Prediction of seasonal climate-induced variations in global food production
Consumers, including the poor in many countries, are increasingly dependent on food imports1 and are thus exposed to variations in yields, production and export prices in the major food-producing regions of the world. National governments and commercial entities are therefore paying increased attent...
| Autores principales: | , , , , , , , |
|---|---|
| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Springer
2013
|
| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/33441 |
| _version_ | 1855526191267577856 |
|---|---|
| author | Iizumi T Sakuma H Yokozawa M Luo JJ Challinor, Andrew J. Brown, M.E. Sakurai G Yamagata T |
| author_browse | Brown, M.E. Challinor, Andrew J. Iizumi T Luo JJ Sakuma H Sakurai G Yamagata T Yokozawa M |
| author_facet | Iizumi T Sakuma H Yokozawa M Luo JJ Challinor, Andrew J. Brown, M.E. Sakurai G Yamagata T |
| author_sort | Iizumi T |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Consumers, including the poor in many countries, are increasingly dependent on food imports1 and are thus exposed to variations in yields, production and export prices in the major food-producing regions of the world. National governments and commercial entities are therefore paying increased attention to the cropping forecasts of important food-exporting countries as well as to their own domestic food production. Given the increased volatility of food markets and the rising incidence of climatic extremes affecting food production, food price spikes may increase in prevalence in future years2, 3, 4. Here we present a global assessment of the reliability of crop failure hindcasts for major crops at two lead times derived by linking ensemble seasonal climatic forecasts with statistical crop models. We found that moderate-to-marked yield loss over a substantial percentage (26–33%) of the harvested area of these crops is reliably predictable if climatic forecasts are near perfect. However, only rice and wheat production are reliably predictable at three months before the harvest using within-season hindcasts. The reliabilities of estimates varied substantially by crop—rice and wheat yields were the most predictable, followed by soybean and maize. The reasons for variation in the reliability of the estimates included the differences in crop sensitivity to the climate and the technology used by the crop-producing regions. Our findings reveal that the use of seasonal climatic forecasts to predict crop failures will be useful for monitoring global food production and will encourage the adaptation of food systems to climatic extremes. |
| format | Journal Article |
| id | CGSpace33441 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2013 |
| publishDateRange | 2013 |
| publishDateSort | 2013 |
| publisher | Springer |
| publisherStr | Springer |
| record_format | dspace |
| spelling | CGSpace334412024-08-27T10:36:01Z Prediction of seasonal climate-induced variations in global food production Iizumi T Sakuma H Yokozawa M Luo JJ Challinor, Andrew J. Brown, M.E. Sakurai G Yamagata T agriculture climate forecasting adaptation Consumers, including the poor in many countries, are increasingly dependent on food imports1 and are thus exposed to variations in yields, production and export prices in the major food-producing regions of the world. National governments and commercial entities are therefore paying increased attention to the cropping forecasts of important food-exporting countries as well as to their own domestic food production. Given the increased volatility of food markets and the rising incidence of climatic extremes affecting food production, food price spikes may increase in prevalence in future years2, 3, 4. Here we present a global assessment of the reliability of crop failure hindcasts for major crops at two lead times derived by linking ensemble seasonal climatic forecasts with statistical crop models. We found that moderate-to-marked yield loss over a substantial percentage (26–33%) of the harvested area of these crops is reliably predictable if climatic forecasts are near perfect. However, only rice and wheat production are reliably predictable at three months before the harvest using within-season hindcasts. The reliabilities of estimates varied substantially by crop—rice and wheat yields were the most predictable, followed by soybean and maize. The reasons for variation in the reliability of the estimates included the differences in crop sensitivity to the climate and the technology used by the crop-producing regions. Our findings reveal that the use of seasonal climatic forecasts to predict crop failures will be useful for monitoring global food production and will encourage the adaptation of food systems to climatic extremes. 2013-10 2013-08-12T09:58:01Z 2013-08-12T09:58:01Z Journal Article https://hdl.handle.net/10568/33441 en Limited Access Springer Iizumi T, Sakuma H, Yokozawa M, Luo JJ, Challinor AJ, Brown ME, Sakurai G, Yamagata T. 2013. Prediction of seasonal climate-induced variations in global food production. Nature Climate Change. |
| spellingShingle | agriculture climate forecasting adaptation Iizumi T Sakuma H Yokozawa M Luo JJ Challinor, Andrew J. Brown, M.E. Sakurai G Yamagata T Prediction of seasonal climate-induced variations in global food production |
| title | Prediction of seasonal climate-induced variations in global food production |
| title_full | Prediction of seasonal climate-induced variations in global food production |
| title_fullStr | Prediction of seasonal climate-induced variations in global food production |
| title_full_unstemmed | Prediction of seasonal climate-induced variations in global food production |
| title_short | Prediction of seasonal climate-induced variations in global food production |
| title_sort | prediction of seasonal climate induced variations in global food production |
| topic | agriculture climate forecasting adaptation |
| url | https://hdl.handle.net/10568/33441 |
| work_keys_str_mv | AT iizumit predictionofseasonalclimateinducedvariationsinglobalfoodproduction AT sakumah predictionofseasonalclimateinducedvariationsinglobalfoodproduction AT yokozawam predictionofseasonalclimateinducedvariationsinglobalfoodproduction AT luojj predictionofseasonalclimateinducedvariationsinglobalfoodproduction AT challinorandrewj predictionofseasonalclimateinducedvariationsinglobalfoodproduction AT brownme predictionofseasonalclimateinducedvariationsinglobalfoodproduction AT sakuraig predictionofseasonalclimateinducedvariationsinglobalfoodproduction AT yamagatat predictionofseasonalclimateinducedvariationsinglobalfoodproduction |