WPS4161

  Changing Farm Types and Irrigation as an Adaptation to Climate

                         Change in Latin American Agriculture1



                                              Robert Mendelsohn
              School of Forestry and Environmental Studies, Yale University USA
                                                         and
                                                    Niggol Seo
                             University of Aberdeen Business School, UK




                                                     Abstract

This paper estimates a model of a farm that treats the choice of crops, livestock, and irrigation as

endogenous.       The model is composed of a multinomial choice of farm type, a binomial choice of

irrigation, and a set of conditional land value functions.                  The model is estimated across over

2000 farmers in Latin America.             The results quantify how farmers adapt their choice of farm

type and irrigation to their local climate.         The results should help governments develop effective

adaptation policies in response to climate change and improve the forecasting of climate impacts.

The paper compares the predicted impacts of climate change using both endogenous and

exogenous models of farm choice.


World Bank Policy Research Working Paper 4161, March 2007

The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of

ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are

less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings,

interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily

represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working

Papers are available online at http://econ.worldbank.org.



1 Funding of this project was provided by the World Bank. We thank Emilio Ruz, Flavio Avila, Jorge
Lozanoff, Luis José María Irias, Magda Aparecida de Lima, Jorge Granados, Jorge González, Flavio Játiva,
Alfredo Albin, Bruno Lanfranco, Rafael Pacheco for their contribution to this project. This project was
funded by the Research Committee of the World Bank under the study 'Climate Change and Rural
Development' that was tasked managed by Ariel Dinar.

1. Introduction


This paper develops a Ricardian farm model that allows farmers to choose the type of

farm and irrigation based on the net productivity of each choice.         Although the

agriculture literature has carefully developed approaches to study the adoption of

irrigation technology (Caswel and Zilberman 1986; Dinar and Yaron 1990; Negri and

Brooks 1990; Dinar and Zilberman 1991; Dinar, Campbell, and Zilberman 1992), the

literature has not explored how adoption may be related to climate.    There have been

several agronomic studies in Latin America of selected crops in a selected country

(Downing 1992; De Siquerira et al. 1994; Magrin et al. 1997; Hofstadter et al. 1997;

Conde et al. 1997) that suggest individual crops would be sensitive to warming.     But

this agronomic literature does not explore how farmers themselves would react to climate

change.    Mathematical programming (MP) has been used to explore how predicted yield

losses from climate change would cause American farmers to change crops (Adams et al.

1994) and switch between crops and livestock (Adams et al. 1999). However, the MP

approach has only been developed for the US and it places all the burden of including

adaptation on the analyst.   To the extent that the analyst is unaware of substitutions

farmers can make or is unaware of reasons farmers cannot make substitutions, there is a

possibility of error.


      This paper presents an alternative methodology for measuring adaptation to climate

by relying on cross sectional evidence.   Cross sectional evidence has been widely used

to measure the link between land value (or net revenue) and climate (Mendelsohn et al.

1994; 1996; 1999; 2001; Mendelsohn and Dinar 2003; Sanghi 1998; Seo et al. 2005;

Kurukulasuriya et al 2006; Kurukulasuriya and Mendelsohn 2006a; Seo and Mendelsohn




                                            2

2006).   These "Ricardian" results provide a consistent welfare measure of the long run

impacts of climate on agriculture.     However, the Ricardian studies do not provide

insight into how farmers are adapting to climate.      By explicitly modeling adaptation,

this paper seeks to explain the Ricardian results and also bridge the gap between the (MP)

approach and the Ricardian approach.


      The theoretical model of the farm allows a farmer to choose among crops, livestock,

and irrigation to maximize profit. Although many farmers in developed countries either

specialize in crops or livestock, many farmers in developing countries choose to do both

activities. We first explore whether farmers who face different climates tend to choose

different types of farming.     Following Kurukulasuriya and Mendelsohn 2006b, the

model is extended to include the choice of irrigation.     We then explore the conditional

net revenue the farmer should expect given the choice of farm type and irrigation.


      The paper is divided into five parts. The next section develops the theory. The third

section describes the survey of over 2000 subsistence and commercial farmers across 7

Latin American countries and other data sources.     The fourth section discusses the cross

sectional results.  The fifth section presents forecasts of impacts from a set of future

climate scenarios.   We compare the forecasts one would make assuming these choices

are endogenous with the results if one assumed the choices were fixed.     We conclude the

paper with the policy implications and the limitations of the paper.


2. Theory


We assume that farmers choose amongst three types of farms: crops only, livestock only,

and a combination of crops-livestock.     For each of the farm types that have crops, the

farmer can also choose to do dryland farming or use irrigation.    Given these choices, the



                                             3

farmer combines inputs to make outputs that maximize land value.     We assume that the

farmer will choose the combination of farm type and irrigation that maximizes expected

net revenues.


      For example, in Figure 1, we show a hypothetical relationship between farm type

and climate.   The picture suggests that each farm type is ideal for a particular climate

range.   As climate changes, farmers switch from one farm type to another.   The overall

response function captures this switching.        However, by explicitly modeling the

switching, analysts can see what changes farmers are making to stay on the maximum

profit locus. .


      The profit each farmer i obtains from choosing farm type j (j=1, 2, or 3) is the

following:




      ij =V(Kj)+1(Kj)                                                                (1)




where K is a vector of exogenous characteristics of the farm. For example, K could

include climate and soils. We identify the choice of farm type with crop prices that

reflect the attractiveness of planting crops versus livestock.   The profit function is

composed of two components: the observable component V and an error term, . The

error term is unknown to the researcher, but may be known to the farmer. The farmer will

choose the farm type that gives him the highest profit. In other words, the farmer will

choose farm type j over all other farm types k if:




                                             4

  *(Kji) >*(Kki)fork  j.[orif 1(Kki)-1(Kji) <V(Kji)-V(Kki)for k  j] (2)




More succinctly, farmer i's problem is:




         argmax[      *(K1i),*(K2i),...,*(KJi)]                                     (3)
               j




The probability Pji of the jth farm type being chosen is




       Pji = Pr[1(Kki ) -1(K ) < Vj -Vk ]  k  j where Vj = V(K ji )                 (4)
                              ji




      Assuming 1 is independently Gumbel distributed and Vk = Kki k +k , the

probability that farmer i will choose farm type j among the 3 farm types is (Chow 1983;

McFadden 1981):




       Pji =     eKji j
                 3  eKkik                         .                               (5)
                 k=1




      The parameters can be estimated by Maximum Likelihood Method, using an

iterative nonlinear optimization technique such as the Newton-Raphson Method. These




                                            5

estimates are CAN (Consistent and Asymptotically Normal) under standard regularity

conditions (McFadden 1999). The probability of choice is identified by both cross price

terms for crops and livestock and adding up constraints across the probabilities.


