Impact of the Common Agricultural Policy (CAP) on the Development of Rural Territories: Principal Component Analysis in SW Spain, Extremadura (2007–2020)

Abstract

The Common Agricultural Policy (CAP) is a European Union (EU) policy aimed at supporting farmers, guaranteeing food security, and promoting sustainable rural development, which has undergone numerous reforms to adapt to the needs of the agricultural sector and society in general. The main objective of this work is to statistically analyse CAP aid in southwest Spain, specifically in Extremadura, a region characterised by areas at risk of depopulation and deep rural areas, during the last two periods of European aid (2007–2013 and 2014–2020). In this study, by means of principal component analysis (PCA), agricultural, economic, and demographic variables were interrelated, together with geographic information systems (GIS), to evaluate their impact on the development of the regional agricultural sector. This methodology will help to identify areas with improvements in their territorial structures and can therefore serve as a basis for their application in other European territories. Through the analysis, we identified areas where the interrelation of the variables showed improvements in their demographic, social, and economic structures, with the municipalities of populations over 10,000 inhabitants in Extremadura being the ones that compose the territorial substructure A. Therefore, this work shows how European agricultural aid can disproportionately favour the most dynamic territories and leave the less developed regions at a disadvantage.

1. Introduction

In recent years, the Common Agricultural Policy (CAP) of the European Union (EU) has undergone significant transformations, marking a substantial evolution (Figure 1) in the way challenges and opportunities in the European agricultural sector are approached [1,2]. The CAP, created with the aim of ensuring food security, promoting rural development, and sustaining the economic viability of farmers in EU member countries [3,4], has responded to changing dynamics both internally and externally. The CAP has also been seeking to improve economic and social disparities between the EU’s agricultural regions, promoting territorial and social cohesion in these regions [5,6,7]. The introduction of rural development policies and programmes has been instrumental in this direction, promoting economic diversification and improving the quality of life in rural areas [8,9,10].

Since the beginning of the 21st century, this policy has developed in order for EU member countries to meet the challenges that began to rise with globalisation, climate change, and product food security [11] and thus seeking a balance between productive efficiency and environmental preservation [12,13], reflected in changes in the allocation of funds and in the incorporation of measures aimed at environmental sustainability [14]. As the EU has grown and expanded, the CAP has had to adapt to a diversity of agricultural realities and changing societal expectations [15,16] in promoting the modernisation and digitalisation of farms in order to improve the efficiency and competitiveness of the sector [17,18] while at the same time being geared towards achieving environmental and climate objectives [19,20].

In the early 2000s, the 2003 reform, which decoupled single payments from production, was implemented, bringing about a drastic change in the CAP. Thus, during the period from 2007 to 2013, the CAP consolidated the changes introduced in the previous reform with the so-known Health Check of 2008, establishing a model in which support was structured around two funds [21,22]: on the one hand, the European Agricultural Guarantee Fund (EAGF) and, on the other, the European Agricultural Fund for Rural Development (EAFRD) as compared to the previous model, in which support was financed exclusively by the European Agricultural Guidance and Guarantee Fund (EAGGF) [23]. In this way, the first fund, the EAGF, was related to farm payments and the Common Market Organisation (CMO) for agricultural products and the second fund, the EAFRD, to rural development policy [24,25]. In the 2014–2020 budgetary framework, and following the 2013 reform, the two-pillar structure established in previous reforms was maintained, although, as a novelty, greater flexibility and linkage between the two was established for the transfer of funds [26,27]. Thus, as established in Article 3 of Regulation (EU) 1306/2013 of the European Parliament and of the Council of 17 December, funds for the financing of agricultural expenditure are made available through the EAGF and the EAFRD [28]. Thus, one of the main objectives of the CAP after these reforms is the consolidation of the two-pillar model, with the first pillar funding direct aids and market measures through the EAGF and the second pillar co-financing rural development support through the EAFRD [29,30,31]. During the last period, the CAP has been oriented towards environmentally and climate friendly agriculture [32,33,34], and based on this, European bodies established as general guidelines the conversion of decoupled support into a multifunctional support system, the consolidation of the two fundamental pillars of the CAP, the reinforcement of the CMO tools [2,35], and the establishment of a more integrated, specific, and territorial approach to rural development [4,36]. CAP aid is allocated based on various criteria, including the characteristics of agricultural production and the sustainable practices implemented on farms [37,38,39], which can lead to significant differences in the distribution of aid between municipalities. Although the size of the population of a municipality does not directly affect the distribution of this aid, the EAFRD channels funds to more disadvantaged rural areas, with the aim of improving the quality of life and promoting economic diversification in these areas [40,41].

