Regenerative agroforestry is associated with improved soil fertility and carbon stocks compared to pasture and eucalyptus in Atlantic Forest soils

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This study evaluated a land-use transition sequence secondary forest → pasture → eucalyptus → regenerative coffee agroforestry across two altitudes (100 and 700 m a.s.l.) in Espírito Santo State to assess whether land-use changes in the Atlantic Forest led to soil depletion. Soil chemical, physical, and microbial attributes, as well as C and N stocks, were evaluated to identify contrasting soil responses patterns. Pasture and eucalyptus showed signs of degradation within this study, exhibiting elevated bulk density (up to 1.60 kg dm⁻³), low base saturation (< 10%), high Al³⁺, and depleted microbial phosphorus ( 50%, with substantial recovery of Ca²⁺, Mg²⁺, and microbial phosphorus to levels comparable to secondary forest. Microbial biomass and mineralization kinetics confirmed improved biological efficiency and faster C turnover. C and N stocks in agroforestry systems recovered up to 80–90% of the forest reference, particularly at higher altitudes, where cooler and moister conditions enhanced organic matter stabilization. Principal component analysis (PCA) distinguished clear more favorable soil attributes gradients, positioning agroforestry between low-input pasture and eucalyptus systems and reference systems. Overall, regenerative coffee agroforestry restored chemical fertility, microbial activity, and organic matter pools, supporting its potential role as an effective strategy for soil for soil quality recovery restoration, C sequestration, and climate mitigation in the studied region. Agroforestry Soil microbial biomass Carbon Sequestration Altitudinal gradient Atlantic Forest Soil fertility Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Land-use change is a major driver of soil organic carbon (SOC) loss and ecosystem degradation in tropical landscapes, reducing soil fertility, microbial activity, and carbon (C) and nitrogen (N) stocks (Dias et al. 2022 ; Menezes et al., 2021 ). In the Atlantic Forest an altitudinally heterogeneous and highly fragmented biome recent evidence demonstrates that soil C and N and related properties are highly sensitive to land use change and altitude (Martins et al. 2015 ; Pinto et al., 2025 ; Vieira et al. 2011 ). Understanding this sensitivity is critical, as the region has experienced extensive conversion of native vegetation to pasture systems and subsequent monocultures can fundamentally alter soil quality and ecosystem function depending on management practices. The conversion of native Atlantic Forest vegetation to extensive pasture systems may represents the first major step in soil degradation. Following deforestation and pasture establishment, soil microbial biomass and basal respiration decline sharply and continue to decrease with pasture age, indicating a progressive loss of biological functionality (Burak et al. 2023 ). This biological decline is accompanied by significant chemical losses: studies replacing native vegetation with agricultural systems, including pastures, documented reduced soil chemical fertility, particularly in exchangeable base levels (Silva et al. 2023 ). At the landscape scale, forest-to-pasture conversion in tropical regions significantly alters soil chemical properties, especially under poor and extensive management resulting in ongoing fertility losses (organic matter, bases and phosphorus) and progressive pasture soil physical degradation (Pinto et al. 2025 ). The magnitude of C loss during this initial conversion is substantial. Across 17 paired sites in Brazilian biomes, native-to-pasture conversion reduced soil C by an average of 7.5 Mg C ha − 1 (0–10 cm) and 11.0 Mg C ha − 1 (0–30 cm) (Assad et al. 2013 ). In some cases, degraded pastures show even more severe losses, with C depletion reaching 47 Mg C ha − 1 in the most degraded systems (Braz et al. 2013 ), demonstrating the substantial impact of extensive pasture degradation on soil C storage and ecosystem function. However, it is important to note that these impacts are context dependent and strongly influenced by soil type, topography, and management history. One strategy adopted to mitigate land degradation in the Atlantic Forest region has been the widespread establishment of eucalyptus plantations