      Conditional on choosing crops, the farmer can also choose irrigation. As with the

farm type model, we assume that the farmer chooses irrigation only if it is more

profitable. We estimate a dichotomous choice model of irrigation, Y, where Y=1 is

irrigation and Y=0 is dryland farming:




                                Yi = 1X +                                            (6)




where X is a k-vector of regressors for the irrigation choice and  is an error term.     The

vector X includes soils and climate. The irrigation choice is identified by the soil clay.

Clay soils generally make irrigation difficult because the soils become water logged.


      In the third stage, we estimate a conditional profit function for each type of farming

based on the available exogenous variables, Z:


                               i =  i + j if Y = j
                                      j                                                 (7)


where Yj is a latent variable explaining the choice of farm type/irrigation, j is the net

profit of farms of type j, Zj is an m-vector of regressors that determine land value, j is an

m-vector of coefficients for farm type j, and the error terms , , and j are jointly

normally distributed, independently of X and Z, with zero expectations.


                 ~ N(0,1)                                                        (8a)



                                              6

                  j ~ N(0, ))                                                               (8b)


                  corr(, j) = 1                                                            (8c)



                  corr(, j) = 2                                                             (8d)




where u j =error from the third stage,  =error from the first stage,  j =error from the
                                                j



second stage,  =standard error from the unconditioned land value regression,
                       j



rj =correlation between the choice error and the land value regression error.


       Because of selection bias, it is possible that the unobserved profitability of a choice

is correlated with the selection of that choice (Heckman 1979).                    Since the farmer

maximizes net revenue conditional on the choice of farm type, the error in the land value

equation may be correlated with the errors in the choice equations. According to Dubin

and McFadden (1984), with the assumption of the following linearity condition:2




                              J                        J
   E(uj |1,...,J ) = j  rj( j -E( j))with rj =0
                                                                                                 (9)
                             j=1                      j=1




The conditional profit functions can be consistently estimated as:



2 See Bourguignon et al. (2004) for the details of the selection bias corrections from the multinomial
choice. They find that Dubin and McFadden's method is preferable to the most commonly used Lee method,
as well as to the Dhal's semi-parametric method in most cases. Monte Carlo experiments also showed that
selection bias correction based on the multinomial logit model can provide fairly good correction for the
outcome equation even when the IIA hypothesis is violated.


                                                   7

      j =xjj+x2jj+j ri     J    Pi lnPi+lnPj+wj
                                            
                                                                                       (10)
                          ij     1-Pi

where the third term on the right hand side is the correction term and wj is the error term.


     In this analysis, we employ land value as the measure of net productivity. With

perfect competition for land, free entry and exit will drive excess profits to zero on the

margin. (Ricardo 1817) In this case, land rents will equal net income per hectare.

Land value will reflect the present value of the net income of each farm:


               
       Vland = t e-rtdt
                   *                                                          (11)
               0



, where r is the market interest rate. (Mendelsohn et al. 1994)


     Land values provide a better measure of climate response because they reflect the

expectation of net revenues across many years. In contrast, annual net revenues reflect

annual outcomes that vary year by year such as weather and prices. Since we are

interested in this analysis in climate not weather impacts, the land value measure is more

relevant. The land value measure also captures the farmer's expectations about other

things that might change in the future. For example, if farmers expect that technical

change will enable them to cultivate the same plot more productively in the future, it will

be reflected in land value.


     In this model, the expected value of a farm, W, is the sum of the probabilities, Pk, of

each farm type times the conditional net revenue of that farm type. That is:




                                              8

                6
      W(C) = Pk (C)*Vk (C)
                                                                               (12)
               k=1




     The change in welfare, W, resulting from a climate change from CAto CB can be

measured as follows.




      Wi =Wi(CB)-Wi(CA)                                                           (13)




3. Data and Background

Farm surveys were pretested and then finalized3. Each survey was translated to Spanish

or Portuguese depending on the country. Farm surveys were collected by country teams

from seven countries in Latin America4. The seven countries include: Argentina, Brazil,

Chile, Colombia, Ecuador, Uruguay, and Venezuela. Random samples of districts were

selected to observe a set of farms over a wide range of climates within each country. In

each country, 15-30 clusters were selected and 20-30 households were interviewed in

each cluster. Cluster sampling was done to control the cost of the survey. The farm

surveys ask questions about farming activities, including crop and livestock production

and costs. The survey was conducted from July 2003 to June 2004. Surveys also record

the climate and weather related perceptions of the farmers. Altogether, a total of 2003



3 Survey forms are available from the authors.
4 We wish to thank Flavio Avila for managing the 7 country data collection process. We
also wish to thank the team leaders of the collection process in each country: A. Albin, R.
Bruno, J. Gonzalez, P. Granados, L. Irias, P. Jativo, J. Lozanoff, R. Pacheco.



                                             9

farms were surveyed.


      Climate data come from two sources: temperature observations came from US

Defense Department Satellites and the rainfall observations came from ground station

data of the World Meteorological Organization.         The satellite temperature measures

proved superior to the weather station observations at least for rural areas of the world

(Mendelsohn et al 2005). The satellites can observe the entire surface of the earth

whereas many rural areas do not have a weather station nearby and so require

interpolation. Unfortunately, the satellites cannot directly measure precipitation and so

the weather station data is the best that can be done at the moment.


      Soil data were obtained from the FAO digital soil map of the world CD ROM. The

data was extrapolated to the district level using Geographical Information System. The

data set reports 116 dominant soil types organized into 26 major groups. We extract

texture and slope of the soils at the district level.


      The analysis relies upon land values and farm characteristics as reported by the

interviewed farmer. In many parts of Latin America, land has been reallocated by the

government. Land use is also restricted in many cases. For example, farmers in Brazil

face official limitations on land clearing. The analysis was not able to control for all of

these imperfections in the land market.            However, separate analyses comparing

Ricardian regressions that use land values and net revenues for the dependent variable

lead to very similar results, suggesting the land value data is consistent and unbiased.


4. Empirical Results


The study identified three types of farms in the region: crop only, livestock only, and

crops/livestock together.    We further break down farms that grow crops by whether or


                                                10

not they use irrigation. Table 1 measures how many farms of each type were in the

sample. Over half of the farms have both livestock and crops, almost one third of the

farms rely solely on crops, and only 13% of the farms just raise animals. Only 26% of

the farms with crops use irrigation. Three fourths of the farms growing crops use

dryland farming.


      Our first analysis seeks to explore how different exogenous factors and specifically

climate affect the choice of farm type. We conduct a multinomial logit omitting the

choice of livestock-only farms for comparison. The results are displayed in Table 2.

