Understanding farmers' influence on land-use change using a participatory Bayesian network approach in a pre-Alpine region in Switzerland

<p>Land-use models can be used to assess the importance of different drivers of land-use change. Local actors make land-use decisions on the basis of both biophysical and policy aspects, but they can also be considered as autonomous drivers as their attitudes and beliefs influence land-use substantially. We use a Bayesian network-based Land-use Modeling Approach (BLUMAP) to analyze influences of local actor characteristics on land-use change in a spatially explicit manner. Our analysis shows that local actor characteristics have a greater influence on land-use change than changes in agricultural policy schemes. Furthermore, focusing on the probabilities of land-use occurrence under different scenarios facilitates the quantification of influences of local actor characteristics on land-use changes and aids in the detection of where land-use changes are more likely to occur. We demonstrate that local actor characteristics could override land-use policy trends; thus, greater consideration should be paid to actors in land-use development processes.</p

Coupling a settlement growth model with an agro-economic land allocation model for securing ecosystem services provision

<p>Mountain landscapes are undergoing rapid land-use changes. Settlement expansion, the intensification of agricultural land-use practices, and farmland abandonment result in a decline of natural and semi-natural habitats and the related ecosystem services (ES). In this context, spatial planning has emerged as a key instrument for the management of ES provision. To better understand trade-offs and interactions between settlement growth and ES provision in a spatially explicit manner, we present a new modeling framework coupling an agent-based, agro-economic optimization model and a cellular-automata-based settlement growth model. The framework is applied in an inner alpine valley in the Valais, Switzerland, which experienced rapid settlement growth in recent years. Results demonstrate how the model framework allows support of local planning processes. Particularly cooperation among municipalities and an explicit consideration of ES can inform spatially explicit ES trade-off decisions under increasing demand for land. We conclude that better informed spatial planning processes support ES provision.</p

Spatial representation of ground-mounted photovoltaic infrastructure (gm-PV), separated by strategy.

NRG-OS = strategy focusing purely on energy effectivity by selecting sites with the highest energy yields (high energy production per year) without considering environmental and social external costs, ESS-OS = strategy selecting the best sites in terms of energy output at the lowest environmental costs, SOC-OS = strategy selecting the best sites in terms of energy at the lowest social costs. Swiss digital elevation model is provided by swisstopo DEM25/200m [23], while Swiss national borders are provided by swisstopo [24].</p

Description of typical Swiss landscapes used in this study.

The “Characteristic landscapes” column refers to characteristic landscapes used as attributes in the discrete choice experiment of the national representative online survey [53, 54, 56]. These characteristic landscapes are upscaled to larger landscape units displayed in the “Name” column (see also Fig 1).</p

Matching the renewable energy installation (REI) attributes of the choice experiment (A) to the REI attributes of a 4 × 4 km square planning unit (B).

Matching the renewable energy installation (REI) attributes of the choice experiment (A) to the REI attributes of a 4 × 4 km square planning unit (B).</p

Overview of the frequencies of selected planning units (PUs), by strategy, in percentage.

37% of the selected planning units of strategy NRG-OS consist of PUs that are also selected by all other trade-off strategies.</p

Spatial representation of wind-energy infrastructure in strategies including ground-mounted photovoltaic infrastructure (gm-PV).

NRG-OS = strategy focusing purely on energy effectivity by selecting sites with the highest energy yields (high energy production per year) without considering environmental and social external costs, ESS-OS = strategy selecting the best sites in terms of energy output at the lowest environmental costs, SOC-OS = strategy selecting the best sites in terms of energy at the lowest social costs. Swiss digital elevation model is provided by swisstopo DEM25/200m [23], while Swiss national borders are provided by swisstopo [24].</p

Group differences of strategies per planning unit (PU) and by external costs.

A TukeyHSD (honestly significance difference) single-step multiple comparison procedure was applied to find means that are significantly different from each other with respect to differences in group sizes.</p

Social preferences of renewable energy scenarios in various landscapes.

It shows the Inverted share of preference (IsoP), with standard error (SE), per characteristic landscape type (n = 1, 8) and scenario (S = 1, 15) of the choice simulation expressing external costs related to social preferences of renewable energy infrastructure (REI) [53]. A higher IsoP value indicates higher costs. IsoP can also be interpreted as the cost of reactance. This study applied the formula IsoP(n,S) = SoPmax−SoP(n,S), where IsoP(n,S) = mean inverted share of preference of landscape type n in scenario S; SoPmax = maximum absolute share of preference; SoP(n,S) = share of preference of landscape type n in scenario S (1 ECUsoc = 1 IsoP).</p

Spatial representation of the combined ecosystem service costs and social costs for the selected planning units (PUs) of all strategies using ground-mounted photovoltaic infrastructure (gm-PV).

Each top bar represents the number of PUs required per strategy to fulfill the desired energy goal of 25 TWh/a. The PUs are classified by their quantile into four classes representing (1) low ecosystem service (ESS) costs and low social costs (grey), (2) low ecosystem service costs and high social costs (green), (3) high ecosystem service costs and low social costs (blue) and (4) high ecosystem service costs and high social costs (red). Swiss digital elevation model is provided by swisstopo DEM25/200m [23], while Swiss national borders are provided by swisstopo [24].</p