A geospatial approach to integrating agricultural management practices and remote sensing data for sugar cane yield prediction

Remote sensing-based estimation of sugar cane yield is constrained by spectral saturation under high-biomass canopies and by the fact that satellite platforms do not directly capture fine-scale edaphic variability or farmer management decisions. Consequently, most existing sugar cane yield models rely primarily on optical indices and neglect the combined effects of radar backscatter, soil and climate gradients, and agronomic management at field scale. This study addresses this gap by evaluating the incremental effect of different information sources on sugar cane yield estimation (t/ha) through progressive incorporation of optical sensor variables (vegetation indices), synthetic aperture radar (SAR), edaphic and meteorological variables, and agronomic management records at the field scale in the Cauca River Valley, Colombia, one of the most productive areas of sugar cane worldwide. Multiple linear regression (ordinary least squares, OLS) and linear mixed-effects models (LMM) were implemented to evaluate how each group of variables improves performance metrics when introduced sequentially. Models based exclusively on SAR information exhibited limited performance (R² < 0.40), whereas combining SAR with optical indices increased predictive capacity (R² < 0.55). Incorporating agronomic management variables further enhanced model accuracy, with the best LMM configuration reaching R² = 0.72 and RMSE = 13.9 t/ha. These results demonstrate that agronomic management decisions increased the explained variance of the model by approximately 19% and reduced prediction error by 4.23 t/ha relative to models without management information. The findings highlight the importance of explicitly representing management in operational and transferable yield estimation models and provide guidance for extending similar multisource approaches to other irrigated sugar cane growing regions. • SAR-optical fusion achieves R² = 0.52 in cloud-prone regions, outperforming SAR alone. • Agronomic management explains more yield variance than all satellite, climate, and soil data combined. • Yield predictions are most reliable during the 5–7 month growth window (R² = 0.80). • Farm-level random effects in linear mixed models capture management-driven yield heterogeneity. • Integrating field management records with geospatial data enables operational yield forecasting in tropical sugarcane systems.