This paper studies the Autonomous Community of Extremadura, a rural region in the southwest of Spain in which the CAP is a fundamental support for the maintenance of the agricultural sector due to its socio-economic characteristics, its agricultural sector that is predominant in many of its territories, and its low productivity as well as its inability to halt the processes of depopulation and ageing. Since Spain joined the European Economic Community (EEC), the CAP has played a fundamental role in supporting the profitability and economic stability of farmers in some areas of the Extremadura region [42]. The CAP has also been a key tool in the modernisation and incorporation of innovative technologies in agriculture in the region, thus improving the efficiency and competitiveness of the sector, which has allowed the adoption of more efficient practices [43]. Therefore, through the different aids and rural development programmes, the CAP has contributed to maintaining the economic viability of many farms in Spanish rural regions [44], which is crucial in facing the specific economic challenges that Extremadura and other rural areas [30] have often presented. Moreover, through this policy, economic diversification and rural development in Extremadura has been promoted, which contributes not only to the economic sustainability of the region but also to social and territorial cohesion by offering employment opportunities outside the agricultural sector [45].

Considering the above, the main objective of this research is to examine the relationship between CAP support and the regional economy of Extremadura as well as the development of the agricultural sector at the municipal level through principal component analysis (PCA) by interrelating agricultural, economic, and demographic variables. Furthermore, in the study of CAP aid, there are several questions that emerge and that this study aimed to resolve, such as whether there is a significant relationship between CAP aid and the economic development of the agricultural sector in the region. Furthermore, it is proposed that the variables used in the PCA can identify patterns in the distribution and impact of this aid. The study also investigated whether there are significant disparities between the different municipalities in terms of the CAP aid they receive and how these disparities affect the socio-economic development of Extremadura’s municipalities. The aim was to use this statistical methodology to detect the areas that are achieving improvements in their demographic, social, and economic structures to serve as cases of good practice for other territories. In relation to previous studies [2,26], this paper uses new periods of analysis (2007–2013) together with previously studied periods (2014–2020) to interpret the interrelation of the variables used by means of the PCA. This approach makes it possible to identify structural changes in CAP support over two different programming periods, highlighting the modifications implemented in this policy.

In the following, taking into account the above considerations, the literature review section is presented. Next, in Section 2, the selected study area is described. This is followed by the methodology used for the development of the research. Section 3 and Section 4 show the results obtained and the discussion of these results. Finally, Section 5 includes the conclusions.

Literature Review

The CAP has been the subject of numerous research studies dealing with various aspects, from, for example, its economic impact to its influence on environmental sustainability and social equity in the European agricultural sector. Several studies have provided valuable data using descriptive analysis, notably Molinero et al. [46], who examined the distribution of agricultural and rural development measures, beneficiaries, and amounts received at the municipal level, highlighting their impact on agricultural structures, or the one carried out by Leco and Pérez [47] on the role of the Basic Payment in Spain, analysing its distribution and potential territorial imbalances within the aid system. Similarly, Martínez et al. [2] carried out a descriptive analysis of CAP aid during the period from 2014 to 2020 in Extremadura, observing the most prominent types of aid in the region and the total distribution over the territory. At the international level, researchers such as Potori et al. and Rumanovska [48,49] have investigated the impact of the CAP in various regions from a theoretical perspective, analysed the changes introduced by the reforms, and assessed how these modifications could affect countries with different agricultural characteristics. A study carried out by Martínez et al. [26] determined the relationships between CAP aid and agricultural, economic, and demographic variables in Extremadura during the 2014–2020 period using a spatial regression model.

Methodologically, this work applies the PCA, a method used in diverse and varied research, due to its multidisciplinary nature and its relevant importance in the field of social sciences and humanities. In relation to this study, the application made by Cárdenas and Nieto [50] stands out, in its analysis of the impact of European policy on the development of rural areas and its exhaustive analysis of EAFRD aid in the 2007–2013 programming period and its relationship with socio-economic development in areas of low population density. Engelmo et al. [51] also used the PCA to study the impact of rural development strategies in Extremadura but with reference to rural tourism. For the specific case of the CAP, Galluzzo [52] analysed the impact of subsidies on agrotourism in Romania, determining the socio-economic variables that have affected this sector and the rural environment where they are distributed. Similarly, Coppola et al. [53] used PCA to understand how factors affecting competitiveness within Italian agriculture vary and how these factors act on the provincial economy. Belliggiano et al. [54] analysed the development of two municipalities located in Italy, each with very different territorial characteristics, through organic farming. They applied a multivariate analysis through a PCA for each of the analysed municipalities with the aim of identifying how both cases boosted the economic diversification that have made them two success stories. Another research highlight is that carried out by Salvati and Carlucci [55], which illustrated a methodology integrating geographic information systems and multivariate statistics (PCA) to provide measures of sustainable development at the municipal scale in Italy, showing the latent disparities between cities in the north and south of this country.

