A Wheat Grazing Model for Simulating Grain and Beef Production: Part II—Model Validation

Zhang, X.‐C.; Hunt, L. A.; Phillips, William A.; Horn, G. W.; Edward, J. B.; Zhang, H.

doi:10.2134/agronj2007.0373

Cited by 8 publications

(6 citation statements)

References 14 publications

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“…The five crop coefficients were previously calibrated for the cultivar Jagger using wheat data measured at the USDA‐ARS Grazinglands Research Laboratory in central Oklahoma during 2004 to 2006 (Zhang et al, 2008a). The calibrated crop coefficients of P1V, P1D, P5, G1, G2, G3, and PHINT for Jagger were 40, 63, 450, 17, 25, 1.5, and 90, respectively [P1V: days at optimum vernalizing temperature required to complete vernalization; P1D: percentage reduction in development rate in a photoperiod 10 h shorter than the optimum relative to that at the optimum; P5: grain‐filling phase duration (°C× d); G1: kernel number per gram canopy weight at anthesis; G2: standard kernel size under optimum conditions (mg kernel −1 ); G3: standard, nonstressed dry weight of a single tiller at maturity (g tiller −1 ); PHINT: interval between successive leaf tip appearance (°C× d)].…”

Section: Methodsmentioning

confidence: 99%

“…The wheat grazing model simulated wheat and cattle growth interactively and dynamically (in a daily time step), and thus was capable of simulating the tradeoffs between livestock and grain production. Zhang et al (2008a) evaluated the wheat grazing model at three locations in central Oklahoma, and reported that when the model was well calibrated, it was capable of predicting aboveground biomass, grain yield, and cattle live weight gain (LWG) for various climate and soil conditions. Using the grazing model, Mauget et al (2009) evaluated the value of El Nino‐Southern Oscillation forecast information for managing dual‐purpose wheat in the southern Great Plains by varying planting dates, N rates, and stocking density.…”

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Variable Environment and Market Affect Optimal Nitrogen Management in Wheat and Cattle Production Systems

et al. 2012

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Fertilizer nitrogen use effi ciency (NUE) is about 33% worldwide. Th e application of crop models has the potential to improve NUE by allowing managers to optimize and synchronize N supply with crop demand. We used a wheat (Triticum aestivum L.) grazing model to predict optimal N rates that maximize either grain yield for a grain-only system or net economic return for a dual-purpose system for selected climate, soil water reserve, and market conditions. Simulated probabilistic distributions of optimal fertilizer N rates for grain-only wheat revealed a 55% chance that optimal N was <40 kg ha -1 when precipitation from August to February was <30 cm, and a 90% chance that optimal N was between 90 and 120 kg ha -1 when precipitation during the same period was >40 cm. Th is result supports a split N application with <45 kg ha -1 applied pre-plant and additional N top-dressed in February according to precipitation and yield potential. Dual-purpose winter wheat with an initially wet soil profi le required about 20, 60, 80, and 90 kg ha -1 of pre-plant N to maximize cattle (Bos taurus) live weight gain for stocking densities of 1, 2, 2.5, and 3 head ha -1 , and about 120 kg N ha -1 to maximize total net economic returns. In contrast, total optimal N for dual-purpose wheat with an initially dry soil ranged from 0 to 90 kg ha -1 , depending on climate. Precipitation between August and February was a good predictor of N requirement. Wheat grazing model was a useful tool for optimizing N management in north-central Oklahoma.

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Section: Methodsmentioning

confidence: 99%

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Variable Environment and Market Affect Optimal Nitrogen Management in Wheat and Cattle Production Systems

et al. 2012

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“…Many researchers throughout the world tested and evaluated the ability of the CERES-Wheat model to simulate top dry biomass and grain yield, and reported satisfactory results (e.g., IAEA-TEC-DOC, 2000; Bannayan et al, 2003;Panda et al, 2003;Rinaldi, 2004). Zhang et al (2008a) evaluated the wheat grazing model at three locations in central Oklahoma, and reported that the model, if well calibrated, has the potential to predict top biomass and grain yields under various climate and soil conditions.…”

