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Article

Identifying Strengths and Obstacles to Climate Change Adaptation in the German Agricultural Sector: A Group Model Building Approach

by
Rodrigo Valencia Cotera
*,
Sabine Egerer
and
María Máñez Costa
Climate Service Center Germany (GERICS), Helmholtz-Zentrum Hereon, Fischertwiete 1, D-20095 Hamburg, Germany
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(4), 2370; https://doi.org/10.3390/su14042370
Submission received: 17 December 2021 / Revised: 14 February 2022 / Accepted: 15 February 2022 / Published: 18 February 2022
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Abstract

:
In the past 30 years, there has been a significant increase in drought events in Europe. It is expected that climate change will make droughts more frequent and intense. This situation is particularly concerning for areas with no drought management culture. This study focuses on North East Lower Saxony (NELS), an important agricultural region in northern Germany. We implement a novel approach to Group Model Building to assess the preparedness of NELS to deal with climate change and droughts. Our novel approach includes the creation of a preliminary model based on individual interviews and a triangulation of information after the workshop. We conclude that stakeholders are aware of climate change, but insufficient attention is given to adaptive solutions mainly because they require high initial investments. Given its existing political infrastructure, the region has the potential to adjust. With efficient government bodies are already in place, beneficial updates could be made to established water withdrawal regulations.

1. Introduction

Of all climate events, droughts affect the most people around the world [1]. In the past 30 years, there has been an increase in drought events in Europe [2]. Prolonged summer heatwaves and droughts are associated with substantial financial, human, and environmental losses. The losses incurred from the 2018 drought were estimated to be EUR 3.3 billion, making it the costliest single-year weather event in Europe [3]. In Austria, for example, the agricultural losses caused by drought are higher than the combined losses due to hail, floods, storms, and frost [4]. Climate change is expected to increase the intensity and frequency of droughts [5], which could further exacerbate this effect [6]. The situation is particularly concerning for Mediterranean and Western Europe [5,7].
Historically, Germany has been considered a water-rich region and its agriculture is mainly rainfed. Since the 1980s, an increase in drought events has been observed with noticeable events in 2003, 2007, 2011 [8], and lately in 2018 and 2019 [3]. At a national level, climate change is expected to reduce water availability during the growing period. A temperature increase of 3 °C would imply an increase of more than 50% in the annual dry periods [9]. As the effects of climate change become more noticeable, the necessity of climate change adaptation becomes more evident. Government agencies, including the German Environment Agency, have suggested adaptation measures. However, the 2018 drought showed the vulnerability of German agricultural systems and the unpreparedness of governance structures to deal with droughts. To better deal with droughts, the system needs to increase its adaptation capacity and drought resilience.
Understanding how stakeholders perceive climate change can shed light on their behavioral intentions and support climate change adaptation [10,11]. Climate change perceptions are influenced by a multitude of factors including age, gender, political beliefs, education, farm size, emotions and personal experiences [10,12,13]. Perceptions, however, are significantly influenced by the level of education and access to weather and climate information [11,13,14,15]. There is extensive evidence suggesting that farmer’s climate change perceptions and awareness significantly influence their adaptation planning and adoption of adaptation practices [11,12,13,15,16,17,18,19]. Therefore, implementing an assessment of perceptions is a prerequisite to identify factors that could affect the adaptation process [11,19] of both of agriculture [14] and water resources [20]. The adaptation process can be defined as a three-step process. First, accurate climate risk perceptions; second, adaptation planning and third, implementation of adaptation [11,19].
Additionally, successful water management and climate change adaptation require the participation of numerous stakeholders including policymakers, farmers, NGOs, the private sector, researchers and communities. Their perceptions are crucial to support decision-makers to develop effective policies [12,13,20]. In particular water management benefits from the practical and critical perspectives of all water users [20]. Stakeholder engagement and participatory modeling are useful approaches to promote strategic decision-making for environmental resource management [21]. Participatory modeling also promotes an exchange of information between science and society [22] in which transdisciplinary knowledge exchange takes place.
Group Model Building (GMB) [23] is a useful and efficient method of implementing participatory modeling processes [24]. In GMB, the main objective is to capture the perception of the participating stakeholders on the problem to be solved. The results provide mental models of the participants’ perceptions of the problem. However, the main challenge in participatory modeling is to find a way around participants’ biases, beliefs, and interests [25]. Therefore, to improve objectivity and comprehensiveness, we propose modifying the GMB suggested by Vennix (1996) [23] to include a preliminary model based on the common perception of the participants and a triangulation process. By triangulation, we mean a combination of methods and comparison of information sources [26]. Through the implementation of our revised GMB approach, we developed causal-loop diagrams [24] together with stakeholders. In this study, we refer to those casual-loop diagrams as “models”. We created the models together with each stakeholder in a series of personal interviews and one group workshop. In total, we produced 20 individual models, a preliminary model, a group model, and a qualitative model.
Motivated by the recent drought events, we implemented our revised approach in North East Lower Saxony (NELS), an important agricultural region in Germany. Because droughts are a new phenomenon in northern Europe, we hypothesize that the agriculture industry in NELS might not be adequately prepared to face droughts associated with climate change. To explore this, we have set three research questions to assess the preparedness of NELS. First, how do stakeholders perceive climate change and drought? Second, are systemic adaptation measures being developed and implemented? Third, what are the possible challenges for adaptation in the region? Testing the preparedness of the region will also support the finding of possible knowledge gaps and needs for science-based services to support climate adaptation in the region. In this case, the need for better-customized climate services was one of the main drivers for this research.
Despite the significant number of studies implemented around the world exploring stakeholders’ perceptions on climate change [10,11,12,14,15,16,17,18,27,28,29], to our knowledge, no previous studies have focused on collecting the perceptions of farmers and other stakeholders in North East Lower Saxony. To fill this gap and support climate change adaptation we implemented this Group Model Building approach.

2. Case Study

This research focuses on NELS (Figure 1), an agricultural region situated in the north of Germany, as this region is representative of other similarly irrigated regions in Northern Europe. Within NELS, 47% of the land (5038.36 km2) is used for agriculture [30], and agriculture represents 2.39% of the gross value added (GVA). This is a high percentage when compared with the 0.85% at national level [31]. The region is located within the transition zone from Atlantic to continental climate [32,33]. This results in a reduction in precipitation from west to east.
NELS is characterized by sandy soils with extremely poor water retention capacity [31]. Despite light soils and insufficient precipitation during the growing season, the region specializes in growing water-demanding crops such as potatoes, sugar beets, and other vegetables. Because of this, farms since the 1950s have invested heavily in irrigation systems. Irrigation water is almost exclusively extracted from aquifers. The most widespread method of irrigation is the sprinkler gun irrigation system [33,34]. It is important to note that the majority of Germany’s irrigated land is located in Lower Saxony and that two thirds of that area is located in NELS [35].
Sustainability 14 02370 g001 550
Figure 1. Left: Location of Lower Saxony (green) in Germany (red). Right: Location of NELS inside Lower Saxony. Maps from Egerer et al. (2021) [36].
Figure 1. Left: Location of Lower Saxony (green) in Germany (red). Right: Location of NELS inside Lower Saxony. Maps from Egerer et al. (2021) [36].
Sustainability 14 02370 g001
Multiple studies have demonstrated that climate change will not only increase the temperatures in Lower Saxony but also cause a change in precipitation patterns [37,38,39]. Scheihing (2019) [39] compared five studies modeling the effect of climate change in Lower Saxony within the reference period (1971–2000). The results show that in Lower Saxony temperatures are expected to increase between 0.6 and 2.2 °C in the middle of the century and increase between 0.6 and 4.9 °C by the end of the century. In the RCP 2.6 scenario, the changes to precipitation are barely detectable. However, in the RCP 8.5 scenario, the yearly average precipitation would increase by 4 to 7 % for the middle of the century, while the summer precipitation would decrease by 6% and winter precipitation would increase by 11%. At a national level, similar trends have already been detected. There has been a pattern of increased winter precipitation and decreased summer precipitation for the period from 1901–2000 for most of the country [40,41].
Observational data (eobs-v19.0e) [42] averaged over NELS show the historical temperature and precipitation trends. For the period 1950–2018, the annual average minimum and maximum temperatures have followed an incremental trend (Figure 2). The total annual precipitation, for the same period, shows a slightly increasing trend while the total precipitation during the growing season (March to August) has followed a slightly decreasing trend (Figure 3). These observations align with the trends identified by Trömel and Schönwiese (2008) [41].

