Abstract
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.
Highlights
Estimating the costs of livestock diseases and control measures in terms of losses and benefits is useful for farmers to optimize their managerial decisions, for researchers to understand the impact of diseases on the livestock sector and for policy makers in planning policies to minimize disease impacts on the livestock sector and any associated public “bads”
Numerous studies have examined the financial impacts of livestock diseases and control measures using methods such as regression modeling, economic welfare analysis, cost-benefit analysis, partial budgets, simulation models, dynamic programming, linear programming, partial equilibrium, and general input-output analysis models [1,2,3,4,5,6,7]
The model results suggest that around 90% of the sampled dairy farms make positive net profit per year under “no-disease” conditions (Figure 1)
Summary
Estimating the costs of livestock diseases and control measures in terms of losses and benefits is useful for farmers to optimize their managerial decisions, for researchers to understand the impact of diseases on the livestock sector and for policy makers in planning policies to minimize disease impacts on the livestock sector and any associated public “bads” (e.g., environment, publicFarm-level Financial Vulnerability to Johne’s health, animal welfare). Cost-benefit analyses, general input-output analysis and partial budget models work at the farm-level but are often focused only on disease/nodisease conditions of a farm to compare and determine the net effect of diseases on the financial performance of healthy vs infected herds These techniques typically isolate a single activity on a farm and assume no interlinking effects between farm activities. In other words, such techniques rarely consider the farming system as a whole when analyzing the economic impact of diseases or of control measures Many of these studies which used welfare analysis, regression and simulation models include interlinked activities within a farm to determine average losses on the farm and generate a national estimate based on those figures. These studies, based on management and production variability within a farm, did not include the variabilities between farms which limits the insight gained from a national-level analysis for individual farmers and for policy makers dealing with agricultural support policies in planning their farm and sector strategies
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
