Digital soil mapping and crop modeling to define the spatially-explicit influence of soils on water-limited sugarcane yield

• Published in: Plant and soil 2024-10, Vol.503 (1-2), p.349-369
• Main Authors: dos Santos, Natasha Valadares, Rizzo, Rodnei, Dias, Henrique Boriolo, Safanelli, José Lucas, Bonfatti, Benito Roberto, Sentelhas, Paulo Cesar, Amorim, Merilyn Taynara Accorsi, Mello, Danilo Cesar, Falcioni, Renan, Francelino, Marcio, Veloso, Gustavo Vieira, Demattê, José A. M.
• Format: Article
• Language: English
• Subjects: Agricultural production; Agriculture; Algorithms; Biomedical and Life Sciences; Brazil; Computer simulation; crop models; Crop production; Crop yield; Crops; data collection; Digital mapping; Ecology; Food processing industry; hydrology; Information processing; Learning algorithms; Life Sciences; Machine learning; Mapping; Modelling; Monte Carlo method; Monte Carlo simulation; Performance evaluation; Plant Physiology; Plant Sciences; prediction; Predictions; Research Article; Simulation models; Soil analysis; soil heterogeneity; Soil improvement; soil map; Soil mapping; Soil maps; Soil properties; Soil Science & Conservation; Soil water; Sugarcane; texture; Uncertainty; yield mapping
• ISSN: 0032-079X; 1573-5036
• DOI: 10.1007/s11104-024-06587-w

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. Highlights • Crop simulation models have limited application due to the lack of soil data. • Digital Soil Mapping was coupled to a sugarcane simulation model to fill the gap of soil information. • Soil attributes and their uncertainties were predicted on a 250-m grid using machine learning algorithm. • A spatially-explicit DSSAT/CANEGRO model was able to represent variations in sugarcane yield at the regional scale; • Sugarcane yield was strongly affected by soil variability and its uncertainties; • Our finds indicate the importance of detailed soil databases and their impact on yield predictions. Graphical abstract