Predicting the distribution of intensive poultry farming in Thailand

Spatial distribution of poultry farms using point pattern modelling: A method to address livestock environmental impacts and disease transmission risks

The distribution of farm locations and sizes is paramount to characterize patterns of disease spread. With some regions undergoing rapid intensification of livestock production, resulting in increased clustering of farms in peri-urban areas, measuring changes in the spatial distribution of farms is crucial to design effective interventions. However, those data are not available in many countries, their generation being resource-intensive. Here, we develop a farm distribution model (FDM), which allows the prediction of locations and sizes of poultry farms in countries with scarce data. The model combines (i) a Log-Gaussian Cox process model to simulate the farm distribution as a spatial Poisson point process, and (ii) a random forest model to simulate farm sizes (i.e. the number of animals per farm). Spatial predictors were used to calibrate the FDM on intensive broiler and layer farm distributions in Bangladesh, Gujarat (Indian state) and Thailand. The FDM yielded realistic farm distributions in terms of spatial clustering, farm locations and sizes, while providing insights on the factors influencing these distributions. Finally, we illustrate the relevance of modelling realistic farm distributions in the context of epidemic spread by simulating pathogen transmission on an array of spatial distributions of farms. We found that farm distributions generated from the FDM yielded spreading patterns consistent with simulations using observed data, while random point patterns underestimated the probability of large outbreaks. Indeed, spatial clustering increases vulnerability to epidemics, highlighting the need to account for it in epidemiological modelling studies. As the FDM maintains a realistic distribution of farm location and sizes, its use to inform mathematical models of disease transmission is particularly relevant for regions where these data are not available.

Evaluating the Economic Impacts of a Cage-Free Animal Welfare Policy in Southeast Asian and Indian Egg Production: A Systematic Review

Animal welfare is increasingly understood to be a key component of sustainable agricultural production. Southeast Asia and India are witnessing an emerging market for cage-free egg production. To evaluate the economic sustainability of cage-free policies in the region, it is critical to understand how this transition will affect farmers' costs and revenues. In this article, we provide an overview of the available information that can inform evaluations of cage-free egg production in Southeast Asia and India. Cage-free egg producers around the world tend to experience higher costs, but these costs are offset by higher revenues. As demand for cage-free eggs is stimulated in Southeast Asia and India by retailer or government policies, we expect that producers will be capable of meeting this demand. In Asia specifically, the dominant cost component is poultry feed. We conclude that the economic viability of egg production in the region is likely to be driven by feed prices and associated government policies, rather than production system per se.

Gridded livestock density database and spatial trends for Kazakhstan

Livestock rearing is a major source of livelihood for food and income in dryland Asia. Increasing livestock density (LSKD) affects ecosystem structure and function, amplifies the effects of climate change, and facilitates disease transmission. Significant knowledge and data gaps regarding their density, spatial distribution, and changes over time exist but have not been explored beyond the county level. This is especially true regarding the unavailability of high-resolution gridded livestock data. Hence, we developed a gridded LSKD database of horses and small ruminants (i.e., sheep & goats) at high-resolution (1 km) for Kazakhstan (KZ) from 2000-2019 using vegetation proxies, climatic, socioeconomic, topographic, and proximity forcing variables through a random forest (RF) regression modeling. We found high-density livestock hotspots in the south-central and southeastern regions, whereas medium-density clusters in the northern and northwestern regions of KZ. Interestingly, population density, proximity to settlements, nighttime lights, and temperature contributed to the efficient downscaling of district-level censuses to gridded estimates. This database will benefit stakeholders, the research community, land managers, and policymakers at regional and national levels.

Molecular epidemiological analyses reveal extensive connectivity between Echinostoma revolutum (sensu stricto) populations across Eurasia and species richness of zoonotic echinostomatids in England

