Data From: Topographic Position Index Predicts Within-field Yield Variation In A Dryland Cereal Production System

We investigated drivers of sub-field spatial variability in yield for 3 crops (hard red winter wheat, Triticum aestivum L. variety Langin; corn, Zea mays L.; and proso millet, Panicum milaceum L.) usings this multi-year dataset from a dryland research farm in northeastern Colorado, USA. The dataset spanned 18 2.6-4.3 ha management units collected over 4 years (2019-2022). The data includes high resolution topographic data collected via real-time kinematic GPS, densely sampled soil texture and chemical properties, and meteorological data from an on-site weather station.
Organization: Department of Agriculture
Last updated: 2025-06-05T09:51:30.391320
Tags: dryland, machine-learning, precision-agriculture, rainfed, random-forest, spatial-variability, topographic-position-index, yield