Data from: Can impacts of climate change and agricultural adaptation strategies be accurately quantified if crop models are annually re-initialized?

Estimates of climate change impacts on global food production are generally based on statistical or process-based models. Process-based models can provide robust predictions of agricultural yield responses to changing climate and management. However, applications of these models often suffer from bias due to the common practice of re-initializing soil conditions to the same state for each year of the forecast period. If simulations neglect to include year-to-year changes in initial soil conditions and water content related to agronomic management, adaptation and mitigation strategies designed to maintain stable yields under climate change cannot be properly evaluated. We apply a process-based crop system model that avoids re-initialization bias to demonstrate the importance of simulating both year-to-year and cumulative changes in pre-season soil carbon, nutrient, and water availability. Results are contrasted with simulations using annual re-initialization, and differences are striking. We then demonstrate the potential for the most likely adaptation strategy to offset climate change impacts on yields using continuous simulations through the end of the 21st century. Simulations that annually re-initialize pre-season soil carbon and water contents introduce an inappropriate yield bias that obscures the potential for agricultural management to ameliorate the deleterious effects of rising temperatures and greater rainfall variability.

气候变化对全球粮食生产影响的评估，通常基于统计模型或基于过程的模型（process-based models）。基于过程的模型能够对气候变化与农事管理下的作物产量响应做出稳健预测。然而，这类模型的应用常存在偏差，究其原因是在预测期内，研究者通常将每年的土壤条件重新初始化为同一状态。若模拟未纳入与农艺管理相关的初始土壤条件与土壤含水量的年际变化，则无法准确评估旨在在气候变化下维持产量稳定的适应与减缓策略。本研究采用一款可规避重初始化偏差的基于过程的作物系统模型，用以证明模拟季前土壤碳、养分与水分可利用性的年际变化与累积变化的重要性。将该模拟结果与采用年度重初始化的模拟结果进行对比，二者差异极为显著。随后，本研究通过持续至21世纪末的连续模拟，验证了最具可行性的适应策略可抵消气候变化对产量造成的影响。采用年度重初始化季前土壤碳与含水量的模拟会引入不合理的产量偏差，掩盖了农事管理可缓解气温上升与降水变异性加剧所带来的有害影响的潜力。