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Farm distribution models developed along a gradient of intensification

Celia Chaiban1, 2*, Daniele Da Re1, 2, Timothy P. Robinson3, Marius Gilbert2, 4 and Sophie Vanwambeke1
  • 1 Earth and Life Institute, Catholic University of Louvain, Belgium
  • 2 Spatial Epidemiology Lab, Free University of Brussels, Belgium
  • 3 Food and Agriculture Organization of the United Nations (Italy), Italy
  • 4 Fonds National de la Recherche Scientifique (FNRS), Belgium

Intensification of livestock production foster the ease and speed at which diseases can emerge and spread [1]. To adequately plan measures limiting epidemic spread, epidemiological models requires farm locations and sizes (in terms of number of animals) [2]–[5]. However, such data are rarely available. In high-income countries where registries are maintained, these are not always accessible for privacy and confidentiality reasons [2]. In middle- and low-income countries (LMIC), when agricultural censuses are conducted, these vary in resolution from one country to another [6]. We aimed at developing farm distribution models (FDM), which would predict both location and number of animals per farm. Furthermore, intensification process which is operating in most LMICs, has been shown to come together with a spatial clustering of farms [4], [7]. As mathematical models are sensitive to this spatial clustering of farms [8], [9], we selected a method which could take it into account to predict farm locations. We selected four countries along a gradient of intensification: Nigeria, Thailand, Argentina and Belgium. These countries are presumably spread along a gradient as the proportion of animals raised in intensive systems increase in line with the per capita Gross Domestic Product (GDP) [10]. First, we explored how the distribution of chicken farms evolved along the spectrum of intensification showed by the four countries. Second, we built FDM based on censuses of commercial farms, recording population and location of chicken farms in each country. The FDM included two successive steps: (i) farm locations were predicted with the Log-Gaussian Cox Processes (LGCP) model from the point pattern analysis field (following a methodology, we already developed [4]) and (ii) population on farms was predicted using a Random Forest model. Finally, we tested our modelling procedure to predict farms locations and sizes in Bangladesh, and compared the predictions with the real data available. The number of chickens per farmer showed distributions which increased from Nigeria, through Thailand and Argentina to Belgium, in line with the GDP per capita gradient. Surprisingly, we did not find such a gradient of farm clustering. Farms in Argentina were the most clustered, followed by Nigeria and Thailand. Belgian farms were more homogeneously distributed, while still being better explained by the cluster model (LGCP model). Our modelling procedure could reproduce the observed datasets with reasonable accuracy in terms of locations and sizes in each of the four country. The LGCP with covariates was shown to produce better results in terms of clustering and cluster locations than random models. The Random Forest model explained 64% of the variance of the training data. The FDM approach could produce a distribution of farms in Bangladesh which was more realistic than a random distribution, however, the intensity of points was underestimated. As expected, the covariates selected in the Random Forest could explain partly the farm size, but could still reproduce a histogram of the distribution of chicken per farms similar to the observed one in Bangladesh. Further improvements of the methodology should explore covariates which would better explain the intensity of farms and farm sizes. However, this methodology could already be helpful to predict the distribution and population of farms in countries where data are scarce.

Acknowledgements

We would like to acknowledge the “Fonds pour la formation à la Recherche dans l'Industrie et dans l'Agriculture” (FRIA) for supporting this project. This research also benefited from computational resources provided by the supercomputing facilities of the Université catholique de Louvain (CISM/UCL) and the Consortium des Équipements de Calcul Intensif en Fédération Wallonie Bruxelles (CÉCI) funded by the Fond de la Recherche Scientifique de Belgique (F.R.S.-FNRS) under convention 2.5020.11.

References

[1] B. A. Jones et al., “Zoonosis emergence linked to agricultural intensification and environmental change,” Proceedings of the National Academy of Sciences, vol. 110, no. 21, pp. 8399–8404, May 2013. [2] C. L. Burdett, B. R. Kraus, S. J. Garza, R. S. Miller, and K. E. Bjork, “Simulating the Distribution of Individual Livestock Farms and Their Populations in the United States: An Example Using Domestic Swine (Sus scrofa domesticus) Farms,” PLOS ONE, vol. 10, no. 11, p. e0140338, Nov. 2015. [3] M. van Andel et al., “Predicting farm-level animal populations using environmental and socioeconomic variables,” Preventive Veterinary Medicine, vol. 145, pp. 121–132, Sep. 2017. [4] C. Chaiban et al., “Point pattern simulation modelling of extensive and intensive chicken farming in Thailand: Accounting for clustering and landscape characteristics,” Agricultural Systems, vol. 173, pp. 335–344, Jul. 2019. [5] M. K. Martin, J. Helm, and K. A. Patyk, “An approach for de-identification of point locations of livestock premises for further use in disease spread modeling,” Preventive Veterinary Medicine, vol. 120, no. 2, pp. 131–140, Jun. 2015. [6] T. P. Van Boeckel et al., “Modelling the distribution of domestic ducks in Monsoon Asia,” Agric Ecosyst Environ, vol. 141, no. 3–4, pp. 373–380, May 2011. [7] H. Steinfeld, P. Gerber, T. D. Wassenaar, V. Castel, and C. de Haan, Livestock’s long shadow: environmental issues and options. Food & Agriculture Org., 2006. [8] A. Reeves, “Construction and evaluation of epidemiologic simulation models for the within- and among-unit spread and control of infectious diseases of livestock and poultry,” Thesis, Colorado State University. Libraries, 2012. [9] M. J. Tildesley and S. J. Ryan, “Disease Prevention versus Data Privacy: Using Landcover Maps to Inform Spatial Epidemic Models,” PLoS Comput Biol, vol. 8, no. 11, Nov. 2012. [10] M. Gilbert et al., “Income Disparities and the Global Distribution of Intensively Farmed Chicken and Pigs,” PLOS ONE, vol. 10, no. 7, p. e0133381, Jul. 2015.

Keywords: Farm distribution models, point pattern analysis, Livestock distribution, chicken farm population, agricultural intensification

Conference: GeoVet 2019. Novel spatio-temporal approaches in the era of Big Data, Davis, United States, 8 Oct - 10 Oct, 2019.

Presentation Type: Regular oral presentation

Topic: Spatial data sources, open data, accessibility and information integration

Citation: Chaiban C, Da Re D, Robinson TP, Gilbert M and Vanwambeke S (2019). Farm distribution models developed along a gradient of intensification. Front. Vet. Sci. Conference Abstract: GeoVet 2019. Novel spatio-temporal approaches in the era of Big Data. doi: 10.3389/conf.fvets.2019.05.00111

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Received: 10 Jun 2019; Published Online: 27 Sep 2019.

* Correspondence: MD. Celia Chaiban, Earth and Life Institute, Catholic University of Louvain, Louvain-la-Neuve, Walloon Brabant, Belgium, celia.chaiban@uclouvain.be