All eight climate coefficients are significant in the cop-only regression and all but the

linear term on summer temperature are significant in the mixed crop-livestock regression.

In order to help interpret the climate coefficients, Table 3 presents the marginal log odds

ratios for annual temperature and precipitation. Crop-only farms are less common in

places with warmer annual temperatures and the effect is significant at the 5% level. In

contrast, precipitation does not influence the choice of crop-only versus livestock only

farms. In contrast, mixed crop-livestock farms are more frequent in warmer places and

this effect is significant. The results imply that livestock-only farms are more likely in

warmer places. The results suggest that farmers tend to choose mixed crop-livestock

farms and livestock-only farms in warmer locations while farmers choose crop-only

farms in cooler locations. Surprisingly, farmers facing higher precipitation are mre

likely to choose livestock-only farms and less likely to pick crop-only farms.


      Soil types Acrisols, Kastanozems, Phaeozems, and Solonetz reduce the likelihood

that crops are grown whereas Gleysols and Lithosols soils increase the probability of

growing crops. Controlling for soils and climate, the Andean countries are more likely




                                             11

to engage in growing crops than the Southern Cone region. This may reflect regional

differences in agricultural or land use policy or regional differences in the demand for

meat (which may be higher in the Southern Cone countries). The coefficient for maize

price is positive and very significant. Maize is a high valued crop. Farmers with higher

maize prices are consequently more likely to choose crops-only. In contrast, the higher

price of potatoes has a negative effect. In this case, potatoes are a low valued crop. If

farmers are reduced to growing potatoes, they are more interested in livestock. The

tomato price is negative for the mixed farms implying that mixing vegetables and

livestock is not profitable.


      The next analysis examines whether or not a farmer adopts irrigation, given that he

has chosen to grow crops. In Table 4, we present two logit regressions of irrigation, one

for farms with crops-only and one for farms with crops-livestock.             Note that the

coefficients for the two models are statistically different. The irrigation choice is not the

same for crop-only and mixed farms. Ideally, we would have liked to have included the

availability of water supplies and a measure of capital constraints.          Unfortunately,

neither variable was available.


      There are many significant explanatory variables in the choice of irrigation equation

for the crops-only farms.   For example, summer precipitation and winter temperature are

significant determinants of whether irrigation is chosen.        In addition, the irrigation

choice depends on soil types. Farms with soil type Acrisols are less likely to choose

irrigation whereas farms with soil type Fluvisols are more likely to choose to irrigate.

The soil variable used to identify the irrigation choice regression, clay texture, is negative

but not significant in the crop-only regression. The selection terms were not significant




                                              12

implying there is no sample selection bias.


         The results for the crop-livestock sample in Table 4 are quite different from the

crop-only results. Summer precipitation is larger and more significant and both winter

temperature and precipitation are significant. Only soil type Fluvisols had a positive

and significant coefficient.    The identifying variable, texture clay, is negative and

significant as expected. Clay is difficult for irrigation because it leads to water logged

fields. The selection terms are again insignificant implying there is no selection bias

problem in the irrigation equations.


      Looking at the marginal effects of annual climate on irrigation in Table 5, we see

that farms in warmer locations are much less likely to choose irrigation. Although

irrigation allows crops to survive higher temperatures, the relative profitability of

irrigation falls as temperatures increase.     Consequently, farmers are more likely to

irrigate in cooler places. Farmers in locations with more rainfall are also less likely to

irrigate. The marginal contribution of irrigation to net revenue (compared to dryland

farming) falls as precipitation increases. Farmers do not need irrigation in places with

high precipitation.


      The third stage of the model estimates the conditional net income for each farm

type. There are five different farm types identified in Table 6: crop only dryland, crop

only irrigated, crop-livestock dryland, crop-livestock irrigated, and livestock only.

Summer temperature is significant in the two crop-only and livestock-only regressions.

Winter temperature is significant in the livestock-only and mixed dryland farms. Summer

precipitation is significant in all but the mixed dryland farms.   Winter precipitation is

significant only in the crop-only dryland and livestock-only regressions.     In farms that



                                            13

grow crops, the temperature squared coefficients are all negative (except for an

insignificant coefficient on winter temperature for crop-only irrigated farms) implying a

hill-shaped relationship. However, for the livestock-only farms, the winter temperature

squared coefficient is large and positive implying a U-shaped relationship. The summer

precipitation squared coefficients are largely negative (except for mixed irrigated farms)

implying a hill-shaped relationship. The winter precipitation squared coefficients are

largely insignificant except for a positive value for crop-only farms and a negative value

for livestock only farms. These results suggest that the marginal impact of temperature

and precipitation will depend on the climate facing the individual farm and will vary

across the sample.


      Table 6 also reveals that soils play a unique role in the net income farmers earn

from each farm type. For example, Acrisols significantly increase the value of irrigated

crop-only farms but are insignificant in all other regressions. Cambisols also increase

the value of irrigated crop-only farms but also livestock-only farms.         In contrast,

Luvisols only increase the value of crop-only dryland farms and Planosols increase the

value of crop-only dryland farms but decrease the value of irrigated crop-only farms and

mixed dryland farms.      The Andean dummy shows that crop only and mixed dryland

farms in the Southern Cone are generally more profitable. Finally the selection terms

are insignificant which suggest that the net revenue regressions are not vulnerable to

sample selection problems.




  Table 7 presents the marginal effects and elasticities of annual temperature and

precipitation.   Crop-only farms in warmer locations have significantly lower net



                                             14

incomes.    These results support the earlier observation that farmers tend to choose

crops-only in cooler locations.      The precipitation has a significant effect only on

livestock-only farms.   Livestock-only farms earn higher incomes in wetter locations.

For the remainder of the farm types, the annual precipitation effects are mixed and

insignificant.


      The temperature elasticities in Table 7 indicate that livestock farms are the most

sensitive to warmer temperatures. Latin American livestock operations depend heavily

on beef cattle which tend to be heat sensitive, a result also found in African livestock

management (Seo and Mendelsohn 2006). Dryland crop farms are also sensitive to heat

as they tend to be located in warm places.         Irrigated crop farms are less sensitive

partially because they are in cooler locations and partially because the irrigation reduces

their vulnerability. The mixed farms are insensitive to temperature partially because

they have a great deal of substitution possibilities to compensate for heat.


      Table 7 also reveals that the net income of livestock-only farms is very sensitive to

precipitation with an elasticity of 3.    Precipitation has very little effect on the net

incomes of the other farm types.         One should not infer from these results that

precipitation has no effect on individual crops. Part of the reason precipitation is having

such little effect is that farmers can switch from one type of crop to another as

precipitation varies.


