Successes and Failures of the Implementation of the Rural Development Programme 2014–2020 Measure “Agri-Environment and Climate” in Lithuania

Abstract

The focus on environment and climate-friendly farming is increasingly important in the European Union (EU) Common Agricultural Policy (CAP). Activities of the Measure M10 “Agri-environment and Climate” of the Rural Development Programme (RDP) 2014–2020 were those policy instruments that pursued environmental and climate goals over large areas under agricultural activities, but their effectiveness is still being questioned. After evaluating implementation successes and failures of the activities of the Measure M10 “Agri-environment and Climate” of the Lithuanian RDP 2014–2020, we aim to contribute to policy instruments that are better designed, more effective, and more attractive for farmers to achieve environmental and climate goals. This research was conducted in several stages: (1) a thorough analysis of the Measure M10 and its implementation; (2) analysis of the Measure M10 activities’ contribution to the policy target areas; (3) multi-criteria evaluation of the activities; (4) survey of beneficiaries and discussions in the focus groups. The data available from the Ministry of Agriculture of the Republic of Lithuania, Agricultural Data Center, and National Paying Agency under Ministry of Agriculture of the Republic of Lithuania was used for the analysis. Analysis of the strategic documents and data on the implementation of Measure M10 was supplemented with results from studies focused on the environmental impact of the implementation of Measure M10 in Lithuania. Multi-criteria evaluation methods were used to arrange the activities of the Measure with respect to the selected indicators. The results from the survey of beneficiaries and discussions in the focus groups let us better clarify the motives, experiences, and preferences of farmers’ participation in the activities of Measure M10. The questionnaire was distributed to 2455 beneficiaries through the National Paying Agency and 342 answers were received back, i.e., 13.9%. Five discussions in focus groups, formed from farmers participating and not participating in the activities, representatives of implementing institutions, and employees of consulting and scientific institutions, were organized. The research has revealed areas for improvement in Measure M10, and suggestions for improvement were prepared to better achieve environmental and climate objectives.

1. Introduction

Agriculture and the environment are interdependent [1,2] as agricultural activities are spread over large areas. Therefore, greater participation of farmers in the agri-environmental measures would ensure that environmentally friendly farming would be implemented in larger areas. To achieve this ambition, there is a need for in-depth studies of the reasons behind the successes and failures of the implementation of agri-environmental measures for better design and success in the future.

Climate change and the increasing number of extreme weather events, the emergence of new plant diseases and pests, and more frequent epidemics of animal diseases are increasing the volatility of agri-food markets and the risks for agricultural operators. Climate change and extreme weather events have added new uncertainties and risks to agricultural yield and therefore for income in various regions of the world [3,4]. Intensive agricultural production and the use of chemicals are degrading the quality of land resources and reducing biodiversity. Agriculture must meet increasing demand for food while minimising negative impacts on ecosystems. Agricultural policies try to stimulate the provision of agricultural ecosystem services [1]. Agri-environmental measures were included in the EU CAP in 1987 [5,6,7]. At that time, EU member states could choose whether to apply them, and from 1992 these measures became mandatory for all EU member states [5,8]. In Lithuania, agri-environmental measures were introduced in preparatory stage before accession the EU in 2004 [9,10]. Since then, the budget share for these measures is constantly increasing [11,12] and with each new programming period agri-environmental measures are reconsidered and improved and more and more farmers decide to participate in these measures and contribute to the environmental goals. As a result of various regulatory reforms, CAP has gradually achieved environmental awareness [13].

At the end of the 2014–2020 rural development programming period, EU member states, including Lithuania, carried out evaluations of the appropriateness, effectiveness, and efficiency of implementation of Measure M10 “Agri-environment and Climate”. The European Commission (EC) emphasizes the importance of evaluating the effectiveness of the implementation of the RDP measures by including these provisions as an obligation in the regulatory documents. EU Regulation No 1303/2013 [14] lays down common provisions on EU funds and EU Regulation No 1305/2013 [15] on support for rural development, in order to improve the quality of the design and implementation of the programmes, as well as to assess their effectiveness, efficiency, and impact, obligates the carrying out of various evaluations of the programmes. At least once during the programming period, an evaluation should assess how support has contributed to the objectives for each priority [14]. In addition, the EC provided guidance on the contents of evaluations in the EU Commission Implementing Regulation 808/2014 [16], laying down rules for the application of EU Regulation No 1305/2013 [15] on support for rural development, and this allows the EC to collect relatively comparable data from EU member states. However, when it comes to the complex Measure M10 “Agri-environment and Climate”, each country’s case must be analysed separately. The results of these evaluations are particularly valuable for highlighting and disseminating examples of good practice. According to the provisions of the discussed EU documents [14,15,16] and RDP 2014–2020 for Lithuania [17], the evaluation of Measure M10 “Agri-environment and Climate” was included in the detailed evaluation plan of the Lithuanian RDP 2014–2020 for the years 2021–2022 [18], the results of which were presented in the final report “Assessment of the Effectiveness of the Measure M10 “Agri-environment and Climate” of the Lithuanian Rural Development Programme 2014–2020” [19] and in its summary report [20]. This article is prepared based on these evaluations and presents the results of further developed research and provides a better understanding of success and failures of Measure M10 implementation. The research results contribute to the debates on better design of agri-environmental measures, while contrary to expectations, very few evaluations of the implementation of Measure M10 “Agri-environment and Climate” were found in the scientific literature. Only one comprehensive study in Poland, by Krzyszczak et al., was carried out [21].

The subject of the research is broad and interdisciplinary. Agri-environment includes various landscapes and associated biodiversity: arable land with all the variety of crops, natural meadows and pastures, forests, and animal husbandry. But, all of them are distributed in a wide area and thus fall under different natural and climatic conditions. And the human factor that determines the management of these natural resources is crucial. The farmer is not only a producer, the farmer often is a landowner, so farm property is therefore both a working tool, an asset, and a place to live [2], and this individually owned land is subjected to numerous regulations. The complexity of the subject determines the fact that research in this area is still relatively rare and fragmented. The same applies for a huge variety of agri-environmental measures applied across countries. Depending on specific needs, different agri-environmental measures are applied in individual countries. The literature presents the fact that not only agri-environmental measures differ but also their adoption success, uptake, and interaction of agricultural and environmental issues across EU member states and regions [22]. The diversity of agri-environmental measures determines the need for case studies. Therefore, research is often experimental in nature and conducted in certain areas as a case study [11,13,23,24,25]. In this manner, researchers are looking for suitable methods to assess the environmental and economic effectiveness of agri-environmental measures. Such research aims to create universal methodologies for assessing the impact of agri-environmental measures and to gain knowledge about the effectiveness of these measures.

