Spatial Dominance of Land Use in Türkiye’s NUTS-2 Regions: An Integrated Quantitative Approach

Spatial Dominance of Land Use in Türkiye’s NUTS-2 Regions: An Integrated Quantitative Approach | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Spatial Dominance of Land Use in Türkiye’s NUTS-2 Regions: An Integrated Quantitative Approach Ebru Ala¹, Büşra Kutlu², Sarıyya Arslan³ This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8089182/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Apr, 2026 Read the published version in Environmental Monitoring and Assessment → Version 1 posted 4 You are reading this latest preprint version Abstract This study examines the spatial dominance levels of forest, agricultural, and water surface areas, as well as the regional differentiation trends across Türkiye’s NUTS-2 regions, using data from 2000–2024. The datasets were compiled from official national sources: the General Directorate of Forestry for forest areas, the Turkish Statistical Institute for agricultural lands, and the Ministry of Environment, Urbanization and Climate Change’s Environmental Status Reports, CORINE Land Cover datasets, and the Wetland Information System for water surfaces. All data were standardized in hectares to ensure comparability across 27 regions. The Location Quotient (LQ) method determined the relative dominance levels of each region in land use types. Principal Component Analysis (PCA) revealed that 84.6% of the total variance was explained by two principal components, while Cluster Analysis identified two main typological land-use groups across Türkiye. The findings indicate a decline in agricultural areas, a relatively stable but regionally differentiated structure in forest areas, and notable changes in water surfaces, particularly in TRC1 and TRB2 regions. The TR61 region (Antalya–Isparta–Burdur) stands out with high values in both agricultural and forest areas, whereas TRB2 (Van–Muş–Bitlis–Hakkâri) exhibits distinct dominance in water surfaces. This study represents one of the first comprehensive attempts to integrate the spatial components of land use in Türkiye through a multidimensional quantitative approach. The results provide a holistic framework for regional planning and sustainable resource management and contribute to the broader discourse on enhancing regional resilience against future multi-dimensional crises and shocks. Spatial dominance LQ analysis PCA Clustering NUTS-2 regions Türkiye Figures Figure 1 Figure 2 Figure 3 1. Introduction Changing socio-economic dynamics on a global scale have initiated a profound transformation in the Earth's climatic cycle. This transformation process creates a decisive impact not only on climate systems but also on the spatial redistribution of land use patterns. Therefore, analyzing the interrelationship between land use and climatic as well as socio-economic processes is of critical importance for assessing sustainable development goals at the spatial level. However, this transformation process does not progress in a self-sustaining manner; in most cases, it results in the degradation of natural ecosystems. According to IPCC reports, the adverse impacts of global warming continue to increase under all scenarios. To limit warming to 1.5°C (with > 50% probability) or below 2°C (with > 67% probability) by the end of the century, greenhouse gas emissions must be rapidly, persistently, and comprehensively reduced; the net-zero CO₂ target must be achieved; and CH₄ emissions, in particular, must be significantly lowered (IPCC, 2023 ). As temperatures rise, the magnitude of climate-related risks continues to expand. The decline in water resources, destruction of forest areas, loss of surface water, uncontrolled urbanization, and unsustainable agricultural activities are the main factors accelerating this process. These increasing pressures at global and regional scales are leading to the irreversible degradation of natural resources. This situation indicates that the management of natural resources must be reconsidered at the spatial level. As of 2025, the world’s average annual forest area loss has reached 3 million hectares, while total water use has climbed to approximately 3,347,084,180 m³ (Worldometer, 2025 ). In Türkiye specifically, total water use reached 57 billion m³ in 2024, with 44 billion m³ allocated for irrigation (General Directorate of State Hydraulic Works (DSİ), 2025 ), During the same period, 27,485 hectares of forest were lost due to fires, and total agricultural land decreased by 5% compared to 2023, declining to 23.5 million hectares (Turkish Statistical Institute (TURKSTAT), 2024a ; Ministry of Agriculture and Forestry (MoAF), 2025 ). These indicators reveal that climatic and environmental pressures are deepening not only globally but also at the national scale. Climate change is causing increasingly arid weather conditions worldwide, directly threatening the livelihoods of communities that depend on agriculture and livestock (IPCC, 2018 ). This situation particularly disrupts the spatial balance among agricultural, forest, and water surface areas; therefore, evaluating these components together has become a fundamental requirement for understanding climatic vulnerabilities at the regional level. Prolonged dry periods, irregular and insufficient rainfall, and the rise in extreme weather events are driving producers toward wetlands that can meet both irrigation and livestock water needs (Dinsa & Gemeda, 2019 ). However, wetlands and forests located in both inland and coastal environments are unique components that provide a wide range of ecosystem services essential for maintaining ecological integrity and supporting human well-being (Kingsford ve diğerleri, 2021; Naidoo ve diğerleri, 2008). Nevertheless, human-induced pressures such as urbanization, agricultural expansion, and industrialization, combined with the adverse effects of climate change, are causing these ecosystems to shrink at an alarming rate (Ye ve diğerleri, 2019). Throughout history, wetlands have served as strategic areas not only for agricultural production but also for transportation, trade, and settlement. However, with the development of drainage and land reclamation techniques, these areas have been converted in many parts of the world for the purpose of gaining agricultural land. This transformation has led to the substantial loss of ecosystems’ natural characteristics, a decline in biodiversity, and the weakening of ecological functions other than crop productivity (Verhoeven & Setter, 2010 ; Hassan ve diğerleri, 2005). There is a direct relationship between agricultural activities and water quality. Historically, the conversion of wetlands into agricultural land has become one of the main factors contributing to surface water pollution through diffuse agricultural contamination. This situation has prompted agricultural research communities to develop new methods aimed at both reducing surface runoff and improving water quality (Cooper & Moore, 2003 ). One of the major paradigm shifts in human history, the Agricultural Revolution, demonstrates that the strategic importance of food production has never diminished throughout time. In the face of a continuously growing global population, sudden climatic changes, and the increasing threat of drought, the sustainability of the agricultural sector and the significance of agricultural policies and decision-making are becoming ever more critical. This reality remains valid not only on a global scale but also for countries such as Türkiye, where agricultural production holds strategic importance (Dede, 2020 ). In this context, the sustainable planning and management of natural areas constitute a critical necessity for the preservation of ecological balance (Kutlu ve diğerleri, 2025). In this context, analyzing the spatial dominance dynamics of land use holds great importance for the development of sustainable planning and urban development policies. In Türkiye, studies examining this relationship on a spatial scale are limited, and particularly, comprehensive quantitative analyses that jointly evaluate forest, agricultural, and water surface areas remain scarce in the literature. This study aims to reveal the spatial dimensions of land use dominance across Türkiye’s NUTS-2 regions within a comprehensive analytical framework. Accordingly, Location Quotient (LQ), Principal Component Analysis (PCA), and Cluster Analysis are applied together to identify spatial dominances and to evaluate interregional structural differences in a multidimensional manner. In doing so, the study seeks to systematically analyze regional land use patterns in Türkiye and to make a methodological contribution to the spatial planning literature. 2. Materials and Methods 2.1. Data Sources and Research Design This study is based on a comparative analysis of land use data for Türkiye’s NUTS-2 regions covering the period 2000–2024 and aims to examine the dominance trends and spatial distribution dynamics of regional land use patterns. The NUTS-2 level is considered the most appropriate spatial unit in Türkiye in terms of both planning scale and statistical comparability, as it allows for the integrated analysis of natural systems and socio-economic indicators while establishing a direct analytical link between regional development and spatial planning policies. The period between 2000 and 2024 was selected in order to observe the long-term spatial traces of Türkiye’s climatic, ecological, and socio-economic transformations. This timeframe corresponds to a transitional phase during which environmental, agricultural, and forestry policies have been re-scaled around the principle of sustainability, and ecosystem-based approaches have gained prominence in spatial planning. Therefore, the temporal comparison enables the analysis of both the spatial concentration trends in land use and the directional shifts in regional dominance. Within the scope of the analysis, three primary land use categories representing the intersection points of ecological and economic systems—forest areas, agricultural lands, and water surfaces—were considered as spatial thresholds. These thresholds were treated as reference components for identifying structural transformations in land use and regional vulnerabilities. All data used in the study were obtained from nationally reliable and verifiable institutional sources: forest area data were provided by the General Directorate of Forestry of the Ministry of Agriculture and Forestry (General Directorate of Forestry (OGM), 2024 ); agricultural land data were derived from the Crop Production Statistics datasets of the Turkish Statistical Institute (Turkish Statistical Institute (TURKSTAT), 2024b ); and data on water surfaces were obtained from the Environmental Status Reports published by the Ministry of Environment, Urbanization and Climate Change (Ministry of Environment, Urbanization and Climate Change, 2024 ), the CORINE Land Cover datasets (Ministry of Agriculture and Forestry (MoAF), 2000 ), and the Wetland Information System (Ministry of Agriculture and Forestry (MoAF), 2024 ). The data collected from these different institutional sources were standardized in hectares (ha) and subjected to a cross-validation process to ensure consistency and comparability across datasets. Overall, the study is structured around a quantitative, comparative, and spatial analytical approach. This approach aims to provide a holistic analytical framework that not only reveals the long-term regional dominance dynamics but also enhances the understanding of how these dynamics influence spatial planning, resource management, and sustainability policies. 