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Graph-based impact analysis as a framework for incorporating practitioner knowledge in dairy herd health management

Published online by Cambridge University Press:  05 September 2017

M. Krieger*
Affiliation:
Department of Animal Nutrition and Animal Health, University of Kassel, Nordbahnhofstrasse 1a, D-37213 Witzenhausen, Germany
E.-M. Schwabenbauer
Affiliation:
Department of Animal Nutrition and Animal Health, University of Kassel, Nordbahnhofstrasse 1a, D-37213 Witzenhausen, Germany
S. Hoischen-Taubner
Affiliation:
Department of Animal Nutrition and Animal Health, University of Kassel, Nordbahnhofstrasse 1a, D-37213 Witzenhausen, Germany
U. Emanuelson
Affiliation:
Department of Clinical Sciences, Swedish University of Agricultural Sciences, SE-750 07 Uppsala, Sweden
A. Sundrum
Affiliation:
Department of Animal Nutrition and Animal Health, University of Kassel, Nordbahnhofstrasse 1a, D-37213 Witzenhausen, Germany
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Abstract

Production diseases in dairy cows are multifactorial, which means they emerge from complex interactions between many different farm variables. Variables with a large impact on production diseases can be identified for groups of farms using statistical models, but these methods cannot be used to identify highly influential variables in individual farms. This, however, is necessary for herd health planning, because farm conditions and associated health problems vary largely between farms. The aim of this study was to rank variables according to their anticipated effect on production diseases on the farm level by applying a graph-based impact analysis on 192 European organic dairy farms. Direct impacts between 13 pre-defined variables were estimated for each farm during a round-table discussion attended by practitioners, that is farmer, veterinarian and herd advisor. Indirect impacts were elaborated through graph analysis taking into account impact strengths. Across farms, factors supposedly exerting the most influence on production diseases were ‘feeding’, ‘hygiene’ and ‘treatment’ (direct impacts), as well as ‘knowledge and skills’ and ‘herd health monitoring’ (indirect impacts). Factors strongly influenced by production diseases were ‘milk performance’, ‘financial resources’ and ‘labour capacity’ (directly and indirectly). Ranking of variables on the farm level revealed considerable differences between farms in terms of their most influential and most influenced farm factors. Consequently, very different strategies may be required to reduce production diseases in these farms. The method is based on perceptions and estimations and thus prone to errors. From our point of view, however, this weakness is clearly outweighed by the ability to assess and to analyse farm-specific relationships and thus to complement general knowledge with contextual knowledge. Therefore, we conclude that graph-based impact analysis represents a promising decision support tool for herd health planning. The next steps include testing the method using more specific and problem-oriented variables as well as evaluating its effectiveness.

Type
Research Article
Copyright
© The Animal Consortium 2017 

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