3. Materials and Methods

Due to its complex cross-scale interactions, conflicting inputs, and multiple potential outcomes, climate change has been described as a wicked problem [43]; and climate change adaptation has been termed the “wicked problem par excellence” [44]. Wicked problems are difficult to formulate and arise as symptoms of other problems, their solutions are difficult to identify and they are not conclusive but rather better or worse [45]. Because wicked problems tend to arise at the boundary between humanity and the environment, solutions spawn unforeseen problems [43].
Diverse approaches have emerged to deal with wicked problems, with examples including combining collaborative and process-driven methods [46] or by combining strategic planning theory and Actor-Network-Theory [47]. A more classical approach that has been recognized as a powerful tool to deal with wicked problems is participatory modeling [25]. Participatory Modelling helps to collect the best knowledge about the system in order to understand its causes, drivers and outcomes [25], and portray it using cognitive mapping [48]. In our case, we use participatory modeling to lay the basis for responding to the three previous presented objectives. Through participatory modeling, we mined the mental database of stakeholders who hold institutional memory regarding system structures, policies, and decision-making. The mental database encompasses all the information in people’s minds, including their impressions and their understanding of the system and how decisions are taken [49]. This information is usually not available in written databases [50].
One way of implementing participatory modeling is through GMB (Figure 4) developed by Vennix, (1996) [23]. GMB is an effective way of capturing the stakeholders’ perceptions and values [51]. Perceptions refers to the way in which people interpret the world by recognizing external stimuli and reacting to them with actions [51]. However, the ultimate goal of a GMB intervention is not only to develop a model of the researched system, but also to delineate a shared understanding of a problem and foster proposed solutions [23] as stakeholders might have different and ambiguous opinions about the problem [52]. GMB is useful when the problem is difficult to identify or when the situation is so complex that no entry point for solutions can be easily identified [53].
In addition to its versatility when confronted with complex problems, GMB afforded us a platform to acquire information from a wide range of actors with diverse backgrounds [54]. Multiple studies have presented GMB as an effective tool to gather, record, and share information and as a means to solve complex issues [55,56,57,58]. With GMB it is possible to model individual as well as collective perspectives and to include them in the planning process [54]. For example, GMB has been used to anticipate and respond to hydrological events [59], to improve management of coastal areas [60], to plan urban agriculture [54] to model the water-energy-food nexus [24], and to manage wetlands [61].
GMB helped us achieve our first objective (stakeholders’ perceptions on climate change and drought) by giving us the opportunity to meet and listen to multiple stakeholders, from farmers and nature protection organizations to water managers and lawmakers and record their perceptions in models. It also helped us to meet our second objective (identify climate change adaptation measures), as the process allowed us to identify individuals and organizations already considering or implementing climate change adaptation measures. It further supported us to meet our third objective (pinpoint barriers to climate change adaptation), as the models serve as a record to be further analyzed. Finally, the process also builds the basis for a possible composite quantitative model.
We have implemented changes to the original method described in Vennix, (1996) [23] and developed a novel approach to GMB (Figure 4) by adding a preliminary model based on individual models and a triangulation process after the group workshop. We propose that these modifications to the method will improve the collection and utilization of information, reduce uncertainty, and compensate for lack of participation during group workshops. These changes should help the modeler achieve a better understanding of the study issue and enhance the model building process. Our approach to GMB is described in the following sections.

3.1. Phase 1: Stakeholder Analysis

A stakeholder analysis is a process used to identify groups, individuals, and organizations affected by a phenomenon in order to prioritize their participation in the decision-making process [62]. These groups, individuals and organizations are referred to as “stakeholders”. By including stakeholders in the model development process, models have a higher chance of being accepted, trusted, and used [25]. People who are excluded from the collaborative process might resist the results [23]. Additionally, an individual’s understanding of the system is limited in scope. For these reasons, it is necessary to include all relevant actors [63]. It is crucial to consider stakeholder analysis before starting the participatory process [62].
We performed the stakeholder analysis following two approaches: brainstorming in a focus group with experts [62,64] and snowball sampling [62,65]. As explained by Reed, (2009) [62], snowball sampling refers to a method in which individuals from the firstly-identified stakeholder groups help identify new stakeholder categories and contacts. By implementing two methods, our goal was to strengthen stakeholder identification and reduce the chances of leaving important parties out. Since stakeholder identification is usually an iterative process [62], additional participants were identified as the study progressed (see Table 1). Our aim was to strengthen the network and increase group diversity. We decided to include no more than twenty stakeholders as suggested by Vennix (1996) [23].
All participants received a formal invitation to contribute to the project. None of the stakeholders was economically compensated for their participation with the exception of travel expenses to the GMB workshop. None of the stakeholders had experience with system dynamics techniques before their participation in the study. However, all of them had completed basic education, with many of them holding M.Sc. and Ph.D. in their fields of work.

3.2. Phase 2: Individual Interviews

We conducted individual interviews to produce individual qualitative models. We chose a semi-structured interview approach, meaning that the topics and questions were developed beforehand. The wording and sequence of the questions could be adapted in situ while ensuring similar information was collected in every interview. During the approximately two-hour-long individual interview, each stakeholder was directly involved in the creation of an individual model. The stakeholders could, at all times during the interview, see their model as well as add and remove components/variables and adjust the connections between them. At the end of the interview, the stakeholder reviewed and validated their individual model. The objective of individual model building is to refine the perception of each stakeholder in order to accurately represent their personal perspective. Having an individual model also serves as a record in case the participant is unable to attend the group workshop. Additionally, the modelers can deepen their understanding of the issue and prepare for the group workshop.

3.3. Phase 3: Preliminary Model

After the individual interviews were held, we performed a desk analysis of the individual models to develop a preliminary qualitative model based on the gathered information to: (a) gain a general impression of the system’s behavior and (b) have a compendium of the stakeholder’s personal perceptions. This process of creating a preliminary model is one of our two modifications to Vennix’s (1996) [23] GMB method.
Our method to create a preliminary model incorporates (but is not equal to) fuzzy-cognitive mapping (FCM). In FCM, identified concepts and the relationship between them are depicted in a graph, with concepts located in nodes and the relationships between them represented by direct angles. To quantify the influence one component has on the other, weights are assigned to the direct angles, either by expert opinion or empirical data [66]. In our case, we chose our components and gave weight to the connections between them based on the individual models. Each individual model was considered as the expert opinion of a stakeholder.
To speed up the construction of the preliminary model, we developed a Python script to extract and order all variables present in all individual models. The script also created the preliminary model based on boundary conditions. First, the components were automatically arranged in a list ranging from the most frequently occurring to the least frequently occurring. Words which were unmistakable synonyms were grouped together. The script also identified how often a connection between two elements occurred in the individual models.
Afterwards, we fixed the boundary conditions by classifying the components and the relationships between them into tiers. Components which were present in 10 to 20 individual models were classified as Tier-1 components, while components occurring in fewer than 10 models were classified as Tier-2. The same classification was applied to the relationships, though relationships rarely repeat in more than 10 models. Because of this they were classified as Tier-1 (occurring in 10 or more models), Tier-2 (in 7 to 9 models), Tier-3 (in 3 to 6 models), and Tier-4 (in 0 to 2 models).
The preliminary model is composed only of Tier-1 components and Tier-1, Tier-2, and Tier-3 relationships. By including only the components mentioned by the majority of our stakeholders, we created a basic structure to capture the common perception of the system. At the same time, by giving weight to the relationships based on their recurrence in the individual models, we distinguished among interactions: low effect (Tier-1), considerable effect (Tier-2), and strong effect (Tier-3). In the model, thin lines denote a low effect, medium thickness lines denote a considerable effect, and thick lines denote a strong effect. This representation of a common perception of the system aims to reduce subjectivity.
As mentioned above, the preliminary model is created to obtain a general overview of the system, but it is not to function as a final representation of it. Its intention is to help the modeler understand what components and connections are important for the majority of the stakeholders. In other words, the preliminary model represents what the majority of the stakeholders believe is the core of the system and it can be interpreted as a common ground. Additionally, participants had the chance to voice their perceptions in the next step (workshop), ensuring that relevant minority information was not lost.

