Problem

The Decision Support System for Agrotechnology Transfer (DSSAT) contains a sunflower model based on CROPGRO. This model assumes some parameters values out of the crop species variability. Besides, this model has not been assessed for simulating grain yield and grain oil content in contrasting environments.

Objective

The main goal of this study was to generate and test a revised CROPGRO-Sunflower model. In addition, we used the revised CROPGRO-Sunflower to quantify crop responses to environmental and management variables.

Methods

Three sunflower models: a revised CROPGRO-Sunflower, the original CROPGRO-Sunflower and the OILCROP-SUN were calibrated and evaluated across contrasting environments. We compared the revised CROPGRO-Sunflower with the original CROPGRO-Sunflower and OILCROP-SUN in terms of ability to simulate crop development, growth, grain yield and grain oil content. Crop responses to soil depth, sowing date, and El Niño-Southern Oscillation (ENSO) effects were quantified using the revised CROPGRO-Sunflower in combination with climatic records for 37 growing seasons to simulate yield in two contrasting environments of Argentina: Balcarce and Reconquista.

Results

Crop growth, grain yield and grain oil content were better simulated by the revised CROPGRO-Sunflower than by OILCROP-SUN. Simulated yield had a root mean square error (RMSE) of 48 g m^-2 with revised CROPGRO-Sunflower and of 119 g m^-2 with OILCROP-SUN. Moreover, RMSE for simulated grain oil concentration was 2% for revised CROPGRO-Sunflower and 11% for OILCROP-SUN. Deep soils and late sowing dates resulted in higher grain yield at Balcarce. Sowing date did not affect grain yield at Reconquista. An effect of the ENSO phases on sunflower grain yield was found. "La Niña" phase was associated with the lowest grain yields at both sites.

Conclusions

Modifications made to the original CROPGRO-Sunflower improved model performance. The revised CROPGRO-Sunflower model can be utilized to simulate crop phenology, growth, grain yield and grain oil concentration over a wide range of environmental conditions.

Implications

This calibrated and evaluated crop simulation model will allow to advance in the quantification of yield gaps and to study the impact of other management practices on sunflower crop production.

中文翻译：

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微调 CROPGRO-Sunflower 模型及其在作物对环境和管理变量响应量化中的应用

问题

农业技术转让决策支持系统(DSSAT) 包含一个基于 CROPGRO 的向日葵模型。该模型假设一些参数值超出了作物品种的变异性。此外，该模型尚未评估用于模拟对比环境中的谷物产量和谷物含油量。

客观的

本研究的主要目标是生成并测试修改后的 CROPGRO-向日葵模型。此外，我们使用修订后的 CROPGRO-Sunflower 来量化作物对环境和管理变量的反应。

方法

三种向日葵模型：修改后的 CROPGRO-Sunflower、原始 CROPGRO-Sunflower 和 OILCROP-SUN 在对比环境中进行了校准和评估。我们将修改后的 CROPGRO-Sunflower 与原始 CROPGRO-Sunflower 和 OILCROP-SUN 在模拟作物发育、生长、谷物产量和谷物含油量的能力方面进行了比较。作物对土壤深度、播种日期和厄尔尼诺-南方涛动 (ENSO) 效应的响应使用修订后的 CROPGRO-向日葵结合 37 个生长季节的气候记录进行量化，以模拟阿根廷两个对比环境中的产量：Balcarce 和 Reconquista。

结果

修正后的 CROPGRO-Sunflower 比 OILCROP-SUN 更好地模拟了作物生长、谷物产量和谷物含油量。模拟产量的均方根误差 (RMSE) 为 48 g m ^-2修改后的 CROPGRO-Sunflower 和 119 g m ^-2 OILCROP-SUN。此外，修正后的 CROPGRO-Sunflower 模拟谷物油浓度的 RMSE 为 2%，OILCROP-SUN 为 11%。深层土壤和较晚的播种日期导致 Balcarce 的谷物产量更高。播种日期不影响 Reconquista 的谷物产量。发现了 ENSO 阶段对向日葵籽粒产量的影响。“拉尼娜”阶段与两个地点的最低粮食产量有关。

结论

对原始 CROPGRO-Sunflower 进行的修改提高了模型性能。修订后的 CROPGRO-向日葵模型可用于模拟各种环境条件下的作物物候、生长、谷物产量和谷物油浓度。

启示

这种经过校准和评估的作物模拟模型将有助于推进产量差距的量化，并研究其他管理措施对向日葵作物生产的影响。