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The primary challenge of the contemporary world is to meet accelerating requirements for food. Limited land, competition between crop and livestock farming and climate change are major challenges. Agroforestry offer a form of sustainable agriculture through the direct provision of food by raising farmers’ incomes and through various ecosystem services. The first essential step in adopting agroforestry is the selection of appropriate tree species that fit local climates. In this paper, we mapped 20 fodder trees and important crops in China using the multi-model ensemble and Ecocrop modelling approach. Relying on the intersectional concept of set theory, the fuzzy logic technique was applied to identify regions where candidate trees could be grown with appropriate crops and livestock. The resulting models provide important insights into the climatic suitability of trees and crops and offer knowledge critical to the proper integration of trees with crops and livestock at specific locations. The results offer support for developing appropriate strategies regarding potential land-use within agroforestry systems in order to maximize ecosystem services and the benefits of sustainable agriculture. Model outputs could easily convert into conventional maps with clearly defined boundaries for site-specific planning for tree-crop-livestock integration. The next step for actualizing an integrated system is to investigate specifically what these different species may contribute to the existing farming systems, quantify the benefits and estimate any possible trade-offs.

Quantitative evidence for the risk of zoonoses and the spread of antimicrobial resistance remains lacking. Here, as part of the UrbanZoo project, we sampled Escherichia coli from humans, livestock and peri-domestic wildlife in 99 households across Nairobi, Kenya, to investigate its distribution among host species in this rapidly developing urban landscape. We performed whole-genome sequencing of 1,338 E. coli isolates and found that the diversity and sharing patterns of E. coli were heavily structured by household and strongly shaped by host type. We also found evidence for inter-household and inter-host sharing and, importantly, between humans and animals, although this occurs much less frequently. Resistome similarity was differently distributed across host and household, consistent with being driven by shared exposure to antimicrobials. Our results indicate that a large, epidemiologically structured sampling framework combined with WGS is needed to uncover strain-sharing events among different host populations in complex environments and the major contributing pathways that could ultimately drive the emergence of zoonoses and the spread of antimicrobial resistance.

Climate change is increasingly putting milk production from cattle-based dairy systems in north sub-Saharan Africa (NSSA) under stress, threatening livelihoods and food security. Here we combine livestock heat stress frequency, dry matter feed production and water accessibility data to understand where environmental changes in NSSA’s drylands are jeopardizing cattle milk production. We show that environmental conditions worsened for ∼17% of the study area. Increasing goat and camel populations by ∼14% (∼7.7 million) and ∼10% (∼1.2 million), respectively, while reducing the dairy cattle population by ∼24% (∼5.9 million), could result in ∼0.14 Mt (+5.7%) higher milk production, lower water (−1,683.6 million m3, −15.3%) and feed resource (−404.3 Mt, −11.2%) demand—and lower dairy emissions by ∼1,224.6 MtCO2e (−7.9%). Shifting herd composition from cattle towards the inclusion of, or replacement with, goats and camels can secure milk production and support NSSA’s dairy production resilience against climate change.

Global livestock supply chains have significantly altered nitrogen (N) flows over past years, thereby threatening environmental and human health. Here, we provide a disaggregated assessment of the livestock sector’s impacts on global N flows and emissions, including international trade. The results show that the sector currently emits 65 Tg N yr−1, equivalent to one-third of current human-induced N emissions and sufficient to meet the planetary boundary for N. Of that amount, 66% is allocated to Asia and 68% is associated with feed production. Most emissions originate from locally produced animal-sourced food, although N emissions embedded in international trade are significant for some importing countries. Given the magnitude of its impacts and its central role in both domestic and international N challenges, the livestock sector urgently requires a global initiative to tackle N pollution while supporting food security. Livestock greenhouse gas emissions are key to the sustainability of food systems. A spatially explicit analysis of global livestock supply chains shows large variability of nitrogen use efficiency across species, systems, production intensities and the origin and management of animal feed.

In May this year, the long-awaited final report from the Review on Antimicrobial Resistance was published. 1 The report estimates that the 700 000 annual deaths currently attributable to infections by drug-resistant pathogens will increase, if unchecked, to 10 million by 2050, running up a bill of US$100 trillion in terms of lost global production between now and then. The reported numbers are somewhat apocalyptic, as they are based on crude projections of current trends and presume the failure of anti-malarial drugs and antiretroviral therapy as well as antibiotics. Nor do they account for the possible entry of new drugs onto the market. There is huge uncertainty associated with the human burden of antimicrobial resistance (AMR) in general and antibiotic resistance in particular (for the purpose of this article the widely used term antimicrobial resistance [AMR] is retained, though the emphasis here is on antibiotic resistance). The precision of the estimates does not really matter much: the point is that the problem is already immense and is growing rapidly. We have been squandering our antibiotic resources for far too long and immediate action is needed on a very large scale if we are to reverse current trends., Transactions of the Royal Society of Tropical Medicine and Hygiene, 110 (7), ISSN:0035-9203

BACKGROUND: Antimicrobial resistance is one of the great challenges facing global health security in the modern era. Wildlife, particularly those that use urban environments, are an important but understudied component of epidemiology of antimicrobial resistance. We investigated antimicrobial resistance overlap between sympatric wildlife, humans, livestock, and their shared environment across the developing city of Nairobi, Kenya. We use these data to examine the role of urban wildlife in the spread of clinically relevant antimicrobial resistance.METHODS: 99 households across Nairobi were randomly selected on the basis of socioeconomic stratification. A detailed survey was administered to household occupants, and samples (n=2102) were collected from the faeces of 75 wildlife species inhabiting household compounds (ie, the household and its perimeter; n=849), 13 livestock species (n=656), and humans (n=333), and from the external environment (n=288). Escherichia coli, our sentinel organism, was cultured and a single isolate from each sample tested for sensitivity to 13 antibiotics. Diversity of antimicrobial resistant phenotypes was compared between urban wildlife, humans, livestock, and the environment, to investigate whether wildlife are a net source for antimicrobial resistance in Nairobi. Generalised linear mixed models were used to determine whether the prevalence of antimicrobial resistant phenotypes and multidrug-resistant E coli carriage in urban wildlife is linked to variation in ecological traits, such as foraging behaviour, and to determine household-level risk factors for sharing of antimicrobial resistance between humans, wildlife, and livestock.FINDINGS: E coli were isolated from 485 samples collected from wildlife between Sept 6,2015, and Sept 28, 2016. Wildlife carried a low prevalence of E coli isolates susceptible to all antibiotics tested (45 [9%] of 485 samples) and a high prevalence of clinically relevant multidrug resistance (252 [52%] of 485 samples), which varied between taxa and by foraging traits. Multiple isolates were resistant to one agent from at least seven antimicrobial classes tested for, and a single isolate was resistant to all antibiotics tested for in the study. The phenotypic diversity of antimicrobial-resistant E coli in wildlife was lower than in livestock, humans, and the environment. Within household compounds, statistical models identified two interfaces for exchange of antimicrobial resistance: between both rodents, humans and their rubbish, and seed-eating birds, humans and their rubbish; and between seed-eating birds, cattle, and bovine manure.INTERPRETATION: Urban wildlife carry a high burden of clinically relevant antimicrobial-resistant E coli in Nairobi, exhibiting resistance to drugs considered crucial for human medicine by WHO. Identifiable traits of the wildlife contribute to this exposure; however, compared with humans, livestock, and the environment, low phenotypic diversity in wildlife is consistent with the hypothesis that wildlife are a net sink rather than source of clinically relevant resistance. Wildlife that interact closely with humans, livestock, and both human and livestock waste within households, are exposed to more antimicrobial resistant phenotypes, and could therefore act as conduits for the dissemination of clinically relevant antimicrobial resistance to the wider environment. These results provide novel insight into the broader epidemiology of antimicrobial resistance in complex urban environments, characteristic of lower-middle-income countries.FUNDING: UK Medical Research Council and CGIAR Research Program on Agriculture for Nutrition and Health.

Growth in demand for livestock-derived foods will likely remain strong in low- and middle- income countries, fueling concerns about expansion of production and the management of natural resources. Recent research suggests that the envisioned negative effects are not inevitable and that benefits such as improved food security can be enhanced. Targeted investments to the livestock sector could help make these happen. We use scenario analysis to explore this question, assessing the impacts of selected investment options in sub-Saharan Africa and South Asia. Our results show that boosting livestock productivity primarily in these two regions could improve food security and producer incomes while limiting greenhouse gas emissions and agricultural water usage. Market-improving investments with similar welfare gains lead to environmental impacts that necessitate complementary investments.

The keeping of livestock has been posited as a risk factor for the emergence of zoonoses and the spread of antimicrobial resistance. However, quantitative evidence regarding the major sources of pathogenic and drug-resistant bacteria and transmission routes between hosts remains lacking. In the largest epidemiological study of this nature to date, we sampled Escherichia coli from humans, livestock, food, wildlife and the environment of 99 households across Nairobi, Kenya to gain a deeper understanding of sharing of bacteria among hosts and potential reservoirs. By analysing whole genome sequencing data from 1,338 E. coli isolates, we reconstruct sharing patterns for the sampled E. coli and its antimicrobial resistance determinants. We find that the diversity and sharing patterns of E. coli is heavily structured by household, which is the primary epidemiological interface for bacterial strain sharing. Strain sharing within households was strongly shaped by host type. We also find evidence for inter-household and inter-host sharing, and importantly, between humans and animals, although this occurs much less frequently. We find similar strain sharing patterns for the E. coli accessory genome, suggesting that it is shaped by recent evolutionary history and is strongly associated with the core genome. Resistome similarity, however, were quite differently distributed across host and household, consistent with their being driven by shared exposure to antimicrobials. Our results indicate that there is potential for the exchange of bacteria between humans, livestock and wildlife in the same household in a tropical urban setting, with wider mixing occurring over a period of months or years, but this does not drive the distribution of antimicrobial resistance.