3.4. Phase 4: Group Model Building Workshop

Following the completion of the preliminary model, we organized a GMB workshop to which all interested parties were invited. In designing the workshop, we followed the four dimensions of GMB as interpreted by Hovmand, (2013) [56]. The result was a GMB workshop with the following dimensions: (1) adaptation of agriculture to climate change was set as the pre-defined problem; (2) the workshop was structured as a group process; (3) the workshop aimed at producing a group model at the end; and (4) the workshop started the process with the “blank slate” approach. Our team consisted of a group facilitator and three notes’ recorders [67].
Because presenting a preliminary model may reduce stakeholders’ feelings of ownership [23], may bias participants, and may create a framing effect [56], the preliminary model was not shown to stakeholders. Stakeholders engaged in a discussion and information exchange, guided and moderated by a facilitator. The facilitator relied on the model to guide the session and to ensure no valuable information was omitted. As the workshop took place, the relationships and components of the model were confirmed by stakeholder responses to questions posed by the facilitator. During the discussion, new components and relationships were added when agreed by all participants.
Besides describing the researched system, the developed model acted as a boundary object [53,68] as it physically represented dependencies across organizations, disciplines, society, and culture. At the same time, during the workshop, the group model was accessible and modifiable by all stakeholders [69]. The creation of boundary objects is crucial to developing and maintaining coherence across social realms [68]. Boundary objects are particularly useful in the context of GMB [53]. The model facilitates knowledge exchange and agreement among participants, which in turn improves the outcome [53]. Additionally, one of the most common complications in model building happens when one stakeholder group, or the facilitator, dominates the model building or when the facilitator ignores someone’s input [53]. In order to avoid this, the interview partners could at all times see the model that was being produced and suggest modifications to the model in any way they thought convenient.

3.5. Phase 5: Triangulation and Analysis

We added a triangulation process as a custom modification to Vennix’s GMB method. Usually, a GMB process ends with a group model produced during the group workshop. This might have disadvantages: not all stakeholders will attend the group workshop, the workshop might be unproductive due to differences of opinion, and participation and interest levels will vary. These situations may contribute to a model only partially representing the studied issue. However, we propose that these drawbacks can be overcome by applying triangulation.
Triangulation has been extensively described by Denzin (2009) [70] as a framework for combining methods, data sources, theories, and observations in an investigation. The term also refers to the process of gathering, comparing, and combining information from different sources [26,49]. This includes interviews, literature, previous studies, transcripts of individual or group sessions and personal observations [26]. Observations refer to direct visual observations, as perceptions of the person’s mood, non-verbal communication or remarks made when the recorder is off can supply additional valuable information [26]. Additionally, in this study, we include observations made during field trips and visits to local farms and facilities (e.g. biogas plant) in our personal observations. During social research, both data and methods can be triangulated [71,72]. Denzin (2009) [70] recommends the use of triangulation since every research method reveals a specific aspect of empirical reality. Interviews, for example, are almost never sufficient to acquire enough data, since people have only partial and local understanding of the system. Furthermore, interview data is often mixed and it could also include false information [49].
Therefore, it is the task of the modeler to triangulate with as many sources of information as possible to fully understand the structures of the system [49]. The modelers should extract the causal structures from the information provided by stakeholders and should validate the model with archival information (previous studies, reports, legislations and numerical data) as well as their own experience and observations [49]. Through triangulation, we can maximize information completeness while garnering cross-validation [73]. Additionally, objectivity can be difficult to achieve when the research is primarily based on the stakeholders’ subjectivity. Each user will respond based on their own past experiences, current mood, and personal idiosyncrasies [70]. Triangulation methods also help achieve an “intersubjective” objectivity [26] and, in most cases, triangulation resolves ambiguity [73].
Based on these parameters, we developed a qualitative system dynamics model representing the structure of the system, built upon the statements given by stakeholders during the interviews and the information captured by the preliminary and group models. First, the water balance of the region was placed as the focal point of the model as as effected by Hassanzadeh al. (2014) [74]. Afterwards, we identified the main causal structures in the preliminary and group models and we verified them with other sources of information coming from numerical and textual databases. The verification or cross-validation process intends to avoid the inclusion of false structures or information in the final model. All information present in the qualitative model can be traced to either the previous models, the literature, or personal observations.
After the completion of the qualitative model, we performed an analysis using the preliminary model, the group model and the qualitative model to answer our three objectives. Because the first two objectives of this research are related to stakeholders’ beliefs and behaviors, the preliminary and the group model were used to corroborate their views and engaged them in the model building process. However, to pinpoint the region’s possible challenges to climate change adaptation, an additional analysis was required. We based this second examination on the triangulated model.

4. Results and Discussion

This section presents the three models generated during this study plus the results of the analysis made based on our three objectives: (1) Determine how stakeholders perceive climate change and drought. (2) Identify whether systemic adaptation measures are being developed and implemented. (3) Outline possible challenges for adaptation in the region to decrease vulnerability. The models are presented in chronological order and they originate from phases 3, 4, and 5 of this study.

4.1. Perceptions on Climate Change and Drought

The models suggest that the majority of the stakeholders are aware of climate change at least to a certain degree, as both, the preliminary and group models include some reference to climate change. In the preliminary model (Figure 5), the climate considerably affects precipitation but the causes of climate change are not shown. In the group model, (Figure 6) stakeholders expressed that CO2 negatively affects the climate. While stakeholders can affirm that they are aware of climate change, deep understanding of its meaning and effects might by missing [18]. A crucial understanding of climate change and its effects is indispensable, as farmers who believe that climate change is human-caused are more inclined to agree that weather patterns are changing and they are more concerned about the impact on their farms [17]. When asked during the interviews if they perceived climate change, most stakeholders expressed that they have noticed changes in precipitation patterns and increasing temperatures. The majority agreed that winters are shorter and warmer, while summers are hotter. As seen in Figure 2 average temperature in the region has indeed been following an incremental trend. This was expected as there is considerable evidence highlighting that farmers’ climate change perceptions are usually in line with the actual climatic data trends [11,13,14,16,19,28].
In regards to drought, in the preliminary model (Figure 5), the effect of drought on the water balance of the region and the factors leading to drought are not represented. Drought is represented as an independent event having a low effect on yields. In the group model (Figure 6), “dry periods” are part of a group of weather events affecting the water balance but they are also not linked to climate change. There is no connection between climate change and droughts in any of the models, indicating that many stakeholders see drought as a weather event that is not necessarily caused by climate change. This could be a common perspective of farmers in northern Europe, as Ibrahim and Johansson (2021) [29] found that farmers in Öland, Sweden, strongly believe that the 2018 drought was part of a natural cycle and half of them do not expect their yields to decrease. They also found out that most of the farmers were climate skeptics and that respondents had no urgency to adapt to climate change. Stakeholders can sometimes falsely believe that climate change would not affect them or that it will affect others sometime in the far future. Low risk perceptions and skepticism slows down and discourages climate change adaptation [10,11,19], while underestimating climate risk perceptions could lead to maladaptation or no adaptation at all [11,19]
Because education and access to information is the main influencing stakeholders’ perceptions on climate change [11,13,14,15], information should be offered to stakeholder to promote informed climate change perceptions. Climate change communication can help increase the risk awareness of stakeholders [10]. Climate change awareness can and should be influenced by information campaigns, access to information and climate change education [11,17,27]. By offering access to information and advisory services, policymakers can strengthen the adaptation capacity of rural communities [13,19,27] and improve farmers’ knowledge and capacity to support mitigation of and adaptation to climate change [11,75]. Climate messages with a negative emotional content that emphasize the threat of climate change have proven the most efficient in increasing climate change adaptation intentions and the risk perception [76].

