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

Perspective: science and the future of livestock industries

Since the 1990s, livestock industries have been forced to respond to major pressures from society, particularly with respect to methane emissions and animal welfare. These challenges are exacerbated by the inevitability of global heating and the effects it will have on livestock productivity. The same challenges also led to questions about the value of animal-sourced foods for feeding the world. The industries and the research communities supporting them are meeting those challenges. For example, we can now envisage solutions to the ruminant methane problem and those solutions will also improve the efficiency of meat and milk production. Animal welfare is a complex mix of health, nutrition and management. With respect to health, the 'One Health' concept is offering better perspectives, and major diseases, such as helminth infection, compounded by resistance against medication, are being resolved through genetic selection. With respect to nutrition and stress, 'fetal programming' and the epigenetic mechanisms involved offer novel possibilities for improving productivity. Stress needs to be minimized, including stress caused by extreme weather events, and solutions are emerging through technology that reveals when animals are stressed, and through an understanding of the genes that control susceptibility to stress. Indeed, discoveries in the molecular biology of physiological processes will greatly accelerate genetic progress by contributing to genomic solutions. Overall, the global context is clear - animal-sourced food is an important contributor to the future of humanity, but the responses of livestock industries must involve local actions that are relevant to geographical and socio-economic constraints.

Investing in health to improve the sustainability of cattle production in the United Kingdom: A narrative review

Livestock health is a key concern for all food system stakeholders and has considerable impacts upon sustainable food production. Improving productivity means that a set quantity of milk or meat may be produced at a lower economic cost, using fewer resources and with reduced greenhouse gas emissions (GHGe); however, diseases that reduce yield, growth or fertility have the opposite effect. The purpose of this narrative review was to assess the breadth of economic and environmental sustainability information relating to cattle health within the literature and to discuss related knowledge gaps within the literature. The mechanisms by which improved awareness and investment can lead to improved cattle health both on-farm and across the wider cattle industry are also appraised; concluding with the opportunities and challenges still outstanding in improving sustainability through livestock health. The economic and environmental impacts of cattle health have not been sufficiently quantified in the literature to draw valid conclusions regarding the sustainability impact of different diseases. Where available, economic data tended to be dated or extremely variable. Furthermore, environmental analyses did not use consistent methodologies and principally focused on GHGe, with little attention paid to other metrics. Although reducing disease severity or occurrence reduced GHGe, published impacts of disease varied from 1% to 40% with little apparent association between GHGe and industry-wide economic cost. An urgent need therefore exists to standardise methodologies and quantify disease impacts using a common baseline with up-to-date data inputs. Given the threat of antimicrobial resistance, improving cattle health through technology adoption and vaccine use would be expected to have positive impacts on social acceptability, especially if these improvements rendered milk and meat more affordable to the consumer. Therefore, it is important for cattle producers and allied industry to take a proactive approach to improving cattle health and welfare, with particular focus on diseases that have the greatest implications for sustainability.

Mechanistic models of Rift Valley fever virus transmission: A systematic review

Rift Valley fever (RVF) is a zoonotic arbovirosis which has been reported across Africa including the northernmost edge, South West Indian Ocean islands, and the Arabian Peninsula. The virus is responsible for high abortion rates and mortality in young ruminants, with economic impacts in affected countries. To date, RVF epidemiological mechanisms are not fully understood, due to the multiplicity of implicated vertebrate hosts, vectors, and ecosystems. In this context, mathematical models are useful tools to develop our understanding of complex systems, and mechanistic models are particularly suited to data-scarce settings. Here, we performed a systematic review of mechanistic models studying RVF, to explore their diversity and their contribution to the understanding of this disease epidemiology. Researching Pubmed and Scopus databases (October 2021), we eventually selected 48 papers, presenting overall 49 different models with numerical application to RVF. We categorized models as theoretical, applied, or grey, depending on whether they represented a specific geographical context or not, and whether they relied on an extensive use of data. We discussed their contributions to the understanding of RVF epidemiology, and highlighted that theoretical and applied models are used differently yet meet common objectives. Through the examination of model features, we identified research questions left unexplored across scales, such as the role of animal mobility, as well as the relative contributions of host and vector species to transmission. Importantly, we noted a substantial lack of justification when choosing a functional form for the force of infection. Overall, we showed a great diversity in RVF models, leading to important progress in our comprehension of epidemiological mechanisms. To go further, data gaps must be filled, and modelers need to improve their code accessibility.