2. Materials and Methods

2.1. Study Area

Extremadura (Figure 2) is in the southwestern part of the Iberian Peninsula, bordering Castilla y León to the north, Castilla-La Mancha to the east, Andalucía to the south, and Portugal to the west. According to the National Statistics Institute (NSI), as of 1 January 2023, Extremadura had a total population of 1,052,523 inhabitants and a population density of 25.28 inhabitants per km², indicating a high dispersion of the population over the territory. The economy of Extremadura has traditionally been linked to agriculture and livestock farming, with both dry and irrigated farming playing an essential role. The agricultural sector in Extremadura had a prominent position in the regional economy in 2022, contributing 7.4% to the gross domestic product (GDP), a figure significantly higher than the Spanish national average (2.5%), according to the NSI. Moreover, during the same year, agricultural assets accounted for 9.8%, also a high figure compared to 3.95% at national level. These data show the relevance of the present study in investigating CAP aid in Extremadura at the municipal level, as it is a region with a still-predominant agricultural structure, so it can serve as a reference for other European territories with these characteristics. This analysis will allow an in-depth observation of the different territorial characteristics of the region and their interrelationships with CAP aid.

The agricultural counties were used as a territorial reference framework since this allows municipalities to be situated within a delimitation from an agrarian point of view, representing areas that show a certain homogeneity both in their productive capacity and in their systems of exploitation and agricultural use as well as a similar economic development [56,57]. Therefore, Extremadura was organised into 22 agricultural counties (

Table 1. Number of municipalities per agricultural counties.

Province	Agricultural County	Municipalities
Badajoz	Alburquerque	6
	Mérida	24
	Don Benito	18
	Puebla de Alcocer	12
	Herrera del Duque	6
	Badajoz	11
	Almendralejo	23
	Castuera	13
	Olivenza	7
	Jerez de los Caballeros	16
	Llerena	18
	Azuaga	11
Cáceres	Cáceres	28
	Trujillo	23
	Brozas	9
	Valencia de Alcántara	8
	Logrosán	12
	Navalmoral de la Mata	34
	Jaraíz de la Vera	14
	Plasencia	33
	Hervás	24
	Coria	38

), 12 of which are located in the province of Badajoz and 10 in the province of Cáceres.

Likewise, it is made up of a total of 388 municipalities, which were used as administrative units for the representation of the variables used in the study, with this being a sufficiently significant sample.

2.2. Data Source

The methodological process of this work is presented in Figure 3.

Firstly, a database was constructed with the demographic, socio-economic, and agricultural variables (

Table 2. Variables used for the study.

Typology	Variable	Calculation (Period)	Source
Demographics	Total population	Average total population	[58]
	Population growth	(Final year population − Initial year population)/Initial year population
	Ageing rate	(Population over 65 years old/Population under 15 years old) × 100
	Migratory balance rate	((Immigrants − Emigrants)/Population) × 100
Agricultural	Number of holdings	Total number of holdings by municipalities	[59]
	UAA	Area (Ha) by municipalities
	Livestock units (Livestock data are expressed in number of heads and converted into livestock units (LU) by applying a coefficient to each species and type of livestock, in order to aggregate different types and species into a common unit [62])	Heads of livestock multiplied according to category
Socio-economic	Unemployment rate	(Unemployed/Labour force) × 100	[60]
	Contracts in the agricultural sector	(Contracts in the agricultural sector/Total contracts) × 100
CAP aids	Direct payments	Total aid amounts	[63,64]
	Non-direct payments
	Competitiveness of agriculture
	LEADER and improvement of the quality of life in rural areas
	Improving the environment and the rural countryside

) that are considered representative of the territorial reality of Extremadura. For the development of this research, the NSI was used as a source of data for the calculation of both demographic variables through the continuous census statistics [58] (total population, population growth, ageing rate, and migration balance rate) and agricultural variables (number of farms, utilised agricultural area (UAA), and livestock units), which were obtained from the agricultural censuses of 2009 and 2020 [59]. The socio-economic variables (unemployment rate and contracts in the agricultural sector) were calculated from data published by the State Public Employment Service (SEPE) [60]. The delimitation of these variables was based on previous work [26] and an exhaustive bibliographical review of the subject of analysis [22,31,54,61].

Subsequently, a process was carried out to compile variables related to CAP aid with which to study it quantitatively. These variables were obtained from two different sources depending on availability, so for the years 2007–2016, they were collected from the Ministry of Agriculture, Rural Development, Population, and Territory of the Regional Government of Extremadura and for 2017–2020 from the portal for open data information of the Spanish Agricultural Guarantee Fund (FEGA). Thus, the annual data correspond to the CAP aid received according to the different financial years from October to October of each year. Due to the fact that the data come from two different sources, the values of the amounts of CAP aid between 2007 and 2016 in the EAGF and EAGF aid series by FEGA [48,49] paying agencies were checked at the annual level, with the aim of observing that the values corresponded to those used for this study. Next, a process of grouping the aid into five major groups, i.e., into five new variables, was carried out to simplify the information by adding up the total amounts of these variables for subsequent application in absolute values in the PCA model. Therefore, grouping CAP aid into five groups is justified for several reasons: In the first place, the experience of previous work has demonstrated the effectiveness of this methodology for understanding the application of aid at the municipal level [2]; secondly, it allows for a homogeneous categorisation of aid, thus facilitating comparative analysis; and finally, it avoids the excessive dispersion of data that would make it difficult to obtain a comprehensive view of the impact of this aid on Extremadura’s society. These new variables include the following: direct payments, non-direct payments, competitiveness of agriculture, LEADER and improvement of the quality of life in rural areas, and improvement of the environment and the rural countryside. These, together with the agricultural, socio-economic, and demographic variables, were correlated in the PCA, establishing the two periods of 2007–2013 and 2014–2020, as these are the last two periods of completed European budgetary programming.

