Land Evaluation Following Updated World Reference Base (WRB) Soil Mapping: A Tool for Sustainable Land Planning in Mediterranean Environments

Abstract

Harmonised land evaluation frameworks are essential for sustainable land planning and policy development. Assessing land suitability is crucial for predicting agricultural and forestry potential but also for mitigating land degradation risks. Current land suitability maps in Portugal vary greatly in scale and methodology. This study presents the first nationally consistent framework to produce a harmonised land suitability map for mainland Portugal at a 1:100,000 scale following a recently updated WRB soil map. The latter was obtained by integrating legacy soil data with delineated land units according to soil-forming factors (climate, lithology, and relief). These land units were used to derive key land qualities, subsequently classified into constraint levels. Following FAO land evaluation principles, four land suitability levels for agriculture and forestry were assigned to 125 land units across three representative areas in southern Portugal. Relief and lithology emerged as main drivers of land suitability. Marginal agricultural lands are largely dominant (65.1–78.0%), followed by non-suitable lands (14.8–28.3%). Forestry suitability is mostly confined to moderate (61.5–69.4%) and marginal (30.6–37.4%) classes, reflecting the higher adaptability of forestry systems. High consistency was observed between the derived suitability classes and the latest land use/land cover map of Portugal. The framework enables decision-makers to identify areas suitable for intensive production while safeguarding lands vulnerable to degradation. It also provides a transferable tool for adaptive landscape management and sustainable land allocation, supporting policy development under changing environmental conditions in Mediterranean regions.

1. Introduction

Land is a finite and irreplaceable foundation for agriculture, biodiversity conservation, ecosystem services provision, and climate change adaptation [1,2]. At the same time, land degradation processes are expanding worldwide, driven by land-use intensification associated with population growth and economic pressures, undermining land productivity and ecosystem resilience [3,4,5,6]. The Mediterranean region is particularly vulnerable to land degradation due to its strong interannual climate variability [7,8]. Climate change intensifies this vulnerability by increasing the frequency and severity of extreme events, such as flash floods, heatwaves, and prolonged droughts [9,10,11]. These challenges underscore the need for robust and systematic land evaluation frameworks [2,4].

Land evaluation is a key tool that offers a systematic and spatially explicit framework to assess the potential and constraints of land for agricultural, forestry, and other rural uses [12,13,14,15]. Integrating land assessment with landscape planning contributes not only to productive use but also to ecosystem services and human health and well-being, bridging scientific knowledge and sustainable spatial planning [16,17]. By integrating biophysical, climatic, and management factors, such assessments allow the identification of land-use options that optimise production while minimising degradation risks, particularly within agricultural, forestry, and mixed-use systems [3,13,15,18]. Land evaluation represents a strategic instrument to address Land Degradation Neutrality (LDN) and to guide restoration and land-use decisions in line with the United Nations Convention to Combat Desertification [19,20], thereby contributing to the achievement of the 2030 Agenda for Sustainable Development [21].

Within the Mediterranean context, characterised by increasing pressure on water resources, accelerated soil degradation, and a higher risk of desertification [8,9], Portugal faces significant land degradation challenges. These challenges are further exacerbated due to the heterogeneity of land evaluation systems across the country. In fact, the soil maps used for that process differ substantially in scale, classification system, and methodology, which has directly contributed to the discrepancies observed in the resulting land evaluation maps. Indeed, in the North and inland Centre regions, land suitability maps for generic agricultural and forestry purposes were developed using a 1:100,000-scale soil mapping [22,23,24] based on the FAO [25] and WRB [26] classification systems (Figure 1). Such soil mapping relied on the prior delineation of land units (or landscape units), an ecologically homogeneous portion of the landscape, characterised by uniform environmental and land-use attributes [27,28,29], providing a conceptual basis for mapping and transferring landscape knowledge from evaluation to application [27]. Thus, it enabled the derivation of data on the land’s ‘characteristics’ and ‘qualities’ following the FAO framework principles [13,30], aiming to identify optimal land use while conserving natural resources. This 1:100,000 suitability map was incorporated into the legal framework governing the National Agricultural Reserve (NAR), a Portuguese land-use protection zoning system, in the above-mentioned regions (Decree-Law No. 199/2015) [31].

In contrast, in the southern and coastal Centre regions (Figure 1; ~55% of the mainland territory), a “soil use capacity” map [32], based on Land-Capability Classification [12], was developed alongside soil mapping efforts at a 1:25,000 scale using the Soil Classification of Portugal [33]. Despite its finer scale (compared to 1:100,000), the 1:25,000 soil map relied on subjective field interpretation with limited analytical data and non-georeferenced profiles [34,35], which compromised the consistency and reliability of the derived “soil use capacity” map. Unlike the Land-Capability Classification [12], it was developed with a focus on rainfed cereal crops, relied on expert-based interpretation, and did not explicitly account for climatic constraints [32].

Although both were included in the NAR legislation [31], the 1:100,000 land suitability and the 1:25,000 “soil use capacity” maps were developed independently, leaving substantial discrepancies in terms of scale, biophysical information, and conceptual criteria across regions. Consequently, the absence of a harmonised land evaluation for mainland Portugal prevents consistent nationwide comparisons and limits the policy relevance of land-use planning tools. Despite the recognised importance of land suitability mapping, in Portugal there is currently no nationally harmonised WRB-based framework integrating updated soil information into land evaluation. Nevertheless, the 1:25,000 legacy soil map is currently being updated to a 1:100,000 scale [36] using the WRB system [37], based on land units delineated according to soil-forming factors (lithology, relief, and climate). This approach ensures consistent soil mapping and may enable the derivation of land qualities, allowing the production of a harmonised land suitability map for the entire mainland.

Building on the FAO land evaluation principles [13,14,15,30,38], adapted to the Portuguese environmental settings, the present study aims to implement and evaluate a harmonised methodology across three study areas to support land suitability mapping at a 1:100,000 scale for mainland Portugal. The proposed approach employs a hierarchical system based on land characteristics and qualities, combining key soil physical and chemical properties with topographic and climatic data [39,40]. This integrated design ensures that land suitability reflects both intrinsic soil properties and external environmental constraints, as recommended by recent advances in land evaluation and sustainable land planning for different environments [41,42,43,44,45,46,47].

The present study is guided by two main hypotheses: (i) the recently updated WRB-based soil map provides data on key land characteristics and qualities to produce a harmonised national land suitability map for general agricultural and forestry uses; (ii) the resulting land suitability map is broadly consistent with current land use/cover patterns [48], thereby providing an initial indication of the approach’s coherence. Through an expeditious methodology, this research provides a scientifically consistent spatial framework that informs evidence-based land-use planning, natural resources conservation, and the improvement of existing policy instruments such as the NAR. The approach is potentially scalable and transferable to other Mediterranean regions facing similar environmental constraints, particularly as a decision-support tool for spatial planning and land management.

2. Materials and Methods

2.1. Study Areas

The present study was conducted in southern Portugal (37°47′–39°40′ N, 7°14′–8°58′ W; Figure 1), focusing on three non-contiguous test areas—Portalegre, Beja, and Coruche—each covering approximately 300,000 ha and representing contrasting geological and geomorphological conditions.