Echinostoma revolutum (sensu stricto) is a widely distributed member of the Echinostomatidae, a cosmopolitan family of digenetic trematodes with complex life cycles involving a wide range of definitive hosts, particularly aquatic birds. Integrative taxonomic studies, notably those utilising nad1 barcoding, have been essential in discrimination of E. revolutum (s.s.) within the 'Echinostoma revolutum' species complex and investigation of its molecular diversity. No studies, however, have focussed on factors affecting population genetic structure and connectivity of E. revolutum (s.s.) in Eurasia. Here, we used morphology combined with nad1 and cox1 barcoding to determine the occurrence of E. revolutum (s.s.) and its lymnaeid hosts in England for the first time, in addition to other echinostomatid species Echinoparyphium aconiatum, Echinoparyphium recurvatum and Hypoderaeum conoideum. Analysis of genetic diversity in E. revolutum (s.s.) populations across Eurasia demonstrated haplotype sharing and gene flow, probably facilitated by migratory bird hosts. Neutrality and mismatch distribution analyses support possible recent demographic expansion of the Asian population of E. revolutum (s.s.) (nad1 sequences from Bangladesh and Thailand) and stability in European (nad1 sequences from this study, Iceland and continental Europe) and Eurasian (combined data sets from Europe and Asia) populations with evidence of sub-population structure and selection processes. This study provides new molecular evidence for a panmictic population of E. revolutum (s.s.) in Eurasia and phylogeographically expands the nad1 database for identification of echinostomatids.

Application of Spatial Risk Assessment Integrated With a Mobile App in Fighting Against the Introduction of African Swine Fever in Pig Farms in Thailand: Development Study

BACKGROUND

African swine fever (ASF), a highly contagious disease affecting both domestic and wild pigs, has been having a serious impact on the swine industry worldwide. This important transboundary animal disease can be spread by animals and ticks via direct transmission and by contaminated feed and fomites via indirect transmission because of the high environmental resistance of the ASF virus. Thus, the prevention of the introduction of ASF to areas free of ASF is essential. After an outbreak was reported in China, intensive import policies and biosecurity measures were implemented to prevent the introduction of ASF to pig farms in Thailand.

OBJECTIVE

Enhancing prevention and control, this study aims to identify the potential areas for ASF introduction and transmission in Thailand, develop a tool for farm assessment of ASF risk introduction focusing on smallholders, and develop a spatial analysis tool that is easily used by local officers for disease prevention and control planning.

METHODS

We applied a multi-criteria decision analysis approach with spatial and farm assessment and integrated the outputs with the necessary spatial layers to develop a spatial analysis on a web-based platform.

RESULTS

The map that referred to potential areas for ASF introduction and transmission was derived from 6 spatial risk factors; namely, the distance to the port, which had the highest relative importance, followed by the distance to the border, the number of pig farms using swill feeding, the density of small pig farms (<50 heads), the number of pigs moving in the area, and the distance to the slaughterhouse. The possible transmission areas were divided into 5 levels (very low, low, medium, high, and very high) at the subdistrict level, with 27 subdistricts in 10 provinces having very high suitability and 560 subdistricts in 34 provinces having high suitability. At the farm level, 17 biosecurity practices considered as useful and practical for smallholders were selected and developed on a mobile app platform. The outputs from the previous steps integrated with necessary geographic information system layers were added to a spatial analysis web-based platform.

CONCLUSIONS

The tools developed in this study have been complemented with other strategies to fight against the introduction of ASF to pig farms in the country. The areas showing high and very high risk for disease introduction and transmission were applied for spatial information planning, for example, intensive surveillance, strict animal movement, and public awareness. In addition, farms with low biosecurity were improved in these areas, and the risk assessment developed on a mobile app in this study helped enhance this matter. The spatial analysis on a web-based platform helped facilitate disease prevention planning for the authorities.

Behavioral-biological surveillance of emerging infectious diseases among a dynamic cohort in Thailand

Background

Interactions between humans and animals are the key elements of zoonotic spillover leading to zoonotic disease emergence. Research to understand the high-risk behaviors associated with disease transmission at the human-animal interface is limited, and few consider regional and local contexts.

Objective

This study employed an integrated behavioral–biological surveillance approach for the early detection of novel and known zoonotic viruses in potentially high-risk populations, in an effort to identify risk factors for spillover and to determine potential foci for risk-mitigation measures.

Method

Participants were enrolled at two community-based sites (n = 472) in eastern and western Thailand and two hospital (clinical) sites (n = 206) in northeastern and central Thailand. A behavioral questionnaire was administered to understand participants’ demographics, living conditions, health history, and animal-contact behaviors and attitudes. Biological specimens were tested for coronaviruses, filoviruses, flaviviruses, influenza viruses, and paramyxoviruses using pan (consensus) RNA Virus assays.

