Under what management and policy scenarios can alley cropping be a competitive alternative in the United States Southeast?

Abstract

Despite statements that it can mitigate financial risk through farm diversification, alley cropping in the United States Southeast has not been comprehensively modeled to estimate potential financial returns and risks. We used a Monte Carlo method to model stochastic financial returns to monocropping agriculture, loblolly pine plantation, and loblolly pine alley cropping in North Carolina, USA, plotting the results from 25,000 iterations to understand financial risk. Under certain scenarios and assumptions, alley cropping did have financial returns comparable to, or potentially higher than monocropping agriculture, but did not lower overall risk as measured by the spread of the distribution of financial returns. Pine plantations on the other hand did have lower risk than both alley cropping and monocropping. Alley cropping with wider 24.4-m alleys performed better than narrow 12.2-m alleys. Allowing the producer to choose a timber rotation length that optimizes financial returns generated the best financial returns for alley cropping, but this assumes perfect knowledge of the manager and is unrealistic. Current policy programs of government payments for commodity crops and cost-share for tree planting, tend to favor monocropping over alley cropping or pine plantation. A hypothetical system of payments for carbon sequestration does increase pine plantation and alley cropping financial returns, but not to the extent that commodity crop programs currently increase monocropping financial returns, and does not reduce risk significantly. Overall, on average agricultural land in North Carolina, alley cropping may be of value to certain producers, but we find those possibilities to be somewhat limited.

  • Citation: Frey, Gregory E.; Cary, Michael A. 2020. Under what management and policy scenarios can alley cropping be a competitive alternative in the United States Southeast?. Agroforestry Systems. 94(6): 2167-2183. https://doi.org/10.1007/s10457-020-00538-x.
  • Keywords: Agroforestry Forestry Monte Carlo Stochastic modelling Risk Policy
  • Posted Date: December 7, 2020
  • Modified Date: December 8, 2020
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