Predicting changes in agricultural yields under climate change scenarios and their implications for global food security

Climate change has direct impacts on current and future agricultural productivity. Statistical meta-analysis models can be used to generate expectations of crop yield responses to climatic factors by pooling data from controlled experiments. However, methodological challenges in performing these meta-analyses, together with combined uncertainty from various sources, make it difficult to validate model results. We present updates to published estimates of crop yield responses to projected temperature, precipitation, and CO2 patterns and show that mixed effects models perform better than pooled OLS models on root mean squared error (RMSE) and explained deviance, despite the common usage of pooled OLS in previous meta-analyses. Based on our analysis, the use of pooled OLS may underestimate yield losses. We also use a block-bootstrapping approach to quantify uncertainty across multiple dimensions, including modeler choices, climate projections from the sixth Coupled Model Intercomparison Project (CMIP6), and emissions scenarios from Shared Socioeconomic Pathways (SSP). Our estimates show projected yield responses of - 22% (maize), - 9% (rice), - 15% (soy), and - 14% (wheat) from 2015 to 2080-2100 under the business-as-usual scenario of SSP5-8.5, which reduce to - 3.8%, - 2.7%, 1.4%, and - 1.5% respectively under the lower emissions scenario of SSP1-2.6. Without mitigation and adaptation, countries in South Asia, sub-Saharan Africa, North America, and Oceania could become at risk of being unable to meet national calorie demand by the end of the century under the most severe emissions scenario.