Geologically, Portalegre and Beja are located within the Iberian (Hesperian) Massif of the European Variscan Belt, dominated by Precambrian and Palaeozoic metamorphic and igneous rocks, with Beja also including sedimentary formations [49,50]. From a geomorphological perspective, both areas belong to the Southern Portuguese Peneplain [51], although Portalegre includes moderately elevated terrain (~300–400 m) and the São Mamede–Marvão range (1025 m), whereas Beja is mostly flat and dissected by quaternary drainage networks. Coruche lies within the Tejo–Sado Cenozoic sedimentary basin, a tectonically stable unit composed of marine and fluvial sediments forming extensive sandy and clay-rich plains [51,52].

All three study areas have a Mediterranean climate (Csa, Köppen–Geiger), with hot, dry summers and mild, wet winters. Portalegre shows greater climatic variability, in terms of mean annual rainfall (MAR) and temperature (MAT), reflecting its broader altitudinal range. Such differences may influence soil development and land use patterns.

Table 1. Area, ranges of altitude (Alt), mean annual rainfall (MAR ^a), mean annual temperature (MAT ^a), and proportional area (%) occupied by each climatic unit ^b and relief unit ^b within each study area: Portalegre (PO), Coruche (CO), and Beja (BE).

Study Areas	Area (ha)	Alt (m)	MAR (mm)	MAT (°C)	Climatic Units (%) c			Relief Units (%) c
					Q2	QL	T2	p	s	o	m
PO	287,161	46–945	591–825	12.4–17.0	99.7	-	0.3	1.6	31.2	41.8	25.3
CO	283,885	0–257	542–696	16.1–16.9	81.4	18.6	-	15.5	41.3	37.4	5.8
BE	300,671	11–391	491–722	16.9–17.6	100	-	-	12.6	43.7	35.4	8.3

^a [53], period 1981–2010; ^b compiled and adapted from [54]. Q2—continental warm land; QL—littoral warm land; T2—temperate continental land; p—plains; s—undulating; o—strongly sloping; m—steep. ^c Percentages may not sum to exactly 100% due to rounding.

presents the proportional distribution of climatic and relief units in each study area; details on such units are provided in

Table A1. Climatic units represented in the study areas of Portalegre (PO), Beja (BE), and Coruche (CO). The table presents each climatic unit and its corresponding code, the mean annual temperature (MAT), and the mean annual temperature of the coldest month (MATcm) and the warmest month (MATwm). Compiled from [54] under the Creative Commons Attribution (CC BY) licence.

Climatic Units	Code	MAT (°C)	MATcm (°C)	MATwm (°C)
Continental warm land	Q2	>14	<10	>22
Littoral warm land	QL	>16	>10	>22
Temperate continental land	T2	12.5 ≤ ₸ ≤ 14	<10	<22

(Appendix A).

Soil distribution reflects the biophysical conditions defined by the interaction of local soil-forming factors [36]. In Beja, soil mapping units (SMUs) associated with Luvisols (LVs) are the most widespread, followed by Regosols (RGs), together covering approximately two-thirds of the area. In Portalegre, SMUs dominated by Regosols, Luvisols, and Cambisols (CMs) occur in roughly equal proportions, comprising around three-quarters of the total area, with substantial rock outcrops at higher elevations. In Coruche, SMUs associated with Regosols are most abundant, representing nearly half of the total soil area, followed by Luvisols.

2.2. Methodological Framework

2.2.1. Overall Workflow

The overall workflow of the soil and land suitability map updating and harmonisation project, within which the present study is embedded, is illustrated in Figure 2. Within this context, in a GIS environment [55], provisional land units were initially delineated based on soil-forming factors. Subsequently, these units were refined using legacy soil data converted to the WRB classification, resulting in a consistent set of spatially homogeneous land units. Thus, each land unit integrates data on soil, climate, and relief and constitutes the basic spatial entity for the land suitability assessment. Land characteristics and derived qualities were evaluated to assign suitability classes for general agricultural and forestry purposes. The resulting harmonised land suitability maps were then compared with current land use/land cover (LULC) to assess their correspondence.

2.2.2. Legacy Soil Data

The legacy soil data used in the present research comprised two main components: (i) the 1:25,000-scale soil mapping [33], based on the Soil Classification of Portugal (SCP), together with 186 reference soil profiles originally used to define its categorical levels [33]; and (ii) an additional dataset of 622 legacy soil profiles compiled from diverse sources within the project area, of which 165 are located within the study areas (corresponding to an average density of approximately one profile per 53 km²).

The 1:25,000 soil map delineates soil mapping units consisting of single soil families, family associations, soil phases, and rock outcrops. It also includes areas classified as “social areas”, corresponding to anthropogenic or built-up features such as settlements, roads, and other infrastructure. The reference soil profiles, mostly collected during the late 1950s and 1960s, generally lack detailed morphological and analytical data and are not georeferenced. For this reason, they were not used as a primary source for the quantitative estimation of land qualities. Instead, priority was given to legacy soil profiles described over the last three decades (1995–2024), which are georeferenced and analytically characterised according to WRB standards. Potential implications associated with the temporal span of the legacy soil data are discussed in Section 4.5 (Limitations and Future Research).

2.2.3. Updated and Harmonised WRB-Based Soil Map

Land evaluation in the present study was based on a WRB soil map currently being updated and harmonised for the entire country at a scale of 1:100,000, developed within a GIS-based workflow detailed in [36]. It constitutes the main biophysical input for the land suitability assessment, providing a consistent and functionally oriented representation of soil spatial variability across the study areas.

The WRB soil map is being produced following a land-systems approach [56,57], in which climate, lithology, and relief (key soil-forming factors) were first mapped as independent spatial layers and subsequently integrated to delineate provisional homogeneous land units (LUs). Climatic units (Table A1; Appendix A) were derived from long-term climatological normals (1981–2010) [53]. Lithological groupings (

Table A2. Proportion of lithological groupings (and corresponding codes) represented in the study areas of Portalegre (PO), Beja (BE), and Coruche (CO), covering 21 of the 23 lithological groupings defined for the entire project area. Adapted from [54] under the Creative Commons Attribution (CC BY) licence.

Rock Type	Lithological Groups	Code	PO (%)	CO (%) a	BE (%) a
Magmatic	Granites and related rocks	g	37.0	2.6	1.5
	Diorites and related rocks	d	0.3	0.3	11.5
	Mafic rocks	b	1.9	0.1	14.5
	Ultramafic rocks	u	0.2	<0.1	0.1
Metamorphic	Quartzites, schists and related rocks	q	5.3	<0.1	13.0
	Gneisses and related rocks	n	7.6	0.8	1.6
	Schists and related rocks	x	30.1	0.7	13.6
	Intermediate-mafic metamorphic rocks	t	1.5	0.4	7.1
	Marbles	m	1.6	<0.1	0.6
Sedimentary	Arenites	r	0.4	34.6	2.8
	Conglomerates	l	<0.1	<0.1	0.4
	Sands	a	<0.1	14.2	2.5
	Alluvium	f	2.3	12.7	2.8
	Colluvium and slope deposits	v	1.8	0.5	0.4
	Unconsolidated or poorly consolidated arenaceous materials	s	1.4	6.3	2.1
	Unconsolidated or poorly consolidated medium-textured materials	sk	<0.1	0.3	0.4
	Clayey materials	k	4.3	6.7	11.0
	Arenaceous materials on sandstones or clayey materials	sr	0.7	18.3	0.3
	Gravelly and sandy and/or clayey materials	cs	2.0	0.4	6.5
	Non-compact limestones	c	0.2	0.2	4.9
	Marls and related rocks	mg	1.1	1.0	2.5

^a Percentages may not sum to exactly 100% due to rounding.

