Soil Quality Indicators for Different Land Uses in the Ecuadorian Amazon Rainforest

Abstract

Deforestation and land-use changes lead to significant soil degradation and erosion, particularly in Amazonian ecosystems, due to the region’s climate and geology. This study characterizes soil quality using physical, chemical, and biological parameters across different land uses. It uses a soil quality index (SQI) based on a minimum data set (MDS), from 19 evaluated parameters. The land uses evaluated were cacao monoculture (CMC), agroforestry systems associated with fruit and timber species (FAFS and TAFS, respectively), and a secondary forest. The SQI was composed of six variables, bulk density (BD), soil organic matter (SOM), urease activity (UR), pH, dehydrogenase activity (DH), and leaf litter, which are considered relevant indicators that allow for an adequate evaluation of soil quality. According to the SQI assessment, FAFS has a moderate-quality rating (0.40), followed by secondary forest (0.35), TAFS (0.33), and CMC (0.30), the last three categorized as low-quality. The methods used are replicable and efficient for evaluating changes in soil properties based on different land uses and management systems in landscapes similar to those of the Ecuadorian Amazon. Also worth mentioning is the potential of agroforestry as a sustainable land-use strategy that can enhance above- and below-ground biodiversity and nutrient cycling. Therefore, implementing agroforestry practices can contribute to long-term soil conservation and the resilience of tropical ecosystems.

1. Introduction

The lush vegetation and ecological diversity of the Amazon basin position the region as a megadiverse reservoir, in so much as it hosts about a quarter of global biodiversity [1,2,3]. It constitutes 50% of the world’s existing forests [2] and plays a fundamental role in climate regulation and the maintenance of biogeochemical cycles [4]. For this reason, the Ecuadorian Amazon, which represents 45% of its territory, constitutes a biodiversity hotspot of global importance [5]. However, in recent times, diversity and natural resources (soil, water, biodiversity, and natural forest) have been altered, mainly by uncontrolled deforestation practices, mining exploitation, and the increase in agricultural and livestock activity [1,2,3,6] or for urban purposes, which is prevalent in most tropical regions [7]. These land-use changes have a direct impact on vegetation and soil, increasing soil bulk density, erosion, nutrient imbalances, and overall soil degradation, which seriously affects soil health and ecosystem functioning [7,8]. Therefore, assessing soil quality is essential to determine the degree of impact of land-use changes, although there is not yet a fully established consensus in the scientific society [3]. Soil quality is defined as “its ability to function within an ecosystem; to sustain or improve animal or plant productivity; to maintain and control environmental quality, and to support the habitability and health of man” [9,10]. Thus, soil quality serves as a major foundation for food security, biodiversity, and environmental sustainability [7].

The soil is an essential component that performs multiple ecological functions and supports several ecosystem services. However, its degradation can generate serious and, in many cases, irreversible repercussions [1,3,11,12]. Generally, Amazonian soils are susceptible to severe soil degradation due to the geological conditions of this region and the fragility of its ecosystems [6]. Owing to the humid and warm tropical climate, the soils are highly weathered, which leads to a predominance of unalterable minerals, such as quartz and low-activity clays, which cause a deterioration in the chemical parameters of the soil. In addition, several studies on Amazonian soils have shown a high organic matter (OM) content, but their fertility is normally low due to soil acidity and the presence of exchangeable aluminum, with low concentrations of available phosphorus and exchangeable bases (K⁺, Ca²⁺, Mg²⁺, and Na⁺) [6,7]. This pattern is characteristic of the region and is largely attributed to the high rainfall. However, the implementation of sustainable management practices, such as forestry, agroforestry, silvopastoral systems, or even reforestation, has the potential to improve soil condition and quality, such as increased organic matter accumulation, reduced soil erosion, and enhanced propagation of beneficial microbial communities, and even contribute significantly to carbon sequestration [5,7,13,14].

Soil quality cannot be determined solely by individual soil properties, as many of them are interdependent [11,15]; it results from the combination of soil physical, chemical, and biological properties [16,17]. However, soil properties that are sensitive to environmental or anthropogenic changes and disturbances can be used as indicators of soil quality [8,18,19,20]. These can be qualitative or quantitative variables that are used to develop indices [18]. Thus far, no universal methodology has been established to define the selection of soil quality indicators. Consequently, the purpose of the soil quality index (SQI) is to quantitatively measure the effect of crop management practices on overall soil health [6,15,16]. Therefore, it is important to consider the selection of variables (physical, chemical, and biological properties of soils) that reflect the ability to respond quickly to changes or disturbances and propose them as quality indicators [6,10]. This means that the use of soil indices can provide early information about the status of soil health. Hence, the soil condition of specific forest or agricultural lands could be assessed by examining several selected soil properties through soil quality assessment, which would increase efficiency and reduce time and cost by identifying a small set of key soil quality parameters, selecting a minimum data set (MDS) through statistical methods [6,7]. This method is an essential step in the quantitative assessment of soil quality, as it minimizes the total data set, maximizes valid information, and reduces data redundancy [14,21]. Moreover, farmers and stakeholders could be guided on a better approach to land-use management and maintaining soil productivity [21,22]. This method could facilitate decision-making and soil management processes at the local or regional levels, as the versatility of this method allows soil quality to be determined in any environment with relative accuracy compared to other methods. Previous studies in tropical Amazonian areas developed by Bravo et al. [3,6], subtropical areas by Zhang et al. [15], and semi-deciduous areas by Agbeshie et al. [7], based on this method, have allowed the evaluation and comparison of soil quality between different land uses, in a quantitative way, and propose specific variables that are selected according to the methodology. For instance, the SQI in Amazonian contexts records values ranging from 0.41 to 0.45 in Amazonian Chakras, from 0.30 to 0.33 in livestock systems, from 0.32 in agrosilvopasture, and from 0.34 to 0.44 in lightly intervened secondary forests [3,6]. In subtropical areas, the SQI is 0.71 for primary forests, 0.42 for natural restorations, and 0.34 for agricultural lands [15]. In semi-deciduous areas, the SQI records values of 0.77 for natural forests, 0.65 for cocoa agroforestry, and 0.59 for agricultural land [7].

