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Parameterization-induced uncertainties and impacts of crop management harmonization in a global gridded crop model ensemble

机译：参数化导致的不确定性以及全球网格化作物模型集合中作物管理协调的影响

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Global gridded crop models (GGCMs) combine agronomic or plant growth models with gridded spatial input data to estimate spatially explicit crop yields and agricultural externalities at the global scale. Differences in GGCM outputs arise from the use of different biophysical models, setups, and input data. GGCM ensembles are frequently employed to bracket uncertainties in impact studies without investigating the causes of divergence in outputs. This study explores differences in maize yield estimates from five GGCMs based on the public domain field-scale model Environmental Policy Integrated Climate (EPIC) that participate in the AgMIP Global Gridded Crop Model Intercomparison initiative. Albeit using the same crop model, the GGCMs differ in model version, input data, management assumptions, parameterization, and selection of subroutines affecting crop yield estimates via cultivar distributions, soil attributes, and hydrology among others. The analyses reveal inter-annual yield variability and absolute yield levels in the EPIC-based GGCMs to be highly sensitive to soil parameterization and crop management. All GGCMs show an intermediate performance in reproducing reported yields with a higher skill if a static soil profile is assumed or sufficient plant nutrients are supplied. An in-depth comparison of setup domains for two EPIC-based GGCMs shows that GGCM performance and plant stress responses depend substantially on soil parameters and soil process parameterization, i.e. hydrology and nutrient turnover, indicating that these often neglected domains deserve more scrutiny. For agricultural impact assessments, employing a GGCM ensemble with its widely varying assumptions in setups appears the best solution for coping with uncertainties from lack of comprehensive global data on crop management, cultivar distributions and coefficients for agro-environmental processes. However, the underlying assumptions require systematic specifications to cover representative agricultural systems and environmental conditions. Furthermore, the interlinkage of parameter sensitivity from various domains such as soil parameters, nutrient turnover coefficients, and cultivar specifications highlights that global sensitivity analyses and calibration need to be performed in an integrated manner to avoid bias resulting from disregarded core model domains. Finally, relating evaluations of the EPIC-based GGCMs to a wider ensemble based on individual core models shows that structural differences outweigh in general differences in configurations of GGCMs based on the same model, and that the ensemble mean gains higher skill from the inclusion of structurally different GGCMs. Although the members of the wider ensemble herein do not consider crop-soil-management interactions, their sensitivity to nutrient supply indicates that findings for the EPIC-based sub-ensemble will likely become relevant for other GGCMs with the progressing inclusion of such processes.
机译：全球网格化作物模型（GGCM）将农业或植物生长模型与网格化的空间输入数据相结合，以估算全球范围内空间明确的作物产量和农业外部性。 GGCM输出的差异是由于使用不同的生物物理模型，设置和输入数据而引起的。 GGCM合奏经常被用于解决影响研究中的不确定性，而无需调查产出差异的原因。这项研究基于参与AgMIP全球网格化作物模型比对倡议的公共领域实地模型环境政策综合气候（EPIC），探索了来自五个GGCM的玉米产量估算的差异。尽管使用相同的作物模型，但GGCM在模型版本，输入数据，管理假设，参数化以及通过品种分布，土壤属性和水文等因素影响作物产量估算的子程序选择方面有所不同。分析显示，基于EPIC的GGCM中的年际产量变异性和绝对产量水平对土壤参数化和作物管理高度敏感。如果假定静态土壤剖面或提供足够的植物养分，则所有GGCM均表现出较高的繁殖能力，具有较高的繁殖能力。对两个基于EPIC的GGCM的设置域的深入比较表明，GGCM的性能和植物胁迫响应在很大程度上取决于土壤参数和土壤过程的参数化，即水文和养分转化，这表明这些经常被忽略的域值得进一步研究。对于农业影响评估，采用GGCM集合及其设置中广泛不同的假设似乎是应对不确定性的最佳解决方案，因为缺乏有关作物管理，品种分布和农业环境过程系数的全面的全球数据。但是，基本假设需要系统的规范才能涵盖代表性的农业系统和环境条件。此外，来自各个领域的参数敏感性之间的相互联系，例如土壤参数，养分周转系数和品种规格，突显了全局敏感性分析和校准需要以集成的方式进行，以避免因忽略核心模型领域而产生偏差。最后，基于单个核心模型对基于EPIC的GGCM的评估与更广泛的集成相关，表明基于同一模型的GGCM的配置差异在结构上的差异要大于一般差异，并且集成均值通过包含结构上的内容而获得了更高的技能不同的GGCM。尽管本文中较宽的集合体的成员未考虑作物-土壤-管理之间的相互作用，但它们对养分供应的敏感性表明，随着此类过程的不断纳入，基于EPIC的子集合体的发现将可能与其他GGCM相关。

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