Skip to main content
Research article - Peer-reviewed, 2017

Capturing systemic interrelationships by an impact analysis to help reduce production diseases in dairy farms.

Krieger, Margret; Hoischen-Taubner, Susanne; Emanuelson, Ulf; Blanco-Penedo, Isabel; de Joybert, Manon; Duval, Julie E.; Sjostrom, Karin; Jones, Philip J.; Sundrum, Albert

Abstract

Production diseases, such as metabolic and reproductive disorders, mastitis, and lameness, emerge from complex interactions between numerous factors (or variables) but can be controlled by the right management decisions. Since animal husbandry systems in practice are very diverse, it is difficult to identify the most influential components in the individual farm context. However, it is necessary to do this to control disease, since farmers are severely limited in their access to resources, and need to invest in management measures most likely to have an effect. In this study, systemic impact analyses were conducted on 192 organic dairy farms in France, Germany, Spain, and Sweden in the context of reducing the prevalence of production diseases. The impact analyses were designed to evaluate the interrelationships between farm variables and determine the systemic roles of these variables. In particular, the aim was to identify the most influential variables on each farm. The impact analysis consisted of a stepwise process: (i) in a participatory process 13 relevant system variables affecting the emergence of production diseases on organic dairy farms were defined; (ii) the interrelationships between these variables were evaluated by means of an impact matrix on the farm-level, involving the perspectives of the farmer, an advisor and the farm veterinarian; and (iii) the results were then used to identify general system behaviour and to classify variables by their level of influence on other system variables and their susceptibility to influence. Variables were either active (high influence, low susceptibility), reactive (low influence, high susceptibility), critical (both high), or buffering (both low). An overall active tendency was found for feeding regime, housing conditions, herd health monitoring, and knowledge and skills, while milk performance and financial resources tended to be reactive. Production diseases and labour capacity had a tendency for being critical while reproduction management, dry cow management, calf and heifer management, hygiene and treatment tended to have a buffering capacity. While generalised tendencies for variables emerged, the specific role of variables could vary widely between farms. The strength of this participatory impact assessment approach is its ability, through filling in the matrix and discussion of the output between farmer, advisor and veterinarian, to explicitly identify deviations from general expectations, thereby supporting a farm-specific selection of health management strategies and measures. (C) 2017 Elsevier Ltd. All rights reserved.

Keywords

Organic farming; Complexity; Participatory approach; Decision support; Impact matrix

Published in

Agricultural Systems
2017, volume: 153, pages: 43-52
Publisher: ELSEVIER SCI LTD

Authors' information

Krieger, M.C.
University of Kassel
Hoischen-Taubner, Susanne
University of Kassel
Swedish University of Agricultural Sciences, Department of Clinical Sciences
Institute of Agrifood Research and Technology (IRTA)
de Joybert, Manon
National Institute of Agricultural Research (INRA)
Duval, Julie
National Institute of Agricultural Research (INRA)
Sjöström, Karin
Swedish University of Agricultural Sciences, Department of Clinical Sciences
Jones, Philip
University of Reading
Sundrum, Albert
University of Kassel

UKÄ Subject classification

Animal and Dairy Science
Other Veterinary Science

Publication Identifiers

DOI: https://doi.org/10.1016/j.agsy.2017.01.022

URI (permanent link to this page)

https://res.slu.se/id/publ/83018