The soil quality indices mostly focus on the physicochemical component, leaving aside biological properties. These indices generally aim to improve soil conditions and, consequently, agricultural productivity. However, what is often overlooked is that the biological component is essential for the development of many soil functions [22,23]. For example, the microbial component of a soil plays an important role in the nutrient cycle, being the main source of nutrients for vegetation due to the different mineralization and decomposition processes in which it participates [7]. Therefore, the importance of identifying, evaluating, and integrating chemical, physical, and biological soil indicators to define threshold limits for monitoring soil quality in areas or regions affected by human activity is recognized [6,16].

In many cases, a lack of knowledge and limited information have been the main barriers to understanding soil functionality, especially in Amazonian settings. Therefore, this study provides the first integrated analysis of cacao-based agroforestry systems—which includes associations with fruit and timber species and cacao monocultures—compared to Amazonian forests, through the development of an integrated SQI for Amazonian contexts, considering biological parameters such as enzyme activities and soil respiration. This allows us to quantify and assess the physicochemical and biological impact of land-use transformation on soil properties, soil microbial communities, and enzymatic activity, using a small number of indicators, known as the MDS. This will facilitate the selection of soil parameters for future research, leading to a reduction in laboratory analysis time and costs, in addition to contributing to the identification of potential and sensitive indicators for Amazonian contexts. From this perspective, the objectives of this study were (1) to characterize the physicochemical and biological properties of soil associated with soil quality in Amazonian Chakra agroforestry systems and tropical rainforests in Ecuador and (2) to establish a soil quality index based on an MDS to assess soil quality across different land uses.

2. Materials and Methods

2.1. Study Area

This research was conducted in communities in the Arosemena Tola and Tena cantons of Napo Province, Ecuador (Figure 1). The region’s climate ranges from humid to humid tropical, with an average annual temperature ranging from 22 °C to 25 °C. The region is characterized by a high relative humidity throughout the year and has an average annual rainfall of approximately 3000 mm. The topography of this area consists of a series of medium-sized hills originating from the eastern slopes of the Ecuadorian Andes mountain range [24,25]. The soils in this area of the region belong to the Andisols order (Soil Survey Staff, 2006), generally presenting a clayey loam texture, while the structure can be granular and blocky. These soils are generally acidic (pH < 5.5) with low natural fertility (low P, K⁺, Ca²⁺, and Mg²⁺ contents) and saturation percentages of bases below 35%. They also have high levels of Fe and Al³⁺ (>1 cmol_c kg⁻¹) [6].

2.2. Land Uses

Due to the climatic conditions, relief, and characteristics of the Ecuadorian Amazon region, it constitutes an optimal space for developing different types of management practices, such as crop associations, forage systems, and forest association systems [5]. The forests of this region are considered part of the evergreen Amazonian–Andean Forest, with medium stratification, high biodiversity, and predominance of species from the families of Fabaceae (e.g., Inga vismifolia Poepp.), Sapotaceae (Pouteria torta (Mart.) Radlk. LC.), and Arecaceae (Iriartea deltoidei Ruiz & Pav.) [26]. Details of the selected land-use types are briefly presented in

Table 1. Description of the types of land uses selected in this study.

Land Use	Description	Main Plant Species
Chakras associated with fruit and timber trees (FAFS and TAFS)	The Amazonian Chakra systems are traditional agroforestry practices developed by Indigenous Amazonian communities. These systems have been maintained for decades as a sustainable way of managing land and maintaining an ecological balance within the ecosystem, as well as cultural heritage [27]. Additionally, Chakras are small-scale biodiverse agroforestry plots, where families cultivate a variety of crops. Chakras hold cultural and spiritual significance as they embody Indigenous worldviews that emphasize harmony between humans and nature [28]. In addition, these management systems serve as a model for sustainable agriculture in the Amazon, balancing food production and environmental conservation. They support Indigenous peoples’ food sovereignty, enhance resilience to climate change, and offer alternatives that reduce the impact of management practices. By preserving traditional knowledge, they contribute to the cultural identity and well-being of Indigenous communities [5,29]. Chakras associated with agroforestry constitute an alternative for management and production that is friendly to the ecosystem, since they resemble the succession of a natural forest [27,28,29] and generally present high adaptation in the tropics [30,31]. In the Ecuadorian Amazon, this type of management has developed traditionally and culturally, forming part of the cultural identity of the Indigenous peoples and nationalities that inhabit the Amazonian territory, and has gradually been established as a diversified cultivation model in association with crops such as cacao, coffee, timber species, and fruit trees, among others. These forest arrangements can vary depending on the geographical location where they are implemented, purpose, soil type, and management practices [32], since they can be a source of food, medicinal resources, construction resources, and habitat, in addition to contributing to nutrient cycling and carbon storage and sequestration [24,33,34].	Amazonian Chakra systems: cacao, cassava, plantains, sugar cane, medicinal plants and trees, ornamental plants, fruit trees and native fruit trees, and timber trees. Fruit trees, e.g., Inga edulis Mart., Citrus sinensis (L.) Osbeck, Terminalia oblonga Ruiz & Pav., Citrus aurantiifolia Christm., Pourouma cecropiifolia Mart., and Bactris gasipaes Kunth. Timber trees, e.g., Cordia alliodora (Ruiz & Pav.) Oken, Piptocoma discolor (Kunth) Pruski., Cedrela odorata L., Schefflera morototoni (Aubl.) Maguire., Persea americana Mill., and Ceiba samauma (Mart.) K. Schum.
Cacao monoculture (CMC)	It is an intensive production system, devoid of tree species, which depends on the use of chemicals, fertilizers, or amendments. From an economic perspective, it could be considered a very efficient production system, but over time, it can become a threat to the remaining natural resources. Given this scenario, and the interest in preserving forests, tropical ecosystems, and their biodiversity, alternatives must be chosen that involve economic, social, cultural, and ecological interests [24,35].	Mainly cacao (Theobroma cacao L.) and coffee (Coffea arabica L., Sp. Pl., or Coffea canephora Pierre ex A. Froehner).
Secondary forest (FOREST)	Tropical secondary forests generally have lush natural vegetation cover and are home to a great biodiversity of flora and fauna. Amazonian forests have a high potential for carbon storage above and below ground, which contributes to a significant reduction in greenhouse gases. In addition, they excel at maintaining a balance between all elements of the ecosystem, are highly efficient, and, at the same time, have the ability to resist and combat global warming [35,36,37]. Furthermore, they have great potential to provide various ecosystem services, such as provisioning, regulating, supporting, and cultural services. These services are important for people’s well-being on a global and local scale [38]. The forest under study corresponds to a lightly disturbed secondary forest, with minor exploitation of forest species and even the influence of the expansion of migratory agricultural and livestock practices, which generates transition zones throughout the region.	Otoba glycycarpa (Ducke) W.A. Rodrigues & T.S. Jaram., Inga sp., Cecropia sciadophylla Mart. LC., Apeiba membranacea Spruce ex Benth., Mabea standleyi Steyerm., Protium sagotianum Marchand LC., Iriartea deltoidea, Chimarrhis glabriflora Ducke., Sterculia colombiana Sprague., Annona papilionella (Diels) H. Rainer LC., and Virola flexuosa A.C. Sm. LC.