2.3. Principal Component Analysis (PCA)

With the aim of understanding the relationship of the selected demographic, socio-economic, and agricultural variables and to represent the reality of the municipalities of Extremadura in terms of the CAP and its relationship with it, the PCA was applied. For this reason, in relation to the objective proposed in this study, the use of PCA is justified by its ability to simplify the analysis and its interpretation by transforming a large set of correlated variables into a smaller set of uncorrelated variables. Likewise, by using PCA, it is possible to identify which original variables contribute most to the variance captured by each component, highlighting which are the most influential in the model, which also allows the identification of similar data groups, which is useful for segmenting regions according to their territorial characteristics based on the selected variables. This is a multivariate technique that seeks to reduce the dimensionality of a set of observed variables by creating new variables called components [65]. These components are the result of combining the original variables [66] and are classified according to the amount of original variance they explain, which allows synthesising the information in the dataset.

Firstly, the communalities are obtained, which indicate the degree of explanation of each variable in all the principal components obtained in the analysis [67]. Thus, the higher the communality value and the closer to 1, the greater its contribution to the whole. Next, the total variance explained is calculated, which shows the degree of explanation of the components obtained whose eigenvalues exceed unity. Finally, the explanatory factor in each municipality is analysed in relation to the principal components, which makes it possible to identify which municipalities are most interrelated with the variables used [51].

This method firstly establishes relationships between variables without considering a specific dependent variable, and secondly, it provides relative scores for, in this case, the different Extremadura municipalities according to the principal components as defined by the interrelation of causal variables so that homogeneous behaviour can be defined in the different areas. In this way, two different models were created, namely one for the 2007–2013 period and the other for the 2014–2020 period, to explain the differences between the two and, at the same time, to delve deeper into the changes that have occurred in the CAP measures and in the socio-economic situation of the Extremadura region at the municipal level.

As a prior step to the PCA, it is necessary to know the efficiency of the correlation of the selected variables, for which there are so-called “contrasts”, such as the Kaiser, Meyer, and Olkin (KMO) sample adequacy measure [55,68]. This permits a comparison of the significance of the correlation coefficients observed with the partial correlation coefficients [61], and its value ranges between 0 and 1 so that if the result is ≥0.75, the idea of carrying out the factor analysis is good; if it is ≥0.5, it is considered acceptable; and if it is <0.5, it is unacceptable [69].

3. Results

The result obtained in the sample adequacy measure KMO is 0.708 for the 2007–2013 period and 0.756 for 2014–2020, so the performance of the PCA with the variables that were selected is good (with values around 0.75 in each period).

Table 3. Communalities by period.

Variables	2007–2013	2014–2020
Direct payments	0.814	0.763
Non-direct payments	0.604	0.556
Competitiveness of agriculture	0.905	0.934
LEADER and improvement of the quality of life in rural areas	0.646	0.688
Improving the environment and the rural countryside	0.892	0.862
Population	0.798	0.787
Population growth	0.950	0.763
Ageing rate	0.757	0.524
Migratory balance rate	0.930	0.815
Unemployment rate	0.664	0.891
Agricultural contracts	0.687	0.950
Number of holdings	0.748	0.645
UAA	0.908	0.905
Livestock units	0.788	0.746

shows the communalities, from which it can be seen that the variables for the 2007–2013 period have extraction values above 0.6 and for the 2014–2020 period at 0.5, with the variable with the lowest value in both periods being non-direct payments. In contrast, the variables that contribute the greatest weight (with values above 0.9) for the 2007–2013 period are aid for the competitiveness of agriculture, population growth, migration balance rate, and UAA. For 2014–2020, aid for the competitiveness of agriculture is also highly explained but surpassed by two variables of agricultural nature, namely agricultural contracts and UAA, leaving behind the demographic variables of the previous period.

Subsequently, the principal components were obtained for both periods. In this study, a total of fourteen components resulted (

Table 4. Total variance explained for both periods.

	2007–2013			2014–2020
Components	Total	% Variance	% Cumulative	Total	% Variance	% Cumulative
1	5.18	37.00	37.00	5.25	37.52	37.52
2	1.90	13.60	50.60	1.87	13.37	50.89
3	1.58	11.25	61.85	1.67	11.92	62.81
4	1.41	10.10	71.95	1.03	7.38	70.19
5	1.02	7.29	79.23	1.00	7.15	77.34
6	0.89	6.36	85.59	0.84	5.96	83.30
7	0.64	4.54	90.13	0.76	5.43	88.73
8	0.46	3.29	93.42	0.44	3.15	91.87
9	0.34	2.43	95.86	0.38	2.71	94.58
10	0.29	2.09	97.95	0.31	2.19	96.78
11	0.10	0.74	98.69	0.23	1.67	98.45
12	0.10	0.68	99.38	0.11	0.81	99.26
13	0.06	0.39	99.77	0.06	0.41	99.67
14	0.03	0.23	100	0.05	0.33	100

