Due to the seasonal increase in soil salt accumulation after cessation of monsoon rains, the simulation of cropping system performance becomes highly challenging in coastal saline cropping areas. Rapidly changing groundwater (GW) dynamics during this period (GW depth and salinity) drive changes in capillary soil moisture rise, soil evaporation and consequent deposition of salts in the crop root zone. Difficulty in simulating this complex cropping environment makes model-based examination of optimal cropping patterns and agronomic management difficult, as one season can be very different to the next. The performance of crops is also difficult to predict under future climate scenarios in these regions, as the impact of both changes in climate and groundwater salinity dynamics on soil status in the crop root-zone changes in complex fashion. No previous simulation study has sought to combine such dynamic and complex elements in simulating crop performance. We calibrated and validated the APSIM model for simulating a broad range of experimental treatments in a rice-pulse cropping system over two seasons, using the example of coastal saline West Bengal, India. This represents a novel evaluation of the APSIM model in simulating the complex mechanisms of seasonal soil water and salinity behavior (as driven solely by daily climate and a dynamic shallow saline water-table), together with the associated crop responses. The model performed well in simulating the observed soil chloride content (CC) and soil water content (SWC) with a high coefficient of determination (R²) for both calibration and validation datasets (CC, R² = 0.91** and 0.89**; SWC, R² = 0.90** and 0.93** respectively) and also acceptable RMSE values. These were well within the bounds of observed experimental error, indicating that the model was simulating system behaviour acceptably. APSIM then successfully simulated the observed crop performance in response to these soil dynamics across 24 unique environmental situations. This illustrates that crop performance in such complex environments can be robustly simulated, and that models like APSIM are a useful tool to translate outputs from other models at different scales (for example climate change from general circulation model's (GCM's), and future changes to groundwater depth and salinity dynamics from regional hydrology models) into changes in cropping system performance. This positions APSIM strongly as a robust research tool for climate change studies on agronomic impacts and adaptations in such regions, as well as for development of decision-support tools to assist farmers in selecting suitable crops, cultivars, their sowing times and optimal agronomic management practices for coastal saline zone under prevailing conditions.

由于季风降雨停止后土壤盐分积累的季节性增加，沿海盐渍作物区的种植系统性能模拟变得非常具有挑战性。在此期间快速变化的地下水 (GW) 动态（GW 深度和盐度）驱动了毛细管土壤水分上升、土壤蒸发和随之而来的作物根区盐分沉积的变化。模拟这种复杂的种植环境的难度使得基于模型的最佳种植模式和农艺管理检查变得困难，因为一个季节可能与下一个季节大不相同。在这些地区的未来气候情景下，作物的表现也难以预测，因为气候变化和地下水盐度动态对作物根区土壤状况的影响以复杂的方式变化。之前没有任何模拟研究试图在模拟作物性能时结合这些动态和复杂的元素。我们以印度西孟加拉沿海盐碱地为例，校准和验证了 AP​​SIM 模型，用于模拟两个季节的稻谷作物种植系统中的各种实验处理。这代表了对 APSIM 模型在模拟季节性土壤水和盐度行为（仅由日常气候和动态浅层咸水位表驱动）以及相关作物响应的复杂机制方面的新评估。该模型在模拟观测到的土壤氯化物含量（CC）和土壤含水量（SWC）方面表现良好，具有较高的决定系数（R 我们以印度西孟加拉沿海盐碱地为例，校准和验证了 AP​​SIM 模型，用于模拟两个季节的稻谷作物种植系统中的各种实验处理。这代表了对 APSIM 模型在模拟季节性土壤水和盐度行为（仅由日常气候和动态浅层咸水位表驱动）以及相关作物响应的复杂机制方面的新评估。该模型在模拟观测到的土壤氯化物含量（CC）和土壤含水量（SWC）方面表现良好，具有较高的决定系数（R 我们以印度西孟加拉沿海盐碱地为例，校准和验证了 AP​​SIM 模型，用于模拟两个季节的稻谷作物种植系统中的各种实验处理。这代表了对 APSIM 模型在模拟季节性土壤水和盐度行为（仅由日常气候和动态浅层咸水位表驱动）以及相关作物响应的复杂机制方面的新评估。该模型在模拟观测到的土壤氯化物含量（CC）和土壤含水量（SWC）方面表现良好，具有较高的决定系数（R 这代表了对 APSIM 模型在模拟季节性土壤水和盐度行为（仅由日常气候和动态浅层咸水位表驱动）以及相关作物响应的复杂机制方面的新评估。该模型在模拟观测到的土壤氯化物含量（CC）和土壤含水量（SWC）方面表现良好，具有较高的决定系数（R 这代表了对 APSIM 模型在模拟季节性土壤水和盐度行为（仅由日常气候和动态浅层咸水位表驱动）以及相关作物响应的复杂机制方面的新评估。该模型在模拟观测到的土壤氯化物含量（CC）和土壤含水量（SWC）方面表现良好，具有较高的决定系数（R² ) 对于校准和验证数据集（CC，R ²  = 0.91** 和 0.89**；SWC，R ² = 0.90** 和 0.93**）以及可接受的 RMSE 值。这些都在观察到的实验误差的范围内，表明该模型模拟了可接受的系统行为。然后，APSIM 成功模拟了观察到的作物性能，以响应 24 种独特环境条件下的这些土壤动态。这说明在这种复杂环境中的作物表现可以被稳健地模拟，并且像 APSIM 这样的模型是一个有用的工具，可以转换其他模型在不同尺度上的输出（例如来自大气环流模型 (GCM) 的气候变化，以及地下水的未来变化）区域水文模型的深度和盐度动态）转化为种植系统性能的变化。