Simulation model for winter wheat can help producers optimize forage

A new, pioneering forage wheat model could provide a valuable technique to researchers exploring the potential of biomass production for cool-season annual forage grasses, according to model developers.

Two researchers at the Texas A&M AgriLife Research and Extension Center in Overton—Monte Rouquette, Texas A&M AgriLife Research plant physiologist, and Prem Woli, AgriLife Research crop modeler—recently published a paper in Agronomy Journal titled “Simulating Winter Wheat Forage Production in the Southern U.S. Using a Forage Wheat Model.” 

It focuses on annual forage grass modeling with the Decision Support System for Agrotechnology Transfer, or DSSAT, suite of crop computer models. Research by Charles Long, AgriLife Research animal breeder and center director; Ray Smith, AgriLife Research plant breeder, and Lloyd Nelson, AgriLife Research plant breeder and professor emeritus, all in Overton, also contributed to the publication. 

DSSAT is a software application program that comprises dynamic crop growth simulation models for more than 40 crops, according to the DSSAT website. The program is supported by a range of utilities and applications for weather, soil, genetics, crop management and observational experimental data. It also includes example data sets for all the crop models included in the suite.

Crop simulation models, including the forage wheat model, simulate growth, development and biomass production as a function of the soil-plant-atmosphere dynamics and management.

The soil-plant-atmosphere system comprises environmental factors such as soil type, weather, temperatures, solar radiation, wind and precipitation, and production management variables including cultivars, planting or harvesting dates, and inputs such as nitrogen fertilizer, Rouquette said.

Like other crop models, the forage wheat model may be used by researchers, educators or students to understand the mechanisms underlying forage wheat biomass production—or by growers or extension agents as a tool for optimizing forage wheat production, Woli said. Users can analyze “what-if” scenarios by manipulating the various factors that affect biomass production.

“The techniques and algorithms used while developing this model may be tremendously useful to other researchers interested in this field,” he said. “That is a significant contribution to annual forage grass modeling. A first annual forage grass model has been incorporated into the DSSAT suite.”

More at Texas A&M AgriLife Research

Share