5. Climate Change Impacts Simulations


In this section, we explore what consequences the cross sectional results imply if climate

changes in the future. There are caveats one must keep in mind to make such forecasts.

First, we assume that comparing a cool farm to a warm farm today is the same as having



                                            15

a farm experience a cool climate today versus a warm climate in the future. If there are

important missing variables in our analysis that are correlated with climate, the

predictions will be biased. Second, we assume that other changes in future conditions

will not affect our climate predictions. For example, changes in technological advances,

growth, and land use will not alter climate impacts. In practice, these future changes are

both likely to occur and likely to have an effect on climate impacts. Future analyses

should take these changes into account, but this is beyond the scope of this paper. We

consequently limit ourselves to examine the impact of climate change on the current

agricultural system. Third, we assume that prices will not change in any of these future

scenarios even if supply changes dramatically. Partially, this can be justified because

prices are determined in a world market and regional changes are not a good predictor of

global changes. However, if prices change, this will tend to reduce the welfare impacts

predicted in this analysis. Fourth, the analysis does not consider carbon fertilization

effects. The increase in carbon dioxide is expected to be beneficial to plants in general

and to specific plants in particular. Carbon fertilization is not taken into account in these

forecasts although it will clearly increase productivity.


     In order to see what impact future climates might have on Latin American

agriculture, we examine three climate scenarios generated by Atmospheric Oceanic

General Circulation Models (AOGCM's). The three models we rely upon provide a

broad array of outcomes from a mild wet scenario to a very hot and dry scenario.

Specifically, the three models are the Parallel Climate Model (PCM) (Washington et al.

2000), the Center for Climate System Research (CCSR) (Emori et al. 1999), and the

Canadian Climate Centre (CCC) (Boer et al. 2000). The climate projections of these

three models for Latin America are presented in Table 8. The PCM is the mildest


                                              16

scenario with small amounts of warming, small increases in summer precipitation, and

large increases in winter precipitation.      The CCC is the harshest scenario with

substantial warming and reductions in summer precipitation. The CCSR scenario lies

between these other scenarios. Temperature increases steadily over this century across

all three models. Precipitation increases and decreases over time in no apparent pattern.


      For each climate scenario, we make two predictions. In one prediction, we assume

that the decision to choose farm type and irrigation is exogenous and will not change.

In the second prediction, we assume these choices are endogenous and will change with

each climate scenario. That is, we predict how each climate scenario will change the

probability each farmer will choose each farm type (using the coefficients in Tables 2)

and the probability of adopting irrigation (using the coefficients in Tables 4).

Combining these results with the changes in the conditional land values yields an

expected change in the land value for each farm for both the exogenous and endogenous

cases.


      Table 9 shows the current distribution of farm types for the sample (the exogenous

case) and how that distribution would change over time (the endogenous case) for each

climate scenario.   The substantial warming associated with CCC and CCSR would

cause the number of crops-only farms to shrink as early as 2020. According to these

two scenarios, this effect would get stronger over time so that by 2100, almost one fourth

of the crops-only farms would be gone in the CCC scenario and one eighth of these farms

would be gone according to the CCSR scenario. According to the CCC and CCSR

scenarios, the crop-only farms would become crop-livestock farms and       livestock only

farms. .The PCM scenario, however, provides a different forecast of impacts. With the




                                            17

milder and wetter PCM scenario, livestock farms would diminish, and crop-livestock

farms would replace them.


      Table 10 shows how irrigation choices would change with warming.            All the

climate scenarios predict an increase in irrigation for crop-only farms and a decrease in

irrigation for mixed farms but the changes are generally not significant.


      Table 11 shows what happens to conditional land values for each farm type-

irrigation possibility. According to the CCC scenario, the land value of all farm types

except livestock-only will fall with warming. The effect is particularly severe for crop-

only dryland farms whose land values fall by almost half by 2100. Crop-livestock farms

are the only exception in the CCC scenarios and their land values increase by one third by

2100. The CCSR scenarios yield qualitatively similar results to the CCC scenarios but

the magnitudes of the effects are about half the size and consequently less significant.

The results for the PCM scenarios, however, are quite different. The land values of

crop-only dryland farms increase in the PCM scenario while net revenues in all other

farm types fall. These PCM predictions, however, are not significantly different from

zero.


         Combining the results from Tables 9, 10, and 11, the overall expected climate

impact on the value of farms is calculated in Table 12. The expected value of future

climate scenarios for the exogenous approach uses the current probabilities of each farm

type and irrigation with the future conditional land values. The difference between the

future predicted land value and current expected land value is the welfare effect of

climate change.


      The baseline expected value of a farm in the sample is about $3400/ha.



                                            18

Examining the endogenous predictions first, the expected value of a Latin American farm

steadily falls over time with the CCC scenario until by 2100, the expected value falls by

28%. With the CCSR scenario, expected values also fall but the magnitude of the effect

is smaller (19% by 2100).       Finally, with the PCM scenario, the expected value initially

increases by 10% by 2020, then fall back to current values. The milder wetter scenario

predicted by the PCM model has little impact on Latin American farmers overall.


      Table 12 also displays the 95% confidence interval around these final results.

These were computed using bootstrapping with 200 draws.             The PCM results are

initially significant in 2020 and then become insignificant. The CCC results are not

quite significant at first but become so by 2060. The CCSR results are only significant

in 2100.


      Finally, Table 12 provides the results for models that treat choice as exogenous

versus endogenous. The exogenous model predicts larger damages and smaller benefits

than the endogenous model in all scenarios. The magnitude of the difference increases

with time. Capturing each farmer's ability to adapt to climate change by adopting

different farm types and irrigation reduces the vulnerability in agriculture. The gap

between the endogenous model and exogenous model is especially large in the CCC

scenario.


6. Conclusion


This study expands on empirical agricultural models of irrigation choice to examine how

such choices are influenced by climate. The paper models the choice of whether to

grow crops, own livestock, and install irrigation and tests whether these choices are

influenced by temperature and precipitation. The purpose of the model is to quantify



                                              19

some of the adaptations that farmers make to adjust to climate. Using cross sectional

evidence, the paper models how Latin American farmers have adapted to the range of

climates across the continent.      Surveys of over 2000 farmers provided detailed

information about crops, livestock and irrigation choices. Relying on a three stage

integrated model of a farm, the choice of farm type, irrigation, and conditional land value

were all calculated.


         The results show that the choice of farm type and irrigation are very sensitive to

climate. Farmers are more likely to pick crops-only in cooler temperatures whereas they

will choose livestock in dryer locations. Farmers are more likely to choose a crop-

livestock combination in hot locations.      Farmers will tend to irrigate in locations that

are both cool and dry. Of course, irrigation also requires access to water sources.