Results

Overall 61/678 (9%) of participants tested positive for the viral families screened which included influenza viruses (75%), paramyxoviruses (15%), human coronaviruses (3%), flaviviruses (3%), and enteroviruses (3%). The most salient predictors of reporting unusual symptoms (i.e., any illness or sickness that is not known or recognized in the community or diagnosed by medical providers) in the past year were having other household members who had unusual symptoms and being scratched or bitten by animals in the same year. Many participants reported raising and handling poultry (10.3% and 24.2%), swine (2%, 14.6%), and cattle (4.9%, 7.8%) and several participants also reported eating raw or undercooked meat of these animals (2.2%, 5.5%, 10.3% respectively). Twenty four participants (3.5%) reported handling bats or having bats in the house roof. Gender, age, and livelihood activities were shown to be significantly associated with participants’ interactions with animals. Participants’ knowledge of risks influenced their health-seeking behavior.

Conclusion

The results suggest that there is a high level of interaction between humans, livestock, and wild animals in communities at sites we investigated in Thailand. This study highlights important differences among demographic and occupational risk factors as they relate to animal contact and zoonotic disease risk, which can be used by policymakers and local public health programs to build more effective surveillance strategies and behavior-focused interventions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07439-7.

Spatial Distribution and Population Estimation of Dogs in Thailand: Implications for Rabies Prevention and Control

Poor management of dog populations causes many problems in different countries, including rabies. To strategically design a dog population management, certain sets of data are required, such as the population size and spatial distribution of dogs. However, these data are rarely available or incomplete. Hence, this study aimed to describe the characteristics of dog populations in Thailand, explore their spatial distribution and relevant factors, and estimate the number of dogs in the whole country. First, four districts were selected as representatives of each region. Each district was partitioned into grids with a 300-m resolution. The selected grids were then surveyed, and the number of dogs and related data were collected. Random forest models with a two-part approach were used to quantify the association between the surveyed dog population and predictor variables. The spatial distribution of dog populations was then predicted. A total of 1,750 grids were surveyed (945 grids with dog presence and 805 grids with dog absence). Among the surveyed dogs, 86.6% (12,027/13,895) were owned. Of these, 51% were classified as independent, followed by confined (25%), semi-independent (21%), and unidentified dogs (3%). Seventy-two percent (1,348/1,868) of the ownerless dogs were feral, and the rest were community dogs. The spatial pattern of the dog populations was highly distributed in big cities such as Bangkok and its suburbs. In owned dogs, it was linked to household demographics, whereas it was related to community factors in ownerless dogs. The number of estimated dogs in the entire country was 12.8 million heads including 11.2 million owned dogs (21.7 heads/km²) and 1.6 million ownerless dogs (3.2 heads/km²). The methods developed here are extrapolatable to a larger area and use much less budget and manpower compared to the present practices. Our results are helpful for canine rabies prevention and control programs, such as dog population management and control and rabies vaccine allocation.

Geographic Drivers of Antimicrobial Use and Resistance in Pigs in Khon Kaen Province, Thailand

In Thailand, pig production has increased considerably in the last decades to meet a growing demand for pork. Antimicrobials are used routinely in intensive pig production to treat infections and increase productivity. However, the use of antimicrobials also contributes to the rise of antimicrobial resistance with potential consequences for animal and human health. Here, we quantify the association between antimicrobial use and resistance rates in extensive and intensive farms with a focus on geographic proximity between farm and drugstores. Of the 164 enrolled farms, 79% reported using antimicrobials for disease prevention, treatment, or as a feed additive. Antimicrobial-resistant E. coli were present in 63% of farms. These drugs included critically important antimicrobials, such as quinolones and penicillins. Medium-scale farms with intensive animal production practices showed higher resistance rates than small-scale farms with extensive practices. Farms with drug-resistant Escherichia coli were located closer to drugstores and a had a higher proportion of disease than farms without drug-resistant E. coli. We found no association between the presence of resistance in humans and antimicrobial use in pigs. Our findings call for actions to improve herd health to reduce the need for antimicrobials and systematic training of veterinarians and drugstore owners on judicious use of antimicrobials in animals to mitigate resistance.

