Data from: Topographic position index predicts within-field yield variation in a dryland cereal production system

Data from: Topographic position index predicts within-field yield variation in a dryland cereal production system

<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...

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Bibliographic Details
Main Author: David Poss (21011022) (author)
Other Authors: Cody Hardy (21228343) (author), Jacob Macdonald (21228230) (author), Robert H. Erskine (21228245) (author), Kyle Douglas-Mankin (9970700) (author), Maysoon M. Mikha (3965930) (author), David Barnard (21078531) (author), Grace L. Miner (21228236) (author), Sushant Mehan (21228247) (author), Adam L. Mahood (21228253) (author)
Published: 2025
Subjects:
Crop and pasture production
Agro-ecosystem function and prediction
Agronomy
Crop and pasture biomass and bioproducts
yield
spatial variability.
machine learning
random forest
rainfed
dryland
precision agriculture
topographic position index
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