; Appendix A) were defined based on geological origin and properties such as silica content, degree of consolidation, and sediment grain size [37,58,59,60]. The 1:200,000 geological map of Portugal (provided by the National Laboratory of Energy and Geology, LNEG) was used as the primary data source to generate this layer. In sedimentary areas, where this geological map offers limited lithological differentiation, additional information from the 1:25,000 legacy soil map was incorporated. When discrepancies occurred, soil map data were prioritised in sedimentary areas, since the geological map often fails to represent relatively thin sedimentary formations overlying materials of a different nature. Relief units (

Table A3. Relief units represented in the study areas of Portalegre (PO), Beja (BE), and Coruche (CO). The table presents each relief unit and its corresponding code, median slope (MD, %), interquartile range (IQR) of slope, and the proportional area (%) occupied by each unit in each study area. Adapted from [54] under the Creative Commons Attribution (CC BY) licence.

Relief Units	Code	MD (%)	IQR	PO (%) a	CO (%)	BE (%)
Steep	m	10.84	2.71	25.3	5.8	8.3
Strongly sloping	o	7.53	2.47	41.9	37.4	35.4
Undulating	s	4.58	0.67	31.2	41.3	43.7
Plains	p	3.12	0.38	1.6	15.5	12.6

^a Percentages may not sum to exactly 100% due to rounding.

; Appendix A) were digitally generated from a digital elevation model (DEM) based on the SRTM 1-arc second dataset, resampled to 25 m resolution and projected to the ETRS89-TM06 reference system. The DEM was processed following the methodology of Hammond [61,62,63] and Morgan & Lesh [64], thereby considering altitude, slope and curvature. Minimum mapping units for each layer and similarity rules were applied to ensure spatial coherence at the target scale.

After establishing the rules for converting the Soil Classification of Portugal to the WRB system [37,65], the national 1:25,000 legacy soil map was intersected with the provisional LUs map. These social areas were incorporated in the provisional LUs map but excluded from the land suitability assessment. Next, the provisional LUs were refined based on the types and proportions of soil units within them. To exemplify, the lithological groupings of alluvium and mafic rocks were subdivided into three sub-groupings, which were distinguished by adding a number after the lithological code. This process resulted in a set of definitive LUs, with a minimum mapping unit of 30 ha. This threshold was selected as the smallest area reliably representable at the 1:100,000 scale, based on preliminary tests with different unit sizes. Each LU defines a distinct soil mapping unit (SMU), except in cases where different LUs exhibit similar types and proportions of soil units, in which case they were considered to represent the same SMU (Figure A1; Appendix A).

Each LU represents a spatially coherent association of WRB Reference Soil Groups and qualifiers, integrating dominant soil-forming processes and key soil functional properties relevant for land evaluation, including soil depth, texture, stoniness, fertility status, drainage and aeration conditions, carbonate and salt content, and water-holding capacity. By explicitly linking soils to their controlling environmental factors, the updated 1:100,000 map improves internal consistency and knowledge about soil properties, making this dataset particularly suitable for regional land suitability assessment.

2.2.4. Land Characteristics and Qualities: Limitation Levels

The assessment of land suitability for agriculture and forestry was based on a set of key biophysical parameters (land characteristics and qualities), selected following FAO land evaluation guidelines [13,14,15,25,30,38] and adapted to the specific environmental conditions of the project area. The set of land qualities considered (

Table A4. Limitation levels for each land quality, with corresponding symbols and units, for the whole project area.

Land Quality	Symbol	Units	Levels
			1	2	3	4
Temperature regime	t	°C	>12.5	10.5–12.5	<10.5	-
Rooting conditions	z	cm	>100	100–50	50–25	<25
Soil fertility	f	cmolc kg−1	>10	10–5	<5	-
Toxicity *	x	-	absent	present	-	-
Aeration (drainage)	a	month	<1	1–2	2–3	>3
Water deficit period	h	month	<5	5–6	6–7	> 7
Natural soil erosion risk—agriculture	ea	t ha−1 yr−1	<10	10–25	25–50	>50
Natural soil erosion risk—forestry	ef	t ha−1 yr−1	<50	50–100	>100	-
Carbonate concentration	c	%	<2	2–25	25–45	>45
Salinity	s	dS m−1	<4	4–16	>16	-
Rock outcrops	d	%	<20	20–50	50–70	>70
Slope inclination	i	%	<5	5–10	10–25	>25

* Toxicity is only documented in ultramafic rocks outside the project area.

—Appendix A) directly affects land use options, mechanisation feasibility, and crop or forest productivity, forming the basis for the assignment of land suitability classes.

Next, the land qualities considered in the present study are outlined, together with a description of how they were derived:

• Temperature regime (t). Mean annual temperature and frost days determined through data on climatological normals (1981–2010) [53].
• Rooting conditions (z). Based on the portion of the profile favourable for root growth and for water and nutrient uptake (effective soil depth). It integrates the A and B horizons and the portion of the C horizon that allows root penetration. For each land unit, the effective soil depth was calculated as the weighted average of the effective depth values of the soil units existing in that land unit. The effective depth for each soil unit was derived from legacy soil profiles by matching their classification to that of the soil unit. This procedure was applied to all land units in the study areas.
• Soil fertility (f). Based on chemical properties related to nutrient retention, particularly cation exchange capacity (CEC at pH 7) and base saturation (Eutric vs. Dystric). For each land unit, CEC and base saturation values were calculated as the weighted average of the values of the soil units existing in that land unit. Values for each soil unit were derived from legacy soil profiles by classification matching. Soil fertility is strongly controlled by the lithological groupings defined for the current project. Three levels of fertility were defined: (i) CEC < 5 cmol·kg⁻¹, mostly Eutric; (ii) 5 ≤ CEC ≤ 10 cmol·kg⁻¹, mostly Eutric; (iii) CEC > 10 cmol·kg⁻¹, Eutric (except Alisols and Acrisols). These thresholds were adopted to maintain consistency with criteria previously established for other regions of Portugal [22,23,24], ensuring a harmonised, national-scale classification of soil fertility.
• Toxicity (x). Considered present only in soils developed on ultramafic rocks, due to high levels of potentially toxic elements (e.g., Ni, Cd) and associated nutrient imbalances. These soils are not representative in the study areas.
• Aeration (drainage) (a). Reflects the frequency and duration of soil water saturation, affecting aeration and plant growth. It was inferred from the WRB Reference Soil Groups and qualifiers and their proportion within each land unit, as well as on the topographic context. For example, Gleysols, Stagnosols, and Planosols show restrictions associated with extended waterlogging.
• Water deficit period (h). The period during which soil water availability is insufficient for plant growth was assessed through a monthly water balance by combining the climatic water balance and soil available water capacity. The climatic water balance was calculated as the difference between precipitation and reference evapotranspiration (ET0), using 1981–2010 climatological normals [53]. Soil available water capacity was estimated for each land unit from the mean effective soil depth and the mean unitary available water capacity (mm cm⁻¹) of the dominant pedological units. In the absence of detailed soil hydraulic measurements, unitary available water capacity values were assigned according to dominant soil texture classes, adjusted for coarse fragments and organic matter content, following [66,67]. Total available water capacity (mm) was then calculated for each homogeneous zone and grouped into standard classes.
• Natural soil erosion risk (e). This limitation describes the potential land sensitivity to soil erosion by water, and its spatial pattern was achieved by using only natural driving forces, that is, climate, soil properties and topography. Then, the potential natural soil loss for each land unit was estimated assuming the absence of vegetation cover and erosion control practices [68,69]. Evaluation of natural soil erosion risk was based on the Revised Universal Soil Loss Equation (RUSLE) [70]:
  • Rainfall erosivity (R): calculated based on established relationships between rainfall erosivity R (MJ·mm·h⁻¹·ha⁻¹·yr⁻¹) and mean annual precipitation (P; mm) using the 1981–2010 climatological normal [53], with a coefficient of determination (r²) > 0.99 [24]: $\mathrm{R}=2.223\ast \mathrm{P}-406.3$.
  • Soil erodibility (K): estimated from soil properties (e.g., texture, organic matter, structure, permeability, and stoniness) of the dominant soil units [24,71,72,73].
  • Topographic factor (LS): derived from slope inclination and slope length calculated from the DEM [74].
  • Cover-management factor (C): set to 0.4 for agricultural land [71], 0.2 for oak woodlands [75], and 0.1 for forest plantations [76].
  • Support practice factor (P): assigned according to erosion control measures, particularly strip cropping and controlled runoff, with P = 0.5 [24,75,76].

