Capturing systemic interrelationships by an impact analysis to help reduce production diseases in dairy farms. (May 2017)

• Record Type: Journal Article
• Authors: Krieger, Margret; Hoischen-Taubner, Susanne; Emanuelson, Ulf; Blanco-Penedo, Isabel; de Joybert, Manon; Duval, Julie E.; Sjöström, Karin; Jones, Philip J.; Sundrum, Albert
• Is Part Of: Agricultural systems. Volume 153(2017)
• Page Start: 43
• Page End: 52
• Publication Date: 2017-05
• Subjects: Organic farming -- Complexity -- Participatory approach -- Decision support -- Impact matrix; Agricultural systems -- Periodicals; Agriculture -- Environmental aspects -- Periodicals; 338.16
• DOI: 10.1016/j.agsy.2017.01.022
• Languages: English
• ISSNs: 0308-521X

Abstract

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. Highlights: Identification of effective management measures on the farm-level is essential for reducing production diseases. Impact analyses were conducted on organic dairy farms to determine typologies of different variables in the farm context. The specific functions of variables varied widely between farms, despite some general tendencies. The method can point out areas crucial for reducing production diseases and support a farm-specific selection of strategies.