Octavio A. Ramirez

Economics of Flexible Versus Conservative Stocking Strategies to Manage Climate Variability Risk

Abstract Recommended strategies for dealing with drought include maintaining a conservative stocking rate, maintaining grazing flexibility by having yearlings as one of multiple enterprises on the ranch, and leaving a significant amount of herbaceous production at the end of the grazing season. We perform an economic analysis of these grazing strategies using a bio-economic multiperiod linear programming model with variable annual forage production and beef prices. We evaluate the economics of conservative versus flexible grazing where stock numbers are adjusted to match forage conditions. The deterministic model estimates that a flexible grazing strategy could nearly double net returns relative to a conservative strategy, but realizing this substantial economic potential means higher production costs, and it depends on a quality climate forecast that is not currently available. Maintaining grazing flexibility was determined to be very important for managing variable forage conditions, and its importance increased with the level of variability. Without annual variation in forage production, over 80% of available forage would optimally be allocated to cow–calf production. As forage variability increased to levels observed on the arid rangelands of the western United States, a 50:50 forage allocation between cow–calf and yearling enterprises was found to be optimal, but optimal cow numbers decreased over time as dry conditions force herd reductions. As compared to a cow–calf ranch only, adding flexible yearling enterprises increased average annual net ranch returns by 14% with conservative stocking and by up to 66% with flexible grazing. Herd expansion beyond a conservative level should occur with yearlings because adjusting cow numbers is too expensive relative to the potential short-term gain, even if forage conditions are known with certainty.

Estimation and use of the inverse hyperbolic sine transformation to model non-normal correlated random variables

During the past .20 years, there has been growing recognition of the consequences of the randomness of crop yields and prices for farm management and agricultural policy decisions. Concomitantly, researchers have recognized the possibility and implications of the non-normality of yields and prices. This study demonstrates a method for estimating multivariate non-normality in crop yields and prices over time, using the inverse hyperbolic sine transformation of corn, soya bean and wheat yields. The resulting estimates are used to simulate a multivariate distribution of crop yields.

A Flexible Parametric Family for the Modeling and Simulation of Yield Distributions

The distributions currently used to model and simulate crop yields are unable to accommodate a substantial subset of the theoretically feasible mean-variance-skewness-kurtosis (MVSK) hyperspace. Because these first four central moments are key determinants of shape, the available distributions might not be capable of adequately modeling all yield distributions that could be encountered in practice. This study introduces a system of distributions that can span the entire MVSK space and assesses its potential to serve as a more comprehensive parametric crop yield model, improving the breadth of distributional choices available to researchers and the likelihood of formulating proper parametric models.

Premium Estimation Inaccuracy and the Actuarial Performance of the US Crop Insurance Program

Purpose - The purpose of this paper is to explore the impact of the levels of inaccuracy associated with three different premium estimation methods, one of which attempts to mimic the protocol currently used by the Risk Management Agency (RMA), on the actuarial performance of the US crop insurance program. Design/methodology/approach - The analyses are conducted using empirically-grounded simulation and other computational methods, under various plausible assumptions about the producers risk aversion behavior and knowledge of his/her actuarially fair premium. Findings - Regardless of the assumed producer knowledge and behavior, it is concluded that the persistently high government subsidy levels required to keep the program solvent could be solely explained by the inaccuracy in the RMAs premium estimates. In other words, the observed need for large subsidies does not necessarily imply that the program is systematically favoring less efficient farmers or particular crops or production areas. Also, contrary to the commonly accepted “adverse selection” argument, it is shown that farmers having more information about their actuarially fair premiums than the insurer is not the reason why high subsidies are needed. Actuarial performance, however, could be improved by using the more elaborate methods exemplified in the paper, as well as larger sample sizes for premium estimation. Originality/value - The paper provides conclusions and recommendations that could substantially reduce the amount of public subsidies needed to keep the US crop insurance program solvent.

Estimation of Crop Yields and Insurance Premiums Using a Shrinkage Estimator

We explore the estimation of crop yields and insurance premiums using a hierarchical Bayes small area estimator. The estimator is evaluated for Area Yield Production (AYP) policy using quasi-simulated corn yields in the United States. Its performance in producing reliable mean county yield and premium estimates is compared to that of a naive estimator. We also investigate the impact of these efficiency improvements on the residual losses between a farm-level policy and AYP. The proposed estimator is found to be substantially more efficient and less biased than the naive estimator.

Insights into the appropriate level of disaggregation for efficient time series model forecasting

This paper provides a potentially valuable insight on how to assess if the forecasts from an autoregressive moving average model based on aggregated data could be substantially improved through disaggregation. It is argued that, theoretically, the absence of moving average (MA) terms indicates that no forecasting efficiency improvements can be achieved through disaggregation. In practice, it is found that there is a strong correlation between the statistical significance of the MA component in the aggregate model and the magnitude of the forecast mean square error (MSE) decreases that can be achieved through disaggregation. That is, if a model includes significant MA terms, the forecast MSE improvements that may be gained from disaggregation could be substantial. Otherwise, they are more likely to be relatively small or non-existent.