Erosion limits were defined considering 10 t ha⁻¹ yr⁻¹ as the maximum tolerable soil loss, which falls within the range commonly accepted in the literature [77].

• Carbonate concentration (c). High total carbonate contents, particularly in Bk, BCk, and Ck horizons, can limit root development and reduce the availability of some micronutrients (Zn, Cu, Fe, Mn, B) and phosphorus, especially in soils with low organic matter. Data (up to 50 cm depth) were derived from the proportion of each soil unit within each land unit, being strongly associated with the defined lithological groupings.
• Salinity (s). Caused by soluble salts, it can strongly limit crop and forest growth. Based on available mapping and profile data (e.g., electrical conductivity of saturation extracts or NaCl content), limitation levels were defined for each land unit. Saline soils are mostly associated with estuarine and coastal areas, having negligible representation in the study areas.
• Rock outcrops (d). Proportion of each land unit covered by rock outcrops, which was derived from the 1:25,000 legacy soil map [33].
• Slope inclination (i). Considered as a limiting factor for agricultural and forestry use based on restrictions to machinery operation, livestock movement, field access, cultural practices, and crop transport. Assigned according to the relief unit associated with each land unit. Limitation levels were coded as follows: plains = 1, undulating = 2, strongly sloping = 3, steep = 4.

All land qualities were classified on a scale from 1 to 4, except for temperature regime and fertility (1–3) and toxicity (1–2). The criteria set for assigning a level to each land quality considered in the present study is shown in Table A4 (Appendix A).

2.2.5. Land Suitability Assessment Framework

Land suitability was assessed following the FAO Land Evaluation approach [13,14,25,30,38], as applied in previous national studies for Portugal (e.g., [22,23,24]), with adaptations incorporating principles from the Global Agroecological Zones (GAEZ) [15]. This assessment follows a rule-based, expert-driven framework, in which suitability classes are assigned using predefined thresholds and limitation criteria rather than predictive or statistical modelling. The evaluation focused on two broad land-use categories: agriculture, including temporary and permanent crops, and pasture; and forestry, including silvopastoral systems. Suitability for each land unit was determined by comparing observed land qualities with the minimum requirements for each land-use category (

Table 2. Maximum allowed level of each land quality within each land suitability class, considering agricultural (A) and forestry (F) uses.

General Uses	Classes	Land Qualities
		t	z	f	x	a	h	e	c	s	d	i
Agriculture	A1 (S1)	1	1	1	1	2	1	1	2	1	1	1
	A2 (S2)	2	2	2	1	3	2	2	3	2	2	2
	A3 (S3)	2	2	3	1	4	3	3	4	3	2	3
	A0 (N)	3	3, 4	-	2	-	4	4	-	-	3	4
Forestry	F1 (S1)	1	2	-	1	1	1	1	1	1	2	2
	F2 (S2)	2	3	-	1	2	2	2	2	2	2	3
	F3 (S3)	3	4	-	1	3, 4	3, 4	3	3, 4	3	3	4
	F0 (N)	-	-	-	2	-	-	4	-	-	4	-

t: temperature regime; z: rooting conditions; f: fertility; x: toxicity; a: aeration; h: water deficit period; e: natural soil erosion risk; c: carbonate concentration; s: salinity; d: rock outcrops; i: slope inclination. Suitability classes A1–A3 and F1–F3 indicate decreasing agricultural and forest suitability, respectively; A0 and F0 indicate non-suitable land. Land quality level: 1 = least limiting; higher values indicate increasing constraints.

). Thresholds were adopted from FAO guidelines and previous national land evaluations to ensure methodological consistency and comparability across regions [22,23,24]. In agricultural terms, A1 corresponds to FAO S1 (high suitability), A2 to S2 (moderate), A3 to S3 (marginal), and A0 to N (non-suitable land). Forestry suitability levels follow the same principle. In both cases, suitability levels were assigned based on the most limiting land quality(ies), with agriculture (A1–A3) lowered if at least three of them were jointly limiting; cumulative limitations were not applied for forestry (F1–F3). Subclasses indicate the most limiting land quality(ies).

Key considerations influencing suitability classification included: (i) agriculture is restricted in colder zones, where only forestry use is feasible; (ii) rooting conditions imposes stricter limitations on agriculture than forestry; (iii) fertility is generally non-limiting for forestry; (iv) carbonates and aeration primarily affects deep-rooted perennial crops; (v) water availability limits rainfed agriculture, although supplementary irrigation can partially compensate; (vi) erosion is more limiting for agriculture than forestry; (vii) and rock outcrops and steep slopes strongly constitute an obstacle to mechanisation, more severely restricting agricultural uses.

The presented framework enabled a consistent, reproducible, and spatially explicit assignment of land suitability classes, supporting the production of harmonised land suitability maps and their subsequent comparison with current land use/land cover.

2.2.6. Correspondence with Land Use and Land Cover Map (LULC)

The correspondence between land suitability and current land use/cover was assessed using the official Portuguese Land Use and Land Cover dataset (LULC) [48]. This vector dataset, with a minimum mapping unit of 1 ha and positional accuracy consistent with a mapping scale of 1:25,000, provides the most up-to-date representation of LULC in mainland Portugal for the reference year 2023.

Across the three study areas collectively, the second-level LULC classes [48] were considered, and their proportional distribution was calculated within each agricultural and forestry suitability class. The following land use/cover types were analysed: (i) agricultural uses—temporary crops, permanent crops, improved pastures, permanent crops (including nurseries), natural grasslands, and heterogeneous agricultural areas; (ii) forestry and semi-natural areas—broadleaved forests (including oak woodlands), coniferous forests, agroforestry and silvopastoral systems, and shrubland; and (iii) other uses—surface water bodies, bare land, artificial territories, and wetlands.

LULC was used exclusively as an independent comparison dataset and not as an input variable for the land suitability assessment. This overlay-based analysis allowed the evaluation of how different land use types are distributed across suitability classes, providing an external assessment of the extent to which the derived land suitability maps reflect and discriminate current land use and cover patterns.

