Farmland Abandonment and Afforestation—Socioeconomic and Biophysical Patterns of Land Use Change at the Municipal Level in Galicia, Northwest Spain

Citation: Corbelle-Rico, E.; López-Iglesias, E. Farmland Abandonment and Afforestation— Socioeconomic and Biophysical Patterns of Land Use Change at the Municipal Level in Galicia, Northwest Spain. Land 2024,13, 1394. https:// doi.org/10.3390/land13091394 Academic Editor: Dingde Xu Received: 30 July 2024 Revised: 26 August 2024 Accepted: 28 August 2024 Published: 30 August 2024 Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). land Article Farmland Abandonment and Afforestation—Socioeconomic and Biophysical Patterns of Land Use Change at the Municipal Level in Galicia, Northwest Spain Eduardo Corbelle-Rico 1,2,* and Edelmiro López-Iglesias 2,3 1 Laboratorio do Territorio, Departament of Agricultural and Forest Engineering, Universidade de Santiago de Compostela, Escola Politécnica Superior de Enxeñería, Campus Terra, 27002 Lugo, Spain 2Centro de Investigación Interuniversitario das Paisaxes Atlánticas Culturais (CISPAC), Cidade da Cultura s/n, 15707 Santiago de Compostela, Spain; [email protected] 3ECOAGRASOC, Department of Applied Economics, Facultade de Ciencias Económicas e Empresariais, Universidade de Santiago de Compostela, Avda. do Burgo s/n, 15782 Santiago de Compostela, Spain *Correspondence: eduar[email protected] Abstract: Over the last few years, new land use planning instruments to reduce the negative consequences of recent land use/cover changes (farmland abandonment, wildfires) have been proposed in Galicia (northwest Spain). Understanding the complex relationship between biophysical constraints, socioeconomic drivers and land use/cover changes is paramount for their successful implementation. In this work, we present an analysis of recent (2005–2017) land use/cover changes in the region, along with a classification of municipalities in homogeneous groups with different patterns of land use and land use change. We then characterize those groups regarding the demographic and employment structure, the economic performance, the characteristics of the primary sector, the land ownership structure and the relative importance of recent wildfire events and the biophysical suitability for the main productions of the primary sector in the region. The results allowed us to identify four different groups of municipalities which are clearly separated by specific patterns of land use (an area where most of the population lives, an area devoted to forest production, another for farming production and a final one dominated by semi-natural covers). These four areas followed a gradient of decreasing levels of population density and economic activity. While land use patterns in different areas could be explained largely by biophysical suitability, the fragmentation of land ownership emerged as a relevant factor, which can explain the greater presence of farmland abandonment—and , therefore, higher wildfire risk—in certain areas. These results offer relevant guidelines for the successful implementation of the new land use planning instruments in the region. Keywords: Galicia; Spain; farmland abandonment; land fragmentation; wildfire risk; cluster analysis 1. Introduction Since the mid-twentieth century, large transformations of the land use system have taken place in Spain as a result of extensive social and economic changes. A dramatic economic improvement that started in the decade of the 1960s, sustained on the growth of the industrial and services sectors [ 1 ], strongly intensified the depopulation of rural areas [ 2 ] and the marginalization of the farming sector within the national economy [ 3 ]. These resulted in the expansion of urban and forest areas, often at the expense of land formerly used for farming [ 4 , 5 ]. The effect of natural constraints, nevertheless, resulted in diverging trajectories at the regional and local scale, with some areas undergoing urbanization and population growth, or agricultural intensification, and others, usually those in mountainous or remote locations, enduring land abandonment and depopulation [6,7]. While the main trends were maintained until today, for the most part, subsequent economic, political and institutional changes had effects on the pace and location of observed land use changes. The accession to the European Economic Community in 1986, Land 2024,13, 1394. https://doi.org/10.3390/land13091394 https://www.mdpi.com/journal/land Land 2024,13, 1394 2 of 19 for example, increased the rate at which farmland abandonment and forest expansion took place in certain areas [ 8 ]. More recently, the financial crisis that started in 2008 greatly reduced, and even reversed, urban growth in some areas of the country [ 9 ]. The successive reforms of the EU Common Agricultural Policy (CAP) may also have introduced slight variations in the previous trends. Oñate et al. [ 10 ], for example, suggest that the decoupling of payments from production, applied since the 2003 Mid-term Review of the CAP, might reduce profit margins in low-income agricultural