Randomized trial demonstrates improved spatial crop yield accuracy in agricultural zones, suggesting enhanced decision-making.
Spatially explicit crop yield information is needed for regional environmental modeling, sustainability assessment, and agricultural decision support, yet official yield statistics are commonly reported only at aggregated administrative scales. This study introduces the NAYD R package, a reproducible geospatial workflow for converting county-level harvested area and yield statistics into spatially explicit production units and zonal clusters while preserving consistency with official records. County-level statistics from the USDA National Agricultural Statistics Service were integrated with USDA Cropland Data Layer crop masks, multi-sensor NDVI products, and satellite-derived evapotranspiration from OpenET SSEBop. An NDVI-based eligibility filter refined crop masks toward reported harvested area, while normalized NDVI and evapotranspiration layers were combined into spatial weighting surfaces and aggregated into contiguous production units and graph-based clusters. Case studies for cotton and winter wheat in Texas showed that the eligibility filter removed approximately 20–40% of CDL-classified pixels while maintaining consistency with reported harvested area and preserving plausible spatial gradients associated with irrigated and dryland systems. Evaluation against independent Texas A&M AgriLife variety trial data indicated that the disaggregated clusters reproduced plausible spatial patterns of yield variability while retaining the county-level NASS constraints. The workflow provides an open-source framework for generating statistically consistent production zones for regional crop modeling, environmental assessment, and sustainable agricultural planning.
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Attia et al. (2026) studied this question.
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