2.2. Data Analysis The analytical framework of this study is built upon a three-stage methodological system comprising the measurement of spatial concentration, the identification of structural dimensions, and the typological classification. These stages were carried out respectively through the Location Quotient (LQ), Principal Component Analysis (PCA), and Cluster Analysis methods. The aim is not only to determine which land use types are dominant across regions but also to reveal the temporal trends, structural patterns, and regional similarity groups of these dominances. In this way, the study examines spatial differentiation in land use through a quantitative, multivariate, and comparative analytical approach. These three methods are designed to complement one another: The LQ Analysis reveals spatial concentration patterns by measuring the relative dominance level of each region compared to the national average. The Location Quotient (LQ) method, applied to determine the relative dominance levels of regions in terms of land use types, allows for the evaluation of the concentration of a specific land use type within a region in comparison to the national average. The formula is expressed as follows: \(\:{A}_{i,j}\) = The area of land use type j (agriculture, forest, or surface water) in region i \(\:{A}_{i}\) = The total land area in region i \(\:L{Q}_{i,j}\:=\:\frac{{A}_{i,j}}{{A}_{i}}\:÷\:\frac{{A}_{j}}{A}\) \(\:{A}_{j}\) = The total area of land use type j in Türkiye \(\:A\) = The total land area in Türkiye LQ value greater than 1.0 indicates that the corresponding land use type is more dominant in the region compared to the national average, whereas an LQ value less than 1.0 signifies a relatively lower concentration. The Principal Component Analysis (PCA ) reveals the common variation structure among land use types. PCA enables dimensionality reduction in multivariate datasets, allowing the variance to be explained through a smaller number of components. In this study, only components with eigenvalues greater than 1 were considered, while components contributing less than 2% to the total variance were excluded from the analysis. The primary objective of PCA is to construct a new set of dimensions that better capture the diversity and underlying structure of the data (Berkhin, 2006 ). The Cluster Analysis was employed to transform the component scores obtained from the PCA into spatially meaningful typologies, thereby identifying regions that exhibit similar land use patterns. This method aims to group units by considering the degrees of similarity and distance among them, allowing for the classification of regions with comparable structural characteristics in terms of land use Dynamics (Ozdamar, 1999 ). The Ward method was selected for the analysis, with the Euclidean distance metric used as the measure of similarity. This approach enabled the grouping of regions that exhibit similar land use patterns, and the results were visualized through a dendrogram graph. The analyses and calculations conducted using the SPSS 29.0 software package represent one of the first quantitative frameworks to examine spatial dominance in Türkiye across three dimensions—agricultural, forest, and wetland areas. The LQ results are presented in tabular form; the PCA findings are visualized through scree plots and biplot graphs; and the cluster analysis results are illustrated using a dendrogram. This multi-stage quantitative approach and integrated analytical framework aim not only to identify statistically significant differences but also to define functionally meaningful typologies in terms of regional spatial planning and resource management. The study seeks to comprehensively analyze the key axes that determine the agriculture–forest–water surface balance across Türkiye’s NUTS-2 regions, the spatial distribution of these axes over time, and the regional clustering patterns that emerge from these dynamics. 3. Findings This section presents the results of the spatial analyses conducted for Türkiye’s NUTS-2 regions over the 2000–2024 period. The findings reveal the regional dominance levels and their temporal variations across three primary land use categories: forests, agricultural areas, and water surfaces. The analyses are interpreted based on the outputs derived from the Location Quotient (LQ), Principal Component Analysis (PCA), and Cluster Analysis methods. This multi-stage approach aims to make the differentiation of land use patterns across Türkiye both quantitatively and spatially visible. First, the LQ results identify the relative dominance levels of each region, revealing overall trends; subsequently, PCA defines the underlying structural components driving these differences. In the final stage, cluster analysis groups regions with similar land use patterns, thereby delineating the spatial typologies of Türkiye. 3.1. LQ Analysis Findings The comparison of Location Quotient (LQ) values for the years 2000 and 2024 reveals significant variations in the spatial dominance levels of agricultural, forest, and water surface areas across Türkiye’s NUTS-2 regions. This analysis evaluates the changes between the two periods in terms of both regional concentration and relative dominance trends, thereby quantitatively illustrating the long-term structural transformations in land use. The findings clearly reveal the differentiation in the spatial distribution of land use types across Türkiye, and the detailed distribution of LQ values by region is presented in Table 1 . Table 1 LQ Values of Agriculture, Forest, and Surface Water in Türkiye’s NUTS-2 Regions (2000–2024) Formula \(\:L{Q}_{i,j}\:=\:\frac{{A}_{i,j}}{{A}_{i}}\:÷\:\frac{{A}_{j}}{A}\) \(\:{A}_{i,j}\) = The area of land use type j (agriculture, forest, or surface water) in region i \(\:{A}_{i}\) = The total land area in region i \(\:{A}_{j}\) = The total area of land use type j in Türkiye \(\:A\) = The total land area in Türkiye Years 2024 2000 NUTS-2 Code Forest Area Agricultural Area Water Surfaces Forest Area Agricultural Area Water Surfaces TR10 1,48 0,44 1,21 1,94 0,75 1,19 TR21 0,81 1,76 0,43 0,97 2,27 0,38 TR22 1,51 0,90 0,61 1,75 1,22 0,54 TR31 1,35 0,95 0.78 1,28 0,78 0,52 TR32 1,78 0,93 0,55 1,58 0,82 0,65 TR33 1,26 1,14 0,86 1,12 1,48 0,65 TR41 1,29 1,05 0,99 1,45 1,42 0,95 TR42 1,82 0,74 0,21 2,31 0,90 0,23 TR51 0,61 1,41 1,71 0,40 2,10 1,32 TR52 0,57 1,45 1,03 0,37 2,45 1,58 TR61 1,76 0,60 0,97 1,42 0,79 1,34 TR62 1,60 0,90 0,86 1,38 1,27 1,72 TR63 1,27 0,97 0,45 1,29 1,44 0,39 TR71 0,22 1,70 2,18 0,14 2,31 0,99 TR72 0,53 1,11 0,48 0,45 1,60 0,31 TR81 2,15 0,38 0,03 2,3 0,85 0,01 TR82 1,81 0,53 0,12 1,92 0,85 0,11 TR83 1,37 1,30 0,61 1,37 1,68 0,50 TR90 1,40 0,67 0,17 1,80 0,38 0,20 TRA1 0,48 0,55 0,14 0,62 0,57 0,19 TRA2 0,12 0,87 0,61 0,14 1,08 0,71 TRB1 0,83 0,53 1,43 0,75 0,62 1,26 TRB2 0,35 0,63 4,30 0,79 0,70 5,70 TRC1 0,64 1,41 2,94 0,42 1,50 1,60 TRC2 0,33 1,58 0,79 0,17 2,28 0,66 TRC3 1,09 0,79 0,20 0,89 1,05 0,26 Türkiye 1 1 1 1 1 1 LQ > 1.0 → The region is specialized / dominant in the related land use type (agriculture, forest, or surface water). LQ = 1.0 → The region’s share in the related land use type is equal to the national average. LQ < 1.0 → The region is less specialized / underrepresented in the related land use type compared to the national level. Source: Calculated by the authors based on data from the (General Directorate of Forestry (OGM), 2024 ; Ministry of Agriculture and Forestry (MoAF), 2000 ; Ministry of Agriculture and Forestry (MoAF), 2024 ; Turkish Statistical Institute (TURKSTAT), 2024a ; Turkish Statistical Institute (TURKSTAT), 2024b ; Ministry of Environment, Urbanization and Climate Change, 2024 ). As shown in Table 1 , the average LQ value for forest areas declined slightly from 1.11 in 2000 to 1.09 in 2024. Although this decrease is relatively small, notable regional variations can be observed. For instance, the TR61 region (Antalya–Isparta–Burdur) recorded the most significant increase in forest dominance (+ 0.34), whereas the TR42 region (Kocaeli–Sakarya–Düzce–Bolu–Yalova) experienced the largest decrease (–0.49). This variation indicates that the spatial significance of forest areas, particularly in the Western Black Sea and Mediterranean regions, has been shaped by shifting land use dynamics. In agricultural areas, the average LQ value decreased from 1.26 in 2000 to 0.97 in 2024, indicating a weakening of regional concentration in agricultural production. The most notable decline occurred in the TR52 region (Konya–Karaman) (–1.00), demonstrating a significant reduction in the relative importance of agriculture within the region. Conversely, the TR90 region (Trabzon–Ordu–Giresun–Rize–Artvin–Gümüşhane) exhibited an increase in agricultural dominance (+ 0.29), suggesting that the agricultural diversity and horticultural activities characteristic of the Eastern Black Sea region have strengthened regional dominance trends. In terms of water surfaces, the average LQ value increased slightly from 0.92 in 2000 to 0.95 in 2024. Although the overall increase is limited, the regional differences are quite striking. A strong rise in water surface dominance was observed in the TRC1 region (Gaziantep–Adıyaman–Kilis) (+ 1.34), which can be attributed to the influence of dam investments and water infrastructure projects in the region. Conversely, the TRB2 region (Van–Muş–Bitlis–Hakkari) recorded a sharp decline (–1.40) in water surface dominance. This decrease is primarily associated with the contraction of lake ecosystems and the impacts of climatic factors. Nevertheless, TRB2 continues to exhibit relatively high values, maintaining its dominance above the national average in terms of water surfaces. Overall, during the 2000–2024 period, the spatial configuration of land use patterns in Türkiye demonstrates a weakening of agricultural dominance, a relative stability in forest areas accompanied by deepening regional disparities, and a divergent trajectory of water surface dominance influenced by localized infrastructure investments and climatic processes. These trends reveal that Türkiye’s land use structure has been reshaped at the regional scale through the combined effects of natural, economic, and political dynamics. 3.2. PCA Analysis Findings The findings obtained from the LQ analysis reveal that land use dominance in Türkiye exhibits distinct regional dynamics. However, in order to elucidate the structural relationships underlying these variations, it is necessary to examine the common variance structure among the variables. For this purpose, Principal Component Analysis (PCA) was conducted to identify the fundamental dimensions through which interregional land use patterns can be explained and to assess the extent to which the spatial trends identified in the LQ analysis statistically covary. The PCA results indicate that the first two components account for 84.6% of the total variance, suggesting that Türkiye’s land use structure is shaped primarily along two fundamental axes (Table 2 ). Table 2 PCA Results for Türkiye’s NUTS-2 Regions: Explained Variance Ratios Total Variance Explained Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings Component Total % of Variance Cumulative % Total % of Variance Cumulative % Total % of Variance Cumulative % 1 3,288 54,798 54,798 3,288 54,798 54,798 2,895 48,246 48,246 2 1,792 29,874 84,673 1,792 29,874 84,673 2,186 36,427 84,673 3 0,682 11,373 96,046 4 0,122 2,040 98,086 5 0,059 0,989 99,075 6 0,055 0,925 100,000 Extraction Method: Principal Component Analysis The first component (54.8%) is characterized by high loadings on agricultural and forest areas, forming the principal axis of differentiation among regions. On this axis, regions with intensive agricultural production—such as those in Central Anatolia (TR51, TR52, TR71, TR72) and the Aegean (TR31, TR32, TR33)—are clearly distinguished from the Black Sea regions (TR81, TR82, TR83) where forest coverage is predominant. The second component (29.9%) represents variations related to water surfaces. Along this axis, regions with extensive water resources (e.g., TRC1) are distinctly separated from those experiencing a decline in water surface areas (e.g., TRB2). This structure demonstrates that spatial dominance in Türkiye can essentially be explained through two main dimensions: (i) the agriculture–forest balance and (ii) the distribution of water surfaces. The third component accounts for 11.3% of the variance, while the contributions of the fourth and subsequent components fall below 2%. Therefore, the analysis is considered meaningful only with reference to the first two components. The scree plot presented in Fig. 1 visually illustrates the dominance of these two components, whereas the biplot in Fig. 2 clearly depicts the positioning of variables and regions along these axes. In addition, the biplot graph (Fig. 2 ) illustrates the positioning of variables and regions within a two-dimensional space. It is clearly observed that agricultural and forest areas are differentiated along the first component axis, whereas water surfaces are distinctly separated along the second component axis. When these findings are evaluated collectively, it can be concluded that land use dominance across Türkiye’s NUTS-2 regions can be interpreted through two primary axes: (i) the agriculture–forest balance and (ii) water surface distribution. The structure revealed by the PCA situates the spatial concentration patterns identified through the LQ analysis within a more integrated analytical framework and uncovers the underlying structural dimensions driving regional differentiation. 