An important aspect of these research studies is the analysis of relationships and perceptions between the levels of policy goal setting and implementation, when policy is formed at the level of a country or region and implementation takes place at the level of an individual farm [22,26]. When the state of the environment depends on the actions of many farmers, results could not be traced back to single farmers and an approach at the territorial level is needed.

Another issue discussed in the literature is the conflict of environmental and economic interests among the different stakeholders [1]. According to Lefebvre et al. [2], agricultural landscapes are described as a multi-scale public good that requires management actions on three scales: (1) the management of landscape elements at farm level, (2) the integration of farms in the agricultural landscape at landscape level, and finally, (3) the conservation of the diversity of agricultural landscapes in the EU as a global public good. The CAP is mainly focused on the first scale and studied how policy instruments could be refined for the CAP to integrate the two other scales [2].

Almost every study within the field mentions that the problem and limitation of the study is the lack of reliable data [1,24,27]. Pe’er et al. [28] also indicated an insufficient set of indicators even for the CAP beyond 2020. The planned Output and Result indicators monitor the administrative and financial implementation, but they are insufficient for an impact evaluation of the CAP objectives and instruments and provide little guidance for steering policy [28]. The large variety of agri-environmental measures and the lack of data make it difficult to study the effectiveness of these measures. Therefore, in many cases, research is based on data collected through surveys, or the problem of missing data is being addressed by finding ways to combine data collected from different sources for different purposes [11].

A lack of research and methodologies is observed in the literature [1,24,27] as well. Scientists use various methods and their combinations when conducting research related to agri-environmental measures. In the literature, we found that among the methods used by researchers, the most common are various surveys [2,21,24,25]. D’Alberto et al. [11] explored the feasibility of combining the non-parametric statistical matching method and the propensity score matching counterfactual approach analysis and tested its usefulness and practicability on a case study represented by selected impacts of the AESs in Emilia-Romagna. Mennig and Sauer [29] used a difference-in-difference propensity score matching estimator to test if AES has an unintended effect on farm productivity. Kazakova-Mateva [30] applied spatial statistics methods in order to explore factors influencing the spatial uptake of environmentally focused area-based measures under the Bulgarian RDP in the period 2014–2020. Bojar et al. [1] employed linear regression analysis to explore an impact of selected agricultural productivity factors on the key performance indicators assessing the level of selected ecosystem services. Research in this area is rarely complete without various surveys and interviews involving farmers and stakeholders [21,24,25,31]. Glumac et al. [25] conducted an online survey based on an analytical hierarchical process to assess the importance that different societal stakeholders in Slovenia and Croatia attach to specific environmental services, as well as to identify which agricultural practices have the highest potential to meet the expectations that the society values most.

It is worth noting that research on this issue is often carried out through projects financed by the EU or national budgets [1,11,21,24]. This fact confirms the relevance of the topic not only in science, but also in politics and society.

The aforementioned problems were also encountered during this research. To address the lack of methodologies, we developed a methodology of several stages that integrates several research methods. In response to the absence of necessary implementation indicators and the general lack of reliable data, we collected the necessary data by analysing rare, specialised studies and conducting a survey. By organising discussions in the focus groups, we aimed to consider the views and evaluations of various stakeholders.

This research was conducted on the principles of the new economics theory of ecosystems formed on the basis of environmental economics and ecological economics. This study contributes to the theory with created methodology, which was verified with empirical calculations.

After evaluating the implementation successes and failures of the activities of the Lithuanian RDP 2014–2020 Measure M10 “Agri-environment and Climate”, we aim to contribute to policy instruments that are better designed, more effective, and more attractive for farmers to achieve environmental and climate goals.

We have formulated the following research hypothesis: The success of the implementation of the Measure “Agri-environment and Climate” depends on its design, farmers’ attitude and behaviour towards the agri-environment, and appropriate support systems.

In the following, our article presents the data sources and the methods used (Section 2); then, in Section 3, we present the results and consider the reasons behind them, while in Section 4, we have a look through the results obtained in comparison with findings from other authors, observe study limitations, and discuss directions for further research.

2. Materials and Methods

This research was conducted in several stages (Figure 1). In the first stage, we analysed the Measure M10 “Agri-environment and Climate” of the Lithuanian RDP 2014–2020 as a whole and deconstructed it by individual activities, reviewing the priorities, target areas, planned indicators, and their levels of achievement. We evaluated the level of implementation regarding the ratio between the supported area and the area that was planned to be supported by distinguishing the level of achievement into quartiles. This four-level classification allowed us to more accurately reflect the different levels of achievement, allowing us to distinguish between failure of implementation, low level of implementation, moderate level of implementation, and successful implementation. This allows us a more detailed assessment of results to identify where improvements are needed and provides a clearer view for planning further interventions based on different levels of success. At this stage, we analysed strategic documents [14,15,16,18] and data on the Measure M10 implementation available from the Ministry of Agriculture of the Republic of Lithuania (MoA), Agricultural Data Center [32], and National Paying Agency under Ministry of Agriculture of the Republic of Lithuania (NPA) [33]. The literature suggested data sources for research concerning Measure M10. Laura Ciobanu gave an overview of the preliminary results of the Romanian RDP 2014–2020 implementation. This study was mainly based on the data taken from the official website of the Romanian National RDP 2014–2020 [34]. Obinna Okereke and Żaneta Wojciechowska [35], for their research on the production potential of agriculture in Poland and the use of funds from selected 2014–2020 RDP measures, including the “Agri-environment and Climate” measure, used data from the Polish Central Statistical Office and the Agency for Restructuring and Modernization of Agriculture. The study carried out by Bojar et al. [1] was conducted based on national sources, mostly the database of the Central Statistical Office. Krzyszczak et al. used statistical data from the official National and EU databases [21]. It is worth mentioning that scientists are waiting for the Farm Accountancy Data Network (FADN) [36] conversion into the Farm Sustainability Data Network (FSDN). The FADN data are planned to be supplemented with environmental variables, and this would allow us to analyse and evaluate the aggregated results, but in the case of the activities of Measure M10, the FSDN is not very promising: the level of aggregation will not allow us to distinguish the effects of the individual activities.

In the second stage, we discussed the contribution of Measure M10 and its activities to the target areas. In the first stage, the collected data were supplemented with the results from the studies focused on the environmental impact of the implementation of Measure M10 in Lithuania [37,38,39,40].