4.2. Adaptation Measures Implemented and under Development in the Region

For our second objective, several adaptation measures were observed in the preliminary and group models and mentioned by stakeholders (Table 2). The most frequently mentioned adaptation measure was the creation of water storage infrastructure, which is present in both the preliminary and the group models (Figure 5 and Figure 6). By storing water, which might come from water treatment plants or industry, the available water of the region could be increased. This could decrease the pressure on groundwater by reducing the need for extracting groundwater for irrigation. The local sugar refinery has already created a water reservoir to store process water, which is later used for irrigation. The water reuse project implemented by the sugar refinery demonstrates the successful incorporation of water storage projects. However, while water reservoirs for agriculture can support climate change adaptation they require high capital investment and have high social and environmental costs [77]. Besides, the scale of this particular project is currently too small to have a significant effect on the amount of available water in the region (Figure 5).
Stakeholders also continuously pointed out the importance of humus formation to promote fertile soils and increase the soil water retention capacity (Figure 5). The majority of the stakeholders are aware that the sandy soils need to be enriched with humus. The humification suggested by stakeholders is closely related to crop rotation and tillage (Figure 5) [78]. Soils with a higher percentage of organic matter are more fertile and have a better water holding capacity, which in turn increases soil moisture and reduces the demand for water (Figure 5) [79]. While stakeholders are clearly aware of this, the current crop rotation and tillage practices do not promote humus formation. It is also important to consider that humification is a process that usually takes decades to complete. There is a widespread belief that no tillage, one of the three principles of Conservation Agriculture, is only possible by using herbicides and pesticides (Figure 5) [29]. Organic farmers, however, stated that their practices promote humification and that they fully enjoyed the benefits of humus-rich soils. One organic farm had been promoting humification for two generations and claimed that they did not even have to irrigate their fields during the 2018 drought.
Renaturalization was mentioned as an option to promote groundwater recharge and improve the state of surface water (Figure 6). Stakeholders mentioned that renaturalization should improve the state of superficial waters. Additionally, by promoting recharge and reducing water retention, the total available water of the region could be increased. There were, however, no major renaturalization projects in the region at the time this study took place. Extensive research should be carried out before implementing land use change projects as there are mixed results explaining the effect of woodlands on groundwater recharge. In some cases, native and exotic woodlands substantially decrease groundwater recharge, when compared to grassland, due to higher rainfall interception [80,81]. In other cases, natural forest and forest plantations have higher degree infiltration as degraded land [81,82]. However, agriculture and forest do have a higher groundwater recharge capacity when compared to urban areas [83].
Digitalization was also suggested as a way to improve the efficiency of agriculture. Digitalization is the base for precision agriculture [84]. New technology allows farmers to increase the efficiency of their irrigation, tillage, fertilizer application, herbicide application, and animal husbandry (Figure 5). Mentioned examples include GPS guided vehicles for fertilizer and pesticide application and soil humidity monitoring systems. Precision agriculture benefits farmers as it means less waste of the previously mentioned resources and, therefore, lower cost [75]. Precision farming has a high potential for mitigation as it also promotes a reduction of emissions and higher yields [75]. Farmers who engage in precision agriculture become more focused on economy but also on environmental benefits and climate change adaptation and mitigation [29]. Several farmers were already investing in new technologies, for example in GPS guided tractors or systems to continuously monitor the humidity in the soil and irrigate their fields only when needed.
Several stakeholders suggested a new crop rotation or diversification of crops as an adaptation measure because of the high water consumption of current crop rotation. Diversification of crops increases the resilience to climate change and drought [75,85,86]. The effect current crops have on water demand is represented in both the preliminary and the group model (Figure 5 and Figure 6). While current crop rotation has a high water-demand, a stakeholder also mentioned that a large proportion of the current infrastructure is built around these crops. Farmers mentioned their reluctance to change crops because it could have a large impact on crop yields and subsequently on their profits (Figure 5). This mirrors the observation in Scandinavia by Ibrahim and Johansson (2021) [29]. While, changing crops could reduce water demand, these changes are costly and could lead to rebounding maladaptation due to the incompatibility with market demand [87]. Market demand incompatibility could also lead to maladaptation through a shift in vulnerability, as the local sugar refinery and the biogas plants, two of the strongest buyers of farmers’ yields, depend on sugar beets and maize.
A financial reserve for crises was also suggested (Figure 6) as an adaptation measure to help agriculture hedge against natural hazards such as droughts, floods, and/or crop failure. There is, however, no consensus around how to create the financial reserve and who should be responsible for it. Financial reserves could benefit the region during extreme events. However, this kind of measure can also increase the farmers’ dependence on government [88]. It may be problematic for stakeholders to rely exclusively on financial bailout. This measure would be available only following catastrophic weather events. There may be the danger of a “moral hazard” [89], where the prospect of a bailout incentivizes stakeholders to neglect adaptation measures. Therefore, considering this financial reserve as an excuse to abandon the implementation of other adaptation measures should be avoided. Doing this could lead to rebounding maladaptation.
Several types of water management strategies are present in the group model (Figure 6). Among them are water rights policy and limitation of use. These water management strategies, such as water usage regulation, have a direct influence on the water balance of the region. The region already has already water management bodies and a solid water usage regulation in place. The current regulation, however, was designed to manage a system with stable climate conditions, and it does not consider droughts or climate change. Traditionally, water management practices have relied on historical data. This practice does not consider the effects of climate change [90,91]. Improved water resources management should take into account the effects of climate change [39,91]. The new strategies should consider short- and long-term demand and rely on available scientific resources such as climate projections and hydrological modeling under climate change scenarios [90]. This could help improve the resilience of the region and benefit all users as competition for water resources intensifies [90]. Apart from climate change, developing successful water management practices has other challenges such as the different interests of stakeholders, the complexity of government networks, and multiple decision makers [92].
Lastly, legal measures at a European level (Figure 6), such as the European Water Framework Directive [93] and Nitrates Directives [94], were also mentioned as mechanisms for climate change adaptation and environmental protection. This perception goes in line with the intentions of the EU policy. Policies such as the EU Cap, the EU Floods Directive, the EU Adaptation Strategy and the EU Water Scarcity and Drought Strategy, are efforts to provide a framework to encourage adaptation at a farm level [75].

4.3. Possible Challenges for Adaptation in NELS

Based on the stakeholder perceptions summarized in the triangulated model (Figure 7), we have identified that NELS has three strengths but also four challenges to overcome (Table 3). The first strength is existing irrigation systems that serve as a buffer during dry periods, as exhibited in the 2018 drought. A second strength is that the region already has water usage regulations. The law has a direct effect on water balance as it controls the amount of water that can be extracted (Figure 6 and Figure 7). However, the current water regulations do not consider either drought events or the effects of climate change. The third strength is that the physical and governmental infrastructure necessary to manage water resources and allocate water rights already exists. According to stakeholders, these government bodies are powerful and have been efficient in controlling water allocation and extraction.
The system’s main vulnerability is the types of crops currently planted. Crops (Figure 5 and Figure 7) directly affect humus formation, but the current crop rotation does not promote this process. Additionally, the current crop rotation has a direct impact on water demand (Figure 7), as it consists mainly of potatoes, sugar beets, and corn, all of which are water-demanding crops. A second challenge is that irrigation use has two drawbacks: high energy cost (Figure 5 and Figure 7) and increased water extraction, which affects water balance and potentially other users (Figure 5, Figure 6 and Figure 7) if no other adaptation measures are implemented.
As a last challenge, the region has a variety of water users with the most important being agriculture, drinking water, industry, and natural habitats (Figure 7). The variety of water users could make water management a controversial environmental policy issue, because of the diversity of interests and the increasing level of conflict among stakeholders, especially during prolonged drought periods. More users also mean higher water extraction, which according to stakeholders could lead to future water conflicts.

