مؤتمر
Farm distribution models developed along a gradient of intensification
| العنوان: | Farm distribution models developed along a gradient of intensification |
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| المؤلفون: | Chaiban, Celia, Da Re, Daniele, Robinso, Thimoty, Gilbert, Marius, Vanwambeke, Sophie, GeoVet 2019. Novel spatio-temporal approaches in the era of Big Data |
| المساهمون: | UCL - SST/ELI/ELIC - Earth & Climate |
| المصدر: | Frontiers in Veterinary Science, Vol. 6, p. [1-2] (2019) |
| بيانات النشر: | Frontiers Media S.A. |
| سنة النشر: | 2019 |
| المجموعة: | DIAL@USL-B (Université Saint-Louis, Bruxelles) |
| الوصف: | 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 ... |
| نوع الوثيقة: | conference object |
| اللغة: | English |
| تدمد: | 2297-1769 |
| العلاقة: | boreal:227613; http://hdl.handle.net/2078.1/227613Test; urn:EISSN:2297-1769 |
| DOI: | 10.3389/conf.fvets.2019.05.00111 |
| الإتاحة: | https://doi.org/10.3389/conf.fvets.2019.05.00111Test http://hdl.handle.net/2078.1/227613Test |
| حقوق: | info:eu-repo/semantics/openAccess |
| رقم الانضمام: | edsbas.2CB41D8E |
| قاعدة البيانات: | BASE |
Full Text Finder | تدمد: | 22971769 |
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| DOI: | 10.3389/conf.fvets.2019.05.00111 |