systems and, therefore, increase farmland abandonment in areas in which these systems are more prevalent. The large changes in the landscape during the second half of the twentieth century in Spain were coupled with significant changes in fire regimes [ 11 , 12 ]. Farmland abandonment increased wildfire risk as it led to increased volumes and spatial continuity of biomass in most of Southern Europe [ 13 ]. Vegetation encroachment, often coupled with (sub)urban expansion, means that wildfires pose a great risk to properties and human lives in the wildland–urban interface [14]. Within this overall context, recent wildfire events in 2017 in Galicia, northwest Spain [ 15 ], raised awareness among regional policymakers about the strong connections between land planning, landscape changes and wildfire risk. This triggered the passing, in May 2021, of the Law for the recovery of agricultural land of Galicia, which set up a combination of zoning regulations, information systems about land markets, the creation of administrative departments specialized in facilitating agreements concerning property rights, and legal instruments to promote the productive recovery of abandoned farmland [ 16 ]. These instruments are aimed at curbing or even reversing farmland abandonment in the region, promoting more adequate land use and thus reducing wildfire risk. These include instruments that were already operating in the region, such as a land bank operating as a mediator between farmers and landowners, as well as completely new ones, thus forming a “toolbox” of instruments at the disposal of the public and the Administration: an attempt to introduce zoning regulations to demarcate the area of forest expansion; “model settlements” to promote farming at the fringes of inhabited areas and thus reduce the amount of biomass in the wildland–urban interface; “agroforestry polygons” intended to promote the joint use of the land in areas of high property fragmentation; and a “farm bank” intended to facilitate the transfer of farm installations from retiring farmers to young ones. However, the implementation of this new legal framework and policy instruments may be hindered by the limited existing knowledge about recent landscape changes, the different patterns of change at the local level and the conditioning factors. In order to achieve this, first, we use the latest land use/cover map series available in Spain (Spanish Land Use Information System, SIOSE) to identify the main patterns of land use/cover change at the municipal level in the region during the period 2005–2017. Second, we use a selection of biophysical and socioeconomic variables to analyze their relationship with the observed land use/cover change patterns. With this analysis, we intend to test the role played by constraints related to physical suitability for the main agricultural and forest productions, or fragmentation of land ownership, as well as to clarify the interaction of land use/cover change with other socioeconomic factors. We based the selection of variables on a review of previous studies for different countries and regions, under the theoretical assumption that these socioeconomic and biophysical variables interact with land use, in the line of Hietel et al. [ 17 , 18 ]. The direction of these interactions is something we try to clarify in the interpretation of the results. Although referring to the case study of the Spanish region of Galicia, our research aims to be of more general interest, especially for other European regions facing similar problems (farmland abandonment, expansion of forest area and increasing wildfire risk in a climate change scenario). The recent approval by the regional Parliament and Government of a new legal framework and policy instruments to address these problems gives greater interest to this case study. This paper aims to contribute to the general literature on land use/cover change in three directions. First, we offer an analysis of land use/cover changes and resulting land use/cover patterns at the local level by producing a typology of Land 2024,13, 1394 3 of 19 municipalities. Second, we test the hypothesis that these land use/cover patterns are largely determined by biophysical constraints, but are also strongly related to socioeconomic variables. Third, we pay special attention to the role played by land ownership structure and the impact of land use/cover patterns on wildfire risk. 2. Materials and Methods 2.1. Study Area In this paper, we focus on the Spanish region (NUTS 2) of Galicia, located in the northwest of Spain (Figure 1). Covering an altitude range from sea level to slightly above 2000 m, the region offers a range of different suitability levels for agricultural and forest production. Small-scale holdings dominate the farming and forest sectors; the average farm-utilized agricultural area was 7.9 ha according to agricultural census data in 2020, with about a third of the Spanish average [ 19 ] as a consequence of a very fragmented land property system (average land plot is currently 0.26 ha and the average area per owner is 1.63 ha [ 