Viva Grass Viewer

The Viva Grass Viewer is a basic module of the Viva Grass tool that is accessible to the general public. It aims to present results of mapping and assessment of the potential of ES, as well the grouping of ES in bundles and interaction amongst ES in agroecosystems. The Viva Grass viewer was implemented for informative and educational purposes, where the user is able to become acquainted with the ES approach, the spatial representation of basic logic behind assessment of ES and the spatial interaction between ES. Contextual data layers available in the Viva Grass Viewer are farmland land use, the potential of selected ES, bundles and trade-offs of ES potential and cold/hot spots of ES potential. The default view (Fig. 3) of the Viewer is a background map with land use data obtained from the IACS database land parcel identification system, representing the main classes of land use in agro-ecosystems: grasslands – semi-natural, permanent, cultivated and arable land. Additionally, abandoned farmland is shown when there is available data. By clicking on the land block of interest, the user can view the potential of ES in a selected field. For informative and educational purposes, the user can change land-use type to view changes in ES potential in case of land-use change. Short descriptions and recommended maintenance practices are provided when available. The potential of ES is the contextual data layer, which is derived from the expert based ES assessment matrix. Distribution of particular ES potential is visualised by selecting the particular service from a drop-down menu. For example, cultivated grassland, which is a monoculture agro-ecosystem, is ploughed at least once in 5 years, fertilised and seeded and, under certain natural conditions, can provide a greater amount of provisioning services associated with biomass production, but has less ability to provide regulating services. Semi-natural grassland, a low-input agro-ecosystem dominated by natural species, potentially delivers an abundance of regulating services, especially those associated with habitat maintenance. Bundles and trade-offs of ES potential are presented in a contextual data layer showing spatial grouping and interactions of ES. The user is able to explore groupings and interactions by choosing one in a drop-down menu. Cold/hot spots of ES potential are available in a contextual layer that gives the number of ES with either low or high values. The user is able to choose different representations of cold/hot spots of ES potential from a drop-down menu. The default choice for “cold/hot spots” is the combined value of “number of ES with high values” and “number of ES with low values”. Willemen et al. (2010) defined cold spots as areas with conflicts between two or more landscape functions, which, in our case, can be described as inappropriate management practice in given natural conditions. Moderate cold spots mostly display one of the trade-offs and planning decisions should be based on these. For defining the meaning of “hotspot”, we follow Bagstad et al. (2016) who defined it as an area which should draw attention of decision-makers, because of high conservation value and high vulnerability. This selection gives a general overview of a selected territory in the context of its current potential to deliver ES. To obtain a specific view on the character of the territory in the context of shortages or abundance of ES potential, the user can choose between additional selections (“Number of ES with high values” or “Number of ES with low values”) to view the actual number of ES with high/low values or by choosing a specific ranked 1-5 value.

Figure 3.  

Default view of the Viva Grass Viewer.

Viva Grass Bio-energy

The Viva Grass Bio-energy decision support system was developed as a tool for assessing grass-based energy resources (Fig. 4) to inform relevant planners/stakeholders about areas with the highest potential for grass for energy (prioritising). It is accessible for registered users only. The analysis of the energy potential includes parameters like grassland area, biomass production and calorific potential for district heating. The Viva Grass Bio-energy module uses additional sources of information to enrich both the base-map and the ES assessment. The 10 semi-natural grassland classes are updated with information about the Annex I*3 habitat type they belong to. Subsequently, quantitative data collected from scientific literature sources is linked to the Annex I habitat types. The module is, therefore, able to provide detailed information to the user about the average biomass production and average grass calorific power per semi-natural grassland type and allows us to select and summarise bio-energy potential from several grasslands. Additionally, the module provides information on the current management status of the selected grasslands, as well as information about the presence of reed encroachment and recommended grazing pressures per habitat type. The bio-energy sub-module was designed with the aim of assessing the availability of grass-based energy sources. It was approbated in Lääne County, Estonia. Grasslands have a potential for energy production as solid biomass heating fuels. Whether grasslands are specifically cultivated for this purpose or the grass mown from permanent and semi-natural meadows is used, grass can be burnt in co-fuelled plants for heat generation. In many cases, the use of grass bales for heating is a feasible alternative to regular biomass-based resources, such as woodchips. The bioenergy sub-module specifically aims at assessing the area, distribution, average production and average calorific potential of different semi-natural grasslands and it is designed to inform relevant planners/stakeholders about areas with the highest potential for grass for energy (prioritising). This is achieved by enriching the Viva Grass base-map with additional information on biomass production and calorific potential in Estonian semi-natural meadows, which is collected from several literature sources (Heinsoo et al. 2010, Melts et al. 2013, Melts et al. 2014b, Melts et al. 2014a). Additionally, the bioenergy sub-module includes information on the estimated demand of heating from grass biomass sources, understood as the amount of inhabitants living in district-heated blockhouses.