Do animal health models meet the needs of organic and conventional dairy farmers in Spain and the UK on disease prevention?

Modelling plays an important role in assessing disease risks and the efficacy of preventative actions. However, the extent to which existing models meet the needs of different groups of dairy farmers around disease prevention is unclear. A questionnaire gathered information on disease prevention actions undertaken by organic and conventional dairy farmers in Spain and the UK, and on their information preferences and needs in relation to such actions. A systematic review of animal health modelling articles was undertaken to compare the expressed needs of dairy farmers for information on disease prevention, with the focus and outputs of existing models. Farmer groups differed in needs when planning disease prevention interventions. Most farmers sourced animal health information from veterinarians. Farmers preferred to use practical experience to judge the efficacy of change. To fulfil the expressed needs of dairy farmers, models need to address specific farming contexts and non-economic impacts of change.

Using internet-based query and climate data to predict climate-sensitive infectious disease risks: a systematic review of epidemiological evidence

The use of internet-based query data offers a novel approach to improve disease surveillance and provides timely disease information. This paper systematically reviewed the literature on infectious disease predictions using internet-based query data and climate factors, discussed the current research progress and challenges, and provided some recommendations for future studies. We searched the relevant articles in the PubMed, Scopus, and Web of Science databases between January 2000 and December 2019. We initially included studies that used internet-based query data to predict infectious disease epidemics, then we further filtered and appraised the studies that used both internet-based query data and climate factors. In total, 129 relevant papers were included in the review. The results showed that most studies used a simple descriptive approach (n=80; 62%) to detect epidemics of influenza (including influenza-like illness (ILI)) (n=88; 68%) and dengue (n=9; 7%). Most studies (n=61; 47%) purely used internet search metrics to predict the epidemics of infectious diseases, while only 3 out of the 129 papers included both climate variables and internet-based query data. Our research shows that including internet-based query data and climate variables could better predict climate-sensitive infectious disease epidemics; however, this method has not been widely used to date. Moreover, previous studies did not sufficiently consider the spatiotemporal uncertainty of infectious diseases. Our review suggests that further research should use both internet-based query and climate data to develop predictive models for climate-sensitive infectious diseases based on spatiotemporal models.

Review: Future consequences of climate change for European Union pig production

Climate change is already a reality for livestock production. In contrast to the ruminant species, little is known about the impacts and the vulnerability of pig European Union (EU) sector to climate warming. This review deals with the potential and the already measurable effects of climate change in pig production. Based on evidences published in the literature, climate change may reduce EU pig productivity by indirectly reducing the availability of crops usually used in pig feeding, spreading the vector or pathogen to new locations and increasing the risk of exposure to cereals contaminated with mycotoxins; and directly mainly by inducing heat stress and increasing the animal's susceptibility to various diseases. Provision of realistic projections of possible impacts of future climate changes on EU pig sector is a prerequisite to evaluate its vulnerability and propose effective adaptation strategies. Simulation modelling approach is the most commonly used approach for exploring the effects of medium or long-term climate change/variability in pig production. One of the main challenges for this modelling approach is to account for both direct and indirect possible effects but also to uncertainties in parameter values that substantially increase the uncertainty estimates for model projections. The last part of the paper focus on the main issues that still need to be overcome for developing a decision support tools for simulating the direct and indirect effect of climate change in pig farms.

Short communication: Identifying key parameters for modelling the impacts of livestock health conditions on greenhouse gas emissions

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.