In the third stage, we performed a multi-criteria evaluation of the activities, which allowed us to evaluate and rank the activities according to farmers’ preferences, environmental benefits, and funds allocated. We used three multi-criteria methods: Simple Additive Weighting (SAW) [41,42,43,44], Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) [42,43,44,45,46,47,48], and Evaluation based on Distance from Average Solution (EDAS) [44,49]. These methods allowed us to evaluate the activities according to a set of indicators aggregated into a single criterion showing the rank of the activity among all activities. The scientific literature [41,42,43,44,45,46,47,48,49] points out that each method has its own internal logic and highlights distinct aspects of the phenomenon being assessed. To minimise the influence of the specificity of the individual methods on the results of the calculations, we evaluated the activities of Measure M10 using these three methods separately and then averaged ranking positions. The multi-criteria assessment was conducted in the following steps: (1) selection indicators; (2) preparation of statistical data for the indicators to be used; (3) data transformation and normalisation; (4) determining the significance of the indicators by estimating their weights; and (5) analysis and evaluation of the results.

The number of indicators that could be included in the multi-criteria assessment was limited. We could only approve the remark expressed by Pe’er et al. that planned Output and Result indicators for Measure M10 monitored the administrative and financial implementation, but they were insufficient for an impact evaluation of the CAP objectives and instruments and provided little guidance for steering policy [28]. In such a situation, we used indicators that we found in the Lithuanian RDP 2014–2020 [18] and Agricultural Data Center [32], and received from the NPA [33]. The following indicators were used for the multi-criteria evaluation: number of beneficiaries (units), supported area (ha) or number of livestock units (LU), amount of support (Eur), area planned to be supported (ha), environmental benefits (number of target values and their percentage contribution), share of support out of the total support (%), the share of the supported area under the individual activity out of the total supported area (%), compensatory payment (Eur/ha), and the ratio between the supported area and the area planned to be supported. The data covered the period of 2018–2022, depending on the maximum values for the implementation of the activities for the area-based activities and the latest data for LU-based activity 10.1.12 “Preservation of the endangered old Lithuanian breeds of animals and domestic birds”.

In the fourth stage, we conducted a survey of beneficiaries and discussions in the focus groups. The results of the survey let us support quantitative results and better clarify the motives, experiences, and preferences of farmers’ participation in the activities of Measure M10. A questionnaire was prepared and distributed to 2455 beneficiaries through the NPA and 342 answers were received back, i.e., 13.9%. This number of respondents at a confidence level of 95% was sufficient to represent the general population and allow for generalising conclusions. After receiving the respondents’ answers to the closed survey questions, we calculated their frequencies, then ranked the results and performed a comparative analysis. We also analysed the answers to thoughtfully prepared open questions and presented the findings. Knowledge was being further deepened by organising five discussions in focus groups, formed from farmers participating and not participating in the activities, representatives of implementing institutions, and employees of consulting and scientific institutions. Focus group experts were also interviewed about the use of weights for multi-criteria evaluation. This stage was planned for our study as various surveys, interviews, and discussions with stakeholders are suggested in the literature. Krzyszczak et al. conducted a survey among polish farmers and stakeholders concerning the implementation effectiveness of the M10 activities [21]. Linares Quero et al. [24], for their analysis of the potential of the CAP 2014–2020 instruments supporting agroecological transitions, used stepwise participatory research methodology. Qualitative techniques were used for data collection in the form of workshops or interviews to promote discussion among stakeholders [24]. Tankosić et al. [31] carried out two stages of research on the application of agri-environmental management practices in the Republic of Serbia. Interviews with the policymakers at the first stage and survey of the organic agricultural producers and of agricultural advisory experts at the second stage were conducted.

3. Results

3.1. Analysis of the Implementation of Measure M10 “Agri-Environment and Climate” of the Lithuanian RDP 2014–2020

There were three quantitative Output indicators, O.5 “Total area (ha)”, O.6 “Physical area supported (ha)”, and O.7 “Number of contracts supported”, provided for all the activities of Measure M10 “Agri-environment and Climate” [16] of the Lithuanian RDP 2014–2020. The target value for the Output indicator O.5 was set at 233,150 ha. Of this, an area of 147,150 ha was foreseen to be supported under all three target areas (4A “Restoring, preserving and enhancing biodiversity, including in Natura 2000 areas, and in areas facing natural or other specific constraints, and high nature value farming, as well as the state of European landscapes”, 4B “Improving water management, including fertiliser and pesticide management” and 4C “Preventing soil erosion and improving soil management”) of the fourth Priority P.4 “Restoring, preserving and enhancing ecosystems related to agriculture and forestry”, with 70,000 ha under the target area 5D “Reducing greenhouse gas and ammonia emissions from agriculture” and 16,000 ha under the target area 5E “Fostering carbon conservation and sequestration in agriculture and forestry” to support the fifth Priority P.5 “Promoting resource efficiency and supporting the shift towards a low carbon and climate resilient economy in agriculture, food and forestry sectors” [17].

The assessment of the actual implementation period (2015–2022) of the Lithuanian RDP 2014–2020 programme showed that 60.8% of the planned Output indicator O.5 “Total area (ha)” of Measure M10 was achieved. The implementation of the objectives of the fourth priority P.4 was not as successful as expected at the planning stage of the programme: three target areas (4A, 4B, and 4C) altogether reached 33.7% of the planned Output value.

The implementation of the priority P.5 has fared better: the Output indicator for the target area 5D was achieved by 63.7%, while the Output indicator for the target area 5E planned value was achieved by three times.

The implementation of the priorities P.4 and P.5 was directly linked to the success or failure of the activities contributing to the target areas (Figure 2).

It is important to stress that the activity “Preservation of the endangered old Lithuanian breeds of animals and domestic birds” (10.1.12) did not foresee monitoring of the number of animals by species. It is necessary to monitor this indicator in the future to assess the performance of this activity.

Table 1. Contribution of the activities of Measure M10 “Agri-environment and Climate” to the corresponding target areas.

