Skip to main content
SpringerLink
Log in

A review of the practice and achievements from 50 years of applying OR to agricultural systems in Britain

  • Original Article
  • Published:
OR Insight

Abstract

This paper will survey how things have changed over nearly 50 years of operational research (OR) applied to agriculture. The first ‘OR group’ was set up at the National Institute of Agricultural Engineering by Dan Boyce in 1969 and is now at Cranfield University. It will examine how, and what, factors have influenced the type of work and the methods used. What applications have stood the test of time and what are just distant memories in paper publications? It will show that agricultural OR has moved on from its early beginnings in agriculture in applying OR techniques with simple analyses, to using and creating complex computer models. While it might be described as alive, it clearly needs to identify itself and its specific contribution to analysing decisions, to set it apart from the ‘anyone can simulate and optimize using a computer’. The skill of holistic systems modelling of combinations of processes at the decision-maker level is as important as the ability to use techniques.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Bradbury, N.J., Whitmore, A.P., Hart, P.B.S. and Jenkinson, D.S. (1993) Modelling the fate of nitrogen in crop, soil in the years following application of N-15-labeled fertiliser to winter-wheat. The Journal of Agriculture Science (Cambridge) 121: 363–379.

    Article  Google Scholar 

  • Garforth, C. et al (2006) Research to Understand, Model the Behaviour, Motivations of Farmers in Responding to Policy Change, EPES 0405-17. London: Department For Environment Food, Rural Affairs.

    Google Scholar 

  • Nelder, J.A. and Mead, R. (1965) A simplex method for function minimization. The Computer Journal 7: 308–313.

    Article  Google Scholar 

Structured review references

    Network analysis

    • Boyce, D.S., Parke, D. and Corrie, W.J. (1971) The identification of optimum production systems by network analysis, dynamic programming. Journal of Agricultural Engineering Research 16 (2): 141–145.

      Article  Google Scholar 

    • Fluck, R.C. and Splinter, W.E. (1966) Optimization of Field Harvesting, Handling Systems by Unit-flow, Shortest Path Techniques. Chicago, IL: American Society of Agricultural Engineers.

      Google Scholar 

    Queuing theory

    • Audsley, E. and Boyce, D.S. (1973) Exact solutions for cyclic transport systems. Journal of Agricultural Engineering Research 18 (3): 217–230.

      Google Scholar 

    • Cooper, K. and Parsons, D.J. (1999) An economic analysis of automatic milking using a simulation model. Journal of Agricultural Engineering Research 73: 311–321.

      Article  Google Scholar 

    • Hansen, A.C., Barnes, A.J. and Lyne, P.W.L. (2002) Simulation modelling of Sugarcane Harvest-to-Mill Delivery Systems. Transactions of the ASAE 45 (3): 531–538.

      Article  Google Scholar 

    • Shukla, L.N., Chisholm, T.S. and Phillips, A.L. (1971) Computer Program for Analysing Harvesting, Loading, Transportation of Sugar Cane. St Joseph, MI: American Society of Agricultural Engineers. ASAE Pap.71-a604.

    Dynamic programming

    • Benjamin, L.R., Milne, A.E., Lutman, P.J.W., Parsons, D.J., Cussans, J. and Storkey, J. (2009) Modelling weed management over a rotation in the UK using stochastic dynamic programming to create a decision tool. Weed Research, in press.

    • Corrie, W.J. and Boyce, D.S. (1972) A dynamic programming method to optimize policies for the multistage harvest of crops with an extended maturity period. Journal of Agricultural Engineering Research 17 (4): 348–354.

      Article  Google Scholar 

    • Fisher, B.S. and Lee, R.R. (1981) A dynamic programming approach to the economic control of weed, disease infestations in wheat. Review of Marketing and Agricultural Economics 49 (3): 175–187.

      Google Scholar 

    • Kennedy, J.O.S. and Stott, A.W. (1993) An adaptive decision making aid for dairy cow replacement. Agricultural Systems 42 (1–2): 25–39.

      Article  Google Scholar 

    • Low, E.M. and Brookhouse, J.K. (1967) Dynamic programming, the selection of replacement policies in commercial egg production. Journal of Agricultural Economics 18 (3): 339–350.

      Article  Google Scholar 

    • Mourits, M.C.M., Huirne, R.B.M., Dijkhuizen, A.A., Kristensen, A.R. and Galligan, D.T. (1999) Economic optimization of dairy heifer management decisions. Agricultural Systems 61 (1): 17–31.