Urbanization can have profound impacts on the distributional ecology of wildlife and livestock, with implications for biodiversity conservation, ecosystem services and human health. A wealth of studies have assessed biotic responses to urbanization in North America and Europe, but there is little empirical evidence that directly links human activities to urban biodiversity in the tropics. Results from a large‐scale field study conducted in Nairobi, Kenya, are used to explore the impact of human activities on the biodiversity of wildlife and livestock with which humans co‐exist across the city. The structure of sympatric wildlife, livestock and human populations are characterized using unsupervised machine learning, and statistical modelling is used to relate compositional variation in these communities to socio‐ecological drivers occurring across the city. By characterizing landscape‐scale drivers acting on these interfaces, we demonstrate that socioeconomics, elevation and subsequent changes in habitat have measurable impacts upon the diversity, density and species assemblage of wildlife, livestock and humans. Restructuring of wildlife and livestock assemblages (both in terms of species diversity and composition) has important implications for the emergence of novel diseases at urban interfaces, and we therefore use our results to generate a set of testable hypotheses that explore the influence of urban change on microbial communities. These results provide novel insight into the impact of urbanization on biodiversity in the tropics. An understanding of associations between urban processes and the structure of human and animal populations is required to link urban development to conservation efforts and risks posed by disease emergence to human health, ultimately informing sustainable urban development policy., We live in a rapidly urbanizing world. Today, more than half of the human race lives in cities, and this number is set to continue to increase, as people migrate to urban areas in search of higher standards of living. Careful planning is required to ensure that detrimental impacts of urban development on the environment and human health are identified and addressed. In this study we use Nairobi—one of the world's fastest growing cities—as a case study with which to explore how and why people's co‐existence with wildlife and livestock varies across a rapidly developing urban landscape. By providing a detailed characterization of the patterns of human and animal co‐existence that emerge from urban socioeconomic and environmental change, and hypotheses for how this influences disease transmission, our results provide a framework within which continuing research on the ecological and epidemiological consequences of rapid urban development in the tropics can be framed.

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.

Improved animal health can reduce greenhouse gas (GHG) emissions intensity in livestock systems while increasing productivity. Integrated modelling of disease impacts on farm-scale emissions is important in identifying effective health strategies to reduce emissions. However, it requires that modellers understand the pathways linking animal health to emissions and how these might be incorporated into models. A key barrier to meeting this need has been the lack of a framework to facilitate effective exchange of knowledge and data between animal health experts and emissions modellers. Here, these two communities engaged in workshops, online exchanges and a survey to i) identify a comprehensive list of disease-related model parameters and ii) test its application to evaluating models. Fifty-six parameters were identified and proved effective in assessing the potential of farm-scale models to characterise livestock disease impacts on GHG emissions. Easy wins for the emissions models surveyed include characterising disease impacts related to feeding.

The role of livestock in supporting human well-being is contentious, with different perceptions leading to polarised opinions. There is increasing concern about the health and environmental impacts of a high rate of consumption of livestock products in high-income countries. These concerns are heightened by an increase in consumption in middle-income countries. On the other hand, livestock support the livelihoods of many people, particularly in low income countries. The benefits of livestock for poor livestock keepers are multiple, including the important role livestock play in supporting crop production in mixed systems, in supplying nutrients and income, and in fulfilling cultural roles. In addition livestock can provide resilience against economic and climate shocks. In view of these apparent positive and negative impacts, the role of livestock in human wellbeing is highly contested, with arguments ‘for’ or ‘against’ sometimes distorted by vested interests or misinterpretation of evidence. The Livestock Fact Check project, undertaken by the Livestock Data for Decisions community of practice, has investigated several ideas concerning livestock commonly taken as ‘fact’. By exploring the provenance of these ‘facts’ we highlight their importance and the risks of both misinterpreting them or using them out of context. Despite the diversity of the livestock sector resulting in equally diverse viewpoints, the project calls for participants in the livestock discourse to adopt a nuanced appreciation of global livestock systems. Judgement of livestock's role in global sustainable diets should be based on clear and well-interpreted information., Highlights • Livestock farming is a contentious and polarised subject in sustainable diet discourse. • Livestock facts are used to support rationales from a variety of positions. • Many ‘facts’ are used without acknowledgement of their provenance or without suitable interpretation. • Knowing what is uncertain or variable is important for fair interpretation, dialogue and rational decisions. • Transparency of evidence related to livestock is the ambition of the Livestock Data for Decisions community of practice.

Poultry production is an important way of enhancing the livelihoods of rural populations, especially in low- and middle-income countries (LMICs). As poultry production in LMICs remains dominated by backyard systems with low inputs and low outputs, considerable yield gaps exist. Intensification can increase poultry productivity, production and income. This process is relatively recent in LMICs compared to high-income countries. The management practices and the constraints faced by smallholders trying to scale-up their production, in the early stages of intensification, are poorly understood and described. We thus investigated the features of the small-scale commercial chicken sector in a rural area distant from major production centres. We surveyed 111 commercial chicken farms in Kenya in 2016. We targeted farms that sell the majority of their production, owning at least 50 chickens, partly or wholly confined and provided with feeds. We developed a typology of semi-intensive farms. Farms were found mainly to raise dual-purpose chickens of local and improved breeds, in association with crops and were not specialized in any single product or market. We identified four types of semi-intensive farms that were characterized based on two groups of variables related to intensification and accessibility: (i) remote, small-scale old farms, with small flocks, growing a lot of their own feed; (ii) medium-scale, old farms with a larger flock and well located in relation to markets and (iii) large-scale recently established farms, with large flocks, (iii-a) well located and buying chicks from third-party providers and (iii-b) remotely located and hatching their own chicks. The semi-intensive farms we surveyed were highly heterogeneous in terms of size, age, accessibility, management, opportunities and challenges. Farm location affects market access and influences the opportunities available to farmers, resulting in further diversity in farm profiles. The future of these semi-intensive farms could be compromised by several factors, including the competition with large-scale intensive farmers and with importations. Our study suggests that intensification trajectories in rural areas of LMICs are potentially complex, diverse and non-linear. A better understanding of intensification trajectories should, however, be based on longitudinal data. This could, in turn, help designing interventions to support small-scale farmers., SCOPUS: ar.j, info:eu-repo/semantics/published

This paper discusses the sustainability of livestock systems, emphasising bidirectional relations with animal health. We review conventional and contrarian thinking on sustainability and argue that in the most common approaches to understanding sustainability, health aspects have been under-examined. Literature review reveals deep concerns over the sustainability of livestock systems; we recognise that interventions are required to shift to more sustainable trajectories, and explore approaches to prioritising in different systems, focusing on interventions that lead to better health. A previously proposed three-tiered categorisation of 'hot spots', 'cold spots' and 'worried well' animal health trajectories provides a mental model that, by taking into consideration the different animal health status, animal health risks, service response needs and key drivers in each system, can help identify and implement interventions. Combining sustainability concepts with animal health trajectories allows for a richer analysis, and we apply this to three case studies drawn from North Africa and the Middle East; Bangladesh; and the Eastern Cape of South Africa. We conclude that the quest for sustainability of livestock production systems from the perspective of human and animal health is elusive and difficult to reconcile with the massive anticipated growth in demand for livestock products, mainly in low- and middle-income countries, as well as the aspirations of poor livestock keepers for better lives. Nevertheless, improving the health of livestock can contribute to health sustainability both through reducing negative health impacts of livestock and increasing efficiency of production. However, the choice of the most appropriate options must be under-pinned by an understanding of agro-ecology, economy and values. We argue that a new pillar of One Health should be added to the three traditional sustainability pillars of economics, society and environment when addressing livestock systems.

The livestock sector is one of the fastest growing subsectors of the agricultural economy and, while it makes a major contribution to global food supply and economic development, it also consumes significant amounts of natural resources and alters the environment. In order to improve our understanding of the global environmental impact of livestock supply chains, the Food and Agriculture Organization of the United Nations has developed the Global Livestock Environmental Assessment Model (GLEAM). The purpose of this paper is to provide a review of GLEAM. Specifically, it explains the model architecture, methods and functionality, that is the types of analysis that the model can perform. The model focuses primarily on the quantification of greenhouse gases emissions arising from the production of the 11 main livestock commodities. The model inputs and outputs are managed and produced as raster data sets, with spatial resolution of 0.05 decimal degrees. The Global Livestock Environmental Assessment Model v1.0 consists of five distinct modules: (a) the Herd Module; (b) the Manure Module; (c) the Feed Module; (d) the System Module; (e) the Allocation Module. In terms of the modelling approach, GLEAM has several advantages. For example spatial information on livestock distributions and crops yields enables rations to be derived that reflect the local availability of feed resources in developing countries. The Global Livestock Environmental Assessment Model also contains a herd model that enables livestock statistics to be disaggregated and variation in livestock performance and management to be captured. Priorities for future development of GLEAM include: improving data quality and the methods used to perform emissions calculations; extending the scope of the model to include selected additional environmental impacts and to enable predictive modelling; and improving the utility of GLEAM output.

Developing countries are experiencing an increase in total demand for livestock commodities, as populations and per capita demands increase. Increased production is therefore required to meet this demand and maintain food security. Production increases will lead to proportionate increases in greenhouse gas (GHG) emissions unless offset by reductions in the emissions intensity (Ei) (i.e. the amount of GHG emitted per kg of commodity produced) of livestock production. It is therefore important to identify measures that can increase production whilst reducing Ei cost-effectively. This paper seeks to do this for smallholder agro-pastoral cattle systems in Senegal; ranging from low input to semi-intensified, they are representative of a large proportion of the national cattle production. Specifically, it identifies a shortlist of mitigation measures with potential for application to the various herd systems and estimates their GHG emissions abatement potential (using the Global Livestock Environmental Assessment Model) and cost-effectiveness. Limitations and future requirements are identified and discussed. This paper demonstrates that the Ei of meat and milk from livestock systems in a developing region can be reduced through measures that would also benefit food security, many of which are likely to be cost-beneficial. The ability to make such quantification can assist future sustainable development efforts.

Agriculture accounts for approximately 10% of global greenhouse gas emissions and is simultaneously associated with impacts on human health through food consumption, and agricultural air pollutant emissions. These impacts are often quantified separately, and there is a lack of modelling tools to facilitate integrated assessments. This work presents a new model that integrates assessment of agricultural systems on (i) human health indirectly through dietary, obesity and malnutrition health risks from food consumption, (ii) human health directly through exposure to air pollutants from agricultural emissions, and (iii) greenhouse gas emissions. In the model, national food demand is the starting point from which the livestock and crop production systems that meet this are represented. The model is applied for 2014–2018 to assess the robustness of the GHG emissions and health burden results that this integrated modelling framework produces compared to previous studies that have quantified these variables independently. Methane and nitrous oxide emissions globally in 2018 were estimated to be 129 and 4.4 million tonnes, respectively, consistent with previous estimates. Agricultural systems were also estimated to emit 44 million tonnes of ammonia. An estimated 4.1 million deaths were associated with dietary health risks, 6.0 million with overweight/obesity, and 730 thousand infant deaths from malnutrition, consistent with previous studies. Agricultural air pollutant emissions were estimated to be associated with 537 thousand premature deaths attributable to fine particulate matter (PM2.5) exposure, and 184 thousand premature deaths from methane-induced ground-level ozone. These health impacts provide substantial opportunities to design integrated strategies that mitigate climate change, and improve human health, and also highlight possible trade-offs that the expansion of agricultural production could have due to increased emissions. The model presented here provides for the consistent evaluation of the implications of different agricultural strategies to meet food demand while minimising human health and climate change impacts.

