About this Dataset

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.
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

Tables

Data Dictionary

@usgov.usda_gov_data_from_topographic_position_index_predicts_5803474e.data_dictionary

5.78 kB
21 rows
5 columns

CREATE TABLE data_dictionary (
  "column_name" VARCHAR,
  "unit" VARCHAR,
  "descriptions" VARCHAR,
  "code_definition" VARCHAR,
  "unnamed_4" VARCHAR  -- Unnamed: 4
);