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Estimating Field-Level Rotations as Dynamic Cycles

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Applied Methods for Agriculture and Natural Resource Management

Part of the book series: Natural Resource Management and Policy ((NRMP,volume 50))

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Abstract

Crop rotation systems are an important part of agricultural production for managing pests, diseases, and soil fertility. Recent interest in sustainable agriculture focuses on low input-use practices which require knowledge of the underlying dynamics of production and rotation systems. Policies to limit chemical application depending on proximity to waterways and flood management require field-level data and analysis. Additionally, many supply estimates of crop production omit the dynamic effects of crop rotations. We estimate a dynamic programming model of crop rotation which incorporates yield and cost intertemporal effects in addition to field-specific factors including salinity and soil quality. Using an Optimal Matching algorithm from the Bioinformatics literature, we determine empirically observed rotations using a geo-referenced panel dataset of 14,000 fields over 13 years. We estimate the production parameters which satisfy the Euler equations of the field-level rotation problem and solve an empirically observed four-crop rotation.

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References

  • Alston, J. M., Andersen, M. A., James, J. S., & Pardey, P. G. (2010). Persistence pays: U.S. agricultural productivity growth and the benefits from public R&D spending. New York: Springer.

    Google Scholar 

  • Antle, J., & Stoorvogel, J. J. (2006). Predicting the supply of ecosystem services from agriculture. American Journal of Agricultural Economics, 88, 1174–1180.

    Article  Google Scholar 

  • Antle, J., & Valdivia, R. O. (2006). Modeling the supply of ecosystem services from agriculture: A minimum data approach. Australian Journal of Agricultural and Resource Economics, 50, 1–15.

    Article  Google Scholar 

  • Beneke, R., & Winterboer, R. (1973). Linear programming applications to agriculture. Ames: Iowa State University Press.

    Google Scholar 

  • Bertsekas, D. P. (1976). Dynamic programming and stochastic control. Mathematics in Science and Engineering, 125, 222–293.

    Google Scholar 

  • Brzinsky-Fay, C., Kohler, U., & Luniak, M. (2006). Sequence analysis with Stata. The Stata Journal, 6, 435–460.

    Article  Google Scholar 

  • Burt, O., & Allison, J. (1963). Farm management decisions with dynamic programming. Journal of Farm Economics, 45(1), 121–136.

    Article  Google Scholar 

  • Csiszar, I. (1991). Why least squares and maximum entropy? An axiomatic approach to inference for linear inverse problems. Annals of Statistics, 19(4), 2032–2066.

    Article  Google Scholar 

  • Doole, G. J. (2008). Optimal management of annual ryegrass (Lolium rigidum Guad.) in phase rotations in the Western Australian Wheatbelt. The Australian Journal of Agricultural and Resource Economics, 52(3), 339–362.

    Google Scholar 

  • Doole, G. J. (2009). A practical algorithm for multiple-phase control systems in agricultural and natural resource economics. Journal of Agricultural and Resource Economics, 34(1), 1–21.

    Google Scholar 

  • El-Nazer, T., & McCarl, B. (1986). The choice of crop rotation: A modeling approach and case study. American Journal of Agricultural Economics, 68(1), 127–136.

    Article  Google Scholar 

  • Heady, E. O. (1948). The economics of rotations with farm and production policy applications. Journal of Farm Economics, 30(4), 645–664.

    Article  Google Scholar 

  • Hennessy, D. A. (2006). On monoculture and the structure of crop rotations. American Journal of Agricultural Economics, 88(4), 900–914.

    Article  Google Scholar 

  • Hildreth, C., & Reiter, S. (1951). On the choice of crop rotation. In T. Koopmans (Ed.), Analysis of production and allocation. New York: Wiley.

    Google Scholar 

  • Jaenicke, E. C. (2000). Testing for intermediate outputs in dynamic DEA Models: Accounting for soil capital in rotational crop production and productivity measures. Journal of Productivity Analysis, 14(3), 247–266.

    Article  Google Scholar 

  • Johnson, S. E. (1933). The theory of combination enterprises on individual farms. Journal of Farm Economics, 15(4), 656–667.

    Article  Google Scholar 

  • Kennedy, J. (1981). An alternative method for deriving optimal fertilizer rates: Comment and extension. Review of Marketing and Agricultural Economics, 49(3), 203–209.

    Google Scholar 

  • Kennedy, J. (1986). Rules for optimal fertilizer carryover: An alternative explanation. Review of Marketing and Agricultural Economics, 54(2), 3–10.

    Google Scholar 

  • Kennedy, J., Whan, I., Jackson, R., & Dillon, J. (1973). Optimal fertilizer carryover and crop recycling policies for a tropical grain crop. Australian Journal of Agricultural Economics, 17(2), 104–113.