Activity	4A	4B	4C	5D	5E	Supported Area, ha	Evaluation of Implementation *
10.1.01 Extensive management of meadows by animal grazing	D	-	-	I	-	26,112	+
10.1.02 Management of specific meadows	I	-	-	D	I	2266	−
10.1.03 Extensive management of wetland	I	I	-	I	D	8114	+ +
10.1.04 Preservation of habitats of the endangered bird—aquatic warbler in natural and semi-natural meadows	D	-	-	-	-	3480	+ +
10.1.05 Preservation of habitats of the endangered bird—aquatic warbler in wetlands	D	-	-	-	-	674	+ +
10.1.06 Strips or plots of melliferous plants or field in arable land	D	-	I	I	-	357	−
10.1.07 Protection of water bodies against pollution and soil erosion in arable land	-	D	I	I	-	21	−
10.1.08 Management of slopes of reclamation ditches	D	-	I	-	-	2825	+/−
10.1.09 Improving the status of water bodies at risk	-	D	I	I	-	7504	+ +
10.1.10 System for cultivation of environmentally friendly fruits and vegetables growing	-	-	D	I	-	7527	+ +
10.1.11 Soil protection	-	I	D	I	-	1077	–
10.1.12 Preservation of the endangered old Lithuanian breeds of animals and domestic birds	D	-	I	-	-	n.a.	n.a.
10.1.13 Cultivation of catch crops on arable land	-	-	I	-	D	39,408	+ +
10.1.14 Stubble fields during the winter	I	-	I	D	I	42,354	+ +

* implementation evaluated regarding ratio between the supported area and the area planned to be supported. n.a.—not applicable; “−“—failure of implementation (<0.25); “+/−“—low level of implementation (≥0.25–<0.5); “+”—moderate level of implementation (≥0.5–<0.75); “+ +”—successful implementation (≥0.75). D—direct impact, I—indirect impact.

shows the activities contributing to the target areas according to the defined priorities. It is important to note that the activities of Measure M10 could contribute to several target areas, and therefore the same hectare could be classified under several target areas. The achievement of the Output indicator was assessed without attributing the same areas to the different target areas. For the calculation of the values of the Output indicators, each activity of the Measure M10 was assigned to the specific target area to which it had direct contribution, as direct and indirect contributions have been distinguished during the programming phase.

3.1.1. Contribution of the Measure to Biodiversity

Contributing to the target area 4A “Restoring, preserving and enhancing biodiversity, including in Natura 2000 areas, and in areas facing natural or other specific constraints, and high nature value farming, as well as the state of European landscapes” of the priority P.4 “Restoring, preserving and enhancing ecosystems related to agriculture and forestry” of the Lithuanian RDP 2014–2020, the measure M10 “Agri-environment and Climate” included six direct and three indirect contributions (Table 1).

The programme envisaged an area of 67,350 ha to achieve the target area 4A. During the period of 2015–2022, the activities of Measure M10 directly related to the achievement of target area 4A were implemented in 33,448 ha, i.e., the achievement of the Output indicator was 49.7%. It can be concluded that these activities have made a substantial contribution to achieving to restore, conserve, and enhance biodiversity, especially in the light that Measure’s M12 “Natura 2000” activity “Support for Natura 2000 on agricultural land” and large areas (52,734 ha) declared under Measure M10 indirectly contributing to the achievement of the objectives.

The analysis of the Lithuanian Ornithological Society’s survey data [37] showed that the activities “Extensive management of wetland” (10.1.03) and “Improving the status of water bodies at risk” (10.1.09) had an exceptional positive impact on bird populations and thus on the biodiversity of the whole agricultural landscape. The other activities analysed under Measure M10 had a lower impact. At the same time, for example, the activity “Extensive management of meadows by animal grazing” (10.1.01) had a minimal impact on rural bird populations; it protected meadows and pastures as bird habitats from degradation by woody vegetation overgrowth.

Similar trends were observed when analysing data from the study carried out by the Center for Environmental Policy [38], which showed that not all the activities of Measure M10 contributed effectively to biodiversity protection. In 2018, of the 24,300 ha declared under the activities related to grassland and wetland management, only 3000 ha were located in the target areas where these activities were most needed, i.e., in areas important for the protection of habitats and/or birds of European Community (EC) importance. On the other hand, appropriate site selection criteria ensured a very high level of protection of biodiversity in the areas covered by three activities of Measure M10: 100% of the areas declared under the activity “Management of specific meadows” (10.1.02), 97% of the areas declared under the activity “Preservation of habitats of the endangered bird—aquatic warbler in wetlands” (10.1.05), and 81% of the areas declared under the activity “Preservation of habitats of the endangered bird—aquatic warbler in natural and semi-natural meadows” (10.1.04) fell within the areas of EC importance for the conservation of habitats and/or birds.

3.1.2. Contribution of the Measure to Water Management

To achieve of the objectives of the target area 4B “Improving water management, including fertiliser and pesticide management” of the priority P.4 “Restoring, preserving and enhancing ecosystems related to agriculture and forestry”, two activities that directly and two that indirectly contribute were included in Measure M10 (Table 1).

The programme foresaw an area of 18,000 ha to achieve these objectives. During the period 2015–2022, the activities of Measure M10 directly linked to the achievement of the objectives of target area 4B were implemented on an area of 7525 ha, i.e., the achievement of the product indicator was 41.8%. It can be said that the activities contributed to the objectives related to the improvement of water management, including the improvement of fertiliser and pesticide management. It is important to note that a significant area (9191 ha) of other activities under Measure M10 contributed indirectly to the achievement of the objectives of target area 4B.

The results of the study carried out by the Centre for Environmental Policy [38] showed that the nutrient balance in the areas where the activities of Measure M10 were implemented was lower than that in conventionally cultivated fields, suggesting that the implementation of Measure M10 had a positive effect on reducing nitrogen and phosphorus overload. It should be noted that in the case of a deficit phosphorus balance, activities of Measure M10 may increase the overall phosphorus deficit.

According to the results of the study by the Centre for Environmental Policy [38], the nitrogen and phosphorus balance is close to neutral in the fields where the activities of the measure are implemented. As the nutrient balances in the areas of Measure M10’s activities are significantly different from those in conventional farms, this undoubtedly has an impact on the overall nutrient balance in the country. The implementation of the activities of Measure M10 has had the greatest effect in the more intensively farmed counties, where traditionally nitrogen balances were mostly in surplus. According to the same study [38], no changes in nitrate–nitrogen concentrations in freshwater bodies were observed as a result of the implementation of the activities under Measure M10. The activities of Measure M10 were effective in reducing nitrogen excess in the areas where they were implemented, but the areas of implementation were relatively small. Kirschke et al. also indicated that the lack of implementation then results in the sustaining of nitrate pollution from agriculture [50]. In addition, some activities of Measure M10 have been implemented in catchment areas where nitrate concentrations were low and there has been no need for pollution reduction. For these reasons, the potential of the activities of Measure M10 to reduce nitrate concentrations in surface water bodies was low [38].

