Performance and sensitivity of the DSSAT crop growth model in simulating maize yield under conservation agriculture
Performance and sensitivity of the DSSAT crop growth model in simulating maize yield under conservation agriculture
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DOI：
10.1016/j.eja.2016.02.001
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发表时间：
2016-05-01
影响因子：
5.2
通讯作者：
Thierfelder, Christian
中科院分区：
文献类型：
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作者：
Corbeels, Marc;Chirat, Guillaume;Thierfelder, Christian
通过保护性农业（CA）的实践，土壤水和养分动态通过农作物残留物覆盖物的存在以及减少或免耕而改变。这些变化可能会对作物产量产生影响。作物生长模型 DSSAT（农业技术转让决策支持系统）最近经过修改，用于模拟这些对作物生长和产量的影响。在本研究中，我们将 DSSAT 应用于赞比亚南部省蒙泽对比耕作和残茬管理实践中种植的玉米 (Zea mays L.) 的长期实验。目的是 (1) 评估 DSSAT 模拟作物对覆盖和免耕响应的能力，以及 (2) 了解 DSSAT 模型输出对输入参数的敏感性，特别关注模型对 CA 实践响应的决定因素。该模型首先针对实验的耕作处理 (CP) 进行参数化和校准，然后通过取消耕作并在模型中应用农作物残留物覆盖物来运行 CA 处理。为了重现 CP 与 CA 处理下观察到的玉米产量，模型中的最佳根系发育仅限于 CP 处理中的上部 22 厘米土层，而在 CA 下根系可以最佳发育至 100 厘米深度。观察到的和模拟的玉米物候、地上总生物量和谷物产量之间的归一化 RMSE 值表明，CA 处理与 CP 处理的模拟效果一样好，并针对后者校准了模型。使用协惯性分析进行全局敏感性分析，以描述 DSSAT 模型对 32 个模型输入参数和作物管理因素的响应。物候品种参数是最有影响力的模型参数。该分析还表明，在 DSSAT 中，覆盖主要影响表层土壤有机碳含量，其次影响土壤总含水量，因为它与模拟土壤水蒸发和径流呈负相关。输入参数或作物管理因素与输出变量之间的相关性在广泛的季节性降雨条件下保持稳定。对模拟玉米产量对 CA 实践模拟的三个关键参数进行的局部敏感性分析表明，DSSAT 对覆盖有反应，特别是当根深受到限制时，即当水是限制作物生长的关键因素时。本研究的结果表明，DSSAT 可用于模拟作物对 CA 的反应，特别是通过模拟覆盖物对土壤水平衡的影响，但 DSSAT 未模拟的其他（通常是特定地点）因素，例如 CP 下的犁盘形成或 CA 下土壤结构的改善，可能需要在模型参数化中考虑，以再现观察到的 CA 与 CP 的作物产量影响。 (C) 2016 Elsevier B.V. 保留所有权利。
With the practice of conservation agriculture (CA) soil water and nutrient dynamics are modified by the presence of a mulch of crop residues and by reduced or no-tillage. These alterations may have impacts on crop yields. The crop growth model DSSAT (Decision Support Systems for Agrotechnology Transfer) has recently been modified and used to simulate these impacts on crop growth and yield. In this study, we applied DSSAT to a long-term experiment with maize (Zea mays L.) grown under contrasting tillage and residue management practices in Monze, Southern Province of Zambia. The aim was (1) to assess the capability of DSSAT in simulating crop responses to mulching and no-tillage, and (2) to understand the sensitivity of DSSAT model output to input parameters, with special attention to the determinants of the model response to the practice of CA. The model was first parameterized and calibrated for the tillage treatment (CP) of the experiment, and then run for the CA treatment by removing tillage and applying a mulch of crop residues in the model. In order to reproduce observed maize yields under the CP versus CA treatment, optimal root development in the model was restricted to the upper 22 cm soil layer in the CP treatment, while roots could optimally develop to 100 cm depth under CA. The normalized RMSE values between observed and simulated maize phenology and total above ground biomass and grain yield indicated that the CA treatment was equally well simulated as the CP treatment, for which the model was calibrated. A global sensitivity analysis using co-inertia analysis was performed to describe the DSSAT model response to 32 model input parameters and crop management factors. Phenological cultivar parameters were the most influential model parameters. This analysis also demonstrated that in DSSAT mulching primarily affects the surface soil organic carbon content and secondly the total soil moisture content, since it is negatively correlated with simulated soil water evaporation and run-off. The correlations between the input parameters or crop management factors and the output variables were stable over a wide range of seasonal rainfall conditions. A local sensitivity analysis of simulated maize yield to three key parameters for the simulation of the CA practice revealed that DSSAT responds to mulching particularly when rooting depth is restricted, i.e., when water is a critical limiting crop growth factor. The results of this study demonstrate that DSSAT can be used to simulate crop responses to CA, in particular through simulated mulching effects on the soil water balance, but other, often site-specific, factors that are not modeled by DSSAT, such as plough pan formation under CP or improved soil structure under CA, may need to be considered in the model parameterization to reproduce the observed crop yield effects of CA versus CP. (C) 2016 Elsevier B.V. All rights reserved.