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Probabilistic Modelling of Cattle Farm Distribution in Australia

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Abstract

A probabilistic Bayesian method called weights of evidence (WofE) was used to develop a synthetic dataset of cattle farm locations at a national scale across Australia. The synthetic dataset was required for the modelling of livestock movements with a view to assessing biosecurity implications. The WofE method is based on the analysis of spatial relationships between evidential patterns with respect to an event, such as the actual location of a farm. The evidential patterns of cattle farms were derived from maps of land use, land tenure, drainage systems, roads, settlements and long-term averaged rainfall. These evidential patterns were used for delineating and ranking land areas suitable for cattle farming. For each evidential pattern statistics such as a positive weight, a negative weight and a contrast were calculated for estimating the degree of correlation between the evidential patterns and known farm locations. The integrated evidential patterns of known farms were then used for estimating posterior probabilities and splitting land into five different classes according to its suitability for farming.

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Notes

  1. Sizes of real cattle farms vary in size and shape. However, for modelling geolocations, neither the size nor shape of cattle farms is taken into account; a farm’s location is presented by a single point which is often situated in the centre of a property or a property block. Bigger farms are separated by longer distances; therefore, cattle properties bigger than 1 km2 will never be associated with the same 1-km2 unit cell from the study region. However, a few farms smaller than 1 km2 could be associated with the same 1-km2 unit cell within the study region. To avoid duplication, the training dataset of farm locations used for modelling purposes only recorded one farm per 1 km2. This ensures that the contribution of any farm from the training dataset into the prior probability will not be overestimated.

  2. The original data and GIS files are available on request by contacting the corresponding author.

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Acknowledgements

Thanks to John Roberts of the Primary Industries & Fisheries, Queensland, Australia and Chris Ryan of the Rural Land Protection Board State Council, New South Wales, Australia for the provision of the validation datasets.

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Authors and Affiliations

  1. Commonwealth Scientific and Industrial Research Organization (CSIRO), Livestock Industries (LI), Private Bag 5, Wembley, WA, 6913, Australia

    I. V. Emelyanova

  2. CSIRO, LI, Locked Bag 1, Armidale, NSW, 2350, Australia

    G. E. Donald & D. J. Miron

  3. CSIRO, LI, Australian Animal Health Laboratory, Private Bag 24, Geelong, VIC, 3220, Australia

    D. A. Henry

  4. Office of the Chief Veterinary Officer, Department of Agriculture, Fisheries and Forestry, GPO Box 858, Canberra, ACT, 2601, Australia

    M. G. Garner

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  1. I. V. Emelyanova
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  2. G. E. Donald
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  5. M. G. Garner
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Correspondence to I. V. Emelyanova.

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Emelyanova, I.V., Donald, G.E., Miron, D.J. et al. Probabilistic Modelling of Cattle Farm Distribution in Australia. Environ Model Assess 14, 449–465 (2009). https://doi.org/10.1007/s10666-008-9140-z

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  • DOI: https://doi.org/10.1007/s10666-008-9140-z

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