3.3. Cluster Analysis Findings The Cluster Analysis, conducted to spatially group the structural differences revealed by the PCA results, typologically classified the land use patterns of Türkiye’s NUTS-2 regions. This analysis identifies clusters of regions exhibiting similar dominance characteristics, thereby making the spatial patterns of land use more clearly observable. The results indicate that Türkiye’s regions are organized into two main groups. The average LQ values for these clusters are presented in Table 3 , while the hierarchical similarity structure among regions is illustrated in Fig. 3 . Table 3 Cluster Analysis Results: Mean LQ Values for Agriculture, Forest, and Surface Water (2000–2024) Cluster N Forest 2024 Agriculture 2024 Surface Water 2024 Forest 2000 Agriculture 2000 Surface Water 2000 Cluster 1 26 1.12 ± 0.56 0.99 ± 0.39 0.82 ± 0.67 1.12 ± 0.66 1.29 ± 0.60 0.74 ± 0.50 Cluster 2 1 0.35 ± – 0.63 ± – 4.30 ± – 0.79 ± – 0.70 ± – 5.70 ± – Total 27 1.09 ± 0.57 0.97 ± 0.39 0.95 ± 0.94 1.11 ± 0.65 1.27 ± 0.60 0.92 ± 1.08 Note: Values are expressed as mean ± standard deviation. According to Table 3 , the first cluster comprises 26 NUTS-2 regions, encompassing the majority of Türkiye’s territory. The regions within this cluster exhibit a trend in which agricultural dominance has declined over the 2000–2024 period, while forest and water surface dominance has remained relatively stable. Based on the cluster averages, LQ values for forest areas remained close to 1.00 in both years, whereas LQ values for agricultural areas showed a marked decrease—from 1.27 in 2000 to 0.97 in 2024. This finding indicates a process of spatial homogenization in agricultural production across the country, accompanied by a reduction in relative dominance in certain regions. Meanwhile, the average LQ value for water surfaces increased slightly from 0.92 in 2000 to 0.97 in 2024, suggesting an overall upward trend that is, however, limited and localized in scale. Accordingly, this cluster represents a broad regional structure characterized by the weakening of agricultural dominance and the relative stability of forest and water surface areas across most parts of Türkiye. As illustrated in Fig. 3 , Türkiye’s NUTS-2 regions are spatially grouped into two main clusters. The first cluster, concentrated across the western, northern, and central parts of the country, is characterized by a balanced land use structure between forest and agricultural areas. In these regions, dominance levels are relatively similar, indicating a high degree of spatial equilibrium and diversity in land use patterns.In contrast, the second cluster consists solely of the TRB2 region (Van–Muş–Bitlis–Hakkâri), which distinctly diverges from all other regions due to its exceptionally high dominance in water surfaces. Nevertheless, between 2000 and 2024, the LQ value for water surfaces in TRB2 declined from 5.70 to 4.30, a decrease that can be attributed to the contraction of lake ecosystems and climatic influences. Despite this decline, TRB2 continues to exhibit the highest water surface dominance in Türkiye, representing the critical role of hydrological factors in shaping spatial dominance patterns. Overall, the clustering results indicate that there is no homogeneous pattern of dominance across Türkiye’s NUTS-2 regions; rather, distinct regional clusters have emerged. Most of the regions within the first cluster exhibit dominance patterns based on a balanced distribution between agricultural and forest areas, whereas the TRB2 region in the second cluster demonstrates strong dominance in water surfaces, underscoring the determinant role of hydrological factors in spatial dominance dynamics. This outcome confirms the validity of the two principal axes—“agriculture–forest” and “water surfaces”—previously identified through PCA. The findings collectively suggest that Türkiye’s land use structure displays regional diversity shaped by the interaction between natural and economic systems, highlighting the need for differentiated spatial planning strategies that account for these region-specific dynamics.koymaktadır. 4. Conclusion This study quantitatively examined land use changes across Türkiye’s NUTS-2 regions for the 2000–2024 period, revealing the spatial dominance patterns of forest, agricultural, and water surface areas. The integrated application of LQ, PCA, and Cluster Analyses demonstrated that Türkiye’s land use structure is not uniform, but rather shaped by distinct regional typologies. The findings indicate a general decline in agricultural areas, a relatively stable yet regionally differentiated pattern in forest areas, and notable changes in water surfaces, particularly across the eastern and southeastern regions. Among these, the TR61 region (Antalya–Isparta–Burdur) stands out with its balanced dominance between forest and agricultural lands, whereas the TRB2 region (Van–Muş–Bitlis–Hakkâri) exhibits a high spatial concentration in water surfaces, underscoring the region’s unique hydrological significance. These trends are consistent with findings from previous studies in the literature. It has been determined that the conversion of forests into agricultural and pasture lands in the Western Black Sea region has weakened ecosystem functions (Palta ve diğerleri, 2025). Similarly, land cover change analyses conducted across Türkiye indicate an increase in forest areas in some regions, while a decreasing trend is observed in others (Şen & Aktürk, 2024 ). In addition, satellite imagery–based studies conducted on Lake Van have identified a contraction in the lake’s surface area between 2014 and 2023, supporting the unique spatial characteristics observed in the TRB2 region (Karakus, 2025 ). These findings can also be interpreted as evidence that changes in water surfaces are influenced by both climatic factors and human-induced pressures. It is evident that the variations in land use cannot be explained solely by natural processes; rather, political, economic, and administrative dynamics play a decisive role. Losses of forest areas associated with energy and mining activities have been found to intensify spatial inequalities (Atmiş ve diğerleri, 2024). Similarly, Ala and Oruç Ertekin have demonstrated that addressing the water–energy–food systems in isolation weakens sustainability and that the lack of integrated governance leads to imbalances in regional resource use (Ala & Ertekin, 2025 ). Furthermore, Ustaoğlu and Aydınoglu identified that urban expansion is predominant in the western regions of Türkiye, whereas agricultural land use dominates in the eastern regions, emphasizing that this differentiation is rooted in socio-economic factors (Ustaoglu & Aydınoglu, 2019 ). These results indicate that the agriculture–forest balance and regional vulnerabilities identified in this study are consistent with the east–west–oriented spatial inequalities observed across the country. In addition, according to the European Environment Agency’s 2017 report, while settlement areas and water surfaces have expanded in Türkiye, agricultural lands have decreased. The report identifies urbanization, industrialization, tourism, energy development, and mining activities as the main drivers of this transformation (European Environment Agency, 2017 ). Population growth and migration dynamics have driven governments to develop new policies and legal frameworks aimed at ensuring sustainable land use and protecting the public interest (Liu & Liu, 2020 ). Similarly, a study conducted in Indonesia revealed that land access and population growth are among the most significant factors influencing changes in land use (Prayitno ve diğerleri, 2018). In the context of Türkiye, demographic pressures and economic trends have also been identified as key drivers behind the contraction of agricultural lands and the expansion of artificial surfaces. At the global scale, the rising population and increasing food demand have driven a shift in land use toward agricultural production (Hassan ve diğerleri, 2005). However, biofuel production (Smeets ve diğerleri, 2007) and afforestation initiatives implemented within the framework of climate-neutral policies (Verhoeven & Setter, 2010 ), have introduced new pressures on natural ecosystems. Moreover, while most existing studies primarily focus on the impacts of urban expansion on wetland ecosystems (Ngoran & Ngoran, 2015 ), relatively fewer have examined the effects of agricultural practices on these fragile environments (Awazi ve diğerleri, 2024). In parallel, rising temperatures, irregular precipitation patterns, and extreme weather events driven by climate change have been shown to accelerate ecosystem degradation in both coastal and inland wetlands (Choudhury ve diğerleri, 2021; Mehvar ve diğerleri, 2019). These findings suggest that the changes in water surface areas observed particularly in the eastern and southeastern regions of Türkiye reflect the regional manifestations of global climatic pressures. In conclusion, the findings from the 2000–2024 period reveal a spatial structure in Türkiye characterized by the decline of agricultural lands, relative stability yet regional differentiation in forest areas, and localized patterns of both contraction and expansion in water surfaces. These results indicate a multidimensional transformation emerging at the intersection of natural, economic, and governance-related dynamics. The integrated application of LQ–PCA–Cluster analyses proposed in this research constitutes one of the first comparative frameworks to evaluate land-use dominance patterns at the national scale, thereby offering a methodological contribution to the literature. Within this context, the study provides critical implications for regional planning and resource management. The development of ecosystem-based planning, sustainable agricultural production strategies, and integrated land management policies will play a pivotal role in enhancing regional resilience against potential climatic and socio-economic shocks. Monitoring spatial dominance in Türkiye is therefore crucial not only for ensuring environmental sustainability, but also for maintaining economic stability and food security. Declarations All authors have read, understood, and have complied as applicable with the statement on “Ethical responsibilities of Authors” as found in the Instructions for Authors. Clinical trial number Not applicable. Funding Not applicable. Author Contribution EA developed the conceptual framework, designed the methodology, and wrote the main sections of the manuscript.BK contributed to data standardization, the environmental assessment framework, and the development of the discussion section.SA prepared the tables and figures (Figures 1–3) and carried out data checking and reference formatting.All authors reviewed and approved the final version of the manuscript. Data Availability Calculated by the authors based on data from the (General Directorate of Forestry (OGM), 2024; Ministry of Agriculture and Forestry (MoAF), 2000; Ministry of Agriculture and Forestry (MoAF), 2024; Turkish Statistical Institute (TURKSTAT), 2024a; Turkish Statistical Institute (TURKSTAT), 2024b; Ministry of Environment, Urbanization and Climate Change, 2024).Turkish Statistical Institute (TURKSTAT). (2024a). Crop Production Statistics. Retrieved September 15, 2025, from https://data.tuik.gov.tr/Kategori/GetKategori?p=Tarim-111Turkish Statistical Institute (TURKSTAT). (2024b). Agricultural Data. Retrieved September 10, 2025, from https://data.tuik.gov.tr/Kategori/GetKategori?p=Tarim-111 General Directorate of Forestry (OGM). (2024). Official statistics. Retrieved September 10, 2025, from https://www.ogm.gov.tr/tr/e-kutuphane/resmi-istatistiklerMinistry of Agriculture and Forestry (MoAF). (2000). CORINE Project.Ministry of Agriculture and Forestry (MoAF). (2024). Wetlands Inventory System (SAYBIS). Retrieved September 10, 2025, from https://saybis.tarimorman.gov.tr/Ministry of Agriculture and Forestry (MoAF). (2025). Retrieved September 10, 2025, from https://istatistik.tarimorman.gov.tr/Sayfa/Detay/2053?utm_Ministry of Environment, Urbanization and Climate Change. (2024). 2024 Provincial Environmental Status Reports. Retrieved September 10, 2025, from https://ced.csb.gov.tr/2024-yili-il-cevre-durum-raporlari-i-113266 References Ala, E., & Ertekin, G. D. (2025). Integrated and Sustainable Food Systems in the Context of the WEF Nexus Approach: An assessment of Istanbul, Ankara, and Izmir. Journal of Planning , 35 (50 Suppl. 1), 18–34. Atmiş, E., Yıldız, D., & Erdönmez, C. (2024). A different dimension in deforestation and forest degradation: Non-forestry uses of forests in Turkey. Land Use Policy , 139 , 107086. Awazi, N. P., Quandt, A., & Ambebe, T. F. (2024). Climate Change and Anthropogenic Pressures on Forested Wetlands and Wetland Ecosystems in Cameroon: Sustainability and Policy Implications. Forestist , 74 (3). Berkhin, P. (2006). A Survey of Clustering Data Mining Techniques. In In Grouping multidimensional data: Recent advances in clustering (pp. 25–71). Berlin, Heidelberg: Springer Berlin Heidelberg. Choudhury, M., Sharma, A., Singh, P., & Kumar, D. (2021). Impact of climate change on wetlands, concerning Son Beel, the largest wetland of North East, India. Global Climate Change , 393–414. Cooper, C. M., & Moore, M. T. (2003). Wetlands and Agriculture. In Achieving sustainable freshwater systems: a web of connections (pp. 221–235). Washington, DC, USA: Island Press. Dede, O. M. (2020). Türkiye Düzey-2 Bölgelerinde Tarım Alanlarının Yerseçim Katsayısı Metodu ile Değerlendirilmesi. Tarım Ekonomisi Araştırmaları Dergisi , 6 (1), 36–48. Dinsa, T., & Gemeda, D. (2019). The Role of Wetlands for Climate Change Mitigation and Biodiversity Conservation. Journal of Applied Sciences and Environmental Management , 23 (7), 1297–1300. European Environment Agency. (2017). Turkey Landcover 2012 Country Fact Sheet. Retrieved October 14, 2025, from https://www.eea.europa.eu/themes/landuse/land-cover-country-fact-sheets/tr-turkey-la ndcover-2012.pdf/view General Directorate of Forestry (OGM). (2024). Official statistics. Retrieved September 10, 2025, from https://www.ogm.gov.tr/tr/e-kutuphane/resmi-istatistikler General Directorate of State Hydraulic Works (DSİ). (2025). Retrieved September 15, 2025, from https://www.dsi.gov.tr/Sayfa/Detay/754?utm_ Hassan, R., Scholes, R., & Ash, N. (2005). Hassan R, Scholes R, Ash N. 2005. Ecosystems and human well-being. Current state and trends . Washington, DC: Island Press. IPCC. (2018). Global Warming of 1.5°C. Retrieved September 15, 2025, from https://www.ipcc.ch/sr15/ IPCC. (2023). Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Retrieved September 10, 2025, from https://www.ipcc.ch/report/ar6/syr/ Karakus, P. (2025). Detection of Water Surface Using Canny and Otsu Threshold Methods with Machine Learning Algorithms on Google Earth Engine: A Case Study of Lake Van. Applied Sciences , 15 (6), 2903. Kingsford, R. T., Bino, G., Finlayson, C. M., Falster, D., Fitzsimons, J., Gawlik, D. E.,.. . Thomas, R. F. (2021). Ramsar Wetlands of International Importance–Improving Conservation Outcomes. Frontiers in Environmental Science , 9 , 643367. Kutlu, B., Rahımbaylı, S., & Akoğlu, M. (2025). Isparta Gölcük Tabiat Parkının Rekreasyon Potansiyelini Belirlemeye Yönelik Bir Araştırma. Düzce Üniversitesi Orman Fakültesi Ormancılık Dergisi , 21 (1), 75–91. Liu, B., & Liu, Z. (2020). The Positive-Externality of Property Right and the Property Right-Regulation: Take the Grain Production-Regulation of Land Use as an Example. Open Journal of Social Sciences , 8 , 455–470. Mehvar, S., Filatova, T., Sarker, M. H., Dastgheib, A., & Ranasinghe, R. (2019). Climate change-driven losses in ecosystem services of coastal wetlands: A case study in the West coast of Bangladesh. Ocean & Coastal Management , 169 , 273–283. Ministry of Agriculture and Forestry (MoAF). (2000). CORINE Project. Retrieved September 10, 2025, from https://corine.tarimorman.gov.tr/corineportal/ Ministry of Agriculture and Forestry (MoAF). (2024). Wetlands Inventory System (SAYBIS). Retrieved September 10, 2025, from https://saybis.tarimorman.gov.tr/ Ministry of Agriculture and Forestry (MoAF). (2025). Retrieved September 10, 2025, from https://istatistik.tarimorman.gov.tr/Sayfa/Detay/2053?utm_ Ministry of Environment, Urbanization and Climate Change. (2024). 2024 Provincial Environmental Status Reports. Retrieved September 10, 2025, from https://ced.csb.gov.tr/2024-yili-il-cevre-durum-raporlari-i-113266 Naidoo, R., Balmford, A., Costanza, R., Fisher, B., Green, R. E., Lehner, B.,.. . Ricketts, T. H. (2008). Global mapping of ecosystem services and conservation priorities. Proceedings of the National Academy of Sciences, 105 (28), 9495–9500. Ngoran, B. S., & Ngoran, S. D. (2015). Urban agriculture and landscape challenges in African cities: an illustration of the Bamenda City Council, Cameroon. Journal of Poverty, Investment and Development , 55–72. Ozdamar, K. (1999). Paket programlar ile istatistiksel veri analizi . Eskişehir: Kaan Kitabevi. Palta, Ş., Tokel, E., Baş, E., & Souza, T. (2025). Effects of forest-to-agriculture conversion on soil arbuscular mycorrhizal fungi in the Western Black Sea Region. Soil and Tillage Research , 252 , 106581. Prayitno, G., Surjono, S., Hidayat, A. R., Subagiyo, A., & Paramasasi, N. K. (2018). Factors that effect to land use change in Pandaan District. IOP Conference Series: Earth and Environmental Science , 202. Smeets, E. M., Faaij, A. P., & Lewandowski, I. M. (2007). Progress in Energy and Combustion Science , 33 , 56–106. Şen, G., & Aktürk, E. (2024). Spatiotemporal forest and land cover change in Türkiye: The role of economic factors in driving environmental transformations. Turkish Journal of Forestry , 25 (2), 176–189. Turkish Statistical Institute (TURKSTAT). (2024a). Crop Production Statistics. Retrieved September 15, 2025, from https://data.tuik.gov.tr/Kategori/GetKategori?p=Tarim-111 Turkish Statistical Institute (TURKSTAT). (2024b). Agricultural Data. Retrieved September 10, 2025, from https://data.tuik.gov.tr/Kategori/GetKategori?p=Tarim-111 Ustaoglu, E., & Aydınoglu, A. C. (2019). Regional variations of land-use development and land-use/cover change dynamics: A case study of Turkey. Remote Sensing , 11 (7), 885. Verhoeven, J. T., & Setter, T. L. (2010). Agricultural use of wetlands: opportunities and limitations. Annals of botany , 105 (1), 155–163. Worldometer. (2025). Retrieved September 15, 2025, from https://www.worldometers.info/water/ Ye, X.-c., Meng, Y.-k., Xu, L.-g., & Xu, C.-y. (2019). Net primary productivity dynamics and associated hydrological driving factors in the floodplain wetland of China’s largest freshwater lake. Science of The Total Environment , 659 , 302–313. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 27 Apr, 2026 Read the published version in Environmental Monitoring and Assessment → Version 1 posted Editorial decision: Revision requested 03 Dec, 2025 Editor assigned by journal 29 Nov, 2025 Submission checks completed at journal 29 Nov, 2025 First submitted to journal 11 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8089182","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":554901414,"identity":"337ab2ee-7a60-4560-b2b5-4348688b30b7","order_by":0,"name":"Ebru Ala¹","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYDACCRBRwCDHwJAAZLARrcWAwRiuhYdYLYkNRGvhl+59JvHDwC59fnuOAcOHssMM9tIH8GuRnHPcTLLHIDl3w5k3Bowzzh1m4OFLwK/F4EYamwSPAXPuBokcA2beNqAWQi6zB2qR/GNQny4/A6jlLzFaDCTS2KR5DA4nMNwAamEkRovEnWPM1jIGxw03nHlWcLDnXDoPzxkCWvhntzHefFNRLS/fnrzxwY8yazn2HgJaUMABBmJichSMglEwCkYBYQAAFPw5Ybvo4JAAAAAASUVORK5CYII=","orcid":"","institution":"Süleyman Demirel University","correspondingAuthor":true,"prefix":"","firstName":"Ebru","middleName":"","lastName":"Ala¹","suffix":""},{"id":554901415,"identity":"c0cd746a-2392-4511-839b-2f7e20ea35c5","order_by":1,"name":"Büşra Kutlu²","email":"","orcid":"","institution":"Süleyman Demirel University","correspondingAuthor":false,"prefix":"","firstName":"Büşra","middleName":"","lastName":"Kutlu²","suffix":""},{"id":554901416,"identity":"d6050f26-07e2-478b-8aab-a40ff97502c6","order_by":2,"name":"Sarıyya Arslan³","email":"","orcid":"","institution":"Süleyman Demirel University","correspondingAuthor":false,"prefix":"","firstName":"Sarıyya","middleName":"","lastName":"Arslan³","suffix":""}],"badges":[],"createdAt":"2025-11-11 17:23:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8089182/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8089182/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10661-026-15390-2","type":"published","date":"2026-04-27T15:57:42+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":99837073,"identity":"cf113692-21dd-443a-91a4-b259fee3e470","added_by":"auto","created_at":"2026-01-08 19:30:02","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":52051,"visible":true,"origin":"","legend":"\u003cp\u003eScree Plot of PCA Results (Explained Variance by Components)\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8089182/v1/ffdefbca3ebb31897434944b.jpg"},{"id":99837075,"identity":"bc57ae73-3972-40a9-b64f-f35b1838bb5e","added_by":"auto","created_at":"2026-01-08 19:30:02","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":87545,"visible":true,"origin":"","legend":"\u003cp\u003ePCA Biplot (Distribution of Regions and Land Use Types on PC1 and PC2 Axes)\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8089182/v1/a55aa83fe8d6f3826c3982ed.jpg"},{"id":100357190,"identity":"946fa557-5e71-4a94-b41b-c90620227f4f","added_by":"auto","created_at":"2026-01-16 07:19:14","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":113538,"visible":true,"origin":"","legend":"\u003cp\u003eHierarchical Cluster Dendrogram of Türkiye’s NUTS-2 Regions (Ward Method)\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8089182/v1/c7aa20d38702d38a39ffda08.jpg"},{"id":108437701,"identity":"f24d5a7d-4ef8-43e4-98bb-06baf9f9c0c9","added_by":"auto","created_at":"2026-05-04 16:02:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":705782,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8089182/v1/00ee9b4e-9997-4658-b579-27307b74573c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Spatial Dominance of Land Use in Türkiye’s NUTS-2 Regions: An Integrated Quantitative Approach","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eChanging socio-economic dynamics on a global scale have initiated a profound transformation in the Earth's climatic cycle. This transformation process creates a decisive impact not only on climate systems but also on the spatial redistribution of land use patterns. Therefore, analyzing the interrelationship between land use and climatic as well as socio-economic processes is of critical importance for assessing sustainable development goals at the spatial level. However, this transformation process does not progress in a self-sustaining manner; in most cases, it results in the degradation of natural ecosystems. According to IPCC reports, the adverse impacts of global warming continue to increase under all scenarios. To limit warming to 1.5\u0026deg;C (with \u0026gt;\u0026thinsp;50% probability) or below 2\u0026deg;C (with \u0026gt;\u0026thinsp;67% probability) by the end of the century, greenhouse gas emissions must be rapidly, persistently, and comprehensively reduced; the net-zero CO₂ target must be achieved; and CH₄ emissions, in particular, must be significantly lowered (IPCC, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). As temperatures rise, the magnitude of climate-related risks continues to expand.\u003c/p\u003e \u003cp\u003eThe decline in water resources, destruction of forest areas, loss of surface water, uncontrolled urbanization, and unsustainable agricultural activities are the main factors accelerating this process. These increasing pressures at global and regional scales are leading to the irreversible degradation of natural resources. This situation indicates that the management of natural resources must be reconsidered at the spatial level. As of 2025, the world\u0026rsquo;s average annual forest area loss has reached 3\u0026nbsp;million hectares, while total water use has climbed to approximately 3,347,084,180 m\u0026sup3; (Worldometer, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In T\u0026uuml;rkiye specifically, total water use reached 57\u0026nbsp;billion m\u0026sup3; in 2024, with 44\u0026nbsp;billion m\u0026sup3; allocated for irrigation (General Directorate of State Hydraulic Works (DSİ), \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), During the same period, 27,485 hectares of forest were lost due to fires, and total agricultural land decreased by 5% compared to 2023, declining to 23.5\u0026nbsp;million hectares (Turkish Statistical Institute (TURKSTAT), \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e; Ministry of Agriculture and Forestry (MoAF), \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These indicators reveal that climatic and environmental pressures are deepening not only globally but also at the national scale.\u003c/p\u003e \u003cp\u003eClimate change is causing increasingly arid weather conditions worldwide, directly threatening the livelihoods of communities that depend on agriculture and livestock (IPCC, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This situation particularly disrupts the spatial balance among agricultural, forest, and water surface areas; therefore, evaluating these components together has become a fundamental requirement for understanding climatic vulnerabilities at the regional level. Prolonged dry periods, irregular and insufficient rainfall, and the rise in extreme weather events are driving producers toward wetlands that can meet both irrigation and livestock water needs (Dinsa \u0026amp; Gemeda, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, wetlands and forests located in both inland and coastal environments are unique components that provide a wide range of ecosystem services essential for maintaining ecological integrity and supporting human well-being (Kingsford ve diğerleri, 2021; Naidoo ve diğerleri, 2008). Nevertheless, human-induced pressures such as urbanization, agricultural expansion, and industrialization, combined with the adverse effects of climate change, are causing these ecosystems to shrink at an alarming rate (Ye ve diğerleri, 2019).\u003c/p\u003e \u003cp\u003eThroughout history, wetlands have served as strategic areas not only for agricultural production but also for transportation, trade, and settlement. However, with the development of drainage and land reclamation techniques, these areas have been converted in many parts of the world for the purpose of gaining agricultural land. This transformation has led to the substantial loss of ecosystems\u0026rsquo; natural characteristics, a decline in biodiversity, and the weakening of ecological functions other than crop productivity (Verhoeven \u0026amp; Setter, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Hassan ve diğerleri, 2005). There is a direct relationship between agricultural activities and water quality. Historically, the conversion of wetlands into agricultural land has become one of the main factors contributing to surface water pollution through diffuse agricultural contamination. This situation has prompted agricultural research communities to develop new methods aimed at both reducing surface runoff and improving water quality (Cooper \u0026amp; Moore, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOne of the major paradigm shifts in human history, the Agricultural Revolution, demonstrates that the strategic importance of food production has never diminished throughout time. In the face of a continuously growing global population, sudden climatic changes, and the increasing threat of drought, the sustainability of the agricultural sector and the significance of agricultural policies and decision-making are becoming ever more critical. This reality remains valid not only on a global scale but also for countries such as T\u0026uuml;rkiye, where agricultural production holds strategic importance (Dede, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In this context, the sustainable planning and management of natural areas constitute a critical necessity for the preservation of ecological balance (Kutlu ve diğerleri, 2025).