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

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US Government

@usgov.department_of_agriculture_data_from_topographic_positi_6f4da9f1

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Department of Agriculture

Dataset Description

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
Organization URL: https://catalog.data.gov/organization/usda
Last updated: 2025-11-21
Tags: dryland, machine learning, precision agriculture, rainfed, random forest, spatial variability., topographic position index, yield


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