Geography of crop yield skewness

Abstract

Analysis of crop yield distributions provides insights into better understanding how natural resources shape agricultural productivity. This study seeks to provide a rigorous theoretical and empirical understanding of the effects of exogenous geographic and climatic factors on the first three moments of crop yields with focus on the third moment. We hypothesize that exogenous factors having beneficial effects on crop production should make crop yield distributions less positively or more negatively skewed. We employ a large crop insurance data set for corn, soybean, and wheat to find general support for our natural-resources-determines-skewness hypothesis. The proposed statistical method optimally uses correlations between the first three moments. It significantly improves estimation performance over existing methods, including the linear moment model which has been commonly applied in the literature.JEL classifications: Q10, Q18, Q50

Modeling skewness with the linear stochastic plateau model to determine optimal nitrogen rates

“…Experiments were conducted at experiment stations and the plots for each replication were 4.9 m × 18.2 m. The Altus experiment was irrigated, but irrigation was not applied in most years due to a restriction from the irrigation district and when irrigation was applied, it was done prior to planting (Department of Plant and Soil Sciences, ); therefore, the irrigated experiments experienced annual variations in applied water. The literature is unclear about the effect of irrigation on yield distributions (Du et al., ; Hennessy, , ). Hennessy's (, ) intuition is that irrigation might increase skewness (less negative) because it eliminates one of the causes of yield shortfalls; however, Du et al () found irrigation to have no significant impact on the skewness of wheat yield distributions.…”

“…The literature is unclear about the effect of irrigation on yield distributions (Du et al., ; Hennessy, , ). Hennessy's (, ) intuition is that irrigation might increase skewness (less negative) because it eliminates one of the causes of yield shortfalls; however, Du et al () found irrigation to have no significant impact on the skewness of wheat yield distributions. Since the level of irrigation was not held constant across years in our datasets, the effect of irrigation on skewness is ambiguous here.…”

Risk‐efficient portfolio crop choice with amended water and irrigation policies in northern Germany

“…More broadly, Hennessy (2009) argues that irrigation might eliminate the left tail of the crop yield distribution which would result in an increasing skewness. This, in turn, is partly contradicted by the findings of a recent study by Du et al (2012) who demonstrate that irrigation boosts the skewness of corn and soybean, while the opposite applies to wheat.…”

Are Actuarial Crop Insurance Rates Fair?: An Analysis Using a Penalized BivariateB-Spline Method

“…We consider the two mean functions . The beta distribution for error and the non-constant scaling factor |z i | 1=5 are included to introduce left skewness in corn yield distributions (Ker and Goodwin, 2000;Du et al, 2012Du et al, , 2015 and increasing yield variation as the historical yield increases (Tannura et al, 2008). Based on this yield model, observed premiums are generated from y i = μ.x i , z i / + ζ i , where ζ i ∼ N.0, 0:1 2 / are measurement errors, the covariates x i (coverage rates) are IID from a uniform distribution on the discrete numbers .0:55, 0:60, : : : , 0:90, 0:95/ and the true premium price is μ.x, z/ = p xz m.z/+|z| 1=5 F.w|z/ dw by equation (3).…”