (ENVISAGE) and to John Nash, Lead Economist, in
the Latin America and Caribbean Region.




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Executive Summary
The impacts of climate change on agriculture are projected to be significant in coming decades, so
response strategies and their likely costs should be evaluated now. Robust crop models are needed
to estimate those impacts on agricultural productivity regionally, nationally, and even sub-
nationally. But existing crop–climate change modeling platforms are not easily accessible to most
stakeholders in developing countries. So there is less testing of those models than there might be,
and there are fewer opportunities for further improvements based on local tests and emerging
data.

That is why this study produced an open-access crop-climate-economic impact modeling platform
for Latin America and the Caribbean that can be extended to other regions—and modified and
improved by users as new crop, climate, and economic datasets become available. The new
platform projects the likely impacts of agroclimatic factors on crop productivity on the basis of
climate projections from two general circulation models and couples it with an economic model
to derive and evaluate a range of climate-change scenarios and likely agricultural productivity and
economic impacts over the next several decades.

The open-access modeling platform developed in this study, the agroecological zone simulator
(AZS), provides biophysical representations of crop growth as a function of agroclimatology and
crop-field management. It is based on the biophysical model applications (BioMA) approach used
by the European Commission Joint Research Centre to investigate climate-change impacts in the
EU. The AZS simulations used:

   ï‚·   The baseline climate (1989 to today) re-sampled to 0.25 degree grid cells (25 kilometers at
       the horizon).
      Two general circulation models—the U.K. Met Office‘s Hadley3 model and the National
       Center for Atmospheric Research (NCAR) model.
   ï‚·   Two special report on emissions scenarios (SRES), A1b and B1, to represent respectively
       a high scenario (business as usual) and low scenario (near carbon dioxide stabilization at
       550 parts per million).
   ï‚·   Two time horizons for simulating risks to crop production, with little time for adaptation
       (2020) and with time for adaptation (2050).

The AZS biophysical results

The main results of the AZS simulations suggest that the prevailing and often expressed view that
Latin America and the Caribbean will continue to be the breadbasket of the future—stepping in to
supply grain to other regions affected by climate change—needs to be tempered and subjected to
further rigorous testing. The AZS estimates confirm and extend previous findings indicating that
the impacts of climate change on agriculture in the region could be significant even by 2020, with
rising risks to maize, soybean, and wheat production in most producing countries by 2050.
Encouragingly, however, adaptation interventions such as the targeted use of irrigation, the
development and use of improved varieties, and the change of sowing dates could significantly
reduce the projected negative impacts of plausible climate shocks. This finding emphasizes the
urgency for adequate investments in adaptation strategies to ensure that the projected shocks to

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agricultural productivity can be reduced. In most cases, such investments may take several
decades to begin having a positive impact.

It is important to note that the time windows in the study represent 30 year climate averages for
the decade in question, so ''2020'' is the average climate change for the period 2005-2034.
Moreover, the baseline for comparing climate regimes is not the 8 year period from 2012 to 2020,
but rather the longer, 35 year interval from 1985 (the midpoint of the 1960-1990 baseline
climatology used by the general circulation models) to 2020, the midpoint of the future time
window. The projected climate impacts on the target crops account for the crop phonological
sensitivities to temperature as well as the potentially large temperature-precipitation changes on
rain fed crops. The projected impacts do not include assumptions regarding technological
advances in the coming decades. To obtain more robust estimates of climate change impacts
relative to today will require additional socio-economic information and modeling that is beyond
the scope of this work.

Wheat

For wheat, yields could be significantly affected by climate change, regardless of the emission
scenario or general circulation model. Percentage yield declines are projected to be deeper in
Mexico, in the Caribbean region, and in the northeastern parts of the continent (Colombia and
Brazil). The projected limited water productivity for 2020 and 2050 could be lower than the
baseline, with southern and western countries less affected. Yield reductions due to the shortening
of the crop cycle leave fewer days to fill grains. The projected yield declines due to disease in
2020 and 2050 could also be significant. Frost damage is expected to affect wheat yields less
seriously in Chile, where shorter crop cycles could also reduce the crop exposure to pathogens,
thus also reducing the pressure of wheat leaf rust. With few exceptions, insufficient water could
affect wheat productivity more than other factors, highlighting the urgent need for developing
drought-tolerant wheat varieties (such as deeper rooting).

With adaptation, the projected yield impacts are markedly less negative for all the production
levels and scenarios considered, but low water availability still limits wheat productivity.
Although the use of genotypes with longer crop cycles could partly compensate for the effect of
climate change in reducing the grain-filling period, the longer crop cycle increases water demand
because of increased transpiration. Except in Chile, disease pressure is expected to fall
everywhere, even without adaptation strategies for leaf rust. Argentina could be most affected by
disease pressure, whereas insufficient water availability appears to be the major wheat yield-
reducing factor in Brazil and Chile.


Soybean

For soybean, yields could be reduced by climate change in 2020 and more so in 2050, though
with different magnitudes throughout the region. Yield losses could be large in Brazil (more than
30% from the baseline) but less pronounced in Argentina, Bolivia, Colombia, and Uruguay. This
can be explained by the greater impact of climate change in Brazil, where the crop cycle is
projected to be shorter than in other parts of Latin America, markedly shortening the soybean
grain-filling period. The impact of rust disease would not increase with warming, except in

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Colombia, where the AZS shows an increase for all combinations of general circulation model
(GCM) emission scenarios. This could be explained by the severity of the increase in temperature
in an already warm environment, given Colombia‘s proximity to the equator, which in turn could
lead to more favorable conditions for pathogens.

With adaptation—such as longer crop-cycle varieties, different sowing dates, and irrigation—the
impacts across all scenarios and time windows could be reduced, especially in Ecuador and
Uruguay. In Argentina the use of varieties with longer cycles could compensate for yield losses
due to climate impacts that reduce the period for crop growth and maturity.


Maize

For maize, climate change could reduce yields throughout Latin America, regardless of the
emission scenario or GCM. This is mainly due to the shorter grain-filling period not being
compensated for by the higher daily biomass accumulation rates and the CO2 fertilization effect.
The countries most affected are likely to be Brazil, Ecuador, Mexico, and Caribbean countries,
where maize is one of the main crops. The Hadley GCM produced the highest losses, except for
Brazil and Ecuador (and for the latter, only for the B1 scenario). Because the yield impacts of
climate change are highly heterogeneous across the region, national adaptation strategies will be
critical in mitigating productivity declines.

Adaptation, especially in the 2020 timeframe, could dampen negative impacts of climate change
on maize yields in most of the region, though yield declines could still be significant in major
maize-producing countries, like Mexico. Higher percentage declines were simulated for the
Hadley GCM than the NCAR, with the A1B emission scenario usually leading to the most severe
declines. Adaptation strategies could limit but not completely offset the climate-change damage to
maize production, even in countries where grey leaf spot disease appears as the most limiting
factor.


Rice

For rice, the AZS estimates show that productivity could, on average, increase across the region.
A major reason for this positive outlook appears to be related to the fact that rice is a
wetland/irrigated crop. Except for Brazil, Mexico, and the Caribbean, the 2020 and 2050
projections are encouraging, with higher productivity projected in most cases. Rice has higher
temperature growth requirements than other crops. Under the current climate (the baseline)
production is slightly reduced by photosynthesis being limited by suboptimal temperatures. With
warmer conditions the negative effect of the shorter grain-filling period would be counterbalanced
by higher biomass accumulation rates because of more favorable conditions for photosynthesis.

The result of these two opposite effects is a general increase in productivity, except in countries
already experiencing warm climates (where thermal conditions for photosynthesis are already
close to optimal and the reduced grain-filling period leads to declines in final yields). In low-
temperature areas (especially Uruguay) climate change could reduce the incidence of pre-
flowering cold shocks inducing sterility. Except for Brazil and the Caribbean the blast disease

                                                                                                13
pressure on the crop could ease, because of thermal and rainfall conditions less favorable for
Pyricularia grisea, the blast disease pathogen.

With adaptation—including the use of different varieties and different sowing dates—future
conditions could be decidedly favorable for rice across the region. Long-cycle genotypes allow
long grain-filling periods and high daily biomass accumulation rates, because of negligible
temperature limits to photosynthesis and the CO2 fertilization effect, higher for the C3 species
than for the C4 (such as maize).1


Economic impacts

The economic impacts of implementing the climate-induced agricultural productivity shocks in
the four focus crops were investigated by coupling of AZS biophysical results to the ENVISAGE
platform. (An example in chapter 4 shows the steps for implementing and potentially applying
such a powerful coupling.) Results were limited by the focus on Latin America, assuming no
impacts in the rest of the world. They thus are relevant to understanding first-order dynamics
following a perturbation, but they would need to be updated with more consistent impact signals
globally.

The aggregate impact of this partial shock could be as high as 1.7% of regional GDP in 2050
under the A1B emission scenario for the Hadley GCM, which produces the most severe yield
shocks in the AZS projections. Although the impact signal is strong, this seems a fairly large
decline in regional GDP, considering that the four affected crops would represent only about
1.3% of regional GDP in 2050 under the baseline scenario. Two reasons for such an outcome
could be climate change affecting other sectors and agriculture having a big (multiplier) effect on
other sectors. The projected impacts tend to accelerate between 2020 and 2050. They are larger
for the Hadley model than the NCAR model, and they tend to be higher for the A1B emission
scenario than the B1 emission scenario. The impacts on global output are negligible.

Most affected across all scenarios and GCMs are Argentina and Brazil, two of the region‘s largest
agricultural producers. Uruguay would be the only country that would see gains in most of the
scenarios—though under the more severe Hadley A1B scenario, even Uruguay could experience a
2.3% loss. Among those least affected are Chile, Ecuador, and Peru. Chile‘s agricultural sector,
small as a share of the total economy in the base year, declines over time—particularly for the
four focus crops. Ecuador and Peru have low production shares in the affected crops. Because this
study focuses on four crops, the impacts for other crops—such as tropical fruits, coffee, and
sugar, which are potentially important parts of GDP—are not included in the estimates.




1
  C3 photosynthesis is called C3 because the CO2 is first incorporated into a 3-carbon compound. It is more efficient
than C4 and CAM plants under cool and moist conditions and under normal light because requires fewer enzymes
and no specialized anatomy. Most plants are C3. C4 photosynthesis is called C4 because the CO2 is first incorporated
into a 4-carbon compound. It photosynthesizes faster than C3 plants under high light intensity and high temperatures.
Examples of C4 plants include maize, sugarcane, millet, and Brachiaria pasture grass.


                                                                                                                  14
The within-country distribution between agriculture and the rest of the economy would tend to
worsen in all countries in the region. In Argentina agricultural value added could drop by 7% in
2020 in the best case and by 32% in 2050 in the worst. The loss in agricultural value added is
significantly less in Brazil than in Argentina in 2050, even though the 2020 impacts are similar.
Some countries (such as Uruguay) may see a rise in agricultural value added.

In a worst case scenario for the four focus crops in Latin America and the Caribbean, the loss in
net export revenues could be $8–11 billion in 2020 and $30–52 billion in 2050. Brazil could bear
the greatest absolute burden followed by Argentina and then Mexico.

Major findings and conclusions

The ability to test dynamic interactions between agroclimatic factors and field management is
critical for policy makers assessing economically efficient investment options. This study
produced a robust set of linked modeling tools and a coherent set of linked databases to facilitate
the evaluation of dynamic interactions of high-resolution agroclimatic and field management
factors affecting crop growth and development. It allowed interactions with a detailed general
equilibrium model to include and test the constraints of realistic socioeconomic factors on
production and welfare.

For Latin America and the Caribbean the projected yield shocks on wheat, maize, and soybean
generated by the AZS platform could result in substantial negative economic impacts in the
aggregate—particularly for Argentina and Brazil, which are heavily invested in the focus crops of
this study.

The findings have several major policy implications.
   1. The estimated negative impacts could be significantly larger than what has previously
       been predicted for Latin America and the Caribbean—and for precisely the two major
       agricultural producers (Brazil and Argentina) expected to play a major role in the global
       food supply chain. This should be a red flag for policy makers deciding on both mitigation
       and adaptation investments to combat global warming via greenhouse gas emissions
       reductions in the LCR countries and globally.
   2. Despite the projected severity of climate change impacts for maize, soybean, and wheat,
       some findings were encouraging. For example, the study simulated the response to the use
       of simple adaptation strategies such as the use of improved varieties, change of sowing
       dates, and modest irrigation and found that although these strategies did not overcome the
       projected damages from climate change, they did reduce the yield shocks to a significant
       degree for some crops. Policy-relevant interventions related to adaptation strategies include:
           a. Allocating adequate research and development (R&D) resources to generate improved
               and adapted plant varieties—currently estimated to take at least a decade and to cost
               US$5–7 million for each new variety).
           b. Expanding high-efficiency irrigation to overcome moisture limitation during grain-
               filling, a major yield-limiting factor in the region for maize, soybean, and wheat. Less
               than 6% of Brazil‗s agriculture and less than 2% of Argentina‗s are irrigated yet these
               are the two countries projected to be hit most severely by climate change and moisture
               limitations for their major commodity crops.


                                                                                                    15
3. One surprise from the study is the estimated positive impact of climate change on rice
   productivity in the region, which highlights to the potential for increasing rice production,
   especially since rice now feeds about 3 billion people worldwide, and global supplies
   expected to tighten significantly in coming years. The key policy-relevant issues for rice
   futures in LCR include:
       a. Raising the current low yield levels of irrigated rice in LCR. Most of the region‗s rice
           is produced in the rainy season to ensure adequate supplies of water for most of the
           growing season. The yield plateau for rainy season rice is about 5 tons per hectare
           because sunlight is often reduced by clouds. If the rice could be produced during the
           dry season with adequate water capture and irrigation, the rice yield plateau could rise
           to between 8 and 12 tons per hectare.
       b. Ensuring adequate water supplies and water control in the landscape. Most of the
           region‗s countries have plans to expand hydroenergy as a part of national low-carbon
           growth strategies. Ensuring that new hydroenergy programs also include irrigation
           would enhance the adaptation capacity for agriculture. Policies that promote
           multipurpose hydro would also permit relocating rice and other agriculture in coastal
           and low-lying areas to areas less vulnerable to major floods and rising sea levels—an
           adaptation-mitigation win-win.
       c. Reducing the environmental footprint of rice. Because flooded rice is a major source
           of methane (a powerful greenhouse gas), adequate R&D funding and policy
           instruments will be needed to catalyze public-private partnerships to develop and
           extend rice field management strategies and the use of improved biological and new-
           generation ―high efficiency-low emissions‖ synthetic fertilizers.
4. The study finding that moisture limitations are likely to strongly influence the reduction of
   future crop yields further emphasizes the importance of current discussions at the global
   level to include soil carbon sequestration in any post-2012 climate agreement. The
   available empirical evidence for a wide variety of soil and agroecosystem types confirms
   that an increase in soil carbon can also result in significant improvements in soil moisture
   holding capacity and thereby reduce the need for irrigation. Appropriate policy and market
   signals are urgently needed to encourage land-based carbon sequestration to facilitate
   better soil and water use and result in more resilient agroecosystems. Sequestration above
   and below ground could also increase rural income opportunities through more diverse
   ecosystem services.
5. The AZS estimates of climate impacts on agricultural productivity at relatively high
   spatial resolution could provide key information to reduce uncertainty for risk-reducing
   insurance and microfinance instruments. Insurance (risk-sharing) is likely to be important
   in future adaptation decisions, whether through traditional indemnity-based insurance or
   through other options that may be more suitable for climate-based insurance, such as
   index-based schemes, weather derivatives, and catastrophe bonds.
6. If a large share of world agriculture faces similar impacts to the AZS projections for the
   region and the four crops studied, initial economic simulations project higher food prices
   than the baseline. So, investments and grants are urgently needed to expand the current
   portfolio of crops in the AZS platform to include subsistence crops, biofuels crops, and
   horticultural crops beyond wheat, soybean, maize, and rice. This will allow more robust
   assessments of the intrasectoral impacts and economic implications in the region and
   across regions.


                                                                                                16
7. Policy makers in the region and elsewhere need more realistic assessments of their relative
   comparative advantages in agriculture and their priorities for investments in adaptive
   strategies. All regions should urgently conduct similar CC-agriculture impact assessments
   to allow a more robust cross-regional comparison of climate change‘s impacts on food
   security, trade, and GDP. To help facilitate participatory and robust analyses, the open-
   access AZS platform developed in this study is now available for further refinement,
   scaling up, and testing in the Latin American and Caribbean Region as well as for rapid
   deployment to and use by other regions.




                                                                                           17
Chapter 1. Introduction
Latin America and the Caribbean is a big region with varied climate, ecosystems, populations,
and cultures. The Fourth Assessment Report of the Intergovernmental Panel on Climate Change
(2007) notes that climate change in the region will affect different ecosystems and sectors over
the coming decades, with specific impacts on agroecosystems. One is decreasing plant and animal
species diversity. A second is shifts in ecosystems composition, biome boundaries, and area
distributions. A third is reductions in the quantity and quality of irrigation water. A fourth is
increasing aridity and desertification. And a fifth is the increasing incidence and impacts of crop
pests and disease.

Agriculture in the region is likely to suffer larger and more direct impacts of climate change than
industry or services. Total economic damage estimated for 2100 ranges from $35 billion
(Mendelsohn and Williams 2004) to more than $100 billion (0.56% of GDP). Under pronounced
warming scenarios the projected losses could already be substantial by 2050 (de la Torre and
others 2009). For cropping systems Cline (2007), based on an average of four climate models, the
projected significant yield losses in Latin America, aggregating declines as follows: –19% for
higher income food-exporting countries; –13.5% for higher income food-importing countries, and
–17% for middle and low income countries. The IPCC AR4 report indicated significant crop yield
losses for Latin America and the Caribbean, including –30% for rainfed maize in Central America
and –15% in Brazil.

Based on the projected yield impacts, Seo and Mendelsohn (2008) estimated average potential
revenue losses to farming households from climate change of 12% for a mild climate-change
scenario in 2100 to 50% for a more severe scenario, after adaptive action by farmers. Mendelsohn
and others (2008) predicted changes in land values as a proxy for the decline in land productivity.
In Mexico, expected to suffer severe effects, the predicted fall in values was greater than the value
of the land itself for 30–85% of all farms, depending on the model and severity of warming.

Agronomic research indicates that the higher temperatures with climatic change will reduce the
production of many crop and livestock groups. Where there is water stress, heat stress or a
combination of the two, the world‘s cereal crops can be vulnerable to even minor changes in
temperature. The agronomy of all crops will be affected by both temperature and precipitation
changes and by the higher atmospheric concentration of carbon dioxide. Rice, for example, is
predicted to experience increased yield due to carbon dioxide fertilization at higher concentrations
(currently around 380 parts per million by volume). But the net yield increase turns negative as
temperature increases by 3 or 4°C. But these projections often hold precipitation constant, and it
is the seasonal availability of water that most heavily influences crop yield changes, perhaps
affecting the largest grain-growing areas in the Asian subcontinent (see, for example IPCC and
others 2007). So, the feedback impacts of climate change on the production of major crops, such
as rice and wheat, are therefore highly uncertain.

The IPCC reports (Reilly and others 1996; Gitay and others 2001; Easterling and others 2007)
review the results of available studies and find negative impacts on crop productivity and yields
for the tropics, but competing evidence for beneficial effects for the high latitudes. At +2 to +3
degrees agricultural prices are expected to be affected, but the impact ranges from –10 to +20%,

                                                                                                  18
depending on the model. But at +3 to +5 degrees agricultural prices are expected to increase 10–
40%, while cereal imports of developing countries are likely to increase 10–40% (Easterling and
others 2007).

In addition to the impacts of climate change through changes in climatic variables, projected
increases in the frequency and severity of extreme events pose serious threats to agricultural
production. Rosenzweig and others (2002) found significant additional impacts of climate change
on U.S. maize production, once effects of excessive soil moisture are included in simulations.
Raddatz (2009) computed that climatic disasters reduce per capita GDP by 0.6% on average, and
that drought and extreme temperatures are major drivers of such impacts (windstorms and
hurricanes having significant effects in Central America and the Caribbean). These data suggest
that agriculture is a major channel for transmitting the effects of climate change to the economy
(de la Torre and others 2009). If the trends of the past four decades continue, climatic disasters
could permanently reduce GDP 2% over a decade.

One study used the inputs of local stakeholders and local experts in diverse agroecological zones
in Latin America to develop regional climate change action plans based on identifying and
prioritizing improved adaptation strategies to climate change (Lee and others 2009). A key
finding is the need for local communities to have access to information and decision support
systems—such as early warning systems (for climate forecasts, extreme weather events, and pest
and disease outbreaks), climate risk maps, and geographic information systems.

The long-term sustainability of agroecosystems and associated livelihoods is unattainable without
sound adaptation strategies. These strategies will need to incorporate not only changes to existing
cropping systems but also point to alternative production systems and environmental services—
and production landscapes for enhanced environmental, social, and economic resilience. The
following questions for decision makers will revolve around monitoring, planning, and
implementation in the coming decades:

   ï‚·   What specific climate changes are expected over time, and what will be their regional
       distribution?
   ï‚·   Which land and water management adaptation practices can best minimize the expected
       impacts, and how can resources be mobilized regionally to implement them?
   ï‚·   What are the expected effects of the anticipated sectoral impacts on trade, employment,
       poverty, and inequality within and across countries?

To answer these questions and develop effective adaptation strategies requires robust and
quantitative assessment tools that can estimate climate-change risks and vulnerabilities for land
use systems, especially within a portfolio of development projects. Once developed, the tools can
then identify and assess opportunities, risks, and vulnerabilities—and help prioritize and
coordinate a range of adaptation actions.

The goal of this study is to enhance regional knowledge and capacity to simulate and assess the
impact of climate change on agroecological zones and land use to 2050. The main objectives are:




                                                                                                19
   ï‚·   To develop a modeling platform for the impact of climate change on agricultural
       productivity (the agroecological zone simulator, or AZS) that is open-access and
       transparent for its data, components, and simulation capabilities.

      To test the platform and derive climate change–crop impact estimates for Latin America
       and the Caribbean for 2020 and 2050.

      To couple the crop impact estimates from the AZS platform with the World Bank‘s
       environmental impacts and sustainability applied general equilibrium model to estimate
       the possible economic impacts of climate change.

The modeling summarizes the impact of climate change on four major crops for Latin America
and the Caribbean—corn, rice, soybean, and wheat—and assesses the economic policy
implications.




                                                                                          20
Chapter 2. The Knowledge Base on Climate Change Projections
and Assessments of Agricultural Impacts and Economic
Implications

Climate change and agriculture

Based on a range of global climate models and development scenarios, it is estimated that the
Earth could experience a global warming of 1.4 to 5.8 °C over the next 100 years. The increasing
atmospheric concentration of carbon dioxide (CO2) and other greenhouse gases is one driver of
global warming. Agriculture both affects and is affected by global warming. Agricultural
emissions of nitrous oxide from fertilizer and manure, of methane from ruminant livestock and
flooded rice, and of CO2 from associated land use and land cover change (deforestation)
contribute about 30% of greenhouse gas emissions to the atmosphere. Such changes in
atmospheric carbon dioxide concentration—and in temperature and rainfall—can influence crop
productivity as follows:

    ï‚·   In general, and assuming no other limiting factors, higher levels of CO2 should stimulate
        photosynthesis for the majority of crop species globally, particularly for wheat, rice,
        barley, cassava, and potato and especially in cooler and wetter habitats. Positive but
        smaller effects on yields should be observed for tropical crops as maize, sugar cane,
        sorghum, and millet, important for the food security of many developing countries and for
        pasture and forage grasses. This is commonly referred to as the ―carbon fertilization
        effect.‖ In reality, however, the negative impacts of the increased crop water requirements
        and increasing incidence of pests may negate any positive effects of CO2 fertilization.

    ï‚·   Temperature influences crop production patterns directly and indirectly. For example,
        higher temperatures may benefit those crops periodically affected by freezes and chills
        but reduce yields of crops that need cool temperatures. Temperature indirectly affects
        crop growth and yields through its effect on crop water demand and on pest cycles and
        invasion dynamics. Of special concern is the expansion of pest ranges into new latitudes
        and altitudes, resulting in situations where farmers and crops would be face unfamiliar
        pests.

    ï‚·   Climate models estimate that both the frequency and intensity of rainfall events will
        change. Although some regions may become wetter, other drought-prone regions may
        suffer longer and more severe dry spells. As rainfall patterns change, there will be impacts
        on snow accumulation at high altitudes and latitudes and changes in surface rainfall runoff,
        with possible reductions of soil moisture and increases in soil erosion and land
        degradation.

Any shock that hits agriculture will also trigger a whole set of responses in the socioeconomic
system—from farm to the global economy. These responses are essentially adaptation strategies
to changing environment and economic conditions. They are of two major kinds: autonomous


                                                                                                 21
reactions, largely outcomes of existing self-regulatory processes, and planned policy
interventions. Adaptation approaches from the farm to national and global scales include:

   ï‚·   Farmers switch to more pest-resistant or drought-tolerant varieties, install irrigation to
       overcome drought and irregular rainfall, and improve drainage to mitigate flooding.

   ï‚·   Countries substitute factors of production in response to changing prices as a function of
       scarcity of one or more of the resources.

   ï‚·   The agricultural sector in different countries is linked through the global economic system
       through flows of production, goods, and services factors. Climate-change shocks to
       agriculture are likely to differ across countries because of nation-specific environmental,
       socioeconomic, and institutional factors. These asymmetries translate into different price
       changes for domestic goods and factors, which in turn stimulate international trade flows
       that may benefit some countries and damage others, working both as buffers against or
       multipliers of the initial impact.

For each of the three adaptation levels, autonomous adaptive responses can be strengthened or
modified through specific national policies, sectoral development strategies, and environmental
laws. Given the complexity of the numerous drivers, responses, and interactions, most
methodological approaches involve various types of modeling and simulation. For example, any
reasonably comprehensive effort to investigate the effect of climate change on agriculture should
in principle involve global climate models, environmental impact models, crop growth models,
land use models, and economic models—and include the following considerations:

   ï‚·   Changes in climate variables: temperature increases and variability, increases in CO2
       concentration, and changes in precipitation patterns.

      Climate change–induced environmental consequences, such as changes in land quality,
       water availability, and the frequency and intensity of extreme events.

   ï‚·   Physiological effects on crop growth and production characteristics.

   ï‚·   Farm-level adaptation strategies, such as varietal changes, changes in sowing timing, and
       irrigation.

   ï‚·   Impacts of the main economic adjustment mechanisms, such as price effects and shifts in
       domestic and international supply and demand.

   ï‚·   Feedbacks of the changed socioeconomic and biophysical conditions to climate.




                                                                                               22
Chapter 3. An Open-Access Climate-Crop Impact Agroecological
Zone Simulation Modeling Platform for Latin America and the
Caribbean
A major output of this study is a robust agroecological zone simulator model platform that
provides the framework for assembling relevant agroclimatology datasets. It includes equations
for data manipulation by the user, calculations of generic crop suitability and water balances, and
climate-crop mapping tools. It facilitates the evaluation of changes in cropping patterns and
growing seasons as a function of projected changes in temperature, precipitation, and evaporative
demands. And it permits investigations of adaptation potentials through simplified adaptive
actions—such as improving drought and flood tolerant varieties, limiting irrigation, and altering
cropping activities (such as sowing dates) in response to shifts in the start of the rainy season.

The agroecological zone simulator (AZS) platform fills a significant gap identified by the Fourth
Assessment Report of the Intergovernmental Panel on Climate Change (2007) and various studies
cited in the previous chapter, highlighting the urgent need for improved estimates of crop yields
in coming decades as a function of interannual climate variability and extreme events and of the
impacts of pest and disease. In addition, the IPCC AR4 also called for existing crop modeling
platforms, including those coupling of biophysics and economics, to be more transparent and
accessible to end users, so that their assumptions and applications could be tested more
extensively and so that their use base could be enriched with contributions from researchers and
users globally.

AZS conceptual framework

Because of the complex interactions between climate, agroecosystems and agromanagement at
farm to regional levels, assessment studies for agriculture rely on a suite of crop models and land
management decision tools (Tubiello and Ewert 2002). For example, assessments have used
dynamic crop models such as the Decision Support System for Agrotechnology Transfer (Tsuji
and others 1994), the Erosion-Productivity Impact Calculator (Williams 1995), and ecosystem
models modified to include some agriculture details, such as TEM (Feltzer and others 2004).
These models compute harvest yields, crop growth, and plant water dynamics as a function of
specialized databases containing soil, climate, and management information.

Dynamic crop models are very detailed at the plant functional and agromanagement level,
differentiating growth (phonological) stages of a crop, and are thus able to capture details
affecting final yield, which are often missed by ecosystem models. The first set of crop models
allows for simulation of more realistic field management activities such as water and fertilizer
management and sowing and harvesting operations. For these reasons, dynamic crop models
require many input data for calibration and validation. They perform best at local to national
scales, if sufficiently detailed ―representative sites‖ can be found to cover the area of study. By
contrast, simplified ecosystem models use generalized crop algorithms. They thus are better
suited to large-scale simulations, requiring less detailed input data. But they may be prone to
larger errors and validation problems, due to the lack of both crop and management detail
(Fischer and others 2005).