\u003c/p\u003e \u003cp\u003eIn this context, analyzing the spatial dominance dynamics of land use holds great importance for the development of sustainable planning and urban development policies. In T\u0026uuml;rkiye, studies examining this relationship on a spatial scale are limited, and particularly, comprehensive quantitative analyses that jointly evaluate forest, agricultural, and water surface areas remain scarce in the literature. This study aims to reveal the spatial dimensions of land use dominance across T\u0026uuml;rkiye\u0026rsquo;s NUTS-2 regions within a comprehensive analytical framework. Accordingly, Location Quotient (LQ), Principal Component Analysis (PCA), and Cluster Analysis are applied together to identify spatial dominances and to evaluate interregional structural differences in a multidimensional manner. In doing so, the study seeks to systematically analyze regional land use patterns in T\u0026uuml;rkiye and to make a methodological contribution to the spatial planning literature.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Data Sources and Research Design\u003c/h2\u003e \u003cp\u003eThis study is based on a comparative analysis of land use data for T\u0026uuml;rkiye\u0026rsquo;s NUTS-2 regions covering the period 2000\u0026ndash;2024 and aims to examine the dominance trends and spatial distribution dynamics of regional land use patterns. The NUTS-2 level is considered the most appropriate spatial unit in T\u0026uuml;rkiye in terms of both planning scale and statistical comparability, as it allows for the integrated analysis of natural systems and socio-economic indicators while establishing a direct analytical link between regional development and spatial planning policies.\u003c/p\u003e \u003cp\u003eThe period between 2000 and 2024 was selected in order to observe the long-term spatial traces of T\u0026uuml;rkiye\u0026rsquo;s climatic, ecological, and socio-economic transformations. This timeframe corresponds to a transitional phase during which environmental, agricultural, and forestry policies have been re-scaled around the principle of sustainability, and ecosystem-based approaches have gained prominence in spatial planning. Therefore, the temporal comparison enables the analysis of both the spatial concentration trends in land use and the directional shifts in regional dominance.\u003c/p\u003e \u003cp\u003eWithin the scope of the analysis, three primary land use categories representing the intersection points of ecological and economic systems\u0026mdash;forest areas, agricultural lands, and water surfaces\u0026mdash;were considered as spatial thresholds. These thresholds were treated as reference components for identifying structural transformations in land use and regional vulnerabilities. All data used in the study were obtained from nationally reliable and verifiable institutional sources: forest area data were provided by the General Directorate of Forestry of the Ministry of Agriculture and Forestry (General Directorate of Forestry (OGM), \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); agricultural land data were derived from the Crop Production Statistics datasets of the Turkish Statistical Institute (Turkish Statistical Institute (TURKSTAT), \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e); and data on water surfaces were obtained from the Environmental Status Reports published by the Ministry of Environment, Urbanization and Climate Change (Ministry of Environment, Urbanization and Climate Change, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), the CORINE Land Cover datasets (Ministry of Agriculture and Forestry (MoAF), \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), and the Wetland Information System (Ministry of Agriculture and Forestry (MoAF), \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The data collected from these different institutional sources were standardized in hectares (ha) and subjected to a cross-validation process to ensure consistency and comparability across datasets.\u003c/p\u003e \u003cp\u003eOverall, the study is structured around a quantitative, comparative, and spatial analytical approach. This approach aims to provide a holistic analytical framework that not only reveals the long-term regional dominance dynamics but also enhances the understanding of how these dynamics influence spatial planning, resource management, and sustainability policies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Data Analysis\u003c/h2\u003e \u003cp\u003eThe analytical framework of this study is built upon a three-stage methodological system comprising the measurement of spatial concentration, the identification of structural dimensions, and the typological classification. These stages were carried out respectively through the Location Quotient (LQ), Principal Component Analysis (PCA), and Cluster Analysis methods.\u003c/p\u003e \u003cp\u003eThe aim is not only to determine which land use types are dominant across regions but also to reveal the temporal trends, structural patterns, and regional similarity groups of these dominances. In this way, the study examines spatial differentiation in land use through a quantitative, multivariate, and comparative analytical approach.\u003c/p\u003e \u003cp\u003eThese three methods are designed to complement one another:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eThe LQ Analysis reveals spatial concentration patterns by measuring the relative dominance level of each region compared to the national average. The Location Quotient (LQ) method, applied to determine the relative dominance levels of regions in terms of land use types, allows for the evaluation of the concentration of a specific land use type within a region in comparison to the national average. The formula is expressed as follows:\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{A}_{i,j}\\)\u003c/span\u003e \u003c/span\u003e= The area of land use type \u003cem\u003ej\u003c/em\u003e (agriculture, forest, or surface water) in region \u003cem\u003ei\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{A}_{i}\\)\u003c/span\u003e \u003c/span\u003e= The total land area in region \u003cem\u003ei\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:L{Q}_{i,j}\\:=\\:\\frac{{A}_{i,j}}{{A}_{i}}\\:\u0026divide;\\:\\frac{{A}_{j}}{A}\\)\u003c/span\u003e \u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{A}_{j}\\)\u003c/span\u003e\u003c/span\u003e= The total area of land use type \u003cem\u003ej\u003c/em\u003e in T\u0026uuml;rkiye\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:A\\)\u003c/span\u003e \u003c/span\u003e= The total land area in T\u0026uuml;rkiye\u003c/p\u003e \u003cp\u003eLQ value greater than 1.0 indicates that the corresponding land use type is more dominant in the region compared to the national average, whereas an LQ value less than 1.0 signifies a relatively lower concentration.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eThe Principal Component Analysis (PCA\u003cb\u003e)\u003c/b\u003e reveals the common variation structure among land use types. PCA enables dimensionality reduction in multivariate datasets, allowing the variance to be explained through a smaller number of components. In this study, only components with eigenvalues greater than 1 were considered, while components contributing less than 2% to the total variance were excluded from the analysis. The primary objective of PCA is to construct a new set of dimensions that better capture the diversity and underlying structure of the data (Berkhin, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe Cluster Analysis was employed to transform the component scores obtained from the PCA into spatially meaningful typologies, thereby identifying regions that exhibit similar land use patterns. This method aims to group units by considering the degrees of similarity and distance among them, allowing for the classification of regions with comparable structural characteristics in terms of land use Dynamics (Ozdamar, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). The Ward method was selected for the analysis, with the Euclidean distance metric used as the measure of similarity. This approach enabled the grouping of regions that exhibit similar land use patterns, and the results were visualized through a dendrogram graph.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe analyses and calculations conducted using the SPSS 29.0 software package represent one of the first quantitative frameworks to examine spatial dominance in T\u0026uuml;rkiye across three dimensions\u0026mdash;agricultural, forest, and wetland areas. The LQ results are presented in tabular form; the PCA findings are visualized through scree plots and biplot graphs; and the cluster analysis results are illustrated using a dendrogram.\u003c/p\u003e \u003cp\u003eThis multi-stage quantitative approach and integrated analytical framework aim not only to identify statistically significant differences but also to define functionally meaningful typologies in terms of regional spatial planning and resource management. The study seeks to comprehensively analyze the key axes that determine the agriculture\u0026ndash;forest\u0026ndash;water surface balance across T\u0026uuml;rkiye\u0026rsquo;s NUTS-2 regions, the spatial distribution of these axes over time, and the regional clustering patterns that emerge from these dynamics.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Findings","content":"\u003cp\u003eThis section presents the results of the spatial analyses conducted for T\u0026uuml;rkiye\u0026rsquo;s NUTS-2 regions over the 2000\u0026ndash;2024 period. The findings reveal the regional dominance levels and their temporal variations across three primary land use categories: forests, agricultural areas, and water surfaces. The analyses are interpreted based on the outputs derived from the Location Quotient (LQ), Principal Component Analysis (PCA), and Cluster Analysis methods. This multi-stage approach aims to make the differentiation of land use patterns across T\u0026uuml;rkiye both quantitatively and spatially visible. First, the LQ results identify the relative dominance levels of each region, revealing overall trends; subsequently, PCA defines the underlying structural components driving these differences. In the final stage, cluster analysis groups regions with similar land use patterns, thereby delineating the spatial typologies of T\u0026uuml;rkiye.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1. LQ Analysis Findings\u003c/h2\u003e \u003cp\u003eThe comparison of Location Quotient (LQ) values for the years 2000 and 2024 reveals significant variations in the spatial dominance levels of agricultural, forest, and water surface areas across T\u0026uuml;rkiye\u0026rsquo;s NUTS-2 regions. This analysis evaluates the changes between the two periods in terms of both regional concentration and relative dominance trends, thereby quantitatively illustrating the long-term structural transformations in land use.