Antibiotic use in pig farms at different levels of intensification-Farmers' practices in northeastern Thailand

Understanding the patterns and drivers of antibiotic use in livestock is crucial for tailoring efficient incentives for responsible use of antibiotics. Here we compared routines for antibiotic use between pig farms of two different levels of intensification in Khon Kaen province in Thailand. Among the 113 family-owned small-scale farms (up to 50 sows) interviewed did 76% get advice from the pharmacy about how to use the antibiotics and 84% used it primarily for treating disease. Among the 51 medium-scale-farms (100–500 sows) belonging to two companies did 100% get advice about antibiotic use from the company’s veterinarian (P<0.0001) and 94% used antibiotics mostly as disease preventive measure (P<0.0001). In 2 small scale farms 3^rd generation cephalosporins, tylosin or colistin were used; antibiotics belonging to the group of highest priority critically important antimicrobials for human medicine. Enrofloxacin, belonging to the same group of antimicrobials, was used in 33% of the small-scale and 41% of the medium-scale farms. In the latter farms, the companies supplied 3–4 antibiotics belonging to different classes and those were the only antibiotics used in the farms. The median and mean estimated expenditure on antibiotics per sow was 4.8 USD (IQR = 5.8) for small-scale farms and 7 USD and 3.4 USD for the medium-scale farms belonging to the two respective companies. Our observations suggest to target the following areas when pig farming transitions from small-scale to medium-scale: (i) strengthening access to professional animal health services for all farmers, (ii) review of the competence and role of veterinary pharmacies in selling antibiotics and (iii) adjustment of farming company animal health protocols towards more medically rational use of antibiotics.

Poultry farm distribution models developed along a gradient of intensification

Efficient planning of measures limiting epidemic spread requires information on farm locations and sizes (number of animals per farm). However, such data are rarely available. The intensification process which is operating in most low- and middle-income countries (LMICs), comes together with a spatial clustering of farms, a characteristic epidemiological models are sensitive to. We developed farm distribution models predicting both the location and the number of animals per farm, while accounting for the spatial clustering of farms in data-poor countries, using poultry production as an example. We selected four countries, Nigeria, Thailand, Argentina and Belgium, along a gradient of intensification expressed by the per capita Gross Domestic Product (GDP). First, we investigated the distribution of chicken farms along the spectrum of intensification. Second, we built farm distribution models (FDM) based on censuses of commercial farms of each of the four countries, using point pattern and random forest models. As an external validation, we predicted farm locations and sizes in Bangladesh. The number of chicken per farm increased gradually in line with the gradient of GDP per capita in the following order: Nigeria, Thailand, Argentina and Belgium. Interestingly, we did not find such a gradient for farm clustering. Our modelling procedure could only partly reproduce the observed datasets in each of the four sample countries in internal validation. However, in the external validation, the clustering of farms could not be reproduced and the spatial predictors poorly explained the number and location of farms and farm sizes in Bangladesh. Further improvements of the methodology should explore other covariates of the intensity of farms and farm sizes, as well as improvements of the methodology. Structural transformation, economic development and environmental conditions are essential characteristics to consider for an extrapolation of our FDM procedure, as generalisation appeared challenging. We believe the FDM procedure could ultimately be used as a predictive tool in data-poor countries.

The Costs, Benefits and Human Behaviours for Antimicrobial Use in Small Commercial Broiler Chicken Systems in Indonesia

There are growing concerns over the threat to human health from the unregulated use of antimicrobials in livestock. Broiler production is of great economic and social importance in Indonesia. This study used a structured questionnaire approach to explore the human behaviours and economic drivers associated with antimicrobial use in small commercial broiler systems in Indonesia (n = 509). The study showed that antimicrobial use was high with farmers easily able to access antimicrobials through local animal medicine, however, it was difficult for farmers to access veterinary advice on responsible antimicrobial use. The most significant finding was that the relative cost of antimicrobials was low, and farmers observed improvements in productivity rates from routine antimicrobial administration. However, farmers seldom kept detailed records on farm productivity or economic costs; this is a hurdle to undertaking a more detailed economic analysis of antimicrobial use. There is a need for further research on the cost-effectiveness of alternative methods of preventing disease and ensuring that feasible alternatives are easily available. Farm-level economics and securing the food supply chain need to be central to any future policy interventions to reduce antimicrobial use in broiler systems in Indonesia and this observation is relevant at a regional and global level.