.

2.3. Field Sampling

For this study, four types of land uses are considered (Table 1), which comprise a total of 11 sampling units: 3 cacao monocultures (CMCs), 4 Amazonian Chakras under agroforestry systems with cacao in association with timber forest species (TAFSs) and 3 in association with fruit tree species (FAFSs), and a secondary forest (FOREST). The experimental design followed systematic sampling, which was evaluated through the sampling of large plots of 1600 m² (40 m × 40 m) according to [39,40]; to increase the number of repetitions, they were divided into three measurement points or subplots (10 m × 10 m), spaced 20 m apart, to represent different sections of the field, and sampling was performed in triplicate. Appropriate measures were taken during data collection and sampling to ensure that the information collected was accurate and representative.

In each sampling subplot (10 m × 10 m), different types of samples were taken. For example, five soil subsamples were collected at two depths (0–10 cm and 10–30 cm) and subsequently homogenized to obtain a composite sample per sampling subplot, for a total of six samples per sampling unit, which were used for the analysis of chemical parameters. As for the biological component, it was only determined at a depth of 0–10 cm. Soil samples were air-dried, ground to pass through a 2 mm sieve, and used for physical and chemical determinations. At the same time, in the central part of the subplot, undisturbed samples were collected with an Uhland-type drill to evaluate physical parameters. Here, with the use of a 0.25 m² quadrant, the material corresponding to dead plant remains (leaf litter) was collected.

2.4. Soil Physical, Chemical, and Biological Analysis

The apparent density (BD) was determined by the cylinder method [41]. A more detailed description of the technique is presented in Text S1 (Supplementary Materials). As for chemical parameters, the hydrogen potential (pH) was measured by potentiometry. Total organic carbon (TOC) was determined by the wet digestion method [42]. Available phosphorus (P) and extractable cations (K⁺, Ca²⁺, and Mg²⁺) were removed using the Olsen extraction solution. P was measured by the molybdenum blue method, while extractable cations were measured using an atomic absorption spectrophotometer [43]. A description of the techniques used for sample analysis in more detail is presented in Text S2 (Supplementary Materials). For the biological component, the soil samples were air-dried at room temperature and sieved (<2 mm). Soil enzyme activities determined according to the techniques described by López-Piñeiro et al. [44] were as follows: dehydrogenase enzyme activity (DH) was measured using INT as a substrate, β-glucosidase enzyme activity (GL), urease enzyme activity (UR), phosphatase enzyme activity (PHO), and arylsulfatase enzyme activity (SU). All enzymatic activities were determined in triplicate [44,45]. Blank assays without soil or substrate were performed at the same time as controls. Soil edaphic respiration (ER) was estimated in the field using a modified version of the static chamber method, while soil basal respiration (BR) was estimated in the laboratory based on the physiological response of microorganisms under minimal conditions of labile substrate sources [46]. Further information is provided in the Supplementary Materials (Text S3).

2.5. Soil Quality Index (SQI)

For the implementation of a comprehensive SQI, three consecutive steps were carried out [7,11,15]: (1) the selection of a minimum data set (MDS) of the determined soil indicators; (2) scoring of the selected MDS indicators; and (3) calculation of the integrated SQI. To select the representative indicators of the MDS, a principal component analysis (PCA) and Pearson’s correlation analysis were performed [10]. In order to select the MDS, only principal components (PCs) that resulted in eigenvalues ≥1 and that explained at least 5% of the total variance were chosen [47]. Then, in each PC, the factors with absolute loading values within 10% of the highest factor loading were selected as vital indicators [15,48]. When more than one indicator was retained in one PC, Pearson’s correlation analysis was used to check whether other indicators should be removed [49]. In this context, if the indicators were adequately correlated (correlation coefficient > 0.6) with each other, only the highest weighted indicator was selected in the PC [47]. After selecting the indicators of the MDS, a non-linear scoring function was used to transform the soil indicators into scores ranging from 0 to 1. The sigmoidal function was calculated using the following equation [50]:

$$S=a/[1+(x/x_0){]}^b$$

(1)

S is the score of the soil indicator; a is the maximum score (a = 1); x corresponds to the value of the indicator; x₀ is the average mean value for each soil indicator; and b corresponds to the value of the equation’s slope. The slope values (b) of −2.5 and 2.5 were used for a ‘more is better’ or ‘less is better’ curve, respectively [49]. Finally, once the scores of the indicators and their weighting values were obtained, the SQI was calculated according to the following equation [11]:

$$SQI={\sum }_{i=1}^{n}Si\times Wi$$

(2)

Wi corresponds to the weighting of the selected PCA indicators; Si is the score of the Equation (1) indicator; and n is the number of indicators selected in the MDS. The SQI for each land use was classified according to the ratings mentioned by Agbeshie et al. [7], as presented in Table S1 of the Supplementary Materials.

2.6. Statistical Analysis

All statistical analyses were performed by IBM SPSS Statistic 25. A one-way analysis of variance (ANOVA) was performed, as well as a Tukey’s mean comparison test (p ≤ 0.05), which was used to examine and compare differences between soil indicators and SQIs between different land-use types at a level of p < 0.05. The principal component analysis (PCA) and correlation matrices between soil indicators were evaluated by Pearson’s correlation analysis. An additional ANOVA was performed on the overall soil quality indicators with SQI and MDS scores to reveal the effect of different land-use types on soil quality.