                                                                                                23
Additional efforts include statistical approaches, where historical production and yield databases
are analyzed in detail against climate databases, to arrive at simple, often one-dimensional
equations that relate regional or national climate regimes to production (Lobell and others 2008).
These can be powerful, especially considering the paucity of data to validate more complicated
models in key study areas in developing countries. But their ability to capture true climate-crop
dynamics under climate regimes outside current experiences is limited.

Still other efforts include empirical approaches, where surface response functions are derived
from more detailed dynamic crop models runs, and then used as a sort of look-up table when
running more extended regional or global simulations. These too can be quite powerful, because
they allow assessing large regional areas in detail while saving on the number of simulations and
thus on the computer power necessary with the full models. But surface responses lack
information on how interactions of key processes may change under different climate regimes. In
the end both statistical and empirical approaches offer poor representations of the agroclimatic
and biophysical processes of importance to crop yields.

By contrast, an agroecological zone (AEZ) modeling framework, synthesizes essential
components of the dynamic crop and ecosystem models described above, offering ways to merge
dynamic details with large-scale outlooks. It uses enough detailed agronomic-based knowledge to
simulate land resource availability and use, farm-level management options, and crop production
potentials. It also uses detailed spatial biophysical and socioeconomic datasets to distribute its
computations at fine grid intervals over large areas, including in some efforts the entire globe
(Fischer and others 2002a). This land-resource inventory is used to assess, for specified
management conditions and levels of inputs, the suitability of crops in rainfed and irrigated
conditions—and to quantify the expected attainable production of cropping activities relevant to
specific agroecological contexts in the study area. The characterization of land resources includes
climate, soils, landform, and present land cover. Crop modeling and environmental matching
procedures identify crop-specific environmental limitations, under various input levels and
management conditions (Fischer and others 2005).

In general, an AEZ framework is characterized by the following basic elements:

   ï‚·   Selecting agricultural production systems with defined input and management
       relationships, and crop-specific environmental requirements and adaptability
       characteristics. These are termed land utilization types (LUT).
   ï‚·   Combining geo-referenced climate, soil, and terrain data in a land-resource database.
   ï‚·   Accounting for spatial land use and land cover, including forests, protected areas,
       population distribution and density, and land for habitation and infrastructure.
   ï‚·   Calculating potential agronomically attainable yields and matching crop and LUT
       environmental requirements with the respective environmental characteristics in the land
       resource database, by land unit and grid cell.
   ï‚·   Assessing crop suitability and land productivity of cropping systems, leading to the AEZ
       simulations.



                                                                                                24
The best known of such efforts is the AEZ FAO/IIASA model, used in a variety of global and
regional assessment studies over the past 20 years (Fischer and others 2002a; Tubiello and Fisher
2007). Coupled to a partial equilibrium economic model, it has been the foundation for many
seminal papers on food security and climate change (Rosenzweig and Parry 1994; Parry and
others 2004).

Although AEZ simulations can be validated in many places, its global outlook implies that
validation cannot be carried out at every grid point considered. One way to overcome this
validation and calibration bottleneck is to provide a transparent platform that could be accessed
by users around the world to allow for continual enrichment of the underlying databases and
validation checks. The AZS output presented here builds substantially on the FAO/IIASA effort
while also making the new platform fully transparent and available for use and field validation by
country and regional stakeholders.

Specifically, the user-friendly open-access AZS platform developed in this study provides for data
and model sharing among all interested member countries in Latin America and the Caribbean.
And it can be readily extended to other regions of the world. Extending the AEZ framework, the
AZS facilitates the analysis of impacts and adaptation responses by:

        ï‚·    Selecting agricultural production systems with defined input and management
             relationships, and crop-specific environmental requirements and adaptability
             characteristics.
        ï‚·    Combining geo-referenced climate, soil, and terrain data in a land resource database.
        ï‚·    Calculating the potential agronomically attainable yield and matching crop
             environmental and management requirements with the respective environmental
             characteristics in the land resource database, by grid cell.
            Computing water-limited, biotic factor–limited, abiotic factor–limited, and actual
             crop yields, by grid cell.
        ï‚·    Assessing crop suitability and land productivity of cropping systems.

Figure 3.1 provides a conceptual framework of the various components and data flows of the AZS
platform.




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Figure 3.1. Flow of information (green=inputs; orange=outputs) linking agroclimatic
datasets to assessments of yield categories depending on land utilization type through
dynamic biophysical simulations




The AZS-BioMA and applications
The core output of this study, the modeling platform AZS-BioMA, provides the biophysical
representation of crop development and growth, as a function of agroclimatology and
management. The AZS is based on the BioMA (Biophysical Model Applications) platform, an
extensible platform for running biophysical models on generic spatial units, based on discrete
conceptual units codified in software components (both for simulation engines and for user
interfaces). The guidelines followed in its development aimed at maximizing:

    ï‚·   Expansion and adaptation with new modeling solutions.
    ï‚·   Ease of customization in new environments.
    ï‚·   Ease of deployment (at national and local research and academic facilities).

This is the first time that such a coherent set of data and models are used to assess the impacts of
climate change on Latin America crops across countries using the same consistent methodology.
Simulations are carried out through modeling solutions, which are discrete simulation engines
where different models are selected and integrated to carry out simulations for a specific goal,
with each solution using extensible components. Third parties can extend BioMA independently


                                                                                                 26
by adding new modeling solutions, using components already in the application or using new
ones.

The crop models compute development, growth, and productivity of selected crops (wheat, maize,
soybean, rice) at each point over a 25 kilometers grid for Latin America, as a function of daily
weather variables, including minimum and maximum temperature, precipitation, and solar
radiation. Each of the grid level–results can be easily aggregated at any scale of interest.

The AZS-BioMA platform is briefly described here to provide a flavor of the kinds of data
categories and the way data are harnessed for desired outputs. A detailed description of the AZS-
BioMA platform is in annex I.


AZS-BioMA inputs


Climate data and weather generation
The climate database was developed in the following steps:

   ï‚·   Identifying a suitable and reliable historical climate database covering the study area.
   ï‚·   Selecting IPCC AR4 emission scenarios (A1B and B1) as inputs for two GCMs (Hadley
       and NCAR).
   ï‚·   Generating the baseline and of the climate change scenarios (using GCMs outputs)
       through a weather generator.

The historical climate data are those produced by the European Centre for Medium-Range
Weather Forecasts (ECMWF), an intergovernmental organization supported by 32 countries.
Among its main activities is the reanalysis of multidecade series of past observations.

Datain this project come from the ECMWF ERA-Interim, a global reanalysis of the data-rich
period since 1989 (www.ecmwf.int/research/era/do/get/era-interim). The ERA-Interim reanalysis
starts in January 1989 and provides meteorological data until today. The ERA-Interim data are,
for our purposes, resampled to 0.25 degree grid cells (25 km x 25 km) to be consistent with other
real time data, like outputs of the ECMWF global circulation model.

Variables available in the ECMWF ERA-Interim database are average surface air temperature,
maximum and minimum surface air temperature, precipitation, evapotranspiration (over water,
bare soil, and based on the Penman-Monteith method), global solar radiation, snow depth, average
wind speed, and water vapor pressure. Other variables, including hourly values, are derived using
the CLIMA libraries (Donatelli and others 2005, 2009; Bregaglio and others 2011).

A first phase involved the use of the weather generator to estimate parameters describing the
features of the climate for each cell, such as monthly and annual trends, level of continentality,
thermal excursion, and rainfall distribution. Once these parameters were estimated, they were
used to generate the baseline climate. The baseline—a series of climate data with the same
features as the historical ones—was regenerated to allow the most unbiased comparisons between
the results of biophysical models based on generated baseline and climate change scenarios.
                                                                                               27
Two IPCC AR4 scenarios (A1B and B1; IPCC 2000) were selected as inputs for two general
circulation models (GCMs): Hadley3 (Gordon and others 2000) and NCAR (Collins and others
2004). The GCMs are realizations of the emission scenario chosen. The Hadley3, maintained and
run by the U.K. Meteorological Office, was chosen because it is a de facto standard. The NCAR
model was chosen because it has been extensively evaluated in the Americas. The time span of
the analysis is 2020 and 2050. The A1B scenario projects rapid economic growth, a global
population peak in 2050 with a rapid introduction of new and more efficient technologies (that is,
the A1 business as usual storyline) combined with a balanced input between fossil and nonfossil
energy sources to support the technological changes envisaged (resulting in the A1B scenario).
The B1 scenario is based on the same storyline as in the A1, but foresees rapid changes in
economic structure that reduce material and carbon intensity by introducing clean and resource-
efficient technologies.

Without being the most extreme, the two emission scenarios selected for this study represent most
of the range of projected temperature increases over the coming decades. It is also worth noting
that for a given emission scenario, the simulation outputs from more than 10 available GCMs
overlap significantly with the two GCMs selected for this study. The emission scenarios differ at
the end of the century, much less at 2050, and very little at 2020, the latter two being the time
span for this study (figure 3.2).

Figure 3.2. Projected mean surface temperature change as a function of IPCC SRES
scenario, indicating mean projected values (solid colors) from a range of GCM simulations
corresponding to each family of greenhouse gas emission scenario (grey bars)




                                                                         IPCC              WG-I
                                                                     (2007)




                                                                                               28
To generate spatially distributed weather data for the biophysical crop models, the Climak
weather generator (Danuso 2002) was used, allowing the generation of synthetic, long-term daily
weather data series through the statistical analysis of shorter term observed weather data at any
location. At the same time, the statistical parameters extracted to characterize a given climate
regime, such as the baseline climate, are easily modified to obtain new synthetic weather series
representing future climate change regimes. This is done by using GCM-driven information, such
as projected mean changes in temperature and precipitation for a given future time window, under
a particular SRES scenario.

A first phase used the weather generator to estimate parameters describing the features of the
climate for each cell, such as monthly and annual trends, level of continentality, thermal
excursion, and rainfall distribution. These parameters, once estimated, generated the baseline
climate (without applying any GCM-derived information to the parameters) and the climate
scenarios (applying results from GCMs to specific parameters).

      The baseline—a series of climate data with the same feature of the historical ones was
       regenerated to allow the most unbiased comparisons of the results of biophysical models
       based on baseline and climate change scenarios (in both cases derived from a generation
       process).

   ï‚·   Regional climate models (RCMs) were not applied, as an intermediate step between the
       GCM outputs and the parameters of the weather generator, because we could not assess, in
       the time frame of the project, whether a homogenous set of models was available to cover
       the Central and Latin America. A heterogeneous set of RCMs, even assuming their ready
       availability, would likely have introduced a bias in the comparisons of different countries.

   ï‚·   The number of years generated is 10, not a larger number such as 50, due to the time and
       resource constraints of the project.

Figure 3.3 presents summer average daily thermal anomalies (°C; difference between climate
change scenario and baseline data) for Hadley-A1B for the two time frames.




                                                                                                29
Figure 3.3. Summer (December, January, February) temperature anomalies obtained by
generating A1B scenarios (2020 on the left, 2050 on the right) with the Hadley GCM




Soils
The soil dataset (Hoogenboom and others 2009) is derived from the updated version of the
―World Inventory of Soil Emission Potentials‖ (WISE version 1.1, Batjes 2002). The WISE 1.1
database was created to provide a basic set of uniform soil data for a wide range of global and
regional environmental studies (such as agroecological zoning and assessments of crop
production). The profiles collected in the database came from five main sources:

   ï‚·   ISIS 4.0, the Soil Information System (van Waveren and Bos 1988) of the ISRIC
       (International Soil Reference and Information Centre).
   ï‚·   SDB, the FAO Soil Database System (FAO 1989).
   ï‚·   A digital soil dataset compiled by the Natural Resources Conservation Service of the
       United States of America (Soil Survey Staff 1996).
   ï‚·   International data gathering activity coordinated by WISE project staff, with national soil
       survey organizations asked to supply descriptions and analysis of profiles representative of
       the units of the Soil Map of the World (FAO-UNESCO 1974).
   ï‚·   Suitable profiles from a survey and stored in the ISRIC library.

Note that the simulations limited to soil water (not considering nitrogen) are sensitive to basic soil
parameters derived from texture and soil depth, because they determine the hydraulic (water flow)
characteristics. A more detailed database that better represented actual soil depths and presence in
a given cell could improve the representativeness of simulations for that cell. But the differences
in the output would not differ markedly except for extremely shallow soils. Figure 3.4 provides
examples of soil water properties stored in the soil dataset.


                                                                                                   30
Figure 3.4. Drainage rate (whole profile) and saturated hydraulic conductivity (top soil).
Crops, distributions (masks), and calendars




The crops considered in the study are wheat, soybean, maize, and rice, fundamental to both food
security and economic trade for Latin America and globally. Indeed, the same set of crops was
used in a recent analysis of current impacts of climate change on food security (Lobell and others
2011). According to 2008 FAO statistics (http://faostat.fao.org), the crop with the most tonnage in
Latin America is sugarcane (table 3.1), even if it is second to soybean in economic production ($).
The second crop in tonnage is soybean. Rice and maize rank third and fourth in economic
production and fourth and third in tonnage. Wheat comes in at eighth in economic production and
sixth in tonnage. Crop masks for wheat, soybean, maize, and rice were derived from the SAGE
Center for Sustainability and the Global Environment—Nelson Institute of Environmental
Studies, University of Wisconsin-Madison (SAGE www.sage.wisc.edu/index.html) (figure 3.5).




                                                                                                31
Figure 3.5. Crop masks (areas where crops are currently grown) used within the project




Source: Crop calendars for Latin America and the Caribbean were downloaded from the SAGE Center for
Sustainability and the Global Environment database (www.sage.wisc.edu/download/sacks/crop_calendar.html).




                                                                                                      32
Table 3.1. Tonnage and economic productivity for the four crops analysed in the project
                                         Crop production (Bt)                  Economic production (M$)

Country                         Maize     Rice     Soybean      Wheat    Maize    Rice    Soybean    Wheat

Argentina                       22,017    1,246    46,238       8,508    2,042    258     9,859      1,234
Bolivia                         1,002     338      1,260        200      60       68      246        29
Brazil                          58,933    12,061   59,242       6,027    1,925    2,523   12,361     877
Chile                                                           1,238                                163
Colombia                                  2,792                                   577
Costa Rica                                248                                     52
Ecuador                         804       1,442                          26       299
Guyana                                    507                                     105
Guyana (Fr)                               9                                       2
Honduras                        536       49                             21       10
Nicaragua                       424       322                            45       67
Panama                                    301                                     63
Paraguay                        2,472     150      6,312        799      158      31      1,309      104
Peru                                      2,776                                   585
Suriname                                  183                                     34

Trinidad and Tobago                       2
Uruguay                                   1,330    880          1,288             278     180        187
Venezuela                       2,996     1,361                          187      189
Total                           89,184    25,117   113,932      18,060   4,464    5,143   23,954     2,595
Rank within Latin America       3         4        2            6        4        3       1          8
and the Caribbean


The AZS-BioMA modeling platform
The modeling platform represents a paradigm shift in current modeling approaches—for three
reasons:

    ï‚·     First, the concept of multiple options for simulation is made available and can be further
          extended, adding modeling approaches, such as the ones implemented in Decision Support
          System for Agrotechnology Transfer models.
    ï‚·     Second, because of the fine resolution used to implement models, users can implement
          variants of modeling approaches. In particular, given the goal of simulating crop growth
          under extreme weather conditions, a curvilinear response to hourly temperature of plant
          development phases and growth, with a decline beyond optimal temperature, is
          implemented (hourly temperatures are derived with good accuracy from daily maximum
          and minimum temperature data). This approach estimates suboptimal rates at high
          temperatures, reproducing biologically known patterns of responses to temperature, and
          leading to estimates of development and growth that are diversified with respect to the

                                                                                                           33
       known accumulation of growing degree days and the yield plateau response to
       temperature.
   ï‚·   Third, the software architecture allows users to easily simulate various production
       levels—such as potential water-limited, nitrogen-limited, and disease-limited
       productivity—thereby allowing deeper analysis of and insights from the production
       system simulated. In particular, biomass growth equations implemented in the current
       version of AZS-BioMA allow users to compute the impacts of elevated CO2 on crop
       growth and yield.

BioMA is the platform currently used at the European Commission Joint Research Centre to
investigate the impacts of climate change on crops in the EU-27, as well as in key production
regions of the world, including Russia and the Commonwealth of Independent States, Latin
America, China, India, and a few countries in Sub-Saharan Africa. Because BioMA is already a
multiregional platform, it can be scaled up fairly easily to provide more intensive coverage of
other regions.




                                                                                            34
Chapter 4. Estimates of Climate Change Impacts for Wheat,
Soybean, Maize, and Rice in Latin America (2020 and 2050)
Crop simulations for each of the four crops and each of the eight climate-change scenarios were
then performed and compared with the baseline results. Such simulation sets were made assuming
no adaptation (the same cultivars and agromanagement used under current conditions) and with
adaptation (allowing for modified plant and management conditions to minimize damage under
future climate regimes). Clearly, the first set of simulation results is in practice rather unrealistic,
given that farmers‘ responses to modified weather conditions are to be expected. Even so, the ―no
adaptation‖ set of results serves as a benchmark to measure the benefits of adaptation actions.

The adaptation solutions investigated here are quite simple, but it can be expected that farmers
will need some time to develop enough experience and confidence for their large-scale
application. And some solutions will require that infrastructure be in place at the time of need. For
both reasons, the adaptation scenarios should be fairly plausible for 2050, but more limited for
2020.

The general definition of adaptation tested through simulation in this project is given by changes
in agricultural management that farmers may implement to alleviate the negative impacts of the
weather scenarios. Adaptation by farmers will occur, to some extent, regardless of any action to
support or steer it from government or local authorities. So, although simulating impact
assessment for ―unchanged systems‖ is a prerequisite to getting insights about system behavior
with the target of developing adaptation strategies, its results should not be considered as one of
the possible ―future scenarios for agriculture.‖

Adaptation tests are run considering three factors:

      Genotype—the duration of the crop cycle evaluated is medium for the analysis of the
       baseline, whereas early and late maturity genotypes are also evaluated in the simulation of
       weather scenarios.
      Planting time—explored by testing the anticipation of planting dates.
      Water supply—implemented using the same rule-based model as the baseline simulation,
       parameterized to provide a medium level of water availability, as detailed in annex I.

Water supply was always active in simulations for irrigated and potentially irrigated crops (maize
and soybean in this study), while all combinations related to genotype and planting time were
explored. Crops were simulated in cells where their relative occupancy was 1% or more of the
agricultural area. The modeling capabilities of the platform allow simulating, for each crop,
adaptation strategies, weather scenarios, and different abstractions of production systems
identified as production levels:

   ï‚·   Potential production (P: crop growth solar radiation and temperature driven).
      Water–limited production (WL: all factors of P and water limitation).

                                                                                                     35
      Abiotic stress–limited production (AL: P and effects due to temperature stresses of
       extreme events for crops).
      Disease–limited production (DL: P and impact from one crop-specific disease).
      Multiple factors–limited production (MFL: P, WL, AL, and DL).

The simulation of potential production is useful to test responses not constrained either by
resources—as quantities—or by technology (or by both). So, estimating multiple factors–limited
production allows estimating the technological gap (for example, we do not use a pivot system to
irrigate weekly, so there are no more than four irrigation events per season by sprinkler) and
water–limited production (for example, no more than 300 millimeters of water available per
season). Notably, negative impacts of climate change on crop productivity can be minimized with
simple adaptation measures—that is, either by planting different genotypes and changing the
timing of sowing (same crop—we test both) or by changing crops (we provide only scenarios of
land suitability for crops).

When water–limited production was simulated, a rule-based agromanagement model to supply
water to crops was used. That is, adaptation for water use is included (adaptation is not
constrained by water availability beyond setting rules and is not constrained by technology). The
water–limited simulations estimate a possible technical adaptation, whereas context specific
constraints (such as no more than 300 millimeters per irrigation season and no more than three
irrigations) can be considered ex-post, evaluating the adaptation scenarios provided, or lead to
another run of simulations.

The simulation of disease–limited production does not include agromanagement to alleviate the
impact of possible increased pressure by plant pathogens due to climate change. These
simulations can be of direct use if no chemical can be applied in a given context; otherwise
simulation results would overestimate the impact of climate change neglecting possible
adaptation. In the latter case, economists could use the quantitative estimates of disease–limited
production in a semiqualitative fashion.

From the above, the assumption of water–limited production is that diseases, if affecting the crop,
will be controlled either chemically or genetically.

The choice of production systems and adaptation focus on the basic food commodity–based
production systems abstracted at the level of ―crops.‖ But the simulation of crop-disease impacts
is innovative.

Limitations

Several assumptions were made while using data and selecting specific modeling solutions and
designing the simulation experiments here. Such assumptions set the limits for using the results of
this analysis. They should be carefully evaluated to avoid introducing conceptual errors in the
final results of an integrated modeling chain, for which the crop biophysical simulations are an
input. The assumptions follow.




                                                                                                36
Weather data
Weather data refer to the ERA-Interim interpolation. The time series representing each 25 km x
25 km grid cell refers to flat land at the predominant elevation above sea level. This makes the
time series more representative of real systems, which are also more uniform in flat land areas.
But the representativeness is more critical for areas where slopes change within cell. In these
cases more detailed analysis using digital elevation models and a smaller spatial scale would
articulate more system performance. But given the target of the study, Latin America and the
Caribbean, the approximation can be considered acceptable.

Furthermore, general circulation models (GCMs) provide estimates primarily of mean
temperature, rainfall, and solar radiation. Given model requirements as inputs, the pattern of
variability of other variables (such as wind, and relative humidity) must also be used. It was kept
unchanged in data layers of scenarios of climate change in these simulations. In addition, GCM
outputs typically lack detailed and quantified indications of realistic changes in the frequency of
extreme events. For this reason, climate variability—the shape of higher order moments—was
kept at present values. The specific distributions—based on thresholds, such as the number of
temperature or water-related stress events—were modified in our time series. But one could
imagine developing climatic ―subscenarios,‖ where the variability of specific climate variables
could be changed in a sensitivity approach—using the features of the weather generator in the
platform.


Model calibration
Model calibration, on literature resources, generally make available reference data for large areas.
It needs to be refined by interacting with local experts and stakeholders, so that the right cultivars
and cropping systems could be simulated as opposed to the idealized types simulated here. For
this reason, although the general impact trends can be considered robust in terms of extensive
regional signals across all four crop types, specific crop-country results need to be interpreted
with caution, since they depend on the specific cultivars in this round of simulations. For weather
data, analysis at a finer spatial scale using local expertise would yield better results for each target
area.


Soils
As for weather data, soils were distributed on a flat surface—terrain. This may alter significantly
the soil–water balance in areas with steep terrain. And in areas where soils are differentiated,
ranging from high to low water-holding capacity, simulation results will represent only a limited
portion of actual results, though they capture the predominant features of the system.


Effects of elevated carbon dioxide
The effects of elevated carbon dioxide (CO2) on crop growth and yield included in the AZS-
BioMA platform are consistent with current findings (Tubiello and others 2008). Even so, it is
widely expected that CO2 responses in farmers‘ fields here will be lower than found
experimentally, so that the functions in the simulations here are likely to overestimate actual field
responses.


                                                                                                     37
Production systems
Production systems were abstracted at the level of ―crop,‖ ignoring possible structure of cropping
systems. If cropping systems were analyzed instead, crop performance in a given cell would result
from its performance in different rotations and under different inputs of resources. And because of
weather data resolution, model calibration, and soils, the simulation results are an abstraction of
production systems for the area—and should thus be compared with actual point data, with
caution. But the goal of the analysis is to estimate basic impact dynamics and adaptation
strategies for the large areas considered.


Adaptation strategies
The adaptation strategies considered basic technical options likely available to farmers today. So,
the alleviation of the impact of climate change was estimated on the basis of the same abstraction
of production system evaluated in the baseline simulations (current conditions).

There was no consideration of agent-based feedback to building adaptation strategies—from
neither agricultural models nor farm models—capable of identifying further options for
production systems (such as new crops), and setting constraints due to technology limits, resource
limits, or both. And given the time span of the analysis, no innovation (new genotypes) was
tested. This hypothesis can be considered mostly adequate for 2020, but is probably quite
conservative for 2050, when even the adapted systems tested would not be very effective at all
sites in alleviating the consequences of climate change. For 2050 complete changes in the
typology of production systems should be tested, rather than simple changes in agromanagement
and resource use evaluated here. This section analyzes simulation results as percentage yield
declines due to climate change—without and with adaptation strategies for 2020 and 2050.

Adaptation strategies for water management, tested in this report, were developed by considering
current irrigation practices throughout the region—including maximum annual applications. But
they do not consider future water availability in the region, either due to sector competition or
hydrological changes from climate change. Sophisticated 2-D hydrological models coupled to
socioeconomic projections, beyond the scope of this study, would be necessary to address this
limitation. Thus the set of adaptation solutions we identified need to be evaluated case-by-case,
on the basis of local expert knowledge and stakeholder involvement.


Results

Figures included in this section present the simulation results (percentage variations from the
baseline) for water–limited and disease–limited yields. Results for the Hadley-A1B climate-
change scenario were consistently more pronounced than for the National Center for Atmospheric
Research (NCAR) projections. Full simulation results for both scenarios are in annex II. Figure
4.1 provides first an aggregated summary by country, indicating crop yield responses by crop
with adaptations, including modifications to irrigation. This set of results was an input to an
economic model to estimate damage to production, price effects including projections of
agricultural value added (see box 4.1 on the environmental impacts and sustainability applied
general equilibrium model).


                                                                                                38
Figure 4.1. Aggregate impacts on crop yields, with adaptation, computed by the AZS-
BioMA platform under 2020 and 2050 NCAR GCM for the A1B scenario




Wheat

Without adaptation, wheat yields could be significantly affected by climate change, regardless of
the emission scenario or GCM (figures 4.2 and 4.3; annex II). Percentage yield declines were
more pronounced in Mexico, in the Caribbean region, and in the northeastern parts of the
continent (Colombia and Brazil). The projected water–limited production for 2020 and 2050
could be lower than in the baseline, with southern and western countries less affected. Yield
reductions are due to the shortening of the crop cycle, due to higher thermal time accumulation,
leaving fewer days available to fill grains. The projected yield declines due to disease in 2020 and
2050 could also significant. Frost damage is expected to affect wheat yields less seriously in
Chile, where shorter cycles could reduce the crop exposure to pathogens, thus reducing also the
pressure of wheat leaf rust. With few exceptions (such as Chile), insufficient water availability
could affect wheat productivity more than other factors, suggesting the development of varieties
with characteristics able to assure higher resistance to water shortages, such as more capability to
deepen the soil portion explored by roots and more favorable leaf angle distribution.

Compared with the simulations carried out without the implementation of adaptation strategies,
projected impacts were decidedly less pronounced for all the production levels and scenarios
considered (figures 4.2 and 4.3; annex II). Impact on water limited yields was still significant
however, with water availability playing a key role in limiting wheat productivity: the use of
genotypes with longer cycles compensated for the climate change effect in reducing the grain
filling period, but increased transpiration demands. Except in Chile, disease pressure decreased
                                                                                                 39
everywhere, though no adaptation strategies specific for leaf rust were applied. The highest
indirect benefits of adaptation on disease–limited productions were simulated for Brazil and
Uruguay and for Latin America and the Caribbean countries. Insufficient water availability played
a major role in Brazil and Chile, whereas disease pressure affected productions especially in
Argentina.

Figure 4.2. Wheat productivity shocks (Hadley A1B) to 2020




                                                                                              40
Figure 4.3. Wheat productivity shocks (Hadley A1B) to 2050




Soybean

Without adaptation strategies, soybean yields could be reduced by climate change in 2020 and
more so in 2050, though with different magnitudes throughout Latin America (figures 4.4 and 4.5;
annex II). Yield losses were larger in Brazil and in the northern part of the continent (more
than -30% from the baseline), but less pronounced in Argentina, Bolivia, Colombia, and Uruguay.

                                                                                             41
By considering projected water–limited production level, yield losses were reduced in Argentina
and Uruguay, whereas in Brazil, Central America and Caribbean regions they suffered reductions.
This could be explained by the greater impact of climate change in Brazil (see D2 for further
details), where the shorter crop cycle is more pronounced than in other parts of Latin America,
markedly shortening the soybean grain-filling period. The impact of rust disease would not
increase with warming, except for Colombia, where it increased for all combinations GCM ï‚´
emission scenario. This can be explained by the severity of the increase in temperature regimes in
a warm environment such as Colombia‘s, in turn leading to more favorable conditions for the
pathogen.

Adaptation strategies (figures 4.4 and 4.5; annex II) reduced the magnitude of impacts across all
scenarios and time windows considered. For example, considering the potential production level,
there were situations with positive impacts of climate change with adaptation (Ecuador and
Uruguay). The most affected country was Brazil, with a maximum percentage of yield losses still
close to –25% (Hadley-A1B). In certain countries, percentage yield decreases were similar
regardless of water management status (Brazil, Colombia, Uruguay, Latin America and the
Caribbean). In others, the climate change impact was larger under water–limited conditions
(Ecuador). In Argentina the use of varieties with longer cycle compensated the climate change
negative effects tending to shorten crop cycles.