     Conditional land values are also dependent on climate.          Cooler than average

temperatures increase land values for all farm types except irrigated crop-livestock farms.

Increased precipitation raises land values for all farm types. However, the net revenues

of some farm types respond more to cooler and wetter conditions than others. The net

revenues of livestock farms are especially sensitive to both temperature and precipitation.

The net revenues of dryland crop-only farms are very sensitive to cooler temperatures.


         Applying these cross sectional results to future climate scenarios reveals some

interesting outcomes. If the future climate scenario is very hot and dry, expected land

values will fall by a third by 2100. Dryland crop-only farming will be especially hard

hit and the amount of irrigation will fall substantially. Crop-livestock operations will be

hurt but less so. If the scenario is hot and dry but not as severe, the impacts will have

the same qualitative direction but the magnitude of the effect will be much smaller.



                                             20

However, if the scenario is mild warming and wetter conditions, crop-only farms will

increase in value and overall farm value will rise. Only livestock will be reduced in the

future. The impacts of climate change consequently depend a great deal on the climate

scenario.


        The overall results suggest that farmers will do a great deal of adaptation in

response to climate change. The results indicate that they will change whether they

grow and own livestock and whether or not they will rely on irrigation. The exogenous

model predicts higher damages and smaller benefits than the endogenous model. The gap

between the models increases over time due to the increasing adaptive behavior and

increasing climate impacts over the long term.      These adaptive decisions which have

been assumed to be exogenous in a great deal of the climate impact literature must be

treated endogenously.


        There are a number of caveats that must be kept in mind in interpreting these

results. First, there was no information about water resources in the analysis and so this

important variable was omitted.      Second, the effect of carbon fertilization was not

captured in the analysis since all the farms in the sample were exposed to the same level

of carbon dioxide. Carbon fertilization is likely to improve future crop productivity and

thus may offset some of the harmful effects predicted in this analysis.        Third, the

influence of technical change is not captured in this study. Future productivity increases

may also offset some of the losses predicted in this analysis. Further, technological

advances in crop breeding could create future crops that are more heat tolerant. Such

possible effects are not considered. Fourth, the paper assumes that commodity and labor

prices would not change with climate. If prices do change, the welfare impacts will be




                                            21

smaller. Finally, the analysis assumes that farmers in the future will be able to adapt as

readily as farmers in the present. That is, the study assumes that the adaptations one

currently sees from place to place can be done across time as climate change unfolds.

All of these factors should be considered when projecting the future outcomes of climate

change.




                                          22

References


Adams, R., M., McCarl, B., A., Segerson, K., Rosenzweig, C., Bryant, K., Dixon, B.,

      Conner, R., Evenson, R., Ojima, D. (1999), "The economic effects of climate

      change on US agriculture." in Mendelsohn and Neumann (eds.), The Impact of

      Climate Change on the United States Economy, Cambridge University Press;

      Cambridge, UK.


Adams, R.M., McCarl, B. 2001, "Agriculture: Agronomic-Economic Analysis" in

      Mendelsohn (ed) Global Warming and the American Economy: A Regional

      Analysis, Edward Elgar Publishing, UK.


Boer, G., G. Flato, and D. Ramsden. 2000. "A transient climate change simulation with

      greenhouse gas and aerosol forcing: projected climate for the 21st century",

      Climate Dynamics 16, 427-450.


Caswell, Margiarette and David Zilberman. 1986. "The Effect of Well Depth and Land

      Quality On the Choice of Irrigation Technology." American J. of Agricultural

      Economics 68: 798-11.


Conde, C., D. Liverman, M. Flores, R. Ferrer, R. Araujo, E. Betancourt, G. Villareal, and

      C. Gay. 1997. "Vulnerability of Rainfed Maize Crops in Mexico to Climate

      Change", Climate Research 9:1-23.


De Siqueira, O.J.F., J.R.B. Farias, and L.M.A. Sans. 1994. "Potential Effects of Global

      Climate Change for Brazilian Agriculture: Applied Simulation Studies for Wheat,

      Maize, and Soybeans", in Rosenzweig, C. and A. Iglesias, eds., Implications of

      Climate Change for International Agriculture: Crop Modeling Study, U.S. EPA,




                                          23

       Washington, DC, USA.


Dinar, Ariel and Dan Yaron. 1990. "Influence of Quality and Scarcity of Inputs on the

       Adoption of Modern Irrigation Technologies." Western J. of Agricultural

       Economics 15(2): 224-33.


Dinar, Ariel and David Zilberman. 1991. "The Economics of Resource-Conservation,

       Pollution-Reduction Technology Selection, The Case of Irrigation Water."

       Resources and Energy 13: 323-48.


Dinar, Ariel, Mark B. Campbell, and D. Zilberman. 1992. "Adoption of Improved

       Irrigation and Drainage Reduction Technologies Under Limiting Environmental

       Conditions." Environmental & Resource Economics 2: 373-98.


Downing, T. E. 1992. Climate Change and Vulnerable Places: Global Food Security and

       Country Studies in Zimbabwe, Kenya, Senegal, and Chile, Research Report No. 1,

       Environmental Change Unit, University of Oxford, Oxford, UK.


Emori, S. T. Nozawa, A. Abe-Ouchi, A. Namaguti, and M. Kimoto. 1999. "Coupled

       Ocean-Atmospheric Model Experiments of Future Climate Change with an

       Explicit Representation of Sulfate Aerosol Scattering", J. Meteorological Society

       Japan 77: 1299-1307.


Heckman, J. 1979. "Sample Selection Bias as a Specification Error", Econometrica 47,

       153-161.


Hofstadter, R., M. Bidegain, W. Baetghen, C. Petraglia, J.H. Morfinao, A. Califra, A.

       Hareau, R. Romero, J. Sauchik, R. Mendez, and A. Roel. 1997. Agriculture Sector

       Assessment, in Assessment of Climate Change Impacts in Uruguay, Montevideo,




                                          24

      Uruguay.


Kurukulasuriya, P. and R. Mendelsohn. 2006a. "A Ricardian Analysis of The Impact of

      Climate Change on African Cropland" World Bank Research Policy Working

      Paper (forthcoming).


Kurukulasuriya, P. and R. Mendelsohn. 2006b. "Modeling Endogenous Irrigation:


The Impact Of Climate Change On Farmers In Africa". World Bank Research Policy

      Working Paper (forthcoming)


Magrin, G., M. Travasso, R. Diaz, and R. Rodriguez. 1997. "Vulnerability of the

      agricultural systems of Argentina to climate change", Climatic Research 9: 31-36.


McCarthy, J., O. Canziani, N. Leary, D. Dokken, and K. White (eds.). 2001. Climate

      Change 2001: Impacts, Adaptation, and Vulnerability, Intergovernmental Panel

      on Climate Change Cambridge University Press: Cambridge.