), but the first two were extracted, in both periods, as they accounted for more than 50% of the total variance explained and were considered sufficient to explain the sample and represent a territorial model of Extremadura in terms of CAP aid. Therefore, these values allow explanation of the sample analysed, although their value is not excessively high due to the complexity of the region under study, as it presents a remarkable demographic, social, economic, and agricultural variability as well as a diverse distribution of support for the development of the agricultural sector [50,61,69]. The decision to retain only two principal components in our study was based on the percentage explanation of the variance explained, where the coefficients obtained in these components are consistent and show expected patterns in terms of their relationship with CAP support, aiding their interpretability and falling in line with the objective proposed in the analysis.

In relation to the above, the decision to extract the first two components was further justified by the results obtained in the sedimentation plot (Figure 4), in which the associated eigenvalues of the explained variance are plotted. Here, it can be seen how, initially, in components 1 and 2, the eigenvalues are high and gradually decrease as more components are extracted. When the eigenvalues decrease and are like each other, the slope of the graph is practically zero. The inflection points in the graph, where the slope changes from steep to minimal, indicate the optimal number of factors to extract. It can be observed that, both in the 2007–2013 (a) and 2014–2020 (b) periods, from component 3 onwards, the slope starts to decrease, being much steeper in the first two components, which, as discussed in the table above, account for more than 50% of the explained variance.

Table 5. Territorial substructures of components 1 and 2 in Extremadura.

Period	Territorial Substructures	Municipalities	Population	Total CAP Amount (EUR)
2007–2013	A (+, +)	28	368,317	1,624,932,207
	B (−, −)	221	190,396	741,664,809.6
	C (+, −)	82	468,130	1,417,338,973
	D (−, +)	57	75,852	385,554,637.3
2014–2020	A (+, +)	33	485,026	1,804,144,438
	B (−, −)	239	172,133	739,398,842.6
	C (+, −)	79	290,835	1,049,518,524
	D (−, +)	37	132,704	681,484,597.4

shows the number of municipalities in Extremadura classified according to the different territorial substructures resulting from the analysis of the values obtained in factors 1 and 2 of the PCA. Four territorial substructures were identified in the region according to their demographic, socio-economic, and agricultural characteristics and in relation to the five CAP support groups: A (positive values in both components), B (negative values in both components), C (positive values in component 1 and negative values in component 2), and D (negative values in component 1 and positive values in component 2). This table shows that substructure A has a high percentage of population for the small number of municipalities that make it up, accounting for more than 30% of the population of Extremadura during the first period of study and almost 45% during the second period. Likewise, the municipalities that make up this substructure accumulated the highest amount of CAP amounts during the study periods. Substructure B shows a totally opposite situation since, in both periods, it is the group with the largest number of municipalities, with figures of over 200, but its represent values of around 15% of the total population of Extremadura in these periods.

The spatial distribution of the different territorial substructures at municipal level (established in Table 5) in Extremadura for the period 2007–2013 and for 2014–2020 is represented in Figure 5, and the main differences between both periods can be observed more clearly. This representation is the result of the grouping at the municipal level of the values obtained in the first two components of each period for each of the selected variables, as shown in the following table (

Table 6. Principal components analysed by period.

	2007–2013		2014–2020
Variables	1	2	1	2
Direct payments	0.84	0.24	0.81	−0.09
Non-direct payments	0.30	0.49	0.64	0.14
Competitiveness of agriculture	0.22	0.92	0.78	0.22
LEADER and improvement of the quality of life in rural areas	0.11	0.77	0.65	0.19
Improving the environment and the rural countryside	0.39	0.86	0.80	0.18
Population	0.83	0.30	0.84	−0.03
Population growth	0.09	0.08	0.12	0.81
Ageing rate	−0.18	−0.04	−0.24	0.51
Migratory balance rate	−0.05	−0.01	−0.09	0.85
Unemployment rate	0.02	0.05	−0.04	−0.10
Agricultural contracts	−0.04	−0.07	−0.05	0.12
Number of holdings	0.76	0.25	0.78	−0.09
UAA	0.93	0.16	0.82	−0.15
Livestock units	0.84	0.07	0.68	−0.19

). In this way, we can check the characteristics of the municipalities that make up Extremadura according to the results obtained in the variables chosen.

