Are Crop Yields Normally Distributed? A Reexamination

Atwood, Joseph A.; Shaik, Saleem; Watts, Myles J.

doi:10.1111/1467-8276.00495

Cited by 83 publications

(38 citation statements)

References 7 publications

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“…Here we applied Monte Carlo methods to derive probability distribution functions of yield risk levels. The approach consists of generating a synthetic series of yield variables using the Monte Carlo method and Latin hypercube sampling (Just & Weninger 1999, Atwood et al 2003 Monte Carlo methods are an important component of uncertainty and probabilistic risk assessments because they allow for the generation of random samples of statistical distributions (Robert & Casella 2004). Monte Carlo methods simulate the behaviour of a system in a nondeterministic manner (stochastic) by using random numbers as opposed to deterministic algorithms.…”

Section: Risk Levelmentioning

confidence: 99%

Climate change and agricultural adaptation: assessing management uncertainty for four crop types in Spain

Iglesias¹,

Quiroga²,

Schlickenrieder³

2010

Clim. Res.

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Climate change inevitably leads to large regional variations in risks and opportunities and is likely to affect most farmers in the Mediterranean in the next decades. The interpretation of climate projections to determine appropriate policy responses is not without difficulties, such as understanding local uncertainty and responses of specific crops to sets of conditions. Here we analyse the potential impacts of climate on agriculture in the Mediterranean -a region that exemplifies other regions of the world that are prone to drought and are likely to experience increased frequency and intensity of droughts in the future. Our analysis relies on understanding the sources of uncertainty derived from climate scenarios, agricultural systems, impact responses and risk levels to support informed decisions for planned agricultural adaptation. We generated multiple projections of impacts based on different models of climate change and crop response in order to capture uncertainties. We used statistical models of yield response and projections of climate change generated from 16 climate scenarios to address the likelihood of projected impacts on traditional Mediterranean farming systems, represented in this study by cereals, grapes, olives and citrus. Results show that uncertainty varies widely by crop and location, and adaptation priorities will therefore depend on the risk focus of adaptation plans.

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Section: Risk Levelmentioning

confidence: 99%

Climate change and agricultural adaptation: assessing management uncertainty for four crop types in Spain

Iglesias¹,

Quiroga²,

Schlickenrieder³

2010

Clim. Res.

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show abstract

“…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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“…Out of the 38 distribution functions fitted, 12 were Weibull, 11 Triangular, 4 Gamma, 7 BetaGeneral, 2 Logistic and lastly, 2 Logistic distributions. Such distributions have been proposed in earlier literature for crop yield modelling in agricultural economics (Atwood et al, 2003;Gallagher, 1987;Nelson and Preckel, 1989;Sherrick et al, 2004;Tolhurst and Ker, 2015). Results of these density fits are available from the authors upon request.…”

Section: Asymmetric Informationmentioning

confidence: 99%

Agricultural insurance policies in Spain and the EU: an analysis of existent and new risk management tools focusing on indirect risk assessment, and asymmetric information

Vera¹

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Are Crop Yields Normally Distributed? A Reexamination

Cited by 83 publications

References 7 publications

Climate change and agricultural adaptation: assessing management uncertainty for four crop types in Spain

Climate change and agricultural adaptation: assessing management uncertainty for four crop types in Spain

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

Agricultural insurance policies in Spain and the EU: an analysis of existent and new risk management tools focusing on indirect risk assessment, and asymmetric information

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