3. Results

3.1. Physicochemical Properties at Two Depths

The physicochemical parameters showed significant differences (p < 0.05) across the different land uses under study (Table S2, Supplementary Materials). BD was significantly higher in the FOREST and FAFS (0.570 ± 0.030 and 0.523 ± 0.211 Mg m⁻³, respectively), both at 10 cm depth and 10–30 cm (0.687 ± 0.035 and 0.687 ± 0.253 Mg m⁻³, respectively), compared to the CMC and TAFS (p < 0.01). The pH at both depths showed the highest significance in the FOREST (5.70 ± 0.134 at 10 cm and 6.22 ± 0.101 at 10–30 cm) compared to the FAFS, TAFS, and CMC (p < 0.001). As for soil organic matter (SOM), at a 10 cm depth, it was significantly higher in the CMC (19.7 ± 3.88%) than in FAFS, TAFS, and FOREST soils (p < 0.01), whereas at a 10–30 cm depth, the SOM was highest in the CMC (12.1 ± 1.22%) and lowest in FOREST soils (3.94 ± 0.258%) (p < 0.001). No significant differences were observed in NH₄⁺ or the Ca²⁺/Mg²⁺ ratio, at any depth across land uses. However, the available P in the FAFS soil showed a significant difference at a 10 cm depth (10.2 ± 5.30 mg kg⁻¹) and was lowest in the FOREST (3.43 ± 0.523 mg kg⁻¹) (p < 0.05) (Table S2, Supplementary Materials). The significance for the exchangeable bases at a 10 cm depth was highest for K⁺, higher for Ca²⁺, and notable for Mg²⁺ in the FOREST (9.43 ± 0.613, 9.91 ± 0.519, and 1.27 ± 0.101 cmol_c kg⁻¹, respectively) compared to the FAFS, TAFS, and CMC, whereas at a 10–30 cm depth, no significant differences were observed in K⁺, Ca²⁺ presents notable significance (p < 0.05), and Mg²⁺ shows higher significance (p < 0.01), just like at a 10 cm depth in the FOREST. The Mg²⁺/K⁺ and Ca²⁺ + Mg²⁺/K⁺ ratios at a 10 cm depth vary significantly (p < 0.05); for both ratios, the FOREST has the lowest value and the agroforestry systems the highest, but at a 10–30 cm depth, the Ca²⁺ + Mg²⁺/K⁺ ratio was high in the FOREST (45.8 ± 17.5) and low in the CMC (16.1 ± 2.40) (p < 0.05), whereas no significant differences were observed in the Mg²⁺/K⁺ ratio.

3.2. Physicochemical and Biological Properties of the Land Uses at 30 cm Depth

The physicochemical parameters at a 30 cm depth showed significant statistical differences (

Table 2. Physicochemical and biological properties under different land-use types.

Parameters	Types of Land Use				ANOVA 1 p-Value
	CMC	FAFS	TAFS	FOREST
Physicochemical depth at 0–30 cm
BD (Mg m−3)	0.307 b (±0.035)	0.632 a (±0.231)	0.457 ab (±0.212)	0.648 a (±0.033)	**
pH	5.09 b (±0.142)	5.04 b (±0.372)	5.01 b (±0.181)	6.05 a (±0.048)	***
SOM (%)	14.6 a (±1.81)	6.59 b (±4.09)	10.5 ab (±5.73)	5.45 b (±0.287)	***
NH4+ (mg kg−1)	67.5 a (±18.5)	57.9 a (±10.9)	60.5 a (±8.43)	37.4 b (±5.82)	**
P (mg kg−1)	4.22 b (±1.56)	7.16 a (±3.63)	4.52 b (±1.48)	3.83 b (±0.195)	*
K+ (cmolc kg−1)	0.136 b (±0.019)	0.123 b (±0.091)	0.141 b (±0.042)	3.25 a (±0.236)	***
Ca2+ (cmolc kg−1)	1.93 b (±0.624)	3.58 ab (±4.16)	2.62 b (±1.01)	6.97 a (±0.503)	**
Mg2+ (cmolc kg−1)	0.343 b (±0.054)	0.710 ab (±0.649)	0.531 b (±0.326)	1.17 a (±0.131)	*
Ca2+/Mg2+	5.49 (±1.58)	4.86 (±1.93)	5.78 (±2.50)	5.89 (±0.288)	n/s
Mg2+/K+	2.64 (±0.279)	6.60 (±5.29)	4.50 (±4.02)	5.17 (±1.73)	n/s
Ca2+ + Mg2+/K+	16.6 (±3.55)	34.3 (±30.1)	23.3 (±11.1)	30.9 (±11.7)	n/s
Biological depth at 0–10 cm
Litter (Mg ha−1)	7.33 (±5.73)	6.37 (±2.38)	10.1 (±4.75)	10.4 (±0.737)	n/s
ER (mg C-CO2 m2 ha−1)	47.2 b (±10.7)	46.3 b (±8.13)	47.5 b (±7.73)	65.5 a (±17.5)	*
BR (mg C-CO2 kg−1 soild−1)	194 (±108)	139 (±137)	136 (±93.6)	106 (±101)	n/s
UR (μg NH4+ g−1 ha−1)	298 (±65.1)	190 (±93.9)	293 (±108)	280 (±52.2)	*
SU (μg pNP g−1 ha−1)	159 a (±39.4)	114 b (±17.2)	153 ab (±21.9)	114 b (±9.81)	***
PHO (μmol pNP g−1 ha−1)	3.53 (±1.10)	3.62 (±1.46)	4.66 (±1.17)	5.27 (±1.03)	*
GL (μmol pNP g−1 ha−1)	0.581 (±0.077)	0.536 (±0.130)	0.531 (±0.140)	0.544 (±0.050)	n/s
DH (μg INTF g−1 ha−1)	0.470 (±0.137)	0.447 (±0.144)	0.488 (±0.306)	0.174 (±0.263)	n/s

¹ p-Value: * p < 0.05; ** p < 0.01; *** p < 0.001; n/s = not significantly different; different letters indicate significant differences between groups. Values presented as ± in parentheses are the standard deviations from the mean. CMC: cacao monoculture; FAFS: fruit agroforestry system; TAFS: timber agroforestry system; BD: bulk density; pH: hydrogen potential; SOM: soil organic matter; NH₄⁺: ammonium; P: available phosphorus; K⁺: exchangeable potassium; Ca²⁺: exchangeable calcium; Mg²⁺: exchangeable magnesium; Ca²⁺/Mg²⁺: calcium–magnesium ratio; Mg²⁺/K⁺: magnesium–potassium ratio; Ca²⁺ + Mg²⁺/K⁺: calcium–magnesium–potassium ratio; Litter: leaf litter, ER: edaphic respiration; BR: basal respiration; UR: urease activity; SU: arylsulfatase activity; PHO: phosphatase activity; GL: β-glucosidase activity; DH: dehydrogenase activity.

