Optimal Stocking Density for Dual-Purpose Winter Wheat Production

Kaitibie, Simeon; Epplin, Francis M.; Brorsen, B. Wade; Horn, G. W.; Krenzer, Eugene G.; Paisley, Steven I.

doi:10.1017/s1074070800005915

Cited by 19 publications

(26 citation statements)

References 17 publications

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“…To determine if there is an economic difference between the two models, the expected profit maximizing stocking densities were determined and are presented in Table 3. For the LRSP model, the expected profit maximizing levels were determined as in Kaitibie et al (2003a). For the SR model, the necessary two‐dimensional integral was derived, and the optimum level was determined by grid search.…”

Section: Data and Applicationmentioning

confidence: 99%

“…Livestock grazing practices generally follow patterns that can be explained by the von Liebig hypothesis, characterized by nonsubstitution between inputs and a gain/yield plateau (Hart et al 1988; Kaitibie et al 2003a). The von Liebig hypothesis suggests that output is directly related to the amount of the most limiting production input.…”

Section: Introductionmentioning

confidence: 99%

“…Thus a response function defines the relationship up to the point where the addition of one more unit of variable nonlimiting input yields zero marginal product. In livestock grazing, several studies (Redmon et al 1995; Pinchak et al 1996; Kaitibie et al 2003a) have shown a linear response plateau function between average daily gain ( ADG ) and forage allowance ( FA ). In addition, Kaitibie et al (2003a) showed that the plateau could be stochastic.…”

Section: Introductionmentioning

confidence: 99%

“…In livestock grazing, several studies (Redmon et al 1995; Pinchak et al 1996; Kaitibie et al 2003a) have shown a linear response plateau function between average daily gain ( ADG ) and forage allowance ( FA ). In addition, Kaitibie et al (2003a) showed that the plateau could be stochastic. Kaitibie et al (2003a) considered only the linear response stochastic plateau (LRSP) function of Tembo et al (2003), and did not consider the alternative switching regression (SR) specification of Berck and Helfand (1990), and Paris (1992).…”

Section: Introductionmentioning

confidence: 99%

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A Cox Parametric Bootstrap Test of the von Liebig Hypotheses

Kaitibie

Nganje

Brorsen

et al. 2007

Canadian J Agri Economics

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"This study uses a Cox parametric bootstrap test to select between two specifications of the von Liebig hypothesis, a switching regression (SR) model, and a linear response function with a stochastic plateau. Specifying the production function as a linear response function with a stochastic plateau yields a superior approximation of the data for livestock gain as a function of forage allowance than the SR approach." Copyright 2007 Canadian Agricultural Economics Society.

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Section: Data and Applicationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Cox Parametric Bootstrap Test of the von Liebig Hypotheses

Kaitibie

Nganje

Brorsen

et al. 2007

Canadian J Agri Economics

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“…The question is then does stocker grazing earn enough added revenue to offset the reduction in wheat grain returns? Kaitibie et al (2003) reported wheat stocker weight gains of 0.99 kg d –1 and an optimal stocking density of 1.48 steers ha –1 , generating 1.47 kg d –1 ha –1 of weight gain. Stockers can be expected to graze for approximately 100 days, yielding a total stocker gain of 147 kg ha –1 .…”

Section: Economic Considerations For Seed Treatments With and Withoutmentioning

confidence: 99%

Insecticide and Fungicide Wheat Seed Treatment Improves Wheat Grain Yields in the U.S. Southern Plains

et al. 2014

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Hard red winter wheat (Triticum aestivum L.) in the U.S. Southern Plains is subject to yield losses due to a wide range of pests. Foliar‐applied pesticides are commonly used to control damaging insect populations, but rescue application of foliar pesticides generally occurs at or above threshold infestation levels, commonly leading to yield losses. Further, excessive precipitation, high winds, or producer labor constraints can delay foliar application beyond economically‐optimal thresholds. Insecticide seed treatment offers a method to potentially reduce wheat yield losses due to pest infestations and avoid timing and efficacy issues sometimes associated with foliar applications. Many wheat seed treatments also include a fungicide that protects wheat yield by enhancing seedling emergence, increasing early‐season vigor, and preventing or reducing fall establishment of fungal diseases such as leaf rust. Our purposes here were to investigate the impact of insecticide and fungicide seed treatment on grain yield, grain volume weight, and economic return of hard red winter wheat in Oklahoma. A three‐year study (2008, 2011, and 2012) was conducted at three to five western Oklahoma locations per year. In addition to seed treatment, stocker cattle were used to graze at some locations during winter months, and our analyses also considered the impact of winter grazing on yield, test weight, and economic return.

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Crop yield response functions in nutrient application: A review

Dhakal

Lange

2021

Agronomy Journal

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The question of the optimal nutrient application in agricultural production continues to gather attention because of its relevance in increasing the profitability of crop production and protecting the environment. Economically optimal nutrient application decision rules are commonly derived by fitting crop yield functions to nutrients. The choice of the appropriate yield functional form thus is critical for estimating unbiased and more precise nutrient recommendations. This review explores recent developments in yield response functions to the nutrient application. We first introduce the concept of the economic theory of production and yield response functions. We then explain, with mathematical equations, some commonly used functional forms: quadratic response, Mitscherlich-Baule, deterministic, and stochastic plateau functions. We also summarize the strengths and weaknesses of each functional form and its implications for nutrient management strategies from an economic perspective. In recent studies, researchers have suggested that stochastic plateau functional forms, both linear and quadratic, could explain yield response to the nutrient application in a better way. Finally, we recommend fitting stochastic plateau functions in conjunction with carryover information in a dynamic optimization setting may yield more accurate optimal nutrient levels. Future research should continue to investigate whether these functional forms perform better in different crop and soil environments.

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Optimal Stocking Density for Dual-Purpose Winter Wheat Production

Cited by 19 publications

References 17 publications

A Cox Parametric Bootstrap Test of the von Liebig Hypotheses

A Cox Parametric Bootstrap Test of the von Liebig Hypotheses

Insecticide and Fungicide Wheat Seed Treatment Improves Wheat Grain Yields in the U.S. Southern Plains

Crop yield response functions in nutrient application: A review

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