To effectively reduce the pollution of water bodies, the experts of the Centre for Environmental Policy recommend intensifying the implementation of measures to reduce the leaching of nutrients into water bodies in the basins of risk water bodies identified by the Ministry of the Environment of the Republic of Lithuania. The basins of the water bodies at risk coincide with the areas where farming is intensive, and the largest nitrogen surplus is left in the soil. To improve the condition of water bodies at risk, it would be appropriate to include activities that promote balanced, precise fertilisation, considering the actual nitrogen reserves in the soil and plant needs. Nitrogen leaching is effectively reduced by intermediate plants, so it would be appropriate to increase the scope of implementation of this measure in the basins of at-risk water bodies [38].

3.1.3. Contribution of the Measure to Soil Management

To contribute to the objectives of target area 4C “Preventing soil erosion and improving soil management” of the fourth priority P.4 “Restoring, preserving and enhancing ecosystems related to agriculture and forestry”, two activities that directly and seven activities that indirectly contributed to the achievement of the objectives were foreseen (Table 1).

The programme envisaged an area of 61,800 ha to achieve these objectives. During the period 2015–2022, the activities of Measure M10 directly related to the achievement of the objectives of target area 4C were implemented on an area of 8604 hectares, i.e., the achievement of the Output indicator was 13.9%. It can be concluded that the activities contributed, albeit slightly, to the achievement of the objectives related to the prevention of soil erosion and improvement of soil management because the area of indirectly contributing activities was implemented on an area of 92,469 ha. It is also important to note that the areas declared under the activities of Measure M11 “Organic farming” directly contributed to the achievement of the objectives of target area 4C.

Analysis of the Centre for Environmental Policy survey data [38] showed that activities requiring the establishment and maintenance of perennial grass strips had the biggest impact on reducing water erosion. However, during the period of 2015–2018 only about one-fifth (in terms of area) of the water erosion mitigation activities of Measure M10 were implemented in erosion-sensitive areas, i.e., areas with a slope gradient greater than 2 degrees.

The experts of the Centre for Environmental Policy indicated positive impact on reducing water-induced soil erosion of the activities “Strips or plots of melliferous plants or field in arable land” (10.1.06) and “Protection of water bodies against pollution and soil erosion in arable land” (10.1.07), “Improving the status of water bodies at risk” (10.1.09), “Cultivation of catch crops on arable land” (10.1.13), and “Stubble fields during the winter” (10.1.14) while establishing and maintaining perennial grass strips, intercropping, leaving stubble over winter, as well as limitation of the activity in areas with a slope gradient of more than 2 degrees reduced water erosion. All other activities of Measure M10 were identified as having a neutral impact on reducing water-induced soil erosion [38].

3.1.4. Contribution of the Measure to the Reduction in Greenhouse Gas and Ammonia Emissions from Agriculture

To contribute to the achievement of the objectives of the target area 5D “Reducing greenhouse gas and ammonia emissions from agriculture” of the priority P.5 “Promoting resource efficiency and supporting the shift towards a low carbon and climate resilient economy in agriculture, food and forestry sectors” Measure M10 “Agri-environment and Climate” provided two activities directly and seven indirectly contributing to the achievement of these objectives.

The programme foresaw an area of 70,000 ha to achieve these objectives. During the period 2015–2022, the activities of Measure M10 directly linked to the achievement of the were implemented on an area of 44,620 ha, i.e., the Output indicator has reached 63.7%. It can be concluded that the activities have made a significant contribution to the achievement of the objectives related to the promotion of resource efficiency given that the area of indirectly contributing activities was 50,712 ha (Table 1).

Based on the methodology and coefficients presented in the study carried out by FPP Consulting [39], it was estimated that during the period of 2015–2022 the implementation of the activities of Measure M10 has resulted the avoidance of 211.7 kt CO₂ eq. greenhouse gas (GHG) and 1.8 kt of ammonia emissions. The main factor contributing to the reduction in GHG and ammonia emissions was the reduced use of fertilisers due to the requirements imposed by the activities.

3.1.5. Contribution of the Measure to Carbon Retention and Sequestration in Agriculture

In order to contribute to the implementation of the objectives of the target area 5E “Fostering carbon conservation and sequestration in agriculture and forestry” of the priority P.5 “Promoting resource efficiency and supporting the shift towards a low carbon and climate resilient economy in agriculture, food and forestry sectors” the Measure M10 “Agri-environment and Climate” foresaw two activities which directly and two activities which indirectly contributed to the realisation of the objectives (Table 1).

The programme foresaw an area of 16,000 ha to achieve these objectives. During the period 2015–2022, the activities of Measure M10 directly linked to the achievement of the objectives of target area 5E were implemented on 47,522 ha, i.e., the achievement of the Output indicator was 297%. It can be concluded that the activities have made a significant contribution to the achievement of the objectives related to carbon conservation and sequestration in agriculture. The area of indirectly contributing activities was 44,620 ha.

Based on the methodology and coefficients presented in the study carried out by FPP Consulting [39], it was estimated that during the period of 2015–2022, the implementation of the activities of Measure M10 has resulted in carbon conservation and sequestration in agriculture. The activities of Measure M10 had a positive impact on carbon sequestration in agricultural land, resulting the avoidance of 319.2 kt CO₂ eq. GHG emissions of carbon dioxide emissions over the 2015–2022 period. The most significant GHG emission avoidance impact was achieved by the activity “Cultivation of catch crops on arable land” (10.1.13), which resulted in a saving of 148.9 kt CO₂ eq. GHG emissions. The activity “Extensive management of meadows by animal grazing” (10.1.01) contributed—saving 112.2 kt CO₂ eq. GHG emissions. However, the experts of the FPP Consulting study [39] pointed out that intercropping can increase or decrease GHG emissions by around 0.01 N/m²/year, as nutrients are not transported out of the soil and are therefore involved in nutrient cycling. In addition, emissions depend on the type of catch crop grown, e.g., nitrous oxide emissions may even increase if legumes are grown, which accumulate more nitrogen.

Obviously, the performance rate depends not only on the area supported but also on the area planned to be supported. It is acknowledged that the target values for some of the indicators for the activities of Measure M10 have not been properly foreseen. Other aspects are also observed when examining individual activities. For example, participation in the activity “Cultivation of catch crops on arable land” (10.1.13) exceeded target value by 6.5 times. Taking into account the technological and crop rotation requirements, catch crops on arable land could account for a maximum of 20% of the arable land, i.e., around 422.7 thousand ha [40] in Lithuania. This suggests that the indicator has not been properly planned for this activity. In the actual situation, an area of around 40,000 ha should have been planned, i.e., 10% of the maximum area of arable land that could be devoted to catch crops. Another reason is the active participation of applicants in this activity due to the ease of implementation of the requirements and attractive compensatory payments compared to other activities.

