Agroforestry, the intentional integration of trees with crops and/or livestock, can lead to multiple economic and ecological benefits compared to trees and crops/livestock grown separately. Field experimentation has been the primary approach to understanding the tree–crop interactions inherent in agroforestry. However, the number of field experiments has been limited by slow tree maturation and difficulty in obtaining consistent funding. Models have the potential to overcome these hurdles and rapidly advance understanding of agroforestry systems. Hi-sAFe is a mechanistic, biophysical model designed to explore the interactions within agroforestry systems that mix trees with crops. The model couples the pre-existing STICS crop model to a new tree model that includes several plasticity mechanisms responsive to tree–tree and tree–crop competition for light, water, and nitrogen. Monoculture crop and tree systems can also be simulated, enabling calculation of the land equivalent ratio. The model’s 3D and spatially explicit form is key for accurately representing many competition and facilitation processes. Hi-sAFe is a novel tool for exploring agroforestry designs (e.g., tree spacing, crop type, tree row orientation), management strategies (e.g., thinning, branch pruning, root pruning, fertilization, irrigation), and responses to environmental variation (e.g., latitude, climate change, soil depth, soil structure and fertility, fluctuating water table). By improving our understanding of the complex interactions within agroforestry systems, Hi-sAFe can ultimately facilitate adoption of agroforestry as a sustainable land-use practice.

Tropical peatlands are known not only for their high, area-based, carbon emissions in response to land-use change but also as hot spots of debate about associated data uncertainties. Perspectives are still evolving on factors underlying the variability and uncertainty. Debate includes the ways of reducing emissions through rewetting, reforestation and agroforestry. A knowledge value-chain that is long and complex links (a) fundamental understanding of peat and peatland processes leading to sciencebased quantification and default values, (b) willingness and (c) ability to act towards emission reduction, and ultimately (d) to local, national and global actions that effectively provide rules, incentives and motivation to conserve peat and reduce emissions. We discuss this value chain, its stakeholders and issues that still remain partially unresolved. We conclude that, to shorten the denial and conspiracy-theory stages of debate that otherwise slow down steps B and C, networks of international and national scientists have to be involved at the early stage of identifying policysensitive environmental issues. Models span part of the knowledge value-chain but transition of analysis units requires specific attention, from soil volumes through area and commodity flows to opportunities for reductions. While drainage of peatlands triggers landscape-scale increases in emissions, factors beyond drainage depth, including nutrient supply, may have a major influence on decomposition rates. Attempts to disentangle the contributions of plant and peat-based respiration in surface flux measurements involve assumptions that cannot be easily verified in comparisons between land uses. With progress on A leading to new internationally accepted defaults and with resistance on step B reduced, the reality of C and lack of working solutions for D is currently constraining further progress.

Managers of agro-ecosystems trade off food production and livelihood strategies against environmental services. They need tools to prospect a wide range of external conditions. Integrated simulation models allow stakeholders to discuss the plausible behaviour of agro-ecosystems and to evaluate dynamic trade-offs, as a basis for planning and policy making in agriculture and natural resource management. However, simulation models need to gain stakeholders’ acceptance before they will be utilized. Gaining stakeholders’ acceptance likely requires salience, credibility and legitimacy. We surveyed the perceptions and expectations of 122 potential model users in four countries, prioritizing these model attributes. A possible shift in user perception was assessed during a participatory model evaluation of a resource management model (FALLOW) for post-tsunami development in West Aceh (Indonesia). Potential model users, comprising natural resource managers, policy makers, lecturers and scientists, ranked salience as the most important characteristic for an integrated simulation model, followed by credibility and legitimacy. Model users’ occupation, prior exposure and interest in using a simulation model did not have a statistically significant influence on users’ perceptions of model attributes.

Migration connects land use in areas of origin with areas of new residence, impacting both through individual, gendered choices on the use of land, labor, and knowledge. Synthesizing across two case studies in Indonesia, we focus on five aspects: (i) conditions within the community of origin linked to the reason for people to venture elsewhere, temporarily or permanently; (ii) the changes in the receiving community and its environment, generally in rural areas with lower human population density; (iii) the effect of migration on land use and livelihoods in the areas of origin; (iv) the dynamics of migrants returning with different levels of success; and (v) interactions of migrants in all four aspects with government and other stakeholders of development policies. In-depth interviews and focus group discussions in the study areas showed how decisions vary with gender and age, between individuals, households, and groups of households joining after signs of success. Most of the decision making is linked to perceived poverty, natural resource and land competition, and emergencies, such as natural disasters or increased human conflicts. People returning successfully may help to rebuild the village and its agricultural and agroforestry systems and can invest in social capital (mosques, healthcare, schools).

This study combined uncertainty analysis of carbon emissions with local stakeholders' perspectives to develop an effective Reducing Emission from Deforestation and Degradation (REDD+) scheme at the district level. Uncertainty of carbon emission estimates depends on scale while local stakeholders' views on plausible REDD+ schemes influence and limit transaction costs. The uncertainty analysis formed the basis for determining an appropriate scale for monitoring carbon emission estimates as performance measures for REDD+ incentives. Our analysis of stakeholder’ perspectives explored (i) potential location and activities for lower emission development pathways, and (ii) perceived fair allocation of REDD+incentives. Our case study focused on frontier forest in Tanjung Jabung Barat District, Jambi, Indonesia. The uncertainty analysis used Monte Carlo simulation techniques using known inaccuracy of land cover classification and variation in carbon stocks assessment per land cover type. With decreasing spatial resolution of carbon emission maps, uncertainty in carbon estimates decreased. At 1 km² resolution uncertainty dropped below 5 %, retaining most of the coarser spatial variation in the district. Fairness, efficiency and transaction cost issues in the design of REDD+ mechanisms were readily recognized by local stakeholders, who converged on an equal allocation to short-term efficiency (emission reduction activities) and long-term fairness (alternative livelihood development). A striking difference occurred in desirable transaction costs (which include monitoring, reporting and verification), with Non-Governmental Organizations (NGOs) aiming for 8 %, while government and researchers accepted transaction costs of 40 %. Feasible measures for emission reduction in the district, derived from a participatory planning process, are compatible with the 1 km² spatial resolution of performance measures.