The Bushland, Texas, maize evapotranspiration, growth, and yield dataset Collection.

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Data Pub Date : 2025-02-04 DOI:10.1038/s41597-025-04539-2

Steven R Evett, Gary W Marek, Paul D Colaizzi, Karen S Copeland, Brice B Ruthardt, Terry A Howell

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引用次数: 0

Abstract

A collection of datasets describing six years of experiments on maize (Zea mays, L.) (corn) is presented (1989, 1990, 1994, 2013, 2016, and 2018). Four weighing lysimeters were used to determine crop evapotranspiration (ET). In-soil and above ground microclimate and ET data are presented on a 15-minute interval as are weather data for all days of the year. Data analysis determined ET, precipitation, irrigation, and dew and frost accumulation on a 15-minute basis from lysimeter mass data. Soil water content data from calibrated neutron probe readings is presented on a periodic basis. Crop planting, harvest, fertilization, pest control, and other agronomic information are presented in agronomic calendars by day of year. Crop growth data are presented on a periodic basis throughout the growing season, as are final crop biomass and yield data. The data are suitable for analysis of effects of irrigation and other agronomic decisions on crop yield and water productivity in the Southern High Plains region of the USA, for model calibration and testing, and for model improvement.

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来源期刊

Scientific Data Social Sciences-Education

CiteScore

11.20

自引率

4.10%

发文量

689

审稿时长

16 weeks

期刊介绍： Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.