Dairy Cow Health and Greenhouse Gas Emission Intensity

The purpose of this review is to identify the main influencing factors related to dairy cow health as it impacts the intensity of greenhouse gas emissions considering known data presented in the literature. For this study, we define the emission intensity as CO2 equivalents per kilogram of milk. In dairy cows, a high dry matter (DM) intake (25 kg/d) leads to an higher absolute methane emission compared to a lower DM intake (10 kg/d). However, the emission intensity is decreased at a high performance level. The emissions caused by DM intake to cover the energy requirement for maintenance are distributed over a higher milk yield. Therefore, the emission intensity per kilogram of product is decreased for high-yielding animals with a high DM intake. Apart from that, animal diseases as well as poor environmental or nutritional conditions are responsible for a decreased DM intake and a compromised performance. As a result, animal diseases not only mean reduced productivity, but also increased emission intensity. The productive life-span of a dairy cow is closely related to animal health, and the impact on emission intensity is enormous. A model calculation shows that cows with five to eight lactations could have a reduced emission intensity of up to 40% compared to animals that have left the herd after their first lactation. This supports the general efforts to increase longevity of dairy cows by an improved health management including all measures to prevent diseases.

Sustainable goat production: modelling optimal performance in extensive systems

Context Strategies for achieving greater ruminant livestock productivity are essential to meet the food demands of growing populations, but sustainable changes are difficult to identify given the inherent complexity of such systems. Systems models can address this issue by allowing the impact of potential changes to be explored. Aims To develop a holistic systems model for goat production in an extensive Mediterranean environment which could allow changes in key management factors influencing the system to be investigated. Methods Initially, a conceptual comprehensive stock-and-flow model of a representative Mediterranean goat production system was constructed. This was used to identify informative indicators that would represent the overall technical and economic performance of the system. Sub-models were then assembled to build the full systems model. The model was parameterised with data collected over 3 years for goat holdings in northern Morocco. Scenario analysis techniques are used to explore the strategies that optimise performance under climate and feed price challenges. Key results Meat production is particularly important during periods of drought when increased meat yields can counteract the expected reduction in milk yields, to protect human food security, prevent excessive rangeland degradation and preserve natural nutritional resources. Feed price shocks during drought can have significant negative impacts on the system and zero feed input is shown to be a more sustainable strategy than reliance on high price feed during drought. Any alternative feed sources need to have a high forage component to reduce grazing periods significantly and promote rangeland preservation. Implications A diverse management strategy with a mixed meat and dairy semi-intensive production is more stable than specialised dairy systems and allows goat production and financial viability of intensification to be maintained under climatic stress; maintaining meat production was necessary to optimise performance. Conclusions The model allows improved insight into management strategies which could optimise animal husbandry performance in goat subsistence systems. However, the work also demonstrates the difficulty of constructing a truly holistic model since, to be practical, such constructs must necessarily be bounded; parameter selection and the limits to the boundaries imposed are inevitably critical.

Climate Change Impact, Adaptation, and Mitigation in Temperate Grazing Systems: A Review

Managed temperate grasslands occupy 25% of the world, which is 70% of global agricultural land. These lands are an important source of food for the global population. This review paper examines the impacts of climate change on managed temperate grasslands and grassland-based livestock and effectiveness of adaptation and mitigation options and their interactions. The paper clarifies that moderately elevated atmospheric CO2 (eCO2) enhances photosynthesis, however it may be restiricted by variations in rainfall and temperature, shifts in plant’s growing seasons, and nutrient availability. Different responses of plant functional types and their photosynthetic pathways to the combined effects of climatic change may result in compositional changes in plant communities, while more research is required to clarify the specific responses. We have also considered how other interacting factors, such as a progressive nitrogen limitation (PNL) of soils under eCO2, may affect interactions of the animal and the environment and the associated production. In addition to observed and modelled declines in grasslands productivity, changes in forage quality are expected. The health and productivity of grassland-based livestock are expected to decline through direct and indirect effects from climate change. Livestock enterprises are also significant cause of increased global greenhouse gas (GHG) emissions (about 14.5%), so climate risk-management is partly to develop and apply effective mitigation measures. Overall, our finding indicates complex impact that will vary by region, with more negative than positive impacts. This means that both wins and losses for grassland managers can be expected in different circumstances, thus the analysis of climate change impact required with potential adaptations and mitigation strategies to be developed at local and regional levels.