3. Results

This section presents the outcomes on the land suitability evaluation. Firstly, it shows the spatial distribution of land suitability classes across the study areas. Next, samples from each area illustrate the arrangement of land units with their corresponding suitability classes and dominant limitations. Finally, considering the three areas collectively, it presents the proportional distribution of land use/land cover (LULC) across agricultural and forestry suitability classes.

3.1. Land Suitability and Study Areas

Figure 3 shows the spatial distribution of agricultural and forest suitability classes in each study area. Overall, the three areas exhibit broadly similar patterns within the same land-use type.

Non-agricultural suitable land (A0) varies between 14.8% (Beja) and 28.3% (Portalegre) (

Table 3. Proportional distribution (%) of land suitability classes and non-suitable land for agricultural and forestry uses (symbols as in Figure 3), as well as water bodies (WB), in Portalegre (PO), Coruche (CO), and Beja (BE). Percentages were adjusted to sum to 100% after excluding social areas (built-up/anthropogenic features).

Study Area	Agriculture					Forestry
	A1	A2	A3	A0	WB	F1	F2	F3	F0	WB
PO	<0.1	0.8	70.6	28.3	0.3	<0.1	61.9	32.9	4.9	0.3
CO	2.5	8.1	65.1	24.3	<0.1	-	69.4	30.6	-	<0.1
BE	0.8	5.4	78.0	14.8	1.0	-	61.5	37.4	0.1	1.0

). Marginal land (A3) is largely dominant in all study areas, ranging from 65.1% to 78.0%. Moderately suitable land (A2) has negligible (0.8%) to limited (8.1%) representation, and highly suitable land (A1) is the least representative class, especially in Portalegre and Beja (<1%). The highest combined proportion of A1 and A2 classes is observed in Coruche (10.6%), mostly associated with alluvial zones.

With respect to forestry suitability, differences between study areas are less pronounced (Table 3). Moderately suitable land (F2) is clearly dominant (61.5–69.4%), with marginal land (F3) also showing substantial representation (30.6–37.4%). Non-suitable land (F0) only has significant representation in Portalegre (4.9%). Highly suitable land (F1) is absent or negligible across the study areas.

3.2. Land Suitability and Land Units

3.2.1. Qualitative Land Suitability Analysis in Each Study Area

Figure 4, Figure 5 and Figure 6 illustrate the spatial relationships between land units, agricultural suitability classes, and the most limiting qualities (subclasses) in samples of each study area, providing a detailed visual overview of how land suitability and constraints are spatially distributed. These samples are considered representative of the respective overall study area.

In the sample of the Beja area (Figure 4), marginal agricultural land (A3) is clearly dominant, followed by non-suitable land (A0). Both highly (A1) and moderately (A2) suitable lands have a low representation. The A3 class occurs mainly in undulating to strongly sloping terrains and across diverse lithological groups. The A1 class is associated with plains dominated by alluvium (‘f’) and A2 with unconsolidated or poorly consolidated medium-textured materials (‘sk’). Non-suitable land is generally in correspondence with schists (‘x’) in steep terrains, as well as with arenites (‘r’) and non-compact limestones (‘c’) in undulating to strongly sloping landscapes.

The dominant constraints (subclasses), which determine the suitability class, are soil water deficit period (h), slope inclination (i), rooting depth (z), and fertility (f), followed by erosion risk (e) and aeration (a) (Figure 4). No clear relationship is evident between the agricultural suitability classes and the dominant limitation, although in non-suitable lands, rooting depth (z) and slope inclination (i) are among the most frequent subclasses.

In the sample of the Portalegre study area (Figure 5), only marginal (A3) and non-suitable (A0) agricultural lands are present. The latter occurs wherever the relief is steep (‘m’), regardless of lithological grouping or climatic unit (Q2 vs. T2), whereas the former corresponds to the remaining relief units. Slope inclination (i) is the most limiting factor in most land units (Figure 5), followed by rooting depth (z) and erosion risk (e), whereas soil water deficit period (h) and fertility (f) occur to a lesser extent.

The sample of the Coruche study area (Figure 6) shows a balanced proportional distribution among moderate (A2), marginal (A3) and non-suitable (A0) agricultural land. Lithological groupings appear to exert a stronger influence on land suitability than relief. A2 class occurs exclusively in areas corresponding to alluvium (‘f’), whereas A0 is predominantly associated with sands (‘a’), most of which are in undulating terrains. The remaining lithological groupings are also related to sedimentary materials, corresponding mainly to the A3 class.

The most common subclass in the Coruche sample (Figure 6) is related to fertility (f), while aeration (a) and rooting depth (z) are also frequent; slope inclination (i) and erosion risk (e) occur to a lesser extent. Relationships are observed between the dominant limitations and lithological groupings. Indeed, fertility is the dominant limitation in land units made of arenites (‘r’), arenaceous materials over sandstones and/or clayey materials (‘sr’), and unconsolidated or poorly consolidated arenaceous materials (‘s’). Aeration and erosion risks constraints prevail in clayey materials (‘k’).

3.2.2. Quantitative Land Suitability Analysis for the Combined Study Areas

To further explore the relationships observed in Section 3.2.1,

Table 4. Proportional distribution (%) of land suitability classes and non-suitable land for agricultural and forestry uses in representative land units (LUs) and their corresponding dominant RSGs, aggregated across the three study areas.

LU	RSG	Agriculture (%)				Forestry (%)
		A1	A2	A3	A0	F1	F2	F3	F0
g.m	RG	-	-	-	100	-	-	100	-
g.s	RG	-	-	100	-	-	69.2	30.8	-
d.m	LV	-	-	-	100	-	-	100	-
d.s	LV	-	1.6	98.4	-	-	100	-	-
b3.o	VR	-	-	95.1	4.9	-	-	100	-
b3.p	VR	-	83.3	16.7	-	-	-	100	-
x.m	LP	-	-	-	100	-	-	100	-
x.s	CM/LV	-	-	100	-	-	96.0	4.0	-
k.o	ST/PL	-	-	87.4	12.6	-	-	100	-
k.p	ST/PL	-	-	100	-	-	-	100	-
r.o	RG	-	-	76.7	23.3	-	83.4	16.6	-
r.p	RG	-	-	100	-	-	40.5	59.5	-
c.o	RG	-	-	-	100	-	-	100	-
c.p	RG	-	-	100	-	-	-	100	-
a1.o	AR	-	-	-	100	-	100	-	-
a1.p	AR	-	-	-	100	-	100	-	-
f1.p	FL-eu	100	-	-	-	-	100	-	-
f2.p	FL-gl	-	100	-	-	-	-	100	-

Lithological groupings: g—granites and related rocks; d—diorites and related rocks; b—mafic (igneous) rocks; x—schists and related rocks; k—clayey materials; r—sandstones; c—non-compact limestones; a—sands; f—alluvium. Relief units: m—steep; o—strongly sloping; s—undulating; p—plains. Climate codes are not included, as only units corresponding to Q2 climate were considered. RGs—Regosols; LVs—Luvisols; VRs—Vertisols; LPs—Leptosols; CMs—Cambisols; STs—Stagnosols; PLs—Planosols; ARs—Arenosols; FLs—Fluvisols; eu—Eutric; gl—Gleyic.

presents the proportional distribution of agricultural and forestry suitability classes for selected representative land units (LUs), together with their dominant WRB Reference Soil Groups (RSGs).