Downscaling livestock census data using multivariate predictive models: Sensitivity to modifiable areal unit problem

The analysis of census data aggregated by administrative units introduces a statistical bias known as the modifiable areal unit problem (MAUP). Previous researches have mostly assessed the effect of MAUP on upscaling models. The present study contributes to clarify the effects of MAUP on the downscaling methodologies, highlighting how a priori choices of scales and shapes could influence the results. We aggregated chicken and duck fine-resolution census in Thailand, using three administrative census levels in regular and irregular shapes. We then disaggregated the data within the Gridded Livestock of the World analytical framework, sampling predictors in two different ways. A sensitivity analysis on Pearson's r correlation statistics and RMSE was carried out to understand how size and shapes of the response variables affect the goodness-of-fit and downscaling performances. We showed that scale, rather than shapes and sampling methods, affected downscaling precision, suggesting that training the model using the finest administrative level available is preferable. Moreover, datasets showing non-homogeneous distribution but instead spatial clustering seemed less affected by MAUP, yielding higher Pearson's r values and lower RMSE compared to a more spatially homogenous dataset. Implementing aggregation sensitivity analysis in spatial studies could help to interpret complex results and disseminate robust products.

Characterizing Antimicrobial Use in the Livestock Sector in Three South East Asian Countries (Indonesia, Thailand, and Vietnam)

A framework was developed to characterize the antimicrobial use/antimicrobial resistance complex in livestock systems in Indonesia, Vietnam, and Thailand. Farm profitability, disease prevention, and mortality rate reduction were identified as drivers toward antimicrobial use in livestock systems. It revealed that antimicrobial use was high in all sectors studied, and that routine preventative use was of particular importance to broiler production systems. Misleading feed labeling was identified as a hurdle to the collection of accurate antimicrobial use data, with farmers being unaware of the antimicrobials contained in some commercial feed. Economic analysis found that the cost of antimicrobials was low relative to other farm inputs, and that farm profitability was precariously balanced. High disease and poor prices were identified as potential drivers toward economic loss. The research indicates that antimicrobial use in small-scale poultry production systems improves feed conversion ratios and overall productivity. However, data were limited to quantify adequately these potential gains and their impacts on the food supply. During the study, all countries embraced and implemented policies on better management of antimicrobial use in livestock and surveillance of antimicrobial resistance. Future policies need to consider farm-level economics and livestock food supply issues when developing further antimicrobial use interventions in the region.

A spatial assessment of Nipah virus transmission in Thailand pig farms using multi-criteria decision analysis

BACKGROUND

Thailand's Central Plain is identified as a contact zone between pigs and flying foxes, representing a potential zoonotic risk. Nipah virus (NiV) has been reported in flying foxes in Thailand, but it has never been found in pigs or humans. An assessment of the suitability of NiV transmission at the spatial and farm level would be useful for disease surveillance and prevention. Multi-criteria decision analysis (MCDA), a knowledge-driven model, was used to map contact zones between local epizootic risk factors as well as to quantify the suitability of NiV transmission at the pixel and farm level.

RESULTS

Spatial risk factors of NiV transmission in pigs were identified by experts as being of three types, including i) natural host factors (bat preferred areas and distance to the nearest bat colony), ii) intermediate host factors (pig population density), and iii) environmental factors (distance to the nearest forest, distance to the nearest orchard, distance to the nearest water body, and human population density). The resulting high suitable areas were concentrated around the bat colonies in three provinces in the East of Thailand, including Chacheongsao, Chonburi, and Nakhonnayok. The suitability of NiV transmission in pig farms in the study area was quantified as ranging from very low to medium suitability.

CONCLUSIONS

We believe that risk-based surveillance in the identified priority areas may increase the chances of finding out NiV and other bat-borne pathogens and thereby optimize the allocation of financial resources for disease surveillance. In the long run, improvements of biosecurity in those priority areas may also contribute to preventing the spread of potential emergence of NiV and other bat-borne pathogens.

Geographical and Historical Patterns in the Emergences of Novel Highly Pathogenic Avian Influenza (HPAI) H5 and H7 Viruses in Poultry