                                                                                               42
Figure 4.4. Soybean productivity shocks (Hadley A1B) to 2020




                                                               43
Figure 4.5. Soybean productivity shocks (Hadley A1B) to 2050




Maize

Climate change could reduce the yields throughout Latin America, regardless to the emission
scenario or GCM (figures 4.6 and 4.7; annex II). This is mainly due to the shorter grain filling
period under the higher thermal time accumulation rates, not being compensated for by the higher
daily biomass accumulation rates and the CO2 fertilization effect (lower in C4 species like maize).

                                                                                                44
The countries most affected were Brazil, Ecuador, Mexico, and Caribbean countries, where maize
is one of the main crops. The Hadley GCM produced the highest losses, except for Brazil and
Ecuador (for the latter, only for the B1 scenario). Abiotic factors did not significantly affected
maize productions, with the only exceptions represented by a slight yield decrease in Mexico,
Central America, and the Caribbean. Considering the heterogeneity of the responses in the area,
the need for adaptations strategies developed at country level is evident.

For the 2020 timeframe, and to a much lower extent in 2050, adaptation strategies could dampen
the negative climate change impacts on grain maize yields in most of the regions of interest
(figures 4.6 and 4.7; annex II), though yield declines could still be relevant in major maize-
producing countries, like Mexico.

Higher percentage decreases were simulated for the Hadley GCM compared with the NCAR one,
with the A1B emission scenario usually leading to the most severe situations. Adaptation
strategies positively concurred to limit climate-change damage to maize production, even in the
countries where the grey leaf spot was the most limiting factor.




                                                                                               45
Figure 4.6. Maize productivity shocks (Hadley A1B) to 2020




                                                             46
Figure 4.7. Maize productivity shocks (Hadley A1B) to 2050




Rice

Except for Brazil, Mexico, and the Caribbean, the 2020 and 2050 projections are encouraging,
with higher productivity projected in most of the cases (figures 4.8 and 4.9; annex II). Rice has
higher thermal (temperature) requirements. Under current climate conditions (the baseline)
production is slightly penalized by photosynthesis being limited by suboptimal temperatures.

                                                                                              47
With warmer conditions the negative effect of the shorter grain-filling period would be
counterbalanced by higher biomass accumulation rates because of the most favorable conditions
for photosynthesis.

The result of these two opposite effects is a general increase in productivity, except in countries
already experiencing warm climates (where thermal conditions for photosynthesis are already
close to optimal and the reduced grain-filling period leads to declines in final yields). In low-
temperature areas (especially Uruguay) climate change could reduce the incidence of
preflowering cold shocks inducing sterility. Except for Brazil and the Caribbean the blast disease
pressure on the crop eases, because of thermal and rainfall conditions that are less favorable for
the blast pathogen Pyricularia grisea.

Adaptation strategies—based on the use of different genotypes and of different sowing dates—
were applied only for the countries where a decrease in production levels was observed (figures
4.8 and 4.9; annex II): Brazil, Ecuador, Mexico, and the Caribbean. The rationale behind the
adaptation was mainly related to the use of genotypes with a longer cycle to compensate for the
climate change effect in shortening the grain-filling period. Sowing dates were also changed.
Results indicate that future conditions will be decidedly favorable for rice across the region.
Long-cycle genotypes allow long grain-filling periods and high daily biomass accumulation rate,
because of negligible thermal limits to photosynthesis and the CO2 fertilization effect, higher for
the C3 species than for the C4 (such as maize). As for wheat, the implementation of adaptation
strategies targeting crop features mainly related to crop cycle length led to indirect benefits in
pathogens pressure (figures 4.2 and 4.3). This could suggest a possible reduction of
agrochemicals in the future in important rice producing countries like Brazil, and the reduced
importance of investing efforts in developing blast-resistant varieties.




                                                                                                48
Figure 4.8. Rice productivity impacts (Hadley A1B) to 2020




                                                             49
Figure 4.9. Rice productivity impacts (Hadley A1B) to 2050.




Agromanagement adaptation
At subnational scales, specific simulations and analyses were carried out to resolve irrigation
solutions necessary to limit, at each simulated grid, the otherwise negative impacts under the no
adaptation scenarios.


                                                                                              50
Region-specific reviews of irrigation information were carried out for the baseline climate,
resulting in simulated irrigation where water was applied a limited number of times during a
crop‘s growing period, and up to 300 millimeters depending on the region. Under the no
adaptation scenarios, these baseline irrigation cases were repeated unchanged. With adaptation,
the crop model was allowed to dynamically compute the need for irrigation, based on soil water
status as determined by the new climate data and altered crop growing conditions. The realism of
these adaptive simulations was maintained by setting the maximum annual irrigation amount to
the same values found for current climate regimes (figures 4.10 and 4.11). Negative impacts of
climate change are thus reduced by allowing more frequent irrigation with increased water
demand.

Figure 4.10. Maize productivity impacts (Hadley A1B) 2020 and 2050




                                                                                             51
Figure 4.11. Soybean productivity impacts (Hadley A1B) 2020 and 2050




Box 4.1. Coupling AZS-BioMA to ENVISAGE for Economic Impact Assessments of climate change

Assessing the potential economic impacts of agricultural productivity shocks can be quantified by coupling
biophysical crop models to partial or general equilibrium economic models. Such biophysics-economics
coupling can be of great interest to policy analysts seeking to derive economic costs and benefits of
specific response strategies, to compare them against a scenario of ―no action.‖

We have developed such linkages between the AZS-BioMA platform and the environmental impacts and
sustainability applied general equilibrium (ENVISAGE) model, a multisector multiregional, global
computable general equilibrium (CGE) model (van der Mensbrugghe 2011). It is calibrated to the GTAP
database with a 2004 base year. The model framework is dynamic, integrating assumptions for population
and labor force growth, for savings and investment, and for sectoral productivity. With differences in
comparative advantage and demand, the model also generates changes in trade patterns on a global scale.

                                                                                                       52
Agricultural productivity in ENVISAGE is assumed to be neutral across all inputs. A change in
agricultural productivity leads to a uniform expansion (or contraction) of the production possibility
frontier:
X r ,cr ,t  Ar ,cr ,t F  L , K , T , M , E ,...
where X is production by region, crop, and time, A is a uniform productivity factor, F is the production
function of labor, capital, land, material inputs (such as seeds and fertilizers), energy, and any other inputs.
Note that ENVISAGE, like most CGE models, uses a production function approach to modeling
agricultural output. Most partial equilibrium models use a supply function approach, where supply is a
function of the market price of the good.

The parameter A grows at some rate influenced by historical experience and is then hit by climate change:
A,cr,t  ANcr,t D r,cr,t (1�r,cr,t )ANcr,t1D r,cr,t (1� r,cr )t ANcr,0D r,cr,t
 r        r,
             D
                 am                    r,
                                          D
                                               am                     r,
                                                                         D
                                                                            am
where AND represents productivity in the absence of damage. AND grows at the rate ï?§ a year, or an average
of over the period 0 to t. The variable Dam is equal to 1 in the base year and is less than 1 if the climate
impact is negative and greater than 1 if the climate impact is positive. Note that if A is 1 in the base year
and grows at 2% a year, agricultural productivity will be 2.5 times higher in 2050 than in 2004—a 10%
shock to agricultural productivity will lower yearly growth to 1.8% a year, resulting by 2050, with climate
change, in yields that are still 2.3 times higher than present. The impact could be much greater in later
decades when climate change is assumed to accelerate and if using a non-linear damage function.

The current coupling passes crop yield shock information (delta changes corresponding to a given climate
change SRES scenario against a baseline without climate change) from AZS-BioMA to ENVISAGE, so
that the latter model can compute costs of such impacts and derive changes in commodity prices and
agricultural value added. For this study, we take the damage estimates from the AZS framework of the two
time periods—2020 and 2050—and interpolate linearly between years (between 2004 and 2020, and again
between 2020 and 2050).

For simplicity, we limited this exercise to climate change–yield shocks in Latin America and the
Caribbean, while assuming a no climate change–impact scenario in the rest of the world. This is equivalent
to investigating the zero-th order implications of a Latin America–only climate perturbation. It is therefore
not an assessment of the full impacts of climate change on Latin American economies. The shocks would
be equivalent to impacts on yields in the absence of any price-induced change in production structure. But
in a general equilibrium framework, the climate-induced productivity shocks will induce changes in
relative prices—particularly of land—and thus substitution across inputs. The impacts on yields will
therefore reflect the exogenous component as well as an endogenous component reflecting a different mix
of land and other inputs.

ENVISAGE results

The economic impacts of implementing the climate-induced agricultural productivity shocks in the four
focus crops are generally negative—consistent with the shocks. At the aggregate level, the magnitude of
the shock will reflect the overall level of the crop-specific shock, the relative importance of the crop in
total production, and the general equilibrium feedback effects—both domestic and global. For example, a
loss in export revenues typically leads to a real depreciation as exports of other goods must rise to
compensate for the change in the trade balance—assumed to be fixed across scenarios.

The aggregate impact of this limited shock on Latin America and the Caribbean GDP could be as high as
1.7% in 2050 under the A1B emission scenario and using the results of the Hadley general circulation

                                                                                                             53
model (GCM). Even considering the strong climate signal, this seems a fairly large decline in regional
GDP, considering that the four affected crops would only represent about 1.3% of total regional GDP in
2050 under the baseline scenario. From this, it can be inferred that the general equilibrium and multiplier
effects are significant. The countries hit most across all scenarios and GCMs are Argentina and Brazil, two
of the largest agricultural producers. Uruguay would be the only country that would see gains in most of
the scenarios—though even under the A1B scenario of the Hadley model, Uruguay would lose and by a
relatively significant 2.3%. Among the least hit are Chile, Ecuador, and Peru. Chile‘s agricultural sector as
a share of the total economy is small in the base year and declines over time—particularly for the four
affected crops. Ecuador and Peru have relatively low production shares in the affected crops. (It is useful
to restate that the other sectors of the economy are not affected by climate change in this scenario—so such
crops as coffee, sugar, and tropical fruits are not affected directly).

The negative impacts tend to accelerate between 2020 and 2050. They are larger for the Hadley model than
the NCAR model, and they tend to be higher for the A1B emission scenario than the B1 emission scenario.
Some of these findings are illustrated in box figure 1, which reflects the loss in agricultural value added in
2050 for the modeled regions and across the four combinations of GCM and emission scenario.

Changes in the region‘s ability to produce will affect global agricultural prices. The crops most affected
are oil seeds, whose prices would rise between 11 and 17% in 2050—relative to a scenario where the four
focus crops are not affected—depending on emission scenario and GCM, with HADA1B producing the
highest rise. Other grains (essentially maize) would see the next largest impact—at around 5%, with little
variation across scenarios and GCMs. Wheat prices would rise only by about 2%, again with little
variation. Rice is an exception. Because its average productivity would rise, prices would drop (slightly)—
as much as 0.7% in the more optimistic climate scenario (NCAR B1). These price changes would be larger
if other regions in the world exhibit similar productivity shocks.

Box figure 1. Change in agricultural value added in 2050 from climate change damage for the four
focus crops

                                           Latin America & Caribbean
                                               Rest of South America
                                         Central America & Caribbean
                                                            Uruguay
                                                             Mexico                             HADB1

                                                                 Peru                           HADA1B
                                                            Ecuador                             NCARB1
                                                           Colombia                             NCARA1B
                                                                 Chile
                                                                 Brazil
                                                           Argentina

  -35       -30      -25       -20      -15       -10       -5            0    5        10


Climate change–induced damage in the four focus crops would generate a large drop in agricultural
exports from Latin America and the Caribbean albeit relative to a situation where the rest of the world is
unaffected by climate change. In aggregate, agricultural exports would decline between 25 and 33% in
2050 compared with a situation where the four focus crops are not hit. Argentina would suffer the largest

                                                                                                           54
percentage loss—between 46 and 67% in 2050. The export decline in Brazil, though significant, would be
much lower than that in Argentina. For most of the other countries regions, the impacts on exports are
mildly positive and potentially even significantly positive for Mexico. Agricultural exports from Uruguay
could be volatile, with a decline of nearly 50% in 2050 in the worst scenario, and a sharp rise of 60% in
2050 in the more optimistic scenario. In value terms the loss in net export revenues for the four crops
considered would be $8–11 billion in 2020, but much higher in 2050 and with a greater range—$30–52
billion. Brazil would take the greatest burden in absolute terms, followed by Argentina and then Mexico.

Main findings of the economic analysis for climate impacts on the four focus crops in LCR were:

    ï‚·   The new shocks generated by the AZS framework could have substantial economic impacts at the
        aggregate level—particularly for Argentina and Brazil, heavily invested in three of the four focus
        crops. If these shocks are representative for a broader set of sectors—such as coffee, fruits and
        livestock—the economic impacts could be amplified and affect a broader set of countries in Latin
        America and the Caribbean.
       A large swathe of the world‘s urban poor could be hurt by higher food prices, which could be even
        higher if a large share of world agriculture confronts impacts similar to those generated by the
        AZS framework.
    ï‚·   A complete and consistent global impact analysis using the AZS framework may eventually reveal
        Latin America‘s enhanced relative comparative advantage in agriculture. But if the comparison is
        with a no climate impact scenario, there is little doubt that Latin American agriculture has much at
        stake and should mitigate the worst consequences of climate change.

Future applications

The results here provide a first example of the kind of analyses that can be performed by using the coupled
AZS-ENVISAGE tools. Future work will need to consider global distributions of climate change shocks,
consistent with those run under the AZS BioMA platform, to better investigate the multiple interactions of
changing productivity potentials, perturbations to supply curves, and price dynamics, both regionally and
internationally. Such coupled platforms are needed to quantify the implications of specific actions in the
agricultural sector, including synergies or offsets arising from the interactions of adaptation and mitigation
needs in the region. With such a coherent framework, the region‘s stakeholders and policy makers could
then analyze a range of scenarios of national to regional interest.

It would be of great interest to run simulations with and without adaptation of crops and crop management,
to quantify the costs and benefits of selected adaptation strategies. It would be also important to enhance
two-way communications between the biophysical and economic models, so that the former could run
realistic adaptation strategies based on socio-economic drivers informed by the latter.

Note:
1. The GTAP database divides world economic activity into 112 regions (of which 94 are individual countries), and
57 sectors (8 of which are crop production, 4 livestock production, and 8 down-stream processed foods). For
modeling purposes, the database has been aggregated to 19 regions—including 8 countries in Latin America and the
Caribbean and two aggregate regions—and 28 sectors with an emphasis on agriculture and food.




                                                                                                              55
Chapter 5. Conclusions and Policy Implications

Although several such platforms exist, the current work helps open several bottlenecks, following
recommendations in Fourth Assessment Report of the Intergovernmental Panel on Climate
Change (2007):

   ï‚·   The basic datasets and biophysical models are fully transparent, both in their validation
       and their availability, including remote access for interested users. Stakeholders around
       the world can now access the platform, evaluate it, test it, and where possible improve it
       by adding or refining datasets, or even by modifying or substituting component code, as
       appropriate for specific areas of study or local agricultural issues.
   ï‚·   The platform, extensible to any world region, is independent of spatial scale, which users
       can modify as more refined datasets for specific regions become available.
   ï‚·   The model allows for explicit, albeit simplified, adaptation of agromanagement, including
       a crop suitability assessment module—to test and evaluate adaptation strategies limiting
       risk under climate change scenarios.
   ï‚·   Explicit links between the biophysical and economic models allow, in principle, two-way
       interactions, with the ability to evaluate economic impacts of specific agromanagement
       solutions identified by the crop models. For future studies, the computable general
       equilibrium (CGE) economic model outputs can test and rank alternative adaptation
       options for updated agroecological zone simulator (AZS) outputs.

Helping policy makers assess the best options


The ability to test dynamic interactions between agroclimatic factors and field management is
important for policy makers to assess economically efficient investment options. For example, the
major findings of this study show:

For Latin America and the Caribbean the projected yield shocks on wheat, soybean, maize, and
rice generated by the AZS platform could result in substantial negative economic impacts at the
aggregate level—particularly for Argentina and Brazil, both heavily invested in three of the four
crops under focus in this study. This finding is a red flag for policy makers in the region and
elsewhere that are concerned about global food security and the role of global trade in mitigating
food insecurity and the possible economic and political insecurity. If these shocks to the focus
crops also hit a broader set of crops, the economic impacts could be amplified and hit a much
broader set of countries in the region. Investments are thus needed to:

   ï‚·   Upgrade the AZS platform to include at least the crops important in current trade (such as
       coffee, fruits, and pastures for livestock).
   ï‚·   Expand the use of the AZS platform across countries to provide greater coherence in
       climate, crop, and economic models and assessment methods.




                                                                                               56
Even simple but realistic adaptation strategies tested with the AZS platform can mitigate (but not
overcome!) the projected negative yield shocks. Policy-relevant interventions related to those
strategies include:

       ï‚·   Allocating adequate research and development (R&D) resources to generate improved
           and adapted plant varieties—currently estimated to take at least a decade and to cost
           US$5–7 million for each new variety).
       ï‚·   Expanding high-efficiency irrigation to overcome moisture limitation during grain-
           filling, a major yield-limiting factor in the region for maize, soybean, and wheat. Less
           than 6% of Brazil‘s agriculture and less than 2% of Argentina‘s are irrigated yet these
           are the two countries projected to be hit most severely by climate change and moisture
           limitations for their major commodity crops.

The estimated gains in rice productivity in Latin America and the Caribbean show the potential
for increasing future production (rice currently feeds more than 3 billion people worldwide, and
future supplies are expected to be tight). Three policy-relevant issues:

          Most of the region‘s rice is produced in the rainy season to ensure adequate supplies of
           water for most of the growing season. The yield plateau for rainy season rice is about
           four tons per hectare because sunlight is often reduced by clouds. If the rice could be
           produced during the dry season with adequate water capture and irrigation, the rice
           yield plateau could rise to between 8 and 12 tons per hectare.
          Most of the region‘s countries have plans to expand hydroenergy as a part of national
           low-carbon growth strategies. Ensuring that new hydroenergy programs also include
           irrigation would enhance the adaptation capacity for agriculture. Policies that promote
           multipurpose hydro would also permit relocating rice and other agriculture in coastal
           and low-lying areas to areas less vulnerable to major floods and rising sea levels—an
           adaptation-mitigation win-win.
       ï‚·   Because flooded rice is a major source of methane (a powerful greenhouse gas),
           adequate R&D funding and policy instruments will be needed to catalyze public-
           private partnerships to develop and extend rice field management strategies and the
           use of improved biological and new-generation ‗high efficiency-low emissions‘
           synthetic fertilizers.

Next steps for World Bank and other donor agency investments, operations, and research

This study highlights the importance of undertaking robust assessments of the likely impacts on
agriculture and the key steps to guide policy makers in the design of a rational economic response
to climate change.

   ï‚·   The most obvious need is to identify and understand the impacts of climate change and
       their associated costs. An economic picture indicates the value at risk and thus the basis
       for an economically efficient adaptation response. Such an assessment clearly requires
       some mapping of down-scaled climate projections and their effect on vulnerable economic
       sectors, such as agriculture.


                                                                                                57
   ï‚·   Once the impacts have been identified and their magnitudes and economic impacts
       determined, it will be necessary to consider the range of adaptation responses, their
       associated costs, and whether they fall within public or private responsibility. The latter
       includes a range of less tangible interventions, such as information to facilitate private
       adaptation.
   ï‚·   The agriculture and land use sector offers significant mitigation potential at low cost.
       Countries should develop a sector emissions budget by concentrating on synergistic
       adaptation-mitigation win-wins, which are more likely to be low cost. Once this budget is
       determined, a range of voluntary and market-based approaches can provide incentives for
       their deployment.

The World Bank and other development agencies can facilitate the rapid development of robust
and synergistic adaptation and mitigation approaches. They can support extending the
development and testing of the AZS platform to a larger suite of traded and subsistence crops and
to other regions. They can also support international and national agricultural research and
development agencies to further refine the AZS platform with local data and to enable local
agencies through appropriate capacity enhancement to use the AZS platform routinely for national
and subnational climate impact assessments for ranking investments.

   ï‚·   The International Center for Tropical Agriculture, based in Cali, Colombia, has expressed
       an interest in hosting the AZS platform for Latin America and the Caribbean with a view
       to further improving the platform and enabling national programs to set up and use their
       own versions. Linking World Bank funded operations to such a regional capacity-
       enhancing agreement would greatly advance the current capacity at most national
       agricultural research agencies.
   ï‚·   The finding that moisture limitations during grain-filling periods for maize, soybean, and
       wheat could limit future yields highlights a major adaptation-mitigation synergy (soil
       carbon sequestration) that could significantly reduce yield losses. Enhancing soil carbon
       not only reduces emissions (mitigation) but can also result in significantly better water and
       nutrient holding capacity of the soils (Fernandes and Motavalli 1997). Such benefits are
       relevant both for commercial farmers and for poor smallholders. But to realize will require
       overcoming governance issues, low technical capacity, high transaction costs, and a lack
       of appropriate baseline and monitoring methodologies. There is a significant opportunity
       to create enabling conditions for further work on mitigation in agriculture in the post-2012
       climate agreement.
   ï‚·   The quantitative AZS estimates of climate impacts on agricultural productivity at high
       spatial resolution provided by this study could provide key information and reduce some
       uncertainty for insurance and microfinance instruments. Insurance is likely to be key in
       future adaptation decisions, whether through traditional indemnity-based insurance or
       through other options that may be more suitable for climate-based insurance, such as
       index-based schemes, weather derivatives, and catastrophe bonds (Fankhauser and others
       2008).

The AZS platform is available for rapid deployment to other regions. For future research, one of
the outcomes that could be generated from the AZS framework is an ensemble of parametric


                                                                                                 58
agricultural-damage functions that could easily be coupled with CGE models and specific
temperature profiles.

   ï‚·   Ideally, the damage function would have the following form:

                                                                     ï?¥
                             D m,c �r,c �r,c TT �r,c TT 
                              ar     0     1
                                                   0
                                                       2
                                                               0

       where Dam is specific to a country/region (r) and crop (c), as well as the ï?¡ parameters,
       but the parameter estimated would also be tied to a specific combination of GCM and crop
       models. The functional form is nonlinear where the non-linear component, ï?¥, could either
       be estimated or imposed. The variable T represents global mean temperature. The
       coefficients could capture the regional temperature change, assuming a stable relationship
       between global and regional temperature changes.
   ï‚·   The AZS framework is generating a huge dataset that could be used to econometrically
       estimate these parameters. In the context of this study, it would produce two families of
       damage estimates—one each for the NCAR and Hadley GCMs. This is another reason to
       extend the overall methodology to encompass all regions and crops.
   ï‚·   Another future research area is to more fully integrate the CGE models with the crop
       models. The CGE models generate results—such as changes in land use and input use—
       that could be reinserted into the crop models to ensure greater consistency. Experience
       with other model couplings shows rapid convergence in most cases.




                                                                                              59
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Annex I. The Agroecological Zone Simulator and Applications

The agroecological zone simulator (AZS) is a realization based on the Biophysical Model
Applications (BioMA) platform, an extensible platform for running biophysical models on
generic spatial units. AZS-BioMA is based on discrete conceptual units codified in software
components (both for simulation engines and user interface). The guidelines followed during its
development aim at maximizing:

       ï‚·   Expansion and adaptation with new modeling solutions.
       ï‚·   Ease of customization in new environments.
       ï‚·   Ease of deployment (at national and local research and academic facilities).


Simulations are carried out through modeling solutions, discrete simulation engines where
different models are selected and integrated to carry out simulations for a specific goal. Each
modeling solution uses extensible components. Third parties can autonomously extend BioMA by
adding new modeling solutions, using components that are already part of the application, or
using new components.
The current version of BioMA includes heterogeneous modeling solutions:

       ï‚·   CropSyst-Water Limited.
       ï‚·   WARM-BlastDisease-Sterility.WOFOST-Water Limited.
       ï‚·   Agricultural Production and Externalities Simulator.
       ï‚·   PotentialDiseaseInfection.
       ï‚·   Diseases (linked to crops).
       ï‚·   ClimIndices.


Crop development and growth

The modeling platform designed for this study to assess the impact of climate change on
agricultural productivity uses three approaches to model crop development and growth, all
established platforms that follow the hierarchical distinction between potential and limited
production. Crop growth is computed through light interception and carbon dioxide assimilation.
Growth-driving processes depend on and are driven by crop phenological development stages,
mainly through degree-day accumulation.
For example, the approaches currently being implemented are:

          CropML-CropSyst (Stöckle and others 1992, 2003) is a cropping system model with a
           generic crop simulator that allows for both determined and perennial species to be
           simulated. This component implements the plant-related part of the original CropSyst
           model, version 3.02.23.
       ï‚·   CropML-WARM (Water Accounting Rice Model, Confalonieri and others 2009a,
           2009b) is a daily time-step model for simulating growth and development of paddy

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            rice crops. The model accounts for all the main processes that characterize this
            peculiar system.
       ï‚· CropML-WOFOST (Van Keulen and Wolf 1986; Boogaard and others 1998) is part of
            the family of crop growth models developed in Wageningen by the school of C.T. de
            Wit.
During the project, CropSyst was used for maize, wheat, and soybean modeling, and WARM was
used for rice modeling. The two models were selected because of their accuracy and robustness
(Confalonieri and others 2010).
Soil water

SoilW is a software component implementing a wide range of alternative methods to simulate
water dynamics into the soil profile, covering all those already in use in major crop modeling
platforms worldwide. The component allows the following processes to be simulated:

       ï‚·     Water redistribution among soil layers.
       ï‚·     Effective plant transpiration (several options available).
       ï‚·     Soil evaporation (several options available).
       ï‚·     Drained water if pipe drains are present (under development).
       ï‚·     Effects of soil tillage and subsequent settling of hydrological properties of the soil
             (field capacity, wilting point, retention functions, conductivity functions, bulk density).


For each process, several approaches are implemented, allowing SoilW to reproduce the behavior
of all the most diffused cropping systems models. For example, three approaches are implemented
for water redistribution. The first is based on a numerical solution of the Richards‘ equation
(Richards 1931), based on the physical concept that water flux between two points is driven by
the pressure gradient between the points themselves and is a function of hydraulic conductivity. In
this approach, water retention curves and hydraulic conductivity as a function of soil water
content and water pressure are needed. This approach is used in several well-known models, such
as CropSyst, SWAP (Van Dam and others 1997) and MACRO (Jarvis 1994). The second
approach—cascading, or tipping bucket—is less demanding in terms of data needs and assumes
that water can move only downward through the soil profile, filling up the layers until field
capacity is reached, with the water exceeding this threshold moving to the deeper layer (Jones and
Ritchie 1990). This approach is used in most of the Decision Support System for Agrotechnology
Transfer models. The third approach is a modification of the cascading one, in which water
movements are reduced by soil hydraulic conductivity, thus allowing water contents to be higher
than field capacity. This approach is used in various simulation models such as SWAT (Neitsch
and others 2002) and WARM (Confalonieri and others 2009a).
Rainfall and irrigation water actually infiltrating the soil (after possible runoff and plant and
mulch interception) is simulated with a library of models implemented in the SoilRE component.
A variety of approaches are also available for subprocess, involved in runoff volume, water
interception by vegetation and mulch, actually infiltrating water. As an example, for runoff,
approaches implementing the curve number and the kinematic wave methods are available.



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Management events (see section on crop management below) involved with soil dynamics, such
as irrigation, are processed by the SoilRE component. During the project, the cascading approach
for simulating soil water redistribution was used because of lack of detail in the available data,
which had to be homogeneous for the whole area considered.
Diseases and abiotic factors affecting productions

In almost all the climate change impact studies, cropping systems modelers have been used to
consider only temperature, radiation, rainfall, atmospheric carbon dioxide concentration, and—in
some cases—irrigation as forces driving crops productivity. Biotic (such as diseases) and abiotic
(such as ozone concentration and frost events) factors were traditionally considered as having a
constant impact on the crops under a changing climate. This assumption is false, because weather
variables have a crucial effect—through, for example, modulating plant-pathogens interactions—
that leads climate to alter the magnitude of the gaps in crops productivity due to diseases. This
consideration is also valid for damages caused by abiotic factors (such as frost), which in most
cases are driven by extreme weather events and are in some cases forecasted to increase by global
climate models.
This project considered the impact of diseases (disease component) and abiotic damages induced
by extreme temperatures (abiotic damage) on crop productions. The disease component simulates
the progress of the epidemics of fungal pathogens by explicitly considering infection, incubation,
latency, infectiousness, sporulation, and spore dispersal, all driven by weather conditions and by
interactions with the host plant. The impact on the host is simulated mainly through lesions to the
photosynthetic tissues. Host resistance (different varieties can greatly differ in terms of
susceptibility to a specific disease) is accounted for in the component and based on a generic
classification of resistance levels. Figure I1 shows the distribution of the pathogens simulated
during the project (the most relevant ones for each of the considered crops) across Latin America.

Figure I1. Geographical distribution of (a) maize Gray leaf spots (Cercospora zeae-maydis),
(b) wheat leaf rust (Puccinia recondita), (c) soybean rust (Phakopsora pachyrizi), and (d) rice
blast disease (Pyricularia oryzae) across Latin America




Damages due to extreme temperature events, such as frost and cold shock–induced sterility, were
simulated using the abiotic damage component, implementing approaches for a variety of abiotic
damages affecting crops, over and above those in use in most other crop modeling platforms. The
models implemented belong to six categories: lodging, frost, cold-induced spikelet sterility, heat-
induced spikelet sterility, ozone, and salinity.