over degraded pastures. However, converting degraded pastureland into eucalyptus plantations does not necessarily reverse the degradation trajectory. Across Brazil, successive eucalyptus rotations on former pasture and agricultural lands have consistently resulted in declines in soil C stocks (Cook et al. 2016 ; Sandoval López et al. 2018 ). Moreover, silvopastoral systems incorporating eucalyptus trees often exhibit lower SOC levels than open pastures (Pinheiro et al. 2021 ). In addition, eucalyptus plantations may lead to a decline in soil microbial quality following land-use conversion, indicating adverse effects on the overall biological functioning of the soil (Behera & Sahani, 2003 ). Further evidence shows that long-established Eucalyptus grandis plantations on former pasturelands experience losses of soil C, nitrogen, and other nutrients confirming that tree planting alone does not guarantee soil restoration (Sandoval López et al. 2018 ). The magnitude of these negative effects, however, varies with species, rotation length, and silvicultural practices. Collectively, these findings indicate that the transition from native forest to pasture and subsequently to eucalyptus plantations can represent a continued trajectory of reduction of soil quality rather than ecological recovery. It is important to note that pasture systems and eucalyptus plantations are not inherently degrading land uses. These systems can maintain soil structure and organic matter (Camargo et al. 2025 ; Freitas et al. 2024 ; Vigo et al. 2024 ). In the present study, the pasture and eucalyptus systems were managed under low external inputs (i.e., no fertilization, liming, or erosion control) on steep slopes typical of the 'Mar de Morros ' landscape. Under these conditions, both systems exhibited signs of soil degradation relative to the secondary forest reference. In this context, limited fertilization, absence of liming, soil compaction, and insufficient ground cover likely accelerated nutrient depletion and soil acidification. However, in regions characterized by steep relief, highly weathered soils, and intense rainfall, such as the ‘ Mar de Morros ’ domain of the Atlantic Forest, pasture and eucalyptus systems established and maintained under low technological input and minimal soil management frequently exacerbates soil physical, chemical, and biological degradation. In these contexts, limited fertilization, absence of liming, soil compaction, and insufficient ground cover accelerate nutrient depletion, acidification, and loss of microbial functionality In contrast, systems combining trees, pastures, and perennial crops such as coffee with continuous organic inputs under regenerative agriculture principles can restore SOC and microbial functions compared with conventional systems (Hanke et al. 2024 ). In a Brazilian coffee agroforestry system with Grevillea robusta , microbial biomass and enzyme activities (β-glucosidase and arylsulfatase) were maintained at levels comparable to those of an adjacent native semi-deciduous tropical forest, indicating that well-designed agroforestry can preserve soil microbial functioning to near-natural levels (dos Santos Bastos et al. 2023 ), suggesting that agroforestry systems can partially reverse the degradation caused by pasture and monoculture conversion. Studies conducted in agroforestry coffee systems within the Atlantic Forest biome in Brazil indicate that these systems can significantly enhance soil biological attributes compared with full-sun coffee systems. Higher levels of total organic carbon and microbial biomass have been reported, along with lower soil CO₂-C losses, suggesting improved biological functioning and greater soil stability under agroforestry management (Thomazini et al., 2015 ). In Santo Antônio do Amparo (Minas Gerais), coffee agroforestry systems intercropped with trees increased soil organic matter and available phosphorus compared with conventional monoculture, while exchangeable bases (K 138 mg dm⁻³; Ca²⁺ 4.9 cmolc dm⁻³; Mg²⁺ 1.2 cmol c dm⁻³) remained similar between systems (Jácome et al. 2020 ). In a Conilon system, agroforestry coffee systems improved key soil chemical and organic attributes compared to full-sun production. Shaded systems increased soil organic matter and cation availability, raising pH and enhancing the contents of exchangeable bases particularly calcium and magnesium