Most land units (LUs) correspond to marginal (A3) or non-suitable (A0) agricultural land (Table 4). A0 is primarily associated with granites (‘g’), diorites (‘d’), and schists (‘x’) in steep terrains (100%). For these lithological groups, suitability shifts from A0 to A3 in less rugged terrains, whereas LUs associated with sands (‘a’) remain unsuited for agriculture regardless of relief. Moderate suitability (A2) is observed in plains and undulating landscapes made of mafic rocks (‘b’) (83.3%) and alluvium (‘f2′) related to Gleyic Fluvisols (‘f2′; 100%), while high suitability (A1) only occurs in alluvium associated with Eutric Fluvisols (‘f1′; 100%).

More than one-third of the LUs are classified as moderately suitable (F2) for forestry purposes, including units associated with alluvium (‘f1′) and sands (‘a’) regardless of relief, as well as granites (‘g’), diorites (‘d’), and schists (‘x’) in undulating terrains. Although forestry suitability tends to decrease in the steepest terrains, LUs related to clayey materials (‘k’) and non-compact limestones (‘c’) are marginally suitable for forestry (F3) regardless of relief. As for agriculture, forestry suitability never decreases when shifting to a less rugged relief class except for sandstones (‘r’).

Some relationships are observed between land suitability and WRB Reference Soil Groups. For example, Leptosols and Arenosols are predominantly non-suitable for agriculture but generally exhibit up to moderate forestry suitability. In contrast, Fluvisols and Vertisols on flatter terrains are among the RSGs with the highest agricultural suitability (A1–A2).

Table 5. Mean and sum of limitation levels in selected representative land units (LUs) and their associated predominant WRB Reference Soil Group(s) (RSGs).

		Land Limitations
LU	RSG	z	f	a	h	ea	ef	c	i	Sum
g.m	RG	2	2	1	2.9	1.9	1.0	1	4	15.8
g.s	RG	2	2	1	2.3	1.2	1.0	1	2	12.5
d.m	LV	2	1	1	2.0	2.4	1.0	1	4	14.4
d.s	LV	2	1	2	1.4	2.0	1.0	1	2	12.4
b3.o	VR	2	1	1	1.8	2.8	1.1	1	3	13.7
b3.p	VR	2	1	2	1.9	1.2	1.0	2	1	12.1
x.m	LP	3	1	1	3.0	3.1	1.1	1	4	17.2
x.s	CM/LV	2	1	1	2.0	1.9	1.0	1	2	11.9
k.o	ST/PL	2	1	3	2.3	2.6	1.0	1	3	15.9
k.p	ST/PL	2	1	4	2.2	1.0	1.0	1	1	13.2
r.o	RG	2	3	1	2.2	2.0	1.0	1	3	15.2
r.p	RG	2	3	1	2.5	1.0	1.0	1	1	12.5
c.o	RG	3	1	1	2.0	3.1	1.1	4	3	18.2
c.p	RG	2	1	1	2.0	1.2	1.0	4	1	13.2
a1.0	AR	3	3	1	2.4	1.0	1.0	1	3	15.4
a1.p	AR	3	3	1	2.0	1.0	1.0	1	1	13.0
f1.p	FL-eu	1	1	2	1.0	1.0	1.0	1	1	9.0
f2.p	FL-gl	1	1	3	1.0	1.0	1.0	1	1	10.0

Codes as in Table 4. For h (water deficit period) and ea/ef (natural soil erosion risk), non-unitary values result from averaging integer limitation levels derived from continuous spatial data within the same land unit.

presents the mean levels of key land limitations for selected representative land units and correspondent predominant WRB Reference Soil Groups, providing a basis for assessing the land qualities most constraining agricultural and forest suitability.

Slope inclination (i) is the only limitation exclusively controlled by relief, while fertility (f) and carbonate content (c) are chiefly associated with lithology. Severe limitations related to rooting conditions (z), water deficit (h), and erosion risk (e) are associated with the steepest terrains, particularly over schists and non-compact limestones. Fertility limitations prevail in LUs corresponding to sands, and aeration constraints in hydromorphic alluvium and clayey materials, particularly in plains. Carbonate-related limitations are only severe in LUs associated with non-compact limestones. As a result, cumulative limitation scores are higher in LUs corresponding to the steepest terrains, and especially for schists (‘x.m’) and non-compact limestones (‘c.o’), while the lowest are in alluvial plains (‘f1.p’), resulting in the highest overall suitability.

3.3. Land Use/Land Cover (LULC) and Land Suitability

Figure 7 shows the percentage distribution of land use/land cover (LULC) within agricultural (top row) and forestry (bottom row) suitability classes, aggregated across the three study areas. Across both agricultural and forestry suitability gradients, decreasing suitability is associated with a marked decline in agricultural uses and a progressive dominance of forestry, shrubland, and other semi-natural land covers.

Overall, as agricultural suitability decreases, the proportion of both permanent and temporary crops declines, while that of forestry increases. The highest suitability class (A1) is dominated by temporary and permanent crops (71.0%), with minor contributions from improved pastures (5.3%). In moderately suitable land (A2), similar patterns are observed, except for improved pastures, which increase almost fourfold. In marginal land (A3), agricultural uses sharply decline, particularly temporary crops (from 42.7% to 14.1%), accompanied by a rise in forestry, which occupies roughly one-third of the area. In non-suitable land (A0), agricultural uses are minimal (20.1%), while forests (broadleaves and coniferous; 29.6%), silvopastoral land (16.9%) and shrubland (12.8%) dominate.

Overall, as forestry suitability decreases, the proportion of permanent and temporary crops declines (Figure 7). The highest suitability class (F1) is dominated by permanent crops (60.3%) and improved pastures (15.4%), indicating that areas highly suitable for forestry are mostly used for agricultural purposes. In F2, permanent crops sharply decline (to 16.5%), while forestry area triples. In non-suitable land (F0), shrubland dominates (39.9%), bare land is no longer negligible (8.7%), forestry cover declines, and agricultural uses become minimal.

4. Discussion

The present research demonstrates that a 1:100,000 land suitability map can be produced simultaneously with the updating of the WRB-based soil map at the same scale. By integrating legacy soil data with land units delineated according to key soil-forming factors (climate, lithology, and relief), this approach supports the derivation of a consistent set of land qualities. Following FAO land evaluation principles [13,14,15,25,30,38], it provides a comprehensive, transparent, and spatially consistent basis for identifying land constraints and defining suitability classes for both agricultural and forestry purposes. This approach improves on the legacy “soil use capacity” map [32], which was based on a mental model with incompletely characterised soil units, did not account for climate outside southern Portugal—limiting its applicability to cooler, higher-elevation regions [22]—and included only three subclasses (erosion risk, excess moisture, and rooting-zone limitations) with only one subclass represented per unit. These flaws are overcome in the current framework, as it: (i) relies on recently updated soil information; (ii) enables national-scale applicability; and (iii) defines 11 land suitability subclasses while explicitly reporting multiple relevant limitations with comparable intensity.

