Precision Nitrogen Management in Dryland Agriculture: Soil and Topographic Drivers of Multi-Year Yield Stability

Variable-rate nitrogen (VRN) management in dryland crop rotations requires prescription maps that remain valid across years and for different crops, and that can be generated from sensors compatible with standard farm equipment. We examined how the ranking of yields in individual fields remained consistent from one season to another, how much of the variation in yields among individual field locations could be attributed to differences in the permanent physical characteristics of those field locations, and if the spatial structure of fertility was transferable among the different crops in a rotational sequence using a multi-seasonal wheat–corn–millet crop rotation dataset from northeast Colorado (n = 721; n = 18 management units; n = 321 location points; 2019–2022). There was a significant positive correlation between wheat yield rankings from non-consecutive growing seasons (ρ = 0.39–0.59), with 80.80% of the total variability explained by spatial effects that are stable over time. Approximately 20% of the within-field yield variability in wheat, the only crop with repeated within-position measurements, could be attributed to permanent differences in physical properties of the field such as topography (TPI) and soils (soil: 2.20%; TPI: 11.50%, both unique; 6.30% both shared), which represented an estimate of the maximum amount of within-field variability possible to explain based on static variables alone in this dataset. After accounting for year and field effects, all three Spearman correlations for each combination of two crops were positive and statistically significant (Wheat–Corn: ρ = +0.29; Wheat–Millet: ρ = +0.48; Corn–Millet: ρ = +0.30), indicating a common spatial fertility structure among all three crops in the rotational sequence. These results suggest that a pedotopographic map created using RTK-GPS elevation data and on-the-go soil sensors provides a partially transferable baseline spatial framework for variable-rate N applications throughout the entire cropping cycle. This baseline would need to include adjustments for average rate applied per crop, while the remainder of the within-field variability (approximately 80%) could be addressed through additional layers of annual sensing (e.g., UAV multispectral indices, active optical sensors, satellite imagery).