A Bayesian Probability Model Can Simulate the Knowledge of Soybean Rust Researchers to Optimize the Application of Fungicides

Asian rust is the main soybean disease in Brazil, causing up to 80% of yield reduction. The use of fungicides is the main form of control; however, due to farmer's concern with outbreaks many unnecessary applications are performed. The present study aims to verify the usefulness of a probability model to estimate the timing and the number of fungicides sprays required to control Asian soybean rust, using Bayesian networks and knowledge engineering. The model was developed through interviews with rust researchers and a literature review. The Bayesian network was constructed with the GeNIe 2.0 software. The validation process was performed by 42 farmers and 10 rust researchers, using 28 test cases. Among the 28 tested cases, generated by the system, the agreement with the model was 47.5% for the farmers and 89.3% for the rust researchers. In general, the farmers overestimate the number. The results showed that the Bayesian network has accurately represented the knowledge of the expert, and also could help the farmers to avoid the unnecessary applications.

To what extent is climate change adaptation a novel challenge for agricultural modellers?

Modelling is key to adapting agriculture to climate change (CC), facilitating evaluation of the impacts and efficacy of adaptation measures, and the design of optimal strategies. Although there are many challenges to modelling agricultural CC adaptation, it is unclear whether these are novel or, whether adaptation merely adds new motivations to old challenges. Here, qualitative analysis of modellers' views revealed three categories of challenge: Content, Use, and Capacity. Triangulation of findings with reviews of agricultural modelling and Climate Change Risk Assessment was then used to highlight challenges specific to modelling adaptation. These were refined through literature review, focussing attention on how the progressive nature of CC affects the role and impact of modelling. Specific challenges identified were: Scope of adaptations modelled, Information on future adaptation, Collaboration to tackle novel challenges, Optimisation under progressive change with thresholds, and Responsibility given the sensitivity of future outcomes to initial choices under progressive change.

A systematic review of non-productivity-related animal-based indicators of heat stress resilience in dairy cattle

INTRODUCTION

Projected temperature rise in the upcoming years due to climate change has increased interest in studying the effects of heat stress in dairy cows. Environmental indices are commonly used for detecting heat stress, but have been used mainly in studies focused on the productivity-related effects of heat stress. The welfare approach involves identifying physiological and behavioural measurements so as to start heat stress mitigation protocols before the appearance of impending severe health or production issues. Therefore, there is growing interest in studying the effects of heat stress on welfare. This systematic review seeks to summarise the animal-based responses to heat stress (physiological and behavioural, excluding productivity) that have been used in scientific literature.

METHODS

Using systematic review guidelines set by PRISMA, research articles were identified, screened and summarised based on inclusion criteria for physiology and behaviour, excluding productivity, for animal-based resilience indicators. 129 published articles were reviewed to determine which animal-based indicators for heat stress were most frequently used in dairy cows.

RESULTS

The articles considered report at least 212 different animal-based indicators that can be aggregated into body temperature, feeding, physiological response, resting, drinking, grazing and pasture-related behaviour, reactions to heat management and others. The most common physiological animal-based indicators are rectal temperature, respiration rate and dry matter intake, while the most common behavioural indicators are time spent lying, standing and feeding.

CONCLUSION

Although body temperature and respiration rate are the animal-based indicators most frequently used to assess heat stress in dairy cattle, when choosing an animal-based indicator for detecting heat stress using scientific literature to establish thresholds, characteristics that influence the scale of the response and the definition of heat stress must be taken into account, e.g. breed, lactation stage, milk yield, system type, climate region, bedding type, diet and cooling management strategies.

