A Multicriteria Decision Analysis Model for Optimal Land Uses: Guiding Farmers under the New European Union’s Common Agricultural Policy (2023–2027)

Abstract

Focusing on sustainability, the new Common Agricultural Policy (2023–2027) sets ambitious goals for water management, as reducing irrigation water use is a vital issue. Cooperation among farmers, relevant authorities, and researchers plays a significant role in achieving these objectives. Therefore, this study applies a multicriteria mathematical programming model to optimize land use, considering water use, profit, labor, and cost. The model was applied to three farmer groups located in Greece and proved to be valuable in the implementation of irrigation water use. Using the same methodology, two additional cases of farmer groups that utilize drylands are presented in complementary ways to investigate how the new CAP affects non-irrigated land uses. Regarding the irrigated case, reducing water usage involves decreasing the land dedicated to crops characterized by high water demand, such as rice, corn, vetch, and clover. This adjustment stems from the necessity to replace irrigated land with non-irrigated land because climate change demands low water consumption for crops and underscores the importance of the new policy framework to promote sustainable agriculture. As for the non-irrigated case, achieving optimal farm planning entails reducing the cultivated areas of vetch, grassland, and sunflower. This result is driven by the need to increase crops receiving primary subsidies, highlighting the necessity for non-irrigated farms to enhance their profitability through the benefits provided by the Common Agricultural Policy. Lastly, it is important to note that this study significantly contributes to guiding decision-makers in achieving alternative agricultural land uses and farm plans while also aiding in the comprehension of the new cross-compliance rules.

1. Introduction

The study of proper water management is considered crucial due to environmental, social, and economic challenges such as climate change, globalization, population growth, wasting water, and dietary habits changes [1,2,3,4], which put pressure on water resources [5]. Additionally, drought [6], water pollution [7,8], and poor water resource management [9] threaten the agricultural sector’s sustainability. Certainly, it has been widely recognized for some time now that the natural resources (soil and water) employed in agricultural practices are no longer viewed as abundant and infinite reserves [10].

The Common Agricultural Policy (CAP) of the European Union has undergone significant reforms over time, adapting to the new challenges and needs of agriculture, markets, and environmental protection [11]. To be more precise, the current policy (2023–2027) focuses on enhancing the sustainability, resilience, and competitiveness of the EU agricultural sector by supporting environmental sensitivity (green architecture and environmental criteria), social justice (redistribution of subsidies and support for young farmers) and increasing competitiveness (digitization and access to markets) [12]. Regarding sustainability enhancement, the new CAP sets ambitious goals for water management, such as reducing the use of irrigation water [13], which is a critical issue. To achieve this goal, the adoption of sustainable irrigation practices, water reuse, and monitoring and management of water resources are promoted [13]. Cooperation among farmers, relevant authorities, and researchers [14,15] is a significant factor in the successful implementation of the aforementioned measures.

Therefore, this paper is developed within the framework of the “Measure 16: Cooperation” project and applies a multicriteria mathematical programming model that contributes to rational water use—through useful land use changes—while considering the main goals of farmers, such as profit maximization, total labor minimization and more. The “Measure 16: Cooperation” of the Greek Rural Development Program (RDP) aims to develop partnerships between stakeholders in the Greek agri-food sector and promote innovation, sustainable development, and competitiveness [16]. Measure 16 comprises two sub-measures. The first one concerns the establishment and operation of business groups for agricultural productivity and sustainability, while the second one focuses on collaboration for environmental projects, practices, and actions addressing climate change. To be precise, through Measure 16, collaborating entities undertake projects such as the same production and marketing of agricultural products, adoption of environmentally friendly practices, development of new technologies, and promotion of research and innovation [16]. All the above demonstrates the significance of “Measure 16: Cooperation“ within the Common Agricultural Policy (CAP) framework. This measure can act as a catalyst for fostering a spirit of collaboration in the agri-food sector, contributing to enhancing the competitiveness of Greek food and agricultural products and promoting sustainable development [16].

