Event Abstract

Back to Event

Toward a time-series of global livestock data over 2000-2015

Daniele Da Re1*, Christoffer Axelsson2, Giuseppina Cinardi3, Timothy P. Robinson3, Sophie O. Vanwambeke1 and Marius Gilbert2
  • 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

High spatial and temporal resolution information on environmental drivers (e.g. climate, primary productivity) are essential to many applications in ecology, economy and health sciences. Livestock densities in particular have an important role in agricultural, socio-economics, food security and epidemiology, thus knowing their spatial and temporal distribution is crucial. For this reason, several methodologies have been proposed to disaggregate livestock census data to continuous gridded density distribution (Wint and Robinson, 2007). However, these products referred only to a particular year and a temporal continuous product is still lacking. Using stratified random forest models (Gilbert et al., 2018) and a set of environmental predictors having a continuous temporal domain (Bontemps et al., 2013; Stevens et al., 2015; Karger et al., 2017; Zhang et al., 2017), we present a first attempt at generating a data product of the global geographic distribution of several livestock species (cattle, buffaloes, horses, sheep, goats, pigs, chickens and ducks) from 2000 to 2015 with a 0.083° spatial resolution (~10k at the equator). Beyond the immediate production of a set of temporally detailed data, we elaborated a methodological framework that we can apply again as new data time points become available. The ability to study livestock distribution and associated processes with temporal depth will be valuable considering the profound changes affecting this activity globally.

Acknowledgements

This project was supported by the FNRS project WISD X302317F.

References

Bontemps, S., P. Defourny, J. Radoux, E. Van Bogaert, C. Lamarche, F. Achard, P. Mayaux, M.Boettcher, C. Brockmann, & G. Kirches (2013) .“Consistent Global Land Cover Maps for Climate Modelling Communities: Current Achievements of the ESA’s Land Cover CCI.” Paper presented at the Proceedings of the ESA Living Planet Symposium, Edinburgh Gilbert, M., Nicolas, G., Cinardi, G., Van Boeckel, T. P., Vanwambeke, S. O., Wint, G. W., & Robinson, T. P. (2018). Global distribution data for cattle, buffaloes, horses, sheep, goats, pigs, chickens and ducks in 2010. Scientific data, 5, 180227. Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P. & Kessler, M. (2017) Climatologies at high resolution for the earth’s land surface areas. Scientific Data 4, 170122. Stevens, F. R., Gaughan, A. E., Linard, C., & Tatem, A. J. (2015). Disaggregating census data for population mapping using random forests with remotely-sensed and ancillary data. PloS one, 10(2), e0107042. Wint, W., & Robinson, T. (2007). Gridded livestock of the world 2007 (No. FAO 636.2 W784 2007). FAO, Roma (Italia). Zhang, Y., Xiao, X., Wu, X., Zhou, S., Zhang, G., Qin, Y., & Dong, J. (2017). A global moderate resolution dataset of gross primary production of vegetation for 2000–2016. Scientific data, 4, 170165.

Keywords: spatio-temporal modelling, random forest, machine learning, livestock density, data sources

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: Da Re D, Axelsson C, Cinardi G, Robinson TP, Vanwambeke SO and Gilbert M (2019). Toward a time-series of global livestock data over 2000-2015. Front. Vet. Sci. Conference Abstract: GeoVet 2019. Novel spatio-temporal approaches in the era of Big Data. doi: 10.3389/conf.fvets.2019.05.00065

Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters.

The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated.

Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed.

For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions.

Received: 06 Jun 2019; Published Online: 27 Sep 2019.

* Correspondence: Mx. Daniele Da Re, Earth and Life Institute, Catholic University of Louvain, Louvain-la-Neuve, Belgium, daniele.dare@uclouvain.be