20 ]). A general trend of farmland abandonment has occurred simultaneously with the sharp decline in agricultural employment and the depopulation of rural areas since the 1960s, but greatly accelerated after the accession of Spain to the European Economic Community in 1986 [ 8 ]. As a consequence of land use/cover changes over the last half century, the territory has undergone intense spatial specialization, with wood production dominating the western third, dairy production dominating the central third, and spontaneous vegetation encroachment being dominant in the eastern mountainous areas. The combination of high natural biomass productivity, forest expansion in the west, and spontaneous vegetation growth in the east, with short periods of drought during the summer, has resulted in a very high risk of wildfires. This is of greater concern in the wildland–urban interface between populated and semi-natural and forest areas [ 21 ], and it caused a steady increase in the yearly public spending in fire suppression equipment and infrastructures, reaching EUR 160 million in 2023 [22]. Figure 1. Location of Galicia in Spain and elevation above sea level. Over the last six decades, the area devoted to crops and pastures was constrained to a small portion of the territory (just over 20%), while the agro-livestock use of hill land, which was very important until the mid-twentieth century, disappeared. This has led to the paradoxical case of a region in which the land has historically been, and remains today, a scarce production factor in the farming sector, characterized at the same time by a significant presence of farmland abandonment [ 23 ]. On the other hand, the forest sector has experienced a significant expansion in the last half century, predominantly based on the plantation of fast-growing species and has positioned the region as the main producer of timber at the national level, accounting for more than half of the total production [24]. Land 2024,13, 1394 4 of 19 2.2. Data Sources The trends of land use/cover change in this study are based on the Spanish Land Use Information System (SIOSE), which is currently the most detailed land use/cover map in Spain. SIOSE is available for the years 2005, 2009, 2011 and 2014 at a 1:25,000 scale (minimum mapping unit of 1–2 hectares depending on the dominant land use/cover) and for the year 2017 at a 1:5000 scale. SIOSE uses a data model that records the proportion of area occupied by different land use/cover categories for each polygon in the map, which sets it apart from more conventional maps that record just one land use/cover category for each patch or polygon. In principle, this new data model makes it possible to produce more precise estimates of the area occupied by each land use/cover category. While we used some of the intermediate editions of SIOSE for the assessment of overall trends in the region, we relied on the first and last available editions (2005 and 2017) for the evaluation of trends at the local (municipal) scale. There are 38 land use/cover classes in SIOSE, which we reclassified to a reduced set of six broad classes: built-up areas, farmland, shrubland, forest plantations, native forests and other areas (Table 1). These simplified classes provide a broad characterization of the pattern of land use in each municipality. For each of the 313 municipalities in the region, we calculated the share (proportion) of municipal area that was occupied by each of the six broad classes in 2005 and 2017. The resulting set of twelve variables was then used as input in a hierarchical cluster analysis, carried out using euclidean distance among observations and the Ward (minimum variance) clustering method. Variables were standardized before the analysis because, although they were naturally constrained to the [0, 1] interval, they presented considerably different ranges and dispersion values. We decided on the appropriate number of clusters by reflecting on the results of twenty different numeric indexes provided in the NbClust package (v. 3.0.1) for R [ 25 ]. The resulting clusters were interpreted using the average proportion of land occupied by the six broad land use/cover classes in each year considered. All spatial and statistical analyses were carried out using GRASS GIS [26] and R programming language [27]. Table 1. Land use/cover classes in the study and original SIOSE classes. Original SIOSE Class Assigned Class Urban and industrial areas, roads, other infrastructures Built-up areas Annual and permanent crops, pastures Farmland Shrublands and rangelands Shrubland Deciduous hardwood forest Native forest Softwood (conifer) forest, perennial hardwood forest Plantation forest Rivers, lakes, rocky areas, burnt areas, beaches Other areas In order to enable the characterization and interpretation of the groups of municipalities formed by the cluster analysis, we compiled an auxiliary set of variables at the municipal scale. These variables were used to describe the differences in biophysical, demographic and socioeconomic characteristics of each group. Variables that work at a finer