The consequential costs of bovine tuberculosis (bTB) breakdowns in England and Wales.

Bovine tuberculosis (bTB) is a globally distributed zoonotic disease with significant economic impacts. Control measures in Great Britain include testing for and culling diseased animals. Farmers receive compensation for the value of culled animals, but not for the consequential costs of having to comply with testing and associated control measures. Such uncompensated costs can be significant. We present results of a survey of 1,600 dairy and beef farm holdings conducted in England and Wales to update and improve estimates of these consequential costs.Estimated costs are positively skewed and show considerable variance, which is in agreement with previous, smaller scale surveys of bTB: most farms experiencing bTB incur modest costs but some suffer significant costs. Testing, movement restrictions and output losses account for over three quarters of total uncompensated costs. Total costs rise with herd size and duration of controls. The composition of consequential costs changes as total costs increase, with an increasing proportion of the costs being associated with output losses and movement restrictions, and a decreasing proportion of costs associated with testing costs. Consequential costs tend to be higher for dairy than beef herds but this is likely due to larger herd sizes for dairy.Overall we find the total farm costs of bTB surpass those compensated for by Government in Great Britain. This study contributes to the public-private cost-sharing debate as farmers bear some of the economic burden of a disease breakdown. The methodology and results presented are crucial for informed Government and farmer decision-making. The identification of potential risk factors in this study was challenging but is of relevance outside GB.

A methodology to assess indirect economic impacts of animal disease outbreaks: A case of hypothetical African swine fever outbreak in Switzerland.

Indirect costs of animal disease outbreaks often significantly exceed the direct costs. Despite their importance, indirect costs remain poorly characterized due to their complexity. In this study, we developed a framework to assess the indirect costs of a hypothetical African swine fever outbreak in Switzerland. We collected data through international and national stakeholder interviews, analysis of national disease control regulations and industry data. We developed a framework to capture the resulting qualitative and quantitative data, categorize the impacts of these regulations, and rank the impacts in order of importance. We then developed a spreadsheet model to calculate the indirect costs of one category of control measure for an individual group of stakeholders. We developed a decision tree model to guide the most economically favourable implementation plan for a given control measure category, under different outbreak scenarios. Our results suggest that the most important measure/impact categories were 'Transport logistics', 'Consumer demand', 'Prevention of wild boar and domestic pig contact' and 'Slaughter logistics'. In our hypothetical scenario, the greatest costs associated with 'Prevention of wild boar and domestic pig contact' were due to assumed partial or total depopulation of fattening pig farms in order to reduce herd size to comply with the simulated control regulations. The model also provides suggestions on the most economically favourable strategy to reduce contact between wild boar and domestic pigs in control areas. Our approach provides a new framework to integrate qualitative and quantitative data to guide disease control strategy. This method could be useful in other countries and for other diseases, including in data- and resource-poor settings, or areas with limited experience of animal disease outbreaks.

Optimizing ex situ genetic resource collections for European livestock conservation.

Ex situ collections offer the potential to reduce extinction risks, affording option to society in maintaining future breeding opportunities for productivity and heritage traits. However, how much should we be seeking to collect and conserve in gene banks, and where? We developed a mathematical model to optimize logistical decisions of breed conservation choices and to evaluate alternative scenarios for efficiently re-allocating genetic materials currently stored in different European gene banks, allowing for cross-country collections, cost and cryogenic capacity differentials. We show how alternative allocations for the breeds that are currently stored in 11 European gene banks could reduce overall conservation costs by around 20% by selecting cryogenic banks that have relatively lower combination of fixed and collection costs, and are geographically closer to collection regions. Our results show that centralizing collections in one gene bank would double the costs, relative to collective European collections approaches. We also calculate marginal costs of collections and show that increasing diversity within the gene banks implies in higher costs per conserved breed.

Correction: A framework for estimating society's economic welfare following the introduction of an animal disease: The case of Johne's disease.

[This corrects the article DOI: 10.1371/journal.pone.0198436.].

A framework for estimating society's economic welfare following the introduction of an animal disease: The case of Johne's disease.

Animal diseases are global issues affecting the productivity and financial profitability of affected farms. Johne's disease is distributed on farms worldwide and is an endemic contagious bacterial infection in ruminants caused by Mycobacterium avium subspecies paratuberculosis. In cattle, the clinical disease manifests itself as chronic enteritis resulting in reduced production, weight loss, and eventually death. Johne's disease is prevalent in the UK, including Scotland. Direct costs and losses associated with Johne's disease have been estimated in previous research, confirming an important economic impact of the disease in UK herds. Despite this, the distributional impact of Johne's disease among milk consumers and producers in Scotland has not been estimated. In this paper, we evaluate the change in society's economic welfare, namely to dairy producers (i.e. infected and uninfected herds) and milk consumers in Scotland induced by the introduction of Johne's disease in the national Scottish dairy herd. At the national-level, we conclude that the economic burden falls mainly on producers of infected herds and, to a lesser extent, milk consumers, while producers of uninfected herds benefit from the presence of Johne's. An infected producer's loss per cow is approximately two times larger in magnitude than that of an uninfected producer's gain. Such economic welfare estimates are an important comparison of the relative costs of national herd prevalence and the wider economic welfare implications for both producers and consumers. This is particularly important from a policy, public good, cost sharing, and human health perspective. The economic welfare framework presented in this paper can be applied to other diseases to examine the relative burden of society's economic welfare of alternative livestock disease scenarios. In addition, the sensitivity analysis evaluates uncertainty in economic welfare given limited data and uncertainty in the national herd prevalence, and other input parameters, associated with Johne's disease in Scotland. Therefore, until the prevalence of Johne's is better understood, the full economic cost to Scottish dairy herds remains uncertain but in the meantime the sensitivity analysis evaluates the robustness of economic welfare to such uncertainties.

Challenges and priorities for modelling livestock health and pathogens in the context of climate change.

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.

Comparing decision-support systems in adopting sustainable intensification criteria.

Sustainable intensification (SI) is a multifaceted concept incorporating the ambition to increase or maintain the current level of agricultural yields while reduce negative ecological and environmental impacts. Decision-support systems (DSS) that use integrated analytical methods are often used to support decision making processes in agriculture. However, DSS often consist of set of values, objectives, and assumptions that may be inconsistent or in conflict with merits and objectives of SI. These potential conflicts will have consequences for adoption and up-take of agricultural research, technologies and related policies and regulations such as genetic technology in pursuit of SI. This perspective paper aimed at comparing a number of frequently used socio-economic DSS with respect to their capacity in incorporating various dimensions of SI, and discussing their application to analyzing farm animal genetic resources (FAnGR) policies. The case of FAnGR policies was chosen because of its great potential in delivering merits of SI. It was concluded that flexible DSS, with great integration capacity with various natural and social sciences, are needed to provide guidance on feasibility, practicality, and policy implementation for SI.