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Abstract
Regardless many claims about their benefits and profitability, the economic value of the information (VOI) provided by precision monitoring systems (PMS) for animal health has been neglected by both developers and analysts. Our study, we propose a method to estimate the value of PMS prior to their implementation and we investigate which characteristics mostly affect the economic value of such systems. We used a case study of two milk biomarkers to monitor subacute ruminal acidosis (SARA) in dairy cows, the fat to protein ratio (FPR) and the fatty acid profile (FAP), which is not yet commercial but has a better accuracy. Economic value is conceptualized as the difference between the economic
performance of a production system that uses the PMS and that does not. A stochastic decision tree economic simulation model was applied to a typical Belgian dairy farm. The model uses data on the disease costs, treatment costs, and prevalence of SARA and on the accuracy characteristics of both biomarkers. Disease and treatments costs as well as prevalence, were inserted as a stochastic distribution to reflect uncertainty and variability. Not monitoring was a better decision than monitoring with the FAP with a 69% probability, while monitoring with the FPR always performed worst. Elasticity analyses revealed an inverse Ushaped relationship between prevalence and economic value and that the FAP's economic value increases with increasing disease costs and with decreasing treatment costs. The economic value reacted differently to improvements in specificity or sensitivity. Precision monitoring tools only provide a value in specific situations regarding prevalence, test
accuracy and disease and treatment costs. In order to avoid a suboptimal use of resources and to focus the development of PMS for those situations and problems where they can provide value, their economic impact should be analyzed prior to their development and
commercialization.
performance of a production system that uses the PMS and that does not. A stochastic decision tree economic simulation model was applied to a typical Belgian dairy farm. The model uses data on the disease costs, treatment costs, and prevalence of SARA and on the accuracy characteristics of both biomarkers. Disease and treatments costs as well as prevalence, were inserted as a stochastic distribution to reflect uncertainty and variability. Not monitoring was a better decision than monitoring with the FAP with a 69% probability, while monitoring with the FPR always performed worst. Elasticity analyses revealed an inverse Ushaped relationship between prevalence and economic value and that the FAP's economic value increases with increasing disease costs and with decreasing treatment costs. The economic value reacted differently to improvements in specificity or sensitivity. Precision monitoring tools only provide a value in specific situations regarding prevalence, test
accuracy and disease and treatment costs. In order to avoid a suboptimal use of resources and to focus the development of PMS for those situations and problems where they can provide value, their economic impact should be analyzed prior to their development and
commercialization.
| Original language | English |
|---|---|
| Pages | 139 |
| Number of pages | 1 |
| Publication status | Published - Mar-2017 |
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Dive into the research topics of 'The economic value of milk biomarkers: case-study of two biomarkers to detect subacute ruminal acidosis in dairy cows'. Together they form a unique fingerprint.Activities
- 1 Organisation and participation in conference
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The International Society for Economics and Social Sciences of Animal Health (ISESSAH)
Wauters, E. (Participant)
27-Mar-2017 → 28-Mar-2017Activity: Participating in or organising an event › Organisation and participation in conference