      Article  Google Scholar 

    • Sells, J.E. (1995) Optimizing weed management using stochastic dynamic programming to take account of uncertain herbicide performance. Agricultural Systems 48: 271–296.

      Article  Google Scholar 

    • Yates, C.M. and Rehman, T.U. (1998) A linear programming formulation of the Markovian decision process approach to modelling the dairy replacement problem. Agricultural Systems 58 (2): 185–201.

      Article  Google Scholar 

    Linear programming

      Livestock feed

      • Chappell, A.E. (1971) Linear programming cuts costs in the production of animal feeds. Operational Research Quarterly 25 (1): 19–26.

        Article  Google Scholar 

      On-farm application

      • Barnard, C.S. and Smith, V.E. (1959) Resource allocation on an east Anglian dairy farm4. Cambridge, UK: Farm Economics Branch, Cambridge University. Occasional Papers No. 6.

      • Butterworth, K. (1985) Practical application of integer programming to farm planning. Journal of the Operational Research Society 36 (2): 99–107.

        Article  Google Scholar 

      • Stewart, J.D. (1961) A Study in the Application of Linear Programming to an Oxfordshire Farm, Miscellaneous Studies No 21 Reading, UK: Department of Agricultural Economics, University of Reading.

        Google Scholar 

      Crop planning

      • Cevaal, P.K. and Oving, R.K. (1979) The Use of Mixed Integer Linear Programming as a Planning Method in Arable Farming. Wageningen, the Netherlands: AGV, Lelystad; IMAG.

        Google Scholar 

      Livestock planning

      • Conway, A.G. and Killen, L. (1987) A linear programming model of grassland management. Agricultural Systems 25 (1): 51–71.

        Article  Google Scholar 

      • Dodd, V.A., Lyons, D.F. and Herlihy, P.D. (1975) System of optimizing the use of animal manures on a grassland farm. Journal of Agricultural Engineering Research 20 (4): 391–403.

        Article  Google Scholar 

      • Morrison, D.A., Kingwell, R.S. and Pannel, D.J. (1986) A mathematical programming model of a crop-livestock farm system. Agricultural Systems 20 (4): 243–268.

        Article  Google Scholar 

      Horticulture planning

      • Annevelink, E. (1992) Operational planning in horticulture: Optimal space allocation in pot-plant nurseries using heuristic techniques. Journal of Agricultural Engineerinng Research 51: 167–177.

        Article  Google Scholar 

      • Audsley, E. (1985) Estimating the effect of different irrigators, transplanters on the profit of an horticultural enterprise. Journal of Agricultural Engineering Research 31 (3): 203–221.

        Article  Google Scholar 

      • Gertych, Z., Miernik, J. and Wojno, J. (1978) Linear programming for the determination of the optimum production structure illustrated by an example from a state nursery. Biuletyn Warzawnikzy 21.

      • Hamer, P.J.C. (1994) A decision support system for the provision of planting plans for Brussels sprouts. Computer and Electronics in Agriculture 11: 97–115.

        Article  Google Scholar 

      • Simpson, I.G., Hales, A.W. and Fletcher, A. (1963) Linear programming under uncertain prices in horticulture. Journal of Agricultural Economics 15 (4): 617–624.

        Article  Google Scholar 

      • Webster, J.P.G. and Nicholson, J.A.H. (1969) An example of linear programming in horticulture. Span 12 (3): 159–161.

        Google Scholar 

      Machinery selection

      • Audsley, E. (1974) A linear programming model of a high temperature grass drying enterprise. Journal of the British Grassland Society 29: 371–388.

        Article  Google Scholar 

      • Audsley, E. (1981) An arable farm model to evaluate the commercial viability of new machines or techniques. Journal of Agricultural Engineering Research 26 (2): 135–143.

        Article  Google Scholar 

      • Audsley, E., Dumont, S. and Boyce, D.S. (1978) An economic comparison of methods of cultivating, planting cereals, sugar beet, potatoes, their interaction with harvesting, timeliness, available labour by linear programming. Journal of Agricultural Engineering Research 23: 282–300.

        Google Scholar 

      • Chamen, W.C.T. and Audsley, E. (1993) A study of the comparative economics of conventional, zero traffic systems for arable crops. Soil and Tillage Research 25: 369–390.

        Article  Google Scholar 

      • Jannot, Ph. and Cairol, D. (1994) Linear programming as an aid to decision-making for investments in farm equipment for arable farms. Journal of Agricultural Engineering Research 59: 173–179.