). BD was significantly higher in the FOREST and FAFS (0.648 ± 0.033 and 0.632 ± 0.231 Mg m⁻³, respectively), compared to in the CMC and TAFS (p < 0.01). The pH showed the highest significance in the FOREST (6.05 ± 0.048) compared to other land uses (p < 0.001). Regarding SOM, it showed the highest significance in the CMC (14.6 ± 1.81%) and was lower in FOREST soils (5.45 ± 0.287%) (p < 0.001). In the case of NH₄⁺, the difference is highly statistically significant, with the FOREST (37.4 ± 5.82 mg kg⁻¹) showing lower levels than the CMC, FAFS, and TAFS (p < 0.01). The available P in FAFS soil (7.16 ± 3.63 mg kg⁻¹) shows a significantly greater difference than the other land uses (p < 0.05). The significance for the exchangeable bases was highest for K⁺ (p < 0.001), higher for Ca²⁺ (p < 0.01), and notable for Mg²⁺ (p < 0.05), higher for the FOREST (3.25 ± 0.236, 6.97 ± 0.503, and 1.17 ± 0.131 cmol_c kg⁻¹, respectively) compared to the FAFS, TAFS, and CMC. On the other hand, no significant differences were observed in the Ca²⁺/Mg²⁺, Mg²⁺/K⁺, or Ca²⁺ + Mg²⁺/K⁺ ratios.

Statistically, the biological parameters also show significant differences. Soil ER was highest in the FOREST (65.5 ± 17.5 mg C-CO₂ m² ha⁻¹), showing a significant difference (p < 0.05) compared to the FAFS, TAFS, and CMC, which exhibited lower values ranging from 46.3 to 47.5 mg C-CO₂ m² ha⁻¹. Meanwhile, for soil BR, no significant differences were recorded across land uses, with values ranging between 106 and 194 mg C-CO₂ m² ha⁻¹. However, enzymatic activity varied among land-use types (CMC, FAFS, TAFS, and FOREST). UR showed notable significance (p < 0.05), with higher values in the CMC (298 ± 65.1 µg NH⁴⁺ g⁻¹ ha⁻¹) than in FAFS, TAFS, and FOREST soils. SU was significantly lower in the FOREST and FAFS (114 ± 9.81 and 114 ± 17.2 µg pNP g⁻¹ ha⁻¹, respectively) than in the CMC and TAFS (p < 0.001). PHO showed significant differences, with values ranging between 3.53 and 5.27 µmol pNP g⁻¹ ha⁻¹ (p < 0.05). In contrast, GL and DH did not show statistically significant differences according to land use (Table 2).

3.3. Assessment of the Soil Quality Index

To obtain the MDS for the 19 soil attributes and determine the SQI for each land use, a PCA was performed. The results revealed four PCs with eigenvalues >1, which explained 73.2% of the total variance (

Table 3. Principal component analysis of soil quality indicators.

Variables	PC1	PC2	PC3	PC4
BD (Mg m−3)	0.92	−0.11	−0.03	0.17
pH	0.38	0.81	−0.04	0.04
SOM (%)	−0.88	0.10	0.11	0.17
NH4+ (mg kg−1)	−0.51	−0.43	0.09	−0.06
P (mg kg−1)	0.61	−0.41	0.21	0.29
K+ (cmolc kg−1)	0.30	0.72	−0.45	0.26
Ca2+ (cmolc kg−1)	0.57	0.70	0.32	0.00
Mg2+ (cmolc kg−1)	0.82	0.40	0.24	0.08
Ca2+/Mg2+	−0.49	0.66	0.14	−0.37
Mg2+/K+	0.88	−0.18	0.15	−0.03
Ca2+ + Mg2+/K+	0.80	0.14	0.38	−0.18
Litter (Mg ha−1)	0.37	0.10	−0.34	−0.68
ER (mg C-CO2 m2 ha−1)	0.10	0.47	−0.46	0.11
BR (mg C-CO2 kg−1 soil d−1)	−0.01	−0.54	−0.35	0.41
UR (μg NH4+ g−1 ha−1)	−0.84	0.31	0.18	0.09
SU (μg pNP g−1 ha−1)	−0.56	0.02	0.06	−0.19
PHO (μmol pNP g−1 ha−1)	0.44	0.44	−0.14	0.45
GL (μmol pNP g−1 ha−1)	−0.57	0.48	0.44	0.30
DH (μg INTF g−1 ha−1)	−0.52	0.00	0.60	0.18
Eigenvalues	1.80	3.68	1.14	1.06
Variance (%)	37.2	19.7	8.84	7.39
Accumulative variance (%)	37.2	56.9	65.8	73.2

Note: Bold factors are considered highly weighted; underlined and bold factors are retained in the minimum data set (MDS) for each principal component (PC1, PC2, PC3, and PC4). BD: bulk density; pH: hydrogen potential; SOM: soil organic matter; NH₄⁺: ammonium; P: available phosphorus; K⁺: exchangeable potassium; Ca²⁺: exchangeable calcium; Mg²⁺: exchangeable magnesium; Ca²⁺/Mg²⁺: calcium–magnesium ratio; Mg²⁺/K⁺: magnesium–potassium ratio; Ca²⁺ + Mg²⁺/K⁺: calcium–magnesium–potassium ratio; Litter: leaf litter; ER: edaphic respiration; BR: basal respiration; UR: urease activity; SU: arylsulfatase activity; PHO: phosphatase activity; GL: β-glucosidase activity; DH: dehydrogenase activity.