The model was applied to three farmer groups located in Greece and proved to be valuable in the implementation of irrigation water use policies and in the achievement of sustainable resource management goals. Using the same methodology, two additional cases of farmer groups that utilize non-irrigated lands are presented. This additional analysis was chosen to be conducted to investigate how the new CAP policy affects the land use of farms that do not use irrigated farming techniques. The five producer groups analyzed in this study were defined by the coordinating body of the project “Measure 16: Cooperation”. Develo** a decision support model (DSM) aiming to adapt to cross-compliance rules and achieve optimal economic efficiency in farms represents a plan with a consistent implementation process. Data collection was initially conducted through a questionnaire for the model’s development. Subsequently, the decision support model was developed, considering the new CAP rules regarding proper water management, as well as other economic, environmental, and social parameters, along with the producers’ real aims (e.g., gross profit maximization). Finally, the multi-criteria analysis method was applied to extract the new land uses [17,18] for each of the five farmer groups.

The multi-criteria analysis method has been applied extensively in the context of agriculture and a research project. Precisely, through a brief literature review [19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45], it emerges that the method is widely used by researchers, demonstrating how to achieve better policy-making procedures and simulate realistic decision-making processes [17]. This method has been used for many years, as various scientists have used it to study the impacts of periodic reforms of the Common Agricultural Policy [46]. A relevant study is that of Bournaris et al. [17], where they examined the “Setting up Young Farmers” measure of the Common Agricultural Policy (CAP). Through this study, the role of the measure in encouraging young and educated farmers to engage more actively in agriculture was essentially highlighted, while a multicriteria mathematical model revealed land uses that can result in better economic results. It is worth mentioning that the European Union’s Rural Development Plan Setting Up Young Farmers measure has also engaged other Greek researchers from time to time [47,48]. Furthermore, Bournaris et al. [49] have developed a multicriteria mathematical programming model to support decision-making in water and land management in Kozani (Greece). The model incorporates vulnerability maps and helps protect water resources from excessive fertilizer use [49]. Additionally, the relevant literature reviews centers on multicriteria analysis as a tool for evaluating agricultural policies [50] and discussing agri-environment schemes [51,52,53]. While the literature on applying the multi-criteria decision analysis method in agriculture is extensive, its practical use among many farmer groups in various Greek rural areas, especially under the conditions of the latest CAP, seems limited.

The article is structured into six sections. Section 2 introduces the study area and details the farmer groups involved. It then closely examines the research methodology, describing the questionnaire design and the chosen analysis method. Section 3 presents the study’s findings, followed by a detailed discussion (Section 4) that interprets the results. Finally, Section 5 concludes the article by explaining the research’s limitations and originality.

2. Materials and Methods

2.1. Study Area and Farmer Groups

The research involves a total of five farmer groups operating in the field of crop production, as they specialize in the production of animal feed, cotton, rice, and grains. The groups are located in the regions of Thessaloniki, Kavala, Kozani, and Serres (Northern Greece) (Figure 1).

The first of the five farmer groups consists of rice farmers and is based in Chalastra, located on the western side of the Regional Unit of Thessaloniki. The next farmer group consists of dried forage and animal feed and is in Lagyna (Regional Unit of Thessaloniki). The third one is located in Chrysoupoli and consists of fodder crops for animal feed producers. Chrisoupoli is an agricultural area located in the Regional Unit of Kavala. The fourth group consists of farmers who are based in Kranidia and are engaged in the production of animal feed. Kranidia is an area that belongs to the Regional Unit of Kozani. Lastly, the fifth group comprises cereal producers based in Mesorachi, within the Regional Unit of Serres. It is widely accepted that agricultural production in the above-mentioned areas plays a significant role in the economy, contributing to the supply of the market and livestock units.

2.2. Questionnaire Design and Data Collection Temporal Structure

To develop the model, data were initially collected using a questionnaire based on the scientific literature [17,18,49]. The questionnaire analysis consists of three sections. The initial section focuses on the demographic details of the farmers involved in the research process. The second one concerns the existent crop plans, which were acquired through the survey of cultivated land. Finally, the third section includes questions regarding the technical and economic data of the production sectors for each of the farmer groups [17,49].

2.3. Methodology—Weighting Goal Programming

According to Moulogianni [54], mathematical programming serves as a mathematical framework that is specially designed for the optimization of the allocation of limited resources through planning or design processes. In this case, multicriteria analysis was chosen as the main tool to promote the rational use of irrigation water through land use changes. This decision is grounded in the capability of a multicriteria decision analysis (MCDA) model to combine various criteria for a utility function while ensuring compliance with policy constraints. Moreover, it considers the available resources, such as land, labor, and capital, making it an important approach for decision-making [17]. The MCDA model considers the multi-functionality of agriculture, which includes variables relating to economic, social, and environmental issues [55]. Considering the farmer and their preferences, the MCDA model emerges as the best choice for the current analysis since it takes into consideration the variety of criteria farmers evaluate when planning their crop plans, hence expanding the traditional concept of profit maximization [17,49].

