In the coming decades the agricultural sector faces many challenges stemming from growing global populations, land degradation, and loss of cropland to urbanization. Although food production has been able to keep pace with population growth on the global scale, periodically there are serious regional deficits, and poverty related nutritional deficiencies affect close to a billion people globally. In this century climate change is one factor that could affect food production and availability in many parts of the world, particularly those most prone to drought and famine.
The purpose of this data set is to provide an assessment of potential climate change impacts on world staple crop production (wheat, rice, and maize) with a focus on quantitative estimates of yield changes based on multiple climate scenario runs. The data set assesses the implications of temperature and precipitation changes for world crop yields taking into account uncertainty in the level of climate change expected and physiological effects of carbon dioxide on plant growth. Adaptation is explicitly considered and incorporated into the results by assessing the country or regional potential for reaching optimal crop yield. Optimal yield is the potential yield non limited by water, fertilizer, and without management constraints. Adapted yields are evaluated in each country as a fraction of the potential yield. The weighting factor combines the ratio of current yields to current yield potential and the economic limitation of the economic country’s agricultural systems.
The baseline year for crop yield changes is the average yield simulated under current climate (1970-2000 baseline). The resulting yield change data were then fed into trade models to assess impacts on prices and overall food production. (Please note that total production changes need to be treated with caution, since production is determined by many factors.) The overall objective is to calculate quantitative estimates of climate change impacts on the amount of food produced globally, and to determine the consequences to world food prices and the number of people at risk of hunger.
This data set is an update to a major crop modeling study by the NASA Goddard Institute for Space Studies (GISS). The initial study was published in 1997, based on output of HadCM2 model forced with greenhouse gas concentration from the IS95 emission scenarios in 1997. Results of the initial study are found in SEDAC's Potential Impacts of Climate Change on World Food Supply: Data Sets from a Major Crop Modeling Study, released in 2001. The co-authors developed and tested a method for investigating the spatial implications of climate change on crop production. The Decision Support System for Agrotechnology Transfer (DSSAT) dynamic process crop growth models, are specified and validated for one hundred and twenty seven sites in the major world agricultural regions. Results from the crop models, calibrated and validated in the major crop-growing regions, are then used to test functional forms describing the response of yield changes in the climate and environmental conditions. This updated version is based on HadCM3 model output along with GHG concentrations from the Special Report on Emissions Scenarios (SRES). The crop yield estimates incorporate some major improvements: 1) consistent crop simulation methodology and climate change scenarios; 2) weighting of model site results by contribution to regional and national, and rainfed and irrigated production; 3) quantitative foundation for estimation of physiological CO2 effects on crop yields; and 4) Adaptation is explicitly considered.
This work links biophysical and statistical models in a rigorous and testable methodology, based on current understanding of processes of crop growth and development. The validated site crop models are useful for simulating the range of conditions under which crops are grown in the world, and provide the means to estimate production functions when experimental field data are not available. The derived functions are appropriate for application in global environmental change studies because they incorporate responses to higher temperatures, changed hydrological regimes, and higher levels of atmospheric CO2. Variables explaining a significant proportion of simulated yield variance in the current climate are crop water (sum of precipitation and irrigation) and temperature during the growing season.