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...

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田间管理情境下的作物产量响应做出可靠预测。然而，这类模型的应用往往存在偏差，这源于当前的通用做法：在预测期内每年都将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逐年变化，则无法对旨在维持气候变化下产量稳定的气候适应与减缓策略进行合理评估。本研究采用一款可规避重新初始化偏差的基于过程的作物系统模型，用以论证模拟季前土壤碳、养分及水分有效性的逐年变化与累积变化的重要性。将本研究结果与采用年度重新初始化的模拟结果进行对比，二者差异十分显著……