Summary How does one deal with a shortcoming in a public service when even a state “pocket of effectiveness” is unlikely to fill it directly? In the Belgian colonial era, South Kivu Province in eastern Democratic Republic of Congo had a vibrant animal production system, which then was shattered by successive wars. The regeneration of Kivu's livestock industry is greatly constrained by the unreliability of the province's state and private animal health services, which have become commercialized and are unable to overcome the asymmetric information problem inherent to the professions and provide the trustworthy effectiveness farmers need to use them effectively. The Congolese state is unlikely to be able to fill this gap itself. Nonetheless, it can be an architect of solutions to the problem. We were able to identify in the local institutional repertoire at least four possible ways policy-makers could use non-governmental organizations to provide missing trustworthiness to the market. This conclusion is hopeful for places with development potential that have weak states and imperfect markets. © 2017 International Livestock Research Institute. Public Administration and Development published by John Wiley & Sons, Ltd.

The planet's mean air and ocean temperatures have been rising over the last century because of increasing greenhouse gas (GHG) emissions. These changes have substantial effects on the epidemiology of infectious diseases. We describe direct and indirect processes linking climate change and infectious diseases in livestock with reference to specific case studies. Some of the studies are used to show a positive association between temperature and expansion of the geographical ranges of arthropod vectors (e.g. Culicoides imicola, which transmits bluetongue virus) while others are used to illustrate an opposite trend (e.g. tsetse flies that transmit a range of trypanosome parasites in sub-Saharan Africa). We further describe a positive association between extreme events: droughts and El Niño/southern oscillation (ENSO) weather patterns and Rift Valley fever outbreaks in East Africa and some adaptation practices used to mitigate the impacts of climate change that may increase risk of exposure to infectious pathogens. We conclude by outlining mitigation and adaptation measures that can be used specifically in the livestock sector to minimize the impacts of climate change-associated livestock diseases.

In recent decades, intensification of animal production has been occurring rapidly in transition economies to meet the growing demands of increasingly urban populations. This comes with significant environmental, health and social impacts. To assess these impacts, detailed maps of livestock distributions have been developed by downscaling census data at the pixel level (10 km or 1 km), providing estimates of the density of animals in each pixel. However, these data remain at fairly coarse scale and many epidemiological or environmental science applications would make better use of data where the distribution and size of farms are predicted rather than the number of animals per pixel. Based on detailed 2010 census data, we investigated the spatial point pattern distribution of extensive and intensive chicken farms in Thailand. We parameterized point pattern simulation models for extensive and intensive chicken farms and evaluated these models in different parts of Thailand for their capacity to reproduce the correct level of spatial clustering and the most likely locations of the farm clusters. We found that both the level of clustering and location of clusters could be simulated with reasonable accuracy by our farm distribution models. Furthermore, intensive chicken farms tended to be much more clustered than extensive farms, and their locations less easily predicted using simple spatial factors such as human populations. These point-pattern simulation models could be used to downscale coarse administrative level livestock census data into farm locations. This methodology could be of particular value in countries where farm location data are unavailable., SCOPUS: ar.j, info:eu-repo/semantics/published

Highlights • First study of antimicrobial resistance (AMR) phenotypes in humans and urban livestock. • Highest AMR carriage in humans, pigs and poultry. • AMR more common in larger households. • Urban livestock keeping is not a risk factor for AMR in humans. • Use of animal manure affects the risk factor of AMR in humans., There are substantial limitations in understanding of the distribution of antimicrobial resistance (AMR) in humans and livestock in developing countries. This papers present the results of an epidemiological study examining patterns of AMR in Escherichia coli isolates circulating in sympatric human (n = 321) and livestock (n = 633) samples from 99 households across Nairobi, Kenya. E. coli isolates were tested for susceptibility to 13 antimicrobial drugs representing nine antibiotic classes. High rates of AMR were detected, with 47.6% and 21.1% of isolates displaying resistance to three or more and five or more antibiotic classes, respectively. Human isolates showed higher levels of resistance to sulfonamides, trimethoprim, aminoglycosides and penicillins compared with livestock (P0.05). These findings revealed a high prevalence of AMR E. coli circulating in healthy humans and livestock in Nairobi, with no evidence to suggest that keeping livestock, when treated as a single risk factor, contributed significantly to the burden of AMR in humans, although the presence of livestock waste was significant. These results provide an understanding of the broader epidemiology of AMR in complex and interconnected urban environments.

Global data sets on the geographic distribution of livestock are essential for diverse applications in agricultural socio-economics, food security, environmental impact assessment and epidemiology. We present a new version of the Gridded Livestock of the World (GLW 3) database, reflecting the most recently compiled and harmonized subnational livestock distribution data for 2010. GLW 3 provides global population densities of cattle, buffaloes, horses, sheep, goats, pigs, chickens and ducks in each land pixel at a spatial resolution of 0.083333 decimal degrees (approximately 10 km at the equator). They are accompanied by detailed metadata on the year, spatial resolution and source of the input census data. Two versions of each species distribution are produced. In the first version, livestock numbers are disaggregated within census polygons according to weights established by statistical models using high resolution spatial covariates (dasymetric weighting). In the second version, animal numbers are distributed homogeneously with equal densities within their census polygons (areal weighting) to provide spatial data layers free of any assumptions linking them to other spatial variables., SCOPUS: ar.j, info:eu-repo/semantics/published

Climate change has the potential to impair livestock health, with consequences for animal welfare, productivity, greenhouse gas emissions, and human livelihoods and health. Modelling has an important role in assessing the impacts of climate change on livestock systems and the efficacy of potential adaptation strategies, to support decision making for more efficient, resilient and sustainable production. However, a coherent set of challenges and research priorities for modelling livestock health and pathogens under climate change has not previously been available. To identify such challenges and priorities, researchers from across Europe were engaged in a horizon-scanning study, involving workshop and questionnaire based exercises and focussed literature reviews. Eighteen key challenges were identified and grouped into six categories based on subject-specific and capacity building requirements. Across a number of challenges, the need for inventories relating model types to different applications (e.g. the pathogen species, region, scale of focus and purpose to which they can be applied) was identified, in order to identify gaps in capability in relation to the impacts of climate change on animal health. The need for collaboration and learning across disciplines was highlighted in several challenges, e.g. to better understand and model complex ecological interactions between pathogens, vectors, wildlife hosts and livestock in the context of climate change. Collaboration between socio-economic and biophysical disciplines was seen as important for better engagement with stakeholders and for improved modelling of the costs and benefits of poor livestock health. The need for more comprehensive validation of empirical relationships, for harmonising terminology and measurements, and for building capacity for under-researched nations, systems and health problems indicated the importance of joined up approaches across nations. The challenges and priorities identified can help focus the development of modelling capacity and future research structures in this vital field. Well-funded networks capable of managing the long-term development of shared resources are required in order to create a cohesive modelling community equipped to tackle the complex challenges of climate change.

This study builds upon earlier work mapping the potential benefits from bovine trypanosomosis control and analysing the costs of different approaches. Updated costs were derived for five intervention techniques: trypanocides, targets, insecticide-treated cattle, aerial spraying and the release of sterile males. Two strategies were considered: continuous control and elimination. For mapping the costs, cattle densities, environmental constraints, and the presence of savannah or riverine tsetse species were taken into account. These were combined with maps of potential benefits to produce maps of benefit-cost ratios. The results illustrate a diverse picture, and they clearly indicate that no single technique or strategy is universally profitable. For control using trypanocide prophylaxis, returns are modest, even without accounting for the risk of drug resistance but, in areas of low cattle densities, this is the only approach that yields a positive return. Where cattle densities are sufficient to support it, the use of insecticide-treated cattle stands out as the most consistently profitable technique, widely achieving benefit-cost ratios above 5. In parts of the high-potential areas such as the mixed farming, high-oxen-use zones of western Ethiopia, the fertile crescent north of Lake Victoria and the dairy production areas in western and central Kenya, all tsetse control strategies achieve benefit-cost ratios from 2 to over 15, and for elimination strategies, ratios from 5 to over 20. By contrast, in some areas, notably where cattle densities are below 20 per km2, the costs of interventions against tsetse match or even outweigh the benefits, especially for control scenarios using aerial spraying or the deployment of targets where both savannah and riverine flies are present. If the burden of human African trypanosomosis were factored in, the benefit-cost ratios of some of the low-return areas would be considerably increased. Comparatively, elimination strategies give rise to higher benefit-cost ratios than do those for continuous control. However, the costs calculated for elimination assume problem-free, large scale operations, and they rest on the outputs of entomological models that are difficult to validate in the field. Experience indicates that the conditions underlying successful and sustained elimination campaigns are seldom met. By choosing the most appropriate thresholds for benefit-cost ratios, decision-makers and planners can use the maps to define strategies, assist in prioritising areas for intervention, and help choose among intervention techniques and approaches. The methodology would have wider applicability in analysing other disease constraints with a strong spatial component.

SCOPUS: no.j, info:eu-repo/semantics/published

Increasing the production of meat and milk within sub-Saharan Africa should provide significant food security benefits. However, greenhouse gas (GHG) emissions represent a challenge, as cattle production in the region typically has high emissions intensity (EI), i.e., high rates of GHG emissions per unit of output. The high EI is caused by the relatively low production efficiencies in the region, which are in turn partly due to endemic cattle diseases. In theory, improved disease control should increase the efficiency and decrease the emissions intensity of livestock production; however quantitative analysis of the potential GHG mitigation effects of improved disease control in Africa is lacking. This paper seeks to respond to this by using a hybrid modelling approach to quantify the production and emissions effects of removing trypanosomiasis from East African cattle production systems. The emissions are quantified for each cattle production system using an excel version of GLEAM, the Food and Agriculture Organization’s Global Livestock Environmental Assessment Model. The results indicate that removing trypanosomiasis leads to a reduction in the emissions intensity per unit of protein produced of between 0% and 8%, driven mainly by the increases in milk yields and cow fertility rates. Despite the limitations, it is argued that the approach provides considerable scope for modelling the GHG impacts of disease interventions.

