Digital Soil Mapping (DSM) can be an alternative data source for spatializing crop models over large areas. The objective of the paper was to evaluate the impact of DSM products and their uncertainties on a crop model’s outputs in an 80 km² catchment in south India. We used a crop model called STICS and evaluated two essential soil functions: the biomass production (through simulated yield) and water regulation (via calculated drainage). The simulation was conducted at 217 sites using soil parameters obtained from a DSM approach using either Random Forest or Random Forest Kriging. We first analysed the individual STICS simulations, i.e., at two cropping seasons for 14 individual years, and then pooled the simulations across years, per site and crop season. The results show that i) DSM products outperformed a classical soil map in providing spatial estimates of STICS soil parameters, ii) although each soil parameters were estimated separately, the correlations between soil parameters were globally preserved, ii) Errors on STICS’ yearly outputs induced by DSM estimations of soil parameters were globally low but were important for the few years with high impacts of soil variations, iii) The statistics of the STICS simulations across years were also affected by DSM errors with the same order of magnitude as the errors on soil inputs and iv) The impact of DSM errors was variable across the studied soil parameters. These results demonstrated that coupling DSM with a crop model could be a better alternative to the classical Digital Soil Assessment techniques. As such, it will deserve more work in the future.

数字土壤测绘 (DSM) 可以成为大面积作物模型空间化的替代数据源。该论文的目的是评估 DSM 产品及其不确定性对 80 km ²作物模型输出的影响印度南部的集水区。我们使用了一种称为 STICS 的作物模型并评估了两种基本的土壤功能：生物量生产（通过模拟产量）和水分调节（通过计算排水）。模拟是在 217 个地点使用从 DSM 方法获得的土壤参数进行的，使用随机森林或随机森林克里金法。我们首先分析了单独的 STICS 模拟，即在 14 个单独年份的两个作物季节，然后汇集了跨年份、每个地点和作物季节的模拟。结果表明 i) DSM 产品在提供 STICS 土壤参数的空间估计方面优于经典土壤图，ii) 尽管每个土壤参数是单独估计的，但土壤参数之间的相关性在全球范围内得以保留，ii) 由 DSM 对土壤参数的估计引起的 STICS 年度输出误差在全球范围内较低，但在土壤变化影响较大的几年中很重要，iii) 跨年 STICS 模拟的统计数据也受到 DSM 误差的影响与土壤输入误差的数量级相同，以及 iv) DSM 误差的影响在所研究的土壤参数中是可变的。这些结果表明，将 DSM 与作物模型相结合可能是经典数字土壤评估技术的更好替代方案。因此，它在未来值得做更多的工作。iii) 跨年 STICS 模拟的统计数据也受 DSM 误差的影响，其数量级与土壤输入误差的数量级相同，以及 iv) DSM 误差的影响在所研究的土壤参数中是可变的。这些结果表明，将 DSM 与作物模型相结合可能是经典数字土壤评估技术的更好替代方案。因此，它在未来值得做更多的工作。iii) 跨年 STICS 模拟的统计数据也受 DSM 误差的影响，其数量级与土壤输入误差的数量级相同，以及 iv) DSM 误差的影响在所研究的土壤参数中是可变的。这些结果表明，将 DSM 与作物模型相结合可能是经典数字土壤评估技术的更好替代方案。因此，它在未来值得做更多的工作。