农业
理论(学习稳定性)
气候变化
作物
环境科学
农业工程
农业经济学
自然资源经济学
业务
经济
地理
计算机科学
林业
工程类
地质学
海洋学
考古
机器学习
作者
Toshichika Iizumi,Toru Sakai,Yoshimitsu Masaki,Kei Oyoshi,T. Takimoto,Hideo Shiogama,Yukiko Imada,David Makowski
出处
期刊:Research Square - Research Square
日期:2025-03-18
标识
DOI:10.21203/rs.3.rs-6174385/v1
摘要
Abstract 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 standard deviation (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 agro-climatic 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.
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