Thus, in Table 6, it can be observed that for the period 2007–2013, in the first component, there is a high influence of direct payments (0.84), population (0.83), number of farms (0.76), UAA (0.93), and livestock units (0.84), which is mainly associated with infrastructure and agricultural resources together with demography. The second component focuses on the competitiveness and quality of life in rural areas, so the variables associated with the competitiveness of agriculture (0.92), LEADER and improvement of the quality of life in rural areas (0.77), and improvement of the environment and the rural countryside (0.86) stand out in this component. With similar results to the previous period, the 2014–2020 results show a first component mainly related to agricultural resources and the demographic structure of Extremadura municipalities, with a high influence of direct payments (0.81), competitiveness of agriculture (0.78), improvement of the environment and the rural countryside (0.80), population (0.84), number of farms (0.78), and UAA (0.82). The notable influence of population growth (0.81), migration balance rate (0.85), and ageing rate (0.51) associate the second component of this period with population dynamics. For both periods, the variables with the least influence in the first component were the ageing Rate and the migration balance rate. Regarding the second component, in the period from 2007 to 2013, the variables associated with agricultural contracts, the ageing rate, and the migration balance rate showed negative coefficients, and during 2014–2020, there was a lower influence of direct payments (−0.09), population (−0.03), number of holdings (−0.09), and UAA (−0.15).

Figure 5 is given to better show the distribution of the different territorial substructures in the study area. Substructure A groups together a total of 28 municipalities in the 2007–2013 period and 33 in the 2014–2020 period. The socio-demographic characteristics of the municipalities that make up this group are differentiated from the results obtained in both periods. Thus, in 2007–2013, we find the main agricultural municipalities of Extremadura, among which Almendralejo, Villafranca de los Barros, Villanueva de la Serena, Miajadas, and Talayuela stand out. As can be seen in the results of Table 5, the municipalities that make up this group are characterised by having high amounts of direct payments, with this variable being one of the most defining for those with positive factors in both components, and also having populations above or around 10,000 inhabitants and positive population growth during this first period, with population being another determining variable for the municipalities that make up this substructure. Another important characteristic is that the municipalities that form part of this substructure are the main regional centres at the agricultural and economic level in their areas, so there is a greater dynamism for attracting agricultural aid, highlighting these high values in agricultural variables such as UAA, livestock units, and the number of holdings. In substructure A of the 2014–2020 period, we find a large part of the municipalities that made up this substructure in the previous period; however, the municipality of Badajoz presents positive values for both components, and Mérida has negative values, moving from substructure A in the first years of study to substructure D but with a low value, so it is not decisive.

On the other hand, substructure B, in which municipalities with negative values for both factors appear, is made up of a total of 221 municipalities in the years 2007–2013 and 239 between 2014 and 2020. The main characteristic of the municipalities that make up this territorial substructure, in both periods, is that they are municipalities with a low population below 3000 inhabitants. This group is the most numerous of the four substructures established due to the territorial characteristics of Extremadura, where the population is widely dispersed over the territory but in which the large proportion of its municipalities does not exceed 5000 inhabitants. As can be seen in Table 6, the ageing and migration balance rates are the most defining variables for the municipalities with both negative factors, which make up this substructure. Therefore, although these municipalities have a high importance of agriculture and livestock farming in their economies, they also present a major problem associated with the high ageing of their population, which resulted in a very accentuated negative population growth during the periods under study. In order to reverse this situation, these municipalities receive large amounts of aid associated with LEADER and improvement of the quality of life in rural areas.

The next two substructures, C and D, are more complex and therefore less well defined. Substructure C is made up of the municipalities that have positive values in the first component and negative values in the second. In the 2007–2013 period, 82 municipalities make up this substructure and 79 in 2014–2020. The municipalities that make up this substructure are relevant to the agricultural sector in Extremadura, mainly due to their dry farming and livestock farming, with large farms standing out. The demographic and economic characteristics of this substructure are very varied, as it is made up of both municipalities with populations of over 10,000 inhabitants and municipalities with smaller populations, the latter showing stagnant population growth in both periods, with population loss accentuated in 2014–2020. In this group, we find municipalities such as Cáceres, Jerez de los Caballeros, Trujillo, Olivenza, and Badajoz, although the latter is only found in the 2007–2013 period because in the following study period, it moves to substructure A, where both components have positive values.

Moreover, substructure D shows negative values in component 1 and positive values in component 2, with 57 municipalities in the first study period and 37 in the second. The municipalities that make up this group stand out for having an ageing index and a migration balance rate with slightly high values. Likewise, these municipalities receive significant aid associated with the EAFRD funds, mainly for LEADER and improvement of the quality of life in rural areas and improvement of the environment and the rural countryside. These municipalities are concentrated in the mountain areas of Extremadura, specifically in the agricultural counties of Hervás and Jaraíz de la Vera and in the south of the district of Navalmoral de la Mata.

Thus, the results obtained show that substructure A is made up of municipalities that receive larger amounts of direct payment aid. These municipalities are the most populated in the region and those where there is a significant development of agro-livestock enterprises. On the other hand, substructure B is made up of the smaller municipalities in the region, which show a very marked rurality, being in mountain and lowlands areas, where LEADER aid and improvement of the quality of life in rural areas are of great importance, as they aim to revitalise these demographically depressed areas. As for the more complex substructures, in C, we find municipalities where rainfed and irrigated land are important, and in D, there are municipalities receiving aid predominantly associated with the EAFRD.