Figure 4.  

Viva Grass Bio-energy module displaying potential gross calorific value and heating plants and demands.

Viva Grass Planner

The Viva Grass Planner is a decision support system designed to implementthe ES concept for spatial planning. The Viva Grass Planner is accessible for registered users; registration is carried out by the system administrator. The Viva Grass Planner consists of two basic sub-modules designed to carry out prioritisation and classification functions, subsequent representation of the results in a map and to provide the possibility to export processed data. Prioritisation is performed by applying Multi-Criteria Decision Support (MCDS) – an accepted scheme for supporting complex decision-making situations with multiple and often conflicting objectives that stakeholders groups and/or researchers value differently (Saarikoski et al. 2016). We developed a MCDS approach for viable grassland management through assessment of ecosystem services and site-specific factors. We followed the scheme proposed by Langemeyer et al. (2016) (Fig. 5). MCDS was carried out in consecutive steps involving definition of a problem, collaborative definition of preferred criteria by stakeholders and experts, weighting of criteria and prioritisation of alternatives. Problems addressed in MCDS were elaborated and defined in round-table meetings of experts and local stakeholders at selected case studies. Round-table meetings began with presentation of ES assessment, ES trade-offs and cold/hotspots, followed by problem-orientated discussion to define MCDS objectives. To evaluate adequacy of different alternatives, clear criteria must be defined. As the whole process of definition of the problem was contextualised in ES assessment, it was possible to use the outcomes of it to define a core set of criteria, thus making them directly connected to ecosystem structure and functions. Additional criteria were developed to meet objectives of a particular MCDS. Thus, the criteria relevant for a particular decision-making context could be selected from the available attributes consisting of the results of ES assessment or from additional data on case specific attributes that were added by the user. To indicate a relative importance of chosen criteria, a weighted sum model was applied. The weighted sum is commonly used to form a comprehensive judgement in case of problematic ranking (Rowley et al. 2012). The Tool user can assign weights ranging from 0-100%, such that the sum of all percentages is equal to 100% (Fig. 6). The total weight index is the sum of the selected components. Weighting scales can be saved and edited. The resulting weighted index can be further divided into priority classes. To create final prioritisation of alternatives, additional classification can be performed by employing supplementary data specified by the objective of the MCDS. Classification can be done both based on performed prioritisation and stand-alone. To perform classification, some GIS skills are needed – writing an expression in SQL syntax. The user also requires knowledge of data structure. To improve the quality of performed analysis, data editing and additional data upload is provided. The user is able to edit and store underlying natural conditions of a selected field in cases when more precise information is available. The calculations of ES potential and interactions amongst ES are recalculated and updated by the Viva Grass tool and subsequently stored in the user account. To indicate a relative importance of a chosen criteria, the tool user can assign weights ranging from 0-100%, such that the sum of all percentages is equal to 100%.

Figure 5.  

Multi-criteria decision support workflow (adapted from Langemeyer et al. (2016)).

Figure 6.  

Screenshot of results of weighting the criteria for landscape management in Viva Grass Planner in Cēsis case study. User defined weights (upper left), legend of ranges of results based on natural brakes (middle left), map of management priority with individual score for each field (map window).

All Viva Grass tool modules were tested and improved in case studies through stakeholder engagement and, based on that, the beta version was developed. Later, the tool was tested in regional workshops in all three countries; all together 150 practitioners in various fields (spatial planning, agricultural consultancy, farmers, researchers) participated in seven all-day workshops. The entire populations of spatial planners and agriculture planners in the respective regions were approached and all who could come to the training sessions participated. Some interested students and farmers participated as well. During the workshops, participants were introduced to the concept of ES and its application in different fields. In the second part of the workshop, participants were introduced to the main functionalities of the Viva Grass tool and how the main results were generated for case studies. Participants were introduced to all modules of the tool and were familiarised with working and weighting different criteria. They developed, in small groups, preliminary case studies for their own localities, discussed weights and assessed the suitability of the tool to support decisions in their cases. Participants were assisted by researchers, who supported implementation of tool functionalities to local cases. After the workshop, participants filled in reports in which they expressed their opinion on the tool’s applicability in their field of activity, its advantages and disadvantages, as well stated their suggestions for further improvement of the Viva Grass tool. These comments were partially incorporated into the final version of the tool.

