Disaggregating Model Bias and Variability when Calculating Economic Optimum Rates of Nitrogen Fertilization for Corn

Kyveryga, Peter; Blackmer, A. M.; Morris, Thomas F.

doi:10.2134/agronj2006.0339

Cited by 36 publications

(41 citation statements)

References 25 publications

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“…7 and 10). Several past studies showed how the choice of functional forms or response models affects the uncertainty in EONR values for individual trials (Cerrato and Blackmer, 1990;Bullock and Bullock, 1994;Kyveryga et al, 2007).…”

Section: A Framework For Improving Nitrogenmentioning

confidence: 99%

“…Metadata analysis is the basis for the evidence that is then used to guide decisions. Kyveryga et al (2007) fitted with the seven yield response functions, namely, linear plateau (LP), quadratic (QD), quadratic plateau (QDP), square root quadratic (SRQ), spherical plateau (SPP), exponential (EX), and exponential plateau (EXP). Also shown are the computed economically optimal nitrogen rates (EONR) for each response function and their 68% confidence bands relative to the abscissa.…”

Section: A Framework For Improving Nitrogenmentioning

confidence: 99%

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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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Section: A Framework For Improving Nitrogenmentioning

confidence: 99%

Section: A Framework For Improving Nitrogenmentioning

confidence: 99%

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

et al. 2018

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“…Data from the trials are fitted to several statistical models to describe relationships between N rates and yields, and solving the models to identify the economic optimum rates (EORs) (Kyveryga et al, 2007). The economic optimum rate is arrived at the point where the marginal cost (MC) of the fertilizer is equal to the marginal revenue (MR) per unit fertilizer (MC=MR) (Kiprono et al, 2010;Kyveryga et al, 2007). The EORs can differ with the growing conditions, locations and the clones and hence, it is important to consider the factors in the local environment that may influence production.…”

Section: Introductionmentioning

confidence: 99%

Economic Evaluation of Foliar NPK Fertilizer on Tea Yields in Kenya

Njogu¹,

Kariuki²,

Kamau³

et al. 2015

JPS

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Economic evaluation analysis of foliar (NPK) fertilizer on tea yields was investigated on three varieties of tea grown in the Kenyan Highlands. Foliar fertilizers trial was setup on three sites, Meru, Kirinyaga and Kericho, comprising of 36 plots per site. Two foliar fertilizer types, Foliar Fertilizer 1 (FF1) and Foliar Fertilizer 2 (FF2), a positive control of Soil Fertilizer (SF) and a blank (FF0) were used to standardize the method. Yield analysis in Meru site showed significant increase (HSD=4.9, p≤0.05) after FF1 full rate application. SF had significant yield increases for all its rates from zero rate (HSD= 49, 44, 19, p≤0.05) for half, full and double rates respectively. FF2 had no yield increase after its application. The economic rates were determined to be full rate for FF1 and SF and half rate for FF2 in Kericho site; half rate for all fertilizer types Kirinyaga site; double rate for FF2 and SF and full rate for FF1 in Meru site. The profitable rates were: double rate for SF across all sites and for FF1 and FF2 in Meru site; full rate for FF1 and half rate for FF2 in Kericho and Kirinyaga sites respectively.

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“…It is noteworthy that the economic benefi ts calculated in Table 4 were not aff ected by the shape of N yield response curves, which are oft en estimated for a wide range of N rates applied in small-plot experiments (Kyveryga et al, 2007). It has been proven that discontinuous N yield response curves (with yield plateaus) will likely show lower potential economic benefi ts from VRN applications than those without yield plateaus (Brennan et al, 2007).…”

Section: Economic Benefi Ts and Fertilizer Savingmentioning

confidence: 90%

“…An ideal approach would be to apply a wide range of N fertilizer rates over a large area of soils, estimate site-specifi c N yield response functions, and to calculate EONRs for many small areas within fi elds (Bullock and Lowenberg-DeBoer, 2007;Hurley et al, 2004;Schmidt et al, 2007). One practical challenge is that farmers are generally uncomfortable with yield losses from applying extremely low N rates or for residual fertilizer eff ects from applications of both extremely low and high N rates (Kyveryga et al, 2007). Second, conducting spatial analysis and testing for statistically signifi cant interactions between the eff ect of N and site-specifi c soil characteristics on corn yield have not yet become a mainstream procedure for practicing agronomists.…”

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confidence: 99%

Categorical Analysis of Spatial Variability in Economic Yield Response of Corn to Nitrogen Fertilization

2011

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Despite growing interests in variable‐rate nitrogen (VRN) fertilizer applications, we still lack basic knowledge and practical methodology for identifying major factors that can be used to guide VRN application to corn (Zea mays L.). The objective was to develop a methodology for identifying a predictable relationship between economic yield response (YR) to N and commonly measured soil and terrain attributes in the presence of spatial dependence. Six 30‐ha no‐till fields in central Iowa were studied during 6 yr. Urea‐ammonium nitrate solution was sidedressed at 112 and 140 kg N ha−1 in alternating strips replicated from 10 to 22 times. Yield responses to the high rate were calculated in a 20 by 25 m grid pattern and classified into profitable and nonprofitable categories within each field. Autologistic models were used to identify which (if any) of the following factors economically affected YR: elevation, apparent soil electrical conductivity (ECa), slope, topographic wetness index (TWI), or digital soil map units. Significant effects of some of the factors were found within 8 of 15 site‐years. Within five of these site‐years, well‐drained areas with lower ECa and TWI, and higher elevation and slope had the higher probability of profitable YR, but these effects were not stable over time. Within the proposed methodology, a high spatial resolution of YR is used that increases the ability to identify areas profitable to N, and farmers can explore VRN possibilities by applying a small fertilizer increment below or above a uniform optimal rate in many alternating strips across fields.

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Disaggregating Model Bias and Variability when Calculating Economic Optimum Rates of Nitrogen Fertilization for Corn

Cited by 36 publications

References 25 publications

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

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

Economic Evaluation of Foliar NPK Fertilizer on Tea Yields in Kenya

Categorical Analysis of Spatial Variability in Economic Yield Response of Corn to Nitrogen Fertilization

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