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Crop management

Dates of irrigation events and the amount of water needed by the crop under current or future
climate conditions were simulated using rule-based models. This is based on the state of the
system, mimicking farmers‘ behavior with respect to management decisions related to resources
availability and on the physical characteristics of the system. A proper set of rules were
implemented in the AgroManagement component.
Maize and soybean were simulated under irrigation conditions, and wheat was simulated under
rainfed conditions. Water limited production was not simulated for rice, since it is grown in the
study region under paddy conditions.
Irrigation events were simulated for both maize and soybean when soil water content fell below
50% of the plant available water, by limiting the total amount of irrigation water in a season to a
maximum of 300 millimeters, and by setting the maximum amount of water to 40 millimeters for
each event. This parameterization of irrigation rule allowed a standard typology of irrigation
practices to be produced, leading to a medium satisfaction of crop water requirements during a
season.
Crop suitability

Crop suitability is an important component of assessment studies, including changes to crops
geographic distribution under climate change in coming decades. On the one hand, it is well
known that crops will respond to specific changes in temperature and precipitation at the locations
where they are currently grown; on the other, it is expected that not all crops and cultivars will
remain suitable within their current geographical ranges, with tendencies to migrate toward higher
latitudes and out of areas already at the margin of production. Yet most crop modeling platforms
present fixed grid simulations of crops—that is, they do not allow for dynamical movements of
ideal crop ranges and thus tend to underestimate likely adaptation responses by farmers. They will
doubtlessly attempt to switch where possible to cultivars and crops better adapted to changing
conditions. By the same token, those model platforms that have excelled in computing suitability
have much less crop modeling detail than does the proposed platform.
The suitability component included in AZS-BioMA implements a variety of approaches for
suitability estimation based on single-cell (that is, threshold-based approaches) or multicell (that
is, multiple regression–based) computations. Among the approaches implemented, some are
retrieved from the literature and based on soil or weather inputs—for example, FAO EcoCrop
(http://ecocrop.fao.org) and Less Favorable Areas (Eliasson and others 2010). Others, developed
during the project, derive a suitability index from simulated variables, such as yields, completion
of crop cycle, and yield gaps due to biotic and abiotic factors affecting productions.
Implementing all the methods allows the methods to be selected in their original configuration or
with categories of variables or parameters excluded from the computation. Another criterion
implemented during the project (district criterion) assumes that crop choices by farmers tend to
aggregate in production districts. This approach cannot be used alone, and the suitability
component allows it to be coupled to all the other methods implemented.



                                                                                                 69
The user can run the AZS-BioMA suitability component by deciding exactly which method to use
or by leaving the component to selecting the most appropriate method in light of the actual data
availability or the exercise that needs to be run.
BioMA is developed at JRC (project leader: M. Donatelli) in close cooperation with the
University of Milan and the Italian Agricultural Research Council. Additional collaboration is
being established with the INRA, France.

AZS use at different scales

This is a key feature of the platform that allows building configurations which access data-layer at
different spatial resolution. The current setting use a grid 25 km x 25 km, but, say 1 km x 1 km
DB can be used allowing detailed analysis for instance of a region in a country. The added value
is that the same tools and methodology can be used to create figures from a region or a country
based on less abstract production systems and technical adaptation strategies. This also sets a
concrete and consistent basis for effective and transparent communication between regional
offices and runners of large area analysis.
Data access as input to analysis external to the AZS platform

Read-only data will be accessible through dedicated web services (cross platform) to authorized
users. This allows applications or utilities to be built that will be able to query data and use them
in local applications. A target use of biophysical simulation output is the link to agroeconomic
models. Figure I2 shows the linking and workflow to develop adaptation strategies. The workflow
may include either agricultural sector models (as in the current project) or farm models. If the
economic model is a farm model, technological and resource-driven constraints can be accounted
for, allowing more context-specific detailed analysis of production systems. The dashed line
connector suggests possible iterations between a bioeconomic farm model and biophysical models
to further improve the definition of adaptation scenarios.
In this project, scenarios are not set to interact with economic models, and the adaptation
strategies, as described in section 1.1, represent basic technical options. The study of further
options could be driven by agricultural sector models needed to investigate, for instance, a
specific crop, or specific constraints in resources.
Figure I2. Possible use of environmental outputs for selecting specific production systems




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In more general terms, the outputs of biophysical analysis (which includes static indicators based
on climate) could be used to build integrated indices to estimate, for instance, land use changes
based on biophysical indicators, using the outputs as proxies for semiqualitative estimates of
climate change impact. The figure highlights a possible use of outputs of environmental interest
for selecting among possible alternate production systems.
Calibration and model evaluation

Calibration and model evaluation must be considered separately for current weather conditions
and for conditions of climate change, as in the following paragraphs.
Process-based, deterministic models like the ones used for simulation in this project are evaluated
against referenced data. This activity, often referred as model ―validation,‖ is done by simulating
the same conditions where the reference data were collected (weather, soil, agricultural
management) and comparing simulation results to data collected from the real system (such as
biomass produced, yield, soil water content). Prior to actually performing model evaluation the
model is calibrated, whereby the value of some model parameters are adjusted to minimize the
difference between simulated and reference data. This is a very delicate process when performed
with process-based models, where parameters have a biophysical meaning; in no case should
calibration lead to parameter values that are out of the range known for the process they refer to.
Once model parameters are calibrated, model evaluation is run as described above against an
independent dataset.
In all cases, model evaluation is run against articulated dataset, in which the context is described
in detail to allow simulating it and the measurements on the state of system regard different
variables and time series. Yield, often the variable of major interest, is the final result of the
simulation of several processes. As such (datasets always being limited in number because they
are very costly), a calibration based only on yield often has multiple solutions, resulting in
unpredictable model performance under changing biophysical contexts. Model outputs such as
crop progression through different development stages (phenology) are typically driven by fewer
factors than yield is.
Models are simplified representations of the real system and must include the essential processes
(as sources of variability of responses) with respect to the goal of the planned analysis. Some
processes can be omitted, in this case adding to the assumptions made for the simulation exercise.
Although acceptable, this has implications for the data that can be used for model calibration and
evaluation: for instance, if a model not simulating water limitation is used, reference data based
on systems where water limitation occurs cannot properly be used either for calibration or
evaluation.
The implications of which dataset can be used for evaluating a crop or cropping system model are
important. Although models tend to simulate crop development and growth as limited by few
factors, actual agricultural production systems, especially in developing countries, show a wide
range of production constraints that can affect production nonlinearly. Unfortunately, yield
statistics are presented as values, rather than ranges; when ranges are available, the upper limit
could be used for calibration and model evaluation, allowing cases that cannot be represented by
models because they include processes or technical mismanagement that increase the yield gap to
be deleted from the reference. Furthermore, the technological gap of a production system can vary

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across regions and countries; when combined with environmental factors, it may lead to a
different resiliency with respect to adverse weather. The impact is again on the usability of yield
statistics to calibrate and evaluate process-based models, because it introduces a further bias as
result of the year-by-year variability.
The models used in this project are well known and peer reviewed, which implies that they have
been evaluated across a broad range of environmental conditions. The analyses carried out in this
project are thus based on such evaluation, relying on data from the scientific literature for model
calibration. This study uses crop production as a level of abstraction for production system,
aiming at representing yield changes (at various production levels) in response to scenarios via a
25 km x 25 km grid. Even if yields are considered at the various production levels mentioned
above, yield estimates represent potential and can have different realizations in different
production systems if analyzed within more specific context constraints. This suggests that this
type of analysis, using the very same tools that allow for different spatial resolutions, could
benefit from a more detailed calibration in specific countries or regions. Yet this level of detail in
the analysis was beyond this project‘s goals and resources; future applications involving local
knowledge and expertise will be necessary to refine simulation results.
A different aspect of model evaluation relates to model use in scenarios of climate change. This
refers to unexplored conditions where there are no site-specific data to represent production
system performance. Because relationships among biophysical processes in the real system are
nonlinear, system performance cannot be estimated using trends and statistical models. Likewise,
empirical parameters whose empiricism is at the same level as the estimate cannot be relied on.
The relationships used in process-based models also have some empiricism, but that empiricism
will be one or more levels below the level of the prediction (figure I3).

Figure I3. Level of prediction and level of empiricism in process-based models (redrawn
from Acock and Acock 1991)




                                                                                                   72
The goal in defining new models is using relationships that are known from physics or chemistry
and parameters that have either a biological or a physical meaning. A process-based model can, in
principle, extrapolate to conditions outside the ones used to develop it. By contrast, a fully
empirical model, as any statistical model, can be used only for the context that originated the data
used to build it.
Given that no evaluation against data can be done for scenarios of climate change impacting on
crop development and growth, to accept their use in such condition a system analysis must be
done confirming model adequacy under the new conditions. This has been done for the models
used in this project, leading to changes in curves of response to temperature. The original models
had a plateau of response to daily maximum air temperature, perfectly adequate in conditions
such as the one of temperate climates, in which temperature rarely reaches levels above the
optimum. Hence, making the plateau approximation is acceptable. However, it is known that rates
of development and growth start decreasing above optimum temperature. For instance, a plateau
model will estimate an overall increase of temperature-driven rates in the linear part of the
response, and same response in the plateau region, when temperature increases as in climate
change scenarios. By contrast, a curvilinear model will estimate a decrease of rates of
development and growth at higher temperatures. The latter is the case in scenarios of climate
change where the steep rise in temperature does not allow for accepting the hypothesis of good
adaptation of crops to environmental conditions, as built in decades of agriculture under variable
weather, but under no steep trend toward higher temperature. This is why the models used include
curvilinear responses to temperature, which do not show any difference of estimates compared
with the original ones under current weather, but start estimating differences at higher
temperatures.
If the assumption of crop adaptation cannot be accepted, another aspect of model structure
adequacy is the response to extreme meteorological events. Extreme events for a crop can be the
values of environmental variables that are beyond the capability of providing a physiological
response by the crop and that may lead to permanent damage or death of the crop. Referring to air
temperature as discussed above, a crop will respond with a given rate to temperature, but if
temperature goes above maximum temperature or below minimum temperature for growth,
permanent damage occurs. These aspects were generally ignored in commonly used modeling
solutions and are now implemented representing one of the simulated production levels.
To summarize, developing and implementing the identified temperature responses and the models
of impact of extreme events allow models originally developed for temperate climates to be used
in scenarios of climate change assuming good crop adaptation.

AZS-BioMA modeling solutions
Two modeling solutions were developed within the current project for the simulation of the four
crops included in the land resources database (wheat, maize, soybean, and rice). They are
customizations of the APES-BioMA solution for maize, soybean, and wheat and of the WARM-
BioMA solution for rice (figure I4). Each solution implements a crop growth and development
simulation model, a soil water hydrological model, a soil temperature model, a model for soil
temperature across the profile, a model for assessing abiotic yield losses, and a model for
simulating biotic yield losses.



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Figure I4. The biophysical models implemented in the APES-BioMA and in the WARM-
BioMA modeling solutions




Source: (a) Stockle and others 2003; (b) Confalonieri and others 2009a; (c) Jones and Ritchie 1990; (d) Richards
1931; (e) adopted by SWAT, Neitsch and others 2002 and ANSWERS-2000, Bouraoui and Dillah 1996; (f) Parton
1984; (g) adopted by SWAT; (h) Ritchie 1991; (i) Confalonieri and others 2004 and Challinor and others 2005; (l)
Salinari and others 2009; (m) Biloni and others 2007.

Sample point simulations
This section presents the results of sample simulations for AZS-BioMA, carried out for a single
season in Brazil for wheat (medium-cycle varieties), for maize (long-cycle varieties), soybean
(short-cycle varieties), and rice (short-cycle varieties). The grid cells chosen are a random sample
within the area where crops are grown, without targeting a representativeness of the conditions
chosen; data are shown purely as examples to illustrate the type of results. For each crop, the
AZS-BioMA parameter editor—showing how the proper parameterization was set—is shown
(figures I5, I8, I11, and I14), followed by simulation results for leaf area index (figures I6, I9, I10,
I12, and I15) and aboveground biomass (figures I7, I10, I13, and I16). For both the simulated
variables, the two screen shot of the AZS-BioMA data viewer show the results for the following
conditions:


                                                                                                             74
      ï‚·   Potential growth (temperature and radiation are the only factors limiting crop growth
          and development);
      ï‚·   Water limited growth (water limitations through soil availability and evaporative
          demand);
      ï‚·   Abiotic factor limited (frost damage to wheat, maize and soybean, and cold shock-
          induced spikelet sterility for rice);
      ï‚·   Biotic, or Diseases limited (impact of the following diseases on crop production:
          wheat rust [Puccinia recondita], maize gray leaf spot [Cercospora zea-maydis],
          soybean rust [Phakopsora pachirizi], rice blast [Pyricularia oryzae]); Actual (Water
          stress, abiotic and biotic factors).

Figure I5. Model parameter editor interface showing the CropSyst parameters for
Wheat_SouthAmerica_3 crop key (variety with medium crop cycle length)




                                                                                              75
Figure I6. Graphic data display interface showing leaf area index (m2 m-2) of wheat
(potential, disease limited, water limited, frost limited, and with all limitations) simulated in
a sample cell of Brazil with the APES-BioMA modeling solution




                                                                                              76
Figure I7. Graphic data display interface showing aboveground biomass (kg ha-1) of wheat
(potential, disease limited, water limited, frost limited, and with all limitations) simulated in
a sample cell of Brazil with the APES-BioMA modeling solution




                                                                                              77
Figure I8. Model parameter editor interface showing the CropSyst parameters for
Maize_SouthAmerica_4 crop key (variety with long crop cycle)




                                                                             78
Figure I9. Graphic data display interface showing leaf area index (m2 m-2) curves of maize
(potential, disease limited, water limited, frost limited, and with all limitations) simulated in
a sample cell of Brazil with the APES-BioMA modeling solution




Note: Frost damages do not affect crop productivity in this simulation.




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Figure I10. Graphic data display interface showing aboveground biomass (kg ha-1) curves
of maize (potential, disease limited, water limited, frost limited, and with all limitations)
simulated in a sample cell of Brazil with the APES-BioMA modeling solution




Note: Frost damages do not affect crop productivity in this simulation.




                                                                                          80
Figure I11. Model parameter editor interface showing the CropSyst parameters for
Soybean_SouthAmerica_2 crop key (variety with short crop cycle)




                                                                              81
Figure I12. Graphic data display interface showing leaf area index (m2 m-2) curves of
soybean (potential, disease limited, water limited, frost limited and with all limitations)
simulated in a sample cell of Brazil with the APES-BioMA modeling solution. Frost
damages do not affect productivity in this simulation.




Note: Frost damages do not affect crop productivity in this simulation.




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Figure I13. Graphic data display interface showing aboveground biomass (kg ha-1) curves
of soybean (potential, disease limited, water limited, frost limited, and with all limitations)
simulated in a sample cell of Brazil with the APES-BioMA modeling solution




Note: Frost damages do not affect crop productivity in this simulation.




                                                                                            83
Figure I14. Model parameter editor interface showing the WARM parameters for
Rice_SouthAmerica_2 crop key (variety with short crop cycle)




                                                                          84
Figure I15. Graphic data display interface showing leaf area index (m2 m-2) curves of rice
(potential, disease limited, water limited, and with all limitations) simulated in a sample cell
of Argentina with the WARM-BioMA modeling solution




                                                                                             85
Figure I16. Graphic data display interface showing storage organs (kg ha-1) curves of rice
(potential, disease limited, water limited, sterility cold limited, and with all limitations)
simulated in a sample cell of Brazil with the APES-BioMA modeling solution




Note: Sterility cold damages do not affect productivity in this simulation.

The following section presents AZS-BioMA data viewer screen shots as sample point outputs related to
simulated soil variables—that is, soil water content (figure I17) and temperature along the soil profile
(figure I18).




                                                                                                     86
Figure I17. Graphic data display interface showing soil water content simulated in a sample
cell of Brazil with the APES-BioMA modeling solution




                                                                                        87
Figure I18. Graphic data display interface showing soil temperature simulated in a sample
cell of Brazil with the APES-BioMA modeling solution




Figure I19 shows how the AZS-BioMA model can be parameterized for simulating the different
pathogens within the APES-BioMA modeling solution.




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Figure I19. Model parameter editor interface showing the options for simulating the three
pathogens: Cercospora Zeae-maydis (maize), Phakospora pachirizi (soybean), and Puccinia
recondita (wheat) accounted for by the APES-BioMA modeling solution in this study




Note: Pyricularia oryzae (rice) is not included in this figure because it is simulated with the second AZS-BioMA
modeling solution (WARM-BioMA).

AZS-BioMA: crop suitability and adaptation capabilities


Suitability
Crop suitability is an important component of assessment studies, including changes to
geographic distribution under climate change in coming decades. If it is well known that crops
will respond to specific changes in temperature and precipitation at the locations where they are
currently grown, it can also be expcted that not all crops and cultivars will remain suitable within
their current geographical ranges, with tendencies to migrate toward higher latitides from areas
already at the margin of known temperature and water limitations. However, most crop modeling
platforms present fixed grid simulations of crops—that is, they do not allow for dynamic
movement of ideal crop ranges—and thus tend to underestimate likely adaptation responses by
farmers, who will doubtlessly attempt to switch to cultivars and crops better adapted to changing
conditions. By the same token, those model platforms that have excelled in computing suitability
have much less crop modeling detail than available under the proposed platform.
Before designing the suitability component, a bibliographic review was conduced to determine
which approaches are commonly adopted for suitability assessments, especially in climate change
impact studies. Most of the available approaches are based on the definition of thresholds for
specific environmental parameters. Examples of this typology of approaches are the FAO

                                                                                                             89
EcoCrop criterion (http://ecocrop.fao.org) and the less favourable areas criterion developed at the
European Commission Joint Research Centre (Eliasson and others 2010). Another approach
recently proposed is based on a multiple regression to relate the percentage of crop presence in an
area to climatic indicators (used as regressors) (Moriondo and others 2010). In this case, the
regressors used were related to temperature, rainfall, heat stress and frost events.
The AZS-BioMA suitability component was developed implementing both these typologies of
approaches: threshold- and multiple regression–based, with the latter developed by extending the
concepts and criteria used in the first typology of methods. Our implementation of all the methods
allow the user to select the methods in their original configuration and with categiories of
variables or parameters excluded from the computation.
We also developed and implemented a district criterion, based on the assumption that crop
choices by farmers tend to aggregate in production districts. This approach cannot be used alone;
the UNIMIsSuitability component allows the user to couple it to all the other methods
implemented. The component can be used in two modalities: single-cell and multicell. The latter
is needed when multiple regression–based methods are selected.
All the approaches implemented will be evaluated and compared using current crop masks (see
deliverable D2; Ref. 4) to provide AZS-BioMA with a default approach.
The user can run the AZS-BioMA suitability component by deciding exactly which method to use
or by leaving the component to selecting the most appropriate method in light of the actual data
availability or the particular exercise that needs to be run (figure I20). This feature was achieved
with a suitable implementation of the strategy design pattern (figure I21). The user can access
methods directly (light and intermediate blue in figure I20) or by selecting context strategies (dark
blue in figure I21) that automatically perform the method selection.




                                                                                                  90
Figure I20. Flow chart of the logic behind the UNIMI.Suitability feature for automatically
selecting the most suitable approach according to the availability of data under specific
conditions




                                                                                       91
Figure I21. Strategy diagram of the AZS-BioMA UNIMI.Suitability component




FAO EcoCrop criterion
The FAO EcoCrop approach is based on two response functions (figures I22 and I23) calculated
by relating climatic data (temperature and rainfall considered during the crops cycles) to crop-
specific parameters (figure I24). The values of these parameters were derived from interviews
with experts and are available at http://ecocrop.fao.org/ecocrop/srv/en/home. After computing the
two response functions, the lowest between temperature and rainfall sauitabilities is selected.

Figure I22. FAO EcoCrop response function for temperature suitability, using parameter
values for maize




                                                                                              92
Figure I23. FAO EcoCrop response function for rainfall suitability using parameter values
for maize




Figure I24. Flow chart of the FAO EcoCrop suitability criterion




                                                                                      93
Less favored areas
The less favored areas approach derives from the work of Eliasson and others (2010), aimed at
evaluating the suitability of generic agriculture areas according to natural constraints. The
guidelines that led to the development of the original version of this approach are scientifically
clear and understandable methodology; key soil, climate, and terrain characteristics–based
criteria; classification related to areas that have natural handicaps to agriculture and not to how
the land is used neither to the payment mechanisms such as eligibility rules, and level of
payments; agricultural areas that include permanent grasslands, permanent crops, and arable land
(forest is not included); and no crop specificity.

Our implementation includes options for using different typologies of information and can be
parameterized for the different crops considered for this project (figure I25). Table I1 lists the
eight criteria adapted from Eliasson and others (2010), together with their description and default
thresholds. Two classes were developed for each criterion, based on relative thresholds: not
limiting and severely or very severely limiting conditions. Criteria are assessed according to the
agronomic law of the minimum (Liebig‘s law). Once one of the considered criteria is rated
severely limiting, the corresponding land is judged to present severe limitations for agricultural
activities in general or for a specific crop (depending on the parameterization adopted). The
criteria are not weighted or given a relative importance. To account for between-year variability
of the length of the growing season, temperature accumulation, heat stress, and soil moisture
balance, these characteristics are classified as severely limiting in a probabilistic approach. A
characteristic is classified as being severely limiting if the probability of exceeding the severe
limit is more than 20% (that is, the constraint occurs in at least 7 years out of 30). The criteria are
applied to single cell points, and the mapping should ensure that the scales of the soil and climate
data are compatible with the scale at which the area is classified.

The need to adapt the criteria selected by Eliasson to the project land resources database (see
deliverable D2; Ref. 4) and to fulfill the possibility of handling them by the user led us to
implement the approach (―Eliasson criterion‖ hereafter) in figure I25. In particular, we clustered
the criteria in categories (climate, soil physics, soil chemistry, and water balance), thus enabling
the choice to process the assessment tests also separately (obtaining criteria for partial suitability),
according to user information and aims.




                                                                                                     94
Figure I25. Flow chart of the less favored areas suitability criterion (Eliasson criterion)




Table I1. Soil and climate criteria for classifying land according to its suitability for generic
agricultural activity
Criterion                     Description                                    Threshold (default)

Climate

Temperature                   Length of growing period defined as the        ≤ 180 days with threshold temperature
                              number of days with daily temperature higher   of 5°C
                              than a user-specified threshold (days)


                              OR

                              Growing degree days accumulated above a
                              user-specified threshold (°C)                  ≤ 1,500 °C with threshold temperature
                                                                             of 5°C

                              Number of periods of consecutive days with     At least one period of 10 consecutive
Heat stress
                              temperature higher than a user-specified       days with daily temperature threshold
                              threshold (-)                                  of 35°C



                                                                                                        95
Soil physics

Drainage                                                                            ≤ 0.5 mm day-1
                                   Daily drainage (mm day-1)

Texture                            Sand (weight %)                                  > 70%

                                   Clay (weight %)                                  > 60%

                                   Organic matter (weight %)                        > 30%

                                   Coarse material (volume %)                       > 15%

Rooting depth                      Maximum rooting depth (cm)
                                                                                    < 30 cm
                                   Change of elevation with respect to the
Slope                                                                               > 15%
                                   planimetric distance

Soil chemistry

Chemical properties                Salinity (electric conductivity dS m-1)          > 4 dS m-1

                                   Sodicity (exchangeable sodium percentage)        > 6%

                                   Gypsum (%)                                       > 15%

Water balance

Non dry day                        Number of days within growing period with        ≤ 90 days
                                   amount of rainfall and soil moisture exceeding
                                   half of potential evapotranspiration (days)

Note: Threshold values distinguish nonlimiting from severely limiting conditions (adapted from Eliasson and others
2010). Climate-related criteria are yearly time scale–based. In our implementation crop-specific thresholds can be
used to extend the criterion to evaluate the suitability for different crops.

Direct crop suitability discriminant
The aim of the second approach implemented in the suitability component—direct crop suitability
disccriminant criterion (DCSD)—is to propose a crop-specific criterion based on the outputs of
cropping systems models. Criteria and related thresholds are presented in table I2. Criteria were
selected according to:

    ï‚·     Actual yield and development stage of the considered crop at the end of the season (that is,
          yield and maximum development stage code criteria).
    ï‚·     Impact of abiotic factors and diseases on potential yield (that is, yield gap for frost,
          diseases, sterility, and water stress criteria).
    ï‚·     Abiotic factors and diseases in terms of events occurrence (that is, stem lodging and
          potential infection events criteria).


We applied the same suitability assessment methodology of the Eliasson criterion (that is,
thresholds processed according to the Liebig‘s law of the minimum) with the possibility of

                                                                                                               96
processing separately different categories of criteria (yield, crop success, abiotic factors, diseases,
and water stress). Compared with the Eliasson criterion, this approach allows for a more direct
evaluation of crop suitability, since all the criteria involved are direct outputs of a cropping
system model. Figure I26 presents the flowchart of the DCSD criterion for crop suitability.
Figure I26. Flow chart of the direct crop suitability discriminant criterion




Table I2. Direct crop suitability discriminant for classifying an area according to its
suitability for a particular crop
Criterion                       Description                 Threshold parameters

Yield suitability

Yield                           Yield of the crop (tha-1)   ThresholdYield

Crop success
                                Maximum development         ThresholdMaxDevelopmentStageCode
Maximum development stage
                                stage code for the crop
code
                                (0: sowing;
                                1: emergence;
                                2: flowering;
                                3: maturity)




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Abiotic suitability
                                    Percentage yield gap due    ThresholdYieldGapFrost
Yield gap frost
                                    to frost conditions (%)

                                    Percentage yield gap due    ThresholdYieldGapSterility
Yield gap sterility
                                    to temperature sterility
                                    conditions (%)

                                    Stem lodging affect the     ThresholdStemLodging
Stem lodging
                                    crop, which still survive
                                    (boolean)

Diseases suitability
                                    Percentage yield gap due    ThresholdYieldGapDiseases
Yield gap diseases
                                    to diseases presence (%)

                                    Number of potential         ThresholdPotInfectionEvents
Potential infection events
                                    infection events (-)


Water stress suitability
                                    Percentage yield gap due    ThresholdYieldGapWaterStress
Yield gap water stress
                                    to conditions of limited
                                    water (%)

Note: Threshold values separate nonlimiting from severely limiting conditions.

Multiple regression based on the Eliasson criterion
The Eliasson criterion has been extended herein and implemented within a multiple regressive
model. In practice, the same variables considered by Eliasson and others (2010) are used as
regressors and related to the crop percentage for a certain area, which therefore represent the
dependent variable. The regression coefficients are then used to predict the percentage of crop
presence for the same area under different conditions for the regressors (such as number of heat
waves) because of climate change or perturbative events.
Multiple regressions for land suitability have been proposed in recent studies, also for evaluating
the impact of climate change (see, for example, Moriondo and others 2010). Based on user needs,
regressors belonging to different categories (climate, soil physics, and the like) could be selected,
as with the standard Eliasson method.

Multiple regression based on the direct crop suitability discriminant criterion
A multiple regression suitability method has been developed also for the DCSD criterion. Based
on user needs, regressors belonging to different categories can be used also for this approach.

District criterion
An accessory criterion, one that accounts for the fact that farmers tend to aggregate crops within
the same production district, has also been developed. Based on the user needs, this district
criterion can be coupled to all the others.


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The criterion (figure I27) is based on the following rules (computation on the cell ï?¡):

       ï‚·   If the percentage of neighboring cells where the same crop is present is higher than a
           user-specified threshold, increase the crop presence percentage of the cell ï?¡ by a user-
           specified relative value.
       ï‚·   If the percentage of neighboring cells where the same crop is present is lower than a
           user-specified threshold, then decrease the crop presence percentage of the cell ï?¡ by a
           user-specified relative value.
       ï‚·   Otherwise, do not modify the crop presence percentage for the cell ï?¡.


Figure I27. Flow chart of the district criterion (to be coupled with one of the other methods)




Sample tests on the component as implemented in the AZS-BioMA Model
All the suitability methods implemented in the component were extensively tested using inputs
explicitly chosen to explore the highest variability of conditions. The following figures present
sample tests carried out using the multiple regression method based on the Eliasson and others
(2010) variables. Four situations are shown: all categories of regressors (climate, soil physics, soil
chemistry, water balance) used; water balance information not used; soil physical and chemical
information not used; only water balance information used. For each situation, how the AZS-
BioMA Model is parameterized (figures I28–I31) and the corresponding results calculated for 15
sample cells (figure I32) are shown.




                                                                                                    99
Figure I28. Screen-shot of the model parameter editor showing a configuration where all the
Boolean parameters are set to true




Figure I29. Screenshot of the model parameter editor showing a configuration where Use
Water Balance Information is set to false




                                                                                       100
Figure I30. Screen-shot of the model parameter editor showing a configuration where Use
Soil Physics Information and Use Soil ChemistryInformation are set to “false�




Figure I31. Screenshot of the model parameter editor showing a configuration where Use
Climate Information, Use Soil Physics Information and Use Soil Chemistry Information are
set to “false�




                                                                                    101
Figure I32. Test results of the application of the multiple regression approach based on the
Eliasson and others (2010) regressors, mapping percentage of crop presence: (A) all
categories of regressors (climate, soil physics, soil chemistry, water balance) used; (B) water
balance regressor not used; (C) soil physical and chemical information not used; (D) only
water balance regressor used




Figures I33–I34 show how the percentage of crop percentage changes when the district criterion
is applied.




