WP1: Beef/dairy farm survey
WP 1 starts the project by developing an understanding of path dependencies in the farming sector. This will be undertaken through applying some of the key concepts of convention and actornetwork theories. Aspects of both ANT (e.g. van der Ploeg & Frouws, 1999) and convention theory (Ponte & Gibbon, 2006) have been used in previous studies of value chains. Two key aspects will be employed. First we will adopt the perspective of symmetrical treatment of human and nonhuman actors (collectively termed ‘actants’) and of social and technical elements (see Latour, 2005). The introduction of ‘non-human’ actors allows us to follow “energy” and “GHG gases” as key non-human actors. Second, we will identify “nodes of particular power through which other actors must proceed” (Clarke, 2005, 61) – which we contend, are likely to represent the key lock-in points for the system.
Operationalising these concepts will involve identifying where “energy” use or GHG emissions are affected by lock-ins within the system. For example, farmers’ may be ‘locked-in’ to the use of imported feedstuff (with a high carbon footprint) through the unavailability of suitable local feed, the need to increase their economies of scale for the farm to promote succession, and so on. In each case the key issue when identifying lock-in will be whether it is possible to reduce the carbon usage through system change and whether system change is being prevented by other features inherent within the system.
We will begin this WP by identifying the local path dependencies of individual farms with the intention of developing a typology of key path dependencies that will represent the likely actions (and lock-ins) of ‘agents’ in the agent based model in WP 4. Issues such as the connection between the efficiency of the farm and the level of path-dependency will also be investigated as it has been suggested that farms running at optimal capacity have little ability to transform individual components (van der Ploeg & Frouws, 1999). Here we will use a conceptual framework based on that of Wilson (2013a) but extend the notion of structural, economic, and socio-psychological lockins by making the notion of structural lock-in more explicit. The key lock-ins to be investigated are;
- Social lock-in (e.g. family size/age, social capital)
- Cultural lock-in (e.g. skills, knowledge)
- Psychological lock-in (e.g. risk averseness, beliefs, attachment to animals)
- Economic lock-in (e.g. debt, income)
- Policy/legal lock-in (e.g. quotas, subsidies, legislation, etc.)
- Environmental and geographical lock-in (e.g. isolation, land quality)
- Infrastructural/technological lock-in (e.g. farm buildings, herd type, feeding system, land availability; mechanisation, fertilizer use)
In order to provide the lock-in with a spatial context (see Orderud & Polickova-Dobiasova, 2010) two study regions will be chosen. These will be Jæren in Rogaland and the Namdalen area in North Trøndelag – representing areas with relatively intensive and extensive agricultural production respectively. From these two case study sites 25 to 30 farmers will be interviewed through a combination of qualitative and quantitative techniques. The quantitative data gathered will include
estimates of energy and fertilizer usage as well as other measures that relate to the overall carbon footprint of the farm.
The focus of the qualitative interview will be on (a) identifying areas that make change from their current farm system difficult (following the above framework of key lock-ins) and focusing on power, control and actants in networks, and (b) cultural, social and psychological lock-in into their current mode of production. Although the measure of energy use will admittedly be crude, it must be emphasized that the focus of the study is not on accurately estimating carbon emissions but on identifying issues of lock-in and path dependency. The issue of carbon emissions will be dealt with through the Bioforsk and NILF experts engaged in the modelling exercise. A further component of the interviews will be on the anticipated impacts of climatic change on their farming systems. This is important in order to create scenarios of change and model likely adaptation to anticipated future events and its impact (thereby addressing sub-area 4 of the call).
Manager and WP Leader, CRR