On the contrary, the target values for “Protection of water bodies against pollution and soil erosion in arable land” (10.1.07) and “Soil protection” (10.1.11) were too optimistic. On the other hand, applicants were reluctant to participate in the “Protection of water bodies against pollution and soil erosion in arable land” (10.1.07) activity due to the more difficult practical conditions of implementation compared to the other activities, while the “Soil protection” (10.1.11) activity competed with the greening payments under the direct payment scheme. As a result, the share of the area supported in these activities in the area to be supported was 0.2% and 2.1% respectively.

When analysing the allocation (mosaic) of the agricultural areas supported and declared under Measure M10 [38], it is important to note that some of the activities of the Measure can only be implemented in certain defined areas. These include “Extensive management of meadows by animal grazing” (10.1.01), “Management of specific meadows” (10.1.02), “Preservation of habitats of the endangered bird—aquatic warbler in wetlands” (10.1.04), “Preservation of habitats of the endangered bird—aquatic warbler in wetlands” (10.1.05), and “Improving the status of water bodies at risk” (10.1.09). However, the status of the sites for these activities should be kept under constant review as their status may change due to the challenges of climate change. According to the beneficiaries and the NPA staff who monitored their fields, one such activity that is particularly affected by climate change [51] is “Extensive management of meadows by animal grazing” (10.1.01).

Other activities of Measure M10 could be implemented throughout the country. The analysis of the data of the Agricultural Data Center [32] showed that the activities of Measure M10 are least implemented in central Lithuania, where intensive farming prevails. This part of the country could benefit most from the implementation of the activities. However, farms in central Lithuania are often characterised by high productivity indicators, which makes it uneconomic for them to participate in Measure M10. It is necessary to look for the activities that would also encourage intensive farms to engage in environmentally beneficial activities. For example, the use of precision tillage techniques, greater crop rotation after the harvest of the main crops, and the cultivation of catch crops that cover the soil surface with their above-ground mass. It would likely result an increase in the agricultural area supported by the Measure and thus in a higher positive environmental impact.

3.2. Multi-Criteria Evaluation

To assess the activities of Measure M10, we used three multi-criteria methods: SAW, TOPSIS, and EDAS. These methods allowed us to evaluate the activities according to set of indicators aggregated into a single criterion showing the rank of the activity among all activities. The data covered the period of 2018–2022, depending on the maximum values for the implementation of the activities for area-based activities and the latest data for LU-based activity 10.1.12.

The indicators for the multi-criteria evaluation have been chosen as follows: number of beneficiaries (units), supported area (ha) or number of livestock units (LU), amount of support (Eur), area planned to be supported (ha), environmental benefits (number of target values and their percentage contribution), share of support out of the total support (%), the share of the supported area under the individual activity out of the total supported area (%), compensatory payment (Eur/ha), and the ratio between the supported area and the area planned to be supported. The data (columns 2–5 in the

Table 2. Indicators, weights, and directions for the multi-criteria evaluation of the activities of Measure M10 “Agri-environment and Climate” of the Lithuanian RDP 2014–2020 in 2022.

Activity	Number of Beneficiaries (Units)	Supported Area (ha)/Number of Livestock Units (LU)	Amount of Support (Eur)	Area Planned to Be Supported (ha)	Environmental Benefits (Number of Target Values and Their Percentage Contribution)	Share of Support out of the Total Support (%)	Share of the Supported Area under the Individual Activity out of the Total Supported Area (%)	Compensatory Payment (Eur/ha)	Ratio between the Supported Area and the Area Planned to Be Supported
10.1.01 Extensive management of meadows by animal grazing	1825	26,112	2,636,613	45,000	1.6	13	0.89	101	0.58
10.1.02 Management of specific meadows *	329	2266	155,919	20,000	2.5	1	0.08	69	0.11
10.1.03 Extensive management of wetland	897	8114	1,686,882	10,000	3.5	8	0.28	208	0.81
10.1.04 Preservation of habitats of the endangered bird—aquatic warbler in natural and semi-natural meadows	184	3480	1,012,727	4500	1	5	0.12	291	0.77
10.1.05 Preservation of habitats of the endangered bird—aquatic warbler in wetlands	14	674	107,384	350	1	1	0.02	159	1.93
10.1.06 Strips or plots of melliferous plants or field in arable land	56	357	30,263	7000	2.5	0	0.01	85	0.05
10.1.07 Protection of water bodies against pollution and soil erosion in arable land *	11	21	4701	10,000	2	0	0.00	221	0.0002
10.1.08 Management of slopes of reclamation ditches **	666	2765	371,270	10,500	1	2	0.09	134	0.26
10.1.09 Improving the status of water bodies at risk	603	7504	1,740,865	8000	2.5	9	0.26	232	0.94
10.1.10 System for cultivation of environmentally friendly fruits and vegetables growing	290	7527	2,457,208	9500	1	12	0.26	326	0.79
10.1.11 Soil protection ***	27	1077	39,524	52,300	2	0	0.04	37	0.02
10.1.12 Preservation of the endangered old Lithuanian breeds of animals and domestic birds *	326	1428	279,853	4638	2	1	0.20	196	0.31
10.1.13 Cultivation of catch crops on arable land	1345	39,408	5,280,977	6000	2	26	1.35	134	6.57
10.1.14 Stubble fields during the winter	2967	42,354	4,192,902	50,000	3	21	1.45	99	0.85
Weights	0.1	0.1	0.1	0.1	0.1	0.1	0.1	0.1	0.2
Direction	max	max	min	max	max	max	max	min	max

* 2021; ** 2019; *** 2018.

were subjected to a logarithmic procedure to avoid distortion of the results.

The weights were chosen to be equal except for the ratio between the supported area and the area planned to be supported, as it reveals the relationship between policy ambitions and farmers’ willingness to contribute to its implementation.

The different multi-criteria evaluation methods show different results of the activities according to the selected indicators (Figure 3). These differences are related to the specificity of the methods as they are based on different aggregation principles (utility functions and reference points). The SAW method comprises indicator values and their weights in one index criterion of the method [41,42,43,44]. TOPSIS method finds an ideal solution with the shortest distance from the best values and with the longest distance from the worst values of the indicators [42,43,44,45,46,47,48], whereas EDAS methods provide a solution based on the compatible distance from the average solution of the alternatives as a reference point [44,49]. Therefore, we used three multi-criteria evaluation methods (SAW, TOPSIS, and EDAS) and then ranked activities with respect to average ranking positions.