4. Discussion

In recent years, the aim of the CAP has been to promote the sustainable development of the European agricultural sector to combat the difficulties suffered by the territories where this sector has a certain relevance in their economies, as is the case of Extremadura. Therefore, the analyses carried out in this research contributed to identifying in greater depth the relationships between CAP aid as grouped into five groups and Extremaduran society (characterised, in this case, through demographic, socio-economic, and agricultural variables) in the 2007–2013 and 2014–2020 periods.

Thus, the results obtained show significant disparities between the municipalities that make up territorial substructures A and B in both periods. By means of the PCA, it was determined that the municipalities with more than 10,000 inhabitants are those that make up substructure A (positive values in component 1 and 2) for both periods due to the high values of the PCA results for variables such as direct payments, population, and the number of farms in these municipalities, as these are associated with the important socio-economic dynamism of intensive agriculture in the areas where they are located. In addition, there are municipalities with larger farms and where livestock farming is the main agricultural activity, such as those located in the southeast of the province of Badajoz in the agricultural counties of Azuaga, Castuera, Llerena, and Puebla de Alcocer. In these places, together with the previously mentioned municipalities, direct payments are more relevant than the rest of the CAP aid. The municipalities of substructure B (negative values for both components) have a heterogeneous distribution in Extremadura, and they are concentrated in the north of the study area, especially in mountainous areas. The territorial characteristics of these areas make it difficult to maximise aid to improve farm yields. This substructure groups together a larger number of municipalities in both periods, which have populations of less than 1000 inhabitants and regressive socio-demographic trends. As a result, the analysis showed disparities in the distribution and grouping of CAP aid in the territory of Extremadura, reflecting important differences between the territorial characteristics of substructures A and B. These substructures present very different situations in the municipalities that comprise them. The more populated and dynamic municipalities have a superior infrastructure that facilitates access to markets and agricultural support services, allowing them to use aid more effectively and competitively. This allows them to achieve economies of scale that smaller municipalities cannot achieve. The research collected all the CAP aids and grouped them into five groups, in which the first two aids, namely direct and non-direct payments, are EAGF aids and the following three, namely competitiveness of agriculture, LEADER and improvement of the quality of life in rural areas, and improvement of the environment and the rural countryside, are the EAFRD aids. Several studies have been carried out in this context and have examined all or part of this support. For example, Nieto and Cárdenas [69] analysed EAFRD support for rural development during the period from 2007 to 2013, where they grouped support according to its purpose in order to optimise the PCA model. This research interpreted the results in a similar way to our study since territorial substructures were created to determine the grouping of positive and negative values according to the results of the first two components for observing how the pattern of both showed similarities in the municipalities that make up the substructures where the negative values associated with EAFRD aid (competitiveness of agriculture, LEADER and improvement of the quality of life in rural areas, and improvement of the environment and the rural countryside) have a greater representation. Another study carried out by Cárdenas and Nieto [50] showed similar results since in their work, the use of the PCA determined how LEADER aid benefits the most dynamic areas of Extremadura, and they determined that the most disadvantaged rural areas have a lower weight, although in their origin, they are focused on their development, which does not help to reduce the imbalances of these territories.

Galluzzo’s work [52] showed the results of the influence of CAP-decoupled aid on rural areas in Romania, which is an important support to improve the socio-economic situation of these areas and avoid their marginalisation, while the results of our research showed that CAP aid is accumulated in the most populated municipalities of Extremadura, which have more productive agricultural structures (mostly related to irrigated and dry land production of vines and olives). Thus, the municipalities with smaller populations and with less favourable economic characteristics are receiving less investment, which is leading to a continuous socio-demographic decline, so this aid is not favourable to reversing this situation. The influence of CAP aid on local development at the municipal level and, more specifically, that of the aid associated with the EAFRD was observed in the results obtained in the work carried out by Belliggiano et al. [54], which showed the disparities that exist between the different Italian municipalities studied. In this work, a group of municipalities with population-attracting factors and with a higher socio-economic growth were identified, as in our study, where the main Extremadura municipalities showed a positive or more stable trend with respect to the municipalities with smaller populations in the region.

On the other hand, several studies on the CAP have provided a theoretical perspective on its impact on the European territorial economy. Studies such as the one carried out by González-Moralejo and Estruch Sanchís [18] have analysed the improvement of competitiveness in Spanish rural regions with the application of the CAP in a descriptive way, showing that the application of this policy is successful in some territories. Taking this into account, our study sought to interrelate CAP aid with the territory, which allowed us to observe that high amounts have been allocated to improve the competitiveness of rural areas. However, despite these efforts, the demographic and socio-economic situation of rural municipalities has not been reversed. Similarly, Alonso and Otero [31] studied the impact of the CAP on population development in rural areas of Asturias, showing that CAP support has a positive influence on population growth in the region. As in our results, it was observed that aid tends to be concentrated in the most populated municipalities, even when their agricultural activity is not significantly high, leaving the more rural municipalities in the background. Thus, the situation described can be compared to the results of Molinero et al. [46] in their research showing the characteristics of CAP payments in Castilla y León, which indicated that the largest amounts are received by the main municipalities of the region and its surroundings. Martinez et al. [2] also carried out a descriptive analysis of CAP aid during the period from 2014 to 2020 in Extremadura, observing the most important types of aid in the region and the total distribution of this aid over the territory and showing its concentration in the most populated municipalities in the region. Meanwhile, Rudnicki et al. (2019) [70] analysed the spatial distribution of European funds in Poland, including the CAP. Their study revealed significant territorial disparities, finding a negative correlation between the distribution of funds and the level of socio-economic development, with this disparity significantly affecting the quality of major centres in Polish regions. Other studies, such as the one carried out by Martínez et al. [26], have determined the relationships of CAP aid with agricultural, economic, and demographic variables in Extremadura during the 2014–2020 period through the use of a spatial regression model, seeking to show the relationship of this aid with the development of the study region.