while also improving phosphorus availability. These changes indicate that agroforestry promotes a more balanced and biologically favorable soil environment relative to conventional monoculture systems (Souza et al. 2024 ). These consistent improvements across multiple regions suggest that regenerative coffee agroforestry can substantially recover soil quality degraded by the pasture-to-monoculture conversion pathway. We propose that regenerative coffee agroforestry systems promote more favorable soil attributes through four interconnected mechanisms. First, greater litter inputs, shading, and microclimate stability increase microbial biomass (MBC), basal respiration, and the microbial quotient (qMIC), restoring biological functionality lost during pasture and monoculture phases. Second, reduced soil disturbance and permanent vegetative cover improve soil fertility by raising pH, enhancing base saturation (V%), and increasing cation exchange capacity (CEC) properties critically depleted during the degradation pathway. Third, improved stabilization of organic matter promotes higher SOC, total N, and profile-level C and N stocks (CS/NS), partially recovering the C and N losses incurred during forest-to-pasture conversion. Finally, litter inputs alter incubation kinetics, increasing the availability of labile C and microbial responsiveness, which supports the biological recovery observed in regenerative systems. Because temperature and moisture vary sharply along short altitudinal gradients in the Atlantic Forest, these regenerative benefits are expected to be amplified at higher elevations (Burak et al. 2023 ; Souza Neto et al. 2011). The combination of cooler temperatures and higher moisture availability at elevated sites should enhance organic matter accumulation, reduce decomposition rates, and support greater microbial biomass and activity compared to low-altitude sites. This altitude dependent response is particularly relevant in the Atlantic Forest, where elevation driven environmental heterogeneity may create distinct windows of opportunity for soil recovery. Based on this conceptual framework, we evaluate the mechanisms expected to underpin soil functional improvements following a complete land-use sequence: secondary native forest (reference system - baseline), pasture (low-input systems first transition), eucalyptus plantation (low-input systems second transition), and regenerative agroforestry coffee (association with more favorable soil conditions). We test the hypothesis that regenerative coffee agroforestry enhances microbial biomass and activity, soil fertility, and C and N stocks relative to low-input pasture and eucalyptus systems evaluated in this study, and that these benefits increase with altitude. This process-based framing provides applied guidance for soil restoration and climate mitigation in Atlantic Forest agricultural landscapes (Marques et al. 2022 ; Tavares et al. 2018 ), demonstrating that regenerative agroforestry represents a viable pathway to recover soil quality and ecosystem function in regions extensively degraded by conventional land-use practices. Materials and Methods 2.1 Study Area and Environmental Context The study was conducted in Alegre (Fig. 1 ), southern Espírito Santo, Brazil, within the Atlantic Forest biome a global biodiversity hotspot under intense land-use pressure. The Caparaó micro-region features strongly undulating relief ranging from 120 to 1,300 m a.s.l., creating distinct environmental conditions across short elevational gradients. Predominant soils are Red–Yellow Latosols and Red–Yellow Argisols (Ferralsols and Acrisols, WRB classification) derived from gneiss/granite parent materials (Cunha et al. 2019 ). To capture environmental heterogeneity and test the hypothesis that regenerative benefits increase with altitude, we selected two contrasting elevations: low elevation (100 m a.s.l.) at Rive (20°44′01.4″S, 41°25′52.6″W) and high elevation (700 m a.s.l.) at Lagoa Seca (20°51′32.2″S, 41°27′34.4″W). Climate transitions from tropical monsoon (Am; mean ≈ 23°C) at low elevation to humid subtropical with dry winter (Cwa; 22°C in the warmest) at higher elevation. Annual rainfall ranges from 1,300–1,500 mm, with 60–70% concentrated between November and March, supporting seasonal dynamics that influence carbon mineralization and microbial activity. 