The 5th epidemic wave in 2016-2017 of avian influenza A(H7N9) virus in China caused more human cases than any previous waves but the factors that may explain the recent range expansion and surge in incidence remain unknown. We investigated the effect of anthropogenic, poultry and wetland information and of market closures on all epidemic waves (1-5). Poultry predictor variables recently became much more important than before, supporting the assumption of much wider H7N9 transmission in the chicken reservoir, that could be linked to increases in pathogenicity. We show that the future range expansion of H7N9 to northern China may translate into a higher risk of coinciding peaks with those of seasonal influenza, leading to a higher risk of reassortments. Live-poultry market closures are showed to be effective in reducing the local incidence rates of H7N9 human cases, but should be paired with other prevention and control measures to prevent transmission.

Several kinds of pressure can lead to the emergence of infectious diseases. In the case of zoonoses emerging from livestock, one of the most significant changes that has taken place since the mid twentieth century is what has been termed the "livestock revolution", whereby the stock of food animals, their productivity and their trade has increased rapidly to feed rising and increasingly wealthy and urbanized populations. Further increases are projected in the future in low and middle-income countries. Using avian influenza as an example, we discuss how the emergence of avian influenza H5N1 and H7N9 in China was linked to rapid intensification of the poultry sector taking place in landscapes rich in wetland agriculture and wild waterfowls habitats, providing an extensive interface with the wild reservoir of avian influenza viruses. Trade networks and live-poultry markets further exacerbated the spread and persistence of avian influenza as well as human exposure. However, as the history of emergence of highly pathogenic avian influenza (HPAI) demonstrates in high-income countries such as the USA, Canada, Australia, the United Kingdom or the Netherlands, this is by no way specific to low and middle-income countries. Many HPAI emergence events took place in countries with generally good biosecurity standards, and the majority of these in regions hosting intensive poultry production systems. Emerging zoonoses are only one of a number of externalities of intensive livestock production systems, alongside antimicrobial consumption, disruption of nutrient cycles and greenhouse gases emissions, with direct or indirect impacts on human health. In parallel, livestock production is essential to nutrition and livelihoods in many low-income countries. Deindustrialization of the most intensive production systems in high-income countries and sustainable intensifications in low-income countries may converge to a situation where the nutritional and livelihood benefits of livestock production would be less overshadowed by its negative impacts on human an ecosystem health., SCOPUS: no.j, info:eu-repo/semantics/published

The large and expanding use of antimicrobials in livestock, a consequence of growing global demand for animal protein, is of considerable concern in light of the threat of antimicrobial resistance (AMR). Use of antimicrobials in animals has been linked to drug-resistant infections in animals (1) and humans (2). In September 2016, the United Nations (UN) General Assembly recognized the inappropriate use of antimicrobials in animals as a leading cause of rising AMR. In September 2018, the interagency group established by the UN Secretary General will report on progress in the global response to AMR, including antimicrobial consumption in animals. We provide a baseline to monitor efforts to reduce antimicrobial use and assess how three global policies might curb antimicrobial consumption in food animal production: (i) enforcing global regulations to cap antimicrobial use, (ii) adherence to nutritional guidelines leading to reduced meat consumption, and (iii) imposing a global user fee on veterinary antimicrobial use., Science, 357 (6358), ISSN:0036-8075, ISSN:1095-9203

Rift Valley fever is a severe disease affecting both humans and animals. The Rift Valley fever virus can be transmitted by body fluids, and the most common way for humans to get infected is from animals. The virus is also vector-borne and can be transmitted by many species of mosquitoes. As with other vector-borne diseases, the epidemiology may vary in response to environmental changes. Here the effects of climate and land use changes on Rift Valley fever, as well as on other vector-borne diseases, are discussed. The effect of irrigation in East Africa on inter-epidemic transmission of RVF is discussed in greater detail, followed by recommendations for future research and actions.

