Background and Aims

To enhance Brazilian sugarcane production sustainably, crop simulation models have been utilized. However, due to the lack of reliable information, particularly concerning soil variability, these models have shown limited performance for specific analyses. This study aims to evaluate Digital Soil Mapping (DSM) as an alternative for filling soil data gaps in crop modeling and to assess the influence of these products on prediction uncertainties. The study site is located in Piracicaba region, Southern Brazil.

Methods

The framework was: (i) a legacy soil data were utilized, and equal-spline equations were applied to standardize the dataset.; (ii) a machine learning (ML) algorithm was used to predict soil attributes and their uncertainties; (iii) pedotransfer functions were applied to obtain soil hydrological properties; (iv) DSSAT/CANEGRO crop model was used to estimate sugarcane yield; (iv) a legacy soil map (LSM), SoilGrids (SG) and a map of attributes derived from regional DSM (RDSM) were compared; (v) a Monte Carlo Simulation (MCS) was conducted with the RDSM maps to evaluate the impact of uncertainties in the estimation of sugarcane yield.

Results

The DSM proved to be a reliable source for use in crop models, reaching similar results to field data. The sugarcane yield map emphasized the model’s sensitivity to soil attributes, with texture and depth significantly impacting yield estimations.

Conclusion

In this sense, coupling DSM and crop modeling is a feasible way to improve yield estimates, especially in countries with limited soil databases.

中文翻译：

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数字土壤测绘和作物建模，用于定义土壤对限水甘蔗产量的空间显性影响

背景和目标

为了可持续地提高巴西甘蔗产量，采用了作物模拟模型。然而，由于缺乏可靠的信息，特别是关于土壤变异性的信息，这些模型在特定分析中表现出有限的性能。本研究旨在评估数字土壤测绘（DSM）作为填补作物建模中土壤数据空白的替代方案，并评估这些产品对预测不确定性的影响。研究地点位于巴西南部皮拉西卡巴地区。

方法

该框架是：（i）利用遗留土壤数据，并应用等样条方程来标准化数据集。 (ii) 使用机器学习（ML）算法来预测土壤属性及其不确定性； (iii) 应用pedotransfer函数来获取土壤水文特性； (iv) DSSAT/CANEGRO 作物模型用于估算甘蔗产量； (iv) 比较了传统土壤图 (LSM)、SoilGrids (SG) 和源自区域 DSM (RDSM) 的属性图； (v) 使用 RDSM 地图进行蒙特卡罗模拟 (MCS)，以评估甘蔗产量估算中不确定性的影响。

结果

事实证明，DSM 是作物模型的可靠来源，可达到与田间数据相似的结果。甘蔗产量图强调了模型对土壤属性的敏感性，质地和深度显着影响产量估计。

结论

从这个意义上说，将 DSM 和作物建模结合起来是提高产量估算的可行方法，特别是在土壤数据库有限的国家。