International Journal of Agricultural Sustainability (Mar 2017)

A systems approach to forecast agricultural land transformation and soil environmental risk from economic, policy, and cultural scenarios in the north central United States (2012–2062)

  • B. L. Turner,
  • M. Wuellner,
  • T. Nichols,
  • R. Gates,
  • L. O. Tedeschi,
  • B. H. Dunn

DOI
https://doi.org/10.1080/14735903.2017.1288029
Journal volume & issue
Vol. 15, no. 2
pp. 102 – 123

Abstract

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Grassland conversion to row-crop production in the north central United States has been a growing threat to socio-economic and environmental sustainability for producers, conservationists, and policy-makers alike. We used a system dynamics model of the region to forecast agriculturally driven land transformation through mid-twenty-first century. The base-case scenario projection showed that farmland area continued to increase, from under 200,000 km2 to over 230,000 km2. Unmitigated, the soil environmental risk (SER) of such changes reached conservative estimates of Dust Bowl-era externalities. Systems analyses show that reducing livestock production costs, doubling conservation compliance requirements, and livestock–cropping integration had the largest impact on grassland conservation and mitigating SER. The largest SER effects came from eliminating conservation incentives or raising cultivation incentives, despite improvements in reduced tillage and enhanced agronomy. Several system archetypes were identified within the policy scenarios: ‘fixes that backfire’ and ‘success-to-the-successful’. For scenarios creating favourable impacts, time delays caused some behaviours to worsen before positive gains were realized. If implemented, patience and persistence to ensure that these scenarios reach their full potential will be necessary. Our scenarios provide quantitative forecasts around measures for sustainable intensification. These projections can aid regional stakeholders in enhancing discussions currently taking place about sustainable agriculture in the region.

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