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

Abstract

Accurate modeling of skewness is needed to increase the actuarial fairness of crop insurance. We test Day's conjecture that crop yield skewness becomes negative as nitrogen rates increase and determine how well a linear response stochastic plateau (LRSP) production function matches the pattern of observed skewness using four long-term nitrogen experiments. Stillwater wheat is consistent with Day's conjecture, but the skewness for Lahoma and Altus wheat yields as well as Altus cotton yields are not. The LRSP assumes normal random effects and can explain only a small part of observed skewness, so a new LRSP with skew-normal random effects is introduced, which comes closer to explaining the observed skewness and should increase the accuracy of nitrogen rate recommendations. Negative skewness reduced optimal nitrogen rates and positive skewness increased optimal nitrogen rates.JEL classifications: C49, D24, Q19

Bayesian Kinked Regression with Unobserved Thresholds: An Application to the von Liebig Hypothesis

“…Although we consider continuous levels of the inputs whereas most experiments will only apply inputs at discrete levels, the simulations are intended to mimic a real experimental situation. In this case, we approximately mirror the data found in Boyer, Brorsen, and Tumusiime (2015). We first consider a single input model with nitrogen (N).…”

“…We repeat this simulation exercise adding a second input, phosphorus (Ph). We are again informed by the Boyer, Brorsen, and Tumusiime (2015), and allow Ph to range uniformly from zero to 100 lbs/acre, with a stipulated threshold of 60 lbs/acre. The true regime coefficients are set to 0.75 for the slope and computed as 1755 for the intercept.…”

Temporal Frequency of Soil Test Information Effects on Returns to Potassium Fertilization in Cotton Production

“…This study used an LRSP functional form assuming the expected plateau yield is normally distributed, but nonnormality in the expected plateau yield could increase or decrease the optimal K rates (Boyer, Brorsen, and Tumusiime, 2015). Nonnormality in the expected plateau yield may give producers an incentive to update information more frequently to sustain higher total K levels.…”

“…Therefore, cotton lint yields were tested with a deterministic quadratic time response function (Just and Weninger, 1999). Similar to cotton yields in Oklahoma (Boyer, Brorsen, and Tumusiime, 2015), a time trend was not present.…”

Subjective beliefs and decision making under uncertainty in the field

“…While Thompson (1986, 1988 trend. Many studies have assumed a quadratic time trend (Schlenker and Roberts, 2009;Boyer et al, 2015). Just and Weninger (1999) emphasized that misspecification of trend could bias the results and advocated use of polynomial trend to choose the best fitting trend polynomial.…”