Alan P. Ker

Increasing Crop Diversity Mitigates Weather Variations and Improves Yield Stability

Cropping sequence diversification provides a systems approach to reduce yield variations and improve resilience to multiple environmental stresses. Yield advantages of more diverse crop rotations and their synergistic effects with reduced tillage are well documented, but few studies have quantified the impact of these management practices on yields and their stability when soil moisture is limiting or in excess. Using yield and weather data obtained from a 31-year long term rotation and tillage trial in Ontario, we tested whether crop rotation diversity is associated with greater yield stability when abnormal weather conditions occur. We used parametric and non-parametric approaches to quantify the impact of rotation diversity (monocrop, 2-crops, 3-crops without or with one or two legume cover crops) and tillage (conventional or reduced tillage) on yield probabilities and the benefits of crop diversity under different soil moisture and temperature scenarios. Although the magnitude of rotation benefits varied with crops, weather patterns and tillage, yield stability significantly increased when corn and soybean were integrated into more diverse rotations. Introducing small grains into short corn-soybean rotation was enough to provide substantial benefits on long-term soybean yields and their stability while the effects on corn were mostly associated with the temporal niche provided by small grains for underseeded red clover or alfalfa. Crop diversification strategies increased the probability of harnessing favorable growing conditions while decreasing the risk of crop failure. In hot and dry years, diversification of corn-soybean rotations and reduced tillage increased yield by 7% and 22% for corn and soybean respectively. Given the additional advantages associated with cropping system diversification, such a strategy provides a more comprehensive approach to lowering yield variability and improving the resilience of cropping systems to multiple environmental stresses. This could help to sustain future yield levels in challenging production environments.

On Technological Change in Crop Yields

Technological change in plant research rarely shifts the entire yield distribution upwards as assumed in the agricultural economics literature. Rather, technologies have been targeted at a specific subpopulation of the yield distribution--for example, drought resistant seeds or so-called racehorse seeds--therefore, it is unlikely technological advancements are equal across subpopulations. In this manuscript we introduce a mixture model of crop yields with an embedded trend function in the component means, which allows different rates of technological change in each mixture or subpopulation. By doing so, we can test some interesting hypotheses that have been previously untestable. While previous literature assumes an equivalent rate of technological change across subpopulations we reject the null in 84.0%, 82.3%, and 64.0% of the counties for corn, soybean, and wheat respectively. Conversely, with respect to stable subpopulations through time (i.e. climate change) we reject in only 12.0%, 5.4%, and 4.6% of the counties for corn, soybean, and wheat respectively. These results have implications for modelling yields, directing funds regarding plant science research, and explaining the prevalence of heteroscedasticity in yield data.

Bayesian Estimation of Possibly Similar Yield Densities: Implications for Rating Crop Insurance Contracts

The Agricultural Act of 2014 solidified insurance as the cornerstone of U.S. agricultural policy. The Congressional Budget Office (2014) estimates that this act will increase spending on agricultural insurance programs by

Transfer Efficiency Analysis of Margin‐Based Programs

5.7 billion to a total of

Nonparametric estimation of possibly similar densities

89.8 billion over the next decade. In light of the sizable resources directed toward these programs, accurate rating of insurance contracts is of the utmost importance to producers, private insurance companies, and the federal government. Unlike most forms of insurance, agricultural insurance is plagued by a paucity of spatially correlated data. A novel interpretation of Bayesian Model Averaging is used to estimate a set of possibly similar densities that offers greater efficiency if the set of densities are similar while seemingly not losing any if the set of densities are dissimilar. Simulations indicate that finite sample performance—in particular small sample performance—is quite promising. The proposed approach does not require knowledge of the form or extent of any possible similarities, is relatively easy to implement, admits correlated data, and can be used with either parametric or nonparametric estimators. We use the proposed approach to estimate U.S. crop insurance premium rates for area-type programs and develop a test to evaluate its efficacy. An out-of-sample game between private insurance companies and the federal government highlights the policy implications for a variety of crop-state combinations. Consistent with the simulation results, the performance of the proposed approach with respect to rating area-type insurance—in particular small sample performance—remains quite promising.