\u003c/p\u003e \u003cp\u003eThe findings clearly reveal the differentiation in the spatial distribution of land use types across T\u0026uuml;rkiye, and the detailed distribution of LQ values by region is presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLQ Values of Agriculture, Forest, and Surface Water in T\u0026uuml;rkiye\u0026rsquo;s NUTS-2 Regions (2000\u0026ndash;2024)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormula\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:L{Q}_{i,j}\\:=\\:\\frac{{A}_{i,j}}{{A}_{i}}\\:\u0026divide;\\:\\frac{{A}_{j}}{A}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{A}_{i,j}\\)\u003c/span\u003e\u003c/span\u003e= The area of land use type \u003cem\u003ej\u003c/em\u003e (agriculture, forest, or surface water) in region \u003cem\u003ei\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{A}_{i}\\)\u003c/span\u003e\u003c/span\u003e= The total land area in region \u003cem\u003ei\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{A}_{j}\\)\u003c/span\u003e\u003c/span\u003e= The total area of land use type \u003cem\u003ej\u003c/em\u003e in T\u0026uuml;rkiye\u003c/p\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:A\\)\u003c/span\u003e\u003c/span\u003e= The total land area in T\u0026uuml;rkiye\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYears\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e2000\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNUTS-2 Code\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForest Area\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAgricultural Area\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWater Surfaces\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eForest Area\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAgricultural Area\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWater Surfaces\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTR10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1,48\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0,44\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1,21\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1,94\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0,75\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1,19\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTR21\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0,81\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1,76\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0,43\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0,97\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e2,27\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0,38\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTR22\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1,51\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0,90\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0,61\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1,75\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1,22\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0,54\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTR31\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1,35\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0,95\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.78\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1,28\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0,78\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0,52\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTR32\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1,78\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0,93\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0,55\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1,58\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0,82\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0,65\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTR33\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1,26\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1,14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0,86\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1,12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1,48\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0,65\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTR41\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1,29\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1,05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0,99\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1,45\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1,42\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0,95\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTR42\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1,82\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0,74\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0,21\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2,31\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0,90\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0,23\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTR51\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0,61\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1,41\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1,71\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0,40\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e2,10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1,32\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTR52\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0,57\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1,45\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1,03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0,37\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e2,45\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1,58\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTR61\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1,76\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0,60\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0,97\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1,42\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0,79\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1,34\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTR62\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1,60\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0,90\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0,86\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1,38\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1,27\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1,72\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e 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align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0,22\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1,70\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2,18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0,14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e2,31\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0,99\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTR72\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0,53\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1,11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0,48\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0,45\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1,60\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0,31\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTR81\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2,15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0,38\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0,03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2,3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0,85\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0,01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTR82\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1,81\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0,53\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0,12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1,92\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0,85\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0,11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTR83\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1,37\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1,30\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0,61\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1,37\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1,68\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0,50\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTR90\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1,40\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0,67\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0,17\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1,80\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0,38\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0,20\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTRA1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0,48\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0,55\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0,14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0,62\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0,57\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0,19\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTRA2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0,12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0,87\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0,61\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0,14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1,08\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0,71\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTRB1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0,83\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0,53\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1,43\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0,75\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0,62\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1,26\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTRB2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0,35\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0,63\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e4,30\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0,79\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0,70\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e5,70\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTRC1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0,64\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1,41\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2,94\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0,42\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1,50\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1,60\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTRC2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0,33\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1,58\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0,79\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0,17\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e2,28\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0,66\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTRC3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1,09\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0,79\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0,20\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0,89\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1,05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0,26\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eT\u0026uuml;rkiye\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eLQ\u0026thinsp;\u0026gt;\u0026thinsp;1.0 \u0026rarr; The region is specialized / dominant in the related land use type (agriculture, forest, or surface water).\u003c/p\u003e \u003cp\u003eLQ\u0026thinsp;=\u0026thinsp;1.0 \u0026rarr; The region\u0026rsquo;s share in the related land use type is equal to the national average.\u003c/p\u003e \u003cp\u003eLQ\u0026thinsp;\u0026lt;\u0026thinsp;1.0 \u0026rarr; The region is less specialized / underrepresented in the related land use type compared to the national level.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSource: Calculated by the authors based on data from the (General Directorate of Forestry (OGM), \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ministry of Agriculture and Forestry (MoAF), \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Ministry of Agriculture and Forestry (MoAF), \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Turkish Statistical Institute (TURKSTAT), \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e; Turkish Statistical Institute (TURKSTAT), \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e; Ministry of Environment, Urbanization and Climate Change, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the average LQ value for forest areas declined slightly from 1.11 in 2000 to 1.09 in 2024. Although this decrease is relatively small, notable regional variations can be observed. For instance, the TR61 region (Antalya\u0026ndash;Isparta\u0026ndash;Burdur) recorded the most significant increase in forest dominance (+\u0026thinsp;0.34), whereas the TR42 region (Kocaeli\u0026ndash;Sakarya\u0026ndash;D\u0026uuml;zce\u0026ndash;Bolu\u0026ndash;Yalova) experienced the largest decrease (\u0026ndash;0.49). This variation indicates that the spatial significance of forest areas, particularly in the Western Black Sea and Mediterranean regions, has been shaped by shifting land use dynamics.\u003c/p\u003e \u003cp\u003eIn agricultural areas, the average LQ value decreased from 1.26 in 2000 to 0.97 in 2024, indicating a weakening of regional concentration in agricultural production. The most notable decline occurred in the TR52 region (Konya\u0026ndash;Karaman) (\u0026ndash;1.00), demonstrating a significant reduction in the relative importance of agriculture within the region. Conversely, the TR90 region (Trabzon\u0026ndash;Ordu\u0026ndash;Giresun\u0026ndash;Rize\u0026ndash;Artvin\u0026ndash;G\u0026uuml;m\u0026uuml;şhane) exhibited an increase in agricultural dominance (+\u0026thinsp;0.29), suggesting that the agricultural diversity and horticultural activities characteristic of the Eastern Black Sea region have strengthened regional dominance trends.