Over the years, the emergence of novel H5 and H7 highly pathogenic avian influenza viruses (HPAI) has been taking place through two main mechanisms: first, the conversion of a low pathogenic into a highly pathogenic virus, and second, the reassortment between different genetic segments of low and highly pathogenic viruses already in circulation. We investigated and summarized the literature on emerging HPAI H5 and H7 viruses with the aim of building a spatio-temporal database of all these recorded conversions and reassortments events. We subsequently mapped the spatio-temporal distribution of known emergence events, as well as the species and production systems that they were associated with, the aim being to establish their main characteristics. From 1959 onwards, we identified a total of 39 independent H7 and H5 LPAI to HPAI conversion events. All but two of these events were reported in commercial poultry production systems, and a majority of these events took place in high-income countries. In contrast, a total of 127 reassortments have been reported from 1983 to 2015, which predominantly took place in countries with poultry production systems transitioning from backyard to intensive production systems. Those systems are characterized by several co-circulating viruses, multiple host species, regular contact points in live bird markets, limited biosecurity within value chains, and frequent vaccination campaigns that impose selection pressures for emergence of novel reassortants. We conclude that novel HPAI emergences by these two mechanisms occur in different ecological niches, with different viral, environmental and host associated factors, which has implications in early detection and management and mitigation of the risk of emergence of novel HPAI viruses.

Species distribution models: A comparison of statistical approaches for livestock and disease epidemics

In livestock industries, reliable up-to-date spatial distribution and abundance records for animals and farms are critical for governments to manage and respond to risks. Yet few, if any, countries can afford to maintain comprehensive, up-to-date agricultural census data. Statistical modelling can be used as a proxy for such data but comparative modelling studies have rarely been undertaken for livestock populations. Widespread species, including livestock, can be difficult to model effectively due to complex spatial distributions that do not respond predictably to environmental gradients. We assessed three machine learning species distribution models (SDM) for their capacity to estimate national-level farm animal population numbers within property boundaries: boosted regression trees (BRT), random forests (RF) and K-nearest neighbour (K-NN). The models were built from a commercial livestock database and environmental and socio-economic predictor data for New Zealand. We used two spatial data stratifications to test (i) support for decision making in an emergency response situation, and (ii) the ability for the models to predict to new geographic regions. The performance of the three model types varied substantially, but the best performing models showed very high accuracy. BRTs had the best performance overall, but RF performed equally well or better in many simulations; RFs were superior at predicting livestock numbers for all but very large commercial farms. K-NN performed poorly relative to both RF and BRT in all simulations. The predictions of both multi species and single species models for farms and within hypothetical quarantine zones were very close to observed data. These models are generally applicable for livestock estimation with broad applications in disease risk modelling, biosecurity, policy and planning.

Spatial analysis and characteristics of pig farming in Thailand

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.

Electronic supplementary material

The online version of this article (doi:10.1186/s12917-016-0849-7) contains supplementary material, which is available to authorized users.

Using Random Forest to Improve the Downscaling of Global Livestock Census Data

Large scale, high-resolution global data on farm animal distributions are essential for spatially explicit assessments of the epidemiological, environmental and socio-economic impacts of the livestock sector. This has been the major motivation behind the development of the Gridded Livestock of the World (GLW) database, which has been extensively used since its first publication in 2007. The database relies on a downscaling methodology whereby census counts of animals in sub-national administrative units are redistributed at the level of grid cells as a function of a series of spatial covariates. The recent upgrade of GLW1 to GLW2 involved automating the processing, improvement of input data, and downscaling at a spatial resolution of 1 km per cell (5 km per cell in the earlier version). The underlying statistical methodology, however, remained unchanged. In this paper, we evaluate new methods to downscale census data with a higher accuracy and increased processing efficiency. Two main factors were evaluated, based on sample census datasets of cattle in Africa and chickens in Asia. First, we implemented and evaluated Random Forest models (RF) instead of stratified regressions. Second, we investigated whether models that predicted the number of animals per rural person (per capita) could provide better downscaled estimates than the previous approach that predicted absolute densities (animals per km2). RF models consistently provided better predictions than the stratified regressions for both continents and species. The benefit of per capita over absolute density models varied according to the species and continent. In addition, different technical options were evaluated to reduce the processing time while maintaining their predictive power. Future GLW runs (GLW 3.0) will apply the new RF methodology with optimized modelling options. The potential benefit of per capita models will need to be further investigated with a better distinction between rural and agricultural populations.

