Climatic Change

, Volume 68, Issue 1–2, pp 113–133

Robustness of Recommended Farm Plans in England under Climate Change: A Monte Carlo Simulation

Article

Abstract

A methodology is described for estimating robustness of recommended farm plans under climate change while maintaining a meaningful representation of the underlying farm system. Monte Carlo Simulation (MCS) of crop yield data is used in conjunction with a fully specified farm-level model and output from a field worktime model. Estimates of farm net margin, enterprise mix (choice and area of enterprises), labour, machinery, storage and animal housing under mean crop yields and field worktimes for current (2000s) and 2050s conditions are generated. MCS is used to estimate the effect of crop yield variation on farm profitability and enterprise mix for the same periods by running the farm-level model with no constraints and running it constrained to the mean data plan. Estimates of robustness, measured as the percentage difference and the probability of exceeding the mean farm net-margin, were calculated from the outputs from these runs. For three representative farm types, mean farm net margin increased; however changes in robustness as shown by percentage difference in farm net margin depended on farm type while the probability of exceeding the mean plan net-margin decreased by 2050 indicating an increase in robustness. The most robust farm type had a diversified mix of enterprises and required no additional fixed resources by the 2050s. The least robust farm type was in a marginal location and mean plan recommendations for the 2050s required additional investment in fixed resources, particularly irrigation. It is concluded that the information provided by the methodology would be particularly useful to farmers: where mean data plans are not robust, MCS results could be used with financial planning techniques to minimise the impact of variability, rather than using high cost inputs to reduce variability per se.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ackrill, R., Ramsden, S. J., and Gibbons, J. M.: 2001, ‘CAP Reform and the Rebalancing of Arable Support for Cereals and Oilseeds: A Farm Level Analysis’, Euro. Rev. Agri. Econ.28: 207–226.Google Scholar
  2. Alderman, G. and Cottrill, B. R.: 1993, Energy and Protein Requirements of Ruminants. An Advisory Manual Prepared by the AFRC Technical Committee on Responses to Nutrients. CAB International, Wallingford, UK.Google Scholar
  3. Bowman A. W., and Azzalini, A.: 1997 Applied Smoothing Techniques for Data Analysis. The Kernel Approach with S-Plus Illustrations, Oxford University Press, Oxford.Google Scholar
  4. Bryant, C. R., Smit, B., Brklachich, M., Johnston, T. R., Smithers, J., Chiotti, Q., and Singh, B.: 2000, ‘Adaptation in Canadian Agriculture to Climatic Variability and Change’, Clim. Change45, 181–201.Google Scholar
  5. Chadwick, L.: 1997, Farm Management Handbook, Scottish Agricultural College, Edinburgh.Google Scholar
  6. Dash: 1997, XPRESS-MP User Guide and Reference Manual, Dash Associates Limited, Blisworth House, Blisworth, Northants, UK.Google Scholar
  7. Dillon, C. R., Shearer, S. A., and Mueller T.: 2001, ‘A Mixed Integer, Nonlinear Programming Model of Innovative Variable Rate Planting Date with Polymer Seed Coatings’. Paper presented at the American Agricultural Economics Association annual meeting, 5–8 August 2001, Chicago, Illinois.Google Scholar
  8. Dowle, K. and Armstrong, A. C.: 1990, ‘A Model for Investment Appraisal of Grassland Drainage Schemes on Farms in the U.K.’, Agri. Water Manag.18, 101–120.Google Scholar
  9. Easterling, W. E., McKenney, M. S., Rosenberg, N. J., and Lemon, K. M.: 1992a, ‘Simulations of Crop Response to Climate Change: Effects with Present Technology and No Adjustments (the ‘dumb farmer’ scenario)’, Agri. Forest Meteor.59, 53–73.Google Scholar
