Accurate simulation of soil temperature can help improve the accuracy of crop growth models by improving the predictions of soil processes like seed germination, decomposition, nitrification, evaporation, and carbon sequestration. To assess how well such models can simulate soil temperature, herein we present results of an inter-comparison study of 33 maize ( L.) growth models. Among the 33 models, four of the modeling groups contributed results using differing algorithms or “flavors” to simulate evapotranspiration within the same overall model family. The study used comprehensive datasets from two sites - Mead, Nebraska, USA and Bushland, Texas, USA wherein soil temperature was measured continually at several depths. The range of simulated soil temperatures was large (about 10–15 °C) from the coolest to warmest models across whole growing seasons from bare soil to full canopy and at both shallow and deeper depths. Within model families, there were no significant differences among their simulations of soil temperature due to their differing evapotranspiration method “flavors”, so root-mean-square-errors (RMSE) were averaged within families, which reduced the number of soil temperature model families to 13. The model family RMSEs averaged over all 20 treatment-years and 2 depths ranged from about 1.5 to 5.1 °C. The six models with the lowest RMSEs were APSIM, ecosys, JULES, Expert-N, SLFT, and MaizSim. Five of these best models used a numerical iterative approach to simulate soil temperature, which entailed using an energy balance on each soil layer. whereby the change in heat storage during a time step equals the difference between the heat flow into and that out of the layer. Further improvements in the best models for simulating soil temperature might be possible with the incorporation of more recently improved routines for simulating soil thermal conductivity than the older routines now in use by the models.

中文翻译：

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玉米土壤温度模拟：33个玉米模型的相互比较

土壤温度的准确模拟可以通过改进对种子发芽、分解、硝化、蒸发和碳固存等土壤过程的预测来帮助提高作物生长模型的准确性。为了评估这些模型模拟土壤温度的效果，我们在此介绍了 33 个玉米 (L.) 生长模型的相互比较研究的结果。在 33 个模型中，四个建模组使用不同的算法或“风格”提供结果，以模拟同一整体模型系列中的蒸散量。该研究使用了来自两个地点的综合数据集：美国内布拉斯加州米德和美国德克萨斯州布什兰，其中连续测量了多个深度的土壤温度。从最冷到最热的模型，模拟土壤温度的范围很大（约 10-15°C），覆盖整个生长季节，从裸露土壤到全冠层，无论是浅层还是深层。在模型族内，由于蒸散方法“风格”不同，对土壤温度的模拟没有显着差异，因此均方根误差（RMSE）在族内进行平均，从而减少了土壤温度模型族的数量至 13。模型系列 RMSE 在所有 20 个治疗年中平均，2 个深度范围为约 1.5 至 5.1 °C。 RMSE 最低的六个模型是 APSIM、ecosys、JULES、Expert-N、SLFT 和 MaizSim。其中五个最佳模型使用数值迭代方法来模拟土壤温度，这需要在每个土壤层上使用能量平衡。由此，在一个时间步长期间储热的变化等于流入该层的热量和流出该层的热量之间的差值。通过纳入比模型现在使用的旧例程更新的模拟土壤热导率例程，可以进一步改进模拟土壤温度的最佳模型。