Author
Archer, David | |
Kludze, Hillarius |
Submitted to: Meeting Proceedings
Publication Type: Proceedings Publication Acceptance Date: 6/1/2006 Publication Date: 6/12/2006 Citation: Archer, D.W., Kludze, H.K. 2006. Transition to organic cropping systems under risk. Proceedings of the American Agricultural Economics Association Annual Meeting. p. 1-24. Interpretive Summary: Farmers are looking for ways to maintain economic viability of their farming operations. One option that is gaining interest is organic farming. However, the three-year certification waiting period, along with changes in equipment needs and the learning process for managing these systems can represent barriers to adoption. We analyzed the risks, returns and best adoption strategies for a representative Minnesota farm switching from conventional to organic cropping systems. Results show that the best strategy is to transition to organic systems as rapidly as possible even with significant learning curves and machinery adjustment costs. This research provides farmers with information needed in making the decision to switch to organic farming and should lead to greater adoption of organic farming systems. Technical Abstract: We analyzed the risks, returns and optimal adoption strategies for a representative Minnesota farm switching from conventional to organic cropping systems. The EPIC simulation model was calibrated based on the yields observed in a farming systems field study. A farm-level simulation model was constructed using the EPIC simulated crop yields and historical prices. Results were compared for an expected utility maximizing farm under a range of risk aversion levels, with and without management learning curves and biological transition effects. A dynamic programming model was then constructed to evaluate the joint effects of machinery replacement decisions, learning curves, and biological transition effects on optimal adoption strategies. Results show that producers will find it optimal to transition to organic systems as rapidly as possible, even with significant learning curves and machinery adjustment costs. |