Kinnunen Pekka, Heino Matias, Sandström Vilma, Taka Maija, Ray Deepak K, Kummu Matti
Water and Development Research Group Aalto University Espoo Finland.
Pellervo Economic Research PTT Helsinki Finland.
Earths Future. 2022 Sep;10(9):e2021EF002420. doi: 10.1029/2021EF002420. Epub 2022 Sep 26.
High crop yield variation between years-caused by extreme shocks on the food production system such as extreme weather-can have substantial effects on food production. This in turn introduces vulnerabilities into the global food system. To mitigate the effects of these shocks, there is a clear need to understand how different adaptive capacity measures link to crop yield variability. While existing literature provides many local-scale studies on this linkage, no comprehensive global assessment yet exists. We assessed reported crop yield variation for wheat, maize, soybean, and rice for the time period 1981-2009 by measuring both yield loss risk (variation in negative yield anomalies considering all years) and changes in yields during "dry" shock and "hot" shock years. We used the machine learning algorithm XGBoost to assess the explanatory power of selected gridded indicators of anthropogenic factors globally (i.e., adaptive capacity measures such as the human development index, irrigation infrastructure, and fertilizer use) on yield variation at a 0.5° resolution within climatically similar regions (to rule out the role of average climate conditions). We found that the anthropogenic factors explained 40%-60% of yield loss risk variation across the whole time period, whereas the factors provided noticeably lower (5%-20%) explanatory power during shock years. On a continental scale, especially in Europe and Africa, the factors explained a high proportion of the yield loss risk variation (up to around 80%). Assessing crop production vulnerabilities on global scale provides supporting knowledge to target specific adaptation measures, thus contributing to global food security.
年份间作物产量的巨大差异——由粮食生产系统遭受的极端冲击（如极端天气）造成——会对粮食生产产生重大影响。这进而给全球粮食系统带来脆弱性。为减轻这些冲击的影响，显然有必要了解不同的适应能力措施如何与作物产量变异性相关联。虽然现有文献提供了许多关于这种关联的局部尺度研究，但尚未有全面的全球评估。我们通过测量产量损失风险（考虑所有年份的负产量异常变化）以及“干旱”冲击年和“炎热”冲击年期间的产量变化，评估了1981 - 2009年期间小麦、玉米、大豆和水稻的报告作物产量变化。我们使用机器学习算法XGBoost，在气候相似区域内以0.5°分辨率评估全球人为因素（即人类发展指数、灌溉基础设施和化肥使用等适应能力措施）的选定网格化指标对产量变化的解释力（以排除平均气候条件的作用）。我们发现，人为因素在整个时间段内解释了40% - 60%的产量损失风险变化，而在冲击年期间这些因素的解释力明显较低（5% - 20%）。在大陆尺度上，特别是在欧洲和非洲，这些因素解释了很大比例的产量损失风险变化（高达约80%）。在全球尺度上评估作物生产脆弱性为确定具体的适应措施提供了支持性知识，从而有助于全球粮食安全。
