• Energy Research

Orchard meadows, a traditional agroforestry system in Switzerland combining the dual use fruit and fodder production, are declining, even though the farmland managed under agri-environmental schemes (AES) has been expanding. Despite increasing interest in agroforestry research for developing sustainable agriculture, it is poorly understood how subsidies contribute to the maintenance of trees on agricultural land and the promotion of farmland biodiversity. Therefore, the objective of the present study is to examine the effects of incentive-based AES on both farmers’ decisions regarding trees and biodiversity by developing an ecological–economic assessment model. To explore cost-effective AES, we explicitly consider the heterogeneity of farm types. We apply this integrated model to the farms in Schwarzbubenland, a small hilly region in Northern Switzerland. Results show that the adoption of AES and the compliance costs of participating in AES considerably vary among farm types, and the current AES do not provide farmers with sufficient payments to maintain any type of orchard meadows, despite the ecological benefits of orchard meadows. The integrating modeling developed in this study enables us to better understand the relationship between subsidies and biodiversity through farmers’ decisions on land use and facilitates the design of cost-effective payments for the maintenance of agroforestry.

Regional impact studies are needed to explore possible adaptation options to climate change. We estimated impacts and adaptation options for future scenarios that feature different assumptions regarding climate, cropping pattern and access to irrigation with two bio-economic farm models. Farm profit, soil organic matter balance and labor input are used as indicators of farm performance. The difference between the baseline and the alternative configurations computed by models is referred as adaptation potential, indicative of the adaptation options including the corresponding changes in cropping patterns. Our results show that as long as there is sufficient access to irrigation water, there is little incentive to change current practices, as farming is at the economic optimum, has a positive soil organic matter balance and labor requirements can be met. Conversely, if irrigation is no longer possible, drastic impacts occur, causing a need to sustainably adjust on-going farm practices. Adaptation through changed crop selection reduced losses to some extent. We conclude that the use of bio-economic models can assist in evaluating the qualitative findings of participatory studies by quantitatively assessing possible climate change impacts and adaptation measures. Strong impacts of climate change, however, cannot be offset by changes in cropping patterns and need further adaptation measures.

This paper presents the results of a sustainability impact assessment (SIA) of policy induced land use changes in Yogyakarta, Indonesia. The regional problems include rapid expansions of urban areas, due to high population pressure, and the conversion of paddy fields and forests into settlements. The objective of this study was to assess the impacts of two land use policies on social, economic, and environmental Land Use Functions (LUFs) in Yogyakarta. The following scenarios were developed for the SIA: a forest protection scenario (S1), a paddy field conservation scenario (S2), and a counterfactual (no policy) scenario of ‘Business As Usual’ (BAU). The Framework for Participatory Impact Assessment (FoPIA) was applied to conduct an expert-based impact assessment. For the specification of the regional sustainability context, a set of nine key LUFs and associated indicators were developed, including three social, three economic, and three environmental sustainability criteria. The resulting scenario impacts of the assessment differed considerably, with positive impacts of the S1 and S2 scenarios on seven of nine LUFs, and negative impacts of the BAU scenario on six LUFs. The perception of the FoPIA method by the regional stakeholders was positive. We conclude that this method contributes toward an enhanced regional understanding of policy effects and sustainability, particularly in data-poor environments.

The impact of climate change and increased irrigation area on future hydrologic and agro-economic conditions was analysed for a representative basin in northeastern Germany using an expanded version of the WBalMO (water balance model) for water management. The model expansion represents various temporally and spatially differentiated irrigation water use processes, including agricultural irrigation, as part of a river basin’s water management. We show that climate changes lead to increased irrigation water demands in the future, which will not always be able to be met. The resulting water deficits were shown for different crops depending on their irrigation priority and the water available. With an increased irrigation area, water deficits will rise. This may limit the profitability of agricultural irrigation. The impacts of climate change on low-flow conditions in the river are much higher than those of the increase in irrigated area alone. Therefore, any additional increases of irrigation will require careful monitoring of water availability to avoid critical impacts on river flows. The expanded model was able to replicate the processes of agricultural irrigation water use and can thus be used to test the impact of policies such as the certification of new irrigation permits.

Smallholder farmers in Northern Ghana face challenges due to weather variability and market volatility, hindering their ability to invest in sustainable intensification options. Modeling can help understand the relationships between productivity, environmental, and economical aspects, but few models have explored the effects of weather variability on crop management and resource allocation. This study introduces an integrated modeling approach to optimize resource allocation for smallholder mixed crop and livestock farming systems in Northern Ghana. The model combines a process-based crop model, farm simulation model, and annual optimization model. Crop model simulations are driven by a large ensemble of weather time series for two scenarios: good and bad weather. The model accounts for the effects of climate risks on farm management decisions, which can help in supporting investments in sustainable intensification practices, thereby bringing smallholder farmers out of poverty traps. The model was simulated for three different farm types represented in the region. The results suggest that farmers could increase their income by allocating more than 80% of their land to cash crops such as rice, groundnut, and soybeans. The optimized cropping patterns have an over 50% probability of increasing farm income, particularly under bad weather scenarios, compared with current cropping systems.