2.2 Land-Use Systems To evaluate the land-use changes from native forest through low-input pasture and eucalyptus systems to regenerative agroforestry, we established a comparative framework encompassing four distinct land-use systems at both elevations. This design allows us to compare soil quality among the different land uses: native forest, pasture, eucalyptus and agroforestry, while accounting for altitude-dependent responses (Fig. 2 ). The land use change pathway in Espírito Santo began during colonization, when smallholders cleared native forests through slash-and-burn practices to establish croplands. Following this initial phase, land use dynamics in the region likely progressed through a sequence characterized by deforestation and the establishment of pastures and small-scale croplands. Many of these areas were subsequently converted into extensive pastures maintained by recurrent burning, which in some cases led to severe soil degradation. To restore productivity, portions of these lands were later converted into eucalyptus plantations, while others underwent natural regeneration into secondary forests. More recently, part of these agricultural areas—both former croplands and, in some cases, directly degraded pastures—have been transitioning into agroforestry systems, particularly regenerative coffee-based systems. Today, the landscape reflects a mosaic of land uses, including degraded pastures, eucalyptus plantations, secondary forests, and a growing share of regenerative agroforestry systems. 2.2.1 Secondary Forest (SF): Baseline Reference System The secondary forest represents the closest available proxy to native Atlantic Forest conditions. It comprises semideciduous seasonal forest with ~ 20 years of prior selective logging history and is currently under natural regeneration without active management (PROBIO 2004; Uruhahy et al. 1983 ). This system serves as the baseline for evaluating degradation in converted systems and recovery potential in agroforestry. 2.2.2 Pasture (PAS) Pastures were established ~ 9 years prior to sampling following forest clearing and consist of Brachiaria decumbens . They have never been renewed, except for occasional burning, show low dry-season cover, and exhibit progressive degradation under minimal management (manual weeding, occasional herbicide; no liming or fertilization), reflecting the common post-deforestation trajectory in the region. 2.2.3 Eucalyptus Plantations (EUC) Eucalyptus plantations on former pasture represent a shift to monoculture that typically does not reverse soil degradation. Species were selected by altitude: Eucalyptus grandis (3 × 3 m; 5 years) at 100 m and Corymbia citriodora (3 × 2 m; 9 years) at 700 m. Both received NPK only at planting. This management mirrors regional practice and is associated with declining soil quality over successive rotations. 2.2.4 Regenerative Agroforestry Coffee (AF) Low elevation (100 m a.s.l.): The agroforestry system consisted of Coffea canephora (robusta coffee) intercropped with Cedrela fissilis (cedar) and Brachiaria grass between coffee rows. Cedrela fissilis was established first at a spacing of 2.5 × 1.0 m and pruned during the initial years to regulate canopy structure. Subsequently, Coffea canephora was planted under the tree canopy at a spacing of 2.5 × 1.0 m (row × plant) and maintained for nine years prior to sampling. This system operated under minimal external inputs, with no mineral fertilization, soil acidity correction, or pest and disease control. Weed management relied on manual practices, and soil cover was maintained through grass and litter accumulation. Continuous litter input from cedar trees contributed to microclimate regulation and supported soil biological recovery, allowing substantial improvement of soil properties relative to the degraded pasture baseline. High elevation (700 m a.s.l.): The agroforestry system comprised Coffea arabica intercropped with Inga sessilis (inga) and banana ( Musa spp.) as shade components, established on former pastureland and maintained for eleven years prior to sampling. Inga sessilis was initially established and pruned after the first two years, after which coffee was planted at a spacing of 2.5 × 1.2 m (row × plant). Weed control was performed by manual hoeing. Liming and mineral fertilization were applied based on soil analysis, using an NPK formulation (20–05–20) at the onset of the rainy season to support coffee productivity while maintaining soil fertility. Periodic pruning of Inga sessilis was conducted to regulate light availability to the coffee plants, ensuring continuous soil cover and sustained organic matter inputs. The longer establishment period compared to the low-elevation system reflects the slower growth rates typical of higher-altitude conditions. 