Process-based crop models are essential tools for representing the fundamental interactions between the cropping environment (weather, soil, and management) and plant development, growth, resource use, and yield formation. Due to these capabilities, crop models are considered as an integral component of smart farming tools for evaluating and improving crop management at field, farm, and regional scales. However, prior to application of a crop model in geospatial decision support tools, its robustness should be established by comparing model predictions with observations from the target cropping environment. The objective of this study was to assess the performance of the Simple Simulation Model (SSM-iCrop) for predicting growth and nitrogen (N) dynamics of winter wheat (Triticum aestivum) cultivars in a temperate environment. Detailed plant and soil data were collected from three field experiments conducted with four widely-grown cultivars under four N application rates in Austria. Variation in N fertilisation and differences in soil properties and weather conditions in the three field experiments generated a wide range of observed crop total dry mass (585–2034 g m⁻²), N uptake (5–32 g N m⁻²), and grain yield (211–898 g m⁻²). The SSM-iCrop model required parameterisation of a relatively small number of plant input parameters. As these parameters could be directly calculated from the experimental data, except for two phenology-related coefficients, there was no need for calibrating the model. In initial simulations, SSM-iCrop was not able to predict the response of leaf area index (LAI) to decreasing N supply. Introducing an additional parameter defining the minimum stem N concentration from emergence to begin grain growth improved the model performance substantially. The simulated time-course of crop attributes through the growing season showed good overall correspondence with observed data. Across the three field experiments, the model performed well in simulating above-ground dry mass (CV=5.9, RMSE=115.6 g N m⁻²), grain yield (CV=1.9, RMSE=60.5 g N m⁻²), total crop N uptake (CV=4.5, RMSE=1.9 g N m⁻²), and grain N content (CV=1.1; RMSE=2.2 g N m⁻²). Overall, The results of this study confirmed the robustness of SSM-iCrop for predicting wheat development, growth, N dynamics, and yield in the target cropping environments. The relatively simple structure and high degree of transparency make the SSM-iCrop suitable for integration in smart farming tools for improving tactical decision making in crop production. This study also highlights the essential role of high-quality detailed experimental data for adequate parameterisation and evaluation of crop models..

基于过程的作物模型是代表种植环境（天气、土壤和管理）与植物发育、生长、资源利用和产量形成之间基本相互作用的重要工具。由于这些功能，作物模型被认为是智能农业工具的一个组成部分，用于在田间、农场和区域范围内评估和改进作物管理。然而，在将作物模型应用于地理空间决策支持工具之前，应通过将模型预测与目标作物环境的观察结果进行比较来确定其稳健性。本研究的目的是评估简单模拟模型 (SSM-iCrop) 预测冬小麦 ( Triticum aestivum ) 生长和氮 (N) 动态的性能。) 温带环境中的栽培品种。详细的植物和土壤数据来自三个田间试验，在奥地利以四种 N 施用率对四种广泛种植的栽培品种进行。三个田间试验中氮肥的变化以及土壤性质和天气条件的差异产生了广泛的观察到的作物总干质量（585-2034 g m ^-2）、N 吸收（5-32 g N m ^-2）和粮食产量（211–898 gm ^-2）。SSM-iCrop 模型需要对相对少量的植物输入参数进行参数化。由于这些参数可以直接从实验数据中计算出来，除了两个物候相关系数外，不需要校准模型。在初始模拟中，SSM-iCrop 无法预测叶面积指数 (LAI) 对减少 N 供应的响应。引入一个额外的参数来定义从出苗到开始晶粒生长的最小茎 N 浓度，显着改善了模型性能。整个生长季节作物属性的模拟时间过程显示出与观测数据良好的整体对应关系。在三个现场实验中，该模型在模拟地上干质量（CV=5.9，RMSE=115.6 g N m ^-2)、谷物产量 (CV=1.9, RMSE=60.5 g N m ^-2 )、作物总氮吸收量 (CV=4.5, RMSE=1.9 g N m ^-2 )和谷物氮含量 (CV=1.1; RMSE=2.2 g N m ^-2 )。总体而言，这项研究的结果证实了 SSM-iCrop 在预测目标种植环境中的小麦发育、生长、氮动态和产量方面的稳健性。相对简单的结构和高度的透明度使 SSM-iCrop 适合集成到智能农具中，以改善作物生产中的战术决策。这项研究还强调了高质量详细实验数据对于作物模型的充分参数化和评估的重要作用。