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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 ,
E.-M. Schwabenbauer ,
S. Hoischen-Taubner ,
U. Emanuelson  and
A. Sundrum
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
*
†E-mail: margret.krieger@uni-kassel.de
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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.

Keywords

dairy cowanimal healthsystem analysisdecision makingimpact matrix
Type
Research Article
Information
animal , Volume 12 , Issue 3 , March 2018 , pp. 624 - 633
Copyright
© The Animal Consortium 2017 

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References

Baldwin, C, MacCormack, A and Rusnak, J 2014. Hidden structure. Using network methods to map system architecture. Research Policy 43, 1381–1397.Google Scholar
Barreto, H and Howland, FM 2006. Introductory econometrics: Using Monte Carlo simulation with Microsoft Excel. Cambridge University Press, Cambridge, New York. xxiii, 774pp.Google Scholar
Bawden, RJ 1991. Systems thinking and practice in agriculture. Journal of Dairy Science 74, 2362–2373.Google Scholar
Béranger, C and Vissac, B 1994. An holistic approach to livestock farming systems: theoretical and methodological aspects. Proceedings of the 2nd International Symposium on Livestock Farming Systems, 63, 5–17.Google Scholar
Brand, A, Noordhuizen, JPTM and Schukken, YH 1996. Herd health and production management in dairy practice. Wageningen Pers, Wageningen, the Netherlands.Google Scholar
Brand, F 2013. Komplexe Systeme. Neue Ansätze und zahlreiche Beispiele. Oldenbourg Verlag, Munich, Germany.CrossRefGoogle Scholar
Darnhofer, I, Lamine, C, Strauss, A and Navarrete, M 2016. The resilience of family farms. Towards a relational approach. Journal of Rural Studies 44, 111–122.Google Scholar
Dillon, JL 1992. The farm as a purposeful system (Miscellaneous Publication No. 10. Department of Agricultural Economics and Business Management, University of New England, Armidale, NSW, Australia.Google Scholar
Gibon, A, Sibbald, AR, Flamant, JC, Lhoste, P, Revilla, R, Rubino, R and Sørensen, JT 1999. Livestock farming systems research in Europe and its potential contribution for managing towards sustainability in livestock farming. Livestock Production Science 61, 121–137.Google Scholar
Godet, M 1979. The crisis in forecasting and the emergence of the ‘prospective’ approach. With case studies in energy and air transport. Pergamon Press, New York, NY, USA.Google Scholar
Gross, JL and Yellen, J 2004. Handbook of graph theory. CRC Press, Boca Raton, FL, USA.Google Scholar
Hoischen-Taubner, S and Sundrum, A 2012. Impact matrix: a tool to improve animal health by a systemic approach. In Tackling the future challenges of organic animal husbandry. 2nd Organic Animal Husbandry Conference, Hamburg, Trenthorst, 12–14 September 2012 (ed. G Rahmann), pp. 139–142. vTI, Braunschweig, Germany.Google Scholar
Hovi, M, Sundrum, A and Thamsborg, S 2003. Animal health and welfare in organic livestock production in Europe. Current state and future challenges. Livestock Production Science 80, 41–53.Google Scholar
Hughner, RS, McDonagh, P, Prothero, A, Shultz, CJ and Stanton, J 2007. Who are organic food consumers? A compilation and review of why people purchase organic food. Journal of Consumer Behaviour 6, 94–110.Google Scholar
Huirne, RB, Harsh, SB and Dijkhuizen, AA 1997. Critical success factors and information needs on dairy farms. The farmer’s opinion. Livestock Production Science 48, 229–238.Google Scholar
International Federation of Organic Agriculture Movements 2006. The IFOAM Basic Standards for Organic Production and Processing. Corrected Version, 2007, International Federation of Organic Agriculture Movements, Bonn, Germany.Google Scholar
Ismael, A, Strandberg, E, Berglund, B, Kargo, M, Fogh, A and Lovendahl, P 2016. Genotype by environment interaction for the interval from calving to first insemination with regard to calving month and geographic location in Holstein cows in Denmark and Sweden. Journal of Dairy Science 99, 5498–5507.Google Scholar