5. Conclusions

We implemented a Group Model Building process based on Vennix (1996) [23] with two additions to the method: (1) an aggregated preliminary model based on the individual models and; (2) a triangulation process to develop a final qualitative model. This novel approach to GMB applied in NELS allowed us to explore our three objectives. Our study showed that creating a preliminary model by merging the individual models displays the shared or common perspective of all stakeholders, and it helps distinguish between interactions with a strong or weak impact on the region’s dynamics. The triangulation process showed that by comparing all the gathered information, the modeler can develop a qualitative model, which can be used to explore the strengths and challenges of the region. This model offers several advantages. First, triangulation processes encourage objectivity by comparing the stakeholders’ perceptions with information from other databases, and the personal observations of the modeler. Second, triangulation helps to compile the information gathered during the research into a single model, which is easier to understand. Third, it does not limit the collection of information to one source as it allows the modeler to include information gathered from other data sources.
Our results suggest that stakeholders are aware of climate change but they lack a deep understanding of the effects that climate change might have on the region and its relation to drought events. Stakeholders are aware of adaptation measures they could implement to cope with these challenges. However, we conclude that NELS is currently unprepared to cope with the effects of climate change. The unpreparedness of NELS lies mainly in the fact that the majority of the adaptation measures suggested by the stakeholders imply large-scale system changes, which require high initial investments or imply an additional risk. Because of this, no major adaptation efforts have been made at either a farm level or regional level. To promote more accurate climate change perceptions and thus trigger adaptation, we propose climate change communication as a solution to underestimate climate risk perceptions. In regards to adaptation measures, we propose large economic incentives as the majority of the suggested adaptation measures require significant initial investments, which farmers are not able to pay for themselves.
Finally, we recommend a better management of water resources taking into account the effects of climate change. This could help improve the resilience of the region and benefit all users as competition for water resources intensifies. Developing successful water management practices has other challenges, including the different interests of stakeholders, the complexity of government networks, and multiple decision makers. Despite the challenges and difficulties, developing a better water management strategy presents an opportunity to initiate the adaptation process without great economic investment. The region could also benefit from the development of a drought management plan. By implementing such a plan, water managers could enforce restrictions and controls to ensure water is available for all actors during drought periods and, at the same time, avoid overexploitation of water resources. This solution is extremely challenging, as it requires a deep analysis of available water resources, a risk analysis process, and changes to the current law. Encouragingly, the NELS region is supported by sound infrastructure and effective governing bodies, which makes tackling this challenge possible.

Author Contributions

Methodology, R.V.C., S.E. and M.M.C.; investigation, R.V.C. and S.E.; writing—original draft preparation, R.V.C.; writing—review and editing, S.E. and M.M.C.; supervision, M.M.C.; funding acquisition, M.M.C. All authors have read and agreed to the published version of the manuscript.

Funding

This work was conducted and financed within the framework of the Helmholtz Institute for Climate Service Science (HICSS), a cooperation between Climate Service Center Germany (GERICS) and Universität Hamburg, Germany.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

We did not report data.

Acknowledgments

The authors will like to thank the Chamber of Agriculture of Lower Saxony for their support during this research and to C.L. and S. Cotera for the language correction. We acknowledge the E-OBS dataset from the EU-FP6 project UERRA (http://www.uerra.eu, accessed on 10 February 2022) and the Copernicus Climate Change Service, and the data providers in the ECA&D project (https://www.ecad.eu, accessed on 10 February 2022).