    Article  Google Scholar 

  • Kullback, J. (1959). Information theory and statistics. New York: Wiley.

    Google Scholar 

  • Langpap, C., Hascic, I., & Wu, J. (2008). Protecting watershed ecosystems through targeted local land use policies. American Journal of Agricultural Economics, 90(3), 684–700.

    Article  Google Scholar 

  • Langpap, C., & Wu, J. (2008). Predicting the effect of land-use policies on wildlife habitat abundance. Canadian Journal of Agricultural Economics, 56(2), 195–217.

    Article  Google Scholar 

  • Levine, R. D. (1980). An information theoretical approach to inversion problems. Journal of Physics A: Mathematical General, 13, 91–108.

    Article  Google Scholar 

  • Livingston, R., Roberts, M., & Zhang, Y. (2012, Nov). Optimal sequential plantings of corn and soybeans under price uncertainty. Working Paper.

    Google Scholar 

  • Mittelhammer, R. C., Judge, G. G., & Miller, D. J. (2003). Econometric foundations. New York: Cambridge University Press.

    Google Scholar 

  • Needleman, S., & Wunsch, C. (1970). A general method applicable to the search for similarities in the amino acid sequence of two proteins. Journal of Molecular Biology, 48(3), 443–453.

    Article  Google Scholar 

  • Orazem, P., & Miranowski, J. (1994). A dynamic model of acreage allocation with general and crop-specific soil capital. American Journal of Agricultural Economics, 76(3), 385–395.

    Article  Google Scholar 

  • Peterson, G. (1955). Selection of maximum profit combinations of livestock enterprises and crop rotation. Journal of Farm Economics, 37(3), 546–554.

    Article  Google Scholar 

  • Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27(3), 379–423.

    Article  Google Scholar 

  • Skilling, J. (1989). The axioms of maximum entropy. In J. Skilling (Ed.), Maximum entropy and Bayesian methods in science and engineering (pp. 173–187). Dordrecht: Kluwer.

    Chapter  Google Scholar 

  • Swanson, E. R. (1956). Application of programming analysis to Corn Belt Farms. Journal of Farm Economics, 38(2), 408–419.

    Article  Google Scholar 

  • Tanaka, K., & Wu, J. (2004). Evaluating the effect of conservation policies on agricultural land use: A site specific modeling approach. Canadian Journal of Agricultural Economics, 52(3), 217–235.

    Article  Google Scholar 

  • Taylor, C. (1983). Certainty equivalence for determination of optimal fertilizer application rates with carry-over. Western Journal of Agricultural Economics, 8(1), 64–67.

    Google Scholar 

  • Tegene, A., Huffman, W., & Miranowski, J. (1988). Dynamic corn supply functions: A model with explicit optimization. American Journal of Agricultural Economics, 70(1), 103–111.

    Article  Google Scholar 

  • Thomas, A. (2002). A dynamic model of on-farm integrated nitrogen management. European Review of Agricultural Economics, 30(4), 439–460.

    Article  Google Scholar 

  • Thoreson, B., Clark, B., Soppe, R., Keller, A., Bastiaanssen, W., & Eckhardt, J. (2009). Comparison of evapotranspiration estimates from remote sensing (SEBAL), water balance, and crop coefficient approaches. In World Environmental and Water Resource Congress, May 17–21 2009, Kansas City, MO, USA, https://doi.org/10.1061/41036(342)437.

  • Wu, J., Adams, R. M., Kling, C. L., & Tanaka, K. (2004). From microlevel decisions to landscape changes: An assessment of agricultural conservation policies. American Journal of Agricultural Economics, 86(1), 26–41.

    Article  Google Scholar 

  • Wu, J., & Babcock, B. (1998). The choice of tillage, rotation, and soil testing practices: Economic and environmental implications. American Journal of Agricultural Economics, 80(3), 494–511.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. ERA Economics, Davis, USA

    Duncan MacEwan & Richard E. Howitt

  2. Department of Agricultural Economics, University of California at Davis, Davis, USA

    Richard E. Howitt

Authors
  1. Duncan MacEwan
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  2. Richard E. Howitt
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Correspondence to Richard E. Howitt .

Editor information

Editors and Affiliations

  1. Silver Spring, MD, USA

    Siwa Msangi

  2. ERA Economics Consulting , Davis, CA, USA

    Duncan MacEwan

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MacEwan, D., Howitt, R.E. (2019). Estimating Field-Level Rotations as Dynamic Cycles. In: Msangi, S., MacEwan, D. (eds) Applied Methods for Agriculture and Natural Resource Management. Natural Resource Management and Policy, vol 50. Springer, Cham. https://doi.org/10.1007/978-3-030-13487-7_9

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