Location: Soil and Water Management ResearchTitle: The Bushland maize for grain datasets: A machine-readable resource
|Evett, Steven - Steve|
|HOWELL, SR., TERRY - Retired ARS Employee|
Submitted to: ASABE Annual International Meeting
Publication Type: Proceedings
Publication Acceptance Date: 4/19/2022
Publication Date: N/A
Interpretive Summary: The scarcity of water resources in the U.S. Southern High Plains is of regional, national and even international concern due to the fact that the region acts as a breadbasket for the nation and world. The majority of agricultural production in this region depends on irrigation, largely dependent on pumping from the Ogallala or High Plains Aquifer. But the aquifers are declining and yield less water every year. Water scarcity is also made worse by increasing temperature brought about by climate change, which causes crop water use to increase. Grain corn is a crop of major importance in the region and serves both the cattle feeding and dairying industries that are regional economic cornerstones. Scientists at the USDA ARS Conservation & Production Research Laboratory at Bushland, Texas, studied grain corn water use over six seasons to determine the decrease in water use that could be obtained by changing management, cultivar, and irrigation methods. They determined that limited deficit irrigation could increase the ratio of yield to water use without causing too much yield decline, thus stretching water further. They also determined that use of subsurface drip irrigation could decrease water use by 10% to 15% compared with conventional sprinkler irrigation, and with equal and sometimes better yield. The data sets are available to other scientists and the general public on the USDA National Agricultural Library online data sharing library (https://doi.org/10.15482/USDA.ADC/1526317). The data sets have already been used to improve crop growth, water use, and yield models used to forecast grain production nationally and to guide water planning locally and regionally.
Technical Abstract: Accurate estimation of crop evapotranspiration (ET) is 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 sets are now available to other scientists and the general public on the USDA National Agricultural Library online data sharing library (https://doi.org/10.15482/USDA.ADC/1526317).