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

References

  1. Cruz, G.; Gravina, V.; Baethgen, W.E.; Taddei, R. A Typology of Climate Information Users for Adaptation to Agricultural Droughts in Uruguay. Clim. Serv. 2021, 22, 100214. [Google Scholar] [CrossRef]
  2. Stein, U.; Özerol, G.; Tröltzsch, J.; Landgrebe, R.; Szendrenyi, A.; Vidaurre, R. European Drought and Water Scarcity Policies. In Governance for Drought Resilience; Bressers, H., Bressers, N., Larrue, C., Eds.; Springer: Cham, Switzerland, 2016; pp. 17–44. ISBN 9783319296715. [Google Scholar]
  3. Ionita, M. On the Curious Case of the Recent Decade, Mid-Spring Precipitation Deficit in Central Europe. npj Clim. Atmos. Sci. 2020, 1–10. [Google Scholar] [CrossRef]
  4. Leitner, M.; Babcicky, P.; Schinko, T.; Glas, N. The Status of Climate Risk Management in Austria. Assessing the Governance Landscape and Proposing Ways Forward for Comprehensively Managing Flood and Drought Risk. Clim. Risk Manag. 2020, 30, 100246. [Google Scholar] [CrossRef]
  5. Samaniego, L.; Thober, S.; Kumar, R.; Wanders, N.; Rakovec, O.; Pan, M.; Zink, M.; Sheffield, J.; Wood, E.F.; Marx, A. Anthropogenic Warming Exacerbates European Soil Moisture Droughts. Nat. Clim. Chang. 2018, 8, 421–426. [Google Scholar] [CrossRef]
  6. Intergovernmental Panel on Climate Change. Global Warming of 1.5 °C, an IPCC Special Report on the Impacts of Global of 1.5 °C above Pre-Industrial Levels; Intergovernmental Panel on Climate Change: Incheon, Korea, 2018. [Google Scholar]
  7. Naumann, G.; Cammalleri, C.; Mentaschi, L.; Feyen, L. Increased Economic Drought Impacts in Europe with Anthropogenic Warming. Nat. Clim. Chang. 2019. [Google Scholar] [CrossRef]
  8. European Environment Agency. Climate Change, Impacts and Vulnerability in Europe 2012; European Environment Agency: Copenhagen, Denmark, 2012.
  9. Thober, S.; Marx, A.; Boeing, F. Auswirkungen Der Globalen Erwärmung Auf Hydrologische Und Agrarische Dürren Und Hochwasser in Deutschland; Helmholtz Centre for Environmental Research—UFZ: Leipzig, Germany, 2018. [Google Scholar]
  10. Maltby, K.M.; Simpson, S.D.; Turner, R.A. Scepticism and Perceived Self-Efficacy Influence Fishers’ Low Risk Perceptions of Climate Change. Clim. Risk Manag. 2021, 31, 100267. [Google Scholar] [CrossRef]
  11. Abid, M.; Scheffran, J.; Schneider, U.A.; Elahi, E. Farmer Perceptions of Climate Change, Observed Trends and Adaptation of Agriculture in Pakistan. Environ. Manag. 2019, 110–123. [Google Scholar] [CrossRef]
  12. Aidoo, D.C.; Boateng, S.D.; Freeman, C.K.; Anaglo, J.N. The Effect of Smallholder Maize Farmers’ Perceptions of Climate Change on Their Adaptation Strategies: The Case of Two Agro-Ecological Zones in Ghana. Heliyon 2021, 7, e08307. [Google Scholar] [CrossRef]
  13. Jha, C.K.; Gupta, V. Farmer’ s Perception and Factors Determining the Adaptation Decisions to Cope with Climate Change: An Evidence from Rural India. Environ. Sustain. Indic. 2021, 10, 100112. [Google Scholar] [CrossRef]
  14. Nyang’au, J.O.; Mohamed, J.H.; Mango, N.; Makate, C.; Wangeci, A.N. Smallholder Farmers’ Perception of Climate Change and Adoption of Climate Smart Agriculture Practices in Masaba South Sub-County, Kisii, Kenya. Heliyon 2021, 7, e06789. [Google Scholar] [CrossRef]
  15. Li, W.; Yuan, K.; Yue, M.; Zhang, L.; Huang, F. Climate Change Risk Perceptions, Facilitating Conditions and Health Risk Management Intentions: Evidence from Farmers in Rural China. Clim. Risk Manag. 2021, 32, 100283. [Google Scholar] [CrossRef]
  16. Snaibi, W.; Mezrhab, A.; Sy, O.; Morton, J.F. Perception and Adaptation of Pastoralists to Climate Variability and Change in Morocco’ s Arid Rangelands. Heliyon 2021, 7, e08434. [Google Scholar] [CrossRef] [PubMed]
  17. Mase, A.S.; Gramig, B.M.; Prokopy, L.S. Climate Change Beliefs, Risk Perceptions, and Adaptation Behavior among Midwestern US Crop Farmers. Clim. Risk Manag. 2017, 15, 8–17. [Google Scholar] [CrossRef]
  18. Hundera, H.; Mpandeli, S.; Bantider, A. Smallholder Farmers’ Awareness and Perceptions of Climate Change in Adama District, Central Rift Valley of Ethiopia. Weather Clim. Extrem. 2019, 26, 100230. [Google Scholar] [CrossRef]
  19. Mahmood, N.; Arshad, M.; Mehmood, Y.; Faisal, M.; Harald, K. Farmers’ Perceptions and Role of Institutional Arrangements in Climate Change Adaptation: Insights from Rainfed Pakistan. Clim. Risk Manag. 2021, 32. [Google Scholar] [CrossRef]
  20. Phan, T.D.; Bertone, E.; Pham, T.D.; Pham, T. V Perceptions and Willingness to Pay for Water Management on a Highly Developed Tourism Island under Climate Change: A Bayesian Network Approach. Environ. Chall. 2021, 5, 100333. [Google Scholar] [CrossRef]
  21. Kotir, J.H.; Smith, C.; Brown, G.; Marshall, N.; Johnstone, R. A System Dynamics Simulation Model for Sustainable Water Resources Management and Agricultural Development in the Volta River Basin, Ghana. Sci. Total Environ. 2016, 573, 444–457. [Google Scholar] [CrossRef]
  22. Williams, D.S. Enhancing Autonomy for Climate Change Adaptation Using Participatory Modeling. Weather Clim. Soc. 2020, 12, 667–678. [Google Scholar] [CrossRef]
  23. Vennix, J.A.M. Group Model Building: Facilitating Team Learning Using System Dynamics; John Wiley & Sons Ltd: Hoboken, NJ, USA, 1996; ISBN 0-471-95355-5. [Google Scholar]
  24. Purwanto, A.; Susnik, J.; Suryadi, F.X.; de Fraiture, C. Using Group Model Building to Develop a Causal Loop Mapping of the Water-Energy-Food Security Nexus in Karawang Regency, Indonesia. J. Clean. Prod. 2019, 240, 118170. [Google Scholar] [CrossRef]
  25. Voinov, A. Participatory Modeling for Sustainability. In Encyclopedia of Sustainable Technologies; Elsevier: Amsterdam, The Netherlands, 2017; pp. 33–39. [Google Scholar]
  26. Akkermans, H. Modelling with Managers: Participative Business Modelling for Effective Strategic Decision-Making; Technische Universiteit Eindhoven: Eindhoven, The Netherlands, 1995. [Google Scholar]
  27. Asare-Nuamah, P.; Botchway, E. Heliyon Comparing Smallholder Farmers’ Climate Change Perception with Climate Data: The Case of Adansi North District of Ghana. Heliyon 2019, 5, e03065. [Google Scholar] [CrossRef] [Green Version]
  28. Elum, Z.A.; Modise, D.M.; Marr, A. Farmer’ s Perception of Climate Change and Responsive Strategies in Three Selected Provinces of South Africa. Clim. Risk Manag. 2017, 16, 246–257. [Google Scholar] [CrossRef]
  29. Ibrahim, M.A.; Johansson, M. Attitudes to Climate Change Adaptation in Agriculture—A Case Study of Öland, Sweden. J. Rural Stud. 2021. [Google Scholar] [CrossRef]
  30. Umweltbundesamt Struktur Der Flächennutzung. Available online: https://www.umweltbundesamt.de/daten/flaeche-boden-land-oekosysteme/flaeche/struktur-der-flaechennutzung#textpart-2 (accessed on 23 October 2019).
  31. Grocholl, J. Effiziente Wassernutzung Im Ackerbau Nord-Ost-Niedersachsens: Möglichkeiten Zur Anpassung an Den Prognostizierten Klimawandel; Lower Saxony Chamber of Agriculture, Uelzen District office: Uelzen, Germany, 2011. [Google Scholar]
  32. Jensen, K.; Knieling, J.; Rechid, D. Charakteristika Der Metropolregion Hamburg. In Kursbuch: Klimaanpassung; TuTech publisher: Hamburg, Germany, 2014; pp. 4–7. [Google Scholar]
  33. LWK Niedersachsen. No Regret—Genug Wasser Für Die Landwirtschaft?! Projektbericht—Kurzfassung; Lower Saxony Chamber of Agriculture, Uelzen district office: Uelzen, Germany, 2008. [Google Scholar]
  34. Wittenberg, H. Groundwater Abstraction for Irrigation and Its Impacts on Low Flows in a Watershed in Northwest Germany. Resources 2015, 4, 566–576. [Google Scholar] [CrossRef]
  35. Ostermann, U. Wasserhaushalt in Nordostniedersachsen Durch Wassernutzung Und—Management Ausgleichen. Wasser und Abfall 2019, 21, 39–46. [Google Scholar] [CrossRef]
  36. Egerer, S.; Valencia-Cotera, R.; Celliers, L.; Máñez Costa, M. A Leverage Points Analysis of a Qualitative System Dynamics Model for Climate Change Adaptation in Agriculture. Agric. Syst. 2021, 189. [Google Scholar] [CrossRef]
  37. Niedersächsisches Ministerium für Umwelt Energie und Klimaschutz. Empfehlung Für Eine Niedersächsische Strategie Zur Anpassung an Die Folgen Des Klimawandels; Niedersächsisches Ministerium für Umwelt Energie und Klimaschutz: Hannover, Germany, 2012. [Google Scholar]
  38. Rechid, D.; Petersen, J.; Jacob, D. Klimaprojektionen Für Die Metropolregion Hamburg. In Kursbuch: Klimaanpassung; TuTech Verlag: Hamburg, Germany, 2014; pp. 8–11. [Google Scholar]
  39. Scheihing, K.W. Klimawandel in Niedersachsen Und Mögliche Folgen Für Die Grundwasserbewirtschaftung: Ein Review. Hydrol. und Wasserbewirtschaft 2019. [Google Scholar] [CrossRef]
  40. Bender, S.; Butts, M.; Hagemann, S.; Smith, M.; Vereecken, H.; Wendland, F. Der Einfluss Des Klimawandels Auf Die Terrestrischen Wassersysteme in Deutschland; Climate Service Center Germany: Hamburg, Germany, 2017. [Google Scholar]
  41. Trömel, S.; Schönwiese, C.D. Robust Trend Estimation of Observed German Precipitation. Theor. Appl. Climatol. 2008, 93, 107–115. [Google Scholar] [CrossRef]
  42. Cornes, R.C.; Van Der Schrier, G.; Jones, P.D.; Besselaar, E.J.M. Van Den An Ensemble Version of the E-OBS Temperature and Precipitation Data Sets. J. Geophys. Res. Atmos. 2018, 9391–9409. [Google Scholar] [CrossRef] [Green Version]