        Article  Google Scholar 

      • Kline, D.E., Bender, D.A., McCarl, B.A. and van Donge, C.E. (1988) Machinery selection using expert systems, linear programming. Computer and Electronics in Agriculture 3: 45–61.

        Article  Google Scholar 

      Land use planning

      • Audsley, E. et al (2006) What can scenario modelling tell us about future European scale land use, what not? Environmental Science & Policy 9: 148–162.

        Article  Google Scholar 

      • Donaldson, A.B., Flichman, G. and Webster, J.P.G. (1995) Integrating agronomic, economic models for policy analysis at the farm level: The impact of CAP reform in two European regions. Agricultural Systems 48: 163–178.

        Article  Google Scholar 

      • Giupponi, C. and Rosato, P. (1999) Agricultural land use changes, water quality: A case study in the watershed of the Lagoon of Venice. Water Science Technology 39 (3): 135–148.

        Article  Google Scholar 

      • Harvey, D.R. (1990) Agricultural Sector Modelling for Policy Development. In: J.G.W. Jones and P.R. Street (eds.) Systems Theory Applied to Agriculture, the Food Chain. London: Elsevier Applied Science, pp. 251–304.

        Google Scholar 

      • Holman I, P. et al (2005a) A regional, multi-sectorial, integrated assessment of the impacts of climate, socio-economic change in the UK. Part I. Methodology. Climatic Change 71 (1): 9–41.

        Article  Google Scholar 

      • Holman, I.P. et al (2005b) A regional, multi-sectorial, integrated assessment of the impacts of climate, socio-economic change in the UK. Part II. Results. Climatic Change 71 (1): 43–73.

        Article  Google Scholar 

      • Oglethorpe, D.R. and O’Callaghan, J.R. (1995) Farm-level economic modelling within a river catchment decision support system. Journal of Environmental Planning, Management 38 (1): 93–106.

        Article  Google Scholar 

      • Rounsevell, M.D.A., Annetts, J.E., Audsley, E., Mayr, T. and Reginster, I. (2003) Modelling the spatial distribution of agricultural land use at the regional scale. Agriculture, Ecosystems and Environment 95 (2–3): 465–479.

        Article  Google Scholar 

      • Veldkamp, A. and Verburg, P.H. (2004) Modelling land use change, environmental impact. Journal of Environmental Management 72 (1–2): 1–3.

        Article  Google Scholar 

      Multi-criteria, uncertainty

      • Annetts, J.E. and Audsley, E. (2002) Multiple objective linear programming for environmental farm planning. Journal of the Operational Research Society 53 (9): 933–943.

        Article  Google Scholar 

      • Darby-Dowman, K., Barker, S., Audsley, E. and Parsons, D.J. (2000) A two-stage stochastic programming with recourse model for determining robust planting plans in horticulture. Journal of the Operational Research Society 51: 83–98.

        Article  Google Scholar 

      • de Koeijer, T.J., Renkema, J.A. and van Mensvoort, J.J.M. (1995) Environmental-economic analysis of mixed crop-livestock farming. Agricultural Systems 48: 515–530.

        Article  Google Scholar 

      • Patten, L.H., Hardaker, J.B. and Pannell, D.J. (1988) Utility-efficient programming for whole-farm planning. Australian Journal of Agricultural Econonmics 32 (2–3): 88–97.

        Google Scholar 

      • Rehman, T. and Romero, C. (1993) The application of the MCDM paradigm to the management of agricultural systems: Some basic considerations. Agricultural Systems 41: 239–255.

        Article  Google Scholar 

      • Schilizzi, S.G.M. and Boulier, F. (1997) ‘Why do farmers do it?’ Validating whole-farm models. Agricultural Systems 54 (4): 477–499.

        Article  Google Scholar 

      Expert systems

      • Amir, I. and Kranz, Y. (1992) Expert system for irrigation planning. Water & Irrigation Review 12 (3).

      • Castro-Tendero, A.J. (1995) SEMAGI – An expert system for weed control decision making in sunflowers. Crop Protection 14 (7): 543–548.

        Article  Google Scholar 

      • Gold, H.J., Wilkerson, G.G., Yu, Y. and Stinner, R.E. (1990) Decision analysis as a tool for integrating simulation with expert systems when risk, uncertainty are important. Computer and Electronics in Agriculture 4 (4): 343–360.

