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Wonsik Kim, Toshichika Iizumi, and Motoki Nishimori

Abstract

Droughts represent an important type of climate extreme that reduces crop production and food security. Although this fact is well known, the global geographic pattern of drought-driven reductions in crop production is poorly characterized. As the incidence of relatively more severe droughts is expected to increase under climate change, understanding the vulnerability of crop production to droughts is a key research priority. Here, we estimate the production losses of maize, rice, soy, and wheat from 1983 to 2009 using empirical relationships among crop yields, a drought index, and annual precipitation. We find that approximately three-fourths of the global harvested areas—454 million hectares—experienced drought-induced yield losses over this period, and the cumulative production losses correspond to 166 billion U.S. dollars. Globally averaged, one drought event decreases agricultural gross domestic production by 0.8%, with varying magnitudes of impacts by country. Crop production systems display decreased vulnerability or increased resilience to drought according to increases in per capita gross domestic production (GDP) in the countries with extensive semiarid agricultural areas. These changes in vulnerability accompany technological improvements represented by per capita GDP increases. Our estimates of drought-induced economic losses in agricultural systems offer a sound basis for subsequent assessments of the costs of adaptation to droughts under climate change.

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Toshichika Iizumi, Yuhei Takaya, Wonsik Kim, Toshiyuki Nakaegawa, and Shuhei Maeda

Abstract

Weather and climate variability associated with major climate modes is a main driver of interannual yield variability of commodity crops in global cropland areas. A global crop forecasting service that is currently in the test operation phase is based on temperature and precipitation forecasts, while recent literature suggests that crop forecasting services may benefit from the use of climate index forecasts. However, no consistent comparison is available on prediction skill between yield models relying on forecasts from temperature and precipitation and from climate indices. Here, we present a global assessment of 26-yr (1983–2008) within-season yield anomaly hindcasts for maize, rice, wheat, and soybean derived using different types of statistical yield models. One type of model utilizes temperature and precipitation for individual cropping areas (the TP model type) to represent the current service, whereas the other type relies on large-scale climate indices (the CI model). For the TP models, three specifications with different model complexities are compared. The results show that the CI model is characterized by a small reduction in the skillful area from the reanalysis model to the hindcast model and shows the largest skillful areas for rice and soybean. In the TP models, the skill of the simple model is comparable to that of the more complex models. Our findings suggest that the use of climate index forecasts for global crop forecasting services in addition to temperature and precipitation forecasts likely increases the total number of crops and countries where skillful yield anomaly prediction is feasible.

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