Section: Wheat Grazing Modelmentioning

confidence: 99%

“…Th e fi ve crop coeffi cients were previously calibrated for the variety Jagger using wheat data measured in central Oklahoma during (Zhang et al, 2008a. Jagger was the dominant cultivar widely grown in Oklahoma in the past several years (http://www.nass.usda.gov/).…”

Section: Crop Coeffi Cientmentioning

confidence: 99%

Optimizing Stocking Rate for Maximum Return to a Wheat–Cattle Enterprise Using Model Simulation and Economics

Zhang

2010

Agronomy Journal

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Managing dual-purpose wheat is complex because of the tradeoff relationship between cattle (Bos taurus) and wheat (Triticum aestivum L.) production. Stocking rate (SR) and planting date are key decision variables of the dual systems. Th e objective was to develop decision support information that help farmers boost profi ts by adapting SR and planting date to particular production and market conditions. A wheat grazing model was used to simulate the systems for seven SR (0-3 head ha-1), fi ve planting dates, three climate scenarios, and two initial soil moisture profi les. Net returns were estimated for three representative markets. Net returns under optimized management doubled those under the business-as-usual for the comparable planting dates, indicating great potentials to boost net returns with better management. Optimal SR depends on markets and forage availability. For market that overwhelmingly favors grain production, grain-only wheat should be used; whereas dual-purpose wheat is preferred under other market conditions. For wet soil at planting, maximum returns are attained in early September planting at high SR of > 2 head ha-1 despite climates; however, the high optimal SR should be adjusted down with delayed planting in wet and average but maintained in dry climate for water conservation. For dry profi le at planting, optimal management is dual-purpose wheat with SR of 3 in wet, grainonly wheat in average, and fallow in dry climate. Had wheat been established in severely dry years, moderate grazing at 0.5 to 1.5 SR could reduce net losses. Under most circumstances, grazing should not be allowed to pass fi rst hollow stem to maximize returns.

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“…4.02) for simulating production of wheat grain and stocker cattle grazing on wheat during late fall and early spring for the U.S. Southern Great Plains. Evaluation and validation of the new model are critical to its acceptance and adoption by potential users and are presented in a companion paper in this issue (Zhang et al, 2008).…”

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A Wheat Grazing Model for Simulating Grain and Beef Production: Part I—Model Development

et al. 2008

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It is a common practice to grow winter wheat (Triticum aestivum L.) as a dual-purpose crop in the U.S. Southern Great Plains to decrease production risk and to increase profit margin through cattle (Bos taurus) production. Crop management of the dual-purpose wheat is complex because of the tradeoffs between beef production and wheat grain yield. A wheat grazing model helps in making optimal decision. The objective of this study was to develop and incorporate a grazing and metabolizable energy-based cattle growth module into the Decision Support Systems for Agrotechnology Transfer (DSSAT) to simulate beef and wheat grain production. The wheat grazing model was comprised of wheat growth, wheat-cattle interaction, and cattle growth components. Wheat growth was simulated by the cropping system model (CSM) of DSSAT.For the wheat-cattle interface, removals of canopy biomass and leaf area by grazing were estimated daily. Predicted grain yield was also reduced by 50 kg ha -1 per day for each day of grazing past the first hollow stem stage. Cattle growth rate was based on a metabolizable energy intake. Maximum voluntary daily intake was estimated based on stocker body weight and forage quality, and is further adjusted for actual forage availability, temperature, and adaptation status during the first two week of grazing to estimate the actual daily intake. Changes in wheat growth processes brought about by grazing, including a grazing effect on the delay of plant phenological development, are not simulated in the model. Field experiments to characterize any such effects are needed to help fine-tune the model.

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A Wheat Grazing Model for Simulating Grain and Beef Production: Part II—Model Validation

Cited by 8 publications

References 14 publications

Variable Environment and Market Affect Optimal Nitrogen Management in Wheat and Cattle Production Systems

Variable Environment and Market Affect Optimal Nitrogen Management in Wheat and Cattle Production Systems

Optimizing Stocking Rate for Maximum Return to a Wheat–Cattle Enterprise Using Model Simulation and Economics

A Wheat Grazing Model for Simulating Grain and Beef Production: Part I—Model Development

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