Income Disparities and the Global Distribution of Intensively Farmed Chicken and Pigs

The rapid transformation of the livestock sector in recent decades brought concerns on its impact on greenhouse gas emissions, disruptions to nitrogen and phosphorous cycles and on land use change, particularly deforestation for production of feed crops. Animal and human health are increasingly interlinked through emerging infectious diseases, zoonoses, and antimicrobial resistance. In many developing countries, the rapidity of change has also had social impacts with increased risk of marginalisation of smallholder farmers. However, both the impacts and benefits of livestock farming often differ between extensive (backyard farming mostly for home-consumption) and intensive, commercial production systems (larger herd or flock size, higher investments in inputs, a tendency towards market-orientation). A density of 10,000 chickens per km2 has different environmental, epidemiological and societal implications if these birds are raised by 1,000 individual households or in a single industrial unit. Here, we introduce a novel relationship that links the national proportion of extensively raised animals to the gross domestic product (GDP) per capita (in purchasing power parity). This relationship is modelled and used together with the global distribution of rural population to disaggregate existing 10 km resolution global maps of chicken and pig distributions into extensive and intensive systems. Our results highlight countries and regions where extensive and intensive chicken and pig production systems are most important. We discuss the sources of uncertainties, the modelling assumptions and ways in which this approach could be developed to forecast future trajectories of intensification.

Global trends in antimicrobial use in food animals

Demand for animal protein for human consumption is rising globally at an unprecedented rate. Modern animal production practices are associated with regular use of antimicrobials, potentially increasing selection pressure on bacteria to become resistant. Despite the significant potential consequences for antimicrobial resistance, there has been no quantitative measurement of global antimicrobial consumption by livestock. We address this gap by using Bayesian statistical models combining maps of livestock densities, economic projections of demand for meat products, and current estimates of antimicrobial consumption in high-income countries to map antimicrobial use in food animals for 2010 and 2030. We estimate that the global average annual consumption of antimicrobials per kilogram of animal produced was 45 mg⋅kg(-1), 148 mg⋅kg(-1), and 172 mg⋅kg(-1) for cattle, chicken, and pigs, respectively. Starting from this baseline, we estimate that between 2010 and 2030, the global consumption of antimicrobials will increase by 67%, from 63,151 ± 1,560 tons to 105,596 ± 3,605 tons. Up to a third of the increase in consumption in livestock between 2010 and 2030 is imputable to shifting production practices in middle-income countries where extensive farming systems will be replaced by large-scale intensive farming operations that routinely use antimicrobials in subtherapeutic doses. For Brazil, Russia, India, China, and South Africa, the increase in antimicrobial consumption will be 99%, up to seven times the projected population growth in this group of countries. Better understanding of the consequences of the uninhibited growth in veterinary antimicrobial consumption is needed to assess its potential effects on animal and human health.

Global trends in antimicrobial use in food animals

Demand for animal protein for human consumption is rising globally at an unprecedented rate. Modern animal production practices are associated with regular use of antimicrobials, potentially increasing selection pressure on bacteria to become resistant. Despite the significant potential consequences for antimicrobial resistance, there has been no quantitative measurement of global antimicrobial consumption by livestock. We address this gap by using Bayesian statistical models combining maps of livestock densities, economic projections of demand for meat products, and current estimates of antimicrobial consumption in high-income countries to map antimicrobial use in food animals for 2010 and 2030. We estimate that the global average annual consumption of antimicrobials per kilogram of animal produced was 45 mg⋅kg(-1), 148 mg⋅kg(-1), and 172 mg⋅kg(-1) for cattle, chicken, and pigs, respectively. Starting from this baseline, we estimate that between 2010 and 2030, the global consumption of antimicrobials will increase by 67%, from 63,151 ± 1,560 tons to 105,596 ± 3,605 tons. Up to a third of the increase in consumption in livestock between 2010 and 2030 is imputable to shifting production practices in middle-income countries where extensive farming systems will be replaced by large-scale intensive farming operations that routinely use antimicrobials in subtherapeutic doses. For Brazil, Russia, India, China, and South Africa, the increase in antimicrobial consumption will be 99%, up to seven times the projected population growth in this group of countries. Better understanding of the consequences of the uninhibited growth in veterinary antimicrobial consumption is needed to assess its potential effects on animal and human health.

The dawn of Structural One Health: a new science tracking disease emergence along circuits of capital

The One Health approach integrates health investigations across the tree of life, including, but not limited to, wildlife, livestock, crops, and humans. It redresses an epistemological alienation at the heart of much modern population health, which has long segregated studies by species. Up to this point, however, One Health research has also omitted addressing fundamental structural causes underlying collapsing health ecologies. In this critical review we unpack the relationship between One Health science and its political economy, particularly the conceptual and methodological trajectories by which it fails to incorporate social determinants of epizootic spillover. We also introduce a Structural One Health that addresses the research gap. The new science, open to incorporating developments across the social sciences, addresses foundational processes underlying multispecies health, including the place-specific deep-time histories, cultural infrastructure, and economic geographies driving disease emergence. We introduce an ongoing project on avian influenza to illustrate Structural One Health's scope and ambition. For the first time researchers are quantifying the relationships among transnational circuits of capital, associated shifts in agroecological landscapes, and the genetic evolution and spatial spread of a xenospecific pathogen.