\u003c/p\u003e \u003cp\u003eIn terms of water surfaces, the average LQ value increased slightly from 0.92 in 2000 to 0.95 in 2024. Although the overall increase is limited, the regional differences are quite striking. A strong rise in water surface dominance was observed in the TRC1 region (Gaziantep\u0026ndash;Adıyaman\u0026ndash;Kilis) (+\u0026thinsp;1.34), which can be attributed to the influence of dam investments and water infrastructure projects in the region. Conversely, the TRB2 region (Van\u0026ndash;Muş\u0026ndash;Bitlis\u0026ndash;Hakkari) recorded a sharp decline (\u0026ndash;1.40) in water surface dominance. This decrease is primarily associated with the contraction of lake ecosystems and the impacts of climatic factors. Nevertheless, TRB2 continues to exhibit relatively high values, maintaining its dominance above the national average in terms of water surfaces.\u003c/p\u003e \u003cp\u003eOverall, during the 2000\u0026ndash;2024 period, the spatial configuration of land use patterns in T\u0026uuml;rkiye demonstrates a weakening of agricultural dominance, a relative stability in forest areas accompanied by deepening regional disparities, and a divergent trajectory of water surface dominance influenced by localized infrastructure investments and climatic processes. These trends reveal that T\u0026uuml;rkiye\u0026rsquo;s land use structure has been reshaped at the regional scale through the combined effects of natural, economic, and political dynamics.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.2. PCA Analysis Findings\u003c/h2\u003e \u003cp\u003eThe findings obtained from the LQ analysis reveal that land use dominance in T\u0026uuml;rkiye exhibits distinct regional dynamics. However, in order to elucidate the structural relationships underlying these variations, it is necessary to examine the common variance structure among the variables.\u003c/p\u003e \u003cp\u003eFor this purpose, Principal Component Analysis (PCA) was conducted to identify the fundamental dimensions through which interregional land use patterns can be explained and to assess the extent to which the spatial trends identified in the LQ analysis statistically covary. The PCA results indicate that the first two components account for 84.6% of the total variance, suggesting that T\u0026uuml;rkiye\u0026rsquo;s land use structure is shaped primarily along two fundamental axes (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePCA Results for T\u0026uuml;rkiye\u0026rsquo;s NUTS-2 Regions: Explained Variance Ratios\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003eTotal Variance Explained\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eInitial Eigenvalues\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eExtraction Sums of Squared Loadings\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eRotation Sums of Squared Loadings\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComponent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e% of Variance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCumulative %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e% of Variance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCumulative %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e% of Variance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCumulative %\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54,798\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54,798\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e54,798\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e54,798\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2,895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e48,246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e48,246\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29,874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84,673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29,874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e84,673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2,186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e36,427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e84,673\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11,373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96,046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98,086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99,075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eExtraction Method: Principal Component Analysis\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe first component (54.8%) is characterized by high loadings on agricultural and forest areas, forming the principal axis of differentiation among regions. On this axis, regions with intensive agricultural production\u0026mdash;such as those in Central Anatolia (TR51, TR52, TR71, TR72) and the Aegean (TR31, TR32, TR33)\u0026mdash;are clearly distinguished from the Black Sea regions (TR81, TR82, TR83) where forest coverage is predominant.\u003c/p\u003e \u003cp\u003eThe second component (29.9%) represents variations related to water surfaces. Along this axis, regions with extensive water resources (e.g., TRC1) are distinctly separated from those experiencing a decline in water surface areas (e.g., TRB2). This structure demonstrates that spatial dominance in T\u0026uuml;rkiye can essentially be explained through two main dimensions: (i) the agriculture\u0026ndash;forest balance and (ii) the distribution of water surfaces.\u003c/p\u003e \u003cp\u003eThe third component accounts for 11.3% of the variance, while the contributions of the fourth and subsequent components fall below 2%. Therefore, the analysis is considered meaningful only with reference to the first two components. The scree plot presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e visually illustrates the dominance of these two components, whereas the biplot in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e clearly depicts the positioning of variables and regions along these axes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn addition, the biplot graph (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) illustrates the positioning of variables and regions within a two-dimensional space. It is clearly observed that agricultural and forest areas are differentiated along the first component axis, whereas water surfaces are distinctly separated along the second component axis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWhen these findings are evaluated collectively, it can be concluded that land use dominance across T\u0026uuml;rkiye\u0026rsquo;s NUTS-2 regions can be interpreted through two primary axes: (i) the agriculture\u0026ndash;forest balance and (ii) water surface distribution. The structure revealed by the PCA situates the spatial concentration patterns identified through the LQ analysis within a more integrated analytical framework and uncovers the underlying structural dimensions driving regional differentiation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Cluster Analysis Findings\u003c/h2\u003e \u003cp\u003eThe Cluster Analysis, conducted to spatially group the structural differences revealed by the PCA results, typologically classified the land use patterns of T\u0026uuml;rkiye\u0026rsquo;s NUTS-2 regions. This analysis identifies clusters of regions exhibiting similar dominance characteristics, thereby making the spatial patterns of land use more clearly observable. The results indicate that T\u0026uuml;rkiye\u0026rsquo;s regions are organized into two main groups. The average LQ values for these clusters are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, while the hierarchical similarity structure among regions is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCluster Analysis Results: Mean LQ Values for Agriculture, Forest, and Surface Water (2000\u0026ndash;2024)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eForest\u003c/p\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAgriculture\u003c/p\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSurface\u003c/p\u003e \u003cp\u003eWater 2024\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eForest\u003c/p\u003e \u003cp\u003e2000\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAgriculture 2000\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSurface\u003c/p\u003e \u003cp\u003eWater 2000\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.35 \u0026plusmn; \u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.63 \u0026plusmn; \u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.30 \u0026plusmn; \u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.79 \u0026plusmn; \u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.70 \u0026plusmn; \u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.70 \u0026plusmn; \u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.92\u0026thinsp;\u0026plusmn;\u0026thinsp;1.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cem\u003eNote: Values are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAccording to Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the first cluster comprises 26 NUTS-2 regions, encompassing the majority of T\u0026uuml;rkiye\u0026rsquo;s territory. The regions within this cluster exhibit a trend in which agricultural dominance has declined over the 2000\u0026ndash;2024 period, while forest and water surface dominance has remained relatively stable. Based on the cluster averages, LQ values for forest areas remained close to 1.00 in both years, whereas LQ values for agricultural areas showed a marked decrease\u0026mdash;from 1.27 in 2000 to 0.97 in 2024. This finding indicates a process of spatial homogenization in agricultural production across the country, accompanied by a reduction in relative dominance in certain regions. Meanwhile, the average LQ value for water surfaces increased slightly from 0.92 in 2000 to 0.97 in 2024, suggesting an overall upward trend that is, however, limited and localized in scale. Accordingly, this cluster represents a broad regional structure characterized by the weakening of agricultural dominance and the relative stability of forest and water surface areas across most parts of T\u0026uuml;rkiye.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, T\u0026uuml;rkiye\u0026rsquo;s NUTS-2 regions are spatially grouped into two main clusters. The first cluster, concentrated across the western, northern, and central parts of the country, is characterized by a balanced land use structure between forest and agricultural areas. In these regions, dominance levels are relatively similar, indicating a high degree of spatial equilibrium and diversity in land use patterns.In contrast, the second cluster consists solely of the TRB2 region (Van\u0026ndash;Muş\u0026ndash;Bitlis\u0026ndash;Hakk\u0026acirc;ri), which distinctly diverges from all other regions due to its exceptionally high dominance in water surfaces. Nevertheless, between 2000 and 2024, the LQ value for water surfaces in TRB2 declined from 5.70 to 4.30, a decrease that can be attributed to the contraction of lake ecosystems and climatic influences. Despite this decline, TRB2 continues to exhibit the highest water surface dominance in T\u0026uuml;rkiye, representing the critical role of hydrological factors in shaping spatial dominance patterns.\u003c/p\u003e \u003cp\u003eOverall, the clustering results indicate that there is no homogeneous pattern of dominance across T\u0026uuml;rkiye\u0026rsquo;s NUTS-2 regions; rather, distinct regional clusters have emerged. Most of the regions within the first cluster exhibit dominance patterns based on a balanced distribution between agricultural and forest areas, whereas the TRB2 region in the second cluster demonstrates strong dominance in water surfaces, underscoring the determinant role of hydrological factors in spatial dominance dynamics. This outcome confirms the validity of the two principal axes\u0026mdash;\u0026ldquo;agriculture\u0026ndash;forest\u0026rdquo; and \u0026ldquo;water surfaces\u0026rdquo;\u0026mdash;previously identified through PCA.\u003c/p\u003e \u003cp\u003eThe findings collectively suggest that T\u0026uuml;rkiye\u0026rsquo;s land use structure displays regional diversity shaped by the interaction between natural and economic systems, highlighting the need for differentiated spatial planning strategies that account for these region-specific dynamics.koymaktadır.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThis study quantitatively examined land use changes across T\u0026uuml;rkiye\u0026rsquo;s NUTS-2 regions for the 2000\u0026ndash;2024 period, revealing the spatial dominance patterns of forest, agricultural, and water surface areas. The integrated application of LQ, PCA, and Cluster Analyses demonstrated that T\u0026uuml;rkiye\u0026rsquo;s land use structure is not uniform, but rather shaped by distinct regional typologies. The findings indicate a general decline in agricultural areas, a relatively stable yet regionally differentiated pattern in forest areas, and notable changes in water surfaces, particularly across the eastern and southeastern regions. Among these, the TR61 region (Antalya\u0026ndash;Isparta\u0026ndash;Burdur) stands out with its balanced dominance between forest and agricultural lands, whereas the TRB2 region (Van\u0026ndash;Muş\u0026ndash;Bitlis\u0026ndash;Hakk\u0026acirc;ri) exhibits a high spatial concentration in water surfaces, underscoring the region\u0026rsquo;s unique hydrological significance.