The Viva Grass Viewer was evaluated as the most usable of the modules (Fig. 7) and Viva Grass Bio-energy as the least usable in the daily work of participants of the workshops. Spatial planning, alongside land management and land-use transformation, were valued as the most suitable applications of the Viva Grass tool, reported by participants of workshops. The main disadvantages, named by the participants of the workshops, were base map data accuracy and reliability (especially soil data), language barrier as the tool’s working language is English and slow performance. The main advantages, named by participants of the workshops, were that the tool was simple and reviewable, it was possible to conduct data processing without having a GIS software and that the tool was novel.

Figure 7.  

Valued usefulness of Viva Grass Tool modules by workshop participants

Prioritisation of areas for landscape maintenance in the Cēsis case study area

The Viva Grass Planner was tested in the Cēsis Municipality case study. The aim was to support landscape management planning at the municipality level. Since 56% of the rural area in the Cēsis Municipality is covered by forest, maintenance of grasslands, as well as removal of shrub in abandoned agriculture land, are essential to preserve the characteristics of the mosaic landscape. The prioritisation model, based on MCDS, was applied to select sites for landscape maintenance or restoration measures. The criteria for prioritisation included the value of four cultural services (recreational, educational, cultural heritage and aesthetic), as well as ecological value (based on the habitats bundle – herbs for medicine, maintaining habitats, global climate regulation, pollination and seed dispersal) (Table 3). The prioritisation model for landscape maintenance is presented in Fig. 8. The results of application of the landscape management prioritisation model are shown in Fig. 9. The testing of the prioritisation model of the Viva Grass planner in the Cēsis case study was performed through an iterative process of stakeholder engagement, including two rounds of meeting with groups of 15 Municipality representatives (spatial planners and tourism experts from the Municipality, farmers and local entrepreneurs). During the first round, the stakeholders assigned weights to the selected ES potential criteria, while during the second round, the prioritisation results, derived from the tool, were examined, weighting of the criteria were adjusted to the priorities of the planning area and concrete management proposals for the specific areas were elaborated. The results are used to formulate proposals to the Cēsis Municipality Development Plan and related Action Plan, defining areas where specific management was required to maintain or improve the landscape value.

Figure 8.  

Prioritisation scheme for landscape planning in Cēsis case study showing workflow (consecutive steps from left to right) for decision support: defining criteria, assigning weights, arranging weighting results, adding additional values (risk index) for classifying priorities of management and assigning management action.

Figure 9.  

The results of landscape management prioritizsation in the Cēsis Municipality showing spatial distribution farmland management priority classes. The highest priority class (1st priority) calls for intensive restoration management (removal of shrubs and Sosnowski's hogweed), 2nd priority class calls for medium restoration measures (i.e. removal of Sosnowski's hogweed from the neighbouring territories - ditches, forest edges), 3rd priority class calls for maintenance practices (i.e. cutting grass more than once a year), lowest categories (4th and 5th priorities) call for monitoring of maintenance actions (i.e. yearly monitoring of management practices in situ or tracking of IACS data).

Green network planning support. The aim of green network (GN) planning MCDS is to guide planners in adaptation of a county-level GN into a rural municipality general plan, stressing the role of grasslands in GN and to identify possible land-use conflicts in GN implementation. The criteria for the inclusion of semi-natural grasslands in the GN of a rural municipality General Plan is based on their capacity to potentially deliver a certain set of ES (Table 4). In terms of the role of semi-natural grasslands inside the GN, ES belonging to the “habitats” and “soils” bundles offer a wide array of environmental benefits. These benefits, such as soil protection or pollination, are not only constrained to a grassland plot itself, but have a wider spatial effect (e.g. increased pollination benefits also in surrounding agricultural land). The GN MCDS scheme is presented in Fig. 10 and output maps in Fig. 11.

Figure 10.  

GN planning scheme showing workflow (consecutive steps from left to right) for decision support: defining criteria, assigning weights, arranging weighting results and assigning selection for scenario building .

Figure 11.  

Three consecutive scenarios of grassland inclusion in Green network of Lääne County, Estonia: (A) Scenario 1: bare minimum - only grasslands belonging to "habitats" bundle (i.e. displaying high or very high values for such ES as maintaining habitats for plant and animal nursery, pollination and seed dispersal, global climate regulations and herbs for medicine) are included in GN, (B) Scenario 2: medium ecological coherence - grasslands from "habitats bundle" and grasslands containing protected species are included in GN, (C) Scenario 3: High ecological coherence - grasslands from habitat bundle, grasslands containing protected species and grasslands form "soil" bundle (displaying high or very high values for such ES as bioremediation by micro-organisms, plants and animals, filtration/storage/accumulation by ecosystems, control of (water) erosion rates and chemical condition of freshwaters) are included in GN.