Efficiency and productivity measurements to analyze farm-level impacts from adoption of biotechnology enhanced soybeans

Abstract

This study focuses on the productivity and on-farm efficiency impacts of adopting biotechnology enhanced soybeans (BES). Previous research suggests the adoption of BES and subsequent time savings resulted in labor allocation to off-farm employment and reduced on-farm efficiency. Using continuous panel data for 129 farms enrolled in the Kansas Farm Management Association (KFMA) with production and financial crop records from 1993 through 2011 that also provided information on their BES adoption experience, this study provides estimates on the technical efficiency, cost efficiency, and Malmquist productivity indexes (MI) with decompositions into efficiency change (EC) and technical change (TC) to provide insights on the impacts of adopting BES for set of sample farms. Using data envelopment analysis to construct nonparametric efficiency frontiers and measurements assuming constant returns-to-scale (CRS) and variable returns-to-scale (VRS) technologies for the farms, this study provides insights on the impact of yield impacts of BES adoption. A biennial Malmquist productivity index (BMI) is developed to consider estimation of the productivity impacts between BES adopters and non-adopters assuming VRS. This analysis used five input categories: Labor, general, direct inputs, maintenance, and energy; and five outputs: corn, soybeans, sorghum, wheat, and other crops. Tobit regression analysis of the panel of Kansas farms provided evidence of a positive impact from adoption of biotechnology enhanced soybeans on on-farm technical efficiency. Kolmogorov-Smirnov goodness-of-fit distributional hypothesis tests showed significant differences between analyzing the farms under CRS and VRS assumptions. T-tests showed a bias existed when assuming CRS if the true underlying technology was VRS in productivity analysis. However, there was not a strong statistically significant difference between the distributions of productivity measures from the underlying populations of BES adopters and non-adopters in the sample of Kansas farms. A revenue-indirect cost efficiency analysis of the sample farms demonstrated that different conclusions were reached under CRS and VRS when considering the differences in the average of the means of estimated efficiency scores and Tobit regression results considering BES adoption. Assuming CRS resulted in positive marginal effects for adopting BES of 0.017 significant at the 5% level. The marginal effect of BES adoption was not statistically significant under VRS.