                                                                                           102
Figure I33. Screenshot of the model parameter editor showing a configuration where
PercentageNeighbouringThresholdForDistrictEffect parameter is set to 75% and
RelativePercentageIncreaseDueToNeighbouring is set to 10% and the district criterion is
activated




Figure I34. Test results of the application of the multiple regression approach based on the
Eliasson and others (2010) regressors, mapping percentage of crop presence




Note: For actual, all categories of regressor are used; for district, all categories of regressors are used and the district
criterion is applied.



                                                                                                                       103
The district criterion increased the percentages of crop presence in the cells surrounded by others
where the same crop is present above a threshold.
Adaptation strategies

Crop assessment studies must take into account adaptation potential and quantify the degree to
which negative impacts can be minimized through management in order to be credible.
Adaptation strategies can be subdivided into two groups: autonomous/short-term adaptations and
planned/long-term adaptations (Tubiello and others 2009; Bates and others 2008; Olesen and
Bindi 2002). Responses implemented by farmers, rural communities, or farmer organizations,
taking into account real or perceived climate change, to optimize production without major
system changes, pertain to the former group; while structural changes guided by interventions at
the regional, national, or international levels, which involves other sectors (such as policy and
research), are planned/long-term adaptations (Carpani and others 2011; Biesbroek and others
2010). We focused on building agromanagement rules for semiautomatic adaptation programs,
and specifically on three main autonomous adaptation strategies: shifting the sowing period of the
four simulated crops, changing the irrigation water volumes, and testing the performance of three
varieties with different crop cycle length.

Sowing
According to Tubiello and others (2000) and Torriani and others (2007), early sowing for summer
crops has positive effects on yield levels. However, Ouda and others (2009) demonstrated that an
early sowing for maize increased season crop length, thus enhancing the irrigation crop
requirements, which reduced water use efficiency under the tested climate change scenarios. The
rules currently implemented in the CRA.AgroManagement component for setting a sowing event
are described below:
RuleAirTemp
Trigger event based on a relative dates window, a rotation year, or a day counter based on daily
average air temperature. If the rule is not triggered during the time window and the parameter
DoItAnyWay is set to true, trigger the rule the last day of the window (figure I35).

Figure I35. RuleAirTemp object model diagram showing the parameters needed by this rule




RuleDate
Triggers an event when a relative date and rotation year is reached (figure I36).

                                                                                               104
Figure I36. RuleDate object model diagram showing the parameters needed by this rule




RuleDatesWindow
Triggers an event based on a relative dates window, a rotation year, or a day counter between
events. The day counter restarts each time the rule is met (figure I37).

Figure I37. RuleDatesWindow object model diagram showing the parameters needed by
this rule




RuleDatesWindowNoRain
Triggers an event based on a relative dates window, a rotation year, a day counter between events,
or a counter for no rainfall. The day counter restarts each time the rule is met, and the rain counter
restarts when there is a rainfall event (figure I38).
Figure I38. RuleDatesWindowNoRain object model diagram showing the parameters
needed by this rule




RuleSoilTempLayer

Rule based on a window date and upper layer soil temperature (figure I39).




                                                                                                  105
Figure I39. RuleSoilTempLayer object model diagram showing the parameters needed by
this rule




The impact associated with these rules for sowing is CropPlanting, which requires the name of the crop
and the planting depth (figure I40).

Figure I40. CropPlanting object model diagram showing the parameters needed by this
impact




Irrigation
For the second adaptation strategy, Ouda and others (2009) noted that irrigation schedule could reduce
yield losses and increase water use efficiency without adding irrigation water. This finding emphasizes the
critical need to use improved irrigation management practices. These practices could reduce irrigation
water losses, enhance plants growth potentialities, and increase final yield, especially under climate change
conditions.

The rules currently implemented in the CRA.AgroManagement component for setting an irrigation event
are described below.

RuleIrrigatePAW

Trigger irrigation within a time window, for a maximum number of times, if plant available water gets
below a given threshold in a soil layer of chosen depth on a specific rotation year (figure I41).

Figure I41. RuleIrrigatePAW object model diagram showing the parameters needed by this
rule.




RulePAWOnce


                                                                                                         106
Trigger irrigation within a range of date, once, if plant available water gets below the upper threshold and
above the lower threshold in a soil layer of chosen depth, on a specific rotation year. If rule is not triggered
during the time window and parameter DoItAnyWay set to true, trigger the rule the last day of the window
(figure I42).

Figure I42. RuleIrrigatePAWOnce object model diagram showing the parameters needed
by this rule




The impact associated to these rule for sowing is IrrigationSimple, which requires the irrigation type and
the irrigation volume (figures I43).

Figure I43. IrrigationSimple object model diagram showing the parameters needed by this
impact




Crop cycle length
Technological advances in production, including crop improvements through breeding (Sinclair
and Muchow 2001; Duvick 2005) or planting varieties with higher water use efficiency could
reduce yield losses to minimal.
This study focused on crop cycle length by developing parameter sets for three varieties for each
crop, with short, medium, and long crop cycles.
After the parameterization of the intermediate variety for each crop (see also section v of this
report), we have built other two varieties in which the growing degree-days needed to reach each
phenological phase are changed by ±20%. After this setting, these new varieties were tested under
the same conditions in which the intermediate one was calibrated in order to verify their
capability to reach the maturity stage under current climate scenario.
Figures I44–I48 show how to use the CRA.AgroManagement component implemented in the
AZS-BioMA model for evaluating the impact of alternative management scenarios to define
adaptation strategies.




                                                                                                            107
Figure I44. Agro Management configuration generator interface showing how to set an agro
management rule (a rule based on a relative date in this example)




Figure I45. Agro Management configuration generator interface showing how to set an agro
management impact (crop planting in this example)




                                                                                    108
Figure I46. Agro Management configuration generator interface showing how to plan an
irrigation set of rules based on soil water content




Figure I47. Available options for setting an irrigation event




                                                                                 109
Figure I48. Agro Management configuration generator interface showing the diagram of the
sample configuration built




AZS-BioMA: System set-up


Calibration and validation, reference data
The modeling solutions within AZS-Bioma were calibrated for the four simulated crops (wheat,
maize, soybean, and rice) before introducing them into the BioMA simulation platform, in order
to test their behavior at diverse South American sites. For each crop, different areas were chosen
to simulate crop development and growth in the grid cells where the fraction of crop coverage is
maximum (source Interim Report – Vegetation Data Set – Crop masks; Ref. 4) and to maximize
the spatial heterogeneity in terms of latitude and climatic conditions. The selected areas for the
four crops are circled in figure I49.




                                                                                              110
Figure I49. Crop distribution of the four simulated crops, with circles indicating areas
selected for the calibration of AZS-Bioma modeling solutions




                                                               <




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For each area, six grid cells were extracted from the climate database with weather series of 10
years (1990–99). Before running the modeling solutions, the meteorological files were analyzed
to obtain information about the inner climatic variability of the sample cells. The results are
shown in the subsequent sections, one for each simulated crop.
Then, an extensive literature review was undertaken to understand the agricultural management
practices in the selected areas and to learn the mean values of yield, leaf area index, and sowing
and harvest dates for each simulated crop (table I3).




                                                                                              111
Table I3. Reference data used for the calibration of crop models parameters describing
morphological and physiological features of three groups of varieties for each crop
Crop State      Latitude Sowing                 Harvest          Leaf area index Yield (t/ha) References
Wheat Argentina 37°S     June 27                December 8                                    Scian and others 2005
                                                                 4–6.7                        Golberg and others 1991
                                                                 5–6.5                        Brisson and others 2001
                         June 12                                                 6            Mendez and others 2007
        Chile    33–37°S June 17; June 22                                        5–6          Campillo and others 2010
                                                                 5                            Acevedo and others 2006
                                                December–January                 5.2–6.7      Acevedo and others 2009
                          August 20;                                                          Lizana and others 2009
                          September 1
        Mexico   27°N     November 14;          April–May          5                          Rodriguez and others 2007
                          December 17
                                                                                  8           Lobell and others 2008
Maize   Argentina 37°S                                                            15          Otegui and others 1996
                          September; early                                                    Otegui and others 1996
                          October
                          September 24;         Physiological      6–8.8                      Luque and others 2006
                          October 6–8           maturity:
                                                March
        Brazil   16°S                                              5.8                        Franca, 1997
                          October–December: February 10–                                      Bergamaschi and others 2007
                          for example,      March 7
                          October 5–10
                                                                                  10          Andrioli and others 2009
        Mexico   17°N     June 28–30; July 11                                                 Tinoco Alfaro and others 2008
                          March 20; April 20;                                     9–12        Avila Perches and others 2009
                          May 1–15
                          April 1; May 8                                          11.8        Mendoza-Elos and others
                                                                                              2006
                                                                   7.4                        Rodríguez and others 2007
                          May 15                November 4                                    Amado Alvarez and others
                                                                                              1998
Soybean Argentina 31°S                                             7                          Canfalone and others 2002
                                                                   7                          Bodrero, 2003
                                                                                  5           Calviño and others 1999.
                          October 1;            March 12–June 2                   5           Mercau and others 2007
                          January 10
                                                                                  5.5         Maehler and others 2003.
        Brazil   16°S                                                             4.4         Sinclair and others 2003
                          November 11;          March 21–27                                   Oya and others 2004
                          November 22
                          October 15;                                                         Avila and others 2003
                          October 30
                          November 15;
                          November 30
                          November 20                                                         Martorano and others 2004
                                                101–123 days after                            Borrmann and others 2009
                                                sowing
                          February 7;           June 6–21          6.5                        Souza and others 2009
                          February 23
Rice    Argentina 31°S    September;                               6                          Brizuela 2009

                                                                                                        112
                               November
                               October 10;           March 7–30                         11.5      Quintero 2009
                               October 28
                                                                                        8.9       Torres 2009
       Brazil        12°S                                                 4–8                     Guimaraes and others 2003
                               November 1–6;                                            5         Guimaraes 2008
                               December 5
                               November 20                                                        Pinheiro and others 1990
                               October 1–15                                                       Buzetti and others 2006
                                                     February–May                                 Bragagnolo, 2006


Parameterization
The parameters of the CropSyst and WARM models (potential production) were calibrated,
starting from the reference values included in the InterimReport (CropDataSet; Ref. 4) in order to
obtain yield and leaf area index values comparable to those found in the literature for the actual
climatic conditions. Table I4 lists the acronyms used for CropSyst model parameters, and table I5
lists the acronyms used for WARM model parameters.
Table I4. CropSyst parameters used to describe crop morphological and physiological
features
                Parameter                                               Unit         Acronym
                Above ground biomass-transpiration coefficient          kPa kg m−3   BTR
                Light to above ground biomass conversion                g MJ−1       LtBC
                Optimum mean daily temperature for growth               °C           Topt
                Maximum water uptake                                    mm day−1     Wup,max
                Leaf water potential at the onset of stomatal closure   J kg−1       Wcr
                Specific leaf area                                      m2 kg−1      SLA
                Stem/leaf partition coefficient                         unitless     SLP
                Leaf duration                                           °C-days      Ldur
                Extinction coefficient for solar radiation              unitless     kc
                Evapotranspiration crop coefficient at full canopy      unitless     Etfull
                Degree-days emergence                                   °C-days      GDDem
                Degree-days peak                                        °C-days      GDDpeak
                Degree-days begin flowering                             °C-days      GDDflo
                Degree-days begin grain filling                         °C-days      GDDgrain
                Degree days physiological maturity                      °C-days      GDDmat
                Base temperature                                        °C           Tb
                Cut-off temperature                                     °C           Tcutoff
                Unstressed harvest index                                unitless     UHI


Table I5. WARM parameters describing rice morphological and physiological features
           Parameter                                                    Unit           Acronym
           Degree days from sowing to emergence                         °C-days        GDDem
           Base temperature for development                             °C             Tb,dev
           Maximum temperature for development                          °C             Tmax,dev
           Degree days from emergence to flowering                      °C-days        GDDflo
           Degree days from flowering to maturity                       °C-days        GDDmat

                                                                                                            113
                 Degree days from maturity to harvest                                      °C-days                  GDDharv
                 Radiation-use efficiency                                                  g MJ−1                   RUEmax
                 Extinction coefficient for solar radiation                                unitless                 kc
                 Base temperature for growth                                               °C                       Tb,gro
                 Optimum T for growth                                                      °C                       Topt,gro
                 Maximum T for growth                                                      °C                       Tmax,gro
                 Specific leaf area at emergence                                           m2 kg−1                  SLAini
                 Specific leaf area end tillering                                          m2 kg−1                  SLAtill
                 Aboveground biomass partition to leaves at emergence                      unitless                 RipL0
                 Leaf duration                                                             °C-days                  Ldur
                 Maximum panicle height                                                    cm                       PanHeightmax


The parameters listed in tables I4–I5 are those adjusted consistently with the results of the
calibrations performed. Below, the results obtained for each simulated crop are presented. The
figures show that the 12 sets of parameters proposed (three groups of varieties ï‚´ four crops) allow
the modeling solutions to simulate leaf area index and biomass data within the range of values
found in the literature for the same conditions.

Wheat
The sample simulations for wheat were carried out in three areas: Mexico, Argentina and Chile.
Figure I50 summarizes the climatic conditions (mean air temperature and global solar radiation
data) for the six grid cells selected for testing the modeling solution. The test aims to reproduce
the reference data on the wheat crop cycle, leaf area index, and yield (figure I51).

Figure I50. Representation of meteorological data for 1990–99 for Mexico, Argentina, and
Chile for wheat calibration
                                                                                                              Global solar radiation
 °C                         Average air temperature                               MJ m-2 d-1
      28
                                                                                   25
      26                                                                           24

      24                                                                           23

      22                                                                           22

                                                                                   21
      20
                                                                                   20
      18
                                                                                   19
      16
                                                                                   18
      14
                                                                                   17
      12                                                                           16

      10                                                                           15
       1990   1991   1992    1993   1994   1995     1996    1997   1998    1999     1990       1991   1992   1993    1994     1995      1996    1997   1998   1999
                                                                          Years                                 Mexico      Argentina   Chile                 Years
                               Mexico   Argentina   Chile




                                                                                                                                                       114
Table I5. Parameterizations adopted for wheat
Process                     Parameter   Average cycle length   Short cycle length   Long cycle length
                            Tb          –1                     –1                   –1
Thermal time accumulation
                            Tcutoff     20                     20                   20
                            GDDem       100                    85                   120
                            GDDpeak     700                    560                  840
Phenology                   GDDflo      900                    720                  1,080
                            GDDgrain    980                    780                  1,176
                            GDDmat      2,000                  1,600                2,400
                            BTR         5                      5                    5
                            LtBC        3                      3                    3
Growth                      SLA         25                     25                   25
                            SLP         2.5                    2.5                  2.5
                            Topt        19                     19                   19
                            ETfull      1.05                   1.05                 1.05
Transpiration               Kc          0.478                  0.478                0.478
                            Wup, max    9                      9                    9
Senescence                  Ldur        600                    600                  600
Harvest                     UHI         0.5                    0.5                  0.5


Figure I51. Reproducing the reference data about the wheat crop cycle, leaf area index, and
yield for Mexico, Argentina, and Chile for wheat calibration




                                                                                                        115
116
Maize
The sample simulations for maize were carried out in three areas: Argentina, Brazil, and Mexico.
Table I6 and figure I52 summarize the climatic conditions (mean air temperature and global solar
radiation data) for the six grid cells selected for testing the modeling solution. The test aims to
reproduce the reference data on the maize crop cycle, leaf area index, and yield (figure I53).

Figure I52. Representation of meteorological data for 1990–99 for Argentina, Brazil, and
Mexico for maize calibration
                                                                                                                 Global solar radiation
 °C                         Average air temperature                                 MJ m-2 d-1
      28
                                                                                     25
      26                                                                             24

      24                                                                             23

      22                                                                             22

                                                                                     21
      20
                                                                                     20
      18
                                                                                     19
      16
                                                                                     18
      14
                                                                                     17
      12                                                                             16

      10                                                                             15
       1990   1991   1992    1993   1994      1995     1996   1997   1998    1999     1990       1991   1992   1993      1994      1995     1996    1997   1998   1999
                                                                            Years                                     Argentina   Brazil   Mexico                 Years
                               Argentina    Brazil   Mexico




Table I6. Parameterizations adopted for maize
Process                                    Parameter          Average cycle length               Short cycle length               Long cycle length
                                           Tb                 8                                  8                                8
Thermal time accumulation
                                           Tcutoff            34                                 34                               34
                                           GDDem              150                                120                              180
                                           GDDpeak            1020                               816                              1,140
Phenology                                  GDDflo             950                                760                              1,224
                                           GDDgrain           1,124                              900                              1,348
                                           GDDmat             1,900                              1,520                            2,280
                                           BTR                8                                  8                                8
                                           LtBC               4                                  4                                4
Growth                                     SLA                28                                 28                               28
                                           SLP                3                                  3                                3
                                           Topt               22                                 22                               22
                                           ETfull             1                                  1                                1
Transpiration                              Kc                 0.47                               0.47                             0.47
                                           Wup, max           12                                 12                               12
Senescence                                 Ldur               1,200                              1,200                            1,200
Harvest                                    UHI                0.43                               0.43                             0.43




                                                                                                                                                            117
Figure I53. Reproducing the reference data about the maize crop cycle, leaf area index, and
yield for Mexico, Argentina, and Brazil for maize calibration




                                                                                       118
Soybean
The soybean sample simulations were carried out in two areas: Argentina and Brazil. Table I7 and
figure I54 summarize the climatic conditions (mean air temperature and global solar radiation
data) for the six grid cells selected for testing the modeling solution. The test aims to reproduce
the reference data on the soybean crop cycle, leaf area, and yield (figure I55).
Figure I54. Representation of meteorological data for 1990–99 for Argentina and Brazil for
soybean calibration
     °C                                                                                                                  Global solar radiation
                                Average air temperature                                     MJ m-2 d-1
          28
                                                                                             25
          26
                                                                                             24
          24
                                                                                             23
          22                                                                                 22

          20                                                                                 21

          18                                                                                 20

                                                                                             19
          16
                                                                                             18
          14
                                                                                             17
          12
                                                                                             16
          10                                                                                 15
           1990   1991   1992    1993   1994        1995       1996   1997   1998    1999     1990       1991   1992   1993   1994     1995         1996   1997    1998   1999
                                                                                    Years
                                                                                                                               Argentina   Brazil                         Years
                                        Argentina     Brazil




                                                                                                                                                             119
Table I7. Parameterizations adopted for soybean
Process                     Parameter   Average cycle length   Short cycle length   Long cycle length
                            Tb          6                      6                    6
Thermal time accumulation
                            Tcutoff     25                     25                   25
                            GDDem       150                    120                  180
                            GDDpeak     1040                   832                  1,248
Phenology                   GDDflo      850                    680                  1,020
                            GDDgrain    930                    744                  1,116
                            GDDmat      1,860                  1488                 2,232
                            BTR         5                      5                    5
                            LtBC        2.5                    2.5                  2.5
Growth                      SLA         28                     28                   28
                            SLP         3                      3                    3
                            Topt        22                     22                   22
                            ETfull      1                      1                    1
Transpiration               Kc          0.45                   0.45                 0.45
                            Wup, max    10                     10                   10
Senescence                  Ldur        900                    900                  900
Harvest                     UHI         0.43                   0.43                 0.43


Figure I55. Reproducing the reference data about the soybean crop cycle, leaf area index,
and yield for Argentina and Brazil for soybean calibration




                                                                                                        120
Rice
The results of the sample simulations carried out for reproducing the reference data about rice
crop cycle, leaf area index, and yield are reported in the following figures. Figure I56 and table I8
summarize the climate conditions (mean air temperature and global solar radiation data) for the
six grid cells chosen for testing the modeling solution (figure I57).
Figure I56. Representation of meteorological data for 1990–99 for Argentina and Brazil for
rice calibration
                                                                                                                    Global solar radiation
 °C                         Average air temperature                                     MJ m-2 d-1
      28
                                                                                         25
      26
                                                                                         24
      24                                                                                 23

      22                                                                                 22

      20                                                                                 21

                                                                                         20
      18
                                                                                         19
      16
                                                                                         18
      14
                                                                                         17
      12                                                                                 16

      10                                                                                 15
       1990   1991   1992    1993   1994        1995       1996   1997   1998    1999     1990       1991   1992   1993   1994    1995     1996   1997   1998   1999
                                                                                Years                                     Argentina   Brazil                    Years
                                    Argentina     Brazil




                                                                                                                                                         121
Table I8. Parameterizations adopted for rice
Process                     Parameter      Average cycle length   Short cycle length   Long cycle length
                            Tb,dev         12                     12                   12
Thermal time accumulation
                            Tmax,dev       42                     42                   42
                            GDDem          70                     56                   84
                            GDDflo         800                    640                  960
Phenology
                            GDDmat         430                    344                  516
                            GDDharv        80                     64                   96
                            RUEmax         2.8                    2.8                  2.8
                            SLAini         27                     27                   27
                            SLAtill        18                     18                   18
                            RipL0          0.6                    0.6                  0.6
Growth
                            Tb,gro         42                     42                   42
                            Tmax,gro       11                     11                   11
                            Topt,gro       27                     27                   27
                            PanHeightmax   100                    100                  100
Transpiration               Kc             0.5                    0.5                  0.5
Senescence                  Ldur           600                    600                  600


Figure I57. Reproducing the reference data about the rice crop cycle, leaf area index, and
yield for Argentina and Brazil for rice calibration




                                                                                                122
Pathogens

Cercospora zeae-maydis (gray leaf spots)
Symptoms: Rectangular, tan-colored lesions that are contained within leaf margins. As lesions
mature they assume a graying cast due to sporulation of the fungi. Under severe disease pressure,
entire leaves can be blighted and lesions can develop on cob sheaths (Carson 1999).
Epidemiology: Cercospora zeae-maydis is an airborne polycyclic fungus. The windborne spores,
named conidia, provide primary inoculum to infect susceptible hybrids of maize, when the
environmental conditions are favorable. The disease can develop when the optimum air
temperature ranges from 22°C to 30°C (Latteral and others 1983) and when relative humidity is
greater than 90%, while the conidia need at least six hours of continuous leaf wetness at
temperatures between 18°C and 25°C for germination (Rupe and others 1982). The pathogen can
penetrate the host leaf stomata through the development of an infection hook. After infection,
flecks can appear within nine days, while the mature lesions become visible and necrotic in 14 or
21 days, marking the beginning of the intensive phase of sporulation. At this stage, high relative
humidity can promote a secondary cycle of infection (Ward and others 1998). This foliar disease,
if not treated, can result in a premature senescence and loss in photosynthetic leaf area, causing
severe yield losses, that can range from 0% to 60% in countries where the disease is endemic
(Ward and others 1996).



                                                                                              123
Geographic distribution: Figure I58 shows the geographic distribution of Cercospora zeae-
maydis.


Figure I58. South American countries in which Cercospora zeae-maydis is present




Parameters:

Parameter                                                Unit       Value                               Source
Maximum air temperature for infection (T maxinf)         °C         36                                  2; 4
Minimum air temperature for infection (T mininf)         °C         10                                  5
Optimum air temperature for infection (T optinf)         °C         20–30; 22–30; 25–30; 24.8           2; 3; 4; 7; 8
Minimum wetness duration for infection (WDmin)           hours      6                                   3; 5
Maximum wetness duration for infection (WDmax)           hours      9                                   3; 5
Maximum air temperature for incubation (T maxinc)        °C         35                                  5
Minimum air temperature for incubation (T mininc)        °C         10                                  5
Optimum air temperature for incubation (Toptinc)         °C         18–25                               5
Minimum incubation duration (MID)                        days       9                                   3
Maximum air temperature for latency (T maxlat)           °C         35°                                 5
Minimum air temperature for latency (T minlat)           °C         10                                  5
Optimum air temperature for latency (T optlat)           °C         18–25                               5
                                                                    14 (susceptible hybrids);
Minimum latency duration (MLD)                           days                                           2; 3; 9
                                                                    16 (moderately resistant hybrids)
Maximum air temperature for infectiousness (T maxness)   °C         35; 32                              2;
Minimum air temperature for infectiousness (T minness)   °C         20                                  2
Optimum air temperature for infectiousness (Toptness)    °C         27.2                                2
Maximum infectiousness duration (MSD)                    days       9                                   6
Maximum air temperature for sporulation (T maxspor)      °C         35; 32                              2;
Minimum air temperature for sporulation (T minspor)      °C         20                                  2
Optimum air temperature for sporulation (T optspor)      °C         27.2; 25                            2;
Minimum rel. humidity for sporulation (RH minspor)       percent    58; 65                              1; 3
Rain for 50% detachment (Rain50)                         mm day-1   2.5                                 10
Maximum catch rain (Rainmax)                             mm day-1   6                                   10

                                                                                                               124
Minimum wind for detachment (Wmin)                     m s-1      1.3 (dehydrated spores)             1
Wind for 50% detachment (W50)                          m s-1      2.4                                 1
Spores at Maximum wind for detachment (Wmaxspor)       percent    0.8                                 10
Maximum wind for detachment (Wmax)                     m s-1      4.7 (hydrated spores)               1
Wetness duration D50 (WD50)                            hours      5                                   10

1: Lepaire and others 2003.          5: Rupe and others 1982.                  8: Latteral and others 1983.
2: Paul and others 2005.             6: Castor and others 1977.                9: Ringer and others 1995.
3: Ward and others 1998.             7: Li and others 2007.                    10: Model default.
4: Beckman and others 1983.


Phakopsora pachyrizi (soybean rust)
Symptoms: Infection begins on the lower first leaves of plants and appears as chlorotic or mosaic-like
areas. The lesions, because of the sporulation, merge forming larger pustules characterized by a tan or red-
brown color. With the progress of maturation, the lesions may become black, due to the formation of
layers of teliospore in the pustules (Rupe and others 2008).

Epidemiology: Soybean rust is an airborne polycyclic fungus. Urediniospores of Phakopsora
pachyrizi are transported readily by air currents and can be carried hundreds of miles in a few
days. The conditions for the development of infection depend on the host vulnerability and on
environmental suitability. Rust spores are able to penetrate the plant cells directly rather than
through natural openings or through wounds in the leaf tissue. Generally, infection occurs when
leaves are wet, the relative humidity is 75–80%, and temperatures are 12–27°C (Jarvie 2009),
with an optimum of 23°C (Magarey and others 2005). At this temperature, some infection occurs
in 6 hours of leaf wetness, but 12 hours are optimal (Rupe and others 2008). After infection,
lesions can appear within 9 days, and the spores are produced in 10–21 days (Kawuki and others
2003). Soybean rust pathogen will go through many cycles of spore production, spore release, and
new infections in a growing season, causing severe yield losses and complete plant defoliation.
Geographic distribution: Figure I59 shows the geographic distribution of Phakopsora phachyrizi.




                                                                                                              125
Figure I59. South American countries in which Phakopsora phachyrizi is present




Parameters:

Parameter                                                Unit       Values                    Sourcea
Maximum air temperature for infection (T maxinf)         °C         28; 28.5                  1; 2; 3
Minimum air temperature for infection (T mininf)         °C         8; 10                     1; 2; 3
Optimum air temperature for infection (Toptinf)          °C         16–28; 15–25; 18–26; 23   1; 2; 3; 13
Minimum wetness duration for infection (WDmin)            hours     6                         1; 2; 9
Maximum wetness duration for infection (WDmax)            hours     12;10                     1; 2
Maximum air temperature for incubation (T maxinc)        °C         30; 29                    9; 12
Minimum air temperature for incubation (T mininc)        °C         15; 12                    9; 12
Optimum air temperature for incubation (T optinc)        °C         24                        12
Minimum incubation duration (MID)                        days       7–8; 5; 6                 1; 4; 10
Maximum air temperature for latency (T maxlat)           °C         42                        5; 6; 9
Minimum air temperature for latency (T minlat)           °C         5                         5; 6; 9
Optimum air temperature for latency (T optlat)           °C         26                        5; 6; 9
Minimum latency duration (MLD)                           days       6; 9                      4; 6; 11
Maximum air temperature for infectiousness (T maxness)   °C         28                        6
Minimum air temperature for infectiousness (Tminness)    °C         10                        2; 3
Optimum air temperature for infectiousness (T optness)   °C         16                        6
Maximum infectiousness duration (MSD)                    days       21; 28                    4; 6
Maximum air temperature for sporulation (T maxspor)      °C         27                        9
Minimum air temperature for sporulation (Tminspor)       °C         10                        2; 3
Optimum air temperature for sporulation (T optspor)      °C         20                        6
Minimum rel. humidity for sporulation (RH minspor)       percent    75–80                     8
Rain for 50% detachment (Rain50)                         mm day-1   2.5                       14
Maximum catch rain (Rainmax)                             mm day-1   6                         14
Minimum wind for detachment (Wmin)                       m s-1      1                         7
Wind for 50% detachment (W50)                            m s-1      2                         7
Spores at Maximum wind for detachment (Wmaxspor)         percent    0.8                       14
Maximum wind for detachment (Wmax)                       m s-1      3                         7
Wetness duration D50 (WD50)                              hours      100                       7


                                                                                                            126
1: Rupe and others 2008.              6: Pivonia and others 2006.             11: Alves 2007.
2: Marchetti and others 1976.         7: Isaard and others 2007.              12: Bonde and others 2007.
3: Magarey and others 2005.           8: Caldwell and others 2002.            13: Kochman and others 1979.
4: Kawuki and others 2003.            9: Del Ponte and others 2008.           14: Model default.
5: Kitani and others 1960.            10: Bourdon and others 1981.