The 2014–2016 JET results are reviewed in the light of their significance for optimising the ITER research plan for the active and non-active operation. More than 60 h of plasma operation with ITER first wall materials successfully took place since its installation in 2011. New multi-machine scaling of the type I-ELM divertor energy flux density to ITER is supported by first principle modelling. ITER relevant disruption experiments and first principle modelling are reported with a set of three disruption mitigation valves mimicking the ITER setup. Insights of the L–H power threshold in Deuterium and Hydrogen are given, stressing the importance of the magnetic configurations and the recent measurements of fine-scale structures in the edge radial electric. Dimensionless scans of the core and pedestal confinement provide new information to elucidate the importance of the first wall material on the fusion performance. H-mode plasmas at ITER triangularity (H = 1 at ßN ~ 1.8 and n/nGW ~ 0.6) have been sustained at 2 MA during 5 s. The ITER neutronics codes have been validated on high performance experiments. Prospects for the coming D–T campaign and 14 MeV neutron calibration strategy are reviewed. This work has been carried out within the framework of the EUROfusion Consortium and has received funding from the Euratom research and training programme 2014–2018 under grant agreement No 633053. Peer Reviewed Article signat per 1.173 autors/es: X. Litaudon35, S. Abduallev39, M. Abhangi46, P. Abreu53, M. Afzal7, K.M. Aggarwal29, T. Ahlgren101, J.H. Ahn8, L. Aho-Mantila112, N. Aiba69, M. Airila112, R. Albanese105, V. Aldred7, D. Alegre93, E. Alessi45, P. Aleynikov55, A. Alfier12, A. Alkseev72, M. Allinson7, B. Alper7, E. Alves53, G. Ambrosino105, R. Ambrosino106, L. Amicucci90, V. Amosov88, E. Andersson Sundén22, M. Angelone90, M. Anghel85, C. Angioni62, L. Appel7, C. Appelbee7, P. Arena30, M. Ariola106, H. Arnichand8, S. Arshad41, A. Ash7, N. Ashikawa68, V. Aslanyan64, O. Asunta1, F. Auriemma12, Y. Austin7, L. Avotina103, M.D. Axton7, C. Ayres7, M. Bacharis24, A. Baciero57, D. Baião53, S. Bailey7, A. Baker7, I. Balboa7, M. Balden62, N. Balshaw7, R. Bament7, J.W. Banks7, Y.F. Baranov7, M.A. Barnard7, D. Barnes7, M. Barnes27, R. Barnsley55, A. Baron Wiechec7, L. Barrera Orte34, M. Baruzzo12, V. Basiuk8, M. Bassan55, R. Bastow7, A. Batista53, P. Batistoni90, R. Baughan7, B. Bauvir55, L. Baylor73, B. Bazylev56, J. Beal110, P.S. Beaumont7, M. Beckers39, B. Beckett7, A. Becoulet8, N. Bekris35, M. Beldishevski7, K. Bell7, F. Belli90, M. Bellinger7, É. Belonohy62, N. Ben Ayed7, N.A. Benterman7, H. Bergsåker42, J. Bernardo53, M. Bernert62, M. Berry7, L. Bertalot55, C. Besliu7, M. Beurskens63, B. Bieg61, J. Bielecki47, T. Biewer73, M. Bigi12, P. Bílková50, F. Binda22, A. Bisoffi31, J.P.S. Bizarro53, C. Björkas101, J. Blackburn7, K. Blackman7, T.R. Blackman7, P. Blanchard33, P. Blatchford7, V. Bobkov62, A. Boboc7, G. Bodnár113, O. Bogar18, I. Bolshakova60, T. Bolzonella12, N. Bonanomi97, F. Bonelli56, J. Boom62, J. Booth7, D. Borba35,53, D. Borodin39, I. Borodkina39, A. Botrugno90, C. Bottereau8, P. Boulting7, C. Bourdelle8, M. Bowden7, C. Bower7, C. Bowman110, T. Boyce7, C. Boyd7, H.J. Boyer7, J.M.A. Bradshaw7, V. Braic87, R. Bravanec40, B. Breizman107, S. Bremond8, P.D. Brennan7, S. Breton8, A. Brett7, S. Brezinsek39, M.D.J. Bright7, M. Brix7, W. Broeckx78, M. Brombin12, A. Brosławski65, D.P.D. Brown7, M. Brown7, E. Bruno55, J. Bucalossi8, J. Buch46, J. Buchanan7, M.A. Buckley7, R. Budny76, H. Bufferand8, M. Bulman7, N. Bulmer7, P. Bunting7, P. Buratti90, A. Burckhart62, A. Buscarino30, A. Busse7, N.K. Butler7, I. Bykov42, J. Byrne7, P. Cahyna50, G. Calabrò90, I. Calvo57, Y. Camenen4, P. Camp7, D.C. Campling7, J. Cane7, B. Cannas17, A.J. Capel7, P.J. Card7, A. Cardinali90, P. Carman7, M. Carr7, D. Carralero62, L. Carraro12, B.B. Carvalho53, I. Carvalho53, P. Carvalho53, F.J. Casson7, C. Castaldo90, N. Catarino53, J. Caumont7, F. Causa90, R. Cavazzana12, K. Cave-Ayland7, M. Cavinato12, M. Cecconello22, S. Ceccuzzi90, E. Cecil76, A. Cenedese12, R. Cesario90, C.D. Challis7, M. Chandler7, D. Chandra46, C.S. Chang76, A. Chankin62, I.T. Chapman7, S.C. Chapman28, M. Chernyshova49, G. Chitarin12, G. Ciraolo8, D. Ciric7, J. Citrin38, F. Clairet8, E. Clark7, M. Clark7, R. Clarkson7, D. Clatworthy7, C. Clements7, M. Cleverly7, J.P. Coad7, P.A. Coates7, A. Cobalt7, V. Coccorese105, V. Cocilovo90, S. Coda33, R. Coelho53, J.W. Coenen39, I. Coffey29, L. Colas8, S. Collins7, D. Conka103, S. Conroy22, N. Conway7, D. Coombs7, D. Cooper7, S.R. Cooper7, C. Corradino30, Y. Corre8, G. Corrigan7, S. Cortes53, D. Coster62, A.S. Couchman7, M.P. Cox7, T. Craciunescu86, S. Cramp7, R. Craven7, F. Crisanti90, G. Croci97, D. Croft7, K. Crombé15, R. Crowe7, N. Cruz53, G. Cseh113, A. Cufar81, A. Cullen7, M. Curuia85, A. Czarnecka49, H. Dabirikhah7, P. Dalgliesh7, S. Dalley7, J. Dankowski47, D. Darrow76, O. Davies7, W. Davis55,76, C. Day56, I.E. Day7, M. De Bock55, A. de Castro57, E. de la Cal57, E. de la Luna57, G. De Masi12, J. L. de Pablos57, G. De Temmerman55, G. De Tommasi105, P. de Vries55, K. Deakin7, J. Deane7, F. Degli Agostini12, R. Dejarnac50, E. Delabie73, N. den Harder38, R.O. Dendy7, J. Denis8, P. Denner39, S. Devaux62,104, P. Devynck8, F. Di Maio55, A. Di Siena62, C. Di Troia90, P. Dinca86, R. D’Inca62, B. Ding51, T. Dittmar39, H. Doerk62, R.P. Doerner9, T. Donné34, S.E. Dorling7, S. Dormido-Canto93, S. Doswon7, D. Douai8, P.T. Doyle7, A. Drenik62,81, P. Drewelow63, P. Drews39, Ph. Duckworth55, R. Dumont8, P. Dumortier58, D. Dunai113, M. Dunne62, I. Ďuran50, F. Durodié58, P. Dutta46, B. P. Duval33, R. Dux62, K. Dylst78, N. Dzysiuk22, P.V. Edappala46, J. Edmond7, A.M. Edwards7, J. Edwards7, Th. Eich62, A. Ekedahl8, R. El-Jorf7, C.G. Elsmore7, M. Enachescu84, G. Ericsson22, F. Eriksson16, J. Eriksson22, L.G. Eriksson36, B. Esposito90, S. Esquembri94, H.G. Esser39, D. Esteve8, B. Evans7, G.E. Evans7, G. Evison7, G.D. Ewart7, D. Fagan7, M. Faitsch62, D. Falie86, A. Fanni17, A. Fasoli33, J. M. Faustin33, N. Fawlk7, L. Fazendeiro53, N. Fedorczak8, R.C. Felton7, K. Fenton7, A. Fernades53, H. Fernandes53, J. Ferreira53, J.A. Fessey7, O. Février8, O. Ficker50, A. Field7, S. Fietz62, A. Figueiredo53, J. Figueiredo53,35, A. Fil8, P. Finburg7, M. Firdaouss8, U. Fischer56, L. Fittill7, M. Fitzgerald7, D. Flammini90, J. Flanagan7, C. Fleming7, K. Flinders7, N. Fonnesu90, J. M. Fontdecaba57, A. Formisano79, L. Forsythe7, L. Fortuna30, E. Fortuna-Zalesna19, M. Fortune7, S. Foster7, T. Franke34, T. Franklin7, M. Frasca30, L. Frassinetti42, M. Freisinger39, R. Fresa98, D. Frigione90, V. Fuchs50, D. Fuller35, S. Futatani6, J. Fyvie7, K. Gál34,62, D. Galassi2, K. Gałązka49, J. Galdon-Quiroga92, J. Gallagher7, D. Gallart6, R. Galvão10, X. Gao51, Y. Gao39, J. Garcia8, A. Garcia-Carrasco42, M. García-Muñoz92, J.-L. Gardarein3, L. Garzotti7, P. Gaudio95, E. Gauthier8, D.F. Gear7, S.J. Gee7, B. Geiger62, M. Gelfusa95, S. Gerasimov7, G. Gervasini45, M. Gethins7, Z. Ghani7, M. Ghate46, M. Gherendi86, J.C. Giacalone8, L. Giacomelli45, C.S. Gibson7, T. Giegerich56, C. Gil8, L. Gil53, S. Gilligan7, D. Gin54, E. Giovannozzi90, J.B. Girardo8, C. Giroud7, G. Giruzzi8, S. Glöggler62, J. Godwin7, J. Goff7, P. Gohil43, V. Goloborod’ko102, R. Gomes53, B. Gonçalves53, M. Goniche8, M. Goodliffe7, A. Goodyear7, G. Gorini97, M. Gosk65, R. Goulding76, A. Goussarov78, R. Gowland7, B. Graham7, M.E. Graham7, J. P. Graves33, N. Grazier7, P. Grazier7, N.R. Green7, H. Greuner62, B. Grierson76, F.S. Griph7, C. Grisolia8, D. Grist7, M. Groth1, R. Grove73, C.N. Grundy7, J. Grzonka19, D. Guard7, C. Guérard34, C. Guillemaut8,53, R. Guirlet8, C. Gurl7, H.H. Utoh69, L.J. Hackett7, S. Hacquin8,35, A. Hagar7, R. Hager76, A. Hakola112, M. Halitovs103, S.J. Hall7, S.P. Hallworth Cook7, C. Hamlyn-Harris7, K. Hammond7, C. Harrington7, J. Harrison7, D. Harting7, F. Hasenbeck39, Y. Hatano108, D.R. Hatch107, T.D.V. Haupt7, J. Hawes7, N.C. Hawkes7, J. Hawkins7, P. Hawkins7, P.W. Haydon7, N. Hayter7, S. Hazel7, P.J.L. Heesterman7, K. Heinola101, C. Hellesen22, T. Hellsten42, W. Helou8, O.N. Hemming7, T.C. Hender7, M. Henderson55, S.S. Henderson21, R. Henriques53, D. Hepple7, G. Hermon7, P. Hertout8, C. Hidalgo57, E.G. Highcock27, M. Hill7, J. Hillairet8, J. Hillesheim7, D. Hillis73, K. Hizanidis70, A. Hjalmarsson22, J. Hobirk62, E. Hodille8, C.H.A. Hogben7, G.M.D. Hogeweij38, A. Hollingsworth7, S. Hollis7, D.A. Homfray7, J. Horáček50, G. Hornung15, A.R. Horton7, L.D. Horton36, L. Horvath110, S.P. Hotchin7, M.R. Hough7, P.J. Howarth7, A. Hubbard64, A. Huber39, V. Huber39, T.M. Huddleston7, M. Hughes7, G.T.A. Huijsmans55, C.L. Hunter7, P. Huynh8, A.M. Hynes7, D. Iglesias7, N. Imazawa69, F. Imbeaux8, M. Imríšek50, M. Incelli109, P. Innocente12, M. Irishkin8, I. Ivanova-Stanik49, S. Jachmich58,35, A.S. Jacobsen83, P. Jacquet7, J. Jansons103, A. Jardin8, A. Järvinen1, F. Jaulmes38, S. Jednoróg49, I. Jenkins7, C. Jeong20, I. Jepu86, E. Joffrin8, R. Johnson7, T. Johnson42, Jane Johnston7, L. Joita7, G. Jones7, T.T.C. Jones7, K.K. Hoshino69, A. Kallenbach62, K. Kamiya69, J. Kaniewski7, A. Kantor7, A. Kappatou62, J. Karhunen1, D. Karkinsky7, I. Karnowska7, M. Kaufman73, G. Kaveney7, Y. Kazakov58, V. Kazantzidis70, D.L. Keeling7, T. Keenan7, J. Keep7, M. Kempenaars7, C. Kennedy7, D. Kenny7, J. Kent7, O.N. Kent7, E. Khilkevich54, H.T. Kim35, H.S. Kim80, A. Kinch7, C. king7, D. King7, R.F. King7, D.J. Kinna7, V. Kiptily7, A. Kirk7, K. Kirov7, A. Kirschner39, G. Kizane103, C. Klepper73, A. Klix56, P. Knight7, S.J. Knipe7, S. Knott96, T. Kobuchi69, F. Köchl111, G. Kocsis113, I. Kodeli81, L. Kogan7, D. Kogut8, S. Koivuranta112, Y. Kominis70, M. Köppen39, B. Kos81, T. Koskela1, H.R. Koslowski39, M. Koubiti4, M. Kovari7, E. Kowalska-Strzęciwilk49, A. Krasilnikov88, V. Krasilnikov88, N. Krawczyk49, M. Kresina8, K. Krieger62, A. Krivska58, U. Kruezi7, I. Książek48, A. Kukushkin72, A. Kundu46, T. Kurki-Suonio1, S. Kwak20, R. Kwiatkowski65, O.J. Kwon13, L. Laguardia45, A. Lahtinen101, A. Laing7, N. Lam7, H.T. Lambertz39, C. Lane7, P.T. Lang62, S. Lanthaler33, J. Lapins103, A. Lasa101, J.R. Last7, E. Łaszyńska49, R. Lawless7, A. Lawson7, K.D. Lawson7, A. Lazaros70, E. Lazzaro45, J. Leddy110, S. Lee66, X. Lefebvre7, H.J. Leggate32, J. Lehmann7, M. Lehnen55, D. Leichtle41, P. Leichuer7, F. Leipold55,83, I. Lengar81, M. Lennholm36, E. Lerche58, A. Lescinskis103, S. Lesnoj7, E. Letellier7, M. Leyland110, W. Leysen78, L. Li39, Y. Liang39, J. Likonen112, J. Linke39, Ch. Linsmeier39, B. Lipschultz110, G. Liu55, Y. Liu51, V.P. Lo Schiavo105, T. Loarer8, A. Loarte55, R.C. Lobel7, B. Lomanowski1, P.J. Lomas7, J. Lönnroth1,35, J. M. López94, J. López-Razola57, R. Lorenzini12, U. Losada57, J.J. Lovell7, A.B. Loving7, C. Lowry36, T. Luce43, R.M.A. Lucock7, A. Lukin74, C. Luna5, M. Lungaroni95, C.P. Lungu86, M. Lungu86, A. Lunniss110, I. Lupelli7, A. Lyssoivan58, N. Macdonald7, P. Macheta7, K. Maczewa7, B. Magesh46, P. Maget8, C. Maggi7, H. Maier62, J. Mailloux7, T. Makkonen1, R. Makwana46, A. Malaquias53, A. Malizia95, P. Manas4, A. Manning7, M.E. Manso53, P. Mantica45, M. Mantsinen6, A. Manzanares91, Ph. Maquet55, Y. Marandet4, N. Marcenko88, C. Marchetto45, O. Marchuk39, M. Marinelli95, M. Marinucci90, T. Markovič50, D. Marocco90, L. Marot26, C.A. Marren7, R. Marshal7, A. Martin7, Y. Martin33, A. Martín de Aguilera57, F.J. Martínez93, J. R. Martín-Solís14, Y. Martynova39, S. Maruyama55, A. Masiello12, M. Maslov7, S. Matejcik18, M. Mattei79, G.F. Matthews7, F. Maviglia11, M. Mayer62, M.L. Mayoral34, T. May-Smith7, D. Mazon8, C. Mazzotta90, R. McAdams7, P.J. McCarthy96, K.G. McClements7, O. McCormack12, P.A. McCullen7, D. McDonald34, S. McIntosh7, R. McKean7, J. McKehon7, R.C. Meadows7, A. Meakins7, F. Medina57, M. Medland7, S. Medley7, S. Meigh7, A.G. Meigs7, G. Meisl62, S. Meitner73, L. Meneses53, S. Menmuir7,42, K. Mergia71, I.R. Merrigan7, Ph. Mertens39, S. Meshchaninov88, A. Messiaen58, H. Meyer7, S. Mianowski65, R. Michling55, D. Middleton-Gear7, J. Miettunen1, F. Militello7, E. Militello-Asp7, G. Miloshevsky77, F. Mink62, S. Minucci105, Y. Miyoshi69, J. Mlynář50, D. Molina8, I. Monakhov7, M. Moneti109, R. Mooney7, S. Moradi37, S. Mordijck43, L. Moreira7, R. Moreno57, F. Moro90, A.W. Morris7, J. Morris7, L. Moser26, S. Mosher73, D. Moulton7,1, A. Murari12,35, A. Muraro45, S. Murphy7, N.N. Asakura69, Y.S. Na80, F. Nabais53, R. Naish7, T. Nakano69, E. Nardon8, V. Naulin83, M.F.F. Nave53, I. Nedzelski53, G. Nemtsev88, F. Nespoli33, A. Neto41, R. Neu62, V.S. Neverov72, M. Newman7, K.J. Nicholls7, T. Nicolas33, A.H. Nielsen83, P. Nielsen12, E. Nilsson8, D. Nishijima99, C. Noble7, M. Nocente97, D. Nodwell7, K. Nordlund101, H. Nordman16, R. Nouailletas8, I. Nunes53, M. Oberkofler62, T. Odupitan7, M.T. Ogawa69, T. O’Gorman7, M. Okabayashi76, R. Olney7, O. Omolayo7, M. O’Mullane21, J. Ongena58, F. Orsitto11, J. Orszagh18, B.I. Oswuigwe7, R. Otin7, A. Owen7, R. Paccagnella12, N. Pace7, D. Pacella90, L.W. Packer7, A. Page7, E. Pajuste103, S. Palazzo30, S. Pamela7, S. Panja46, P. Papp18, R. Paprok50, V. Parail7, M. Park66, F. Parra Diaz27, M. Parsons73, R. Pasqualotto12, A. Patel7, S. Pathak46, D. Paton7, H. Patten33, A. Pau17, E. Pawelec48, C. Paz Soldan43, A. Peackoc36, I.J. Pearson7, S.