Control of helminth ruminant infections by 2030

Helminth infections have large negative impacts on production efficiency in ruminant farming systems worldwide, and their effective management is essential if livestock production is to increase to meet future human needs for dietary protein. The control of helminths relies heavily on routine use of chemotherapeutics, but this approach is unsustainable as resistance to anthelmintic drugs is widespread and increasing. At the same time, infection patterns are being altered by changes in climate, land-use and farming practices. Future farms will need to adopt more efficient, robust and sustainable control methods, integrating ongoing scientific advances. Here, we present a vision of helminth control in farmed ruminants by 2030, bringing to bear progress in: (1) diagnostic tools, (2) innovative control approaches based on vaccines and selective breeding, (3) anthelmintics, by sustainable use of existing products and potentially new compounds, and (4) rational integration of future control practices. In this review, we identify the technical advances that we believe will place new tools in the hands of animal health decision makers in 2030, to enhance their options for control and allow them to achieve a more integrated and sustainable approach to helminth control in support of animal welfare and production.

Impact of subclinical mastitis on greenhouse gas emissions intensity and profitability of dairy cows in Norway

Impaired animal health causes both productivity and profitability losses on dairy farms, resulting in inefficient use of inputs and increase in greenhouse gas (GHG) emissions produced per unit of product (i.e. emissions intensity). Here, we used subclinical mastitis as an exemplar to benchmark alternative scenarios against an economic optimum and adjusted herd structure to estimate the GHG emissions intensity associated with varying levels of disease. Five levels of somatic cell count (SCC) classes were considered namely 50,000 (i.e. SCC50), 200,000, 400,000, 600,000 and 800,000cells/mL (milliliter) of milk. The effects of varying levels of SCC on milk yield reduction and consequential milk price penalties were used in a dynamic programming (DP) model that maximizes the profit per cow, represented as expected net present value, by choosing optimal animal replacement rates. The GHG emissions intensities associated with different levels of SCC were then computed using a farm-scale model (HolosNor). The total culling rates of both primiparous (PP) and multiparous (MP) cows for the five levels of SCC scenarios estimated by the model varied from a minimum of 30.9% to a maximum of 43.7%. The expected profit was the highest for cows with SCC200 due to declining margin over feed, which influenced the DP model to cull and replace more animals and generate higher profit under this scenario compared to SCC50. The GHG emission intensities for the PP and MP cows with SCC50 were 1.01kg (kilogram) and 0.95kg carbon dioxide equivalents (CO₂e) per kg fat and protein corrected milk (FPCM), respectively, with the lowest emissions being achieved in SCC50. Our results show that there is a potential to reduce the farm GHG emissions intensity by 3.7% if the milk production was improved through reducing the level of SCC to 50,000cells/mL in relation to SCC level 800,000cells/mL. It was concluded that preventing and/or controlling subclinical mastitis consequently reduces the GHG emissions per unit of product on farm that results in improved profits for the farmers through reductions in milk losses, optimum culling rate and reduced feed and other variable costs. We suggest that further studies exploring the impact of a combination of diseases on emissions intensity are warranted.

Vulnerability of global food production to extreme climatic events

It is known that the frequency, intensity or duration of the extreme climatic events have been changing substantially. The ultimate goal of this study was to identify current vulnerabilities of global primary food production against extreme climatic events, and to discuss potential entry points for adaptation planning by means of an explorative vulnerability analysis. Outcomes of this analysis were demonstrated as a composite index where 118 country performances in maintaining safety of food production were compared and ranked against climate change. In order to better interpret the results, cluster analysis technique was used as a tool to group the countries based on their vulnerability index (VI) scores. Results suggested that one sixth of the countries analyzed were subject to high level of exposure (0.45-1), one third to high to very high level of sensitivity (0.41-1) and low to moderate level of adaptive capacity (0-0.59). Proper adaptation strategies for reducing the microbial and chemical contamination of food products, soil and waters on the field were proposed. Finally, availability of data on food safety management systems and occurrence of foodborne outbreaks with global coverage were proposed as key factors for improving the robustness of future vulnerability assessments.