  43. Sun, J.; Yang, K. The Wicked Problem of Climate Change: A New Approach Based on Social Mess and Fragmentation. Sustainability 2016, 8, 1312. [Google Scholar] [CrossRef] [Green Version]
  44. Termeer, C.; Dewulf, A.; Breeman, G. Governance of Wicked Climate Adaptation Problems. In Climate Change Management; Springer: Berlin/Heidelberg, Germany, 2013; pp. 27–39. [Google Scholar]
  45. Webber, M.M. Dilemmas in a General Theory of Planning. Policy Sci. 1973, 4, 155–169. [Google Scholar]
  46. Elia, G.; Margherita, A. Can We Solve Wicked Problems? A Conceptual Framework and a Collective Intelligence System to Support Problem Analysis and Solution Design for Complex Social Issues. Technol. Forecast. Soc. Chang. 2018, 133, 279–286. [Google Scholar] [CrossRef]
  47. Tietjen, A.; Jørgensen, G. Landscape and Urban Planning Translating a Wicked Problem: A Strategic Planning Approach to Rural Shrinkage in Denmark. Landsc. Urban Plan. 2016, 154, 29–43. [Google Scholar] [CrossRef] [Green Version]
  48. Eden, C. Cognitive Mapping. Eur. J. Oper. Res. 1988, 36, 1–13. [Google Scholar] [CrossRef]
  49. Sterman, J.D. Bussiness Dynamics. Systems Thinking and Modeling for a Complex World; Shelstad, J.J., Ed.; McGraw-Hill Higher Education: New York, NY, USA, 2000; ISBN 007238915X. [Google Scholar]
  50. Forrester, J.W. Policies, Decisions and Information Sources for Modeling. Eur. J. Oper. Res. 1992, 59, 42–63. [Google Scholar] [CrossRef]
  51. Carmona Costa, M. Risk Perception and Governance Performance in Multi-Sector Partnerships. The Case Study of the Permanent Drought Commission of the Jucar River; Universität Hamburg: Hamburg, Germany, 2016. [Google Scholar]
  52. Rouwette, E.; Bleijenbergh, I.; Vennix, J. Group Model-Building to Support Public Policy: Addressing a Conflictcted Situation in a Problem Neighbourhood. Syst. Res. Behav. Sci. 2016, 78, 64–78. [Google Scholar] [CrossRef]
  53. Black, L.J. When Visuals Are Boundary Objects in System Dynamics Work. Syst. Dyn. Rev. 2013, 29, 70–86. [Google Scholar] [CrossRef] [Green Version]
  54. Rich, K.M.; Rich, M.; Dizyee, K. Participatory Systems Approaches for Urban and Peri-Urban Agriculture Planning: The Role of System Dynamics and Spatial Group Model Building. AGSY 2016. [Google Scholar] [CrossRef]
  55. Zagonel, A.A. Model Conceptualization in Group Model Building: A Review of the Literature Exploring the Tension between Representing Reality and Negotiating a Social Order. In Proceedings of the 2002 International Conference of the System Dynamics Society, Palermo, Italy, 28 July–1 August 2002; pp. 1–68. [Google Scholar]
  56. Hovmand, P.S. Group Model Building Approaches to Group Model Building. In Community Based System Dynamics; Springer: New York, NY, USA, 2013; pp. 17–30. [Google Scholar]
  57. Berard, C. Group Model Building Using System Dynamics: An Analysis of Methodological Frameworks. Electron. J. Bus. Res. Methods 2010, 8, 35–45. [Google Scholar]
  58. Stave, K. Participatory System Dynamics Modeling for Sustainable Environmental Management: Observations from Four Cases. Sustainability 2010, 2, 2762–2784. [Google Scholar] [CrossRef] [Green Version]
  59. Máñez, M.; Osorio, J.; Kovalevsky, D. WP-13 Deliverable D13.1 Generic Integrative Modeling Approach Guideline; Climate Service Center Germany: Hamburg, Germany, 2017. [Google Scholar]
  60. Vugteveen, P.; Rouwette, E.; Stouten, H.; Van Katwijk, M.M.; Hanssen, L. Developing Social-Ecological System Indicators Using Group Model Building. Ocean Costal Manag. 2015, 109, 29–39. [Google Scholar] [CrossRef] [Green Version]
  61. Chen, H.; Chang, Y.; Chen, K. Integrated Wetland Management: An Analysis with Group Model Building Based on System Dynamics Model. J. Environ. Manag. 2014, 146, 309–319. [Google Scholar] [CrossRef]
  62. Reed, M.S.; Graves, A.; Dandy, N.; Posthumus, H.; Hubacek, K.; Morris, J.; Prell, C.; Quinn, C.H.; Stringer, L.C. Who’s in and Why? A Typology of Stakeholder Analysis Methods for Natural Resource Management. J. Environ. Manag. 2009, 90, 1933–1949. [Google Scholar] [CrossRef] [PubMed]
  63. Sterman, J.D. All Models Are Wrong: Reflections on Becoming a Systems Scientist. Syst. Dyn. Rev. 2002, 18, 501–531. [Google Scholar] [CrossRef]
  64. Al-samarraie, H.; Hurmuzan, S. A Review of Brainstorming Techniques in Higher Education. Think. Ski. Creat. 2017. [Google Scholar] [CrossRef]
  65. Pattison, P.E.; Robins, G.L.; Snijders, T.A.B.; Wang, P. Conditional Estimation of Exponential Random Graph Models from Snowball Sampling Designs. J. Math. Psychol. 2013, 57, 284–296. [Google Scholar] [CrossRef]
  66. Malek, Ž. Fuzzy-Logic Cognitive Mapping: Introduction and Overview of the Method. In Environmental Modeling with Stakeholders; Springer International Publishing: Cham, Switzerland, 2017; pp. 127–143. [Google Scholar]
  67. Richardson, G.P.; Andersen, D.F. Teamwork in Group Model Building. Syst. Dyn. Rev. 1995, 11, 113–137. [Google Scholar] [CrossRef] [Green Version]
  68. Star, S.L.; Grisemer, J.R. Institutional Ecology, “translations” and Boundary Objects: Amateurs and Professionals in Berkley’s Museum of Vertebrate Zoology, 1907–1936. Soc. Stud. Sci. 1989, 19, 387–420. [Google Scholar] [CrossRef]
  69. Black, L.J.; Andersen, D.F. Using Visual Representations as Boundary Objects to Resolve Conflict in Collaborative Model-Building Applications. Syst. Res. Behav. Sci. 2012, 29, 194–208. [Google Scholar] [CrossRef] [Green Version]
  70. Denzin, N.K. The Research Act: A Theoretical Introduction to Sociological Methods; Routledge: London, UK, 2009; ISBN 9780202362489. [Google Scholar]
  71. Bryman, A. Social Research Methods, 4th ed.; Oxford University Press: Oxford, UK, 2012; ISBN 978–0–19–958805–3. [Google Scholar]
  72. Kaivo-oja, J. Towards Better Participatory Processes in Technology Foresight: How to Link Participatory Foresight Research to the Methodological Machinery of Qualitative Research and Phenomenology? Futures 2017, 86, 94–106. [Google Scholar] [CrossRef]
  73. Tischer, D. Collecting Network Data from Documents to Reach Non-Participatory Populations. Soc. Netw. 2020. [Google Scholar] [CrossRef]
  74. Hassanzadeh, E.; Elshorbagy, A.; Wheater, H.; Gober, P. Managing Water in Complex Systems: An Integrated Water Resources Model for Saskatchewan, Canada. Environ. Model. Softw. 2014, 58, 12–26. [Google Scholar] [CrossRef]
  75. European Environment Agency. Climate Change Adaptation in the Agriculture Sector in Europe; European Environment Agency: Copenhagen, Denmark, 2019.
  76. Brosch, T. ScienceDirect Affect and Emotions as Drivers of Climate Change Perception and Action: A Review. COBEHA 2021, 42, 15–21. [Google Scholar] [CrossRef]
  77. International Water Management Institute. Water Policy Brief; International Water Management Institute: Colombo, Sri Lanka, 2009. [Google Scholar]
  78. Marcinkeviciene, A.; Boguzas, V.; Balnyte, S.; Pupaliene, R.; Velicka, R. Influence of Crop Rotation, Intermediate Crops, and Organic Fertilizers on the Soil Enzymatic Activity and Humus Content in Organic Farming Systems. Eurasian Soil Sci. 2013, 46, 198–203. [Google Scholar] [CrossRef]
  79. Drastig, K.; Prochnow, A.; Libra, J.; Koch, H.; Rolinski, S. Irrigation Water Demand of Selected Agricultural Crops in Germany between 1902 and 2010. Sci. Total Environ. 2016, 569–570, 1299–1314. [Google Scholar] [CrossRef] [PubMed]
  80. Fan, J.; Oestergaard, K.T.; Guyot, A.; Lockington, D.A. Estimating Groundwater Recharge and Evapotranspiration from Water Table Fluctuations under Three Vegetation Covers in a Coastal Sandy Aquifer of Subtropical Australia. J. Hydrol. 2014, 519, 1120–1129. [Google Scholar] [CrossRef] [Green Version]
  81. Ouyang, Y.; Jin, W.; Grace, J.M.; Obalum, S.E.; Zipperer, W.C.; Huang, X. Journal of Hydrology: Regional Studies Estimating Impact of Forest Land on Groundwater Recharge in a Humid Subtropical Watershed of the Lower Mississippi River Alluvial Valley. J. Hydrol. Reg. Stud. 2019, 26, 100631. [Google Scholar] [CrossRef]
  82. Krishnaswamy, J.; Bonell, M.; Venkatesh, B.; Purandara, B.K.; Rakesh, K.N.; Lele, S.; Kiran, M.C.; Reddy, V.; Badiger, S. The Groundwater Recharge Response and Hydrologic Services of Tropical Humid Forest Ecosystems to Use and Reforestation: Support for the ‘Infiltration-Evapotranspiration Trade-off Hypothesis’. J. Hydrol. 2013, 498, 191–209. [Google Scholar] [CrossRef]
  83. Lamichhane, S.; Shakya, N.M. Journal of Hydrology: Regional Studies Alteration of Groundwater Recharge Areas Due to Land Use/Cover Change in Kathmandu Valley, Nepal. J. Hydrol. Reg. Stud. 2020, 26, 100635. [Google Scholar] [CrossRef]
  84. European Commission. A European Strategic Long-Term Vision for a Prosperous, Modern, Competitive and Climate Neutral Economy; European Commission: Brussels, Belgium, 2018. [Google Scholar]
  85. Intergovernmental Panel on Climate Change. Climate Change and Land; Intergovernmental Panel on Climate Change: Incheon, Korea, 2019. [Google Scholar]
  86. Shikuku, K.M.; Winowiecki, L.; Twyman, J.; Eitzinger, A.; Perez, J.G.; Mwongera, C.; Läderach, P. Climate Risk Management Smallholder Farmers’ Attitudes and Determinants of Adaptation to Climate Risks in East Africa. Clim. Risk Manag. 2017, 16, 234–245. [Google Scholar] [CrossRef]