-P. Pehkonen112, E. Peluso95, C. Penot55, A. Pereira57, R. Pereira53, P.P. Pereira Puglia7, C. Perez von Thun35,39, S. Peruzzo12, S. Peschanyi56, M. Peterka50, P. Petersson42, G. Petravich113, A. Petre84, N. Petrella7, V. Petržilka50, Y. Peysson8, D. Pfefferlé33, V. Philipps39, M. Pillon90, G. Pintsuk39, P. Piovesan12, A. Pires dos Reis52, L. Piron7, A. Pironti105, F. Pisano17, R. Pitts55, F. Pizzo79, V. Plyusnin53, N. Pomaro12, O.G. Pompilian86, P.J. Pool7, S. Popovichev7, M.T. Porfiri90, C. Porosnicu86, M. Porton7, G. Possnert22, S. Potzel62, T. Powell7, J. Pozzi7, V. Prajapati46, R. Prakash46, G. Prestopino95, D. Price7, M. Price7, R. Price7, P. Prior7, R. Proudfoot7, G. Pucella90, P. Puglia52, M.E. Puiatti12, D. Pulley7, K. Purahoo7, Th. Pütterich62, E. Rachlew25, M. Rack39, R. Ragona58, M.S.J. Rainford7, A. Rakha6, G. Ramogida90, S. Ranjan46, C.J. Rapson62, J.J. Rasmussen83, K. Rathod46, G. Rattá57, S. Ratynskaia82, G. Ravera90, C. Rayner7, M. Rebai97, D. Reece7, A. Reed7, D. Réfy113, B. Regan7, J. Regaña34, M. Reich62, N. Reid7, F. Reimold39, M. Reinhart34, M. Reinke110,73, D. Reiser39, D. Rendell7, C. Reux8, S.D.A. Reyes Cortes53, S. Reynolds7, V. Riccardo7, N. Richardson7, K. Riddle7, D. Rigamonti97, F.G. Rimini7, J. Risner73, M. Riva90, C. Roach7, R.J. Robins7, S.A. Robinson7, T. Robinson7, D.W. Robson7, R. Roccella55, R. Rodionov88, P. Rodrigues53, J. Rodriguez7, V. Rohde62, F. Romanelli90, M. Romanelli7, S. Romanelli7, J. Romazanov39, S. Rowe7, M. Rubel42, G. Rubinacci105, G. Rubino12, L. Ruchko52, M. Ruiz94, C. Ruset86, J. Rzadkiewicz65, S. Saarelma7, R. Sabot8, E. Safi101, P. Sagar7, G. Saibene41, F. Saint-Laurent8, M. Salewski83, A. Salmi112, R. Salmon7, F. Salzedas53, D. Samaddar7, U. Samm39, D. Sandiford7, P. Santa46, M.I.K. Santala1, B. Santos53, A. Santucci90, F. Sartori41, R. Sartori41, O. Sauter33, R. Scannell7, T. Schlummer39, K. Schmid62, V. Schmidt12, S. Schmuck7, M. Schneider8, K. Schöpf102, D. Schwörer32, S.D. Scott76, G. Sergienko39, M. Sertoli62, A. Shabbir15, S.E. Sharapov7, A. Shaw7, R. Shaw7, H. Sheikh7, A. Shepherd7, A. Shevelev54, A. Shumack38, G. Sias17, M. Sibbald7, B. Sieglin62, S. Silburn7, A. Silva53, C. Silva53, P.A. Simmons7, J. Simpson7, J. Simpson-Hutchinson7, A. Sinha46, S.K. Sipilä1, A.C.C. Sips36, P. Sirén112, A. Sirinelli55, H. Sjöstrand22, M. Skiba22, R. Skilton7, K. Slabkowska49, B. Slade7, N. Smith7, P.G. Smith7, R. Smith7, T.J. Smith7, M. Smithies110, L. Snoj81, S. Soare85, E. R. Solano35,57, A. Somers32, C. Sommariva8, P. Sonato12, A. Sopplesa12, J. Sousa53, C. Sozzi45, S. Spagnolo12, T. Spelzini7, F. Spineanu86, G. Stables7, I. Stamatelatos71, M.F. Stamp7, P. Staniec7, G. Stankūnas59, C. Stan-Sion84, M.J. Stead7, E. Stefanikova42, I. Stepanov58, A.V. Stephen7, M. Stephen46, A. Stevens7, B.D. Stevens7, J. Strachan76, P. Strand16, H.R. Strauss44, P. Ström42, G. Stubbs7, W. Studholme7, F. Subba75, H.P. Summers21, J. Svensson63, Ł. Świderski65, T. Szabolics113, M. Szawlowski49, G. Szepesi7, T.T. Suzuki69, B. Tál113, T. Tala112, A.R. Talbot7, S. Talebzadeh95, C. Taliercio12, P. Tamain8, C. Tame7, W. Tang76, M. Tardocchi45, L. Taroni12, D. Taylor7, K.A. Taylor7, D. Tegnered16, G. Telesca15, N. Teplova54, D. Terranova12, D. Testa33, E. Tholerus42, J. Thomas7, J.D. Thomas7, P. Thomas55, A. Thompson7, C.-A. Thompson7, V.K. Thompson7, L. Thorne7, A. Thornton7, A.S. Thrysøe83, P.A. Tigwell7, N. Tipton7, I. Tiseanu86, H. Tojo69, M. Tokitani67, P. Tolias82, M. Tomeš50, P. Tonner7, M. Towndrow7, P. Trimble7, M. Tripsky58, M. Tsalas38, P. Tsavalas71, D. Tskhakaya jun102, I. Turner7, M.M. Turner32, M. Turnyanskiy34, G. Tvalashvili7, S.G.J. Tyrrell7, A. Uccello45, Z. Ul-Abidin7, J. Uljanovs1, D. Ulyatt7, H. Urano69, I. Uytdenhouwen78, A.P. Vadgama7, D. Valcarcel7, M. Valentinuzzi8, M. Valisa12, P. Vallejos Olivares42, M. Valovic7, M. Van De Mortel7, D. Van Eester58, W. Van Renterghem78, G.J. van Rooij38, J. Varje1, S. Varoutis56, S. Vartanian8, K. Vasava46, T. Vasilopoulou71, J. Vega57, G. Verdoolaege58, R. Verhoeven7, C. Verona95, G. Verona Rinati95, E. Veshchev55, N. Vianello45, J. Vicente53, E. Viezzer62,92, S. Villari90, F. Villone100, P. Vincenzi12, I. Vinyar74, B. Viola90, A. Vitins103, Z. Vizvary7, M. Vlad86, I. Voitsekhovitch34, P. Vondráček50, N. Vora7, T. Vu8, W.W. Pires de Sa52, B. Wakeling7, C.W.F. Waldon7, N. Walkden7, M. Walker7, R. Walker7, M. Walsh55, E. Wang39, N. Wang39, S. Warder7, R.J. Warren7, J. Waterhouse7, N.W. Watkins28, C. Watts55, T. Wauters58, A. Weckmann42, J. Weiland23, H. Weisen33, M. Weiszflog22, C. Wellstood7, A.T. West7, M.R. Wheatley7, S. Whetham7, A.M. Whitehead7, B.D. Whitehead7, A.M. Widdowson7, S. Wiesen39, J. Wilkinson7, J. Williams7, M. Williams7, A.R. Wilson7, D.J. Wilson7, H.R. Wilson110, J. Wilson7, M. Wischmeier62, G. Withenshaw7, A. Withycombe7, D.M. Witts7, D. Wood7, R. Wood7, C. Woodley7, S. Wray7, J. Wright7, J.C. Wright64, J. Wu89, S. Wukitch64, A. Wynn110, T. Xu7, D. Yadikin16, W. Yanling39, L. Yao89, V. Yavorskij102, M.G. Yoo80, C. Young7, D. Young7, I.D. Young7, R. Young7, J. Zacks7, R. Zagorski49, F.S. Zaitsev18, R. Zanino75, A. Zarins103, K.D. Zastrow7, M. Zerbini90, W. Zhang62, Y. Zhou42, E. Zilli12, V. Zoita86, S. Zoletnik113, I. Zychor65 and JET Contributorsa // EUROfusion Consortium JET, Culham Science Centre, Abingdon, OX14 3DB, United Kingdom / 1 Aalto University, PO Box 14100, FIN-00076 Aalto, Finland / 2 Aix Marseille Université, CNRS, Centrale Marseille, M2P2 UMR 7340, 13451, Marseille, France / 3 Aix-Marseille Université, CNRS, IUSTI UMR 7343, 13013 Marseille, France / 4 Aix-Marseille Université, CNRS, PIIM, UMR 7345, 13013 Marseille, France / 5 Arizona State University, Tempe, AZ, United States of America / 6 Barcelona Supercomputing Center, Barcelona, Spain / 7 CCFE, Culham Science Centre, Abingdon, Oxon, OX14 3DB, United Kingdom / 8 CEA, IRFM, F-13108 Saint Paul Lez Durance, France / 9 Center for Energy Research, University of California at San Diego, La Jolla, CA 92093, United States of America / 10 Centro Brasileiro de Pesquisas Fisicas, Rua Xavier Sigaud, 160, Rio de Janeiro CEP 22290-180, Brazil / 11 Consorzio CREATE, Via Claudio 21, 80125 Napoli, Italy / 12 Consorzio RFX, corso Stati Uniti 4, 35127 Padova, Italy / 13 Daegu University, Jillyang, Gyeongsan, Gyeongbuk 712-174, Republic of Korea / 14 Departamento de Física, Universidad Carlos III de Madrid, 28911 Leganés, Madrid, Spain / 15 Department of Applied Physics UG (Ghent University) St-Pietersnieuwstraat 41 B-9000 Ghent, Belgium / 16 Department of Earth and Space Sciences, Chalmers University of Technology, SE-41296 Gothenburg, Sweden / 17 Department of Electrical and Electronic Engineering, University of Cagliari, Piazza d’Armi 09123, Cagliari, Italy / 18 Department of Experimental Physics, Faculty of Mathematics, Physics and Informatics Comenius University Mlynska dolina F2, 84248 Bratislava, Slovakia / 19 Department of Materials Science, Warsaw University of Technology, PL-01-152 Warsaw, Poland / 20 Department of Nuclear and Quantum Engineering, KAIST, Daejeon 34141, Korea / 21 Department of Physics and Applied Physics, University of Strathclyde, Glasgow, G4 ONG, United Kingdom / 22 Department of Physics and Astronomy, Uppsala University, SE-75120 Uppsala, Sweden / 23 Department of Physics, Chalmers University of Technology, SE-41296 Gothenburg, Sweden / 24 Department of Physics, Imperial College London, London, SW7 2AZ, United Kingdom / 25 Department of Physics, SCI, KTH, SE-10691 Stockholm, Sweden / 26 Department of Physics, University of Basel, Basel, Switzerland / 27 Department of Physics, University of Oxford, Oxford, OX1 2JD, United Kingdom / 28 Department of Physics, University of Warwick, Coventry, CV4 7AL, United Kingdom / 29 Department of Pure and Applied Physics, Queens University, Belfast, BT7 1NN, United Kingdom / 30 Dipartimento di Ingegneria Elettrica Elettronica e Informatica, Università degli Studi di Catania, 95125 Catania, Italy / 31 Dipartimento di Ingegneria Industriale, University of Trento, Trento, Italy / 32 Dublin City University (DCU), Dublin, Ireland / 33 Ecole Polytechnique Fédérale de Lausanne (EPFL), Swiss Plasma Center (SPC), CH-1015 Lausanne, Switzerland / 34 EUROfusion Programme Management Unit, Boltzmannstr. 2, 85748 Garching, Germany / 35 EUROfusion Programme Management Unit, Culham Science Centre, Culham, OX14 3DB, United Kingdom / 36 European Commission, B-1049 Brussels, Belgium / 37 Fluid and Plasma Dynamics, ULB—Campus Plaine—CP 231 Boulevard du Triomphe, 1050 Bruxelles, Belgium / 38 FOM Institute DIFFER, Eindhoven, Netherlands / 39 Forschungszentrum Jülich GmbH, Institut für Energie- und Klimaforschung—Plasmaphysik, 52425 Jülich, Germany / 40 Fourth State Research, 503 Lockhart Dr, Austin, TX, United States of America / 41 Fusion for Energy Joint Undertaking, Josep Pl. 2, Torres Diagonal Litoral B3, 08019, Barcelona, Spain / 42 Fusion Plasma Physics, EES, KTH, SE-10044 Stockholm, Sweden / 43 General Atomics, PO Box 85608, San Diego, CA 92186-5608, United States of America / 44 HRS Fusion, West Orange, NJ, United States of America / 45 IFP-CNR, via R. Cozzi 53, 20125 Milano, Italy / 46 Institute for Plasma Research, Bhat, Gandhinagar-382 428, Gujarat State, India / 47 Institute of Nuclear Physics, Radzikowskiego 152, 31-342 Kraków, Poland / 48 Institute of Physics, Opole University, Oleska 48, 45-052 Opole, Poland / 49 Institute of Plasma Physics and Laser Microfusion, Hery 23, 01-497 Warsaw, Poland / 50 Institute of Plasma Physics AS CR, Za Slovankou 1782/3, 182 00 Praha 8, Czechia / 51 Institute of Plasma Physics, Chinese Academy of Sciences, Hefei 230031, People’s Republic of China / 52 Instituto de Física, Universidade de São Paulo, Rua do Matão Travessa R Nr.187 CEP 05508-090 Cidade Universitária, São Paulo, Brasil / 53 Instituto de Plasmas e Fusão Nuclear, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal / 54 Ioffe Physico-Technical Institute, 26 Politekhnicheskaya, St Petersburg 194021, Russian Federation / 55 ITER Organization, Route de Vinon, CS 90 046, 13067 Saint Paul Lez Durance, France / 56 Karlsruhe Institute of Technology, PO Box 3640, D-76021 Karlsruhe, Germany / 57 Laboratorio Nacional de Fusión, CIEMAT, Madrid, Spain / 58 Laboratory for Plasma Physics Koninklijke Militaire School—Ecole Royale Militaire, Renaissancelaan 30 Avenue de la Renaissance B-1000, Brussels, Belgium / 59 Lithuanian energy institute, Breslaujos g. 3, LT-44403, Kaunas, Lithuania / 60 Magnetic Sensor Laboratory, Lviv Polytechnic National University, Lviv, Ukraine / 61 Maritime University of Szczecin, Waly Chrobrego 1-2, 70-500 Szczecin, Poland / 62 Max-Planck-Institut für Plasmaphysik, D-85748 Garching, Germany / 63 Max-Planck-Institut für Plasmaphysik, Teilinsitut Greifswald, D-17491 Greifswald, Germany / 64 MIT Plasma Science and Fusion Centre, Cambridge, MA 02139, United States of America / 65 National Centre for Nuclear Research (NCBJ), 05-400 Otwock-Świerk, Poland / 66 National Fusion Research Institute (NFRI), 169-148 Gwahak-ro, Yuseong-gu, Daejeon 305-806, Republic of Korea / 67 National Institute for Fusion Science, Oroshi, Toki, Gifu 509-5292, Japan / 68 National Institute for Fusion Science, Toki, 509-5292, Japan / 69 National Institutes for Quantum and Radiological Science and Technology, Naka, Ibaraki 311-0193, Japan / 70 National Technical University of Athens, Iroon Politechniou 9, 157 73 Zografou, Athens, Greece / 71 NCSR ‘Demokritos’, 153 10, Agia Paraskevi Attikis, Greece / 72 NRC Kurchatov Institute, 1 Kurchatov Square, Moscow 123182, Russian Federation / 73 Oak Ridge National Laboratory, Oak Ridge, TN 37831-6169, United States of America / 74 PELIN LLC, 27a, Gzhatskaya Ulitsa, Saint Petersburg, 195220, Russian Federation / 75 Politecnico di Torino, Corso Duca degli Abruzzi 24, I-10129 Torino, Italy / 76 Princeton Plasma Physics Laboratory, James Forrestal Campus, Princeton, NJ 08543, United States of America / 77 Purdue University, 610 Purdue Mall, West Lafayette, IN 47907, United States of America / 78 SCK-CEN, Nuclear Research Centre, 2400 Mol, Belgium / 79 Second University of Napoli, Consorzio CREATE, Via Claudio 21, 80125 Napoli, Italy / 80 Seoul National University, Shilim-Dong, Gwanak-Gu, Republic of Korea / 81 Slovenian Fusion Association (SFA), Jozef Stefan Institute, Jamova 39, SI-1000 Ljubljana, Slovenia / 82 Space and Plasma Physics, EES, KTH SE-100 44 Stockholm, Sweden / 83 Technical University of Denmark, Department of Physics, Bldg 309, DK-2800 Kgs Lyngby, Denmark / 84 The ‘Horia Hulubei’ National Institute for Physics and Nuclear Engineering, Magurele-Bucharest, Romania / 85 The National Institute for Cryogenics and Isotopic Technology, Ramnicu Valcea, Romania / 86 The National Institute for Laser, Plasma and Radiation Physics, Magurele-Bucharest, Romania / 87 The National Institute for Optoelectronics, Magurele-Bucharest, Romania / 88 Troitsk Insitute of Innovating and Thermonuclear Research (TRINITI), Troitsk 142190, Moscow Region, Russian Federation / 89 University of Electronic Science and Technology of China, Chengdu, People’s Republic of China / 90 Unità Tecnica Fusione, ENEA C. R. Frascati, via E. Fermi 45, 00044 Frascati (Roma), Italy / 91 Universidad Complutense de Madrid, Madrid, Spain / 92 Universidad de Sevilla, Sevilla, Spain / 93 Universidad Nacional de Educación a Distancia, Madrid, Spain / 94 Universidad Politécnica de Madrid, Grupo I2A2, Madrid, Spain / 95 Università di Roma Tor Vergata, Via del Politecnico 1, Roma, Italy / 96 University College Cork (UCC), Ireland / 97 University Milano-Bicocca, piazza della Scienza 3, 20126 Milano, Italy / 98 University of Basilicata, Consorzio CREATE, Via Claudio 21, 80125 Napoli, Italy / 99 University of California, 1111 Franklin St., Oakland, CA 94607, United States of America / 100 University of Cassino, Consorzio CREATE, Via Claudio 21, 80125 Napoli, Italy / 101 University of Helsinki, PO Box 43, FI-00014 University of Helsinki, Finland / 102 University of Innsbruck, Fusion@Österreichische Akademie der Wissenschaften (ÖAW), Innsbruck, Austria / 103 University of Latvia, 19 Raina Blvd., Riga, LV 1586, Latvia / 104 University of Lorraine, CNRS, UMR7198, YIJL, Nancy, France / 105 University of Napoli ‘Federico II’, Consorzio CREATE, Via Claudio 21, 80125 Napoli, Italy / 106 University of Napoli Parthenope, Consorzio CREATE, Via Claudio 21, 80125 Napoli, Italy / 107 University of Texas at Austin, Institute for Fusion Studies, Austin, TX 78712, United States of America / 108 University of Toyama, Toyama, 930-8555, Japan / 109 University of Tuscia, DEIM, Via del Paradiso 47, 01100 Viterbo, Italy / 110 University of York, Heslington, York YO10 5DD, United Kingdom / 111 Vienna University of Technology, Fusion@Österreichische Akademie der Wissenschaften (ÖAW), Austria / 112 VTT Technical Research Centre of Finland, PO Box 1000, FIN-02044 VTT, Finland / 113 Wigner Research Centre for Physics, PO Box 49, H-1525 Budapest, Hungary

