In recent years, agroforestry has gained increasing attention as an option to simultaneously alleviate poverty, provide ecological benefits, and mitigate climate change. The present study simulates small-scale farmers’ agroforestry adoption decisions to investigate the consequences for livelihoods and the environment over time. To explore the interdependencies between agroforestry adoption, livelihoods, and the environment, an agent-based model adjusted to a case study area in rural Indonesia was implemented. Thereby, the model compares different scenarios, including a climate change scenario. The agroforestry system under investigation consists of an illipe (Shorea stenoptera) rubber (Hevea brasiliensis) mix, which are both locally valued tree species. The simulations reveal that farmers who adopt agroforestry diversify their livelihood portfolio while increasing income. Additionally, the model predicts environmental benefits: enhanced biodiversity and higher carbon sequestration in the landscape. The benefits of agroforestry for livelihoods and nature gain particular importance in the climate change scenario. The results therefore provide policy-makers and practitioners with insights into the dynamic economic and environmental advantages of promoting agroforestry.
1 January 2020 in Journal of Artificial Societies and Social Simulation
The successful adoption of innovations depends on the provision of adequate information to farmers. In rural areas of developing countries, farmers usually rely on their social networks as an information source. Hence, policy-makers and program-implementers can benefit from social di usion processes to e ectively disseminate information. This study aims to identify the set of farmers who initially obtain information ('seeds') that optimises di usion through the network. It systematically evaluates di erent criteria for seed selection, number of seeds, and their interaction e ects. An empirical Agent-Based Model adjusted to a case study in rural Zambia was applied to predict di usion outcomes for varying seed sets ex ante. Simulations revealed that informing farmers with the most connections leads to highest di usion speed and reach. Also targeting village heads and farmers with high betweenness centrality, who function as bridges connecting di erent parts of the network, enhances di usion. An increased number of seeds improves reach, but the marginal e ects of additional seeds decline. Interdependencies between seed set size and selection criteria highlight the importance of considering both seed selection criteria and seed set size for optimising seeding strategies to enhance information di usion.
Socio-Ecological System modelling projects are becoming increasingly complicated, with multiple actors and aspects being the norm. Such projects can cause problems for the modellers when this involves different elements, goals, philosophies, etc., all pulling in different directions – we call this “Chimaera Modelling.” Although such situations are common when you talk to modellers, they do not seem to be explicitly discussed in the literature. In this paper, we attempt to turn this perceived “inside” phenomenon into an “outside” phenomenon and to start a debate to increase transparency among the modelling community. We discuss the different aspects which may be relevant to this problem to start this debate, including: the underlying philosophy, modelling goals, extent of choice the modellers have, different stages of modelling, and kinds of actors that are involved. We further map out some of the dimensions with which Chimaera Modelling connects. We briefly discuss these and propose to the community as a whole to work on their methodological development, feasibility, risks and applicability as their resolution is far beyond the scope of this paper. We end with a brief description of the broad possible approaches to such situations. Our main message is a call for recognition of Chimaera Modelling as a likely side-effect of multi-stakeholder, multi-purpose projects, and to take this into account proactively at the project team level and be transparent about the tensions and contradictions that underly such modelling.