Evett et al. (2025) The Bushland, Texas, alfalfa, soybean, sunflower, and winter wheat evapotranspiration, growth, and yield dataset collections

Identification

• Journal: Scientific Data
• Year: 2025
• Date: 2025-12-17
• Authors: Steven R. Evett, Gary W. Marek, Paul D. Colaizzi, Karen S. Copeland, Brice B. Ruthardt
• DOI: 10.1038/s41597-025-06225-9

Research Groups

• USDA Agricultural Research Service, Conservation & Production Research Laboratory, Bushland, Texas, USA
• USDA–ARS Ogallala Aquifer Program (consortium including Kansas State University, Texas A&M AgriLife Research, Texas A&M AgriLife Extension Service, Texas Tech University, and West Texas A&M University)

Short Summary

This paper presents comprehensive, quality-controlled datasets on evapotranspiration (ET), growth, and yield for alfalfa, soybean, sunflower, and winter wheat, collected over multiple years using large weighing lysimeters and extensive instrumentation at Bushland, Texas. These high-resolution datasets are designed for calibrating and testing crop simulation models and analyzing water productivity in the Southern High Plains.

Objective

• To present four collections of quality-controlled datasets (alfalfa, soybean, sunflower, winter wheat) suitable for model calibration, testing, and improvement, and for analyzing the effects of weather, irrigation, and agronomic decisions on crop yield and water productivity in the Southern High Plains region of the USA.

Study Configuration

• Spatial Scale: Experiments conducted at the USDA-ARS Conservation and Production Laboratory, Bushland, Texas, USA (35.186714° N, 102.094189° W, 1170 m above MSL) on Pullman soil series. Data collected from four large, precision weighing lysimeters (3 m x 3 m horizontally, ~2.3 m deep) centered within 4.44 hectare square fields.
• Temporal Scale:
  • Alfalfa: 1996–1999
  • Soybean: 1995, 2003, 2004, 2010, 2019
  • Sunflower: 2009, 2011
  • Winter Wheat: 1989–1990, 1991–1992, 1992–1993
  • Data intervals: 15-minute for ET, microclimate, and weather data; periodic for soil water content, crop growth, biomass, and yield.

Methodology and Data

• Models used:
  • Evapotranspiration (ET) calculated using the soil water balance equation: ∆S = P + DW + I + F + R + CW + V (where ∆S is change in soil water storage, P is precipitation, DW is dew and frost accumulation, I is irrigation, F is subsurface soil water flux, R is runon/runoff, CW is scale counterweight adjustments, and V is other virtual mass changes).
  • A custom Excel spreadsheet was developed and used for applying the water balance equation, quality control, and event flagging.
• Data sources:
  • Weighing lysimeters: Measured mass changes (converted to relative soil water storage in mm depth of water) with an accuracy of 0.05 mm or better, used for ET, irrigation, precipitation, dew/frost accumulation, and drainage tank emptying.
  • In-soil sensors: Soil heat flux, soil temperature, and soil volumetric water content at various depths.
  • Above-ground microclimate instruments: Precipitation, wind speed, air temperature, humidity, radiant energy (incoming/reflected shortwave and longwave, photosynthetically active radiation (PAR)), and surface temperature.
  • Weather station: Replicate calibrated sensors at 2 m and 10 m heights over a mowed grass surface for solar irradiance (W m⁻²), wind speed (m s⁻¹), relative humidity (%), air temperature (°C), barometric pressure (kPa), and precipitation (mm).
  • Neutron probe: Field-calibrated readings for soil water content from 0.10 m to 2.40 m depth in 0.20 m increments.
  • Agronomic calendars: Daily logs of field operations (tillage, planting, fertilization, pesticide application, irrigation events and methods, plant measurements, harvest, lysimeter maintenance).
  • Plant growth and yield measurements: Plant emergence, stand density, height, leaf area index, row width, above-ground biomass (undried and dried), head/pod mass, population density, machine yield, hand harvest data (total biomass, dry yield, yield at standard moisture content), and harvest index.

Main Results

• Four comprehensive, quality-controlled dataset collections are publicly available, covering alfalfa (4 years), soybean (5 years), sunflower (2 years), and winter wheat (3 seasons) experiments.
• Each collection comprises six distinct datasets: Agronomic Calendars, Growth and Yield Data, Weighing Lysimeter Data, Evapotranspiration, Irrigation, Dew/frost - Water Balance Data, Standard Quality Controlled Research Weather Data, and Soil Water Content Data.
• Data are provided at high temporal resolution (15-minute intervals for ET, microclimate, and weather) and include periodic measurements for crop growth, biomass, yield, and soil water content.
• The weighing lysimeter ET data demonstrate an accuracy of 0.05 mm or better, and rigorous quality control procedures, including a custom Excel spreadsheet implementing the water balance equation, were applied to ensure data reliability.

Contributions

• Provides a unique, long-term, high-resolution, and quality-controlled experimental dataset from large weighing lysimeters, which are invaluable for agro-hydrological research and are rare globally.
• Offers a comprehensive suite of interconnected variables (evapotranspiration, crop growth, yield, soil water content, microclimate, and detailed agronomic practices) for multiple major crops, enabling holistic studies of crop water use and productivity.
• The datasets are specifically designed and validated for the calibration, testing, and improvement of crop simulation models and energy/water balance models.
• Includes extensive documentation (data dictionaries, agronomic calendars) to ensure the usability, interpretability, and reproducibility of the data for a wide range of scientific applications.

Funding

• USDA–ARS Ogallala Aquifer Program

Citation

@article{Evett2025Bushland,
  author = {Evett, Steven R. and Marek, Gary W. and Colaizzi, Paul D. and Copeland, Karen S. and Ruthardt, Brice B.},
  title = {The Bushland, Texas, alfalfa, soybean, sunflower, and winter wheat evapotranspiration, growth, and yield dataset collections},
  journal = {Scientific Data},
  year = {2025},
  doi = {10.1038/s41597-025-06225-9},
  url = {https://doi.org/10.1038/s41597-025-06225-9}
}