ABSTRACT
Social learning is a process suited to developing understanding and concerted action to tackle complex resource dilemmas, such as freshwater management. Research has begun to recognise that in practice social learning encounters a variety of institutional challenges from the shared habits and routines of stakeholders (organised by rules, norms and strategies) that are embedded in organisational structures and norms of professional behaviour. These institutional habits and routines influence the degree of willingness to engage with stakeholders, and expectations of behaviours in social learning processes. Considering this, there has been a call to understand how institutions influence social learning and emergent outcomes. We addresses this by presenting a heuristic for implementing social learning cognisant of institutional context to answer three questions: (i) How institutional influences impact implementation of social learning design; (ii) how implementation of social learning design modifies institutions influencing social learning; and (iii) how these changes in design and institutions together shape social learning outcomes? To answer these questions a freshwater planning exercise was designed, implemented and evaluated as a social learning process with community groups in two New Zealand catchments. Incorporating participatory reflection enabled the project team to modify social learning design to manage institutional influences hindering progress toward outcomes. Findings emphasise that social learning is underpinned by participants' changing assumptions about what constitutes the institution of learning itself-from instruction to a dynamic, collective and emergent process. Reflecting on these assumptions also challenged participants' expectations about their own and others' behaviours and roles in freshwater planning.
Subject(s)
Social Learning , Community Participation , Fresh Water , Humans , Learning , New ZealandABSTRACT
Can we develop land use policy that balances the conflicting views of stakeholders in a catchment while moving toward long term sustainability? Adaptive management provides a strategy for this whereby measures of catchment performance are compared against performance goals in order to progressively improve policy. However, the feedback loop of adaptive management is often slow and irreversible impacts may result before policy has been adapted. In contrast, integrated modelling of future land use policy provides rapid feedback and potentially improves the chance of avoiding unwanted collapse events. Replacing measures of catchment performance with modelled catchment performance has usually required the dynamic linking of many models, both biophysical and socio-economic-and this requires much effort in software development. As an alternative, we propose the use of variable environmental intensity (defined as the ratio of environmental impact over economic output) in a loose coupling of models to provide a sufficient level of integration while avoiding significant effort required for software development. This model construct was applied to the Motueka Catchment of New Zealand where several biophysical (riverine water quantity, sediment, E. coli faecal bacteria, trout numbers, nitrogen transport, marine productivity) models, a socio-economic (gross output, gross margin, job numbers) model, and an agent-based model were linked. An extreme set of land use scenarios (historic, present, and intensive) were applied to this modelling framework. Results suggest that the catchment is presently in a near optimal land use configuration that is unlikely to benefit from further intensification. This would quickly put stress on water quantity (at low flow) and water quality (E. coli). To date, this model evaluation is based on a theoretical test that explores the logical implications of intensification at an unlikely extreme in order to assess the implications of likely growth trajectories from present use. While this has largely been a desktop exercise, it would also be possible to use this framework to model and explore the biophysical and economic impacts of individual or collective catchment visions. We are currently investigating the use of the model in this type of application.
