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RESOURCE MODELLING: ITS NATURE AND USE

Published online by Cambridge University Press:  31 May 2012

Peter Sands
Peter Sands*
Affiliation:
Commonwealth Scientific and Industrial Research Organization, Division of Forestry and Forest Products, Stowell Avenue, Hobart, Australia
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Abstract

To improve management of resources, such as agricultural crops or forests, scientists attempt to analyse the resource systems and to predict the consequences or outcomes of interventions. They construct models of interactions of components of the systems, drawing on knowledge and experience. In agriculture, five types of models have become common — empirical, crop–weather, crop–growth, crop–system, and crop–process. The models aim mainly to predict crop yields when a series of actions are taken. They differ markedly in complexity, from a simple regression to a series of mechanistic relations aimed at simulating the crop system. The uses to which a model is to be put, and by whom it will be used, are major determinants of the nature of the model so modellers must work with the potential users. In fact, modelling is an exercise in human relations as much as in science. All things being equal, the simpler the model is that meets the objectives of the users, the better are the chances of its being used.

Résumé

Afin d'améliorer la régie des ressources telles que les cultures et les forêts, les scientistes tentent d'analyser les systèmes supportant ces ressources et de prédire les conséquences ou résultats d'interventions diverses. Ils inventent des modèles des interactions entre les composantes de ces systèmes en se référant aux connaissances et aux expériences acquises. En agriculture, cinq types de modèles sont d'usage courant : empirique, culture–météo, culture–croissance, culture–système et culture–fonction. Ces modèles tentent en général de prédire les rendements, à la suite d'une série d'interventions spécifiques. Ils diffèrent de façon marquée quant à leur complexité, variant de régressions simples, à des ensembles complexes de relations mécanistiques simulant le système. Les usages et les usagers auxquels il est destiné sont des éléments qui déterminent la nature d'un modèle, de sorte que le modélisateur doit travailler de concert avec ses usagers potentiels. En fait, la modélisation est une exercice de relations humaines autant qu'une science. Plus un modèle répondant aux objectifs visés est simple, plus grandes sont les chances qu'il soit utilisé.

Type
Research Article
Information
The Memoirs of the Entomological Society of Canada , Volume 120 , Supplement S143 , 1988 , pp. 5 - 10
Copyright
Copyright © Entomological Society of Canada 1988

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References

Barlow, N.D. 1983. The role of modelling in practical pest management, pp. 41–51 in Cameron, P.J., Wearing, C.H., and Kain, W.M. (Eds.), Proceedings of Australasian Workshop on Development of IPM, Auckland, New Zealand, 20–22 July 1982, Government Printer, Auckland. 231 pp.Google Scholar
Barlow, N.D. 1985. A model for pest assessment in New Zealand sheep pastures. Agric. Syst. 18: 1–37.Google Scholar
Boote, K.J., Jones, J.W., Mishoe, J.W., and Berger, R.D.. 1983. Coupling pests to crop growth simulators to predict yield reductions. Phytopathology 73: 1581–1587.Google Scholar
Brook, K.D., and Hearn, A.B.. 1983. Development and implementation of SIRATAC: a computer based cotton management system. Paper prepared for a conference on computers in agriculture, University of Western Australia, Perth, August.Google Scholar
Conway, G.R. 1977. Mathematical models in applied ecology. Nature 269: 291–297.Google Scholar
Cuff, W., and Baskerville, G.. 1983. Ecological modelling and management of spruce budworm infested firspruce forests of New Brunswick, Canada, pp. 93–98 in Lauenroth, W.K., Skogerboe, G.V., and Flug, M. (Eds.), Analysis of Ecological Systems: State-of-the-art in Ecological Modelling, Elsevier, Amsterdam. 992 pp.Google Scholar
Cuff, W.R., and Hardman, J.M.. 1980. A development of the Leslie matrix formulation for restructuring and extending an ecosystem model: the infestation of stored wheat by Sitophilus oryzae. Ecol. Model. 9: 281–305.Google Scholar
Fitzpatrick, E.A., and Nix, H.A.. 1970. The climatic factor in grassland ecology, pp. 1–26 in Moore, R.M. (Ed.), Australian Grasslands, Australian National University Press, Canberra.Google Scholar
Forester, J.W. 1968. Principles of systems. Wright-Allen Press, Cambridge, MA.Google Scholar
Gass, S.I. 1983. Decision-aiding models: validation, assessment and related issues for policy analysis. Operations Research 31: 603–631.Google Scholar
Getz, W.M., and Gutierrez, A.P.. 1982. A perspective on systems analysis in crop production and insect pest management. Annu. Rev. Ent. 27: 447–466.Google Scholar
Goutzamanis, J.J., and Connor, D.J.. 1977. A simulation model of the wheat crop. School of Agriculture, La Trobe University, Melbourne, Bulletin 1.Google Scholar
Hearn, A.B., and da Roza, G.. 1985. A simple model for crop management applications for cotton (Gossypium hirsutum L.). Field Crops Res. 12: 49–69.Google Scholar
Hughes, R.D., and Sands, P.J.. 1979. Modelling bushfly populations. J. Appl. Ecol. 16: 117–139.Google Scholar
Norton, G.A., and Holling, C.S. (Eds.). 1979. Pest management. Pergamon Press, Oxford.Google Scholar
Passioura, J.B. 1973. Sense and nonsense in crop simulation. J. Aust. Inst. Agric. Sci. 39: 181–183.Google Scholar
Passioura, J.B. 1979. Accountability, philosophy and plant physiology. Search (Syd.) 10: 347–350.Google Scholar
Sands, P. 1988. Is small beautiful? Pros and cons of simple, appropriate models, in Leech, J., McMurtrie, R.E., and West, P.W. (Eds.), Modelling Trees, Stands and Forests, University of Melbourne, Melbourne, Forestry Bulletin.Google Scholar
Sands, P., and Hughes, R.D.. 1976. A simulation model of seasonal changes in the value of cattle dung as a food resource for an insect. Agric. Meteorol. 17: 161–183.Google Scholar
Sands, P., and Hughes, R.D.. 1977. Mathematical model of the survival rates of female bushflies (Musca vetustissima Walker) (Diptera: Muscidae) as inferred from field populations. Bull. ent. Res. 67: 675–683.Google Scholar
Seif, E., and Pederson, D.G.. 1978. Effect of rainfall on the grain yield of spring wheat, with an application. Aust. J. Agric. Res. 29: 1107–1115.Google Scholar
Stapper, M. 1986. Modelling plant growth and development, pp. 249–272 in McLean, G.D., Garett, R.G., and Ruesink, W.G. (Eds.), Plant Virus Epidemiology: Monitoring, Modelling and Predicting Outbreaks, Academic Press, Sydney.Google Scholar
Sutherst, R.W., Norton, G.A., Barlow, N.D., Conway, G.R., Birley, M., and Comins, H.N.. 1979. An analysis of management strategies for cattle tick (Boophilus microplus) control in Australia. J. Appl. Ecol. 16: 352–392.Google Scholar
Thornley, J.H.M. 1980. Research strategy in the plant sciences. Plant Cell Environ. 3: 233–236.Google Scholar
Watt, K.E.F. 1964. The use of mathematics and computers to determine optimal strategy and tactics for a given insect pest control problem. Can. Ent. 96: 202–220.Google Scholar
Welch, S.M., Croft, B.A., and Michels, M.F.. 1981. Validation of pest management models. Environ. Ent. 10: 425–432.Google Scholar