Mapping the global distribution of livestock

Livestock contributes directly to the livelihoods and food security of almost a billion people and affects the diet and health of many more. With estimated standing populations of 1.43 billion cattle, 1.87 billion sheep and goats, 0.98 billion pigs, and 19.60 billion chickens, reliable and accessible information on the distribution and abundance of livestock is needed for a many reasons. These include analyses of the social and economic aspects of the livestock sector; the environmental impacts of livestock such as the production and management of waste, greenhouse gas emissions and livestock-related land-use change; and large-scale public health and epidemiological investigations. The Gridded Livestock of the World (GLW) database, produced in 2007, provided modelled livestock densities of the world, adjusted to match official (FAOSTAT) national estimates for the reference year 2005, at a spatial resolution of 3 minutes of arc (about 5×5 km at the equator). Recent methodological improvements have significantly enhanced these distributions: more up-to date and detailed sub-national livestock statistics have been collected; a new, higher resolution set of predictor variables is used; and the analytical procedure has been revised and extended to include a more systematic assessment of model accuracy and the representation of uncertainties associated with the predictions. This paper describes the current approach in detail and presents new global distribution maps at 1 km resolution for cattle, pigs and chickens, and a partial distribution map for ducks. These digital layers are made publically available via the Livestock Geo-Wiki (http://www.livestock.geo-wiki.org), as will be the maps of other livestock types as they are produced.

Predicting hotspots for influenza virus reassortment

TOC summary: Reassortment is most likely to occur in eastern China, central China, or the Nile Delta in Egypt.

The 1957 and 1968 influenza pandemics, each of which killed ≈1 million persons, arose through reassortment events. Influenza virus in humans and domestic animals could reassort and cause another pandemic. To identify geographic areas where agricultural production systems are conducive to reassortment, we fitted multivariate regression models to surveillance data on influenza A virus subtype H5N1 among poultry in China and Egypt and subtype H3N2 among humans. We then applied the models across Asia and Egypt to predict where subtype H3N2 from humans and subtype H5N1 from birds overlap; this overlap serves as a proxy for co-infection and in vivo reassortment. For Asia, we refined the prioritization by identifying areas that also have high swine density. Potential geographic foci of reassortment include the northern plains of India, coastal and central provinces of China, the western Korean Peninsula and southwestern Japan in Asia, and the Nile Delta in Egypt.

Improving risk models for avian influenza: the role of intensive poultry farming and flooded land during the 2004 Thailand epidemic

Since 1996 when Highly Pathogenic Avian Influenza type H5N1 first emerged in southern China, numerous studies sought risk factors and produced risk maps based on environmental and anthropogenic predictors. However little attention has been paid to the link between the level of intensification of poultry production and the risk of outbreak. This study revised H5N1 risk mapping in Central and Western Thailand during the second wave of the 2004 epidemic. Production structure was quantified using a disaggregation methodology based on the number of poultry per holding. Population densities of extensively- and intensively-raised ducks and chickens were derived both at the sub-district and at the village levels. LandSat images were used to derive another previously neglected potential predictor of HPAI H5N1 risk: the proportion of water in the landscape resulting from floods. We used Monte Carlo simulation of Boosted Regression Trees models of predictor variables to characterize the risk of HPAI H5N1. Maps of mean risk and uncertainty were derived both at the sub-district and the village levels. The overall accuracy of Boosted Regression Trees models was comparable to that of logistic regression approaches. The proportion of area flooded made the highest contribution to predicting the risk of outbreak, followed by the densities of intensively-raised ducks, extensively-raised ducks and human population. Our results showed that as little as 15% of flooded land in villages is sufficient to reach the maximum level of risk associated with this variable. The spatial pattern of predicted risk is similar to previous work: areas at risk are mainly located along the flood plain of the Chao Phraya river and to the south-east of Bangkok. Using high-resolution village-level poultry census data, rather than sub-district data, the spatial accuracy of predictions was enhanced to highlight local variations in risk. Such maps provide useful information to guide intervention.