Iizumi Toshichika, Sakai Toru, Masaki Yoshimitsu, Oyoshi Kei, Takimoto Takahiro, Shiogama Hideo, Imada Yukiko, Makowski David
Institute for Agro-Environmental Sciences, National Agriculture and Food Research Organization, 3-1-3 Kannondai, Tsukuba, Ibaraki 305-8604, Japan.
Japan International Research Center for Agricultural Sciences, 1-1 Ohwashi, Tsukuba, Ibaraki 3058-686, Japan.
PNAS Nexus. 2025 Mar 22;4(4):pgaf099. doi: 10.1093/pnasnexus/pgaf099. eCollection 2025 Apr.
Agricultural research and development (R&D) has increased crop yields, but little is known about its ability to increase yield stability in the context of increasingly frequent extreme weather events. Using a grid yield dataset, we show that from 2000 to 2019, the SD of yield anomalies for maize, rice, wheat, and soybean increased in 20% of the global harvested area. Based on random forest models relating yield anomaly to climate, soil, management, and public R&D expenditure, we show that cumulative agricultural R&D expenditure, proportion of growing season exposed to optimal hourly temperatures, and dry and very wet days are key factors explaining crop yield variability. An attribution analysis based on large ensemble climate simulations with and without human influence on the global climate shows that unfavorable agroclimatic conditions due to climate change has increased SD, while higher R&D expenditure has led to more contrasting trends in SD over 2000-2019. Although R&D has continued steadily in most countries, this study indicates that the progress made in R&D since 2000 may have lagged behind the unfavorable effect of climate change on yield variability.
农业研发提高了作物产量，但在极端天气事件日益频繁的背景下，其提高产量稳定性的能力却鲜为人知。利用网格产量数据集，我们发现，在2000年至2019年期间，全球20%的收获面积中，玉米、水稻、小麦和大豆产量异常的标准差有所增加。基于将产量异常与气候、土壤、管理和公共研发支出相关联的随机森林模型，我们发现，累计农业研发支出、生长季节暴露于最佳小时温度的比例以及干旱和非常潮湿的天数是解释作物产量变异性的关键因素。一项基于有无人类对全球气候影响的大集合气候模拟的归因分析表明，气候变化导致的不利农业气候条件增加了标准差，而更高的研发支出导致2000年至2019年期间标准差出现了更明显的对比趋势。尽管大多数国家的研发一直在稳步推进，但这项研究表明，自2000年以来研发取得的进展可能落后于气候变化对产量变异性的不利影响。
