A Fertilizer Production Surface with Specification of Economic Optima for Corn Grown on Calcareous IDA Silt Loam

Heady, Earl O.; Pesek, John

doi:10.2307/1233014

Cited by 41 publications

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“…Two Additional Analyses Heady and Pesek [9] pointed out that some farmers may not use the optimum because of capital limitations. In such cases, the recommended rate would be a point on the expansion path but below the optimal rates.…”

Section: Results Of the Analysismentioning

confidence: 99%

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A Comparison of Annual Versus Average Optima for Fertilizer Experiments

Doll

1972

American J Agri Economics

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Section: Results Of the Analysismentioning

confidence: 99%

“…Pesek [9] published their pioneering article on corn-fertilizer response functions. At that time, they stated that although there was a " ... long-standing knowledge of the need for improved knowledge of the cropfertilizer marginal coefficients, relatively little progress has been made" [9, p. 466].…”

Section: S E V E N T E E N Years Have Passed Since Heady Andmentioning

confidence: 99%

A Comparison of Annual Versus Average Optima for Fertilizer Experiments

Doll

1972

American J Agri Economics

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“…Previous work on crop response to nitrogen fertilizer has usually used either limiting nutrient response functions or polynomial models. Plateau functional forms tend to best fit data from field studies (Heady and Pesek 1954, Lanzer and Paris 1981, Grimm, Paris, and Williams 1987. Past studies have often assumed that the parameters of the yield function are nonstochastic or "limited" stochastic (some parameters are considered stochastic and others are not), and that all model errors are independent.…”

Section: Introductionmentioning

confidence: 86%

Determining Optimal Levels of Nitrogen Fertilizer Using Random Parameter Models

Tumusiime¹,

Brorsen

Mosali³

et al. 2011

J. Agric. Appl. Econ.

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The parameters of yield response functions can vary by year. Past studies usually assume yield functions are nonstochastic or "limited" stochastic. In this study, we estimate rye-ryegrass yield functions where all parameters are random. Optimal nitrogen recommendations are calculated for two yield response functions: linear response plateau and Spillman-Mitscherlich. Nonstochastic models are rejected in favor of stochastic parameter models. However, the economic benefits of using fully stochastic models are small since optimal nitrogen rates do not differ greatly between stochastic and nonstochastic models. Previous work on crop response to nitrogen fertilizer has usually used either limiting nutrient response functions or polynomial models. Plateau functional forms tend to best fit data from field studies (Heady and Pesek 1954, Lanzer and Paris 1981, Grimm, Paris, and Williams 1987. Past studies have often assumed that the parameters of the yield function are nonstochastic or "limited" stochastic (some parameters are considered stochastic and others are not), and that all model errors are independent. This assumption often leads to estimating the parameter values of the assumed yield function by ordinary least squares. Research suggests, however, that parameters of yield response functions can vary by year.3 Random parameter models have been suggested by Berck and Helfand (1990), Paris (1992), Wallach (2002), andTembo et al (2008). Berck and Helfand (1990), and Paris (1992) consider linear response plateau models where the intercept and plateau parameters are random, but without random effects. Tembo et al (2008) adds uncorrelated random effects to the intercept and plateau, but not to the slope. Of these studies, only Makowski and Wallach (2002) treat all of the model parameters as random. Makowski and Wallach (2002) consider a linear-plus-plateau function in which wheat yield response is related to N uptake, and nitrogen uptake is related to applied nitrogen.We consider three crop response functions: the linear response plateau (LRP), the Spillman-Mitscherlich, and the quadratic; and we make all model parameters random. Our random parameter model lets parameters vary stochastically by year. The data used are annual rye-ryegrass forage data collected from a long-term nitrogen fertilization experiment in southcentral Oklahoma. We conduct nested likelihood ratio tests to choose between nonstochastic and stochastic models (Greene, 2008), and evaluate the economic value of using the alternative models by comparing expected profit. The ultimate goal is to make optimal nitrogen rate recommendations for cool season cereal rye (S.cereale)-ryegrass (Lolium multiflorum Lam)forage producers in southern Oklahoma. Determining the Profit Maximizing Level of Nitrogen FertilizerConsider a risk-neutral forage producer whose objective is to maximize expected net returns from winter cereal rye-ryegrass forage. The producer seeks to maximize expected net return above nitrogen cost:(1)where is the producer"s net return a...

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“…The MRTN name reflects the fact that this system calculates the maximum (economic) return to nitrogen fertilizer (RTN). While fertilizer rate recommendations based on response trials and economic return is not a new concept (Johnson, 1953;Heady and Pesek, 1954), emphasis on maximum return and adjustment for varying N and corn prices disappeared as implementation of the yield-goal system relating N fertilization requirement to attainable yield became popular. Also, adjustment for N/corn grain price ratio can improve rate recommendations (Kim et al, 2013).…”

Section: Maximum Return To Nitrogen Recommendation Systemmentioning

confidence: 99%

Strengths and Limitations of Nitrogen Rate Recommendations for Corn and Opportunities for Improvement

et al. 2018

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A gronomy J our n al • Volume 110 , I ssue 1 • 2 018 1 T he goal of an N recommendation system is to accurately estimate the gap between the N provided by the soil and the N required by the plant. Accurately estimating this gap depends on the ability of the recommendation system to accurately estimate fi eld or subfi eld specifi c economically optimal nitrogen rates (EONR). Current recommendation systems are not as accurate as needed to provide consistently reliable estimates of N needs across years at the fi eld or subfi eld scale. Uncontrollable factors like temperature, rainfall timing, intensity and amount, and interactions of temperature and rainfall with factors such as N source, timing and placement, plant genetics, and soil characteristics combine to make N rate recommendations for an individual fi eld or rates for subfi elds a process guided as much by science as by the best professional judgement of farmers and farm advisors.Substantial evidence has accumulated that EONRs can vary widely across fi elds, within fi elds and over years in the same fi eld for a wide range of crops and geographies. Examples ABSTRACTNitrogen fi xation by the Haber-Bosch process has more than doubled the amount of fi xed N on Earth, signifi cantly infl uencing the global N cycle. Much of this fi xed N is made into N fertilizer that is used to produce nearly half of the world's food. Too much of the N fertilizer pollutes air and water when it is lost from agroecosystems through volatilization, denitrifi cation, leaching, and runoff . Most of the N fertilizer used in the United States is applied to corn (Zea mays L.), and the profi tability and environmental footprint of corn production is directly tied to N fertilizer applications. Accurately predicting the amount of N needed by corn, however, has proven to be challenging because of the eff ects of rainfall, temperature, and interactions with soil properties on the N cycle. For this reason, improving N recommendations is critical for profi table corn production and for reducing N losses to the environment. Th e objectives of this paper were to review current methods for estimating N needs of corn by: (i) reviewing fundamental background information about how N recommendations are created; (ii) evaluating the performance, strengths, and limitations of systems and tools used for making N fertilizer recommendations; (iii) discussing how adaptive management principles and methods can improve recommendations; and (iv) providing a framework for improving N fertilizer rate recommendations.

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A Fertilizer Production Surface with Specification of Economic Optima for Corn Grown on Calcareous IDA Silt Loam

Cited by 41 publications

References 0 publications

A Comparison of Annual Versus Average Optima for Fertilizer Experiments

A Comparison of Annual Versus Average Optima for Fertilizer Experiments

Determining Optimal Levels of Nitrogen Fertilizer Using Random Parameter Models

Strengths and Limitations of Nitrogen Rate Recommendations for Corn and Opportunities for Improvement

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