). The first component (PC1) explained 37.2% of the total variation (Figure 2) and was highly weighted by the absolute values of BD, SOM, P, Mg²⁺, Mg²⁺/K⁺ ratio, Ca²⁺ + Mg²⁺/K⁺ ratio, and UR (

Table 4. Normalization equation of scoring curves.

Parameter	BD	SOM	UR	pH	DH	Litter
Average	0.48	10.1	265	5.13	0.44	8.35
Curve type	Less is better	More is better	More is better	More is better	More is better	More is better
Slope (b)	2.5	−2.5	−2.5	−2.5	−2.5	−2.5
Normalization equation	$S=a/[1+$ $(x/0.48){]}^b$	$S=a/[1+$ $(x/10.1){]}^b$	$S=a/[1+$ $(x/265){]}^b$	$S=a/[1+$ $(x/5.10){]}^b$	$S=a/[1+$ $(x/0.44){]}^b$	$S=a/[1+$ $(x/0.44){]}^b$
Weighting value	38.2	38.2	38.2	23.4	9.61	8.11

BD: bulk density; SOM: soil organic matter; pH: hydrogen potential; UR: urease activity; DH: dehydrogenase activity; Litter: leaf litter.

). BD and SOM showed a high correlation (r = −0.95;

Table 5. Pearson’s correlation of principal indicators selected for the assessment of the SQI.

Parameters	BD	pH	SOM	P	K+	Ca2+	Mg2+/K+	Litter	UR	SU	DH
BD	1	0.21	−0.95 **	0.65 **	0.24	0.46 **	0.78 **	0.27	−0.78 **	−0.49 **	−0.46 **
pH		1	−0.13	−0.06	0.81 **	0.79 **	0.16	0.16	−0.04	−0.22	−0.24
SOM			1	−0.53 **	−0.27	−0.38 *	−0.75 **	−0.26	0.75 **	0.46 **	0.45 **
P				1	−0.17	0.19	0.54 **	−0.04	−0.61 **	−0.39 *	−0.17
K+					1	0.51 **	0.02	0.14	0.07	−0.24	−0.34
Ca2+						1	0.38 *	0.22	−0.31	−0.21	−0.15
Mg2+/K+							1	0.32	−0.74 **	−0.37 *	−0.35 *
Litter								1	−0.18	0.10	−0.40 *
UR									1	0.53 **	0.37 *
SU										1	0.33
DH											1

* The correlation is significant at the 0.05 level (bilateral); ** the correlation is significant at the 0.01 level (bilateral); BD: bulk density; pH: hydrogen potential; SOM: soil organic matter; P: available phosphorus; K⁺: exchangeable potassium; Ca²⁺: exchangeable calcium; Mg²⁺/K⁺: magnesium–potassium ratio; Litter: leaf litter; UR: urease activity; SU: arylsulfatase activity; DH: dehydrogenase activity.

); therefore, due to the higher load and ease of measurement, BD and SOM were selected as indicators in PC1, as well as UR, by the degree of significant association (p < 0.01). The second principal component (PC2) explained 19.7% of the total variation (Figure 2) and was heavily weighted on pH, K⁺, and Ca²⁺. For this component, pH and K⁺ showed a strong correlation (r = 0.81, p < 0.01); therefore, pH was selected to represent PC2.

PC3 and PC4 were highly weighted on DH and Litter, respectively (Table 3). Due to the moderate correlation between DH and SOM with r = 0.45 (p < 0.01; Table 5), DH and Litter were selected as indicators for PC3 and PC4, respectively. In summary, the MDS for calculating the SQI was based on BD, SOM, pH, UR, DH, and Litter, from the results of the PCA shown in Table 4.

3.4. Correlation Between Physical, Chemical, and Biological Indicators

The Pearson’s correlation analysis (Table 5) revealed significant associations between physical, chemical, and biological soil quality indicators. BD and SOM exhibited a strong negative correlation (r = −0.95; p < 0.01), as with UR, SU, and DH (r = −0.78, r = −0.49, and r = 0.46, respectively), but this same physical parameter showed a significant positive correlation with available P (r = 0.65) and the Mg²⁺/K⁺ ratio (0.78) (p < 0.01). pH showed a strong positive correlation with exchangeable K⁺ and Ca²⁺ (r = 0.81 and r = 0.79; p < 0.01). As for the SOM correlation, this indicator showed a significant positive correlation with enzymatic activities such as UR with r = 0.75, SU with r = 0.46, and DH with r = 0.45 (p < 0.01); on the contrary, available P presented a negative correlation with UR and SU (r = −0.61, p < 0.01, and r = −0.39, p < 0.05, respectively). The Mg²⁺/K⁺ ratio also showed a negative correlation with enzymatic activities, UR (r = −0.74; p < 0.01), SU, and DH (r = −0.37 and r = −0.35, p < 0.05, respectively). Litter was representative in PC4, but it did not show any correlation with the other quality indicators.

The calculation of the SQI for the different selected land uses was carried out based on the following equation, based on [3,11]:

$$SQI=0.38\times S\left(BD\right)+0.38\times S\left(SOM\right)+0.23\times S\left(pH\right)+0.38\times S\left(UR\right)+0.09\times S\left(DH\right)+0.08\times S\left(Litter\right)$$

(3)

In general, the SQI did not show significant differences between the land uses under study (p < 0.05). However, the SQI values of the land uses ranged between 0.30 and 0.40 (Figure 3), categorizing the CMC, TAFS, and FOREST as low-quality, while the FAFS achieved moderate quality based on Table S2 (Supplementary Materials). This is followed by the FOREST and TAFS. These results clearly indicate that agroforestry stands out compared to monocultures in terms of soil quality, species diversity, and the production of associated crops such as cacao, coffee, and other products that make up the Amazonian Chakras.

