Possible factors determining global-scale patterns of crop yield sensitivity to drought

In recent decades, droughts have critically limited crop production, inducing food system shocks regionally and globally. It was estimated that crop yield variability in around one-third to three-fourths of global harvested areas is explained significantly by drought, revealing the notable vulnerability of crop systems to such climate-related stressors. However, understanding the key factors determining the global pattern of crop yield sensitivity to drought is limited. Here, we investigate a wide range of physical and socioeconomic factors that may determine crop-drought vulnerability in terms of yield sensitivity to drought based on the Standardized Precipitation Index at 0.5° resolution from 1981 to 2016 using machine learning approaches. The results indicate that the spatial variations of the crop-drought sensitivity were mainly explained by environmental factors (i.e., annual precipitation, soil water-holding capacity, soil acidity, annual potential evapotranspiration) and crop management factors (i.e., fertilizer rate, growing season). Several factors might have a positive effect in mitigating crop-drought vulnerability, such as annual precipitation, soil water holding capacity, and fertilizer rate. This study quantitatively assesses the possible effect of various determinants which might control crop vulnerability to drought. This understanding may provide insights for further studies addressing better crop vulnerability measures under future drought stress.

The economic value of stochastic climate information for agricultural adaptation in a semi-arid region in Austria

Identifying efficient adaptation measures in land and water use requires integrated approaches and a spatially and temporally explicit representation of water demand and supply. Stochastic climate information may further improve adaptation assessments to reduce the risk of misinterpretation of climate signals. We aim at developing an integrated modeling framework (IMF) that meets these requirements for assessing impacts of three stochastic climate scenarios (DRY, SIMILAR, WET), and regional irrigation water restrictions on land and water use. Furthermore, impacts on regional net benefits and the economic value of stochastic climate information (VOI) are assessed. The VOI is defined as the difference between regional net benefits with and without efficient adaptation of land and water use to a specific climate scenario. The IMF has been applied to the semi-arid Seewinkel region in Austria. Considering efficient adaptation, regional net benefits amount to 8 M€ and irrigation water use to 8.4 Mm³ in a DRY climate scenario. In a WET climate scenario and a scenario with SIMILAR conditions compared to the past, regional net benefits amount to 38 and 20 M€ and irrigation water use to 41 and 21 Mm³, respectively. High regional net benefits are obtained through an expansion of vineyards, irrigation, and fertilization. On average, the VOI is highest if land and water use is efficiently adapted to DRY but a WET scenario is realized (506 €/ha/a) and lowest with efficient adaptation to WET but the realization of a SIMILAR scenario (58 €/ha/a).