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Sample Size Requirements for Assessing Statistical Moments of Simulated Crop Yield Distributions

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Author Info

  • Niklaus Lehmann

    ()
    (Institute for Environmental Decisions, Agrifood & Agri-environmental Economics Group, ETH Zurich, Sonneggstrasse 33/SOL C7, Zurich CH-8092, Switzerland)

  • Robert Finger

    ()
    (Agricultural Economics and Rural Policy Group, Wageningen University, Hollandseweg 1, Room 2114, NL-6706 KN Wageningen, The Netherlands)

  • Tommy Klein

    ()
    (Air Pollution/Climate Group, Agroscope Research Station ART, Reckenholzstrasse 191, Zurich CH-8046, Switzerland)

  • Pierluigi Calanca

    ()
    (Air Pollution/Climate Group, Agroscope Research Station ART, Reckenholzstrasse 191, Zurich CH-8046, Switzerland)

Abstract

Mechanistic crop growth models are becoming increasingly important in agricultural research and are extensively used in climate change impact assessments. In such studies, statistics of crop yields are usually evaluated without the explicit consideration of sample size requirements. The purpose of this paper was to identify minimum sample sizes for the estimation of average, standard deviation and skewness of maize and winterwheat yields based on simulations carried out under a range of climate and soil conditions. Our results indicate that 15 years of simulated crop yields are sufficient to estimate average crop yields with a relative error of less than 10% at 95% confidence. Regarding standard deviation and skewness, sample size requirements depend on the degree of symmetry of the underlying population’s distribution. For symmetric distributions, samples of 200 and 1500 yield observations are needed to estimate the crop yields’ standard deviation and skewness coefficient, respectively. Higher degrees of asymmetry increase the sample size requirements relative to the estimation of the standard deviation, while at the same time the sample size requirements relative to the skewness coefficient are decreased.

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Bibliographic Info

Article provided by MDPI, Open Access Journal in its journal Agriculture.

Volume (Year): 3 (2013)
Issue (Month): 2 (April)
Pages: 210-220

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Handle: RePEc:gam:jagris:v:3:y:2013:i:2:p:210-220:d:24727

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Related research

Keywords: crop yield distributions; statistical moments; sample sizes requirements; crop models; stochastic weather generator; climate change;

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References

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  1. Finger, Robert & Lazzarotto, Patrick & Calanca, Pierluigi, 2010. "Bio-economic assessment of climate change impacts on managed grassland production," Agricultural Systems, Elsevier, vol. 103(9), pages 666-674, November.
  2. anonymous, 2000. "The brave new world of agriculture," EconSouth, Federal Reserve Bank of Atlanta, issue Q3, pages 8-13.
  3. Ben Groom & Phoebe Koundouri & Celine Nauges & Alban Thomas, 2008. "The story of the moment: risk averse cypriot farmers respond to drought management," Applied Economics, Taylor & Francis Journals, vol. 40(3), pages 315-326.
  4. Ardian Harri & Cumhur Erdem & Keith H. Coble & Thomas O. Knight, 2009. "Crop Yield Distributions: A Reconciliation of Previous Research and Statistical Tests for Normality," Review of Agricultural Economics, Agricultural and Applied Economics Association, vol. 31(1), pages 163-182.
  5. Liu, Junguo & Williams, Jimmy R. & Zehnder, Alexander J.B. & Yang, Hong, 2007. "GEPIC - modelling wheat yield and crop water productivity with high resolution on a global scale," Agricultural Systems, Elsevier, vol. 94(2), pages 478-493, May.
  6. Lehmann, Niklaus & Finger, Robert & Klein, Tommy & Calanca, Pierluigi & Walter, Achim, 2013. "Adapting crop management practices to climate change: Modeling optimal solutions at the field scale," Agricultural Systems, Elsevier, vol. 117(C), pages 55-65.
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