Kok, K 2009. The potential of Fuzzy Cognitive Maps for semi-quantitative scenario development, with an example from Brazil. Global Environmental Change 19, 122–133.Google Scholar
Krieger, M, Hoischen-Taubner, S, Emanuelson, U, Blanco-Penedo, I, de Joybert, M, Duval, JE, Sjöström, K, Jones, PJ and Sundrum, A 2017a. Capturing systemic interrelationships by an impact analysis to help reduce production diseases in dairy farms. Agricultural Systems 153, 43–52.CrossRefGoogle Scholar
Krieger, M, Sjöström, K, Blanco-Penedo, I, Madouasse, A, Duval, JE, Bareille, N, Fourichon, C, Sundrum, A and Emanuelson, U 2017b. Prevalence of production disease related indicators in organic dairy herds in four European countries. Livestock Science 198, 104–108.CrossRefGoogle Scholar
Kristensen, E and Jakobsen, EB 2011. Challenging the myth of the irrational dairy farmer; understanding decision-making related to herd health. New Zealand Veterinary Journal 59, 1–7.CrossRefGoogle ScholarPubMed
Lam, TJGM, Jansen, J, van den Borne, BHP, Renes, RJ and Hogeveen, H 2011. What veterinarians need to know about communication to optimise their role as advisors on udder health in dairy herds. New Zealand Veterinary Journal 59, 8–15.Google Scholar
LeBlanc, SJ, Lissemore, KD, Kelton, DF, Duffield, TF and Leslie, KE 2006. Major Advances in Disease Prevention in Dairy Cattle. Journal of Dairy Science 89, 1267–1279.CrossRefGoogle ScholarPubMed
Linss, V and Fried, A 2009. Advanced Impact Analysis: The ADVIAN® method – an enhanced approach for the analysis of impact strengths with the consideration of indirect relations. Papers and Preprints, Department of Innovation Research and Sustainable Resource Management (BWL IX) 1/2009, University of Technology, Chemnitz, Germany.Google Scholar
Nir, O 2003. What are production diseases, and how do we manage them? Acta Veterinaria Scandinavica Suppl 98, 21–32.Google Scholar
Noordhuizen, JP and da Silva, JC 2009. Animal hygiene and animal health in dairy cattle Operations. The Open Veterinary Science Journal 3, 17–21.Google Scholar
Onnela, J-P, Saramäki, J, Kertész, J and Kaski, K 2005. Intensity and coherence of motifs in weighted complex networks. Physical Review E 71, 65103.Google Scholar
Özesmi, U and Özesmi, SL 2004. Ecological models based on people’s knowledge: a multi-step fuzzy cognitive mapping approach. Ecological Modelling 176, 43–64.Google Scholar
Sands, DM 1986. Farming systems research. Clarification of terms and concepts. Experimental Agriculture 22, 87–104.CrossRefGoogle Scholar
Sundrum, A 2014. Organic livestock production. In Encyclopedia of agriculture and food systems (ed. NK van Alfen), pp 287–303. Academic Press, Oxford, UK.CrossRefGoogle Scholar
Sundrum, A 2015. Metabolic disorders in the transition period indicate that the dairy cows’ ability to adapt is overstressed. Animals 5, 978–1020.CrossRefGoogle ScholarPubMed
Vaarst, M, Winckler, C, Roderick, S, Smolders, G, Ivemeyer, S, Brinkman, J, Mejdell, CM, Whistance, LK, Nicholar, P, Walkenhorst, M, Leeb, C, March, S, Henriksen, BIF, Stöger, E, Gratzer, E, Hansen, B and Hubner, J 2011. Animal health and welfare planning in organic dairy Cattle farms. The Open Veterinary Science Journal 5, 19–25.CrossRefGoogle Scholar
van Soest, FJS, Mourits, MCM and Hogeveen, H 2015. European organic dairy farmers’ preference for animal health management within the farm management system. Animal 9, 1875–1883.Google Scholar
Velmans, M 1999. Intersubjective science. Journal of Consciousness Studies 6, 299–306.Google Scholar
Vester, F 2007. The art of interconnected thinking. Tools and concepts for a new approach to tackling complexity. MCB Verlag, Munich, Germany.Google Scholar
Wolfram Research 2015. Mathematica, Version 10.1. Wolfram Research, Inc, Champaign, Illinois, USA.Google Scholar
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