scale ( e.g., that of individual land plots) are often used in land use/cover change analysis [ 28 ], but here, we focused only on variables available at the municipal level, as the clustering exercise was intended to produce groups of municipalities. Furthermore, many socioeconomic and demographic variables that potentially interact with the land use system are only available from public statistical records at the municipal level. When choosing the appropriate reference year or period for these auxiliary (context) variables, we considered two possible options. Choosing a point in time previous to the time frame of the cluster analysis (2005–2017) would be preferable if the auxiliary variables were used to explain why different municipalities underwent different land use trajectories. On the contrary, choosing a point in time after the dates of the cluster analysis would simply allow us to describe the characteristics of municipalities showing a specific land use pattern or land use change trajectory. For this choice, we considered that the period Land 2024,13, 1394 5 of 19 of time for the cluster analysis was not particularly long (12 years), and, because of that and the fact that we base the analysis on the percentage of each use (not its rate of change) at the beginning and end of the time period, the results showed that the cluster analysis captured different patterns of land use at the municipal level rather than radically different trajectories of land use change. For this reason, we opted to use current (the most recent) values of the context variables. As such, we do not see socioeconomic variables in this study as drivers of land use/cover change, but rather as variables that interact with land use, in the line with the work of Hietel et al. [ 17 , 18 ]. Instead, we conceptualize explanatory variables as “suites of interacting factors [that] work in conjunction with one another” [ 29 ] and that have co-evolved with land use/cover patterns as a result of two-way feedback effects between the land use system and the socioeconomic context along time. As described in the previous section, the primary sector in the study area has undergone a marked process of specialization in dairy farming and wood production. For this reason, we used a suitability map for maize (Zea mays L.) production [ 30 ] and for white eucalyptus (Eucalyptus globulus Labill) production [ 31 ] as a way to capture the biophysical suitability for these activities, as maize is one of the main forage crops used by dairy farms in the region, and E. globulus is the single tree species responsible for up to one-half of wood production in the region. We computed the suitable area for each as a share of total municipal area. As for socioeconomic variables, based on a review of previous studies for different countries and regions [ 17 , 18 , 28 , 32 – 35 ], we selected 21 indicators classified into five groups: demographic, employment structure, general economic, agricultural and forestry sectors, and land ownership (Table 2). The literature offers examples of the kind of “suites of interacting factors” associated to different land use/cover change processes. For example, Hietel et al. [ 18 ], in their study of a region in Central Germany between 1945 and 1999, found that the high density of farms, high levels of employment in agriculture and high livestock density were associated with areas maintaining a large proportion of agricultural area, while high population density and a high presence of industrial employment were associated with areas that underwent significant urbanization. On the contrary, they found that small average farm size and low levels of non-agricultural employment were associated with areas where farming activity declined. Another example which combines the analysis of biophysical and socioeconomic variables is that of De Freitas et al.’s study [ 32 ] in their analysis of an area in southern Brazil, where agricultural intensification appeared to be related to a combination of biophysical (interfluvial areas, smooth relief, high landscape fragmentation) and socioeconomic characteristics (large average farm size, low population density), while extensification took place in areas with similar biophysical conditions but lower economic development. Punzo et al. [ 35 ], in a study of urban expansion in Italy, found a positive relationship between a combination of increasing population density and economic development (GDP per capita, employment rates) and increasing levels of land consumption for urban development. In line with these findings, in our study area, we expect to find a positive relationship between population density, population growth and employment in non-agricultural sectors with the expansion of urban areas; a positive relationship between agricultural employment, farm density, average farm size and other indicators of a dynamic agricultural sector, with higher percentages of agricultural land; a positive relationship between land ownership fragmentation and farmland abandonment; and a relationship of very low population and agricultural