Multi-criteria mathematical programming has been applied over time to find constructive land use changes and discover optimal farm plans based on the aforementioned criteria. This is something that is proved by reviewing a wide range of the relative literature [17,18,49,56]. Sumpsi et al. (1993, 1997) [57,58] and Amador et al. (1998) [59] developed methodologies for simulating agricultural systems using multi-criteria techniques. Specifically, they propose weighted goal programming as a methodology for decision-making analysis [57,58,59]. In general, this methodology has been used for farm planning [60,61,62,63,64], decoupling [46], environmental management [65] and water agricultural policy evaluation [66,67]. The final case deals with this study’s question regarding land use changes and rational water use within the context of the new CAP (2023–2027).

In the context of this study, this methodology essentially aims to estimate an objective utility function, allowing the decision-making processes of farmers to be simulated. This reduces irrigation water use while maximizing profit and achieving other objectives. The implementation of this methodology consists of three main parts [17]. Firstly, it begins by identifying a preliminary set of objectives likely to hold the highest importance for farmers. This task can be effectively accomplished through questionnaires and descriptive methods, which provide valuable insights into farmers’ priorities. Following this, it establishes a pay-off matrix corresponding to the set of objectives. Lastly, using this matrix, a set of weights is computed to capture and reflect farmers’ preferences accurately [17,49].

The first part involves establishing a set of objectives «f1(x), f2(x)…fn(x)» that essentially represent the real goals of the farmers [59] such as profit maximization, cost, and labor minimization [17]. Once the objectives are set, the analysis proceeds to the second part, where a pay-off matrix is determined [18]. The elements of this matrix are computed by optimizing an objective in each different row [49]. Once the pay-off matrix is obtained, the analysis proceeds to the third step, where the following system of equations q is solved [17,18,49]:

$${\sum }_{j=1}^q{w}_j{f}_{ij}=f_i, \mathrm{i}=1, 2, \dots .\mathrm{q}; \mathrm{and} {\sum }_{j=1}^q{w}_j=1$$

(1)

where:

• w_j: The weights attached to each objective represent the actual behavior of the farmer.
• f_ij: The pay-off matrix elements.
• f_i: Τhe outcome obtained for the i-th objective based on the observed crop distribution.

Τhe system described above typically does not produce a specific set of weights. Therefore, the search for the optimal solution involves minimizing the sum of deviational variables that most align with the closest set of weights [17]. To achieve this, a weighted goal program can be formulated using percentage deviation variables [68]. The solution will be derived by solving the following linear programming (LP) model [17]:

$$Min{\sum }_{i=1}^q\frac{n_i+p_i}{f_i} \mathrm{subject} \mathrm{to}: {\sum }_{j=1}^q{w}_j{f}_{ij}+n_i-p_i=f_i, \mathrm{i}=1, 2, \dots .\mathrm{q}; \mathrm{and} {\sum }_{j=1}^q{w}_j=1$$

(2)

where:

• p_i: The positive deviational variable, measuring the degree of over-achievement for the i-th objective concerning a given target.
• n_i: The negative deviational variable that assesses the variance between the actual value and the modeled solution for the i-th objective.

The following figure provides a visual representation of the aforementioned process (Figure 2).

2.4. Model Specification

In the case of this project, the MCDA model comprises the following elements:

2.4.1. Variables

Each farmer group has a set of variables ** options, including soft and hard wheat, barley, alfalfa, clover, and corn, receive not only direct payments but also coupled payments from the CAP [12,13]. It is also important to note that rapeseed can receive support for organic farming methods if the farmers choose this direction [13].

Mesorrachi’s farmer group’s 100% sunflower abandonment is notable but not surprising, given that similar results have been reported for cotton [54], sugar beet [54,69], soft wheat, and barley [49,54], either due to low profitability, high costs, or unfavorable production conditions for these crops, or because the conditions required by the model were not met.