Puccinia recondite (wheat leaf rust)
Symptoms: Small brown pustules develop on the leaf blades in a random scatter distribution. They may
group into patches in serious cases. Infectious spores are transmitted through the soil. Onset of the disease
is slow but accelerated in temperatures above 15°C, making it a disease of the mature cereal plant in
summer, usually too late to cause significant damage in temperate areas. Losses of 5–20% are normal but
may reach 50% in severe cases.

Epidemiology: Puccinia recondita can infect with dew periods of three hours or less at temperature about
20°C. At lower temperatures, longer dew periods are required. Most of the severe epidemics occur when
uredinia or latent infections survive the winter at some threshold level on the wheat crop or where spring
sown wheat is the recipient of exogenous inoculum before heading (Roelfs and others 1992). Disease
spread can be very rapid under favorable environmental conditions. A single uredinium can produce about
3,000 spores a day over 20 days.

Variation in severity of rust epidemics in this area depends on differences in crop maturity at the time of
infection by primary inoculum, host resistance used, and environmental conditions (Eversmeyer and
Kramer 2000).

Geographic distribution: Figure I60 shows the geographic distribution of Puccinia recondita.


Figure I60. South American countries in which Puccinia recondita is present




                                                                                                         127
Parameters:

Parameter                                                  Unit        Value                       Source
Maximum air temperature for infection (T maxinf)           °C          30; 20; 30–32               1, 2, 3, 4, 5, 6, 7
Minimum air temperature for infection (T mininf)           °C          2.6; 2; 5; 3                1, 2, 3, 4, 5, 7, 8
Optimum air temperature for infection (T optinf)           °C          20–25; 20; 25               1, 3, 4, 5, 6, 7
Minimum wetness duration for infection (WDmin)             days        3; 4; 4–6                   1, 2, 5, 7
Maximum wetness duration for infection (WDmax)             days        14; 16                      1, 2
Maximum air temperature for incubation (T maxinc)          °C          32.2; 35                    9, 10
Minimum air temperature for incubation (T maxinc)          °C          5; 10; 15; 15.6             2, 3, 9, 11
Optimum air temperature for incubation (T optinc)          °C          20; 26.5; 25; 23.9–26.7 2, 3, 9, 10, 11
Minimum incubation duration (MID)                          days        6; 7; 8; 8.3; 10            2, 3, 9, 11, 12
Maximum air temperature for latency (Tmaxlat)              °C          32.2; 35                    9, 10
Minimum air temperature for latency (T minlat)             °C          5; 10; 15; 15.6             2, 3, 9, 11
Optimum air temperature for latency (T optlat)             °C          20; 26.5; 25; 23.9–26.7 2, 3, 9, 10, 11
Minimum latency duration (MLD)                             days        6; 7; 8; 8.3; 10            2, 3, 9, 11, 12
Maximum air temperature for infectiousness (T maxness)     °C          30–32; 33; 35               2, 4, 5, 13
Minimum air temperature for infectiousness (T minness)     °C          10; 20                      2, 4, 14
Optimum air temperature for infectiousness (T optness)     °C          25                          4, 14
Maximum infectiousness duration (MSD)                      days        65                          11, 15
Maximum air temperature for sporulation (T maxspor)        °C          30–32; 33; 35               2, 4, 5, 13
Minimum air temperature for sporulation (T minspor)        °C          10; 20                      2, 4, 14
Optimum air temperature for sporulation (T optspor)        °C          25                          4, 14
Minimum rel. humidity for sporulation (RH minspor)         percent     90                          16
Rain for 50% detachment (Rain50)                           mm day-1 2.5–4.9                        17
Maximum catch rain (Rainmax)                               mm day-1 2.5–4.9                        17
Minimum wind for detachment (Wmin)                         m s-1       1.3; 0.5–1.5                17, 18
Wind for 50% detachment (W50)                              m s-1       5                           20
Spores at Maximum wind for detachment (Wmaxspor)           percent     0.8                         20
Maximum wind for detachment (Wmax)                         m s-1       6                           20
Wetness duration D50 (WD50)                                days        1; 1–2                      1, 19
1: Magarey and others 2005.              9: Eversmeyer and Kramer 2000.               17: Sache 2000.
2: Wójtowicz 2007.                       10: Roelsf and others 1992.                  18: Geagea and others 1997.
3: Clifford and Harris 1981.             11: Tomerlin and others 1983.                19: Stuckey and Zadocks 1989.
4: Singh and others 1992.                12: Kovalenko and others 2004.               20: Model default
5: NAPPFAST Pest record 2003.            13: Rapilly 1979.
6: Wiese and Ravenscroft 1979.           14: Dick and Johnson 1983.
7: Vallavieille Pope and others 1995.    15: Mehta and Zadocks 1970.
8: Angus and others 1981.                16: Agricultural Development and Advisory Service.

Pyricularia oryzae (blast disease)
Symptoms: At the beginning, the symptoms occur with the appearance of gray-green spots on the leaves,
with a darker border. As lesions mature they become whitish-greenish with brown-reddish edges, and they
assume an oval-elliptic form. When the sporulation starts, the center of the spots turn ash grey. Dark-
brown spots also come about on the culm and on the panicle; node damages can lead to breakage of the
culm (Castejón-Muñoz 2007).

Epidemiology: Pyricularia oryzae is an ascomycete that can infect rice crop by means of airborne conidia,
when the environmental conditions are suitable. The pathogen overwinters inside affected rice straw
remaining from the previous year. From these straws primary inoculum develops. The production of
conidia occurs when air temperature and air relative humidity rise. After the infection process fulfilled, the
mycelium develops inside the host tissue and then causes lesions in which conidia are formed. An
epidemic breaks out when several cycles are repeated. The factors that could enhance the severity of an
epidemic are the number of conidia produced by the lesions, the suitability of meteorological conditions,

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and the inner resistance of the cultivar. Also agricultural management practices could affect the dynamics
of blast disease epidemics.

Geographic distribution: Figure I61 shows the geographic distribution of Pyricularia oryzae.

Figure I61. South American countries in which Pyricularia oryzae is present




For this pathogen, no literature review was needed since its simulation will be carried out via the
UNIMI.BlastDisease component (see also section iii of this document), implementing a specific model for
this disease.

Land resources database

All the data sets (climate, crop, soil, vegetation) were created and standardized within a dedicated
GIS developed and described for the previous deliverable (D2 – January 30; Ref. 4).
For some areas, the soil information discussed for D2 (derived from Hogenboom and others 2009)
was not available. After searching for alternatives and evaluating different options, we identified
another soil database (SOTERLAC version 2.0 database; 1:5 million scale) from the World Data
Centre for Soils (www.isric.org/content/world-data-centre-soils). The Hogenboom and others
(2009) database is more suitable for cropping systems model applications. However, the
SOTERLAC database has a more regular cover of the Latin America and Caribbean: 1,660
representative soil profiles, corresponding to an average density of profile observation of 0.09 per
1,000 km2 (Batjes 2005). Therefore, the SOTERLAC database information will be used to
complete the soil database delivered for D2, and the soil properties not available (but needed by
the AZS-BioMA model) will be derived using pedotransfer functions.
Figure I62 shows the information available in the SOTERLAC database, and figure I63 shows the
regularity in the distribution of Latin American and Caribbean sites for which SOTERLAC
profiles are available.

                                                                                                      129
Figure I62. Occurrence, in percentage, of the attributes in the SOTERLAC 2.0




                                                                               130
Figure I63. Spatial distribution of the soil profiles of SOTERLAC 2.0




Table I9 lists the main information of layers, profiles, and soils stored in SOTERLAC 2.0. For a
comparison with the information in the Hogenboom database, see appendix C of the deliverable
D2 (Ref. 4).

Table I9. List of the main information enclosed in SOTERLAC 2.0 related to layers, profiles,
and soil components
Name   Label                       Description
                                               Layer
PRID   Profile-ID                  Code for the representative profile
HONU   Horizon number              A consecutive number, starting with the surface horizon
                                   Diagnostic horizon - Revised Legend of the FAO-UNESCO Soil Map of
DIAH   Diagnostic horizon
                                   the World (FAO 1988)
                                   Diagnostic property - Revised Legend of the FAO-Unesco Soil Map of
DIAP   Diagnostic property
                                   the World (FAO 1988)
HODE   Horizon designation         Master horizon with subordinate characteristics (FAO 1990)

                                                                                                 131
HBDE    Lower depth                 The average depth of the lower boundary in centimeters
HBDI    Distinctness of transition  Abruptness of horizon boundary to underlying horizon (FAO 1990)
SCMO    Colour - moist soil         The Munsell color of the moist soil
SCDR    Colour - dry soil           The Munsell color of the dry soil
STGR    Grade of structure          Grade of structure (FAO 1990)
STSI    Size of structure elements  Size of structure elements (FAO 1990)
STTY    Type of structure           Type of structure (FAO 1990)
                                    Classes of volume (%) of rock or mineral fragments (> 2mm) in the soil
MINA Abundance of coarse fragments
                                    matrix (FAO 1990)
MINS Size of coarse fragments       Size of dominant rock or mineral fragments in classes (FAO, 1990)
                                    Weight (%) of particles 2.0–1.0 mm (very coarse sand) in fine earth
SDVC Very coarse sand
                                    fraction
SDCO Coarse sand                    Weight (%) of particles 1.0–0.5 mm (coarse sand) in fine earth fraction
SDME Medium sand                    Weight (%) of particles 0.5–0.25 mm (medium sand) in fine earth fraction
SDFI Fine sand                      Weight (%) of particles 0.25–0.1 mm (fine sand) in fine earth fraction
SDVF Very fine sand                 Weight (%) of particles 0.1–0.05 mm (very sand) in fine earth fraction
SDTO Total sand                     Weight (%) of particles 2.0–0.05 mm (total sand) in fine earth fraction
STPC Silt                           Weight (%) of particles < 0.002 mm (silt) in fine earth fraction
CLPC Clay                           Weight (%) of particles < 0.002 mm (clay) in fine earth fraction
PSCL Particle size class            Particle size class as derived from the particle size analysis
BULK Bulk density                   The bulk density in kg per cubic dm
MCT1 Soil moisture (field capacity) Soil moisture (%) at tension 1 (field capacity, –33 KPa)
TEN2 Water tension 2                Water tension 2 (KPa)
MCT2 Soil moisture (tension 2)      Soil moisture (%) at tension 2
TEN3 Water tension 3                Water tension 3 (KPa)
MCT3 Soil moisture (tension 3)      Soil moisture (%) at tension 3
TEN4 Water tension 4                Water tension 4 (KPa)
MCT4 Soil moisture (tension 4)      Soil moisture (%) at tension 4
MCT5 Soil moisture (wilting point)  Soil moisture (%) at tension 5 (wilting point, –1,500KPa)
HYDC Hydraulic conductivity         The saturated hydraulic conductivity in cm/hour
INFR Infiltration rate              The basic infiltration rate in cm/hour
PHAQ pH (H2O)                       pH (H2O) in a supernatant suspension of a 1:2.5 soil - water mixture
PHKC pH (KCl)                       pH (KCl) in a supernatant suspension of a 1:2.5 soil - 1M KCl mixture
ELCO Electrical conductivity        The electrical conductivity of saturation extract (dS/m)
SONA Soluble Na                     The soluble Na+ content of the saturated paste in cmol(c) / liter
SOCA Soluble Ca                     The soluble Ca++ content of the saturated paste in cmol(c) / liter
SOMG Soluble Mg                     The soluble Mg++ content of the saturated paste in cmol(c) / liter
SOLK Soluble K                      The soluble K+ content of the saturated paste in cmol(c) / liter
SOCL Soluble Cl                     The soluble Cl- content of the saturated paste in cmol(c) / liter
SSO4 Soluble SO4                    The soluble SO4-- content of the saturated paste in cmol(c) / liter
SCO3 Soluble CO3                    The soluble CO3- content of the saturated paste in cmol(c) / liter
HCO3 Soluble HCO3                   The soluble HCO3- content of the saturated paste in cmol(c) / liter
EXCA Exchangeable Ca                The exchangeable Ca in cmol(+) / kg
EXMG Exchangeable Mg                The exchangeable Mg in cmol(+) / kg
EXNA Exchangeable Na                The exchangeable Na in cmol(+) / kg
EXCK Exchangeable K                 The exchangeable K in cmol(+) / kg
EXAL Exchangeable Al                The exchangeable Al in cmol(+) / kg
EXAC Exchangeable acidity           The exchangeable acidity, as determined in 1N KCl, in cmol(+) / kg
CECS CEC soil                       The cation-exchange capacity of the soil at pH 7.0 in cmol(+) / kg

                                                                                                        132
TCEQ   Total carbonate equivalent       The content of carbonates in g / kg
GYPS   Gypsum                           The gypsum content in g / kg
TOTC   Total carbon                     The content of total organic carbon in g / kg
TOTN   Total nitrogen                   The content of total N in g / kg
P2O5   P2O5                             The P2O5 content in mg / kg
PRET   Phosphate retention              The phosphate retention in percent
FEDE   Fe, dithionite extractable       The Fe fraction, in weight (%), extractable in dithionite
FEPE   Fe, pyrophosphate extractable    The Fe fraction, in weight (%), extractable in pyrophosphate at PH 10
ALDE   Al, dithionite extractable       The Al fraction, in weight (%), extractable in dithionite
ALPE   Al, pyrophosphate extractable    The Al fraction, in weight (%), extractable in pyrophosphate at PH 10
CLAY   Clay mineralogy                  The dominant type of mineral in the clay fraction
                                                     Profile
PRID   Profile-ID                       Code for the representative profile
                                        ID for the owner, institute or organization that holds (part of) the national
PDID   Profile database-ID
                                        soil profile database
                                        Latitude in decimal degrees. Latitudes in the southern hemisphere are
LATI   Latitude
                                        negative
                                        Longitude in decimal degrees. Longitudes in the western hemisphere are
LNGI   Longitude
                                        negative
ELEV   Elevation                        The elevation of the representative profile in meters above sea level
SAYR   Sampling year                    The year in which the profile was described and sampled
MNUM   Sampling month                   The month in which the profile was described and sampled
LABO   Lab-ID                           ID for the soil laboratory that analyzed the samples
DRAI   Drainage                         Present drainage class of a soil component represented by this profile
INFR   Infiltration rate                Basic infiltration rate category (BAI 1991)
ORGA   Surface organic matter           Surface organic matter - thickness and degree of decomposition
                                        Classification according to World reference base for soil resources
WRBC WRB Classification
                                        (WRB) - full name
                                        Classification according to World reference base for soil resources
WRBS   WRB Classification - specifier
                                        (WRB) - specifier
                                        Characterization of the profile - revised legend of the FAO-Unesco Soil
CLAF   FAO classification
                                        Map of the World Legend (FAO 1988)
                                        The year of publication of the version of the FAO Legend used for the
CLAV   Classification version
                                        profile's characterization
                                        Any potentially limiting factor related to (sub)surface features of the
PHAS   FAO phase
                                        terrain
                                        The original national classification of the profile, if different from the
CLAN   National classification
                                        FAO classification
STAX   Soil Taxonomy                    Classification according to Soil Taxonomy
                                                Soil components
ISOC   ISO country code                 ISO-3166 country code (1994)
SUID   SOTER unit-ID                    The identification code of a SOTER unit on the map and in the database
                                        The sequence number of the terrain component in the terrain (largest
TCID   Terrain component number
                                        comes first)
                                        The sequence number of the soil component in the terrain component
SCID   Soil component number
                                        (largest comes first)
PRID   Profile-ID                       Code for the representative profile


                                                                                                                133
                                      The number of reference profiles that was considered for the selection of
NRPR   Number of reference profiles
                                      the representative profile
POSI   Position                       The relative position of the soil component within the terrain component
RKSC   Surface rockiness              The percentage coverage of rock outcrops - classes (FAO 1990)
                                      The percentage cover of coarse fragments (> 0.2 cm), completely or partly
STSC   Surface stoniness
                                      at the surface - classes (FAO 1990)
                                      Characterization of the erosion or deposition type according to FAO
ERTY   Erosion/deposition type
                                      (1990)
                                      The area affected by erosion or deposition. Classes according to UNEP-
ERAA   Area affected
                                      ISRIC (1988)
ERDE   Erosion degree                 Degree of erosion (FAO 1990)
                                      The degree in which the soil surface has a tendency to capping and
SCAP   Sensitivity to capping
                                      sealing (FAO 1990)
                                      Estimated depth to which root growth is unrestricted by physical or
RDEP   Rootable depth
                                      chemical impediments— classes after FAO (1990)
       Relation with other soil       The relationship between this soil component and adjoining soil
RELA
       components                     components




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Annex II. Crop Yield Simulation Results
Wheat, 2020, No Adaptation

Table II1. Percentage impact (no adaptation) of climate change on wheat yields for potential,
water limited, abiotic damaged, diseased, and actual production levels; projections for 2020
                              Climate change      Climate change impact (percentage difference compared to
Country                       scenario                            the baseline productivity
                                                  Potential    Water        Abiotic     Diseased   Actual
                                                               limited     damaged
Argentina                     Hadley, A1B, 2020       –13.74      –21.70       –13.74       –22.33  –31.23
Argentina                     Hadley, B1, 2020        –15.45      –22.68       –15.45       –33.39  –31.38
Argentina                     NCAR, A1B, 2020         –16.40      –23.67       –16.40       –23.70  –30.97
Argentina                     NCAR, B1, 2020          –15.99      –23.60       –15.99       –23.61  –31.74
Brazil                        Hadley, A1B, 2020       –21.96      –26.05       –21.96       –26.02  –30.78
Brazil                        Hadley, B1, 2020        –24.69      –28.24       –24.69       –27.67  –31.34
Brazil                        NCAR, A1B, 2020         –30.47      –34.76       –30.47       –32.80  –36.47
Brazil                        NCAR, B1, 2020          –24.83      –29.99       –24.83       –27.34  –32.40
Chile                         Hadley, A1B, 2020        +0.20      –24.43        +1.99       –12.40  –33.03
Chile                         Hadley, B1, 2020         –0.33      –23.49        +2.04       –12.70  –31.62
Chile                         NCAR, A1B, 2020          –0.97      –21.44        +1.34       –13.47  –30.02
Chile                         NCAR, B1, 2020           –0.85      –21.51        +1.93       –13.43  –29.80
Colombia                      Hadley, A1B, 2020       –30.50      –32.80       –30.50       –29.29  –28.61
Colombia                      Hadley, B1, 2020        –29.81      –32.14       –29.81       –28.63  –28.14
Colombia                      NCAR, A1B, 2020         –29.62      –31.99       –29.62       –27.50  –26.11
Colombia                      NCAR, B1, 2020          –25.91      –28.45       –25.91       –26.24  –25.60
Ecuador                       Hadley, A1B, 2020       –16.12      –18.03       –16.12       –17.07  –18.57
Ecuador                       Hadley, B1, 2020        –15.40      –17.94       –15.40       –16.25  –18.49
Ecuador                       NCAR, A1B, 2020         –12.63      –14.82       –12.63       –14.16  –15.91
Ecuador                       NCAR, B1, 2020          –10.55      –12.85       –10.55       –12.35  –14.24
Mexico                        Hadley, A1B, 2020       –39.99      –36.93       –39.99       –41.25  –37.69
Mexico                        Hadley, B1, 2020        –36.59      –33.18       –36.59       –37.83  –33.99
Mexico                        NCAR, A1B, 2020         –37.27      –32.04       –37.27       –39.18  –32.40
Mexico                        NCAR, B1, 2020          –34.14      –29.74       –34.14       –35.61  –29.77
Peru                          Hadley, A1B, 2020        –5.43       –7.29        –7.18        –3.45   –4.56
Peru                          Hadley, B1, 2020         –4.89       –7.48        –6.68        –2.84   –4.80
Peru                          NCAR, A1B, 2020          –4.11       –5.91        –6.02        –2.00   –3.13
Peru                          NCAR, B1, 2020           –3.38       –5.31        –5.08        –1.21   –2.18
Uruguay                       Hadley, A1B, 2020       –17.65      –17.05       –17.65       –15.66  –14.55
Uruguay                       Hadley, B1, 2020        –22.37      –20.15       –22.37       –19.55  –16.05
Uruguay                       NCAR, A1B, 2020         –25.91      –24.53       –25.91       –22.36  –18.58
Uruguay                       NCAR, B1, 2020          –23.04      –23.06       –23.04       –19.71  –17.98
Rest of South America         Hadley, A1B, 2020       –13.90      –21.59       –13.93       –22.29  –31.02
Rest of South America         Hadley, B1, 2020        –15.61      –22.61       –15.64       –23.36  –31.18
Rest of South America         NCAR, A1B, 2020         –16.71      –23.77       –16.75       –23.82  –30.94
Rest of South America         NCAR, B1, 2020          –16.09      –23.50       –16.12       –23.54  –31.54
Central America & Caribbean   Hadley, A1B, 2020       –39.89      –36.87       –39.89       –41.20  –37.64
Central America & Caribbean   Hadley, B1, 2020        –36.53      –33.16       –36.53       –37.80  –33.96
Central America & Caribbean   NCAR, A1B, 2020         –37.19      –32.04       –37.19       –39.14  –32.39
Central America & Caribbean   NCAR, B1, 2020          –34.06      –29.72       –34.06       –35.57  –29.75




                                                                                            135
Maize, 2020, No Adaptation

Table II2. Percentage impact (no adaptation) of climate change on maize yields for potential,
water limited, abiotic damaged, diseased, and actual production levels; projections for 2020
                              Climate change      Climate change impact (percentage difference compared to
Country                       scenario                            the baseline productivity
                                                  Potential    Water        Abiotic     Diseased   Actual
                                                               limited     damaged
Argentina                     Hadley, A1B, 2020       –16.07      –16.46       –16.07       –15.60  –15.17
Argentina                     Hadley, B1, 2020        –10.75      –11.11       –10.75       –10.37  –10.01
Argentina                     NCAR, A1B, 2020         –14.69      –14.82       –14.69       –14.33  –13.70
Argentina                     NCAR, B1, 2020          –14.13      –14.35       –14.13       –13.70  –13.15
Brazil                        Hadley, A1B, 2020       –27.86      –28.32       –27.86       –27.83  –27.14
Brazil                        Hadley, B1, 2020        –20.30      –20.84       –20.30       –20.07  –19.61
Brazil                        NCAR, A1B, 2020         –38.09      –38.49       –38.09       –38.94  –37.98
Brazil                        NCAR, B1, 2020          –34.05      –34.49       –34.05       –35.04  –34.16
Chile                         Hadley, A1B, 2020       –11.23      –10.36       –11.23       –11.43  –10.16
Chile                         Hadley, B1, 2020         –8.64       –7.11        –8.64        –8.82   –6.94
Chile                         NCAR, A1B, 2020         –11.84      –11.03       –11.84       –12.10  –10.86
Chile                         NCAR, B1, 2020          –12.62      –11.87       –12.62       –12.90  –11.72
Colombia                      Hadley, A1B, 2020       –19.16      –20.16       –19.16       –16.89  –16.69
Colombia                      Hadley, B1, 2020        –18.20      –19.18       –18.20       –16.11  –15.91
Colombia                      NCAR, A1B, 2020         –19.85      –20.87       –19.85       –18.54  –18.21
Colombia                      NCAR, B1, 2020          –16.70      –17.75       –16.70       –14.54  –14.18
Ecuador                       Hadley, A1B, 2020       –37.18      –38.20       –37.18       –37.76  –37.95
Ecuador                       Hadley, B1, 2020        –34.82      –36.07       –34.82       –35.33  –35.68
Ecuador                       NCAR, A1B, 2020         –34.25      –35.07       –34.25       –34.70  –34.76
Ecuador                       NCAR, B1, 2020          –30.62      –31.67       –30.62       –30.65  –31.13
Mexico                        Hadley, A1B, 2020       –28.25      –28.83       –28.42       –29.77  –30.09
Mexico                        Hadley, B1, 2020        –24.42      –25.12       –24.63       –25.49  –25.86
Mexico                        NCAR, A1B, 2020         –24.48      –25.21       –24.76       –25.88  –26.38
Mexico                        NCAR, B1, 2020          –22.72      –25.53       –23.02       –24.10  –24.70
Peru                          Hadley, A1B, 2020       –13.95      –15.55       –13.95       –13.93  –13.87
Peru                          Hadley, B1, 2020        –12.05      –13.67       –12.05       –12.13  –12.26
Peru                          NCAR, A1B, 2020          –9.96      –11.92        –9.96        –8.85   –9.03
Peru                          NCAR, B1, 2020           –9.17      –11.22        –9.17        –8.22   –8.46
Uruguay                       Hadley, A1B, 2020       –13.86      –14.40       –13.86       –13.51  –13.24
Uruguay                       Hadley, B1, 2020         –8.68       –9.27        –8.68        –8.45   –8.31
Uruguay                       NCAR, A1B, 2020         –12.99      –13.28       –12.99       –12.99  –12.48
Uruguay                       NCAR, B1, 2020          –14.06      –14.51       –14.06       –13.74  –13.33
Rest of South America         Hadley, A1B, 2020       –15.65      –16.76       –15.65       –14.95  –14.80
Rest of South America         Hadley, B1, 2020        –12.90      –14.04       –12.90       –12.17  –12.13
Rest of South America         NCAR, A1B, 2020         –13.97      –15.17       –13.97       –13.21  –13.10
Rest of South America         NCAR, B1, 2020          –13.16      –14.43       –13.16       –12.25  –12.17
Central America & Caribbean   Hadley, A1B, 2020       –27.92      –28.51       –28.08       –29.33  –29.63
Central America & Caribbean   Hadley, B1, 2020        –24.19      –24.89       –24.39       –25.16  –25.51
Central America & Caribbean   NCAR, A1B, 2020         –24.30      –25.04       –24.57       –25.62  –26.10
Central America & Caribbean   NCAR, B1, 2020          –22.50      –23.31       –22.79       –23.77  –24.34




                                                                                               136
Soybean, 2020, No Adaptation

Table II3. Percentage impact (no adaptation) of climate change on soybean yields for
potential, water limited, abiotic damaged, diseased, and actual production levels;
projections for 2020
                              Climate change      Climate change impact (percentage difference compared to
Country                       scenario                            the baseline productivity
                                                  Potential    Water        Abiotic     Diseased   Actual
                                                               limited     damaged
Argentina                     Hadley, A1B, 2020       –24.42      –15.98       –24.42       –24.41  –15.01
Argentina                     Hadley, B1, 2020        –15.63       –6.31       –15.63       –15.63   –5.18
Argentina                     NCAR, A1B, 2020         –14.97       –5.28       –14.97       –14.97   –4.13
Argentina                     NCAR, B1, 2020          –18.17       –9.07       –18.17       –18.16   –7.99
Brazil                        Hadley, A1B, 2020       –39.04      –38.71       –39.04       –39.04  –37.25
Brazil                        Hadley, B1, 2020        –31.74      –31.37       –31.74       –31.74  –29.71
Brazil                        NCAR, A1B, 2020         –39.86      –39.59       –39.86       –39.86  –38.12
Brazil                        NCAR, B1, 2020          –36.29      –36.03       –36.29       –36.29  –34.47
Chile                         Hadley, A1B, 2020
Chile                         Hadley, B1, 2020
Chile                         NCAR, A1B, 2020
Chile                         NCAR, B1, 2020
Colombia                      Hadley, A1B, 2020      –24.98      –24.79      –24.98       –29.12     –29.09
Colombia                      Hadley, B1, 2020       –23.00      –22.80      –23.00       –26.78     –26.74
Colombia                      NCAR, A1B, 2020        –18.44      –18.07      –18.44       –22.07     –21.78
Colombia                      NCAR, B1, 2020         –19.20      –19.00      –19.20       –20.50     –20.34
Ecuador                       Hadley, A1B, 2020      –48.73      –50.56      –48.73       –48.73     –48.80
Ecuador                       Hadley, B1, 2020       –46.22      –48.20      –46.22       –46.22     –46.35
Ecuador                       NCAR, A1B, 2020        –44.98      –47.01      –44.98       –44.98     –45.11
Ecuador                       NCAR, B1, 2020         –39.63      –41.86      –39.63       –39.73     –39.74
Mexico                        Hadley, A1B, 2020
Mexico                        Hadley, B1, 2020
Mexico                        NCAR, A1B, 2020
Mexico                        NCAR, B1, 2020
Peru                          Hadley, A1B, 2020
Peru                          Hadley, B1, 2020
Peru                          NCAR, A1B, 2020
Peru                          NCAR, B1, 2020
Uruguay                       Hadley, A1B, 2020      –10.66      –11.51      –10.66       –10.57     –10.42
Uruguay                       Hadley, B1, 2020       –18.06      –18.88      –18.06       –18.00     –17.92
Uruguay                       NCAR, A1B, 2020        –15.63      –16.52      –15.63       –15.53     –15.52
Uruguay                       NCAR, B1, 2020         –13.93      –14.48      –13.93       –13.82     –13.45
Rest of South America         Hadley, A1B, 2020      –23.99      –15.83      –23.99       –23.98     –14.85
Rest of South America         Hadley, B1, 2020       –15.71       –6.74      –15.71       –15.70      –5.62
Rest of South America         NCAR, A1B, 2020        –15.00       –5.66      –15.00       –14.98      –4.52
Rest of South America         NCAR, B1, 2020         –18.04       –9.25      –18.04       –18.03      –8.18
Central America & Caribbean   Hadley, A1B, 2020      –32.26      –32.70      –32.26       –35.89     –35.88
Central America & Caribbean   Hadley, B1, 2020       –30.08      –30.58      –30.08       –33.47     –33.47
Central America & Caribbean   NCAR, A1B, 2020        –26.60      –26.99      –26.60       –30.02     –29.86
Central America & Caribbean   NCAR, B1, 2020         –25.71      –26.30      –25.71       –27.38     –27.29