4. Discussion

4.1. Effects of Land-Use Types on Soil Properties

In our study, the soil’s properties varied significantly depending on land use (Table 3 and Table 4). Numerous authors have reported variations in soil physical, chemical, and biological attributes in a tropical context [3,18]. This behavior is attributed to the effect of soil-forming factors and processes, as well as the historical background of land use [6]. The soil’s BD is one of the variables most sensitive to changes in land use [6], and its behavior has a great influence on other attributes, such as SOM, UR, SU, and DH (Table 5), which means that the higher the BD, the lower the biological activity and decomposition of SOM; similar findings were reported by [7,8]. However, it is worth mentioning that changes in soil microbiota can affect soil processes, both positively and negatively. These changes in soil microbial communities are generally due to alterations in the physicochemical characteristics of the soil (e.g., organic carbon, nutrients, bulk density, pH, and moisture), as well as in the quantity, quality, and distribution of crop residues (leaf litter) [51]. This is why SOM is essential for soil enzymatic activity. The higher the SOM content, the greater the biological activity, as it is expected to improve the available energy and nutrients in the soil [20].

SOM is one of the most important factors in an SQI due to its positive effect on soil properties, just like the pH. Furthermore, it is the central indicator of soil quality and health, which is strongly affected by agricultural management [3,52]. The percentage of SOM found in the land uses of this study is considered high (>5%), as reported by [3,6] in similar Amazonian systems. Furthermore, agroforestry systems and forests are linked to the historical use of forest cover in the Ecuadorian Amazon, contributing to greater carbon storage [13,24,53]. On the other hand, the downward movement of cations in the soil is influenced by anions formed during the mineralization of OM, which form ionic pairs that are transported with water [54]. Additionally, microbial decomposition of OM releases CO₂, which converts to bicarbonate (HCO₃⁻), releasing H⁺ ions and thus lowering soil pH [21].

Generally, phosphorus (P) availability in Amazonian soils is low, which is corroborated by the findings of this study (Table 2 and Table S2, Supplementary Materials) and those reported for Amazonian soils under similar management systems [6]. Other studies have shown a similar pattern, with greater P availability in agroforestry systems (AFSs) compared to forest [7]. Likewise, studies conducted in the Brazilian Amazon have reported higher P availability in Chakras than in forests [4]. As reported by [11], the low levels of NH₄⁺ observed in the forest may be associated with urea fertilization in the management systems examined in this study, as well as with the presence of nitrogen-fixing plant species such as legumes and Inga.

Exchangeable K⁺ is easily leachable and released from above-ground biomass in tropical soils, processes that are influenced by the intense rainfall in the Amazon. Despite the constant leaching of K⁺, it can be recycled by the roots. Therefore, the higher the tree density, the greater the biomass input that contributes to the supply of exchangeable K⁺ to the soil, as confirmed by the reports in [8] and the findings of this study (Table 2). Furthermore, the study in [7], regarding the interchangeable bases (K⁺, Ca²⁺, and Mg²⁺) in agroforestry systems with cacao and natural forest, recorded characteristics similar to our findings. Low concentrations of exchangeable cations are frequently observed in Amazonian soils [4]. But these cations together with organic matter significantly improve soil quality [55]. Our study also shows that soil nutrients were highest in the surface layer and decreased with depth. This is likely associated with the accumulation of dead wood and leaf litter on the surface. However, when considering the depth factor, SOM, NH₄⁺, P, K⁺, Ca²⁺, and Mg²⁺ decreased, while pH and BD increased (Table S2, Supplementary Materials). This may be associated with biological activity and primarily root penetration, according to [4,6,15,56].

Regarding the biological component, FOREST soils stand out, exhibiting greater soil respiration and better microbial enzymatic activity than the FAFS, TAFS, and CMC systems (Table 2), confirming their role as reservoirs of the functionality of biological processes that develop in the soil. This is consistent with the findings of [12,57], which reported greater biological activity and basal respiration in forest ecosystems due to a higher contribution of OM, plant density, species diversity, and diverse plant–microbe interactions. According to [46], soil ER is higher in agroforestry and forestry systems due to better temperature regulation, along with a greater quantity and diversity of leaf litter. These factors, together, foster an optimal niche, promoting a more diverse microbial community [46,56], in addition to providing more stable food webs for soil microbes and suitable habitats [14]. Other studies affirm that incorporating tree species into cultivation systems such as coffee or cacao significantly improves their sustainability. Trees help diversify the environment and promote soil biological activity, nutrient cycling, and long-term carbon storage above and below ground. This is largely due to their ability to increase enzymatic activity through a greater volume of fine roots, the production of root exudates, and a higher input of leaf litter [23,58]. Furthermore, the findings of this study reaffirm that forest-like systems, such as the Amazonian Chakras, can enhance the physicochemical and biological quality of soils and, importantly, serve as a model for sustainable agriculture in the Amazon by balancing food production with environmental conservation. By supporting Indigenous food sovereignty, Chakras strengthen resilience to climate change, provide sustainable alternatives to harmful land-use practices, and help preserve traditional knowledge [5,28].

4.2. Effects of Land-Use Types on Soil Quality Index (SQI)

Land-use changes can improve soil quality, but the improvement varies depending on the type of conversion carried out, as in this study and several others [21]. Soil quality can be influenced by many factors, such as lithology and geography, soil-forming factors, type of land use and management, type of vegetation, and human activity [6,15]. In this study, six indicators (BD, SOM, pH, UR, DH, and Litter), with highly weighted factors, were selected in the MDS for evaluation. All six factors are related to one or more soil functions (e.g., water and nutrient retention and transport, soil structure, aeration, etc.) which influence soil pore structure and the capacity of soil to accept, store, and release water and nutrients [6,8]. Similar to our study, Lenka et al. [59] considered SOM, BD, and pH, along with available nitrogen, sulfur, K⁺, P, and soil respiration, as potential indicators of soil quality. They assumed that these indicators reflect the four key soil functions, water retention and structure, nutrient supply, biological activity, and fundamental properties limiting productivity, in an intensively cultivated region of northern India. Previous studies have also identified TOC, BD, and total nitrogen as reliable indicators of soil quality [8,15]. Similarly, Agbeshie et al. [7] assessed the SQI under varying land-use types in a tropical region of Ghana, also highlighting pH, SOM, total nitrogen, the presence of cadmium, and silt as the MDS. In Amazonian contexts, SOM, BD, leaf litter, and aeration porosity were selected as indicators of soil physicochemical quality (structure) across different types of land uses, reflecting their importance due to their greater contribution to the integrated quality index [6]. Bravo-Medina et al. [3], in their assessment of the SQI in different pastoral, agricultural, and forestry management systems in the Ecuadorian Amazon, also consider SOM and BD as part of the MDS. Thus, with a minimum of soil variables and minimal data acquisition costs, it will be possible to obtain information on the soil quality of a study site. However, these indicators may vary by area [55]. Therefore, converting degraded areas into mulched agroforestry systems is important not only for protecting the soil from erosion but also for enhancing its properties, particularly organic matter content and, consequently, soil structure, due to the substantial input of leaf litter on the surface.

