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, <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.

本研究依托美国科罗拉多州东北部一处旱地试验农场的多年期数据集，探究了3种作物产量的田块内空间变异驱动因子：硬红冬小麦（普通小麦*Triticum aestivum* L.，品种Langin）、玉米（*Zea mays* L.）以及粟（*Panicum milaceum* L.）。该数据集涵盖2019—2022年共4年间采集的18个面积为2.6~4.3公顷的田间管理单元相关数据，包含通过实时动态GPS采集的高分辨率地形数据、高密度采样获取的土壤质地与化学属性数据，以及场内气象站的气象观测数据。