The consistency observed between land units, dominant biophysical limitations, and resulting suitability classes suggests the robustness of the proposed approach. In fact, land unit-based frameworks have proven effective for soil mapping and land-use planning in heterogeneous landscapes, supporting optimal land-use allocation and targeted soil and land management interventions [42,43,47,78,79,80,81,82,83]. Moreover, by treating land units as integrated expressions of geology, geomorphology, climate, and soils, the framework moves beyond single-factor approaches, reinforcing holistic land suitability assessment and enhancing transferability to other complex Mediterranean regions. Although these three factors were considered, our results indicated that at the regional scale, lithology and terrain configuration exert a stronger differentiation of land potential than climate, consistent with previous studies [39,78,79,80,81].

4.1. Land Suitability Classes and Land Units

Our results reveal a clear and systematic relationship between land suitability, relief, lithology, and soil characteristics. Across the three study areas, marginal agricultural land (71.2%) is overwhelmingly dominant, with only small proportions of land classified as moderately (4.8%) or highly (1.1%) suitable. This pattern is consistent with previous maps for Northern [22,23] and inland Centre regions [24], although those areas show a higher proportion of non-suitable land, largely reflecting geomorphological (stepper relief; Figure 1) and climatic constraints rather than methodological differences. Overall, the predominance of marginal to non-suitable agricultural land across mainland Portugal mirrors typical Mediterranean constraints, including steep terrain, shallow or coarse-textured soils, and recurrent soil water deficit [8,11,84].

Land units corresponding to alluvial plains are among the most suitable areas for agricultural purposes, especially when the water table does not impose aeration constraints. Such land units, where Eutric Fluvisols are dominant soil units [36], usually exhibit favourable combinations of effective soil depth and water availability [33,37,85]. This pattern agrees with the legacy “soil use capacity” map [32], where alluvial units are also among the most suitable for agriculture. Given their limited extent, these areas are particularly important for agricultural planning and protection [31,86]. Land units related to mafic rocks in flatter terrains, where Vertisols are largely dominant, show moderate agricultural suitability due to restricted effective soil depth and seasonal water deficit. Although Vertisols are further constrained by workability and shrink–swell cracking [85], these characteristics were not assessed here, as was the case in the “soil use capacity” evaluations [32]. Nevertheless, in contrast to the legacy “soil use capacity” map, which often considered these soils highly suitable under a minimum effective depth of 45 cm, the present framework applies stricter thresholds (≥100 cm) and explicitly incorporates climatic constraints, resulting in more conservative classifications [15,32].

While highly and moderately suitable lands are mostly associated with alluvium, marginal and non-suitable lands occur in steep relief across several lithological groupings. Exceptions include land units developed on sands, arenites, and non-compact limestones, which are marginal or even non-suitable regardless of relief, typically due to low fertility, restricted soil depth, low water availability, and/or high carbonate content. In the legacy “soil use capacity” map [32], these soil limitations are likewise associated with low use capacity. Leptosols (effective depth ≤ 25 cm) severely restrict crop rooting and are therefore considered unsuitable for agriculture, though they still allow marginal forestry due to the greater tolerance of trees to limited depth [85]. Likewise, coarse-textured Arenosols (type 1 sands, ‘a1′) have very low water retention, constraining rainfed crops, whereas deeper tree rooting may access groundwater and permit moderate forestry suitability [23,85]. These constraints explain the predominance of low agricultural but comparatively higher forestry suitability observed for these RSGs (Table 4).

Most land units fall within moderate (F2) forestry suitability, reflecting the potential of the study areas for general forestry purposes. Alluvial units are an exception, with lower forestry than agricultural suitability due to aeration constraints associated with periodic flooding and shallow groundwater, which limit tree rooting and productivity [87]. Non-suitable forestry areas are restricted to high-elevation land units with >70% rock outcrops, mainly composed of quartzites and schists, stressing the combined influence of lithology and relief on rock outcrop abundance and, consequently, land suitability [88].

Land suitability generally decreases with increasing relief ruggedness, except for forestry on land units made of arenites, where suitability declines as relief changes from strongly sloping to plain, due to soil aeration, which reduces forestry suitability [89,90]. Marginal suitability in clayey and non-compact limestone substrates regardless of relief, reflects the particularly limiting effects of poor aeration [89] and high carbonate content [85] for forestry uses.

4.2. Land Suitability Subclasses and Land Units

The analysis of land subclasses reinforces the interpretation of the dominant biophysical controls on land suitability. The most frequent limitations are slope inclination (i), soil water deficit period (h), rooting conditions (z), and soil fertility (f). Slope inclination is a quality exclusively determined by relief, strongly constraining mechanisation and erosion risk in steeper areas. Fertility is mainly controlled by lithology, rooting conditions by lithology and topography, and the soil water deficit period by the combined effects of lithology, relief, and climate.

In the present study, climatic-related land qualities, particularly temperature regime (t), soil water deficit period (h), and erosion risk (e), are represented as continuous spatial layers, allowing a spatially resolved assessment of land suitability, in which a single land unit may exhibit different suitability levels depending on its geographical location [91]. Although mean annual temperatures in the study areas consistently exceed 12.5 °C (thermal limitation level = 1), other regions of mainland Portugal experience lower mean annual temperatures [22,23,24] that impose increasing thermal constraints (10.5–12.5 °C, t = 2; <10.5 °C, t = 3). In the study areas, the three climatic units considered (QL, Q2, T2) capture meaningful variations in minimum and maximum temperature, air humidity, rainfall, evapotranspiration, and consequently in soil water deficit (h), providing a more detailed land evaluation. Although climate is not limiting in the present areas, its influence should be considered at the national scale [22,23,24,54].

In Mediterranean regions, expected climate changes [8,9,11] may intensify key limitations, particularly soil water deficit (h) and erosion risk (e), with negative effects on rooting conditions (z) and plant growth [92,93]. Conversely, higher thermal units may locally extend the growing season and enable double cropping [94]. They may also promote a northward shift in land suitability [95,96,97] and in the distribution of Mediterranean crops (e.g., maize, vineyards) and forest species (e.g., cork oak, Quercus suber L.), partly offsetting declining suitability in southern regions [98,99,100].

4.3. Land Use/Cover Patterns and Land Suitability

Our study indicates a high correspondence between land suitability and current land use/cover patterns [48], despite differences in scale and classification schemes between both maps, suggesting that the framework reliably reflects underlying biophysical constraints. Areas with higher agricultural suitability are predominantly occupied with temporary and permanent crops, whereas non-suitable lands are mainly occupied by forest, silvopastoral systems, or shrubland [48]. This pattern suggests that land suitability remains a major constraint on land-use allocation at regional scales, agreeing with studies developed in both Mediterranean [89,101] and other environments [40,80,102,103,104]. Nevertheless, a considerable proportion of agricultural activity (~20%) persists in non-suitable areas, increasing potential degradation risks. This trend may primarily reflect the possibility of certain limitations to be mitigated through management practices (e.g., irrigation, fertilisation, drainage, and ripping) [85,90], as well as socio-economic pressures, such as land scarcity, economic incentives, or traditional management [13,105].

Improved pastures are mainly associated with marginal agricultural suitability classes (22.4%) and, although their benefits depend on management practices [90], can enhance soil quality by increasing soil organic carbon [106], improving structure, promoting biodiversity, and reducing erosion, thereby contributing to climate change mitigation and adaptation [107,108].