In their contribution, Viana et al. [8] recognize BD as a potential indicator of degraded areas and undisturbed forests, and in our SQI, BD is part of the MDS, as is SOM, the latter being one of the most influential characteristics in land reclamation, playing a fundamental role in nutrient cycling, soil quality, and agricultural production. SOM and BD could play an important role in monitoring soil quality [6,7]. For this reason, Gao et al. [14] consider these parameters representative of land-use differentiation. On the other hand, some SQIs consider pH to be the chemical indicator with the greatest contribution. This parameter is one of the most important properties for determining soil quality due to its direct influence on soil chemical reactions, nutrient availability, and the control of the diversity and activity of microorganisms that perform important functions in the soil. Likewise, it is a variable that responds to changes in the management of different land uses, particularly those that depend on the use of chemical fertilizers [17,52]. Furthermore, despite being basic nutrients, available P and K⁺ are recognized as quality indicators of the SQI for subtropical ecosystems of China [15]. In contrast, soil biological properties are more dynamic and sensitive and respond rapidly to environmental disturbances and changes in land use and management. Therefore, they have the advantage of serving as early signals of soil degradation and quality loss [17,20,51]. Enzymatic activity, for example, is involved in most soil processes, and enzyme functions are extremely important and play a vital role in the nitrogen, phosphorus, and carbon cycles. In particular, dehydrogenase activity provides a comprehensive understanding of microbial processes in soil, as it is unique to living systems and also indicates the rate of oxidation of OM and, therefore, the availability of nutrients. Therefore, due to its relationship with important soil processes, its determination has been studied as an indicator of different soil quality conditions [17,20,60].

The SQI in this study positioned the CMC system as having the lowest quality compared to the other selected uses (Figure 3). This pattern may be linked to the management practices associated with this type of land use. As a monoculture reliant on external chemical inputs, it reflects a decline in soil fertility [7,14,17]. FAFSs are classified as having moderate soil quality (Figure 3), assuming that, in forest and forest-like systems, the highest percentage of SOM comes from dense vegetation and high surface litter, which adds OM and incorporates high-quality nutrients into the soil after decomposition, modulates soil biological processes, and reduces runoff [7]. In the Amazon region, it is common to find soils with a high OM content, which improves physical indicators (BD, saturated hydraulic conductivity, and porosity) and positively influences the final value of the SQI [6]. However, the quality of the forest according to this SQI is categorized as low; this behavior is attributed to the fact that the forest under study is a secondary forest that has undergone intervention, and this may be due to the expansion of migratory farming and livestock practices, along with deforestation practices in the rainforest, which create certain transition zones in the region [16]. But a lower SQI is reported by Bravo-Medina et al. [3] for a secondary forest in the neighboring province, while in agroforestry systems, it is greater than in the secondary forest, as in our findings. On the contrary, in silvopastoral systems, the SQI is lower than that reported for the secondary forest and Amazonian Chakra systems [6] and similar to what was found in our study (Figure 3). This suggests that among the land-use systems studied, Amazonian Chakra agroforestry systems are effective in restoring soil quality and reintegrating degraded lands into productive and environmentally sustainable uses. This effect can be explained by what has been observed in the Brazilian Amazon, where the interaction between fruit and timber trees and shade crops in agroforestry systems improved soil conditions and quality [16]. Several studies claim that soil quality varies depending on soil type, land cover type, and environmental conditions [6,7,55]. Since quality indicators are not universal, their selection requires consideration of the specific conditions of each region [17]. Future research should prioritize optimizing agroforestry to promote above- and below-ground biodiversity and improve ecosystem services, ensuring economic viability and ecological resilience in the face of climate change.

5. Conclusions

The properties and quality of soils under different land-use types were assessed by applying statistical tests, computing a soil quality index, and using principal component analysis to integrate physical, chemical, and biological soil properties into the index, thereby clarifying the relationship between land-use types and soil fertility. The results revealed significant differences in the evaluated soil properties, demonstrating that land-use change substantially influenced them. Regardless of the number of indicators considered, agroforestry systems exhibited the highest soil quality, followed by the secondary forest, while the cacao monoculture showed the lowest quality. According to the minimum data set, the variables with the greatest weight were bulk density, soil organic matter, pH, litter, and urease and dehydrogenase activity. It has been shown that a set of parameters integrated into the so-called soil quality index can provide efficient information on the overall condition of soils in Amazonian ecosystems. This allows soil quality to be determined with relative accuracy compared to other methods while reducing analysis time and costs.

According to the findings, agroforestry is positioned as a viable and sustainable alternative to intensive monocultures, offering a balance between soil conservation and agricultural productivity. Undoubtedly, forest systems enhance carbon sequestration, promote microbial diversity and activity, and improve soil structure, in addition to mitigating the risks of soil degradation. However, the effectiveness of agroforestry systems depends on the selection of plant species and management practices, which should be the framework for government programs. Support for the implementation of forest-type systems should be provided by research centers such as INIAP, universities such as the Amazon State University (UEA) and the Amazon Regional University (IKIAM), and government programs focused on minimizing the impact of deforestation. Ultimately, the integration of so-called Chakra-type agroforestry systems into inclusive policy decision-making and the participation of stakeholders such as regional and local governments are essential to promoting resilient and sustainable soil management in the Ecuadorian Amazon, given that these regions face increasing environmental and socioeconomic pressures. Therefore, future research should focus on finding alternatives that contribute to enhancing the composition of agroforestry systems, with the goal of improving biodiversity and carbon sequestration, thus contributing to sustainable land management in these ecosystems.