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估算通常基于统计模型或基于过程的模型。基于过程的模型能够对气候变化和管理措施下的农业产量响应提供可靠预测。然而，由于在预测期内每年都将土壤条件重新初始化为相同状态的常见做法，这些模型的应用往往存在偏差。若模拟忽略了与农艺管理相关的初始土壤条件和含水量的年际变化，则无法正确评估旨在维持气候变化下稳定产量适应和缓解策略。我们应用一种避免重新初始化偏差的基于过程的作物系统模型，以证明模拟季前土壤碳、养分和水分有效性的年际变化及累积变化的重要性。将结果与采用年度重新初始化的模拟结果对比，差异显著。随后，我们通过21世纪末的连续模拟，展示了最可能适应策略抵消气候变化对产量影响的潜力。每年重新初始化季前土壤碳和含水量的模拟会引入不当产量偏差，并掩盖农业管理改善气温上升和降雨变率增大带来有害影响的潜力