Predicting farm-level animal populations using environmental and socioeconomic variables

van Andel, Mary and Jewell, Christopher Parry and McKenzie, Joanna and Hollings, Tracey and Robinson, Andrew and Burgman, Mark and Bingham, Paul and Carpenter, Tim (2017) Predicting farm-level animal populations using environmental and socioeconomic variables. Preventive Veterinary Medicine, 145. pp. 121-132. ISSN 0167-5877

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Accurate information on the geographic distribution of domestic animal populations helps biosecurity authorities to efficiently prepare for and rapidly eradicate exotic diseases, such as Foot and Mouth Disease (FMD). Developing and maintaining sufficiently high-quality data resources is expensive and time consuming. Statistical modelling of population density and distribution has only begun to be applied to farm animal populations, although it is commonly used in wildlife ecology. We developed zero-inflated Poisson regression models in a Bayesian framework using environmental and socioeconomic variables to predict the counts of livestock units (LSUs) and of cattle on spatially referenced farm polygons in a commercially available New Zealand farm database, Agribase. Farm-level counts of cattle and of LSUs varied considerably by region, because of the heterogeneous farming landscape in New Zealand. The amount of high quality pasture per farm was significantly associated with the presence of both cattle and LSUs. Internal model validation (predictive performance) showed that the models were able to predict the count of the animal population on groups of farms that were located in randomly selected 3 km zones with a high level of accuracy. Predicting cattle or LSU counts on individual farms was less accurate. Predicted counts were statistically significantly more variable for farms that were contract grazing dry stock, such as replacement dairy heifers and dairy cattle not currently producing milk, compared with other farm types. This analysis presents a way to predict numbers of LSUs and cattle for farms using environmental and socio-economic data. The technique has the potential to be extrapolated to predicting other pastoral based livestock species.

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Journal Article
Journal or Publication Title:
Preventive Veterinary Medicine
Additional Information:
This is the author’s version of a work that was accepted for publication in Preventive Veterinary Medicine . Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Preventive Veterinary Medicine, 145, 2017 DOI: 10.1016/j.prevetmed.2017.07.005
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01 Jun 2018 09:18
Last Modified:
13 Oct 2023 10:50