Ranking results of the multi-criteria evaluation of the activities of Measure M10 are presented in the

Table 3. Ranking results of the multi-criteria evaluation of the activities of Measure M10 “Agri-environment and Climate” of the Lithuanian RDP 2014–2020 programme.

Activity	Rank
	SAW	TOPSIS	EDAS	Average
10.1.01 Extensive management of meadows by animal grazing	3	7	4	4
10.1.02 Management of specific meadows	7	11	10	9
10.1.03 Extensive management of wetland	4	5	3	3
10.1.04 Preservation of habitats of the endangered bird—aquatic warbler in natural and semi-natural meadows	12	8	9	10
10.1.05 Preservation of habitats of the endangered bird—aquatic warbler in wetlands	13	3	8	7–8
10.1.06 Strips or plots of melliferous plants or field in arable land	10	13	13	13
10.1.07 Protection of water bodies against pollution and soil erosion in arable land	14	12	14	14
10.1.08 Management of slopes of reclamation ditches	11	10	11	11–12
10.1.09 Improving the status of water bodies at risk	5	6	5	5
10.1.10 System for cultivation of environmentally friendly fruits and vegetables growing	8	4	7	6
10.1.11 Soil protection	6	14	12	11–12
10.1.12 Preservation of the endangered old Lithuanian breeds of animals and domestic birds	9	9	6	7–8
10.1.13 Cultivation of catch crops on arable land	1	1	1	1
10.1.14 Stubble fields during the winter	2	2	2	2

. The highest ranked among all the activities under Measure M10 were “Cultivation of catch crops on arable land” (10.1.13), “Stubble fields during the winter” (10.1.14), “Extensive management of wetland” (10.1.03), and “Extensive management of meadows by animal grazing” (10.1.01). These ranking results were due to the good performance of the activities in terms of the high number of applicants, the area supported, the Result indicators and the levels of compensatory payments compared to other activities.

Multi-criteria evaluation has revealed that the worst performing Activities were “Protection of water bodies against pollution and soil erosion in arable land” (10.1.07), “Strips or plots of melliferous plants or field in arable land” (10.1.06), “Management of slopes of reclamation ditches” (10.1.08), and “Soil protection” (10.1.11). The main general reasons were over-optimistic planning of the activities’ Output indicators, significantly lower compensatory payments compared to similar activities (e.g., “Management of specific meadows” (10.1.02) and “Extensive management of meadows by animal grazing” (10.1.01)). Moreover, a more detailed analysis of these activities showed that applicants were unwilling to participate in the activity “Protection of water bodies against pollution and soil erosion in arable land” (10.1.07) due to the complexity of its practical implementation. To meet the requirements for this activity, the farmer had to drive the machinery through the crop fields and reach the banks of water bodies, i.e., relatively small areas. This action results in additional transport costs, labour costs, and loss of income due to lost harvests.

It is important to note that these results are in line with the assessments of the experts in all focus groups. It is suggested to pay attention to the activities that ranked last. A future revision of these activities and their requirements and payment levels would be necessary to better achieve the environmental goals. This would have a positive impact on potential beneficiaries.

3.3. Survey Results

It is important to note that these results are in line with the questionnaire survey results that showed that the most popular activity among the respondents was “Stubble fields during the winter” (10.1.14) (Figure 4). Some respondents answered that this activity was quite innovative as it corresponds to the application of no-till technologies, where environmentally friendly sowing directly into the stubble was applied. One more reason for choosing this activity was related to the set of requirements, which were clear and easy to implement. In terms of the compensatory payment level, it is important to note that of all respondents, as many as 80% mentioned that the level of the compensatory payment partially or fully compensated additional costs and income foregone.

Another popular activity was “Extensive management of meadows by animal grazing” (10.1.01). According to the respondents, the requirements set for this activity met their expectations, but the compensatory payment level was only appropriate if there were no additional costs associated with grazing.

The third most popular activity was “Cultivation of catch crops on arable land” (10.1.13). Respondents were positive about the environmental benefits of this activity. They were also quite positive about the clarity of the requirements, while stressing that implementation was not difficult in practice. The compensatory payment level was only partially satisfactory for the majority of respondents (80%). This was related to the payments’ granting rules, as the same area could not be granted twice.

These most popular activities among the participants of the questionnaire could be examples of good practice for the least popular activities: “Soil protection” (10.1.11), “Protection of water bodies against pollution and soil erosion in arable land” (10.1.07), and “Preservation of habitats of the endangered bird—aquatic warbler in wetlands” (10.1.05).

There were some differences between the results of the analysis of the factual data and the results of the questionnaire survey. These could appear due to the uneven distribution of the respondents by the activity of the Measure compared to all beneficiaries.

To the question “Why did you choose these particular activities?” the respondents mostly answered that it was because of the desire to contribute improving the environment (49%). Another reason mentioned was easy to implement requirements and conditions for the provision of support set for applicants (36%), and 15% of respondents indicated that participation was motivated by the support. Among other reasons, the respondents also mentioned the topography of the area, soil improvement, technologies (no-till) used in the farm, and other conditions (keeping bees).

It is important to note that most of the respondents (80%) answered that information on the possibility to participate in the activities of Measure M10 reached beneficiaries in a timely and sufficient manner. The most information about the possibility to participate in the activities of Measure M10 was received from the NPA and employees of the ward (31% each). A slightly lower flow of information came from the staff of municipal agricultural departments and the media (24% and 21%, respectively). One-fifth of the respondents were recommended by another person to participate in the activities of Measure M10. Respondents also received information from the staff of the Ministry of Agriculture (16%). Several respondents additionally mentioned other sources of information, such as the staff of the Lithuanian Agricultural Advisory Service, the Lithuanian Chamber of Agriculture, the Lithuanian Farmers’ Union, and the Lithuanian Grain Growers’ Association.

The requirements and conditions for granting support were relatively clear and easy to implement in the farms. This was confirmed by 83% of respondents, and 17% of respondents mentioned that the requirements and conditions for granting support were too difficult to implement. Respondents solved this problem by studying rules in depth by themselves. Respondents made some following suggestions regarding the requirements and conditions to attract more potential applicants: the information could be more specific and less voluminous, and it should be communicated through the municipalities, the NPA, and the MoA.

Two-thirds of respondents reported that compensatory payments did not fully compensated additional costs and income foregone. There was an almost equal split between positive and negative responses as to whether compensatory payments covered additional costs and income foregone because of participation in the activities.

