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

Niklaus Lehmann (), Robert Finger, Tommy Klein and Pierluigi Calanca
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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

Agriculture, 2013, vol. 3, issue 2, 1-11

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.

Keywords: crop yield distributions; statistical moments; sample sizes requirements; crop models; stochastic weather generator; climate change (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2013
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