Crop‐Yield Distributions Revisited

Ramírez, Octavio A.; Misra, Sukant K.; Field, James E.

doi:10.1111/1467-8276.00106

Cited by 110 publications

(75 citation statements)

References 11 publications

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“…The results of this study may depend to some degree on how the technology trend is modeled; it is, therefore, worth considering other options. Because explicit modeling of the underlying technological processes would require a knowledge of crop variety and management, two practical options for modeling the yield technology trend remain-(i) fitting a given trend parameterization (e.g., polynomial) using least squares (Just and Weninger 1999), and (ii) assuming the form of the probability density function (PDF) of detrended yield, and of the form of the technology trend, with subsequent determination of the trend using maximum likelihood (Moss and Shonkwiler 1993;Ramirez et al 2003). Because the form of the PDF of yield cannot be determined a priori (e.g., Atwood et al 2003), we use the first of these methods to determine alternative technology trends; linear ( y ϭ a ϩ bt), quadratic ( y ϭ a ϩ bt ϩ ct 2 ), and cubic ( y ϭ a ϩ bt ϩ ct 2 ϩ dt 3 ) trends were fitted to either the whole time series or piecewise to each half of the time series (section 3b).…”

Section: B Weather and Yield Datamentioning

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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Section: B Weather and Yield Datamentioning

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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“…In the second step the crop yields are detrended. For this purpose a variety of regression models such as linear (Goodwin and Mahul, 2004;Adhikari et al, 2012), quadratic (Lu et al, 2008;Adhikari et al, 2012), and polynomials (Ramirez et al, 2003) have been used in the literature. In addition, Deng et al (2008) and Vedenov and Barnett (2004) applied log-linear model.…”

Section: Yield Estimation Approachesmentioning

confidence: 99%

Designing a whole-farm revenue insurance for agricultural crops in Zanjan province of Iran

Ghahremanzadeh

Mohammadrezaei

Dashti

et al. 2018

EARN

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ABSTRACT:The purpose of this article is to design and empirically evaluate the Whole Farm Insurance (WFI) over the conventional insurance programs in Zanjan province of Iran. Historical farm-level and county-level data were used to estimate yield and price density functions. Both parametric and nonparametric methods were applied for predicting the future values and the PQH simulation method was utilized to calculate premium rates. Results revealed that loss ratios of the WFI are lower for farmers who insured more than one crop. Additionally, utilizing WFI reduces premiums. Moreover, premiums obtained from nonparametric method are relatively lower compared to the parametric approach.KEYWORDS: Indemnity, Iran, price risk, whole-farm insurance, yield risk, Zanjan.Diseño de un seguro de ingresos de toda la granja para cultivos agrícolas en la provincia de Zanjan de Irán RESUMEN: El propósito de este artículo es diseñar y evaluar empíricamente el Seguro Agrario Integral (SAI) con respecto a los programas de seguros convencionales en la provincia de Zanjan de Irán. Se usaron datos históricos a nivel de explotación y de comarca para estimar las funciones de rendimiento y de densidad de precios. Se aplicaron métodos paramétricos y no paramétricos para predecir los valores futuros y se utilizó el método de simulación SAI para calcular las tasas de primas. Los resultados revelaron que los índices de pérdida del SAI son más bajos para los agricultores que aseguraron más de un cultivo. Además, la utilización del SAI reduce las primas. Las primas obtenidas del método no paramétrico son relativamente más bajas en comparación con el enfoque paramétrico.PALABRAS CLAVE: Indemnización, Irán, riesgo de precio, seguro de granja completa, riesgo de rendimiento, Zanjan.JEL classification/Clasificación JEL: G22, C15, C53, C63.

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“…As revealed by extensive studies of historical and more recent data on yields (Day 1965;Gallagher 1986;Ramirez et al 2003;Sherrick et al 2004), the form of the distribution depends on the crop, and may depend on the average yield level and many local or regional conditions (mainly climatic). Biological and technical progress has allowed mean values and standard deviations for yields to increase considerably over the last two centuries.…”

Section: Types Of Statistical Distributionmentioning

confidence: 99%

“…This problem has been discussed widely (Gallagher 1986;Just and Weninger 1999;Atwood et al 2003;Ramirez et al 2003), with many cases of skewed distributions (both negative and positive) being described.…”

Section: Types Of Statistical Distributionmentioning

confidence: 99%

A probability distribution for crop yields in Poland

Górski¹

2009

Geogr. Pol.

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An important source of risk in agricultural production is the variability to crop yields refl ecting irregularly changing weather. This variability may be described as a stochastic process that has a function of density. Analyses of historical data on crop yields reveal that the function of density changed from right-skewed to left-skewed, along with increasing mean yields. All examined yields of crops cultivated recently in Poland demonstrate the left skew, which does not diminish with the aggregation of acreage. A fairly good approximation of the probability distribution for actual yields may be obtained using the log-normal distribution with an inverted abscissa.

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Crop‐Yield Distributions Revisited

Cited by 110 publications

References 11 publications

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

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

Designing a whole-farm revenue insurance for agricultural crops in Zanjan province of Iran

A probability distribution for crop yields in Poland

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