employment densities with land abandonment or more extensive land use. Finally, considering the fact that wildfires have affected a large portion of the total area in the region over the last several decades, we included several variables related to the number of wildfires and burnt area, taken from the statistics of the Spanish Ministry of the Environment. Land 2024,13, 1394 6 of 19 Table 2. List of variables used to describe biophysical and socioeconomic characteristics of the municipalities within the study area. Variable (Units, Year) Source Demographic Population density (inhab/km, 2022) Galician Statistics Institute (IGE), demographic data Population change (%, 2000–2022) Ageing index (%, 2022) 1 Population over 65 years (%, 2022) Employment by economic sectors Agriculture and forestry (%, 2022) Galician Statistics Institute (IGE), affiliations to social security Industry (%, 2022) Construction (%, 2022) Services (%, 2022) Economic Gross domestic product per area (EUR/km², 2020) Galician Statistics Institute (IGE), gross domestic product by municipalities Gross disposable income per inhabitant (EUR/inhab, 2020) Galician Statistics Institute (IGE), household income by municipalities Agriculture and forestry Density of farm labor (workers/km2, 2022) Galician Statistics Institute (IGE), affiliations to social security Farm density (farms/km2, 2020) Spanish Statistics Institute (INE), agricultural census Average farm size (ha, 2020) Spanish Statistics Institute (INE), agricultural census Stocking density (livestock equivalent units/km2, 2020) Spanish Statistics Institute (INE), agricultural census Bovine stocking density (animals/km2, 2022) Galician Statistics Institute (IGE), cattle registration Wood extraction (m3/km2-year, 2019–2021) Galician Statistics Institute (IGE) and Regional Ministry for Rural Affairs Land ownership Landowner/inhabitant ratio (2022) Cadastral Statistics, Spanish Ministry of Economics and IGE (demographic data) Average property size (ha/owner, 2022) Cadastral Statistics, Spanish Ministry of Finances Average plot size (ha, 2022) Cadastral Statistics, Spanish Ministry of Finances Average number of plots per owner (num., 2022) Proportion of municipal area occupied by common land (%, 2022) Galician Statistics Institute (IGE) and Regional Ministry for Rural Affairs Wildfires Wildfires smaller than 1 ha (events/km2, 2006–2015) Wildfire Statistics, Spanish Ministry for Ecologic Transition and Demographic Challenge Wildfires larger than 1 ha (events/km2, 2006–2015) Wildfires, total (events/km2, 2006–2015) Burnt area—forest, as percentage of municipal area (%, 2006–2015) Burnt area—shrubland, as percentage of municipal area (%, 2006–2015) Burnt area—total, as percentage of municipal area (%, 2006–2015) Biophysical suitability Areas suitable for maize production (percentage of municipal area) Díaz-Fierros and Gil, 1984 [30] Areas suitable for Eucaliptus globulus (percentage of municipal area) Calvo de Anta, 1992 [31] 1Ageing index defined as the ratio between population over 65 years old and population of 20 years or less. As most of the variables evaluated in this study present a skewed distribution, we used a Kruskal–Wallis Rank Sum Test to evaluate the existence of stochastic dominance among the resulting clusters of municipalities for each of the analyzed variables. The test indicates whether it is likely that an observation in one cluster presents greater values for a given Land 2024,13, 1394 7 of 19 variable than an observation in any other group. The results of the test, indicating whether there are significant differences among clusters for a given variable, were complemented by median and median absolute deviation values, along with boxplot and whiskers graphics in order to interpret these differences. 3. Results 3.1. Observed Trends of Land Use/Cover Change The estimated figures produced using SIOSE editions from 2005 to 2017 indicate a net expansion of built-up areas (1.69%) and especially of plantation forests (37.87%), while the remaining classes showed a shrinking trend all along the period: a slight reduction in the case of shrubland ( − 1.13%) and much more relevant for farmland ( − 8.40%), native forests ( − 13.02%) and other areas ( − 40.49%) (Table 3). The sharp reduction in the “other areas” is probably largely explained by methodological changes in the 2017 edition of SIOSE (its most detailed scale), in addition to fluctuations in the area affected by forest fires. Excluding this, the main trends can be summarized by a decrease in farmland and native forest, and an expansion of plantation forests and built-up areas. Table 3. Area covered by different land use/cover classes in Galicia (values in square kilometers). Authors’ elaboration from SIOSE 2005, 2009, 2014 and 2017. Land Use/Cover Class Year Variation 2005–2017 2005 2009 2014 2017 Built-up areas 1896.49 1916.19 1984.41 1928.73 +1.69% Farmland 6933.70 6900.55 6718.50 6351.03 −8.40% Shrubland 9216.91 9546.39 8736.65 9112.73 −1.13% Plantation forests 5248.73 5063.77 5674.31 7236.78 +37.87% Native forests 4404.62 4424.97 4791.65 3830.99 −13.02% Other