Mendelsohn, R., W. Nordhaus and D. Shaw. 1994. "The Impact of Global Warming on

      Agriculture: A Ricardian Analysis", American Economic Review 84: 753-771.


Mendelsohn, R. and A. Dinar. 1999. "Climate Change Impacts on Developing Country

      Agriculture" World Bank Research Observer 14: 277-293.


Mendelsohn, R., A. Dinar and A. Sanghi. 2001. "The Effect of Development on the

      Climate Sensitivity of Agriculture", Environment and Development Economics 6:

      85-101.


Mendelsohn, R., A. Dinar, and L. Williams. 2006a. "The Distributional Impact of Climate

      Change On Rich and Poor Countries" Environment and Development Economics

      11: 1-20.



                                          25

Mendelsohn, R., A. Basist, F. Kogan, and P. Kurukulasuriya. 2006b. "Measuring Climate

        Change Impacts with Satellite versus Weather Station Data", Climatic Change

        (forthcoming).


Negri, D. H. and D. H. Brooks. 1990. "Determinants of Irrigation Technology Choice."

        Western Journal of Agricultural Economics 15: 213-23.


Reilly, J., et al. 1996. "Agriculture in a Changing Climate: Impacts and Adaptations." In

        IPCC (Intergovernmental Panel on Climate Change), Watson, R., M. Zinyowera,

        R. Moss, and D. Dokken, eds., Climate Change 1995: Impacts, Adaptations, and

        Mitigation of Climate Change: Scientific-Technical Analyses, Cambridge

        University Press: Cambridge.


Ricardo, D. 1817. "On the Principles of Political Economy and Taxation", John Murray,

        London.


Rosenzweig, C., and D. Hillel. 1998. Climate Change and the Global Harvest: Potential

        Impacts of the Greenhouse Effect on Agriculture Oxford University Press, New

        York.


Seo, N. S., R. Mendelsohn, and M. Munasinghe. 2005. "Climate Change and Agriculture

        in Sri Lanka: A Ricardian Valuation", Environment and Development Economics

        10: 581-596.


Seo, N. S., and R. Mendelsohn. 2006. "The Impact of Climate Change on Livestock

        Management in Africa: A Structural Ricardian Analysis", World Bank Research

        Policy Working Paper, Washington D.C..


Singh, I., L. Squire, and J. Strauss. 1986. "A Survey of Agricultural Household Models:




                                             26

      Recent Findings and Policy Implications", World Bank Economic Review 1: 149-

      179.


Washington, W., et al. 2000. "Parallel Climate Model (PCM): Control and Transient

      Scenarios". Climate Dynamics 16: 755-774.




                                        27

Table 1: Number of Farms of Each Type


                            Dryland       Irrigated    All
    Crop Only                 360           277        637
Crop and Livestock            948           179        1127
  Livestock Only              268             1        269
         All                  1576          457        2003




                                     28

Table 2: Multinomial Logit Model of Farm Type Selection


                                                            Both Crops and
                                    Crops Only              Livestock
Var.                                Est.           Chi-sq         Est.  Chi-sq
Intercept                               -1.939         0.74    -1.787     0.69

Summer Temperature                       0.717         9.21     0.333     2.23
Summer Temperature2                     -0.038        34.79    -0.024    14.98

Summer Precipitation                    -0.040        60.73    -0.029    34.02
Summer Precipitation2                    0.000        40.40     0.000    22.54

Winter Temperature                       1.020        92.60     1.000    95.56
Winter Temperature2                     -0.015        25.04    -0.017    31.44

Winter Precipitation                    -0.026        18.73    -0.022    13.72
Winter Precipitation2                    0.000         6.23     0.000     7.03

Soil Acrisols                           -0.018        12.02    -0.018    15.61

Soil Gleysols                            0.016         2.71     0.001     0.02

Soil Lithosols                           0.008         2.48     0.008     2.43

Soil Kastanozems                        -0.014         4.48    -0.005     0.71

Soil Phaeozems                          -0.010         7.71    -0.013    13.46

Soil Solonetz                           -0.016         5.10    -0.015     5.04

Maize Price                              1.095        15.66     1.233    19.71

Potato Price                           -21.434        56.30    -0.820     4.46

Tomato Price                            -0.828         1.98    -5.031    20.43

Andean Dummy                             2.460        81.49     2.105    62.06

Commercial Dummy                        -0.025         0.06    -0.129     1.73

** denotes the statistics estimate is significant at the 1% level.




                                              29

Table 3: Bootstrap Marginal Climate Effects on Farm Type Selections


                            Crop Only          Crop and Livestock    Livestock Only
Baseline                               27.5%                   50.3%             22.4%

                               (25.7%, 29.5%)          (48.6%, 53.2%)    (18.2%, 24.9%)

Temperature (C°)                        -1.6%                    0.5%              1.0%

                                (-2.0%, -0.9%             (0.0%, 1.3%      (0.2%, 1.5%)

Precipitation (mm/mo)                 -0.13%                   0.01%             0.11%

                             (-0.15%, -0.07%)         (-0.02%, 0.07%)    (0.05%, 0.15%)

* Numbers in parentheses are 95% confidence intervals.




                                           30

Table 4: Logit Model of Irrigation


                                                        Both Crop and Livestock
                               Crop Only Farms          Farms
Var.                           Est.          Chi-sq     Est.          Chi-sq
Intercept                             2.411       1.67          0.654       0.24

Summer Temperature                   0.021        0.01          0.058       0.11
Summer Temperature2                 -0.003        0.14         -0.002       0.08

Summer Precipitation                 -0.011       6.06         -0.020      21.54
Summer Precipitation2                0.000        0.65          0.000      13.11

Winter Temperature                  -0.274        6.24          0.143       2.91
Winter Temperature2                   0.011       9.97         -0.006       5.40

Winter Precipitation                -0.006        2.34         -0.010       6.72
Winter Precipitation2                0.000        0.08          0.000       0.85

Soil Acrisols                       -0.023        6.40         -0.009       1.45

Soil Cambisols                      -0.019        2.59         -0.002       0.06

Soil Ferralsols                     -0.005        1.11         -0.001       0.07

Soil Gleysols                       -0.014        1.48          0.014       2.85

Soil Fluvisols                       0.025      13.30           0.025      17.91

Texture Clay                        -0.303        1.57         -0.485       4.60

Andean dummy                         0.139        0.12         -0.850       6.82

Selection mixed                     -0.552        1.69

Selection livestock                  0.138        0.26

Selection crops                                                -0.260       0.30

Selection livestock                                             0.670       2.71




** denotes the estimate is significant at1% level and * at 5% level.




                                            31

Table 5: Bootstrap Marginal Effects on Irrigation Choice


                                        Crop Only          Both Crop and Livestock
         Baseline Prob.                            46.2%                      19.8%

                                          (40.8%, 49.4%)             (17.5%, 21.9%)

    Marginal Temp Effects                          -0.6%                      -0.5%

                                            (-2.9%, 0.9%)             (-1.3%, -0.2%)

    Marginal Prec Effects                          -0.2%                      -0.2%

                                           (-0.3%, -0.1%)             (-0.2%, -0.1%)

* Numbers in parentheses are 95% confidence intervals.




