The Bushland Maize for Grain Datasets: A Machine-readable Resource

Abstract

<b><sc>Abstract.</sc></b> Accurate data on crop evapotranspiration (ET), yield, and crop water productivity are important for effective irrigation scheduling to improve crop water productivity, irrigation scheme management, long term water resource planning and management, and for use in crop simulation models to improve accuracy of yield estimates. However, all crop ET estimation methods must be tested against ground truth – ET as measured by mass balance in crop fields – and improved so as to estimate ET as accurately as possible. Mass balance measurement of ET depends on solving the soil water balance in which ET is the sum of the change of water stored in the soil profile to well below the root zone, irrigation, precipitation, the sum of any runon and runoff, and any soil water flux into or out of the soil profile. Effective means of measuring the profile change in storage include the neutron probe and large weighing lysimeters. The USDA ARS weighing lysimeter team at Bushland, Texas, measured ET of maize grown for grain in 1989, 1990, 1994, 2013, 2016, and 2018 using both weighing lysimeters and the neutron probe. Along with those measurements, the team measured crop growth and yield, weather, and irrigation applied. These data are presented, along with cropping calendars for each season, as machine readable files available to the public via the USDA ARS National Agriculture Library (NAL) Ag Data Commons internet site. The cropping calendars contain dates of important field operations, including dates, amounts, and kinds of fertilizers and pesticides applied. The weather data include daily sums and averages as well as 15-minute mean data for all days of the year, and include solar irradiance, air temperature and humidity, wind speed, air pressure, and precipitation. Some seasons of the Bushland maize data have already been used by the Agricultural Model Intercomparison and Improvement Project (AgMIP) maize modeling team, the OPENET team, and several universities for projects as varied as testing and improvement of eddy covariance methods of ET estimation, remote sensing-based ET estimation, and crop model testing and improvement. The data may be found in the NAL Ag Data Commons at: https://doi.org/10.15482/USDA.ADC/1526317.