An economic assessment of alternative antimicrobial use scenarios on pig farms.

This paper explores the theoretical economic outcome of management changes that result in different levels of antimicrobial use (AMU) in two types of UK pig farm. A static farm economic pig production model (FEPM) was used on a representative 'Top-third' most profitable farm and a representative 'Mid-range' profitable farm. Three AMU theoretical management scenarios were investigated; (a) management changes leading to a reduction of AMU by 35% (AMU35); (b) more extensive management changes leading to a reduction of AMU by 95% (AMU95); and (c) implementing depopulation of the herd (AMU Depop). A sensitivity analysis was conducted to determine the effect of increases or decreases in pig revenue and feed price on farm gross margin under these scenarios. Over a single year, the AMU35 scenario was estimated to have a small positive impact (+3%) on both farm types. The other two AMU reduction scenarios had higher AMU reduction on farms but required higher variable cost and hence they resulted in lower farm profitability. There was a substantial reduction (up to -50%) in farm gross margin under these two AMU reduction scenarios in the modeled short-term time-period. The impact of the alternative AMU scenarios was slightly higher on a farm representing the 'Top-third' farm type, reducing farm gross margin further by 7% compared to the 'Mid-range' farm. Nevertheless, both farm types stay profitable under all three AMU scenarios. The results showed that in the modeled short-term implementing management changes that result in a reduction of on-farm AMU by 35% had a good economic outcome. In practice, the other two scenarios would be considered as longer-term strategies. Although both require higher initial costs to implement, the improved biosecurity and hygiene will benefit from lower disease occurrence for a longer term. Farm gross margins were, however, found to be highly sensitive to changes on market prices especially increasing feed prices. An increase of more than 15% in feed price moved a profitable farm into a loss-making farm. It will be economically challenging for uptakes of these, or similar, AMU reduction scenarios on farms if the market prices become un-favorable to pig farmers.

Association of ABO blood group with susceptibility to SARS-CoV-2 infection in Rupandehi district of Nepal.

Objectives: Recent studies after the outbreak of coronavirus disease 2019 have shown an association of the ABO blood group to the susceptibility of severe acute respiratory syndrome coronavirus 2 infection. Anti-A and anti-B antibodies, carbohydrate clustering, interleukin-6 levels and host transmembrane protease serine subtype 2 were suggested to cause the variable susceptibility of severe acute respiratory syndrome coronavirus 2 infection to the ABO blood groups. This study aims to find the association of the ABO blood group with severe acute respiratory syndrome coronavirus 2 infection susceptibility in Nepal. Methods: Population-based matched case-control study was conducted from October 2021 to February 2022 in Rupandehi district of Nepal. A total of 1091 reverse transcription-polymerase chain reaction confirmed coronavirus disease 2019 cases and 2182 controls were included in the study by convenient sampling method. Results: A statistically significant association of severe acute respiratory syndrome coronavirus 2 infection was observed for the blood group AB between cases and controls (11.5% vs 8.5%; odds ratio = 1.4, 95% confidence interval = 1.10-1.78). However, there was no association of severe acute respiratory syndrome coronavirus 2 infection for blood group A (26.7% vs 28.23%; odds ratio = 0.93, 95% confidence interval = 0.79-1.09), B (26.9% vs 29.84%; odds ratio = 0.86, 95% confidence interval = 0.73-1.02) and O (34.9% vs 33.41%; odds ratio = 1.07, 95% confidence interval = 0.92-1.25). Conclusion: This study reported slightly more susceptibility to severe acute respiratory syndrome coronavirus 2 infection among individuals with blood group AB.

Financial Impacts of Liver Fluke on Livestock Farms Under Climate Change-A Farm Level Assessment.

Liver fluke infection (fascioliasis) is a parasitic disease which affects the health and welfare of ruminants. It is a concern for the livestock industry and is considered as a growing threat to the industry because changing climatic conditions are projected to be more favorable to increased frequency and intensity of liver fluke outbreaks. Recent reports highlighted that the incidence and geographic range of liver fluke has increased in the UK over the last decade and estimated to increase the average risk of liver fluke in the UK due to increasing temperature and rainfall. This paper explores financial impacts of the disease with and without climate change effects on Scottish livestock farms using a farm-level economic model. The model is based on farming system analysis and uses linear programming technique to maximize farm net profit within farm resources. Farm level data from a sample of 160 Scottish livestock farms is used under a no disease baseline scenario and two disease scenarios (with and without climate change). These two disease scenarios are compared with the baseline scenario to estimate the financial impact of the disease at farm levels. The results suggest a 12% reduction in net profit on an average dairy farm compared to 6% reduction on an average beef farm under standard disease conditions. The losses increase by 2-fold on a dairy farm and 6-fold on a beef farm when climate change effects are included with disease conditions on farms. There is a large variability within farm groups with profitable farms incurring relatively lesser economic losses than non-profitable farms. There is a substantial increase in number of vulnerable farms both in dairy (+20%) and beef farms (+27%) under the disease alongside climate change conditions.

Financial Vulnerability of Dairy Farms Challenged by Johne's Disease to Changes in Farm Payment Support.

Johne's disease is an endemic contagious bacterial infection of ruminants which is prevalent in the United Kingdom and elsewhere. It can lower financial returns on infected farms by reducing farm productivity through output losses and control expenditures. A farm-level analysis of the economics of the disease was conducted taking account of farm variability and different disease prevalence levels. The aim was to assess the financial impacts of a livestock disease on farms and determine their financial vulnerability if farm support payments were to be removed under future policy reforms. A farm-level optimization model, ScotFarm, was used on 50 Scottish dairy farms taken from the Farm Business Survey to determine the impacts of the disease. A counterfactual comparison of five alternative "disease" scenarios with a "no-disease" scenario was carried out to evaluate economic impact of the disease. The extent of a farm's reliance on direct support payments was considered to be an indicator of their financial vulnerability. Under this definition, farms were grouped into three financial vulnerability risk categories; "low risk," "medium risk," and "high risk" farms. Results show that farms are estimated to incur a loss of 32% on average of their net profit under a standard disease prevalence level. Farms in the "low risk" and "medium risk" categories were estimated to have a lower financial impact of the disease (22 and 28% reduction on farm net profit, respectively) which, along with their lower reliance on farm direct support payments, indicate they would be more resilient to the disease under future changes in farm payment support. On the contrary, farms in the "high risk" category were estimated to have a reduction of 50% on their farm net profit. A majority of these farms (61%) in the "high risk" category move from being profitable to loss making under the standard disease scenario when farm support payments are removed. Of these, 15% do so because of the impact of the disease. These farms will be more vulnerable if changes were to be made in farm support payments under future agricultural policy reforms.

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.

Predicting the interface morphologies of silicon films on arbitrary substrates: application in solar cells.

A three-dimensional model that predicts the interface morphologies of silicon thin-film solar cells prepared on randomly textured substrates was developed and compared to experimental data. The surface morphologies of silicon solar cells were calculated by using atomic force microscope scans of the textured substrates and the film thickness as input data. Calculated surface morphologies of silicon solar cells are in good agreement with experimentally measured morphologies. A detailed description of the solar cell interface morphologies is necessary to understand light-trapping in silicon single junction and micromorph tandem thin-film solar cells and derive optimal light-trapping structures.