                                           32

Table 6: Conditional Ricardian Model


                            Crop Only Dryland      Crop Only Irrigated   Livestock Only
Var.                        Est.          T-stat.  Est.         T-stat.  Est.       T-stat.
Intercept                     -3331.45      -0.81    -2275.49       -1.05  -5970.85     -2.90

Summer Temperature              439.59       1.61      729.83        2.74    427.52      2.03
Summer Temperature2              -16.80     -2.14       -18.81      -2.27     -3.13     -0.58

Winter Temperature              340.28       1.61      -322.90      -1.34   -745.58     -6.80
Winter Temperature2              -11.39     -1.59         5.50       0.68     16.97      6.82

Summer Precipitation              19.41      2.66        34.34       3.08     52.24      6.80
Summer Precipitation2             -0.03     -2.38        -0.07      -2.33     -0.16     -6.04

Winter Precipitation             -11.85     -2.81        16.10       1.43      9.33      3.44
Winter Precipitation2              0.04      2.48        -0.06      -1.09     -0.05     -3.75

Soil Acrisols                     14.12      1.41        94.95       2.22      2.92      0.97

Soil Cambisols                     2.05      0.16        57.86       1.54     27.65      4.36

Soil Gleysols                     -9.51     -0.90        21.98       0.70      4.16      1.22

Soil Phaeozems                   -16.04     -1.12       -30.84      -2.80

Soil Kastanozems                   2.29      0.23        -4.58      -0.48      2.06      0.75

Soil Luvisols                     18.06      2.13         8.82       0.99

Soil Planozols                    11.70      2.58    -1830.86       -2.73      2.97      1.50

Andean dummy                  -2940.12      -6.03      996.55        1.04   -669.79     -1.01

Selection irrigation            -321.22     -0.41

Selection dryland                                    -2275.49       -1.05

Selection crop                                                              -739.20     -1.70

Selection crop/livestock                                                       9.58      0.02

Adjusted R-sq                             0.28                  0.19                0.41
** denotes the estimate is significant at 1% level and * at 5% level.




                                             33

Table 6: Conditional Ricardian Model: Continued


                                  Mixed Non-Irrigated       Mixed Irrigated
Var.                              Est.          T-stat.     Est.          T-stat.
Intercept                              -1091.42      -0.98       -847.00      -0.46

Summer Temperature                        82.26       0.66        245.37       1.16
Summer Temperature2                       -2.13      -0.58          -5.01     -0.76

Winter Temperature                       352.23       5.70         54.59       0.35
Winter Temperature2                      -12.45      -6.75          -4.05     -0.89

Summer Precipitation                       5.97       1.70        -12.54      -1.18
Summer Precipitation2                     -0.01      -1.17           0.05      2.27

Winter Precipitation                      -0.10      -0.04          -2.59     -0.33
Winter Precipitation2                      0.00      -0.31          -0.03     -0.86

Soil Acrisols                             -3.17      -0.85           2.20      0.21

Soil Cambisols                            -2.29      -0.51           4.39      0.52

Soil Gleysols                             -6.53      -1.04          -0.06      0.00

Soil Fluvisols                            -1.00      -0.09           3.75      0.49

Soil Kastanozems                           1.12       0.19         -11.79     -0.40

Soil Luvisols                              2.47       0.74          -6.54     -1.02

Soil Planosols                            -5.43      -2.26          -1.84     -0.40

Andean dummy                           -1373.39      -5.33       -728.35      -1.31

Selection irrigation                     665.19       1.47

Selection dryland                                                -243.93      -0.64

Selection crop
Selection crop/livestock


Adjusted R-sq                                   0.21                      0.34
** denotes the estimate is significant at`1% level and * at 5% level.




                                             34

Table 7: Bootstrap Marginal Climate Effects and Elasticities on Conditional Income


Farm Type                    Temperature     Precipitation  Temperature        Precipitation

                                    Marginal Effects                   Elasticities

                                      -188                 0           -1.59              0.03

Dryland Crop Only               (-314, -92)         (-10, 5)   (-2.71, -0.95)    (-0.69, 1.11)

                                      -210               21            -1.05              0.65

Irrigated Crop Only             (-346, -35)        (-13, 62)   (-1.88, -0.18)    (-0.39, 1.61)

                                         -5                4           -0.06              0.36

Crop/livestock Dryland            (-80, 49)          (-7, 9)   (-1.09, 0.69)     (-0.78, 0.95)

                                        -24               -7           -0.28             -0.46

Crop/livestock Irrigated          (-38, 93)        (-38, 11)   (-0.44, 1.21)     (-2.93, 0.66)

                                        -61              15            -1.90              2.98

Livestock Only                   (-122, 67)          (4, 23)   (-4.41, 1.91)      (1.09, 4.41)

* Numbers in parentheses are 95% confidence intervals.
* Climate elasticities (% change in net revenue per percentage change in climate
variable) are in parenthesis.




                                            35

Table 8: AOGCM Climate Scenarios


                 NOW        2020       2060      2100

Temperature Summer (°C)

    CCC                19.9        +1.5      +2.8       +5.0

    CCSR               19.9        +1.2      +2.1       +3.1

     PCM               19.9        -0.1      +0.7       +1.4


Temperature Winter (°C)

    CCC                16.4        +1.3      +2.6       +5.2

    CCSR               16.4        +1.4      +2.3       +3.2

     PCM               16.4        +1.2      +1.9       +2.6


Precipitation Summer (mm/mo)

    CCC                162        -2.5%    -11.7%     -12.3%

    CCSR               162       +1.9%     +2.5%       -2.5%

     PCM               162        -3.1%    +2.5%      +1.9%


Precipitation Winter (mm/mo)

     CCC                 75       -2.7%     -5.3%     +1.3%

    CCSR                 75      +1.3%      -4.0%      -6.7%

     PCM                 75      +32.0%    +32.0%     +22.7%




                                     36

Table 9: Bootstrap Probabilities of Each Farm Type with Climate Change


                           Crop Only            Crop- Livestock    Livestock Only
Baseline                                 63.1%              27.0%                7.9%