5. Conclusions

The use of statistical models that interrelate variables such as the PCA model and their spatial representation in a GIS helped to interpret and improve the results of our study, thus contributing to show the effect that aid can have on the development of the economy of a territory with such a rural character, as is the case of Extremadura. It can therefore be affirmed that the municipalities located around consolidated agricultural systems, both extensively associated with livestock and intensive-based irrigation systems, are where the PCA values were more significant. For this reason, the irrigated areas of Extremadura (agricultural regions of Don Benito, Coria, and some of the municipalities of the regions of Trujillo, Mérida, or Badajoz) play a fundamental role in the competitiveness of the agricultural sector in the region because this system improves the yield, profitability, and economic stability of the sector. Like irrigation, extensive livestock farms in the agricultural regions of Cáceres, Olivenza, Jerez de los Caballeros, and Trujillo show the consolidation and development of this system within the region due to its importance in the municipalities that compose them. Therefore, it was observed that in the municipalities of the counties where these agricultural systems have been developed, there is an important relationship between certain aids and the rest of the variables used for this study that influences the socio-economic development of these populations.

Consequently, the significance of this aid in Extremadura is herein demonstrated, as Extremadura was the fourth Spanish region to receive the largest amount of aid under the current European policy in the periods analysed. Thus, there has been a concentration of aid in the most dynamic areas of Extremadura, namely the region’s most populated municipalities, giving rise to a significant difference in the CAP’s distribution of amounts. It can therefore be seen that aid is distributed primarily in the areas with the highest concentration of population, where the most productive farming systems are located, and, in those areas, where natural land constraints are limited. That means that there is a clear disparity in the funds received by the municipalities, with funds being directed towards those that are also located around the principal communication roads and have optimal physical characteristics, which has permitted a competitive use of the territory, whether in the agricultural or livestock sector.

Thus, although the CAP provides tools to the different EU member states to support their agricultural systems, with the aim of achieving the objectives set out in the successive reforms and seeking to improve the situation of the European agricultural sector and the quality of life in their rural areas, in the case of Extremadura, the results remain unsatisfactory despite decades of work. While there is no dispute that this support has a highly positive impact on the agricultural economy of the region, promoting competitiveness and innovation in agriculture and enabling the promotion of agricultural technology and the development of quality products for improved production efficiency, it is not distributed in an equitable manner. As such, the implementation methods applied to highly ruralised territories help to identify significant weaknesses in the CAP, which help to reveal that European aid often disproportionately favours the most dynamic territories, leaving less developed regions at a disadvantage. It is essential to address the challenges faced by smaller municipalities in achieving economies of scale in taking advantage of CAP support. An effective strategy to enable these areas to benefit more efficiently is to encourage cooperation between beneficiaries and the formation of consortia, which would allow them to pool resources and efforts. This could include the sharing of infrastructure and services as well as the implementation of joint projects that amplify the impact of support. In terms of setting a population threshold to optimise the use of CAP support, this recommendation needs to be adapted to the characteristics of each region. This aspect will be explored in future research in greater depth, proposing an approximate range based on empirical data and relevant case studies. This understanding focuses on the need to improve the management of European support to the agricultural sector, ensuring a fairer and more efficient distribution of resources that can adequately address the needs of all farming communities, especially the most vulnerable and rural regions.

As future lines of research, it is considered necessary to compare CAP aid data between territories in countries with similar socio-economic and territorial characteristics in order to determine the disparities that exist between the development of the agricultural sector in different areas and the application of CAP aid to them. Also, for future work, the per capita analysis of CAP aid in the different municipalities of Extremadura will be considered in order to understand in greater depth the economic differences in the distribution of this aid. This can be enhanced by conducting detailed studies of various CAP measures to assess their broader economic contributions. Such studies should explore whether support for initiatives like aid to young farmers, the inclusion of women in agriculture, repopulation and reforestation of at-risk areas, and ecological farming is increasing. Additionally, examining how these contributions impact public administration and policy development could provide valuable insights into their effectiveness and inform future strategies. Furthermore, the use of diverse methods and spatial analysis is highly beneficial for examining results from multiple perspectives. This approach contributes to a more comprehensive interpretation of the data and improves the ability to optimise the distribution of funds. The application of various analytical techniques allows a better assessment of the impact of public policies such as the CAP. This not only improves resource allocation but also informs strategic adjustments to improve policy outcomes and address regional disparities.