  87. Schipper, E.L.F. Primer Maladaptation: When Adaptation to Climate Change Goes Very Wrong. One Earth 2020, 3, 409–414. [Google Scholar] [CrossRef]
  88. Rakgwale, T.J.; Oguttu, J.W. International Journal of Disaster Risk Reduction the Impact of the 2014–2016 Drought in Greater Letaba Local Municipality: How the Farmers Coped and Factors That Were Significantly Associated with Loss of Animals. Int. J. Disaster Risk Reduct. 2020, 50, 101869. [Google Scholar] [CrossRef]
  89. Holmström, B. Moral Hazard and Observability. Bell J. Econ. 1979, 10, 74–91. [Google Scholar] [CrossRef] [Green Version]
  90. Ludwig, F.; Van Slobbe, E.; Cofino, W. Climate Change Adaptation and Integrated Water Resource Management in the Water Sector. J. Hydrol. 2014, 518, 235–242. [Google Scholar] [CrossRef]
  91. Werner, K.; Svedin, L. Climate Risk Management States, Water, and Climate: Who’s Planning for Change? Clim. Risk Manag. 2017, 16, 59–72. [Google Scholar] [CrossRef]
  92. Pluchinotta, I.; Pagano, A.; Tsoukiàs, A. A System Dynamics Model for Supporting Decision-Makers in Irrigation Water Management. J. Environ. Manag. 2018, 223, 815–824. [Google Scholar] [CrossRef]
  93. European Commission. Directive 2000/60/EC of the European Parliament and of the Council of 23 October 2000 Establishing a Framework for Community Action in the Field of Water Policy; European Commission: Brussels, Belgium, 2000; Volume L327. [Google Scholar]
  94. European Commission. Council Directive Concering the Protection of Waters against Pollution Caused by Nitrates from Agricultural Sources (91/676/EEC); European Commission: Brussels, Belgium, 1991. [Google Scholar]
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Figure 2. Annual average minimum (blue) and maximum temperature (orange) in NELS for the period 1950–2018.
Figure 2. Annual average minimum (blue) and maximum temperature (orange) in NELS for the period 1950–2018.
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Figure 3. Total annual precipitation (blue) and total precipitation during the growing season (orange) in NELS for the period 1950–2018.
Figure 3. Total annual precipitation (blue) and total precipitation during the growing season (orange) in NELS for the period 1950–2018.
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Figure 4. The five stages of our approach to GMB process, from top to bottom. First, the stakeholder analysis phase. Second, the individual interviews producing individual models. Third, the analysis of the individual models to produce the preliminary model. Fourth, the GMB workshop to generate a group model. Fifth, the triangulation and analysis to build the qualitative model.
Figure 4. The five stages of our approach to GMB process, from top to bottom. First, the stakeholder analysis phase. Second, the individual interviews producing individual models. Third, the analysis of the individual models to produce the preliminary model. Fourth, the GMB workshop to generate a group model. Fifth, the triangulation and analysis to build the qualitative model.
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Figure 5. The preliminary model including all Tier-1 components. The thickness of the arrows represents the tier level, with low effect relationships (Tier-1) shown by thin blue arrows, considerable effect (Tier-2) by medium thick green arrows, and strong effect (Tier-3) by the thick red arrows. The binary operators represent the nature of the relationship, as they indicate if the effect is positively or negatively related to the cause.
Figure 5. The preliminary model including all Tier-1 components. The thickness of the arrows represents the tier level, with low effect relationships (Tier-1) shown by thin blue arrows, considerable effect (Tier-2) by medium thick green arrows, and strong effect (Tier-3) by the thick red arrows. The binary operators represent the nature of the relationship, as they indicate if the effect is positively or negatively related to the cause.
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Figure 6. The group model developed during the GMB session showing in detail the water balance and the water-demanding sectors of the region. The left side of the model shows factors influencing the water balance, while the right side shows the factors and sectors influencing regional water use.
Figure 6. The group model developed during the GMB session showing in detail the water balance and the water-demanding sectors of the region. The left side of the model shows factors influencing the water balance, while the right side shows the factors and sectors influencing regional water use.
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Figure 7. The triangulated model, product of triangulation and compilation of the gathered data. A “+” indicates a proportional effect and a “-“ indicates an inversely proportional effect. In the model the main balancing and reinforcing feedback loops are marked; with (R) for reinforcing (or positive) and (B) for balancing (or negative) feedback loops.
Figure 7. The triangulated model, product of triangulation and compilation of the gathered data. A “+” indicates a proportional effect and a “-“ indicates an inversely proportional effect. In the model the main balancing and reinforcing feedback loops are marked; with (R) for reinforcing (or positive) and (B) for balancing (or negative) feedback loops.
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Table
Table 1. Participants in the study.
Table 1. Participants in the study.
FieldOrganization
Farmers (n = 8)Traditional agriculture (n =6)
Organic agriculture (n = 2)
Government agencies (n = 8)Chamber of Agriculture of Lower Saxony (n = 3)
Water supply and management agencies (n = 2)
Lower Saxony State Department for Water, Coastal and Nature Conservation (n = 1)
Ministry of Agriculture (n = 1)
Ministry of the Environment (n = 1)
NGOs (n = 2)Greenpeace (n = 1)
BUND (n = 1)
Other organizations (n = 2)Farmers’ association (n = 1)
Irrigation association (n = 1)
Table
Table 2. Identified suggested and implemented adaptation measures and corresponding models.
Table 2. Identified suggested and implemented adaptation measures and corresponding models.
Adaptation MeasurePrel. ModelGroup ModelCurrently ImplementedChallenge
Water storageYesYesAt a small scaleHigh initial investment
HumificationYesNoSome farmersExtremely slow and dependent on crop rotation
RenaturalizationNoYesNoPossible reduction of groundwater recharge and loss of usable land
DigitalizationYesNoSeveral farmersInitial investment
New crop rotationYesNoNoRebounding and shift in vulnerability
Financial reserveNoYesNoRebounding and shift in vulnerability
Water managementNoYesYesCover everyone’s needs
EU legal measuresNoYesYesCould create new externalities
Table
Table 3. Strengths and challenges of the region in coping with possible future problems caused by climate change.
Table 3. Strengths and challenges of the region in coping with possible future problems caused by climate change.
CharacteristicSignificancePossible Consequences
StrengthsIrrigation systemsAbility to compensate for and cope with dry periodsHigher yield than rainfed agriculture
Water usage regulationControlled and managed use of water resourcesConservation of water resources while covering users’ needs
Infrastructure and government bodiesGovernment and water management bodies already existEfficient and fast implementation of measures
ChallengesIrrigation systemsMore water extraction
More energy consumption		Increased pressure on water balance
Increase in costs and reduction of profit
Water usage regulation does not consider climate projectionsInability to cope with drought events
Incremental water extraction as temperatures rise
Increased extraction during drought events		Economic loss in at least one sector
Cumulative pressure on water balance
Drastic pressure on water balance
Crop rotationHigh water demand
Not ideal for humification		Possible ever-increasing water demand under climate change
Multiple water usersAllocating water for all users during drought events
More water extraction		Water conflicts
Increased pressure on water balance
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Valencia Cotera, R.; Egerer, S.; Máñez Costa, M. Identifying Strengths and Obstacles to Climate Change Adaptation in the German Agricultural Sector: A Group Model Building Approach. Sustainability 2022, 14, 2370. https://doi.org/10.3390/su14042370

AMA Style

Valencia Cotera R, Egerer S, Máñez Costa M. Identifying Strengths and Obstacles to Climate Change Adaptation in the German Agricultural Sector: A Group Model Building Approach. Sustainability. 2022; 14(4):2370. https://doi.org/10.3390/su14042370

Chicago/Turabian Style

Valencia Cotera, Rodrigo, Sabine Egerer, and María Máñez Costa. 2022. "Identifying Strengths and Obstacles to Climate Change Adaptation in the German Agricultural Sector: A Group Model Building Approach" Sustainability 14, no. 4: 2370. https://doi.org/10.3390/su14042370

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