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Measuring Disaster Crop Production Losses Using Survey Microdata : Evidence from Sub-Saharan Africa (Inglês)

Every year, disasters account for billions of dollars in crop production losses in low- and middle-income countries and particularly threaten the lives and livelihoods of those depending on agriculture. With climate change accelerating, this burden will likely increase in the future and accurate, micro-level measurement of crop losses will be important to understand disasters’ implications for livelihoods, prevent humanitarian crises, and build future resilience. Survey data present a large, rich, highly disaggregated information source that is trialed and tested to the specifications of smallholder agriculture common in low- and middle-income countries. However, to tap into this potential, a thorough understanding of and robust methodology for measuring disaster crop production losses in survey microdata is essential. This paper exploits plot-level panel data for almost 20,000 plots on 8,000 farms in three Sub-Saharan African countries with information on harvest, input use, and different proxies of losses; household and community-level data; as well data from other sources such as crop cutting and survey experiments, to provide new insights into the reliability of survey-based crop loss estimates and their attribution to disasters. The paper concludes with concrete recommendations for methodology and survey design and identifies key avenues for further research.


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    Markhof,Yannick Valentin, Ponzini,Giulia, Wollburg,Philip Randolph

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    Documento de trabalho sobre pesquisa de políticas

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    Measuring Disaster Crop Production Losses Using Survey Microdata : Evidence from Sub-Saharan Africa

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    flood; complete primary education; post disaster needs assessment; disaster risk reduction strategies; Damage and Loss Assessment; plant productivity; crop loss; crop and livestock; living standard measurement; regression of log; access to information; access to insurance; climate change impact; impact of disaster; crop production; survey data; attainable yield; harvest area; crop yield; annual crop; crop model; perennial crop; survey design; sensor data; accurate estimate; input use; crop management; farmer; measurement error; gold standard; soil fertility; rainfall data; smallholder agriculture; panel data; production loss; severe shocks; basis risk; crop damage; agricultural season; drought exposure; rainy season; explanatory power; geographic area; production process; weather shock; future research; statistical model; high resolution; farming practice; insurance payout; administrative cost; insurance contract; daily data; plant growth; crop phenology; productivity gap; diminishing return; literature studies; risk preference; weather forecast; rainfall index; expected return; agricultural yield; information asymmetry; monitoring tool; data requirement; spatial resolution; several reasons; smallholder farming; causal relationship; insurance claim; administrative effort; moral hazard; adverse selection; average rainfall; respondent fatigue; positive value; horizontal axis; visual aid; initial observation; significant factor; harvest ratio; graphical analysis; meteorological data; severe drought; vegetation index; production ratio; respective year; Drought Resilience; survey methodology; sorghum plant; improved seed; inorganic fertilizer; household characteristic; dependency ratio; agricultural asset; fixed effect; standard error; predictive power; average productivity; survey questions; insurance literature; smallholder livestock; weather station; satellite data; methodological research; assessment approach; adverse events; income reduction; cognitive ability; flood area; high frequency; yield data; disaster losses; average yield; idiosyncratic risk; household survey; individual characteristic; farm level; crop type; production quantities; hectare variable; covariate shock; drought losses; harvested crop; rainfall intensity; weather condition; crop disease; harvest quantity; humanitarian crisis; environmental loss; open access; development policy; physical asset; Learning and Innovation Credit; recent estimates; agricultural input; asset use; poverty eradication; intertemporal variation; Research Support; land area; survey methods; data system; disaggregated level; need assessment; agricultural data; knowledge gap; experimental data; agricultural household; mathematical model



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