        Article  Google Scholar 

      • Plant, R.E. (1989) Artificial intelligence methods for scheduling crop management actions. Agricultural Systems 31 (1): 127–155.

        Article  Google Scholar 

      Other optimization

      • Audsley, E., Milne, A.E. and Paveley, N. (2006) A foliar disease model for use in wheat disease management decision support systems. Annals of Applied Biology 147: 161–172.

        Article  Google Scholar 

      • Boyce, D.S. and Rutherford, I. (1972) A deterministic combine harvester cost model. Journal of Agricultural Engineering Research 17 (3): 261–270.

        Article  Google Scholar 

      • Dumont, A.G. and Boyce, D.S. (1974) Probabilistic simulation of weather variables. Journal of Agricultural Engineering Research 19 (4): 131–145.

        Article  Google Scholar 

      • Jalvingh, A.W., Dijkhuizen, A.A. and van Arendonk, J.A.M. (1992) Dynamic probabilistic modelling for reproduction, replacement management in sow herds: General aspects, model description. Agricultural Systems 39 (2): 133–152.

        Article  Google Scholar 

      • Kuo, S.F., Merkley, G.P. and Liu, C.W. (2000) Decision support for irrigation project planning using a genetic algorithm. Agricultural Water Management 45 (3): 243–266.

        Article  Google Scholar 

      • Parsons, D.J. (1998) Optimizing silage harvesting plans in a grass, grazing simulation using the revised simplex method, a genetic algorithm. Agricultural Systems 56 (1): 29–44.

        Article  Google Scholar 

      • Parsons, D.J. and Te Beest, D. (2004) Optimizing fungicide applications on winter wheat using genetic algorithms. Biosystems Engineering 88 (4): 401–410.

        Article  Google Scholar 

      • Stacey, K.F., Parsons, D.J., Frost, A.R., Fisher, C., Filmer, D. and Fothergill, D. (2004) An automatic growth, nutrition control system for broiler production. Biosystems Engineering 89 (3): 363–371.

        Article  Google Scholar 

      • Audsley, E. et al (1997) Harmonisation of Environmental Life Cycle Assessment for Agriculture. Final Report, Concerted Action AIR3-CT94-2028. Brussels, Belgium: European Commission, DG VI Agriculture.

        Google Scholar 

      Non-optimization

      • Audsley, E. and Wheeler, J.A. (1978) The annual cost of machinery using actual cash flows. Journal of Agricultural Engineering Research 23: 189–201.

        Article  Google Scholar 

      • Fraser, I. and Cordina, D. (1999) An application of DEA to irrigated dairy farms in Northern Victoria, Australia. Agricultural Systems 59: 267–282.

        Article  Google Scholar 

      • Milne, A.E., Paveley, N., Audsley, E. and Parsons, D.J. (2007) A model of the effect of fungicides on disease-induced yield loss, for use in wheat disease management decision support systems. Annals of Applied Biology 151: 113–125.

        Article  Google Scholar 

      • Parke, D., Dumont, A.G. and Boyce, D.S. (1978) A mathematical model to study forage conservation methods. Journal of the British Grassland Society 33: 261–272.

        Article  Google Scholar 

      • Williams, A.G., Audsley, E. and Sandars, D.L. (2006) Final Report to Defra on Project IS0205: Determining the Environmental Burdens, Resource use in the Production of Agricultural, Horticultural Commodities. London: Department for Environment, Food, Rural Affairs (Defra).

        Google Scholar 

      Download references

      Acknowledgements

      The authors thank Dr Lluis Plà (University of LLeida) for his thoughtful comments.

      Author information

      Authors and Affiliations

      1. Centre for Natural Resources Management, Building 42a, Cranfield University, Bedford, MK43 0AL, UK

        Eric Audsley & Daniel L Sandars

      Authors
      1. Eric Audsley
        View author publications

        You can also search for this author in PubMed Google Scholar

      2. Daniel L Sandars
        View author publications

        You can also search for this author in PubMed Google Scholar

      Corresponding author

      Correspondence to Eric Audsley.

      Rights and permissions

      Reprints and permissions

      About this article

      Cite this article

      Audsley, E., Sandars, D. A review of the practice and achievements from 50 years of applying OR to agricultural systems in Britain. OR Insight 22, 2–18 (2009). https://doi.org/10.1057/ori.2008.1

      Download citation

      • Published:

      • Issue Date:

      • DOI: https://doi.org/10.1057/ori.2008.1

      Keywords

      Search

      Navigation