An agronomically informed framework for temporally transferable yield prediction

Abstract Purpose Yield maps are essential for agricultural decision-making, yet existing methods are often crop- or machine-specific, limiting their broader applicability. Methods This study introduces an agronomically informed framework for temporally transferable yield prediction that combines optimal phenological moment (OPM) selection with a spatial yield redistribution strategy: density-based yield mapping (DBYM). The framework relies on vegetation indices derived from satellite imagery acquired at physiologically meaningful crop stages and on independently known field-scale production values to guide spatial yield allocation. The method was evaluated across 10 growing seasons involving coffee, sugarcane, and wheat under both manual and mechanized production systems in commercial fields located in Brazil and Australia. In addition to DBYM, a simple linear regression baseline using the same OPM-derived inputs was evaluated to investigate the contribution of agronomically informed temporal input selection itself. Results Results demonstrated that OPM-based inputs alone already provided strong predictive capability across seasons, while DBYM improved the spatial coherence of predictions, particularly under sparse calibration scenarios. The index of agreement ranged from 0.73 to 0.94, while Moran’s bivariate index was significant ( p < 0.01) for all DBYM predictions, confirming spatial consistency between observed and predicted yield patterns. RMSE values were comparable to those reported in machine learning and deep learning yield prediction studies, ranging from 0.21 to 0.28 Mg ha⁻¹ for coffee, 5.43–6.22 Mg ha⁻¹ for sugarcane, 0.15–0.26 Mg ha⁻¹ for wheat in Brazil, and 0.44–0.61 Mg ha⁻¹ for wheat in Australia. Conclusion The results suggest that predictive performance can be affected by the agronomic relevance of acquisition timing. By combining agronomically informed temporal inputs with a flexible spatial redistribution framework, DBYM enables the extension of yield map time series across seasons and provides a scalable and resource-efficient alternative for precision agriculture applications in fields lacking dedicated yield monitoring systems.