Spatial Analysis of UAS-generated Data to Evaluate Nitrogen Fertilizer Placement Alternatives, Predict and Map End-of-Season Rice Yield

Urea deep placement (UDP), which directly targets nitrogen (N) to the root zone, has shown promise in enhancing crop uptake, yield, and environmental sustainability compared to other N placement methods (non-UDP). This study utilized unmanned aerial systems (UAS or drones) to evaluate the impact of different N placement strategies on rice spectral signatures across three production zones, with an average farmer field size of 11.4 hectares. The research focused on analyzing Normalized Difference Vegetation Index (NDVI) and Optimized Soil Adjusted Vegetation Index (OSAVI) reflectance from UAS flight data collected during the booting stage in three rice health zones defined as low (NDVI 0.728), medium (NDVI 0.874), and high (NDVI 0.914). End-of-season rice grain yields revealed a nonlinear relationship with NDVI on the plot scale, whereas OSAVI exhibited a significant linear correlation with plot yields across the different zones. Spatial analysis, supported by the Jenks natural breaks algorithm, categorized OSAVI reflectance into four homogeneous groups, enabling predictions of rice yields at the plot scale and extrapolating plot yields to farmer field levels. This approach refined assessment outcomes, effectively distinguishing between UDP and non-UDP treatments. The UDP N management consistently outperformed other N placement methods, with yield increases of 9.9% at the plot level and 14.3% at the farmer field level. Additionally, this spatial analysis facilitated the generation of high-resolution digital yield maps, providing a valuable tool for precision agriculture to optimize N management and enhance overall productivity in rice cultivation.