It is important to note that three-quarters of the respondents plan to continue to participate in the activities related to the agri-environment, while 17% stated the opposite. The main reasons for deciding not to participate were NPA sanctions due to inaccurate information on declared areas from different sources when this is not under the applicant’s control, too long a commitment period, and inflexible commitments linked to natural and climatic conditions. Respondents suggest strengthening and improving the advisory services at the ward level by providing individual advice.

4. Discussion, Concluding Remarks, and Recommendations

Improvements of the design of the activities of Measure M10 would increase supported areas leading to better achievement of environmental and climate goals and effective use of EU support.

The results of the survey showed that some applicants lacked information about suitable activities and the procedures for fulfilling their commitments. Analogous results were obtained by Krzyszczak et al., where the respondents who did not participate in Measure M10 most frequently explained themselves by noting a lack of information about the programme, bureaucratic limitations, or doubts concerning the profitability of participation [21]. Bartolini and Vergamini revealed that one of the reasons of poor environmental outcomes was information asymmetry between farmers and public administrators [12]. Information asymmetry between farmers and public administration emerges already at the stage of determining the amount of the payment, when assessing production technologies and compliance costs. When the payment exceeds the compliance costs, the farmers receive the difference as rent. However, when the payment is lower than the compliance costs, farmers with higher compliance costs choose not to participate in the programme. Linares Quero et al., among others, indicated the same problem that farmers often lack the knowledge to properly implement the practices [24]. It is necessary to spread more information through the media and television programs dedicated to the farmers because not everyone uses the Internet.

The compensatory payments in force for the period of 2015–2021, which were set in 2013 and calculated based on data from 2009–2011 or 2010–2012, became inadequate by the end of the programming period. Provision should be made for the possibility of recalculating compensatory payments considering changes in the prices of products and in the costs of materials, energy, and labour needed to produce them, as well as technological progress. Linares Quero et al. also pointed out that the financial support provided did not always sufficiently compensate income losses [24]. On the contrary, Mennig and Sauer found that schemes designed for arable land overcompensated farmers and thus failed to comply with World Trade Organization rules that compensatory payments for agri-environmental schemes should distort neither trade nor production [29]. These differences are due to the application of the average payment rate in different areas, resulting in undercompensation in some areas and overcompensation in others.

For activities where grass strips are established on arable land, compensatory payments should also include additional transportation and labour costs compensating expenses for reaching fields located far from each other, as well as the income foregone due to harvest losses when accessing the part of the field where environmental obligations are implemented with agricultural machinery. Similarly, the results of the study carried out by Vannucci et al. revealed that direct and indirect costs of planting or maintaining hedgerows, although to be refined, could be covered by policy tools [26].

Shorter terms of commitment would positively affect farmers’ participation in Measure M10 for the first time. Later, having the skills and seeing the benefits it is likely farmers would continue to participate more contributing to the implementation of environmental objectives.

It is worth noting that sanctioning was more focused on punishing beneficiaries than achieving environmental goals. Sanctions were applied not only to the activity but to the entire support for the farm. For this reason, farmers decided not to participate in the activities of Measure M10. It would be meaningful to apply more flexible sanctioning. Linares Quero et al. also noted that high penalties for making mistakes negatively affects the willingness of farmers to implement other agroecological practices [24].

In order to attract more farmers, it is recommended to provide the priority to receive investment support for those participating in Measure M10 and introduce shorter terms for receiving compensatory payments.

Analysis has revealed that the activities of Measure M10 were least implemented in central Lithuania, where intensive farming predominates. In this part of the country, the implementation of the activities could bring the greatest benefits. However, farms in central Lithuania often have high productivity indicators and therefore it is not economically beneficial for them to participate in the activities of Measure M10. A different situation was observed in Poland by Okereke and Wojciechowska: the most active farmers were from the Northwest of Poland, with high agricultural production potential [35]. Bartolini and Vergamini identified the spatial heterogeneity as one of the reasons of lower-than-expected environmental impacts [12]. Tankosić et al. concluded that the policymakers and developers in the Republic of Serbia should carefully consider the distinctive characters of the regions and, in the future, can strive to develop targeted agri-environmental policies [31]. It is suggested to design such activities that would attract intensive farms to implement environment-friendly practices. For example, applying precise tillage technologies, greater crop rotation after harvesting the main crops, growing catch crops that cover the soil surface with their green mass.

Focus group discussions revealed that inspection of the implementation of the activities of Measure M10 was time-consuming and expensive due to the different deadlines set for individual committed works. This often required NPA specialists going to the same fields three to four times, as inspections could not be executed remotely. It is proposed to reduce the number of inspections by refusing insignificant requirements and controls. Additionally, inspections were problematic because some committed works did not have a specific deadline and might depend on natural conditions, and support could be paid to all beneficiaries only after all inspections were completed.

A lack of Result and Impact assessment indicators was fully revealed during the investigation. Similarly, Pe’er et al. [28], among other suggestions, recommended to redesign the set of Result indicators using best knowledge, to link Result indicators with existing data monitored and reported by farmers, and to expand the list of “Impact” indicators to cover all CAP objectives. We fully agree and support Vannucci et al. [26], who concluded that the result-based approach attributes aid on the basis of results that have been obtained and demands to have reliable measures of the current state of the environment and of the improvements that have been obtained.

Therefore, our research hypothesis that the success of the implementation of the Measure “Agri-environment and Climate” depends on its design, farmers’ attitude and behaviour towards the agri-environment, and appropriate support systems was confirmed.

This study contributes to the theory of ecosystem economics with created methodology, which was verified with empirical calculations.

However, this study has several limitations. The first limitation is related to the data availability and was widely discussed throughout the study. We admit that despite all efforts, we could not fully reveal the exact impact of all the activities of Measure M10. The CAP monitoring system should be supplemented with indicators necessary for evaluations and impact assessments. The second limitation is related to the sample size, which is incomplete and may cause results generalisation. The second limitation is related to the survey sample: while it was possible to interview a few respondents, the overall framework of the survey did not sufficiently include potential beneficiaries of these activities. Therefore, the latter aspect could be better explored in further research, while it is important to find out the reasons for non-participation and try to solve them. A larger sample size would allow for more accurate results and better reflect the real reasons for farmers’ participation, avoiding the emergence of individual opinions. Then it would be possible to draw more reliable conclusions and claim that the results of the study accurately reflect the current situation. Since the practical implementation of agri-environmental measures is voluntary and depends on the farmers’ decisions, further research could focus more on the human and his or her choices and behaviour. More broadly, the need for ongoing research and the collection of the necessary data and the need for integrated interdisciplinary research and wider coverage would be highly desirable.