Topographic Position Index Predicts Within-Field Yield Variation in a Dryland Cereal Production System

Agricultural systems exhibit a large degree of within-field yield variability. We require a better understanding of the drivers of this variability in order to optimally manage croplands. We investigated drivers of sub-field spatial variability in yield for three crops (hard red winter wheat, Tritic...

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Vydané v:Agronomy (Basel) Ročník 15; číslo 6; s. 1304
Hlavní autori: Macdonald, Jacob A., Barnard, David M., Mankin, Kyle R., Miner, Grace L., Erskine, Robert H., Poss, David J., Mehan, Sushant, Mahood, Adam L., Mikha, Maysoon M.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.06.2025
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ISSN:2073-4395, 2073-4395
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Abstract Agricultural systems exhibit a large degree of within-field yield variability. We require a better understanding of the drivers of this variability in order to optimally manage croplands. We investigated drivers of sub-field spatial variability in yield for three crops (hard red winter wheat, Triticum aestivum L. variety Langin; corn, Zea mays L.; and proso millet, Panicum milaceum L.) usings a multi-year dataset from a dryland research farm in northeastern Colorado, USA. The dataset spanned 18 2.6–4.3 ha management units, over 4 years, and included high-resolution topographic data, densely sampled soil properties, and on-site weather data. We modeled yield for each crop separately using random forest regression and evaluated model performance using spatially blocked cross-validation. The topographic position index (TPI) and increasing percent sand had a strong negative effect on yield, while the nitrogen application rate (N) and total soil carbon had strong positive effects on yield in both the wheat and millet models. Remarkably, TPI had almost as large of an effect size as N, and outperformed other more commonly used topographic predictors of yield such as the topographic wetness index (TWI), elevation, and slope. Despite the size and quality of our dataset, cross-validation results revealed that our models account for approximately one-quarter of the total yield variance, highlighting the need for continued research into drivers of spatial variability within fields.
AbstractList Agricultural systems exhibit a large degree of within-field yield variability. We require a better understanding of the drivers of this variability in order to optimally manage croplands. We investigated drivers of sub-field spatial variability in yield for three crops (hard red winter wheat, Triticum aestivum L. variety Langin; corn, Zea mays L.; and proso millet, Panicum milaceum L.) usings a multi-year dataset from a dryland research farm in northeastern Colorado, USA. The dataset spanned 18 2.6–4.3 ha management units, over 4 years, and included high-resolution topographic data, densely sampled soil properties, and on-site weather data. We modeled yield for each crop separately using random forest regression and evaluated model performance using spatially blocked cross-validation. The topographic position index (TPI) and increasing percent sand had a strong negative effect on yield, while the nitrogen application rate (N) and total soil carbon had strong positive effects on yield in both the wheat and millet models. Remarkably, TPI had almost as large of an effect size as N, and outperformed other more commonly used topographic predictors of yield such as the topographic wetness index (TWI), elevation, and slope. Despite the size and quality of our dataset, cross-validation results revealed that our models account for approximately one-quarter of the total yield variance, highlighting the need for continued research into drivers of spatial variability within fields.
Audience Academic
Author Poss, David J.
Mahood, Adam L.
Erskine, Robert H.
Mikha, Maysoon M.
Mankin, Kyle R.
Barnard, David M.
Miner, Grace L.
Mehan, Sushant
Macdonald, Jacob A.
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  surname: Mikha
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CitedBy_id crossref_primary_10_1016_j_biocon_2025_111400
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Snippet Agricultural systems exhibit a large degree of within-field yield variability. We require a better understanding of the drivers of this variability in order to...
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StartPage 1304
SubjectTerms Agricultural land
Agricultural production
Arid zones
Carbon content
Crops
Datasets
dryland
Farming systems
Generalized linear models
Hypotheses
Machine learning
Meteorological data
Millet
Nitrogen
Precipitation
Production data
rainfed
random forest
Soil properties
Soils
spatial variability
Topography
Triticum aestivum
Variability
Wheat
Winter wheat
yield
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Title Topographic Position Index Predicts Within-Field Yield Variation in a Dryland Cereal Production System
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