Site-specific crop management is based on the postulate of varying soil and crop requirements in a field. Therefore, a field is separated into homogenous management zones, using available data to adapt management practices environment to maximize productivity and profitability while reducing environmental impacts. Due to advancing sensor technologies, crop growth and yield data on more minor scales are common, but soil data often needs to be more appropriate. Crop growth models have shown promise as a decision support tool for site-specific farming. The Decision Support System for Agrotechnology Transfer (DSSAT) is a widely used point-based model. To overcome the problem of inappropriate soil input data problem, this study introduces an external plug-in program called Soil Profile Optimizer (SPO), which uses the current DSSAT v4.8 to calibrate soil profile parameters on a site-specific level. Developed as an inverse modelling approach, the SPO can calibrate selected soil profile parameters by targeting available in-season plant data. Root Mean Square Error (RMSE) and normalized RMSE as error minimization criteria are used. The SPO was tested and evaluated by comparing different simulation scenarios in a case study of a 3-yr field trial with maize. The scenario with optimized soil profiles, conducted with the SPO, resulted in an R² of 0.76 between simulated and observed yield and led to significant improvements compared to the scenario conducted with field scale soil profile information (R² 0.03). The SPO showed promise in using spatial plant measurements to estimate management zone scale soil parameters required for the DSSAT model.

特定地点的作物管理基于田间不同土壤和作物需求的假设。因此，将一个油田分成同质的管理区域，利用可用数据来适应管理实践环境，以最大限度地提高生产力和盈利能力，同时减少对环境的影响。由于传感器技术的进步，更小尺度的作物生长和产量数据很常见，但土壤数据通常需要更合适。作物生长模型已显示出作为特定地点农业决策支持工具的前景。农业技术转让决策支持系统（DSSAT）是一种广泛使用的基于点的模型。为了克服土壤输入数据不适当的问题，本研究引入了一个名为土壤剖面优化器（SPO）的外部插件程序，它使用当前的 DSSAT v4.8 在特定地点的水平上校准土壤剖面参数。SPO 是一种逆向建模方法，可以通过针对可用的当季植物数据来校准选定的土壤剖面参数。使用均方根误差 (RMSE) 和归一化 RMSE 作为误差最小化标准。通过比较玉米 3 年田间试验案例研究中的不同模拟场景，对 SPO 进行了测试和评估。使用 SPO 进行的优化土壤剖面情景，模拟产量和观测产量之间的 R ^{2为 0.76，与使用田间规模土壤剖面信息进行的情景 (R 2} 0.03)相比，显着改善。SPO 在利用植物空间测量来估计 DSSAT 模型所需的管理区规模土壤参数方面表现出了良好的前景。