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.
Resources
3 resources available
-
Modeling_data.csv
CSV -
Data_dictionary.csv
CSV -
Study_site_DEM.tif
TIF
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Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| accrualPeriodicity | R/P1Y |
| bureauCode |
[
"005:18"
]
|
| contactPoint |
{
"fn": "Douglas-Mankin, Kyle",
"hasEmail": "mailto:Kyle.Mankin@usda.gov"
}
|
| description | <p dir="ltr">We investigated drivers of sub-field spatial variability in yield for 3 crops (hard red winter wheat, <i>Triticum aestivum</i> L. variety Langin; corn, <i>Zea mays</i> L.; and proso millet, <i>Panicum milaceum</i> 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.</p> |
| distribution |
[
{
"@type": "dcat:Distribution",
"title": "Modeling_data.csv",
"format": "csv",
"mediaType": "text/csv",
"downloadURL": "https://ndownloader.figshare.com/files/54132527"
},
{
"@type": "dcat:Distribution",
"title": "Data_dictionary.csv",
"format": "csv",
"mediaType": "text/csv",
"downloadURL": "https://ndownloader.figshare.com/files/54132539"
},
{
"@type": "dcat:Distribution",
"title": "Study_site_DEM.tif",
"format": "tif",
"mediaType": "image/tiff",
"downloadURL": "https://ndownloader.figshare.com/files/54132677"
}
]
|
| identifier | 10.15482/USDA.ADC/28914434.v1 |
| keyword |
[
"dryland",
"machine learning",
"precision agriculture",
"rainfed",
"random forest",
"spatial variability.",
"topographic position index",
"yield"
]
|
| license | https://creativecommons.org/publicdomain/zero/1.0/ |
| modified | 2025-11-21 |
| programCode |
[
"005:040"
]
|
| publisher |
{
"name": "Agricultural Research Service",
"@type": "org:Organization"
}
|
| spatial |
"{"type": "Polygon", "coordinates": [[[-103.1482923836, 40.1494161291], [-103.1479314553, 40.1626982725], [-103.1199392655, 40.1622469882], [-103.120305641, 40.1489650556], [-103.1482923836, 40.1494161291]]]}"
|
| temporal | 2019-01-01/2022-12-31 |
| title | Data from: Topographic position index predicts within-field yield variation in a dryland cereal production system |