areas 1876.30 1724.87 1671.22 1116.49 −40.49% 3.2. Results of the Cluster Analysis The results of the cluster analysis divided the 313 municipalities into four clusters (Figure 2, Table 4). Cluster 1, the smallest of them all (32 municipalities and 4.58% of regional area), comprises the main urban areas in the region and some of their surroundings. Accordingly, it presents the highest share of built-up areas (23% in 2005, 25% in 2017), but also a high share of plantation forests (25% in 2005, 26% in 2017), both of which are clearly above the regional average values. Cluster 2 (116 municipalities, 31.25% of regional area) is clearly the domain of plantation forests (31% of area in 2005, 40% in 2017), albeit with a significant presence of built-up areas, also higher than the regional average. For the most part, this cluster is limited to the coastal areas of the region, although it includes municipalities in higher-elevation areas along the main valleys. Cluster 3 (54 municipalities, 25.34% of regional area) occupies areas of medium elevation, mostly in the northern half of the region (with the notable exception of areas in the Limia valley in the southern half). It is the domain mainly of farmland (39% in 2005, 36% in 2017) and, to a much lesser extent, of native forests (17% in 2005, 14% in 2017), both of them above the regional average. Furthermore, while this was no longer the case in 2017, built-up areas were around the regional average in 2005. Cluster 4 (111 municipalities, 38.81% of regional area) is clearly the domain of shrublands (41% in 2005, 44% in 2017) and native forests (19% in 2005, 18% in 2017). These municipalities are located in the highest elevations in the region, less suitable for farming and plantation forests. In general, most of the regional trends in the period 2005–2017 mentioned above are observable in all or almost all the clusters. For example, the contraction of farmland area and the expansion of forest plantations are present in all clusters, although at different rates. Land 2024,13, 1394 8 of 19 Something similar is observed for the increase in built-up areas (only with the exception of Cluster 3) and the reduction in native forest (the only nuance is its stability in Cluster 1). The main divergence is found in the trend of shrubland area, which declines in Clusters 1 and 2, while it expands in Cluster 3 and especially in Cluster 4 (where it reinforces its dominance). Figure 2. Results of the clustering of municipaliies into homogeneous groups of land use/cover in 2005 and 2017. Table 4. Distribution of total area, number of municipalities, average elevation and percentage of area covered by different land uses/covers for each cluster of municipalities. Values of land use/cover higher than regional average are highlighted in bold. Variable Cluster 1 Cluster 2 Cluster 3 Cluster 4 Galicia (Total) Number of municipalities 32 116 54 111 313 Area (km2)1353 9244 7496 11,480 29,572 Area (%) 4.58 31.25 25.34 38.81 100 Average elevation (m asl) 125 232 532 646 502 Plantation forest 2005 (%) 24.83 30.79 11.09 10.75 17.75 Plantation forest 2017 (%) 26.39 39.74 16.77 16.97 24.47 Native forest 2005 (%) 7.22 8.78 17.10 19.27 14.90 Native forest 2017 (%) 7.20 6.71 14.03 17.95 12.96 Farmland 2005 (%) 21.58 21.56 38.54 15.32 23.45 Farmland 2017 (%) 19.79 20.06 36.24 13.17 21.48 Shrublands 2005 (%) 19.22 26.05 24.10 41.29 31.17 Shrublands 2017 (%) 16.85 21.63 24.51 43.96 30.82 Built-up areas 2005 (%) 22.81 7.17 6.57 3.75 6.41 Built-up areas 2017 (%) 24.51 7.43 5.74 4.17 6.52 Other areas 2005 (%) 4.32 5.63 2.57 9.59 6.35 Other areas 2017 (%) 5.25 4.41 2.70 3.76 3.78 3.3. Biophysical and Socioeconomic Characterization of Clusters A summary of median values of all the biophysical and socioeconomic variables for the four clusters of municipalities formed by land use trends is presented in Table 5. A graphical representation can also be found in Appendix A(Figures A1–A3). All variables showed significant differences among clusters ( α< 5%) in the Kruskal–Wallis test, with the exception of the percentage of employment working in services. Land 2024,13, 1394 9 of 19 Demographic variables show a clear gradient from Cluster 1 to Cluster 4: along this gradient, population density decreases (from 530 inhab/km 2 in Cluster 1 to 20 inhab/km 2 in Cluster 4) and population ageing increases (median ageing index of 135% in Cluster 1, 537% in Cluster 4). Of all groups, only Cluster 1 showed a positive change in population in the last two decades, while all the other clusters showed increasing rates (from Cluster 2 to Cluster 4) of population decline. If we analyze the relationship between these demographic variables and land use (Tables 4and 5), some expected facts can be seen for the two extreme groups, such as very high demographic densities and population growth in Cluster 1 (the one with the largest expansion of built-up areas); an opposite demographic structure and dynamics (very low densities, strong population decline and marked aging) in Cluster 4, where land use is characterized by the predominance of shrublands and native