Spatial analysis and characteristics of pig farming in Thailand

Abstract

Background: In Thailand, pig production intensified significantly during the last decade, with many economic, epidemiological and environmental implications. Strategies toward more sustainable future developments are currently investigated, and these could be informed by a detailed assessment of the main trends in the pig sector, and on how different production systems are geographically distributed. This study had two main objectives. First, we aimed to describe the main trends and geographic patterns of pig production systems in Thailand in terms of pig type (native, breeding, and fattening pigs), farm scales (smallholder and large-scale farming systems) and type of farming systems (farrow-to-finish, nursery, and finishing systems) based on a very detailed 2010 census. Second, we aimed to study the statistical spatial association between these different types of pig farming distribution and a set of spatial variables describing access to feed and markets.

Results: Over the last decades, pig population gradually increased, with a continuously increasing number of pigs per holder, suggesting a continuing intensification of the sector. The different pig-production systems showed very contrasted geographical distributions. The spatial distribution of large-scale pig farms corresponds with that of commercial pig breeds, and spatial analysis conducted using Random Forest distribution models indicated that these were concentrated in lowland urban or peri-urban areas, close to means of transportation, facilitating supply to major markets such as provincial capitals and the Bangkok Metropolitan region. Conversely the smallholders were distributed throughout the country, with higher densities located in highland, remote, and rural areas, where they supply local rural markets. A limitation of the study was that pig farming systems were defined from the number of animals per farm, resulting in their possible misclassification, but this should have a limited impact on the main patterns revealed by the analysis.

Conclusions: The very contrasted distribution of different pig production systems present opportunities for future regionalization of pig production. More specifically, the detailed geographical analysis of the different production systems will be used to spatially-inform planning decisions for pig farming accounting for the specific health, environment and economical implications of the different pig production systems.

Keywords: Intensive pig farm; Random forest; Spatial distribution; Sustainable development; Two-part model.

Fig. 1

Decision rules identifying pig farming systems. Left side shows the proposed classification of the smallholders and large-scale farming systems according to the pig numbers, with holders raising less than 50 pigs being considered as smallholders (<5 pigs per holder for backyard and 5–50 pigs per holder for smallholder commercial) and holders with 50or more pigs considered as large-scale farming system (50–500 pigs per holder for small, 500–5000 pigs per holder for moderate, and >5000 pigs per holder for large). Right side shows a proposed classification of farming system according to pig types, with i) farrow-to-finish system if the holder includes all types of breeding pig (boar, sow, and piglet) as well as fattening pigs, ii) nursery system, if the holder includes all types of breeding pig (but no fatting pigs), and ii) finishing system if holder includes only fattening pigs

Fig. 2

Spatial datasets used as predictor variables for modeling the pig distribution in Thailand. The variables (1 km resolution) include; a Travel time to the capital city (Bangkok) (log10 of time) [44], b Travel time to the provincial capitals (log10 of time) [44], c rainfed croplands (proportion within a square kilometer) [43], d irrigated croplands (proportion within a square kilometer) [43], e elevation (log10 of meter) [42], and f human population density (log10 of number of human per a square kilometer) [39]

Fig. 3

Temporal distribution pattern of pig population. Top shows comparisons between human and pig populations in Thailand over the past 50 years (1964–2013), which bar plot shows the number of human population (left y-axis) and line plot shows the number of pig population (right y-axis). Left bottom shows trends in numbers of pig holders in Thailand over the past 10 years. Right bottom shows an average size of pig holding in Thailand over the past 10 years

Fig. 4

Spatial distributions of pig population in Thailand in 2010. The upper row shows the distribution of pig density by types: all pigs (a), native pigs (b), breeding pigs (c), and fattening pigs (d). The lower row shows the distribution of all pig farms (e), smallholder farms (f) and large-scale farms (g) The lower right hand map (h) shows the nine regional administrative areas

Fig. 5

Partial dependent plots of the fitted function (Y-axis) and the predictor variables (X-axis). Response variables include: native pig density (NaPig), breeding pig density (BrPig), fattening pig density (FatPig), Large-scale farm density (LF), and smallholder density (SM). The predictor variables include: a travel time to the capital city (TCapCity), b travel time to the provincial capitals (TProCap), c elevation, d rainfed croplands, d irrigated croplands, and e human population density