Skip to main content
ARS Home » Plains Area » Lubbock, Texas » Cropping Systems Research Laboratory » Wind Erosion and Water Conservation Research » Research » Publications at this Location » Publication #362526

Research Project: Optimizing Water Use Efficiency for Environmentally Sustainable Agricultural Production Systems in Semi-Arid Regions

Location: Wind Erosion and Water Conservation Research

Title: Precision Agriculture: Water and Nutrient Management

item Lascano, Robert
item GOEBEL, TIMOTHY - Texas Tech University
item BOOKER, JON - Texas Tech University

Submitted to: Encyclopedia of Environmental Management
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/10/2019
Publication Date: N/A
Citation: N/A

Interpretive Summary: Precision agriculture (PA) refers to the practice of managing agronomic inputs according to specific needs across the landscape. The major impediment to the adoption of PA is the development of decision-support systems that provide guidelines on which, when and where a specific input should be applied. Research in PA, focusing on factors controlling crop variability, has described useful process relationships, and these results are supporting the development of decision-support systems. An example is the integration of crop simulation models with geographic information data of soil and elevation, real-time weather, and management information systems. Models such as the Precision Agricultural- Landscape Modeling System that can calculate the energy, water, nutrient, and carbon balance across the landscape at a 5 to 10 m resolution provide the desired integration of field-scale data. These landscape-scale models can provide a decision-support framework to manage agronomic inputs to maximize economic crop yield while minimizing environmental hazards. Adoption of PA will continue to increase given the demand for a safe food and fiber supply of high quality

Technical Abstract: Precision agriculture (PA) refers to the practice of managing agronomic inputs according to specific needs across the landscape. A major obstacle to adopt PA is the development of site-specific decision support systems to provide guidelines on the management of water and nutrient for crop production. For this purpose, a comprehensive simulation model known as the Precision Agricultural Landscape Modeling System (PALMS) was combined with the cotton simulation model known as GOSSYM to manage water and nitrogen on a center pivot irrigated system for two growing seasons, 2010 and 2011, in the semiarid Texas High Plains. The combined model, PALMSCot (62,000 lines of code written in Fortran-77), was applied to a ¼-section of land irrigated with a 400 m center pivot sprinkler system. Inputs to the model were represented by: (i) two-dimensional grid-based layers providing model domain, crop and vegetation extent, topography, and daily irrigation files (hourly resolution); (ii) three-dimensional grid-based soil texture class and initial soil water content across the model domain and in 26 profile layers; (iii) text files of hourly weather measurements (with 15-min rain amounts) and a field settings file containing factors considered consistent across the entire field, including initial soil temperature and organic carbon, tillage and fertilization operations, and surface water model settings; and (iv) soil hydraulic parameters assigned by soil texture class within the model code and a text file of cotton growth parameters. We used a 20 × 20 m grid system to input soil properties and elevation on a Pullman clay loam with a 0 – 1 % slope, near Floydada, TX. Weather data was obtained from a nearby Mesonet weather station. The 2010 season was used to calibrate the model by comparing measured and simulated values of cotton lint yield and the 2011 season was used to simulate different scenarios of water and nitrogen application that would result in the highest cotton lint yield across the landscape. Results from this exercise showed that the adoption of site-specific management would be enhanced if management decision are linked to profitability.