In the last two decades, two important avian influenza viruses infecting humans emerged in China, the highly pathogenic avian influenza (HPAI) H5N1 virus in the late nineties, and the low pathogenic avian influenza (LPAI) H7N9 virus in 2013. China is home to the largest population of chickens (4.83 billion) and ducks (0.694 billion), representing, respectively 23.1 and 58.6% of the 2013 world stock, with a significant part of poultry sold through live-poultry markets potentially contributing to the spread of avian influenza viruses. Previous models have looked at factors associated with HPAI H5N1 in poultry and LPAI H7N9 in markets. However, these have not been studied and compared with a consistent set of predictor variables. Significant progress was recently made in the collection of poultry census and live-poultry market data, which are key potential factors in the distribution of both diseases. Here we compiled and reprocessed a new set of poultry census data and used these to analyse HPAI H5N1 and LPAI H7N9 distributions with boosted regression trees models. We found a limited impact of the improved poultry layers compared to models based on previous poultry census data, and a positive and previously unreported association between HPAI H5N1 outbreaks and the density of live-poultry markets. In addition, the models fitted for the HPAI H5N1 and LPAI H7N9 viruses predict a high risk of disease presence for the area around Shanghai and Hong Kong. The main difference in prediction between the two viruses concerned the suitability of HPAI H5N1 in north-China around the Yellow sea (outlined with Tianjin, Beijing, and Shenyang city) where LPAI H7N9 has not spread intensely., SCOPUS: ar.j, info:eu-repo/semantics/published

