Integrated or monofunctional landscapes? Agent-based modelling for evaluating the socioeconomic implications of land use interventions
The effectiveness of land sharing and land sparing (LS/LS) approaches to conservation in the face of rising agricultural demands has been widely debated. While numerous studies have investigated the LS/LS framework from an ecological lens (yield-biodiversity relationship) the relevance of the framework to real life depends on broader considerations. Some of the key caveats include: i) limited knowledge regarding the feasibility of interventions given diverse stakeholders’ interests, ii) the social acceptability (uptake) of these contrasting strategies to direct land users, and iii) limited knowledge regarding their impacts on individuals’ livelihoods and food security. Without considering these social science dimensions proponents of the framework risk an incomplete picture that is not grounded in local realities and can paradoxically force into opposition the very conservation and development interests they seek to reconcile. Using a Companion Modelling approach, which comprises the development of a role-playing game (RPG) and an agent-based model (ABM), this thesis addressed these caveats. The research was based in the Nilgiris of Western Ghats India, a tropical agricultural system at the forest frontier. The main findings show that through engaging local stakeholders in a participatory process, plausible land use strategies that align with their objectives could be identified. Stakeholders proposed three land use interventions. Two of them resemble a form of land sparing (‘monofunctional’ landscapes) on the farms: sparing land for Wildflower Meadows or Tree Plantations while increasing yield on the remaining land. The third intervention asks farmers to accept yield penalties for Intercropping more trees on their farms, a form of land sharing (‘integrated’ landscapes). In terms of decision-making regarding the adoption of these three interventions by direct land users, the study reveals several findings. Firstly there are three main types of motivations that influence farmers’ decision to adopt interventions, in order of importance: monetary benefits, pro-environmental motivations and social norms. Secondly, land use, the type of management preferred on the farm and whether land users accept trees on the farm or not are factors that influence what type of interventions is socially acceptable on individual farms. These factors have been detected in the in-depth household survey and also validated by the RPG. When assessing the adoption of the three interventions, ex ante their implementation, using an ABM, there are some important differences observed between the interventions. Wildflower Meadows is the intervention adopted by the largest number of households, whereas Intercropping is adopted across the largest area of land. Forest Plantations is significantly more unpopular than the other two interventions. The third line of investigation, about the outcomes of adoption, has important policy implications. Adding a socioeconomic dimension to the ecological one adds a level of complexity and creates a less straightforward choice between the LS/LS strategies. None of the three interventions can provide optimal outcomes for production, aspects of biodiversity conservation, livelihoods and food security. Each intervention has indicators that score better compared to the other two interventions. The findings demonstrate that the ecological focus of the LS/LS framework is insufficient to deal with real-world complexities and lends itself to overly simplistic policy prescriptions. More meaningful policies could be achieved when bridging natural and social sciences to better understand the merits and limitations of the LS/LS approaches.
The outcomes of research show that both the synergies and trade-offs which occur from economic, social and environmental objectives are important in defining sharing-sparing interventions that are locally feasible, in line with the Sustainable Development Goals and that ultimately lead to better long-term conservation of biodiversity.