forests; and the percentage (albeit very small) of both built-up areas and farmland. However, other findings are less predictable—even striking. We highlight two: (i) the area occupied by plantation forest reaches the highest values in Clusters 1 and 2, i.e., those with higher population densities; and (ii) Clusters 3 and 4 have similar demographic structure and dynamics (only somewhat worse in 4), while the land use patterns are very different (high proportion of farmland in Cluster 3) in contrast to the dominance of shrublands in Cluster 4. Gross disposable income per inhabitant, a global indicator of the level of economic development, shows a consistent picture with demographic dynamics, decreasing as we move from Cluster 1 to Cluster 4 (from 15,417 to 11,525 EUR/inhab). The same happens for gross domestic product (GDP) per area (density or pressure of economic activities on the territory). It shows a negative gradient from Cluster 1 to Cluster 4, with greater differences (from 9632 to 283 EUR/km2). Based on these data, the sectoral structure of employment in the first three clusters fits that which was expected: the lower the level of economic development, the greater the percentage of agricultural employment and lower the percentage of employment in the industry, construction and services, although employment in the services sector seem in this case remarkably stable across the four groups of municipalities. This relationship is broken, however, in Cluster 4: this includes the municipalities with the lowest economic development and the most regressive demographics, but the percentage of agricultural employment is clearly lower than in Cluster 3. Therefore, we can characterize Cluster 4 by a marked deagrarianization given its level of economic development. This employment structure must be put in relation to the low percentage of farmland in Cluster 4, the lowest of the four clusters. As it could be expected, most indicators of farming activity (density of farm labour, average farm size, stocking density) reach the highest value in Cluster 3, usually followed by Cluster 2. Overall, these indicators show a clear positive relationship with the area occupied by farmland. The only exception is farm density, indicating that for the total area of farmland, farm size is more relevant than their density (the number of farms). Stocking density values, particularly those of cattle, underline the specialization of the farming sector in Cluster 3. Wood extraction shows the highest values in Cluster 2, followed by Clusters 1 and 3, and very low levels in Cluster 4. As expected, this variable has a clear positive relationship with some nuances, with the weight of plantation forest as the conditioning factor and a result of afforestation. Variables related to land ownership follow a clear gradient in the first three clusters. Both the average size of plot and the average area per landowner record the lowest values in Cluster 1, increasing in Cluster 2 and especially in Cluster 3 (in short, less land fragmentation as population density decreases). However, this relationship is broken in Cluster 4, the one with the lowest demographic densities. Compared to Cluster 3, municipalities of Cluster 4 are characterized by two notes: the high proportion of municipal area occupied by common land (units with an average size above 200 ha); and the much higher fragmentation of individual private property land, reflected in a smaller area per owner, a smaller average Land 2024,13, 1394 16 of 19 0.0 2.5 5.0 7.5 10.0 1234 workers/km² Density of farm labor, 2020 0 10 20 30 1 2 3 4 farms/km² Density of farms, 2020 0 10 20 30 40 1 2 3 4 ha Farm size, 2020 0 100 200 300 400 1 2 3 4 LU/km² Stocking density, 2020 0 50 100 150 200 1 2 3 4 LU/km² Bovine stocking density, 2020 0 300 600 900 1200 1 2 3 4 m³/km²−year Wood extraction, 2019−2021 0 2 4 6 8 1 2 3 4 landowners/inhabitants, 2022 0 5 10 1 2 3 4 ha/owner Average property size, 2022 0.3 0.6 0.9 1.2 1234 Cluster ha/plot Average plot size, 2022 5 10 15 1 2 3 4 Cluster plots/owner Plots per owner, 2022 Figure A2. Boxplots showing values of socioeconomic and biophysical variables at the municipal level for the 4 clusters of municipalities (2 of 3). Land 2024,13, 1394 17 of 19 0 20 40 60 80 1 2 3 4 % Area of common land, 2022 0.01 0.10 1.00 10.00 1234 events/km² (log scale) Wildfires smaller than 1 ha, 2006−2015 0.01 0.10 1.00 1234 events/km² (log scale) Wildfires larger than 1 ha, 2006−2015 0.01 0.10 1.00 1234 events/km² (log scale) Wildfires, total, 2006−2015 0 10 20 30 40 1 2 3 4 % Burnt area − forest, 2006−2015 0 20 40 60 1 2 3 4 % Burnt area − shrubland, 2006−2015 0 20 40 60 1 2 3 4 % Burnt area − total, 2006−2015 0 25 50 75 1 2 3 4 Cluster % Area suitable for maize 0 25 50 75 100 1 2 3 4 Cluster % Area suitable for eucalyptus Figure A3. Boxplots showing values of socioeconomic and biophysical variables at the municipal level for the 4 clusters of municipalities (3 of 3). Land 2024,13, 1394 18 of 19 References 1. Prados de la Escosura, L.; Sánchez-Alonso, B. 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