                                                                                               137
Rice, 2020, No Adaptation

Table II4. Percentage impact (no adaptation) of climate change on rice yields for potential,
water limited, abiotic damaged, diseased, and actual production levels; projections for 2020
                              Climate change      Climate change impact (percentage difference compared to
Country                       scenario                            the baseline productivity
                                                  Potential    Water        Abiotic     Diseased   Actual
                                                               limited     damaged
Argentina                     Hadley, A1B, 2020       –1.62                     –1.28       –0.13    +0.20
Argentina                     Hadley, B1, 2020        +0.21                     +0.24       +1.47    +1.49
Argentina                     NCAR, A1B, 2020         +0.56                     +0.85       +1.09    +1.37
Argentina                     NCAR, B1, 2020          –0.07                     +0.27       +0.53    +0.85
Brazil                        Hadley, A1B, 2020       –7.56                     –7.32       –4.03    –3.78
Brazil                        Hadley, B1, 2020        –5.46                     –5.28       –2.58    –2.40
Brazil                        NCAR, A1B, 2020         –4.05                     –3.76       –1.17    –0.87
Brazil                        NCAR, B1, 2020          –4.23                     –3.96       –1.48    –1.21
Chile                         Hadley, A1B, 2020
Chile                         Hadley, B1, 2020
Chile                         NCAR, A1B, 2020
Chile                         NCAR, B1, 2020
Colombia                      Hadley, A1B, 2020       +8.89                   +8.89      +12.32    +12.32
Colombia                      Hadley, B1, 2020        +7.39                   +7.39      +10.39    +10.39
Colombia                      NCAR, A1B, 2020         +9.29                   +9.29      +13.50    +13.50
Colombia                      NCAR, B1, 2020          +9.59                   +9.59      +13.00    +13.00
Ecuador                       Hadley, A1B, 2020       –1.12                   –1.12       +6.06     +6.06
Ecuador                       Hadley, B1, 2020        –2.08                   –2.08       +4.93     +4.93
Ecuador                       NCAR, A1B, 2020         +1.10                   +1.10       +7.29     +7.29
Ecuador                       NCAR, B1, 2020          +1.14                   +1.14       +7.28     +7.28
Mexico                        Hadley, A1B, 2020       –9.16                   –9.16       –6.57     –6.57
Mexico                        Hadley, B1, 2020        –9.34                   –9.34       –6.85     –6.85
Mexico                        NCAR, A1B, 2020         –9.23                   –9.23       –7.04     –7.04
Mexico                        NCAR, B1, 2020          –9.46                   –9.46       –7.05     –7.05
Peru                          Hadley, A1B, 2020       +2.00                   +2.00       +6.98     +6.98
Peru                          Hadley, B1, 2020        +2.02                   +2.02       +6.05     +6.05
Peru                          NCAR, A1B, 2020         +4.31                   +4.31       +8.89     +8.89
Peru                          NCAR, B1, 2020          +3.33                   +3.33       +6.93     +6.93
Uruguay                       Hadley, A1B, 2020       +0.19                   +2.03       +0.72     +2.54
Uruguay                       Hadley, B1, 2020        +0.38                   +1.83       +1.17     +2.62
Uruguay                       NCAR, A1B, 2020         +2.68                   +4.46       +2.77     +4.53
Uruguay                       NCAR, B1, 2020          +1.16                   +3.04       +1.18     +3.03
Rest of South America         Hadley, A1B, 2020       +1.32                   +1.33       +4.32     +4.33
Rest of South America         Hadley, B1, 2020        +1.77                   +1.77       +4.53     +4.53
Rest of South America         NCAR, A1B, 2020         +3.92                   +3.93       +6.31     +6.32
Rest of South America         NCAR, B1, 2020          +3.37                   +3.38       +5.32     +5.33
Central America & Caribbean   Hadley, A1B, 2020       –2.84                   –2.84       –1.74     –1.74
Central America & Caribbean   Hadley, B1, 2020        –2.98                   –2.98       –1.92     –1.92
Central America & Caribbean   NCAR, A1B, 2020         –2.26                   –2.26       –1.26     –1.26
Central America & Caribbean   NCAR, B1, 2020          –2.15                   –2.15       –1.21     –1.21




                                                                                             138
Wheat, 2050, No Adaptation

Table II5. Percentage impact (no adaptation) of climate change on wheat yields for potential,
water limited, abiotic damaged, diseased, and actual production levels; projections for 2050
                              Climate change      Climate change impact (percentage difference compared to
Country                       scenario                            the baseline productivity
                                                  Potential    Water        Abiotic     Diseased   Actual
                                                               limited     damaged
Argentina                     Hadley, A1B, 2050       –25.69      –32.84       –25.69       –29.70  –35.70
Argentina                     Hadley, B1, 2050        –21.48      –29.40       –21.48       –26.34  –33.46
Argentina                     NCAR, A1B, 2050         –26.36      –33.43       –26.36       –29.87  –35.42
Argentina                     NCAR, B1, 2050          –16.48      –25.64       –16.48       –22.04  –29.91
Brazil                        Hadley, A1B, 2050       –51.50      –54.32       –51.50       –50.48  –50.50
Brazil                        Hadley, B1, 2050        –35.01      –38.85       –35.01       –34.98  –37.01
Brazil                        NCAR, A1B, 2050         –40.52      –45.05       –40.52       –39.27  –40.88
Brazil                        NCAR, B1, 2050          –23.42      –29.66       –23.42       –22.93  –27.33
Chile                         Hadley, A1B, 2050        +0.33      –28.96        +5.08        –9.01  –29.16
Chile                         Hadley, B1, 2050         +1.20      –27.54        +3.61        –9.49  –31.10
Chile                         NCAR, A1B, 2050          –0.16      –23.08        +4.38        –9.54  –23.76
Chile                         NCAR, B1, 2050           +2.67      –21.95        +5.63        –8.74  –25.95
Colombia                      Hadley, A1B, 2050       –64.97      –68.03       –64.97       –58.32  –56.99
Colombia                      Hadley, B1, 2050        –52.76      –56.51       –52.76       –47.16  –46.23
Colombia                      NCAR, A1B, 2050         –55.83      –60.19       –55.83       –49.53  –48.06
Colombia                      NCAR, B1, 2050          –28.28      –35.17       –28.28       –23.07  –22.02
Ecuador                       Hadley, A1B, 2050       –27.32      –33.37       –27.32       –21.29  –22.71
Ecuador                       Hadley, B1, 2050        –23.95      –28.97       –23.95       –19.71  –21.33
Ecuador                       NCAR, A1B, 2050         –21.03      –27.96       –21.03       –14.86  –16.78
Ecuador                       NCAR, B1, 2050          –13.19      –19.23       –13.19        –9.17  –11.01
Mexico                        Hadley, A1B, 2050       –66.71      –62.16       –66.71       –68.59  –62.21
Mexico                        Hadley, B1, 2050        –55.95      –52.82       –55.95       –56.66  –51.60
Mexico                        NCAR, A1B, 2050         –57.67      –52.19       –57.67       –59.46  –50.85
Mexico                        NCAR, B1, 2050          –43.02      –39.10       –43.02       –43.74  –36.90
Peru                          Hadley, A1B, 2050        –7.08      –15.46        –7.85        –2.16   –2.78
Peru                          Hadley, B1, 2050         –5.46      –12.70        –6.76        –1.07   –2.73
Peru                          NCAR, A1B, 2050          –4.94      –12.70        –6.07        +0.98   +1.68
Peru                          NCAR, B1, 2050           –1.78       –8.36        –3.55        +3.84   +3.68
Uruguay                       Hadley, A1B, 2050       –35.51      –34.48       –35.51       –29.96  –24.54
Uruguay                       Hadley, B1, 2050        –24.42      –22.92       –24.42       –19.32  –14.17
Uruguay                       NCAR, A1B, 2050         –36.14      –35.48       –36.14       –30.15  –24.24
Uruguay                       NCAR, B1, 2050          –24.31      –26.49       –24.31       –18.60  –16.59
Rest of South America         Hadley, A1B, 2050       –26.32      –33.34       –26.34       –30.16  –35.95
Rest of South America         Hadley, B1, 2050        –21.69      –29.44       –21.72       –26.52  –33.35
Rest of South America         NCAR, A1B, 2050         –26.49      –33.48       –26.52       –29.91  –35.32
Rest of South America         NCAR, B1, 2050          –16.47      –25.47       –16.50       –21.85  –29.57
Central America & Caribbean   Hadley, A1B, 2050       –66.69      –62.25       –66.69       –68.55  –62.18
Central America & Caribbean   Hadley, B1, 2050        –55.92      –52.87       –55.92       –56.63  –51.57
Central America & Caribbean   NCAR, A1B, 2050         –57.65      –52.31       –57.65       –59.42  –50.83
Central America & Caribbean   NCAR, B1, 2050          –42.87      –39.04       –42.87       –43.66  –36.81




                                                                                               139
Maize, 2050, No Adaptation

Table II6. Percentage impact (no adaptation) of climate change on maize yields for potential,
water limited, abiotic damaged, diseased, and actual production levels; projections for 2050
                              Climate change      Climate change impact (percentage difference compared to
Country                       scenario                            the baseline productivity
                                                  Potential    Water        Abiotic     Diseased   Actual
                                                               limited     damaged
Argentina                     Hadley, A1B, 2050       –31.53      –32.77       –31.53       –30.69  –29.92
Argentina                     Hadley, B1, 2050        –22.40      –23.49       –22.40       –21.60  –20.94
Argentina                     NCAR, A1B, 2050         –29.43      –30.43       –29.43       –28.68  –27.69
Argentina                     NCAR, B1, 2050          –17.92      –19.03       –17.92       –17.20  –16.54
Brazil                        Hadley, A1B, 2050       –68.35      –69.06       –68.35       –69.61  –68.21
Brazil                        Hadley, B1, 2050        –53.44      –54.33       –53.44       –54.19  –53.02
Brazil                        NCAR, A1B, 2050         –59.52      –60.37       –59.52       –60.84  –59.34
Brazil                        NCAR, B1, 2050          –40.88      –41.82       –40.88       –41.56  –40.40
Chile                         Hadley, A1B, 2050       –19.94      –20.02       –19.94       –20.03  –18.87
Chile                         Hadley, B1, 2050        –15.24      –14.83       –15.24       –15.37  –13.84
Chile                         NCAR, A1B, 2050         –21.79      –22.15       –21.79       –21.84  –20.98
Chile                         NCAR, B1, 2050          –11.62      –11.51       –11.62       –11.84  –10.51
Colombia                      Hadley, A1B, 2050       –38.10      –41.30       –38.10       –40.91  –40.89
Colombia                      Hadley, B1, 2050        –32.74      –35.32       –32.74       –32.75  –32.62
Colombia                      NCAR, A1B, 2050         –33.13      –36.67       –33.13       –34.32  –34.28
Colombia                      NCAR, B1, 2050          –21.36      –24.54       –21.36       –18.80  –18.50
Ecuador                       Hadley, A1B, 2050       –74.50      –74.81       –74.50       –77.51  –75.49
Ecuador                       Hadley, B1, 2050        –63.50      –63.89       –63.50       –67.08  –65.06
Ecuador                       NCAR, A1B, 2050         –62.04      –63.39       –62.04       –65.47  –64.19
Ecuador                       NCAR, B1, 2050          –42.91      –44.49       –42.91       –44.06  –43.94
Mexico                        Hadley, A1B, 2050       –44.55      –46.46       –44.97       –43.46  –44.07
Mexico                        Hadley, B1, 2050        –41.66      –43.37       –41.97       –41.63  –42.25
Mexico                        NCAR, A1B, 2050         –36.73      –38.91       –37.08       –37.08  –37.66
Mexico                        NCAR, B1, 2050          –28.09      –30.31       –28.49       –28.39  –29.12
Peru                          Hadley, A1B, 2050       –30.69      –34.32       –30.69       –31.38  –30.44
Peru                          Hadley, B1, 2050        –24.34      –27.66       –24.34       –24.85  –24.20
Peru                          NCAR, A1B, 2050         –20.39      –25.70       –20.39       –18.31  –18.38
Peru                          NCAR, B1, 2050          –11.24      –16.03       –11.24        –9.56   –9.97
Uruguay                       Hadley, A1B, 2050       –31.47      –32.73       –31.47       –30.96  –30.19
Uruguay                       Hadley, B1, 2050        –20.23      –21.56       –20.23       –16.69  –19.16
Uruguay                       NCAR, A1B, 2050         –26.54      –27.75       –26.54       –26.07  –25.20
Uruguay                       NCAR, B1, 2050          –15.46      –16.82       –15.46       –14.90  –14.30
Rest of South America         Hadley, A1B, 2050       –33.67      –36.39       –33.67       –34.42  –33.83
Rest of South America         Hadley, B1, 2050        –25.86      –28.34       –25.86       –25.60  –25.15
Rest of South America         NCAR, A1B, 2050         –26.54      –30.02       –26.54       –25.90  –25.72
Rest of South America         NCAR, B1, 2050          –15.71      –18.96       –15.71       –14.27  –14.21
Central America & Caribbean   Hadley, A1B, 2050       –44.23      –46.18       –44.63       –43.30  –43.89
Central America & Caribbean   Hadley, B1, 2050        –41.28      –43.01       –41.58       –41.28  –41.87
Central America & Caribbean   NCAR, A1B, 2050         –36.57      –38.79       –36.91       –36.96  –37.51
Central America & Caribbean   NCAR, B1, 2050          –27.85      –30.10       –28.23       –28.06  –28.76




                                                                                               140
Soybean, 2050, No Adaptation

Table II7. Percentage impact (no adaptation) of climate change on soybean yields for
potential, water limited, abiotic damaged, diseased, and actual production levels;
projections for 2050
                              Climate change      Climate change impact (percentage difference compared to
Country                       scenario                            the baseline productivity
                                                  Potential    Water        Abiotic     Diseased   Actual
                                                               limited     damaged
Argentina                     Hadley, A1B, 2050       –44.60      –38.23       –44.60       –44.61  –37.58
Argentina                     Hadley, B1, 2050        –30.63      –22.76       –30.63       –30.63  –21.89
Argentina                     NCAR, A1B, 2050         –35.07      –27.54       –35.07       –35.07  –26.76
Argentina                     NCAR, B1, 2050          –20.35      –11.46       –20.35       –20.34  –10.42
Brazil                        Hadley, A1B, 2050       –79.56      –79.42       –79.56       –79.56  –79.05
Brazil                        Hadley, B1, 2050        –66.24      –66.06       –66.24       –66.24  –65.37
Brazil                        NCAR, A1B, 2050         –66.77      –66.58       –66.77       –66.76  –65.88
Brazil                        NCAR, B1, 2050          –45.04      –44.82       –45.04       –45.04  –43.52
Chile                         Hadley, A1B, 2050
Chile                         Hadley, B1, 2050
Chile                         NCAR, A1B, 2050
Chile                         NCAR, B1, 2050
Colombia                      Hadley, A1B, 2050      –46.57      –46.37      –46.57       –56.22     –56.23
Colombia                      Hadley, B1, 2050       –38.58      –38.40      –38.58       –47.61     –47.62
Colombia                      NCAR, A1B, 2050        –28.20      –27.78      –28.20       –35.58     –35.28
Colombia                      NCAR, B1, 2050         –21.47      –21.17      –21.47       –26.22     –26.00
Ecuador                       Hadley, A1B, 2050      –86.21      –86.56      –86.21       –86.21     –86.25
Ecuador                       Hadley, B1, 2050       –76.94      –77.65      –76.94       –76.94     –77.02
Ecuador                       NCAR, A1B, 2050        –75.93      –76.68      –75.93       –75.93     –76.02
Ecuador                       NCAR, B1, 2050         –56.68      –58.19      –56.68       –56.68     –56.77
Mexico                        Hadley, A1B, 2050
Mexico                        Hadley, B1, 2050
Mexico                        NCAR, A1B, 2050
Mexico                        NCAR, B1, 2050
Peru                          Hadley, A1B, 2050
Peru                          Hadley, B1, 2050
Peru                          NCAR, A1B, 2050
Peru                          NCAR, B1, 2050
Uruguay                       Hadley, A1B, 2050      –23.59      –25.83      –23.59       –23.49     –23.37
Uruguay                       Hadley, B1, 2050       –37.57      –39.85      –37.57       –37.49     –37.44
Uruguay                       NCAR, A1B, 2050        –15.82      –18.15      –15.82       –15.71     –15.37
Uruguay                       NCAR, B1, 2050         –29.24      –31.65      –29.24       –29.15     –28.87
Rest of South America         Hadley, A1B, 2050      –43.95      –37.81      –43.95       –43.95     –37.10
Rest of South America         Hadley, B1, 2050       –30.85      –23.34      –30.85       –30.84     –22.42
Rest of South America         NCAR, A1B, 2050        –34.47      –27.22      –34.47       –34.46     –26.37
Rest of South America         NCAR, B1, 2050         –20.63      –12.15      –20.63       –20.62     –11.05
Central America & Caribbean   Hadley, A1B, 2050      –59.07      –59.00      –59.07       –66.86     –66.84
Central America & Caribbean   Hadley, B1, 2050       –50.55      –50.60      –50.55       –57.91     –57.90
Central America & Caribbean   NCAR, A1B, 2050        –43.16      –43.04      –43.16       –49.83     –49.62
Central America & Caribbean   NCAR, B1, 2050         –32.58      –32.82      –32.58       –37.04     –36.89




                                                                                               141
Rice, 2050, No Adaptation

Table II8. Percentage impact (no adaptation) of climate change on rice yields for potential,
water limited, abiotic damaged, diseased, and actual production levels; projections for 2050
                              Climate change      Climate change impact (percentage difference compared to
Country                       scenario                            the baseline productivity
                                                  Potential    Water        Abiotic     Diseased   Actual
                                                               limited     damaged
Argentina                     Hadley, A1B, 2050        +0.25                    +0.68       +3.96    +4.38
Argentina                     Hadley, B1, 2050         +2.39                    +2.81       +4.59    +5.00
Argentina                     NCAR, A1B, 2050          +4.81                    +5.26       +7.75    +8.19
Argentina                     NCAR, B1, 2050           +7.08                    +7.47       +8.49    +8.86
Brazil                        Hadley, A1B, 2050       –15.24                   –14.96       –7.70    –7.40
Brazil                        Hadley, B1, 2050        –11.09                   –10.80       –5.36    –5.05
Brazil                        NCAR, A1B, 2050          –2.33                    –2.01       +3.35    +3.69
Brazil                        NCAR, B1, 2050           +2.29                    +2.60       +6.62    +6.95
Chile                         Hadley, A1B, 2050
Chile                         Hadley, B1, 2050
Chile                         NCAR, A1B, 2050
Chile                         NCAR, B1, 2050
Colombia                      Hadley, A1B, 2050      +10.61                  +10.61      +21.50      +21.50
Colombia                      Hadley, B1, 2050       +10.27                  +10.27      +18.12      +18.12
Colombia                      NCAR, A1B, 2050        +15.38                  +15.38      +26.53      +26.53
Colombia                      NCAR, B1, 2050         +17.72                  +17.72      +25.33      +25.33
Ecuador                       Hadley, A1B, 2050       –1.90                   –1.90      +12.75      +12.75
Ecuador                       Hadley, B1, 2050        –1.92                   –1.92      +10.38      +10.38
Ecuador                       NCAR, A1B, 2050         +4.91                   +4.91      +19.17      +19.17
Ecuador                       NCAR, B1, 2050          +7.23                   +7.23      +16.93      +16.93
Mexico                        Hadley, A1B, 2050      –15.01                  –15.01      –11.57      –11.57
Mexico                        Hadley, B1, 2050       –14.79                  –14.79      –11.86      –11.86
Mexico                        NCAR, A1B, 2050         –7.79                   –7.79       –4.47       –4.47
Mexico                        NCAR, B1, 2050          –6.06                   –6.06       –2.81       –2.81
Peru                          Hadley, A1B, 2050       +7.08                   +7.08      +18.73      +18.73
Peru                          Hadley, B1, 2050        +5.38                   +5.38      +14.33      +14.33
Peru                          NCAR, A1B, 2050        +10.65                  +10.65      +20.64      +20.64
Peru                          NCAR, B1, 2050          +9.54                   +9.54      +16.58      +16.58
Uruguay                       Hadley, A1B, 2050       +5.80                   +8.24       +7.32       +9.75
Uruguay                       Hadley, B1, 2050        +5.98                   +8.33       +7.28       +9.62
Uruguay                       NCAR, A1B, 2050         +9.36                  +11.88      +10.78      +13.29
Uruguay                       NCAR, B1, 2050          +9.09                  +11.36      +10.37      +12.64
Rest of South America         Hadley, A1B, 2050       +3.29                   +3.30       +8.40       +8.41
Rest of South America         Hadley, B1, 2050        +4.14                   +4.15       +7.74       +7.75
Rest of South America         NCAR, A1B, 2050         +8.46                   +8.48      +12.75      +12.76
Rest of South America         NCAR, B1, 2050         +10.16                  +10.17      +13.64      +13.65
Central America & Caribbean   Hadley, A1B, 2050       –3.89                   –3.89       –0.07       –0.07
Central America & Caribbean   Hadley, B1, 2050        –3.91                   –3.91       –1.70       –1.70
Central America & Caribbean   NCAR, A1B, 2050         +1.50                   +1.50       +5.64       +5.64
Central America & Caribbean   NCAR, B1, 2050          +3.29                   +3.29       +6.23       +6.23




                                                                                               142
Wheat, 2020, With Adaptation

Table II9. Percentage impact (with adaptation) of climate change on wheat yields for
potential, water limited, abiotic damaged, diseased, and actual production levels;
projections for 2020
                              Climate change      Climate change impact (percentage difference compared to
Country                       scenario                            the baseline productivity
                                                  Potential    Water        Abiotic     Diseased   Actual
                                                               limited     damaged
Argentina                     Hadley, A1B, 2020        –4.62      –12.67        –4.62       –18.62  –25.51
Argentina                     Hadley, B1, 2020         –6.46      –13.78        –6.46       –19.85  –25.73
Argentina                     NCAR, A1B, 2020          –8.48      –15.90        –8.48       –21.30  –25.97
Argentina                     NCAR, B1, 2020           –7.66      –15.39        –7.66       –20.55  –26.39
Brazil                        Hadley, A1B, 2020       –12.99      –18.88       –12.99       –13.57  –16.16
Brazil                        Hadley, B1, 2020        –11.64      –17.35       –11.64       –12.12  –13.35
Brazil                        NCAR, A1B, 2020          –8.56      –15.12        –8.56        –8.46  –11.08
Brazil                        NCAR, B1, 2020          –14.02      –19.64       –14.02       –13.20  –15.78
Chile                         Hadley, A1B, 2020        +4.34      –21.25        +4.85       –12.45  –32.57
Chile                         Hadley, B1, 2020         +3.90      –19.97        +4.74       –12.75  –31.07
Chile                         NCAR, A1B, 2020          +3.03      –18.16        +4.22       –13.73  –29.44
Chile                         NCAR, B1, 2020           +2.99      –18.01        +4.15       –13.98  –29.43
Colombia                      Hadley, A1B, 2020       –17.88      –20.48       –17.88       –17.75  –18.37
Colombia                      Hadley, B1, 2020        –16.77      –19.40       –16.77       –17.21  –18.16
Colombia                      NCAR, A1B, 2020         –18.74      –21.33       –18.74       –18.27  –17.55
Colombia                      NCAR, B1, 2020          –13.74      –16.54       –13.74       –15.84  –16.48
Ecuador                       Hadley, A1B, 2020       –10.13      –12.21       –10.13       –11.31  –12.43
Ecuador                       Hadley, B1, 2020         –9.27      –11.69        –9.27       –10.45  –11.93
Ecuador                       NCAR, A1B, 2020          –6.59       –8.93        –6.59        –8.49   –9.83
Ecuador                       NCAR, B1, 2020           –4.53       –6.97        –4.53        –6.82   –8.17
Mexico                        Hadley, A1B, 2020      +21.26        –8.93      +21.26        +36.88   +2.47
Mexico                        Hadley, B1, 2020       +25.83        –4.31      +25.83        +42.00   +7.81
Mexico                        NCAR, A1B, 2020        +22.12        –6.09      +22.12        +35.92   +4.55
Mexico                        NCAR, B1, 2020         +17.98        –6.02      +17.98        +30.87   +4.34
Peru                          Hadley, A1B, 2020        +1.76       –0.48        +0.40        +3.07   +3.25
Peru                          Hadley, B1, 2020         +2.27       –0.83        +0.89        +3.68   +2.92
Peru                          NCAR, A1B, 2020          +2.76       +0.88        +1.40        +4.15   +4.94
Peru                          NCAR, B1, 2020           +3.50       +1.29        +2.08        +5.08   +5.59
Uruguay                       Hadley, A1B, 2020        –7.12      –10.06        –7.12        –0.60   +4.10
Uruguay                       Hadley, B1, 2020         –8.71      –10.54        –8.71        –1.68   +4.67
Uruguay                       NCAR, A1B, 2020          –6.23      –10.47        –6.23        +1.06   +4.74
Uruguay                       NCAR, B1, 2020           –7.03       –7.95        –7.03        +1.22   +7.84
Rest of South America         Hadley, A1B, 2020        –4.81      –12.67        –4.84       –18.27  –24.99
Rest of South America         Hadley, B1, 2020         –6.49      –13.67        –6.52       –19.39  –25.11
Rest of South America         NCAR, A1B, 2020          –8.48      –15.73        –8.50       –20.81  –25.40
Rest of South America         NCAR, B1, 2020           –7.48      –15.06        –7.51       –19.93  –25.65
Central America & Caribbean   Hadley, A1B, 2020      +20.88        –9.10      +20.88        +36.67   +2.34
Central America & Caribbean   Hadley, B1, 2020       +25.42        –4.53      +25.42        +41.78   +7.65
Central America & Caribbean   NCAR, A1B, 2020        +21.73        –6.31      +21.73        +35.72   +4.42
Central America & Caribbean   NCAR, B1, 2020         +17.67        –6.17      +17.67        +30.69   +4.22