The predominance of permanent crops (60.3%) and improved pastures (15.4%) within the highest forestry suitability class (F1) indicates that areas highly suitable for forestry often also exhibit high agricultural potential. This overlap reflects multifunctional land use shaped by management and socio-economic factors rather than biophysical suitability alone, reinforcing that land suitability maps represent potential rather than prescriptive land use [13,14,90,105,109,110,111,112,113].

4.4. Policy, Land-Use Planning and Management Implications

The present study provides a consistent framework for land evaluation, supporting evidence-based land-use planning and management. Explicitly linking suitability classes to identifiable land qualities helps planners and decision-makers target areas for intensive use, conservation, or restoration, promoting sustainable land-use strategies that contribute to resilient landscapes, food production, ecosystem services, and human well-being [3,15,16]. The framework is particularly relevant in Mediterranean regions, where updated land suitability maps are crucial for climate change adaptation [114].

By producing a national harmonised land suitability map (1:100,000), our approach enables overcoming inconsistencies in the frameworks underpinning the National Agricultural Reserve [31] and the National Ecological Network [86]. Beyond addressing the limitations of the legacy “soil use capacity” map, it allows the readjustment of the boundaries of these legal instruments [31,86], supporting improved land management and policy integration. The 1:100,000 suitability map also facilitates detailed studies on land suitability for irrigation and highlights areas with higher agricultural potential where fine-scale land suitability mapping should be prioritised [22,23,24,115]. Additionally, it provides a basis for establishing land market values and defining property taxes for rural holdings.

The proposed framework directly addresses the Updated Guidelines for Applying Common Criteria to Identify Agricultural Areas with Natural Constraints [84], under the Common Agricultural Policy [116]. So, it strengthens the technical basis for instruments designed to compensate farmers in constrained areas and promote sustainable land management. Nationally consistent and spatially explicit information on land potential and limitations supports evidence-based, equitable policy implementation, contributing to territorial cohesion and the maintenance of agriculture in marginal lands.

Finally, the framework addresses land degradation neutrality objectives, since operationalising zero net land degradation (ZNLD) requires accurate mapping of land potential and constraints [3,117]. It also supports the objectives of the EU Soil Monitoring and Resilience Directive (Directive (EU) 2025/2360) [118], which promotes systematic soil monitoring and sustainable management across Member States. By linking land suitability levels to spatial planning and policy instruments, the approach facilitates compliance with emerging EU soil and environmental policies and contributes directly to several Sustainable Development Goals (SDG 2, Zero Hunger; SDG 13, Climate Action; SDG 15, Life on Land) [21].

4.5. Limitations and Future Research

The present study shares limitations common to regional and national land suitability assessments regarding data availability and map resolution. The WRB-based soil map (1:100,000 scale) limits detection of fine-scale variability required by some users. Land units delineation was affected by two main limitations. First, the lithology layer relied on two data sources—a 1:200,000 geological map and a 1:25,000 legacy soil map—potentially introducing inconsistencies. This issue was addressed by prioritising the soil map info in sedimentary areas, where the geological map often fails to capture thin sedimentary formations crucial for soil profile development and properties, thereby minimising the constraint associated with the eight-fold scale discrepancy. Second, the relief layer considered a minimum mapping unit of 250 ha, potentially omitting fine-scale details. It should be noted that this latter option only reflects on the data presented in Table A3 (Appendix A) and does not affect the precision of the erosion risk assessment, since the calculation of the topographic factor (LS) is directly derived from the 25 m DEM, thus ensuring maximum accuracy in this procedure.

Several soil properties were inferred rather than directly measured, particularly hydraulic attributes and available water capacity, estimated from texture-based values and pedotransfer assumptions, introducing additional uncertainty in suitability class boundaries at local scales. In addition, although the legacy soil profile data used in this study were mostly collected during the last three decades and post-date the major land-use changes that occurred in mainland Portugal during the 1970s and 1980s [106,109], certain soil properties (e.g., organic carbon, salinity and pH) may have undergone subsequent changes due to management practices or environmental pressures. Furthermore, the rule-based evaluation framework relies on predefined thresholds, making suitability classes sensitive to these limits; although thresholds follow FAO recommendations and previous national applications, uncertainty remains, especially locally. No independent field validation was conducted, and comparisons with current land use/land cover data serve solely as descriptive consistency checks rather than formal validation. Consequently, the resulting maps are primarily tools for regional planning and strategic decision-making, not site-specific applications. Finally, socio-economic, technological, and cultural factors, as well as specific land management policies, were not explicitly incorporated, despite their potential great influence on current land use and productivity [13,14,105,113,119,120].

Future research should incorporate field validation and remote sensing to quantify uncertainties in inferred soil properties and suitability classes. Integrated, interdisciplinary frameworks linking biophysical suitability with socio-economic and institutional drivers are recommended [119]. Further refinement could involve higher-resolution digital soil mapping, explicit climate change scenarios, and management-dependent indicators, allowing suitability classes to better reflect both biophysical potential and practical feasibility. Future studies could also focus on land suitability for specific crop or forest species, identifying crop rotation systems and management practices that improve soil qualities over time [40,41,42,43,47]. Integration of machine learning and predictive GIS approaches would enable dynamic map updating and scenario analysis, while the use of unit-specific thresholds for key soil indicators [121] could strengthen links between land evaluation, evolving soil conditions, and sustainable land management.

5. Conclusions

The current study presents a consistent framework to produce a harmonised land suitability map for mainland Portugal at a 1:100,000 scale following recently updated WRB-based soil information. The integration of soil, relief and climatic data, provided by the prior delineation of land units, offers a transparent, scalable and reproducible basis for assessing land suitability for agriculture and forestry purposes. By linking land suitability levels to quantifiable biophysical constraints, this framework provides a basis for identifying areas with higher productive potential and those where environmental limitations may favour more conservative or protective management options.

Across the study areas, agricultural suitability is predominantly marginal to non-suitable, whereas forestry suitability is largely moderate to marginal. Land units with higher agricultural suitability are mainly associated with alluvial plains and, secondarily, with flattened areas developed on mafic rocks. Given their limited extent, these areas can be identified as potentially important for targeted protection or irrigation development within land-use planning processes. Although moderate forestry suitability is widespread, climatic specificities play a key role in determining forest species allocation, according to the relative influence of the ocean (e.g., Littoral Land) and warmer and drier environments (e.g., Continental Warm Land). While currently less influential than relief and lithology, climate is expected to gain importance under climate change, particularly through longer water deficit periods and higher erosion risk, potentially modifying future suitability patterns.

The land suitability evaluation broadly reflects current land use/cover patterns, although socio-economic, historical, and management factors can lead to agricultural practices in less suitable areas, emphasising that suitability maps represent biophysical potential rather than prescriptive land use. The proposed framework can support several practical applications, including overcoming inconsistencies between legacy maps, supporting CAP-related policy implementation, promoting land degradation neutrality and biodiversity conservation, and enabling evidence-based spatial planning. In Mediterranean environments, where water scarcity, soil degradation, and extreme events are prevalent, the updated map can support adaptive management.

Study limitations include map resolution, inferred soil properties, and omission of socio-economic and management factors. Therefore, future research should integrate higher-resolution spatial data, climate change scenarios, and dynamic management indicators, potentially supported by remote sensing, machine learning, and predictive GIS models. Such developments would enable more refined assessments of land suitability, improved representation of emerging constraints, and the implementation of scenario-based planning approaches under changing environmental conditions.