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United States Department of Agriculture

Agricultural Research Service

Research Project: INTEGRATION OF CLIMATE VARIABILITY AND FORECASTS INTO RISK-BASED MANAGEMENT TOOLS FOR AGRICULTURE PRODUCTION AND RESOURCE CONSERVATION Title: A wheat grazing model for simulating grain and beef production: Part II - model validation

Authors
item Zhang, Xunchang
item Hunt, Leslie - UNIV OF GUELPH CANADA
item Phillips, William
item Horn, Gerald - OKLA STATE UNIV
item Edwards, J - OKLA STATE UNIV
item Zhang, H - OKLA STATE UNIV

Submitted to: Agronomy Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: April 3, 2008
Publication Date: September 1, 2008
Citation: Zhang, X.J., Hunt, L.A., Phillips, W.A., Horn, G.W., Edwards, J., Zhang, H.L. 2008. A wheat grazing model for simulating grain and beef production: Part II - model validation. Agronomy Journal. 100(5):1248-1258.

Interpretive Summary: Millions of hectares of winter wheat are grown in the U.S. Southern Great Plains as a dual-purpose crop. Management of dual-purpose wheat is complex due to the tradeoffs between grain and beef production. A wheat grazing computer model was developed for potential decision support. However, the model must be fully evaluated before it can be put into use. The goal of this work is to evaluate the ability of the newly developed wheat grazing model to predict fall-winter forage and grain yields of winter wheat as well as daily weight gains per steer grazing on wheat pasture at three Oklahoma State University experimental stations. Three independent field experiments were used in the evaluation. The first was a variety trial in which fall-winter forage and grain yields were harvested. The second was a planting date experiment in which forage in the fall-winter period and grain yields were measured as a function of planting dates. The third was a dual-purpose wheat and steer grazing experiment in which standing crop biomass, steer weight gain, and grain yields were monitored. Overall results show that the model, if well calibrated, has the capability to predict fall-winter forage and grain yield and daily steer weight gain well enough for use in some decision support systems. The model has the potential to be used by extension specialists to maximize economic return to the wheat and beef production systems.

Technical Abstract: Model evaluation is a prerequisite to its adoption and successful application. The objective of this paper is to evaluate the ability of a newly developed wheat grazing model to predict fall-winter forage and grain yields of winter wheat (Triticum aestivum L.) as well as daily weight gains per steer grazing on wheat pasture at three Oklahoma State University experimental stations. Three independent field experiments were used in the evaluation. The first was a variety trial in which fall-winter forage and grain yields were harvested. The second was a planting date experiment in which forage in the fall-winter period and grain yields were measured as a function of planting dates. The third was a dual-purpose wheat and steer grazing experiment in which standing crop biomass, steer weight gain, and grain yields were monitored. For the variety trials at two locations, the overall model efficiency (ME) was 0.102 for fall-winter forage prediction and 0.367 for grain yield. For the planting date experiment, the ME was 0.615 for predicting fall-winter forage yields and 0.409 for grain yields when a root downward extension rate of 20 mm/d was used instead of the default value of 30 mm/d. In the steer grazing experiment, the relationship between average daily weight gain and forage allowance (an indicator of grazing pressure) was adequately represented by the model. For the measured and simulated total steer weight gains during the entire grazing period, and for a wide range of stocking rates and grazing durations, the ME was 0.616. Overall results show that the model, if well calibrated, has the capability to predict fall-winter forage and grain yield and daily steer weight gain well enough for use in some decision support systems.

Last Modified: 10/21/2014
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