Incorporating dynamic crop growth processes and management practices into a terrestrial biosphere model for simulating crop production in the United States: Toward a unified modeling framework
Incorporating dynamic crop growth processes and management practices into a terrestrial biosphere model for simulating crop production in the United States: Toward a unified modeling framework
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DOI：
10.1016/j.agrformet.2022.109144
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发表时间：
2022-10
影响因子：
6.2
通讯作者：
Y. You;H. Tian;S. Pan;Hao Shi;Zihao Bian;A. Gurgel;Yawen Huang;D. Kicklighter;Xin‐Zhong Liang;Chaoqun Lu;J. Melillo;R. Miao;Naiqing Pan;J. Reilly;W. Ren;R. Xu;Jia Yang;Qiang Yu;Jingting Zhang
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作者：
Y. You;H. Tian;S. Pan;Hao Shi;Zihao Bian;A. Gurgel;Yawen Huang;D. Kicklighter;Xin‐Zhong Liang;Chaoqun Lu;J. Melillo;R. Miao;Naiqing Pan;J. Reilly;W. Ren;R. Xu;Jia Yang;Qiang Yu;Jingting Zhang
不同利益群体（例如农民、发展机构和政策制定者）的农业决策通常在不同规模（例如地块、景观和国家）上进行。目前，辅助决策的工具要么专注于小规模的管理指导，要么专注于大规模的评估，忽视了跨尺度的联系和相互作用，因此可能无法提供稳健和一致的指导和评估。在这里，我们开发了先进的农业建模框架，将传统作物模型在代表作物生长过程和管理实践方面的优势整合到陆地生物圈模型（TBM）、动态土地生态系统模型（DLEM）中，以满足跨尺度的应用需求（例如适应和减缓）。具体来说，动态作物生长过程，包括作物特定的物候发育、碳分配、产量形成、生物固氮过程以及耕作、覆盖种植和遗传改良等管理实践，在DLEM中得到了明确的体现。根据站点尺度观测对新模型进行了评估，结果表明该模型总体表现良好，季节尺度上叶面积指数的平均归一化均方根误差为19.91%，地上生物量的平均归一化均方根误差为17.46%，年产量的平均归一化均方根误差为14.42%。然后将该模型应用于模拟美国本土1960年至2018年的玉米、大豆和冬小麦产量。模拟作物产量的空间格局与地面调查数据一致。我们的模型还捕捉了三种作物全国总产量的长期趋势和年际变化。这项研究证明了将传统作物建模技术融入TBM以建立统一的建模框架的重要性，该框架具有解决不同时空尺度的气候影响、适应和缓解的潜力。
Agricultural decision-making by different interest groups (e.g., farmers, development agents and policy makers) usually takes place on different scales (e.g., plot, landscape and country). Currently, tools to assist decision-making are either dedicated to small-scale management guidance or large-scale assessment, which ignore the cross-scale linkages and interactions and thus may not provide robust and consistent guidance and assessment. Here, we developed an advanced agricultural modeling framework by integrating the strengths of conventional crop models in representing crop growth processes and management practices into a terrestrial biosphere model (TBM), the Dynamic Land Ecosystem Model (DLEM), to meet the cross-scale application needs (e.g., adaptation and mitigation). Specifically, dynamic crop growth processes, including crop-specific phenological development, carbon allocation, yield formation, biological nitrogen fixation processes, and management practices such as tillage, cover cropping and genetic improvements, were explicitly represented in DLEM. The new model was evaluated against site-scale observations and the results showed that the model performed generally well, with an average normalized root mean square error of 19.91% for leaf area index and 17.46% for aboveground biomass at the seasonal scale and 14.42% for annual yield. Then the model was applied to simulate corn, soybean, and winter wheat productions in the conterminous United States from 1960 to 2018. The spatial patterns of simulated crop productions were consistent with ground survey data. Our model also captured both the long-term trends and interannual variations of the total national productions of the three crops. This study demonstrates the significance of fusing conventional crop modeling techniques into TBMs to establish a unified modeling framework, which holds the potential to address climate impacts, adaptation and mitigation across varied spatiotemporal scales.