Are Crop Yields Normally Distributed?

Just, Richard E.; Weninger, Quinn

doi:10.2307/1244582

Cited by 213 publications

(121 citation statements)

References 2 publications

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“…Overall, the both price and yield distributions have different characteristics from the normal distributions as indicated by the literature (e.g. Just and Weninger, 1999). The application of normal distribution to the price and yield calibration may not be appropriate simulation results.…”

Section: Crop Price and Yield Distributionsmentioning

confidence: 92%

Farm Level Analysis of Risk and Risk Management Strategies and Policies

Kimura

Thi

2011

OECD Food, Agriculture and Fisheries Papers

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Section: Crop Price and Yield Distributionsmentioning

confidence: 92%

Farm Level Analysis of Risk and Risk Management Strategies and Policies

Kimura

Thi

2011

OECD Food, Agriculture and Fisheries Papers

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“…Model parameterizations are simplifications of crop processes that inevitably result in some error. Any yield data will also have associated error, and the necessary separation of the time series into underlying technology trends (defined as a monotonic increase in yield over time due to nonclimatic factors, such as improved yield varieties and an increased use of fertilizer) and interannual variability also adds uncertainty (e.g., Just and Weninger 1999;Yu et al 2001). Input management data, such as planting date, plant population density, and crop variety, have associated uncertainties that will impact the ability of the model to reproduce reality (i.e., model skill).…”

Section: Introductionmentioning

confidence: 99%

Simulation of Crop Yields Using ERA-40: Limits to Skill and Nonstationarity in Weather–Yield Relationships

Challinor

Wheeler

Slingo

et al. 2005

Journal of Applied Meteorology

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Reanalysis data provide an excellent test bed for impacts prediction systems, because they represent an upper limit on the skill of climate models. Indian groundnut (Arachis hypogaea L.) yields have been simulated using the General Large-Area Model (GLAM) for annual crops and the European Centre for Medium-Range Weather Forecasts (ECMWF) 40-yr reanalysis (ERA-40). The ability of ERA-40 to represent the Indian summer monsoon has been examined. The ability of GLAM, when driven with daily ERA-40 data, to model both observed yields and observed relationships between subseasonal weather and yield has been assessed. Mean yields were simulated well across much of India. Correlations between observed and modeled yields, where these are significant, are comparable to correlations between observed yields and ERA-40 rainfall. Uncertainties due to the input planting window, crop duration, and weather data have been examined. A reduction in the root-mean-square error of simulated yields was achieved by applying bias correction techniques to the precipitation. The stability of the relationship between weather and yield over time has been examined. Weather-yield correlations vary on decadal time scales, and this has direct implications for the accuracy of yield simulations. Analysis of the skewness of both detrended yields and precipitation suggest that nonclimatic factors are partly responsible for this nonstationarity. Evidence from other studies, including data on cereal and pulse yields, indicates that this result is not particular to groundnut yield. The detection and modeling of nonstationary weather-yield relationships emerges from this study as an important part of the process of understanding and predicting the impacts of climate variability and change on crop yields.

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“…Previous research has shown that crop yield data are often times skewed and do not follow a normal distribution [29][30][31]. The D'Agostino-Pearson K 2 test was used to see if the data were normally distributed [1,32].…”

Section: Discussionmentioning

confidence: 99%

Economic Evaluation of Switchgrass Feedstock Production Systems Tested in Potassium-Deficient Soils

et al. 2013

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Limited information is available about the economic benefits and costs associated with managing switchgrass (Panicum virgatum L.) produced for bioenergy feedstock in the K-deficient soils common in the southern Great Plains. The objectives of this study were to determine the most economical production system for harvesting and managing N and K fertilizations on switchgrass stands and to determine how sensitive the results are to various feedstock and fertilizer market price scenarios. A 4-year agronomic field experiment was conducted on a K-deficient site in South Central Oklahoma; the treatments included two harvest systems (summer and winter (SW) and winter only (W)), two N rates (0 and 135 kg ha −1 ), and two K rates (0 and 67 kg ha −1 ). Enterprise budgeting techniques and mixed ANOVA models were used to determine and compare the effects of eight harvest/N/K systems on yield, total cost, revenue, and net return. The harvest/N/K systems evaluated included SW/0/0, SW/0/67, SW/135/0, SW/135/67, W/0/0, W/0/67, W/135/0, and W/ 135/67. Results revealed the SW/135/67 system produced significantly (P >0.0001) greater average yield compared to the other systems; however, the SW/0/0 system was the most (P >0.0001) economical, realizing an average net return of $415 ha −1 . Compared to the base-case net return of the SW/0/ 0 system, the value of the additional yield generated with the SW/135/67 system was less than the costs associated with the extra nutrients and additional harvest activity. For feedstock prices greater than $110 Mg −1 , the most economical system shifted from the SW/0/0 to favor the SW/135/67 system.

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Are Crop Yields Normally Distributed?

Cited by 213 publications

References 2 publications

Farm Level Analysis of Risk and Risk Management Strategies and Policies

Farm Level Analysis of Risk and Risk Management Strategies and Policies

Simulation of Crop Yields Using ERA-40: Limits to Skill and Nonstationarity in Weather–Yield Relationships

Economic Evaluation of Switchgrass Feedstock Production Systems Tested in Potassium-Deficient Soils

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