2.2.5. Experimental Design Context As a result of the study being conducted in established, commercial production areas, the experiment did not follow a classical replicated plot design. Instead, it was structured as a comparative–mensurative experiment, in which the four land-use systems (secondary forest, pasture, eucalyptus, and agroforestry) were evaluated in contrasting but internally homogeneous production areas at two altitudes. Within each land-use and altitude combination, three composite soil samples (pseudo replicates) were collected from randomized locations to characterize within-area variability. This experimental approach is consistent with field-based ecological and agricultural studies conducted under operational constraints, where true replication at the field scale is not feasible (Hurlbert, 1984 ). Similar designs have been successfully applied in studies assessing land-use and management impacts on soil properties (Arevalo et al., 2009 ). While this design limits statistical inference beyond the specific conditions studied (i.e., we cannot generalize about all pastures or eucalyptus plantations), it provides high ecological realism and allows for a robust comparison of the soil attributes associated with each land-use system under local management practices. Therefore, treatment effects should be interpreted as differences associated with the land-use systems as implemented in this specific landscape, not as inherent properties of the land-use types themselves. 2.3 Sampling Design and Analytical Soil sampling was conducted in February (late rainy season) to capture seasonal conditions when microbial activity and organic matter dynamics are most active. For chemical and biological analysis samples were collected in triplicate for each land-use and elevation combination at depth of 0–0.10 m. For carbon stocks samples were also collected ate the depth of 0.10–0.40 m. 2.3.1 Chemical and Physical Characterization Subsamples (0–0.10 m) were air-dried and sieved (< 2 mm) for chemical analyses following Embrapa standards (Teixeira et al. 2017 ). Measured attributes included pH (H₂O), available P and K, exchangeable Ca 2+ , Mg 2+ , and Al 3+ ; derived attributes included potential acidity (H + Al), SOC, total N (TN), CEC, and base saturation (V%). Undisturbed cores at 0–0.10 and 0.10–0.40 m were collected for bulk density; particle size distribution was determined to contextualize edaphic variation (Teixeira et al. 2017 ). 2.3.2 Microbial Biomass and Quotient Microbial biomass C, N, and P (C-MB, N-MB, P-MB) were quantified by irradiation–extraction on fresh soils, stored at ~ 4°C, sieved (< 2 mm), subjected to 2.5 MeV gamma irradiation, and extracted with 0.5 M K₂SO₄ (Brookes et al. 1985; Mendonça and Matos 2017 ). C-MB was calculated with k C = 0.33 (Vance et al. 1987 ). The microbial quotient was computed as qMIC = (C-MB/SOC) × 100, an indicator of microbial allocation and efficiency (Sparling 1992 ). 2.3.3 Soil Organic Matter Mineralization and Lability A 4 × 2 × 2 factorial (four land uses × two elevations × litter presence/absence) with three replicates assessed substrate lability and microbial responsiveness. Fresh 0–0.10 m soils (50 g) were incubated in closed systems for 60 days; cumulative CO₂ was periodically measured (Mendonça and Matos 2017 ). For litter treatments, 5 g of plant residue from each respective land use was added to simulate in-situ inputs (Grugiki et al. 2017 ). Cumulative CO₂ (Y) was fitted to a logistic model: Y = a / [1 + e^(−(b + cx))] where a is the asymptotic maximum CO₂ (labile C pool), b the horizontal displacement (lag), c the growth rate (mineralization kinetics), and t ½ = − b / c the time to half of maximum CO₂. Lower t ½ indicates faster mineralization and higher microbial activity. 