\u003c/p\u003e \u003cp\u003eThese trends are consistent with findings from previous studies in the literature. It has been determined that the conversion of forests into agricultural and pasture lands in the Western Black Sea region has weakened ecosystem functions (Palta ve diğerleri, 2025). Similarly, land cover change analyses conducted across T\u0026uuml;rkiye indicate an increase in forest areas in some regions, while a decreasing trend is observed in others (Şen \u0026amp; Akt\u0026uuml;rk, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In addition, satellite imagery\u0026ndash;based studies conducted on Lake Van have identified a contraction in the lake\u0026rsquo;s surface area between 2014 and 2023, supporting the unique spatial characteristics observed in the TRB2 region (Karakus, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These findings can also be interpreted as evidence that changes in water surfaces are influenced by both climatic factors and human-induced pressures.\u003c/p\u003e \u003cp\u003eIt is evident that the variations in land use cannot be explained solely by natural processes; rather, political, economic, and administrative dynamics play a decisive role. Losses of forest areas associated with energy and mining activities have been found to intensify spatial inequalities (Atmiş ve diğerleri, 2024). Similarly, Ala and Oru\u0026ccedil; Ertekin have demonstrated that addressing the water\u0026ndash;energy\u0026ndash;food systems in isolation weakens sustainability and that the lack of integrated governance leads to imbalances in regional resource use (Ala \u0026amp; Ertekin, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Furthermore, Ustaoğlu and Aydınoglu identified that urban expansion is predominant in the western regions of T\u0026uuml;rkiye, whereas agricultural land use dominates in the eastern regions, emphasizing that this differentiation is rooted in socio-economic factors (Ustaoglu \u0026amp; Aydınoglu, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These results indicate that the agriculture\u0026ndash;forest balance and regional vulnerabilities identified in this study are consistent with the east\u0026ndash;west\u0026ndash;oriented spatial inequalities observed across the country.\u003c/p\u003e \u003cp\u003eIn addition, according to the European Environment Agency\u0026rsquo;s \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e report, while settlement areas and water surfaces have expanded in T\u0026uuml;rkiye, agricultural lands have decreased. The report identifies urbanization, industrialization, tourism, energy development, and mining activities as the main drivers of this transformation (European Environment Agency, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Population growth and migration dynamics have driven governments to develop new policies and legal frameworks aimed at ensuring sustainable land use and protecting the public interest (Liu \u0026amp; Liu, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Similarly, a study conducted in Indonesia revealed that land access and population growth are among the most significant factors influencing changes in land use (Prayitno ve diğerleri, 2018). In the context of T\u0026uuml;rkiye, demographic pressures and economic trends have also been identified as key drivers behind the contraction of agricultural lands and the expansion of artificial surfaces.\u003c/p\u003e \u003cp\u003eAt the global scale, the rising population and increasing food demand have driven a shift in land use toward agricultural production (Hassan ve diğerleri, 2005). However, biofuel production (Smeets ve diğerleri, 2007) and afforestation initiatives implemented within the framework of climate-neutral policies (Verhoeven \u0026amp; Setter, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), have introduced new pressures on natural ecosystems. Moreover, while most existing studies primarily focus on the impacts of urban expansion on wetland ecosystems (Ngoran \u0026amp; Ngoran, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), relatively fewer have examined the effects of agricultural practices on these fragile environments (Awazi ve diğerleri, 2024). In parallel, rising temperatures, irregular precipitation patterns, and extreme weather events driven by climate change have been shown to accelerate ecosystem degradation in both coastal and inland wetlands (Choudhury ve diğerleri, 2021; Mehvar ve diğerleri, 2019). These findings suggest that the changes in water surface areas observed particularly in the eastern and southeastern regions of T\u0026uuml;rkiye reflect the regional manifestations of global climatic pressures.\u003c/p\u003e \u003cp\u003eIn conclusion, the findings from the 2000\u0026ndash;2024 period reveal a spatial structure in T\u0026uuml;rkiye characterized by the decline of agricultural lands, relative stability yet regional differentiation in forest areas, and localized patterns of both contraction and expansion in water surfaces. These results indicate a multidimensional transformation emerging at the intersection of natural, economic, and governance-related dynamics. The integrated application of LQ\u0026ndash;PCA\u0026ndash;Cluster analyses proposed in this research constitutes one of the first comparative frameworks to evaluate land-use dominance patterns at the national scale, thereby offering a methodological contribution to the literature.\u003c/p\u003e \u003cp\u003eWithin this context, the study provides critical implications for regional planning and resource management. The development of ecosystem-based planning, sustainable agricultural production strategies, and integrated land management policies will play a pivotal role in enhancing regional resilience against potential climatic and socio-economic shocks. Monitoring spatial dominance in T\u0026uuml;rkiye is therefore crucial not only for ensuring environmental sustainability, but also for maintaining economic stability and food security.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAll authors have read, understood, and have complied as applicable with the statement on \u0026ldquo;Ethical responsibilities of Authors\u0026rdquo; as found in the Instructions for Authors.\u003c/p\u003e\u003cp\u003e \u003ch2\u003eClinical trial number\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eEA developed the conceptual framework, designed the methodology, and wrote the main sections of the manuscript.BK contributed to data standardization, the environmental assessment framework, and the development of the discussion section.SA prepared the tables and figures (Figures 1\u0026ndash;3) and carried out data checking and reference formatting.All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eCalculated by the authors based on data from the (General Directorate of Forestry (OGM), 2024; Ministry of Agriculture and Forestry (MoAF), 2000; Ministry of Agriculture and Forestry (MoAF), 2024; Turkish Statistical Institute (TURKSTAT), 2024a; Turkish Statistical Institute (TURKSTAT), 2024b; Ministry of Environment, Urbanization and Climate Change, 2024).Turkish Statistical Institute (TURKSTAT). (2024a). Crop Production Statistics. Retrieved September 15, 2025, from https://data.tuik.gov.tr/Kategori/GetKategori?p=Tarim-111Turkish Statistical Institute (TURKSTAT). (2024b). Agricultural Data. Retrieved September 10, 2025, from https://data.tuik.gov.tr/Kategori/GetKategori?p=Tarim-111 General Directorate of Forestry (OGM). (2024). Official statistics. Retrieved September 10, 2025, from https://www.ogm.gov.tr/tr/e-kutuphane/resmi-istatistiklerMinistry of Agriculture and Forestry (MoAF). (2000). CORINE Project.Ministry of Agriculture and Forestry (MoAF). (2024). Wetlands Inventory System (SAYBIS). Retrieved September 10, 2025, from https://saybis.tarimorman.gov.tr/Ministry of Agriculture and Forestry (MoAF). (2025). Retrieved September 10, 2025, from https://istatistik.tarimorman.gov.tr/Sayfa/Detay/2053?utm_Ministry of Environment, Urbanization and Climate Change. (2024). 2024 Provincial Environmental Status Reports. Retrieved September 10, 2025, from https://ced.csb.gov.tr/2024-yili-il-cevre-durum-raporlari-i-113266\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAla, E., \u0026amp; Ertekin, G. D. (2025). Integrated and Sustainable Food Systems in the Context of the WEF Nexus Approach: An assessment of Istanbul, Ankara, and Izmir. \u003cem\u003eJournal of Planning\u003c/em\u003e, \u003cem\u003e35\u003c/em\u003e(50 Suppl. 1), 18\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAtmiş, E., Yıldız, D., \u0026amp; Erd\u0026ouml;nmez, C. (2024). A different dimension in deforestation and forest degradation: Non-forestry uses of forests in Turkey. \u003cem\u003eLand Use Policy\u003c/em\u003e, \u003cem\u003e139\u003c/em\u003e, 107086.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAwazi, N. P., Quandt, A., \u0026amp; Ambebe, T. F. (2024). 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Net primary productivity dynamics and associated hydrological driving factors in the floodplain wetland of China\u0026rsquo;s largest freshwater lake. \u003cem\u003eScience of The Total Environment\u003c/em\u003e, \u003cem\u003e659\u003c/em\u003e, 302\u0026ndash;313.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Spatial dominance, LQ analysis, PCA, Clustering, NUTS-2 regions, Türkiye","lastPublishedDoi":"10.21203/rs.3.rs-8089182/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8089182/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examines the spatial dominance levels of forest, agricultural, and water surface areas, as well as the regional differentiation trends across T\u0026uuml;rkiye\u0026rsquo;s NUTS-2 regions, using data from 2000\u0026ndash;2024. The datasets were compiled from official national sources: the General Directorate of Forestry for forest areas, the Turkish Statistical Institute for agricultural lands, and the Ministry of Environment, Urbanization and Climate Change\u0026rsquo;s Environmental Status Reports, CORINE Land Cover datasets, and the Wetland Information System for water surfaces. All data were standardized in hectares to ensure comparability across 27 regions.\u003c/p\u003e \u003cp\u003eThe Location Quotient (LQ) method determined the relative dominance levels of each region in land use types. Principal Component Analysis (PCA) revealed that 84.6% of the total variance was explained by two principal components, while Cluster Analysis identified two main typological land-use groups across T\u0026uuml;rkiye.\u003c/p\u003e \u003cp\u003eThe findings indicate a decline in agricultural areas, a relatively stable but regionally differentiated structure in forest areas, and notable changes in water surfaces, particularly in TRC1 and TRB2 regions. The TR61 region (Antalya\u0026ndash;Isparta\u0026ndash;Burdur) stands out with high values in both agricultural and forest areas, whereas TRB2 (Van\u0026ndash;Muş\u0026ndash;Bitlis\u0026ndash;Hakk\u0026acirc;ri) exhibits distinct dominance in water surfaces. This study represents one of the first comprehensive attempts to integrate the spatial components of land use in T\u0026uuml;rkiye through a multidimensional quantitative approach. The results provide a holistic framework for regional planning and sustainable resource management and contribute to the broader discourse on enhancing regional resilience against future multi-dimensional crises and shocks.\u003c/p\u003e","manuscriptTitle":"Spatial Dominance of Land Use in Türkiye’s NUTS-2 Regions: An Integrated Quantitative Approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-08 19:29:56","doi":"10.21203/rs.3.rs-8089182/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-03T21:41:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-29T07:24:47+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-29T07:23:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Monitoring and Assessment","date":"2025-11-11T17:17:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f3c13b24-4d27-4b53-a7c6-1849a54f0131","owner":[],"postedDate":"January 8th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T16:01:27+00:00","versionOfRecord":{"articleIdentity":"rs-8089182","link":"https://doi.org/10.1007/s10661-026-15390-2","journal":{"identity":"environmental-monitoring-and-assessment","isVorOnly":false,"title":"Environmental Monitoring and Assessment"},"publishedOn":"2026-04-27 15:57:42","publishedOnDateReadable":"April 27th, 2026"},"versionCreatedAt":"2026-01-08 19:29:56","video":"","vorDoi":"10.1007/s10661-026-15390-2","vorDoiUrl":"https://doi.org/10.1007/s10661-026-15390-2","workflowStages":[]},"version":"v1","identity":"rs-8089182","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8089182","identity":"rs-8089182","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}