Establishing a spatially explicit monitoring system for phosphorus use efficiency for crop fields

Abstract We evaluated a geospatially explicit phosphorus (P) use efficiency (PUE) monitoring method in crop fields using proximal sensing, field observations, and machine learning. Corn ( Zea mays L.) yield and grain protein content were measured using an Ag Leader yield monitor and a CropScan sensor near Riesel, Texas. Topsoil P (0–15 cm) and grain P levels were analyzed for samples collected at strategic field locations. A random forest model was trained to predict PUE using soil electrical conductivity (EC a ) from a Veris instrument and topographic variables as predictors ( R 2 = 0.78, root mean squared error = 0.01). CropScan sensor effectively estimated grain P content, supporting field‐wide PUE upscaling. EC a and elevation were the primary drivers of PUE variation. The resulting maps are valuable for monitoring PUE in crop fields and guiding variable‐rate fertilizer applications. This scalable approach provides a robust framework for monitoring nutrient dynamics and efficiency, informing precision management strategies to enhance yield and sustainability in crop production systems.