2.3.4 Carbon and Nitrogen Stocks SOC was determined by wet oxidation (Yeomans & Bremner 1988 ) and TN by Kjeldahl digestion (Tedesco et al. 1995 ). Profile-level carbon and nitrogen stocks (CS/NS) were computed as: CS/NS = [(SOC or TN) × Bd × d] / 10 where SOC or TN (g kg⁻¹), bulk density Bd (g cm⁻³), and layer thickness d (cm). Stocks to 0–0.40 m evaluate recovery in AF relative to SF. 2.4 Statistical Analysis Descriptive statistics (mean, SD) were computed for each attribute within each land use and elevation, and overall means/SDs were also calculated. ANOVA was performed in SISVAR to test treatment effects, with significance at 5% and 1% by the F-test (Ferreira 2019 ). For soil carbon stocks, only means were computed. A Principal Component Analysis (PCA) was performed to identify the main gradients of variation among physical, chemical, and biological soil attributes across land-use systems and altitudes. The analysis was conducted to the depth 0.00–0.10 m using standardized data based on the correlation matrix. Variables with absolute correlations ≥ 0.30 with each principal component were considered significant contributors. The PCA allowed visualization of relationships between soil attributes and land-use systems through biplots. Analyses were performed using R software (Lê & Husson 2008 ). Results 3.1. Microbial functioning and CO 2 Mineralized At the 100 m altitude, there is a significant difference among land uses for microbial variables (Table 1 ). No clear pattern was detected for microbial biomass carbon (C–MB), the highest values were found under eucalyptus management (576.13 µg kg⁻¹), followed by agroforestry (505.85 µg kg⁻¹) whereas secondary forest (464.86 µg kg − 1 ) and pasture (434.37 µg kg⁻¹) showed lower values. N–MB presented no significant variation across land uses (p > 0.05), averaging 46.7 µg kg − 1 . In contrast, P–MB differed (p < 0.01), with secondary forest exhibiting the highest values (23.44 µg kg − 1 ) and pasture the lowest (9.13 µg kg − 1 ). The microbial coefficient (qMic) also varied among systems (p < 0.01), ranging from 1.95 µg kg − 1 in secondary forest to 6.70 µg kg − 1 in pasture. At the 700 m altitude, overall higher C–MB values were obtained compared to 100 m, and at this altitude, the secondary forest had the highest values (Table 1 ). Secondary forest had the highest values of the C–MB (580.00 µg kg − 1 ), followed by pasture (475.20 µg kg − 1 ), eucalyptus (420.72 µg kg − 1 ) and agroforestry (413.66 µg kg − 1 ). N–MB remained unchanged (p > 0.05), while P–MB decreased significantly under eucalyptus (1.67 µg kg − 1 ) and pasture (2.30 µg kg − 1 ) compared with secondary forest (7.96 µg kg − 1 ) (p < 0.01). The qMic had moderate variation among systems (p < 0.05), with higher values under agroforestry (4.76) and pasture (3.57) compared with secondary forest (2.59). Table 1 Carbon, Nitrogen and Phosphorus of MB and microbial coefficient ( q Mic) for different land use systems, at different altitudes 100 m and 700 m at depth 0.00–0.10 m Microbial functioning Secondary forest Pasture Eucaliptus Agroforestry system Mean S.D. 1 F 2 µg kg − 1 ------------------------------------------------100 m ------------------------------------------------ C–MB 464,86 434,37 576,13 505,85 495,30 45,69 * N–MB 55,70 45,06 41,08 45,00 46,71 4,50 ns P–MB 23,44 9,13 15,87 9,39 14,46 5,20 ** q Mic 1,95 6,70 5,75 2,73 4,28 1,94 ** -----------------------------------------------700 m ----------------------------------------------- C–MB 580,00 475,20 420,72 413,66 472,40 55,21 * N–MB 56,08 49,13 41,80 51,27 49,57 4,11 ns P–MB 7,96 2,30 1,67 7,22 4,79 2,80 ** q Mic 2,59 3,57 3,27 4,76 3,55 0,62 * 1/ S.D. Standart of deviation; 2/ F test (ANOVA); *(p < 0.05); **(p < 0.01); ns not significant. 3.2. Soil fertility and chemical functionality Table 2 presents the chemical and physical attributes of soils (0.00–0.10 m) under different land-use systems at two altitudes. At both altitudes, clear contrasts were obtained among land uses, overall secondary forest and agroforestry soils generally presented better quality, while the conversion to pasture and eucalyptus was associated with the decrease of the soil quality. In general, higher BD was found in the pasture management (1.10 kg dm − 3 at 100 m and 1.60 kg dm − 3 at 700 m), suggesting physical degradation following vegetation removal. Whereas agroforestry maintained lower bulk density values (1.02 and 1.10 kg dm − 3 ) lower to the forest condition in both altitudes. Table 2 Chemical and physical attributes of soil collected at 0.00-0.10 m depth of at two altitudes for each land use systems in the southern region of the