Global disease suitability models are essential tools to inform surveillance systems and enable early detection. We present the first global suitability model of highly pathogenic avian influenza (HPAI) H5N1 and demonstrate that reliable predictions can be obtained at global scale. Best predictions are obtained using spatial predictor variables describing host distributions, rather than land use or eco-climatic spatial predictor variables, with a strong association with domestic duck and extensively raised chicken densities. Our results also support a more systematic use of spatial cross-validation in large-scale disease suitability modelling compared to standard random cross-validation that can lead to unreliable measure of extrapolation accuracy. A global suitability model of the H5 clade 2.3.4.4 viruses, a group of viruses that recently spread extensively in Asia and the US, shows in comparison a lower spatial extrapolation capacity than the HPAI H5N1 models, with a stronger association with intensively raised chicken densities and anthropogenic factors., SCOPUS: ar.j, info:eu-repo/semantics/published

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., SCOPUS: ar.j, info:eu-repo/semantics/published

Decision-making and financial planning for tsetse control is complex, with a particularly wide range of choices to be made on location, timing, strategy and methods. This paper presents full cost estimates for eliminating or continuously controlling tsetse in a hypothetical area of 10,000 km 2 located in south-eastern Uganda. Four tsetse control techniques were analysed: (i) artificial baits (insecticide-treated traps/targets), (ii) insecticide-treated cattle (ITC), (iii) aerial spraying using the sequential aerosol technique (SAT) and (iv) the addition of the sterile insect technique (SIT) to the insecticide-based methods (i–iii). For the creation of fly-free zones and using a 10% discount rate, the field costs per km 2 came to US$283 for traps (4 traps per km 2 ), US$30 for ITC (5 treated cattle per km 2 using restricted application), US$380 for SAT and US$758 for adding SIT. The inclusion of entomological and other preliminary studies plus administrative overheads adds substantially to the overall cost, so that the total costs become US$482 for traps, US$220 for ITC, US$552 for SAT and US$993 – 1365 if SIT is added following suppression using another method. These basic costs would apply to trouble-free operations dealing with isolated tsetse populations. Estimates were also made for non-isolated populations, allowing for a barrier covering 10% of the intervention area, maintained for 3 years. Where traps were used as a barrier, the total cost of elimination increased by between 29% and 57% and for ITC barriers the increase was between 12% and 30%. In the case of continuous tsetse control operations, costs were estimated over a 20-year period and discounted at 10%. Total costs per km 2 came to US$368 for ITC, US$2114 for traps, all deployed continuously, and US$2442 for SAT applied at 3-year intervals. The lower costs compared favourably with the regular treatment of cattle with prophylactic trypanocides (US$3862 per km 2 assuming four doses per annum at 45 cattle per km 2 ). Throughout the study, sensitivity analyses were conducted to explore the impact on cost estimates of different densities of ITC and traps, costs of baseline studies and discount rates. The present analysis highlights the cost differentials between the different intervention techniques, whilst attesting to the significant progress made over the years in reducing field costs. Results indicate that continuous control activities can be cost-effective in reducing tsetse populations, especially where the creation of fly-free zones is challenging and reinvasion pressure high.

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 down-scaling 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., SCOPUS: ar.j, info:eu-repo/semantics/published

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Intensification of animal production can be an important factor in the emergence of infectious diseases because changes in production structure influence disease transmission patterns. In 2004 and 2005, Thailand was subject to two highly pathogenic avian influenza epidemic waves and large surveys were conducted of the poultry sector, providing detailed spatial data on various poultry types. This study analysed these data with the aim of establishing the distributions of extensive and intensive poultry farms, based on the number of birds per holder. Once poultry data were disaggregated into these two production systems, they were analysed in relation to anthropogenic factors using simultaneous autoregressive models. Intensive chicken production was clustered around the capital city of Bangkok and close to the main consumption and export centres. Intensively-raised ducks, mainly free-grazing, showed a distinct pattern with the highest densities distributed in a large area located in the floodplain of the Chao Phraya River. Accessibility to Bangkok, the percentage of irrigated areas and human population density were the most important predictors explaining the geographical distribution of intensively-raised poultry. The distribution of extensive poultry showed a higher predictability. Extensive poultry farms were distributed more homogeneously across the country and their distribution was best predicted by human population density.

Global concerns over the emergence of zoonotic pandemics emphasize the need for high-resolution population distribution mapping and spatial modelling. Ongoing efforts to model disease risk in China have been hindered by a lack of available species level distribution maps for poultry. The goal of this study was to develop 1 km resolution population density models for China's chickens, ducks, and geese. We used an information theoretic approach to predict poultry densities based on statistical relationships between poultry census data and high-resolution agro-ecological predictor variables. Model predictions were validated by comparing goodness of fit measures (root mean square error and correlation coefficient) for observed and predicted values for ¼ of the sample data which was not used for model training. Final output included mean and coefficient of variation maps for each species. We tested the quality of models produced using three predictor datasets and 4 regional stratification methods. For predictor variables, a combination of traditional predictors for livestock mapping and land use predictors produced the best goodness of fit scores. Comparison of regional stratifications indicated that for chickens and ducks, a stratification based on livestock production systems produced the best results; for geese, an agro-ecological stratification produced best results. However, for all species, each method of regional stratification produced significantly better goodness of fit scores than the global model. Here we provide descriptive methods, analytical comparisons, and model output for China's first high resolution, species level poultry distribution maps. Output will be made available to the scientific and public community for use in a wide range of applications from epidemiological studies to livestock policy and management initiatives., JOURNAL ARTICLE, SCOPUS: ar.j, info:eu-repo/semantics/published

In January 2006, a major cold spell affected Europe, coinciding with an increase of H5N1 influenza virus detected in wild birds, mostly dead mute swans, starting along the River Danube and the Mediterranean coast line. Subsequently H5N1 detections in wild birds were concentrated in central and western parts of Europe, reaching a peak in mid February. We tested the hypothesis that the geographic distribution of these H5N1 infections was modulated by the long-term wintering line, the 0 °C isotherm marking the limit beyond which areas are largely unsuitable for wintering waterfowl. Given the particularly cold 2005-2006 European winter, we also considered the satellite-derived contemporary frost conditions. This brought us to select the long-term maximum rather than the mean January 0 °C isotherm as the best approximation for the 2005-2006 wintering line. Our analysis shows that H5N1 detection sites were closer to the wintering line than would be expected by chance, even when the geographic distribution of water bird wintering sites was accounted for. We argue that partial frost conditions in water bodies are conducive to bird congregation, and this may have enhanced H5N1 transmission and local spread. Because the environmental virus load also would build up in these hot spots, H5N1 virus may have readily persisted during the spring, at least in cooler areas. We conclude that H5N1 introduction, spread, and persistence in Europe may have been enhanced by the cold 2005-2006 winter., Journal Article, SCOPUS: ar.j, info:eu-repo/semantics/published

The central role played by livestock in the livelihoods of rural households in the developing world is seldom fully appreciated by policy makers, development agencies and donors. Knowledge gaps in the geographic distribution and environmental determinants of farming systems, especially if viewed through the livestock lens, compound this problem. We have produced a map of pastoral, agro-pastoral and mixed farming systems across Eastern Africa, by analysing datasets collected in the framework of livelihood analysis. Input data were gathered between 2000 and 2007 by various emergency and development agencies for Djibouti, Eritrea, Kenya, Somalia, Uganda and parts of Ethiopia and Sudan. A quantitative definition of the production systems is adopted, based on the ratio of livestock- to crop-derived income. The resulting livelihood-based map of livestock production systems was compared through correspondence analysis to an alternative livestock production systems map, produced independently from environmental data. Convergence between the two mapping approaches was evident. The geographic distribution of the livestock production systems was also modelled using multivariate analysis of remotely sensed and other geospatial datasets. Models show high statistical accuracy, and were thus used to fill the gaps in the observed distribution of livestock production systems. Finally, selected environmental factors underpinning the systems (agro-climatology, human and livestock populations and land cover) were analysed in detail, enabling the livestock production systems to be characterized in terms of them. The regional scope of the map, as well as its direct link with a vast amount of livelihood information, render it a valuable tool for a range of development and research applications, including those related to global change.