  10. Easterling, W. E., Rosenberg, N. J., Lemon, K. M., and McKenney, M. S.: 1992b,‘Simulations of Crop Responses to Climate Change: Effects with Present Technology and Currently Available Adjustments (the ‘smart farmer’ scenario)’, Agri. Forest Meteor.59, 75–102.Google Scholar
  11. Easterling, W. E., Crosson, P. R., Rosenberg, N. J., McKenney, M. S., Katz, L. A., and Lemon, K. M.: 1993, ‘Agricultural Impacts of and Responses to Climatic Change in the Missouri-Iowa-Nebraska-Kansas (MINK) region’, Clim. Change24, 23–61.Google Scholar
  12. Hossell, J., Ramsden, S. J., Gibbons, J. M., Harris, D., Matthews, A., Clarke, J., and Pooley, J.: 2001, ‘The Timescale of Potential Farm Level Responses and Adaptations to Climate Change in England and Wales’, DEFRA Report CC0333, DEFRA, London.Google Scholar
  13. Hulme, M. and Jenkins, G.: 1998, ‘Climate Change Scenarios for the United Kingdom’, Scientific Report, Technical Report No. 1, UK Climate Impacts Programme.Google Scholar
  14. Ihaka, R. and Gentleman, R.: 1996, R: ‘A Language for Data Analysis and Graphics’, J. Comput. Graph. Stat.5, 299–314.Google Scholar
  15. Kaiser, H. M., Riha, S. J., Wilks, D. S., Rossiter, D. G., and Sampath, R.: 1993, ‘A Farm-Level Analysis of Economic and Agronomic Impacts of Gradual Climate Warming’, Amer. J. Agr. Econ.75, 387–398.Google Scholar
  16. Kimball, B. A., Mauney, J. R., Nakayama, F. S., and Idso, S. B. N.: 1983, ‘Effects of Increasing Atmospheric CO2 on Vegetation’, Vegetation104/105, 65–75.Google Scholar
  17. Mendelsohn, R., Nordhaus, W. D., and Shaw, D.: 1994, ‘The Impact of Global Warming on Agriculture: A Ricardian Analysis’, Amer. Econ. Rev.84, 753–771.Google Scholar
  18. Nix, J.: 1999, Farm Management Pocketbook, Wye College, University of London, thirtieth edition (2000).Google Scholar
  19. Pannell, D. J., Malcolm, B., and Kingwell, R. S.: 2000, ‘Are We Risking Too Much? Perspectives on Risk in Farm Modelling’, Agri. Econ.23, 69–78.Google Scholar
  20. Ramsden, S. J., Gibbons, J. M., and Wilson, P.: 1999, ‘Impacts of changing Relative Prices on Farm Level Dairy Production in the United Kingdom’, Agri. Systems62, 201–215.Google Scholar
  21. Ramsden, S. J., Wilson, P., and Gibbons, J.: 2000, ‘Adapting to Agenda 2000 on Combinable Crop Farms’, Farm Manag.10, 606–618.Google Scholar
  22. Reilly, J. and Schimmelpfennig, D.: 2000, ‘Irreversibility, Uncertainty, and Learning: Portraits of Adaptation to Long-Term Climate Change’, Clim. Change45, 253–278.Google Scholar
  23. Schimmelpfennig, D.: 1996, ‘Uncertainty in Economic Models of Climate-Change Impacts’, Clim. Change33, 213–234.Google Scholar
  24. Schneider, S. H., Easterling, W. E., and Mearns, L. O.: 2000, ‘Adaptation: Sensitivity to Natural Variability, Agent Assumptions and Dynamic Climate Changes’, Clim. Change45, 203–221.Google Scholar
  25. Silverman, B. W.: 1986, ‘Density Estimation for Statistics and Data Analysis’, Chapman and Hall, London.Google Scholar
  26. Weatherhead, E. K., Knox, J. W., Morris, J., Hess, T. M., Bradley, R. I., and Sanders, C. L.: 1997, Irrigation Demand and on-Farm Water Conservation in England and Wales. MAFF Project Number OC9219, Cranfield University, Silsoe, UK.Google Scholar
  27. Williams, J. R., Dyke, P. T., Fuchs, W. W., Benson, V. W., Rice, O. W., and Taylor, E. D.: 1990, EPIC-Erosion/productivity Impact Calculator. 2: User Manual, U.S. Department of Agriculture, Technical Bulletin No. 1768, Washington, D.C., U.S.A.Google Scholar
  28. Zeyuan, Q., Prato, T., and Kaylen, M.: 1998, ‘Watershed-Scale Economic and Environmental Tradeoffs Incorporating Risks: A Target MOTAD Approach’, Agri. Res. Econ. Rev.27, 231.Google Scholar

Copyright information

© Kluwer Academic Publishers 2005

Authors and Affiliations

  1. 1.Division of Agricultural SciencesUniversity of NottinghamLoughboroughEngland

Personalised recommendations