Department of Agriculture

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

Related Datasets

Data From: Crop- And Weather-dependent Yield And Wind Erosion Benefits From A Conservation Practices System

@usgov
Data From: Random Forest Regression To Predict Farinograph Traits From GlutoPeak Output In Wheat Wild Relative Backcross Lines

@usgov
The Bronson Files, Dataset 7, Field 13, 2015

@usgov
Data From: Crop Diversity Effects On Near-Surface Soil Condition Under Dryland Agriculture

@usgov
A Regionally-adapted Implementation Of Conservation Agriculture Delivers Rapid Improvements To Soil Properties Associated With Crop Yield Stability

@usgov
The Bronson Files, Dataset 8, Field 113, 2016

@usgov

Data From: Crop- And Weather-dependent Yield And Wind Erosion Benefits From A Conservation Practices System

Data From: Random Forest Regression To Predict Farinograph Traits From GlutoPeak Output In Wheat Wild Relative Backcross Lines

The Bronson Files, Dataset 7, Field 13, 2015

Data From: Crop Diversity Effects On Near-Surface Soil Condition Under Dryland Agriculture

A Regionally-adapted Implementation Of Conservation Agriculture Delivers Rapid Improvements To Soil Properties Associated With Crop Yield Stability

The Bronson Files, Dataset 8, Field 113, 2016