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

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.

Resources

Name Format Description Link
8 https://ndownloader.figshare.com/files/54132527
8 https://ndownloader.figshare.com/files/54132539
48 https://ndownloader.figshare.com/files/54132677

Tags

  • rainfed
  • topographic-position-index
  • yield
  • random-forest
  • spatial-variability
  • dryland
  • precision-agriculture
  • machine-learning

Topics

Categories