                                                                                               143
Maize, 2020, With Adaptation

Table II10. Percentage impact (with adaptation) of climate change on maize yields for
potential, water limited, abiotic damaged, diseased, and actual production levels;
projections for 2020
                              Climate change      Climate change impact (percentage difference compared to
Country                       scenario                            the baseline productivity
                                                  Potential    Water        Abiotic     Diseased   Actual
                                                               limited     damaged
Argentina                     Hadley, A1B, 2020        –9.80      –10.12        –9.80       –10.42   –8.95
Argentina                     Hadley, B1, 2020         –4.26       –4.57        –4.26        –4.88   –3.61
Argentina                     NCAR, A1B, 2020          –7.05       –7.08        –7.05        –7.60   –6.12
Argentina                     NCAR, B1, 2020           –7.30       –7.44        –7.30        –7.83   –6.38
Brazil                        Hadley, A1B, 2020       –14.91      –14.63       –14.91       –20.85  –19.08
Brazil                        Hadley, B1, 2020         –8.09       –7.82        –8.09       –14.46  –12.85
Brazil                        NCAR, A1B, 2020         –27.75      –27.57       –27.75       –33.47  –31.36
Brazil                        NCAR, B1, 2020          –26.04      –25.88       –26.04       –31.75  –29.66
Chile                         Hadley, A1B, 2020        –6.94       –6.89        –6.94        –8.19   –6.32
Chile                         Hadley, B1, 2020         –3.77       –2.99        –3.77        –5.02   –2.53
Chile                         NCAR, A1B, 2020          –7.83       –7.77        –7.83        –9.15   –7.33
Chile                         NCAR, B1, 2020           –8.40       –8.38        –8.40        –9.75   –7.92
Colombia                      Hadley, A1B, 2020       –11.43      –12.00       –11.43       –10.38  –10.15
Colombia                      Hadley, B1, 2020        –11.29      –11.85       –11.29       –10.40  –10.17
Colombia                      NCAR, A1B, 2020         –10.55      –11.14       –10.55        –9.93   –9.66
Colombia                      NCAR, B1, 2020           –7.22       –7.85        –7.22        –6.87   –6.60
Ecuador                       Hadley, A1B, 2020        –8.46       –9.50        –8.46        –8.28   –8.93
Ecuador                       Hadley, B1, 2020         –6.27       –7.07        –6.27        –6.01   –6.51
Ecuador                       NCAR, A1B, 2020          –5.67       –6.65        –5.67        –5.40   –6.01
Ecuador                       NCAR, B1, 2020           –1.00       –2.72        –1.00        –0.58   –2.02
Mexico                        Hadley, A1B, 2020       –17.47      –19.57       –17.67       –19.35  –21.53
Mexico                        Hadley, B1, 2020        –14.12      –16.19       –14.46       –16.13  –18.35
Mexico                        NCAR, A1B, 2020         –13.99      –16.13       –14.30       –15.76  –18.01
Mexico                        NCAR, B1, 2020          –12.51      –14.77       –12.80       –14.45  –16.83
Peru                          Hadley, A1B, 2020        –8.64       –9.78        –8.64        –9.15   –8.34
Peru                          Hadley, B1, 2020         –6.31       –7.40        –6.31        –7.03   –6.24
Peru                          NCAR, A1B, 2020          –4.10       –5.57        –4.10        –3.94   –3.33
Peru                          NCAR, B1, 2020           –2.12       –3.65        –2.12        –1.82   –1.31
Uruguay                       Hadley, A1B, 2020        –9.14       –9.60        –9.14        –9.59   –8.52
Uruguay                       Hadley, B1, 2020         –3.91       –4.48        –3.91        –4.39   –3.58
Uruguay                       NCAR, A1B, 2020          –7.50       –7.74        –7.50        –8.07   –6.95
Uruguay                       NCAR, B1, 2020           –8.91       –9.33        –8.91        –9.28   –8.17
Rest of South America         Hadley, A1B, 2020       –23.52      –24.39       –23.52       –24.95  –23.72
Rest of South America         Hadley, B1, 2020         –7.51       –8.29        –7.51        –7.62   –6.95
Rest of South America         NCAR, A1B, 2020          –7.41       –8.32        –7.41        –7.44   –6.82
Rest of South America         NCAR, B1, 2020           –6.80       –7.59        –6.80        –7.03   –6.33
Central America & Caribbean   Hadley, A1B, 2020       –17.25      –19.29       –17.45       –19.05  –21.14
Central America & Caribbean   Hadley, B1, 2020        –14.01      –16.02       –14.34       –15.93  –18.07
Central America & Caribbean   NCAR, A1B, 2020         –13.85      –15.94       –14.16       –15.56  –17.72
Central America & Caribbean   NCAR, B1, 2020          –12.31      –14.51       –12.59       –14.19  –16.48




                                                                                               144
Soybean, 2020, With Adaptation

Table II11. Percentage impact (with adaptation) of climate change on soybean yields for
potential, water limited, abiotic damaged, diseased, and actual production levels;
projections for 2020
                              Climate change      Climate change impact (percentage difference compared to
Country                       scenario                            the baseline productivity
                                                  Potential    Water        Abiotic     Diseased   Actual
                                                               limited     damaged
Argentina                     Hadley, A1B, 2020       –14.16       –4.57       –14.16       –14.16   –3.50
Argentina                     Hadley, B1, 2020         –5.59       +4.91        –5.59        –5.58   +6.12
Argentina                     NCAR, A1B, 2020          –4.18       +6.78        –4.18        –4.17   +8.02
Argentina                     NCAR, B1, 2020           –7.67       +2.61        –7.67        –7.66   +3.78
Brazil                        Hadley, A1B, 2020       –25.94      –25.19       –25.94       –25.94  –23.64
Brazil                        Hadley, B1, 2020        –14.84      –14.01       –14.84       –14.85  –12.17
Brazil                        NCAR, A1B, 2020         –23.57      –22.79       –23.57       –23.57  –21.14
Brazil                        NCAR, B1, 2020          –20.63      –19.90       –20.63       –20.63  –18.18
Chile                         Hadley, A1B, 2020
Chile                         Hadley, B1, 2020
Chile                         NCAR, A1B, 2020
Chile                         NCAR, B1, 2020
Colombia                      Hadley, A1B, 2020      –16.57      –18.54      –16.57      –14.41      –14.76
Colombia                      Hadley, B1, 2020       –19.99      –21.69      –19.99      –12.83      –12.91
Colombia                      NCAR, A1B, 2020        –14.17      –15.62      –14.17       –7.55       –7.15
Colombia                      NCAR, B1, 2020         –10.45      –12.21      –10.45       –4.37       –4.27
Ecuador                       Hadley, A1B, 2020       –1.42       –4.09       –1.42       –1.42       –1.45
Ecuador                       Hadley, B1, 2020        +1.70       –1.00       +1.70       +1.70       +1.73
Ecuador                       NCAR, A1B, 2020         +2.33       –0.46       +2.33       +2.33       +2.30
Ecuador                       NCAR, B1, 2020         +10.44       +7.33      +10.44      +10.44      +10.36
Mexico                        Hadley, A1B, 2020
Mexico                        Hadley, B1, 2020
Mexico                        NCAR, A1B, 2020
Mexico                        NCAR, B1, 2020
Peru                          Hadley, A1B, 2020
Peru                          Hadley, B1, 2020
Peru                          NCAR, A1B, 2020
Peru                          NCAR, B1, 2020
Uruguay                       Hadley, A1B, 2020       +8.68       +8.66       +8.68       +7.80       +9.32
Uruguay                       Hadley, B1, 2020       +16.14      +16.03      +16.14      +15.42      +16.88
Uruguay                       NCAR, A1B, 2020        +10.47      +10.67      +10.47      +10.25      +11.81
Uruguay                       NCAR, B1, 2020          +9.61       +9.53       +9.61       +9.12      +10.44
Rest of South America         Hadley, A1B, 2020      –14.13       –4.90      –14.13      –14.13       –3.84
Rest of South America         Hadley, B1, 2020        –5.58       +4.51       –5.58       –5.57       +5.72
Rest of South America         NCAR, A1B, 2020         –4.29       +6.26       –4.29       –4.28       +7.50
Rest of South America         NCAR, B1, 2020          –7.74       +2.14       –7.74       –7.73       +3.31
Central America & Caribbean   Hadley, A1B, 2020      –11.30      –13.53      –11.30       –9.40       –9.64
Central America & Caribbean   Hadley, B1, 2020       –12.84      –14.91      –12.84       –7.56       –7.62
Central America & Caribbean   NCAR, A1B, 2020         –8.85      –10.78       –8.85       –4.10       –3.88
Central America & Caribbean   NCAR, B1, 2020          –3.62       –5.87       –3.62       +0.97       +0.98




                                                                                               145
Rice, 2020, With Adaptation

Table II12. Percentage impact (with adaptation) of climate change on rice yields for
potential, water limited, abiotic damaged, diseased, and actual production levels;
projections for 2020
                              Climate change      Climate change impact (percentage difference compared to
Country                       scenario                            the baseline productivity
                                                  Potential    Water        Abiotic     Diseased   Actual
                                                               limited     damaged
Argentina                     Hadley, A1B, 2020       –1.62                     –1.28        –0.13   +0.20
Argentina                     Hadley, B1, 2020        +0.21                     +0.24        +1.47   +1.49
Argentina                     NCAR, A1B, 2020         +0.56                     +0.85        +1.09   +1.37
Argentina                     NCAR, B1, 2020          –0.07                     +0.27        +0.53   +0.85
Brazil                        Hadley, A1B, 2020       +3.26                     +3.60        +8.50   +8.86
Brazil                        Hadley, B1, 2020        +5.62                     +5.97       +10.22  +10.58
Brazil                        NCAR, A1B, 2020         +6.76                     +7.11       +11.32  +11.69
Brazil                        NCAR, B1, 2020          +6.50                     +6.85       +10.90  +11.26
Chile                         Hadley, A1B, 2020
Chile                         Hadley, B1, 2020
Chile                         NCAR, A1B, 2020
Chile                         NCAR, B1, 2020
Colombia                      Hadley, A1B, 2020       +8.89                   +8.89      +12.32      +12.32
Colombia                      Hadley, B1, 2020        +7.39                   +7.39      +10.39      +10.39
Colombia                      NCAR, A1B, 2020         +9.29                   +9.29      +13.50      +13.50
Colombia                      NCAR, B1, 2020          +9.59                   +9.59      +13.00      +13.00
Ecuador                       Hadley, A1B, 2020       +9.51                   +9.51      +19.19      +19.19
Ecuador                       Hadley, B1, 2020        +8.57                   +8.57      +17.76      +17.76
Ecuador                       NCAR, A1B, 2020        +12.09                  +12.09      +20.70      +20.70
Ecuador                       NCAR, B1, 2020         +11.47                  +11.47      +20.21      +20.21
Mexico                        Hadley, A1B, 2020       +1.21                   +1.21       +4.14       +4.14
Mexico                        Hadley, B1, 2020        +1.02                   +1.02       +3.97       +3.97
Mexico                        NCAR, A1B, 2020         +1.32                   +1.32       +3.93       +3.93
Mexico                        NCAR, B1, 2020          +1.12                   +1.12       +3.90       +3.90
Peru                          Hadley, A1B, 2020       +2.00                   +2.00       +6.98       +6.98
Peru                          Hadley, B1, 2020        +2.02                   +2.02       +6.05       +6.05
Peru                          NCAR, A1B, 2020         +4.31                   +4.31       +8.89       +8.89
Peru                          NCAR, B1, 2020          +3.33                   +3.33       +6.93       +6.93
Uruguay                       Hadley, A1B, 2020       +0.19                   +2.03       +0.72       +2.54
Uruguay                       Hadley, B1, 2020        +0.38                   +1.83       +1.17       +2.62
Uruguay                       NCAR, A1B, 2020         +2.68                   +4.46       +2.77       +4.53
Uruguay                       NCAR, B1, 2020          +1.16                   +3.04       +1.18       +3.03
Rest of South America         Hadley, A1B, 2020       +1.32                   +1.33       +4.32       +4.33
Rest of South America         Hadley, B1, 2020        +1.77                   +1.77       +4.53       +4.53
Rest of South America         NCAR, A1B, 2020         +3.92                   +3.93       +6.31       +6.32
Rest of South America         NCAR, B1, 2020          +3.37                   +3.38       +5.32       +5.33
Central America & Caribbean   Hadley, A1B, 2020       +3.09                   +3.09       +4.97       +4.97
Central America & Caribbean   Hadley, B1, 2020        +2.27                   +2.27       +4.11       +4.11
Central America & Caribbean   NCAR, A1B, 2020         +3.00                   +3.00       +4.77       +4.77
Central America & Caribbean   NCAR, B1, 2020          +3.19                   +3.19       +4.87       +4.87




                                                                                               146
Wheat, 2050, With Adaptation

Table II13. Percentage impact (with adaptation) of climate change on wheat yields for
potential, water limited, abiotic damaged, diseased, and actual production levels;
projections for 2050
                              Climate change      Climate change impact (percentage difference compared to
Country                       scenario                            the baseline productivity
                                                  Potential    Water        Abiotic     Diseased   Actual
                                                               limited     damaged
Argentina                     Hadley, A1B, 2050       –16.50      –24.02       –16.50       –24.78  –29.23
Argentina                     Hadley, B1, 2050        –12.59      –20.88       –12.59       –22.33  –27.47
Argentina                     NCAR, A1B, 2050         –17.72      –25.19       –17.72       –25.80  –29.37
Argentina                     NCAR, B1, 2050           –7.84      –17.31        –7.84       –18.90  –24.21
Brazil                        Hadley, A1B, 2050       –46.21      –50.45       –46.21       –42.11  –42.30
Brazil                        Hadley, B1, 2050        –26.11      –31.62       –26.11       –22.71  –23.71
Brazil                        NCAR, A1B, 2050          –7.72      –15.42        –7.72        –4.04   –6.89
Brazil                        NCAR, B1, 2050          –28.29      –33.91       –28.29       –23.07  –24.77
Chile                         Hadley, A1B, 2050        +5.54      –25.19        +8.38        –8.96  –28.36
Chile                         Hadley, B1, 2050         +5.54      –23.91        +6.23        –9.96  –30.41
Chile                         NCAR, A1B, 2050          +4.74      –19.21        +7.97        –9.73  –22.69
Chile                         NCAR, B1, 2050           +6.65      –18.68        +8.37        –9.41  –25.35
Colombia                      Hadley, A1B, 2050       –57.92      –61.41       –57.92       –50.29  –50.13
Colombia                      Hadley, B1, 2050        –43.68      –47.98       –43.68       –37.60  –38.03
Colombia                      NCAR, A1B, 2050         –48.17      –53.07       –48.17       –41.29  –40.85
Colombia                      NCAR, B1, 2050          –18.21      –25.74       –18.21       –14.11  –13.84
Ecuador                       Hadley, A1B, 2050       –22.79      –29.06       –22.79       –16.73  –17.25
Ecuador                       Hadley, B1, 2050        –19.02      –24.27       –19.02       –14.62  –15.48
Ecuador                       NCAR, A1B, 2050         –16.56      –23.65       –16.56       –10.45  –11.34
Ecuador                       NCAR, B1, 2050           –7.53      –13.83        –7.53        –3.99   –4.86
Mexico                        Hadley, A1B, 2050       –34.70      –46.72       –34.70       –27.37  –39.34
Mexico                        Hadley, B1, 2050         –6.25      –28.86        –6.25        +7.04  –17.49
Mexico                        NCAR, A1B, 2050        +10.58       –13.93      +10.58        +24.96   –0.63
Mexico                        NCAR, B1, 2050          –19.92      –32.04       –19.92       –11.03  –21.34
Peru                          Hadley, A1B, 2050        –2.59      –10.44        –3.54        +2.92   +3.98
Peru                          Hadley, B1, 2050         –0.81       –7.44        –2.37        +3.94   +4.28
Peru                          NCAR, A1B, 2050          +0.20       –7.12        –1.11        +6.50   +9.02
Peru                          NCAR, B1, 2050           +5.28       –1.20        +3.99       +10.08  +12.00
Uruguay                       Hadley, A1B, 2050       –26.53      –27.49       –26.53       –15.59   –9.74
Uruguay                       Hadley, B1, 2050        –12.51      –11.83       –12.51        –2.72   +7.11
Uruguay                       NCAR, A1B, 2050          –6.55      –12.70        –6.55        +4.06   +6.68
Uruguay                       NCAR, B1, 2050          –20.98      –21.96       –20.98        –9.51   –2.31
Rest of South America         Hadley, A1B, 2050       –17.39      –24.80       –17.41       –25.12  –29.41
Rest of South America         Hadley, B1, 2050        –12.89      –21.04       –12.92       –22.11  –27.11
Rest of South America         NCAR, A1B, 2050         –17.79      –25.19       –17.82       –25.42  –28.93
Rest of South America         NCAR, B1, 2050           –7.57      –16.92        –7.60       –18.16  –23.38
Central America & Caribbean   Hadley, A1B, 2050       –34.93      –46.94       –34.93       –27.45  –39.40
Central America & Caribbean   Hadley, B1, 2050         –6.61      –29.14        –6.61        +6.87  –17.61
Central America & Caribbean   NCAR, A1B, 2050        +10.01       –14.51      +10.01        +24.71   –0.86
Central America & Caribbean   NCAR, B1, 2050          –19.90      –31.95       –19.90       –11.04  –21.30




                                                                                               147
Maize, 2050, With Adaptation

Table II14. Percentage impact (with adaptation) of climate change on maize yields for
potential, water limited, abiotic damaged, diseased, and actual production levels;
projections for 2050
                              Climate change      Climate change impact (percentage difference compared to
Country                       scenario                            the baseline productivity
                                                  Potential    Water        Abiotic     Diseased   Actual
                                                               limited     damaged
Argentina                     Hadley, A1B, 2050       –26.89      –27.39       –26.89       –27.02  –25.39
Argentina                     Hadley, B1, 2050        –16.95      –17.46       –16.95       –17.16  –15.67
Argentina                     NCAR, A1B, 2050         –23.83      –24.21       –23.83       –23.98  –22.36
Argentina                     NCAR, B1, 2050          –11.18      –11.76       –11.18       –11.48  –10.08
Brazil                        Hadley, A1B, 2050       –64.13      –64.29       –64.13       –67.82  –65.84
Brazil                        Hadley, B1, 2050        –41.52      –41.62       –41.52       –45.59  –43.50
Brazil                        NCAR, A1B, 2050         –36.36      –36.53       –36.36       –41.59  –39.34
Brazil                        NCAR, B1, 2050          –36.36      –36.53       –36.36       –41.59  –39.35
Chile                         Hadley, A1B, 2050       –15.77      –15.14       –15.77       –16.88  –14.54
Chile                         Hadley, B1, 2050        –11.76      –11.27       –11.76       –12.96  –10.65
Chile                         NCAR, A1B, 2050         –16.74      –16.49       –16.74       –18.05  –15.83
Chile                         NCAR, B1, 2050           –8.30       –8.51        –8.30        –9.59   –7.76
Colombia                      Hadley, A1B, 2050       –35.70      –37.30       –35.70       –39.08  –38.93
Colombia                      Hadley, B1, 2050        –27.58      –28.94       –27.58       –29.39  –29.18
Colombia                      NCAR, A1B, 2050         –14.13      –15.84       –14.13       –12.62  –12.39
Colombia                      NCAR, B1, 2050          –14.13      –15.84       –14.13       –12.62  –12.39
Ecuador                       Hadley, A1B, 2050       –54.75      –55.16       –54.75       –59.40  –56.89
Ecuador                       Hadley, B1, 2050        –40.80      –41.47       –40.80       –45.12  –43.19
Ecuador                       NCAR, A1B, 2050         –14.34      –16.01       –14.34       –14.56  –15.26
Ecuador                       NCAR, B1, 2050          –14.34      –16.01       –14.34       –14.55  –15.26
Mexico                        Hadley, A1B, 2050       –43.40      –45.00       –43.72       –43.93  –45.19
Mexico                        Hadley, B1, 2050        –37.47      –39.53       –37.73       –38.23  –40.06
Mexico                        NCAR, A1B, 2050         –20.79      –23.32       –21.10       –22.28  –24.30
Mexico                        NCAR, B1, 2050          –20.79      –23.32       –21.10       –22.27  –24.29
Peru                          Hadley, A1B, 2050       –26.93      –28.66       –26.93       –29.46  –27.97
Peru                          Hadley, B1, 2050        –17.87      –19.85       –17.87       –20.16  –19.22
Peru                          NCAR, A1B, 2050         –14.62      –17.89       –14.62       –14.36  –13.88
Peru                          NCAR, B1, 2050           –6.59       –8.96        –6.59        –6.84   –5.82
Uruguay                       Hadley, A1B, 2050       –28.17      –28.73       –28.17       –28.38  –27.02
Uruguay                       Hadley, B1, 2050        –16.07      –16.78       –16.07       –16.33  –15.14
Uruguay                       NCAR, A1B, 2050         –22.37      –22.89       –22.37       –22.54  –21.15
Uruguay                       NCAR, B1, 2050          –10.64      –11.38       –10.64       –10.89   –9.63
Rest of South America         Hadley, A1B, 2050       –17.38      –18.59       –17.38       –17.94  –17.36
Rest of South America         Hadley, B1, 2050        –20.57      –21.99       –20.57       –21.78  –20.97
Rest of South America         NCAR, A1B, 2050         –10.56      –12.21       –10.56       –10.45   –9.58
Rest of South America         NCAR, B1, 2050          –17.45      –19.32       –17.45       –17.52  –16.80
Central America & Caribbean   Hadley, A1B, 2050       –43.10      –44.69       –43.40       –43.74  –44.95
Central America & Caribbean   Hadley, B1, 2050        –37.09      –39.13       –37.35       –37.91  –39.67
Central America & Caribbean   NCAR, A1B, 2050         –20.56      –23.05       –20.85       –21.96  –23.90
Central America & Caribbean   NCAR, B1, 2050          –20.56      –23.05       –20.85       –21.95  –23.89




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Soybean, 2050, With Adaptation

Table II15. Percentage impact (with adaptation) of climate change on soybean yields for
potential, water limited, abiotic damaged, diseased, and actual production levels;
projections for 2050
                              Climate change      Climate change impact (percentage difference compared to
Country                       scenario                            the baseline productivity
                                                  Potential    Water        Abiotic     Diseased   Actual
                                                               limited     damaged
Argentina                     Hadley, A1B, 2050       –34.00      –28.47       –34.00       –34.02  –25.68
Argentina                     Hadley, B1, 2050        –19.33      –12.02       –19.33       –19.33   –9.17
Argentina                     NCAR, A1B, 2050         –24.34      –16.43       –24.34       –24.34  –14.66
Argentina                     NCAR, B1, 2050           –7.71       +0.33        –7.71        –7.70   +3.76
Brazil                        Hadley, A1B, 2050       –73.67      –73.80       –73.67       –73.67  –72.99
Brazil                        Hadley, B1, 2050        –55.64      –56.62       –55.64       –55.64  –54.37
Brazil                        NCAR, A1B, 2050         –30.57      –32.61       –30.57       –30.57  –28.44
Brazil                        NCAR, B1, 2050          –30.57      –32.61       –30.57       –30.57  –28.44
Chile                         Hadley, A1B, 2050
Chile                         Hadley, B1, 2050
Chile                         NCAR, A1B, 2050
Chile                         NCAR, B1, 2050
Colombia                      Hadley, A1B, 2050      –38.88      –43.80      –38.88       –44.03     –44.19
Colombia                      Hadley, B1, 2050       –29.64      –34.07      –29.64       –33.17     –33.43
Colombia                      NCAR, A1B, 2050        –13.80      –20.17      –13.80       –16.24     –15.85
Colombia                      NCAR, B1, 2050         –11.32      –16.42      –11.32        –5.39      –5.13
Ecuador                       Hadley, A1B, 2050      –65.31      –66.63      –65.31       –65.31     –65.39
Ecuador                       Hadley, B1, 2050       –45.69      –49.05      –45.69       –45.69     –45.72
Ecuador                       NCAR, A1B, 2050        –10.13      –16.80      –10.13       –10.13     –10.22
Ecuador                       NCAR, B1, 2050         –10.13      –16.80      –10.13       –10.13     –10.22
Mexico                        Hadley, A1B, 2050
Mexico                        Hadley, B1, 2050
Mexico                        NCAR, A1B, 2050
Mexico                        NCAR, B1, 2050
Peru                          Hadley, A1B, 2050
Peru                          Hadley, B1, 2050
Peru                          NCAR, A1B, 2050
Peru                          NCAR, B1, 2050
Uruguay                       Hadley, A1B, 2050      –12.59      –13.14      –12.59       –12.66     –11.18
Uruguay                       Hadley, B1, 2050        +2.68       +0.51       +2.68        +2.48      +3.94
Uruguay                       NCAR, A1B, 2050         +9.16       +6.86       +9.16        +8.83     +10.34
Uruguay                       NCAR, B1, 2050          +9.16       +6.86       +9.16        +8.83     +10.34
Rest of South America         Hadley, A1B, 2050      –33.98      –28.71      –33.98       –33.99     –25.93
Rest of South America         Hadley, B1, 2050       –19.31      –12.33      –19.31       –19.30      –9.49
Rest of South America         NCAR, A1B, 2050        –24.37      –16.78      –24.37       –24.37     –15.01
Rest of South America         NCAR, B1, 2050          –7.77       –0.11       –7.77        –7.76      +3.30
Central America & Caribbean   Hadley, A1B, 2050      –47.53      –51.22      –47.53       –51.82     –51.92
Central America & Caribbean   Hadley, B1, 2050       –34.72      –38.79      –34.72       –37.60     –37.76
Central America & Caribbean   NCAR, A1B, 2050        –12.09      –18.60      –12.09       –13.56     –13.35
Central America & Caribbean   NCAR, B1, 2050         –11.31      –16.93      –11.31        –7.57      –7.44




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Rice, 2050, With Adaptation

Table II16. Percentage impact (with adaptation) of climate change on rice yields for
potential, water limited, abiotic damaged, diseased, and actual production levels;
projections for 2050
                              Climate change      Climate change impact (percentage difference compared to
Country                       scenario                            the baseline productivity
                                                  Potential    Water        Abiotic     Diseased   Actual
                                                               limited     damaged
Argentina                     Hadley, A1B, 2050       +0.25                     +0.68        +3.96   +4.38
Argentina                     Hadley, B1, 2050        +2.39                     +2.81        +4.59   +5.00
Argentina                     NCAR, A1B, 2050         +4.81                     +5.26        +7.75   +8.19
Argentina                     NCAR, B1, 2050          +7.08                     +7.47        +8.49   +8.86
Brazil                        Hadley, A1B, 2050       –3.64                     –3.32        +5.50   +5.85
Brazil                        Hadley, B1, 2050        +0.10                     +0.43        +7.62   +7.98
Brazil                        NCAR, A1B, 2050         +9.35                     +9.71       +17.33  +17.71
Brazil                        NCAR, B1, 2050         +13.43                   +13.80        +19.75  +20.15
Chile                         Hadley, A1B, 2050
Chile                         Hadley, B1, 2050
Chile                         NCAR, A1B, 2050
Chile                         NCAR, B1, 2050
Colombia                      Hadley, A1B, 2050      +10.61                  +10.61      +21.50      +21.50
Colombia                      Hadley, B1, 2050       +10.27                  +10.27      +18.12      +18.12
Colombia                      NCAR, A1B, 2050        +15.38                  +15.38      +26.53      +26.53
Colombia                      NCAR, B1, 2050         +17.72                  +17.72      +25.33      +25.33
Ecuador                       Hadley, A1B, 2050       +8.37                   +8.37      +26.39      +26.39
Ecuador                       Hadley, B1, 2050        +8.27                   +8.27      +24.31      +24.31
Ecuador                       NCAR, A1B, 2050        +15.35                  +15.35      +32.81      +32.81
Ecuador                       NCAR, B1, 2050         +17.65                  +17.65      +30.58      +30.58
Mexico                        Hadley, A1B, 2050       –6.08                   –6.08       –2.26       –2.26
Mexico                        Hadley, B1, 2050        +4.18                   +4.18       +7.93       +7.93
Mexico                        NCAR, A1B, 2050         +1.81                   +1.81       +5.66       +5.66
Mexico                        NCAR, B1, 2050          +4.18                   +4.18       +7.93       +7.93
Peru                          Hadley, A1B, 2050       +7.08                   +7.08      +18.73      +18.73
Peru                          Hadley, B1, 2050        +5.38                   +5.38      +14.33      +14.33
Peru                          NCAR, A1B, 2050        +10.65                  +10.65      +20.64      +20.64
Peru                          NCAR, B1, 2050          +9.54                   +9.54      +16.58      +16.58
Uruguay                       Hadley, A1B, 2050       +5.80                   +8.24       +7.32       +9.75
Uruguay                       Hadley, B1, 2050        +5.98                   +8.33       +7.28       +9.62
Uruguay                       NCAR, A1B, 2050         +9.36                  +11.88      +10.78      +13.29
Uruguay                       NCAR, B1, 2050          +9.09                  +11.36      +10.37      +12.64
Rest of South America         Hadley, A1B, 2050       +3.29                   +3.30       +8.40       +8.41
Rest of South America         Hadley, B1, 2050        +4.14                   +4.15       +7.74       +7.75
Rest of South America         NCAR, A1B, 2050         +8.46                   +8.48      +12.75      +12.76
Rest of South America         NCAR, B1, 2050         +10.16                  +10.17      +13.64      +13.65
Central America & Caribbean   Hadley, A1B, 2050       +0.24                   +0.24       +4.68       +4.68
Central America & Caribbean   Hadley, B1, 2050        +0.61                   +0.61       +3.53       +3.53
Central America & Caribbean   NCAR, A1B, 2050         +6.05                   +6.05      +10.90      +10.90
Central America & Caribbean   NCAR, B1, 2050          +8.40                   +8.40      +12.13      +12.13




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Table II17. Application of coupled AZS-ENVISAGE platform: change in agricultural value
added from climate–induced damages in the four focus crops (%)
                                NCARA1B        NCARB1          HADA1B          HADB1
Countries/regions             2020   2050   2020    2050    2020    2050   2020    2050
Argentina                     –7.0  –28.7   –9.7   –15.3   –13.7   –32.3   –8.6   –21.5
Brazil                        –8.6  –10.3   –7.9   –11.8    –8.5   –15.3   –6.1   –14.2
Chile                         –2.2   –1.1   –2.3    –0.9    –2.8    –1.3   –2.5    –1.4
Colombia                      –1.9   –2.4   –1.5    –2.0    –1.9    –4.9   –2.2    –3.8
Ecuador                       –0.5   –0.9   –0.3    –1.0    –0.4    –2.4   –0.3    –1.7
Peru                           0.2   –0.3    0.4     0.0     0.1    –0.9    0.0    –0.4
Mexico                        –1.2   –3.5   –1.1    –3.8    –1.7   –10.4   –1.2    –8.4
Uruguay                        1.0    2.5    0.5     3.1     0.0    –4.5    1.3     1.2
Central America & Caribbean   –0.3    0.5    0.0     0.2    –0.6    –4.6   –0.5    –3.0
Rest of South America          2.2    1.0    1.5     2.5     2.0     4.9    2.0     3.5
Latin America & Caribbean     –3.5   –8.0   –3.4    –7.0    –4.2   –12.1   –3.0    –9.9




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