In the case of 100% sunflower abandonment, it can be conjectured that this specific crop was entirely excluded from the optimal crop plan because there is a lack of extra coupled payments from the CAP [69] since the profits were very low due to the high production costs. In this case, the findings support previous research on the general impact of CAP subsidies on crop choice [73,74,75,76,77]. This is evident from other studies on the impact of economic policies and financial incentives on decision-makers’ behavior [76]. Increasing or decreasing all the above-mentioned cultivated lands achieves the objectives of the model (profit maximization, cost minimization, labor minimization). As a result of the suggested land uses, the two farmer groups (in Kranidia and Mesorrachi) are more profitable and require less labor [17,18,49,56,69].

5. Conclusions

The study of proper water management in agriculture is considered crucial as drought, pollution, and poor water resource management threaten the sustainability of the agricultural sector. Focusing on sustainability, the new Common Agricultural Policy (2023–2027) sets ambitious goals for water management, as reducing irrigation water use is a critical issue. Therefore, this paper is developed within the framework of the ”Measure 16: Cooperation” initiative and applies a multicriteria mathematical programming model that contributes to optimal land uses. Also, it considers the rational water use and the main goals of farmers, such as profit maximization, and cost and labor minimization. The model was applied to three farmer groups located in Greece. Using the same methodology, two additional cases of farmer groups that utilize drylands are presented complementarily to investigate how the new CAP policy affects land uses of farms that do not use irrigated farming techniques.

From the present analysis, several conclusions can be drawn. Multi-criteria decision analysis can be considered a valuable tool in implementing water use policies, as it can indicate optimal land uses and achieve more effective irrigation management practices. This is evidenced by the results of the analysis, as the reduction in land dedicated to crops such as rice, corn, alfalfa, and vetch (Thessaloniki, Lagyna, and Chrisoupoli) implies potential water savings, allowing farmers to retain or even enhance their profitability. In addition, it turned out that this methodology is valuable since it can combine economic and environmental factors. This is explained by the fact that this study considered both economic sustainability (profit, cost, labor) and environmental sustainability (water use) when making land-use decisions in agriculture.

The present multicriteria decision analysis (MCDA) model, focusing on crop diversity, promotes economic and environmental sustainability. This is evident because the current model investigates alternative crops with low water demand and greater economic benefits, informing farmer groups about how they should operate. Farmers can thus be helped and, in the future, adapt to conditions related to environmental protection and market demands. This approach contributes to the effective management of agricultural activities, with a focus on environmental protection and the rural areas’ long-term sustainability assurance.

Farmers generally prefer less labor-intensive practices or prioritize achieving higher profits even if it requires more working hours. In this case, the results of the multicriteria decision analysis (MCDA) model can inform farmers about profitability and labor intensity and guide them to make special decisions by following different land use strategies. Last but not least, it is worth mentioning that through the implementation of this study, the significant impact of the Common Agricultural Policy on land use decisions is highlighted. This refers to the well-known subsidies that play a significant role in farmers’ cultivation choices and land management.

This study offers valuable insights into optimizing land use for water management and profitability in Greek agriculture. However, certain limitations need to be addressed for future research directions. The study focuses on three irrigated and two non-irrigated groups of farmers in Northern Greece. Extending the model’s application to more farmer groups and other Greek regions with varying climates, soil types, and crop selections would strengthen the conclusions about the new Common Agricultural Policy (CAP). For this reason, it is essential to expand the sample size in future studies, both in terms of the number of farmers involved and the geographical scope. The model emphasizes only water use, profit, cost, and labor. Future iterations could incorporate additional sustainability indicators such as soil health, biodiversity, and greenhouse gas emissions to provide a more holistic picture. Acknowledging the limitations of using data from a single year, this study also aimed to provide a snapshot of the current situation and demonstrate the implementation of the MCDA model within a specific timeframe. However, incorporating longer-term averages for crop yields and prices can be valuable and aligns with a more comprehensive analysis. This consideration can be incorporated into future iterations of this research to enhance the reliability of the current findings.

Furthermore, the study includes information regarding local crops, aiming at the integrated management of agricultural areas. Data collection, model development, understanding, and compliance with the multiple CAP rules by farmers are critical steps that ensure not only the success of this model but also the maximum possible subsidy allocation according to each farmer’s capabilities. It is also believed that the overall process will encourage producers—through the understanding of the new cross-compliance rules—to choose more efficient crops while maintaining existing ones, always to increase their profitability. It is also important to note that the success of this model depends on the adoption of new land uses, which will be confirmed through the conduct of a future study.