                                (51.1%, 77.3%)         (11%, 43%)       (2.9%, 12.9%)

2020
         CCC                              -6.3%             +2.9%              +3.3%

                                 (-7.5%, -5.1%)       (0.9%, 4.9%)       (1.7%, 4.9%)

         CCSR                             -5.0%             +2.3%              +2.7%

                                 (-6.2%, -3.8%)       (2.1%, 2.5%)       (2.5%, 3.2%)

         PCM                               2.4%             +2.0%               -4.7%

                                  (-0.4%, 6.2%)      (-0.2%, 4.2%)     (-7.9%, -1.5%)

2060
         CCC                             -11.4%             +6.1%              +4.7%

                                (-14.6%, -8.2%)      (0.9%, 11.3%)       (4.0%, 8.7%)

         CCSR                             -7.0%             +4.7%              +2.2%

                                 (-9.6%, -5.4%)       (1.5%, 7.9%)       (0.2%, 4.2%)

         PCM                              -1.1%             +4.1%               -2.7%

                                  (-4.3%, 2.1%)       (1.3%, 6.9%)     (-4.9%, -0.5%)

2100
         CCC                            -23.1%              +7.5%             +13.7%

                               (-29.2%, -17.1%)    (-2.5%, 18.2%)       (3.7%, 23.4%)

         CCSR                           -12.7%              +6.5%              +5.4%

                                (-17.1%, -8.3%)      (1.3%, 11.7%)      (0.6%, 10.2%)

         PCM                              -2.1%             +5.3%               -2.8%

                                  (-5.5%, 1.3%)       (2.5%, 8.3%)     (-4.8%, -0.8%)

*Calculated from coefficients in Table 2.
* Numbers in parentheses are 95% confidence intervals.




                                             37

Table 10: Bootstrap Irrigation Probabilities with Climate Change


               Now                2020               2060            2100
 Crop Only
    CCC        32.6%                         +1.4%             +4.5%          +8.0%
               (13.3%, 51.6%)       (-2.8%, 5.6%)       (-4.3%, 9.1%) (-8.4%, 16.2%)
   CCSR        32.6%                         +1.3%             +2.7%          +3.8%
               (13.3%, 51.6%)       (-1.9%, 4.5%)       (-2.9%, 5.6%) (-8.4%, 13.0%)
    PCM        32.6%                         +1.8%             +1.8%          +3.0%
               (13.3%, 51.6%)       (-1.8%, 5.4%)       (-1.2%, 3.2%)  (-0.4%, 6.4%)
  Crop and
  Livestock
    CCC                   23.5%               -1.3%            -2.2%          -5.5%
                 (9.5%. 37.6%)      (-2.7%, 0.1%)       (-4.4%, 0.0%) (-11.1%, 1.1%)
   CCSR                   23.5%               -2.1%            -2.2%          -2.6%
                 (9.5%. 37.6%)      (-3.5%, -0.7%)     (-4.2%, -0.2%)  (-5.8%, 0.6%)
    PCM                   23.5%               -2.6%            -3.4%          -2.8%
                 (9.5%. 37.6%)      (-5.3%, 0.0%)      (-6.2%, -0.6%) (-5.0%, -0.6%)
*Calculated from coefficients in Table 4.
* Numbers in parentheses are 95% confidence intervals.




                                             38

Table 11: Bootstrap Impact of Climate Change on Conditional Land Values


              Crop Only     Crop Only       Crop-         Crop-            Livestock
              Dryland       Irrigated       Livestock     Livestock        Only
                                            Dryland       Irrigated
Baseline              4312             4443          2325            2832          2083

                (469, 7734)     (1409, 7493) (1185, 3390)    (1409, 4233)    (985, 3178)

2020
  CCC                  -408            -201          -104            -198          +152

               (-668, -142)      (-525, 80)     (-206, -2)     (-518, 120)   (-148, 454)

  CCSR                 -342            -125          -121             -119         +106

               (-565, -122)      (-384, 104)   (-223, -19)     (-369, 141)    (-94, 307)

  PCM                 +661             -248          -102            -441           -310

               (-260, 1465)      (-491, 159)    (-222, 18)    (-1201, 339)   (-733, 116)

2060
  CCC                  -927            -409          -277            -426          +327

              (-1535, -326)     (-1369, 564)   (-477, -77)     (-673, 128)   (-324, 933)

  CCSR                 -585            -219          -214              -21         +216

               (-925, -245)      (-876, 459)    (354, -73)     (-262, 229)   (-126, 556)

  PCM                 +457             -291          -136            -379           -274

               (-357, 1145)      (-523, 138)     (-273, 2)       (-766, -3)   (-624, 86)

2100
  CCC                 -1919            -931          -650            -582          +740

              (-3349, -508)    (-2190, 1212) (-1060, -240)    (-1084, -80)  (-265, 1890)

  CCSR                -1057            -516          -334             -117         +403

              (-1756, -355)     (-1142, 538)  (-556, -112)     (-597, 365)    (-97, 907)

  PCM                 +365             -348          -204            -317           -231

               (-145, 1155)      (-745, 288)   (-345, -73)     (-743, 121)   (-644, 169)

*Calculated from coefficients in Table 6.
* Numbers in parentheses are 95% confidence intervals.




                                           39

Table 12: Climate Change Impacts on Expected Land Value


             Exogenous Impact   Endogenous Impact     Exogenous    Endogenous

             ($/ha)             ($/ha)                % change     % change

 Baseline                 3412                   3412
 2020

 CCC             -237 (-480, -1)     -185 (-750 to 32)      -6.9%            -5.4%
 CCSR            -196 (-397, -2)     -143 (-563 to 45)      -5.7%            -4.2%
 PCM            159 (-392, 695)       +332 (4 to 868)        4.7%             9.7%
 2060

 CCC            -538 (-1101, 41)   -463 (-1465 to -26)     -15.8%           -13.6%
 CCSR            -323 (-527, 32)     -350 (-868 to 34)      -9.5%           -10.3%
 PCM              58 (-414, 525)    +80 (-133 to 378)        1.8%             2.3%
 2100

 CCC         -1122 (-2185, 129)    -957 (-2460 to -98)     -32.9%           -28.0%
 CCSR           -601 (-1143, 30)   -648 (-1473 to -63)     -17.6%           -19.0%
 PCM               1 (-383, 525)     -36 (-316 to 517)       0.1%            -1.1%
95% confidence intervals are in parentheses. They were estimated using 200 bootstrap
repetitions.




                                           40

Figure 1: Ricardian Model of Net Income and Precipitation




Net _I ncome




                                          Preci pat i on

            Farm_Type   Al l           Crops             Li vest oc   Mi xed




                                         41