Process-based crop growth models have become indispensable tools for investigating the effects of genetic, management, and environmental factors on crop productivity. One source of uncertainty in crop model predictions is model parameterization, i.e. estimating the values of model input parameters, which is carried out very differently by crop modellers. One simple (SSM-iCrop) and one detailed (APSIM) maize (Zea mays L.) model were partially or fully parameterized using observed data from a 2-year field experiment conducted in 2016 and 2017 at the UFT (Universitats- und Forschungszentrum Tulln, BOKU) in Austria. Model initialisation was identical for both models based on field measurements. Partial parameterization (ParLevel_1) was first performed by estimating only those parameters related to crop phenology. Full parameterization (ParLevel_2) was then conducted by estimating parameters related to phenology plus those affecting dry mass production and partitioning, nitrogen uptake, and grain yield formation. With ParLevel_1, both models failed to provide accurate estimation of LAI, dry mass accumulation, nitrogen uptake and grain yield, but the performance of APSIM was generally better than SSM-iCrop. Full parameterization greatly improved the performance of both crop models, but it was more effective for the simple model, so that SSM-iCrop was equally well or even better compared to APSIM. It was concluded that full parameterization is indispensable for improving the accuracy of crop model predictions regardless whether they are simple or detailed. Simple models seem to be more vulnerable to incomplete parameterization, but they better respond to full parameterization. This needs confirmation by further research.

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作物模型最佳性能的完整参数化很重要：简单和详细玉米模型的相互比较

基于过程的作物生长模型已成为研究遗传、管理和环境因素对作物生产力影响的不可或缺的工具。作物模型预测中的一个不确定性来源是模型参数化，即估计模型输入参数的值，作物建模者执行的方法非常不同。一个简单的 (SSM-iCrop) 和一个详细的 (APSIM) 玉米 (Zea mays L.) 模型使用 2016 年和 2017 年在 UFT（Universitats- und Forschungszentrum Tulln , BOKU) 在奥地利。基于现场测量的两种模型的模型初始化是相同的。部分参数化 (ParLevel_1) 首先通过仅估计与作物物候相关的参数来执行。然后通过估计与物候相关的参数以及影响干物质生产和分配、氮吸收和谷物产量形成的参数来进行完整参数化 (ParLevel_2)。使用 ParLevel_1，两种模型都未能提供对 LAI、干物质积累、氮吸收和谷物产量的准确估计，但 APSIM 的性能总体上优于 SSM-iCrop。全参数化极大地提高了两种裁剪模型的性能，但对简单模型更有效，因此与 APSIM 相比，SSM-iCrop 效果相同甚至更好。得出的结论是，无论是简单还是详细，全参数化对于提高作物模型预测的准确性都是必不可少的。简单的模型似乎更容易受到不完整参数化的影响，但它们更好地响应全参数化。这需要进一步研究证实。