Abstract
A traditional approach in agricultural and veterinary research is focussing on the biological perspective where large cattle-databases are used to analyse the dairy herd. This approach has yielded valuable insights. However, recent research indicates that this knowledge-base can be further increased by examining agricultural and veterinary challenges from other perspectives. In this paper we suggest three perspectives that may supplement the biological perspective in agricultural and veterinary research; the economic-, the managerial-, and the social perspective. We review recent studies applying or combining these perspectives and discuss how multiple perspectives may improve our understanding and ability to handle cattle-health challenges.
Highlights
IntroductionOn the contrary, when farmers call for help on herd health management issues, the challenges may be highly embedded in the economic, managerial, and social contexts of the farm
Agricultural and veterinary-related problems rarely take place in isolation
Despite early research showing the potential of adopting alternative perspectives [1], the traditional approach in agricultural and veterinary research has been a focus on biological perspectives
Summary
On the contrary, when farmers call for help on herd health management issues, the challenges may be highly embedded in the economic, managerial, and social contexts of the farm. Knowing the economic status of the farm, the way farmers think, and the working climate at the farm may be valuable when we address issues as cattle-health challenges. Many agricultural and veterinary-related problems may be better understood and handled when we have rich information about the context in which these problems occur. Despite early research showing the potential of adopting alternative perspectives [1], the traditional approach in agricultural and veterinary research has been a focus on biological perspectives. A widely used approach in veterinary and agricultural research is to address biological questions by analysing large cattle databases [2,3].
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