state of Espírito Santo Soil attributes 1 Secondary forest Pasture Eucaliptus Agroforestry system Secondary forest Pasture Eucaliptus Agroforestry system Mean S.D. 12 F 13 -------------------------100 m------------------------- -------------------------700 m------------------------- Sand (%) 1 46.53 38.72 42.36 32.74 56.32 51.71 56.90 72.54 48.70 9.64 - Silt (%) 1 23.97 28.27 3.22 30.60 9.84 5.56 4.42 7.18 15.52 10.11 - Clay (%) 1 29.50 33.01 54.41 36.67 33.85 42.73 38.68 20.28 35.78 6.98 - Bd (kg dm − 3 ) 2 1.22 1.10 1.19 1.02 1.19 1.60 1.08 1.10 1.20 0.11 ** pH (H 2 O) 3 4.66 5.12 4.95 6.30 4.66 4.87 5.09 6.28 5.24 0.67 ns P (mg dm − 3 ) 4 1.44 0.44 0.83 2.20 0.85 0.50 0.54 2.47 1.16 0.79 ** K (mg dm − 3 ) 5 44.86 32.68 57.36 156.06 60.32 23.80 20.51 285.03 85.08 91.56 ** Ca + 2 (cmol c dm³) 6 0.66 0.67 0.98 1.34 1.62 0.66 0.32 3.36 1.20 0.97 ** Mg + 2 (cmol c dm −3 ) 6 0.59 0.34 0.62 0.62 0.48 0.08 0.09 1.59 0.55 0.47 ** Al + 3 (cmol c dm − 3 ) 6 0.46 0.16 0 0 1.25 0.96 1.02 0 0.48 0.52 ** H + Al (cmol c dm 3 ) 7 6.74 4.73 4.02 2.50 8.91 7.61 7.20 4.27 5.75 2.18 ns SOC (dag kg − 1 ) 8 1.70 1.02 1.00 1.67 1.63 1.64 1.30 1.44 1.44 0.24 ns NT (dag kg − 1 ) 9 0.17 0.10 0.14 0.17 0.16 0.14 0.11 0.10 0.14 0.02 ns CEC (t) 10 1.82 1.26 1.74 2.36 3.51 1.75 1.49 5.68 2.59 1.07 ** BS (%) 11 16.81 18.75 30.25 48.58 20.20 9.51 6.10 57.10 28.74 14.55 ** 1 Pipette method (Slow stirring); ²Volumetric Ring Method (Teixeira et al. 2017 ); 3 pH in water (1:2.5 ratio); 4 Mehlich-1 extractor and colorimetric determination; 5 Mehlich-1 extraction and flame photometry determination; 6 extraction with 1 mol L -1 potassium chloride and titration; 7 extraction with 0.5 mol L -1 calcium acetate, pH 7.0 and titration (Teixeira et al. 2017 ); 8 Soil organic carbon, wet oxidation with potassium dichromate and sulfuric acid (Yeomans & Bremner 1988 ); 9 Total nitrogen, determined by soil digestion with sulfuric acid and hydrogen peroxide, followed by steam distillation (Kjeldahl) (Tedesco et al. 1995 ); 10 Effective cation exchange capacity; 11 Base saturation; 12 Standard deviation; 13 F test (ANOVA). Soil acidity was high across systems (pH 4.6–6.3), with no significant differences (p > 0.05). However, consistent declines in pH and nutrient availability were evident after forest conversion, particularly under pasture. Available P remained low in all systems (0.44–2.47 mg dm − 3 ) but tends to increase in agroforestry at both altitudes. Available K and exchangeable Ca 2+ , and Mg 2+ differed significantly (p < 0.01), showing strong depletion in pasture and eucalyptus soils and comparatively higher levels under agroforestry within this study. At 700 m, agroforestry reached 285.03 mg dm − 3 K, 3.36 cmol c dm − 3 Ca 2+ , and 1.59 cmol c dm⁻³ Mg 2+ , compared to minimal levels in pasture (23.8 mg dm − 3 K, 0.66 cmol c dm − 3 Ca 2+ , 0.08 cmol c dm − 3 Mg 2+ ). Exchangeable Al 3+ also varied (p < 0.01), being absent under agroforestry and eucalyptus and highest under forest and pasture at 700 m (1.02–1.25 cmol c dm − 3 ), reflecting higher acidity and base loss in degraded areas. The potential acidity (H + Al) did not differ significantly but tended to be lower in agroforestry (2.50–4.27 cmol c dm − 3 ) than in forest and pasture. SOC and NT were not significantly affected (p > 0.05), averaging 1.44 dag kg − 1 and 0.14 dag kg − 1 , respectively, however, at the altitude 100 m high numerical values were found in the forest and agroforestry systems and at 700 m in the pasture, forest and agroforestry. In contrast, CEC and BS presented strong responses to land use (p < 0.01). Agroforestry reached the highest values at both altitudes (CEC: 2.36–5.68 cmol c dm − 3 ; BS: 48.6–57.1%), while pasture and eucalyptus exhibited the lowest. Overall, soils under pasture and eucalyptus reflected the typical pattern of chemical and physical degradation following forest conversion, with compaction, nutrient depletion, and higher acidity. In contrast, agroforestry systems consistently improved soil bulk density and fertility indicators, recovering CEC, base saturation, and nutrient stocks toward levels observed in the secondary forest. 3.3. C–N sequestration (0–40 cm) Across both altitudes (100 m and 700 m), soil C and N stocks at 0–40 cm depth were strongly influenced by land use. Secondary forests maintained the highest total C stocks (≈ 45–55 t C ha⁻¹) and N stocks (≈ 4–5 t N ha⁻¹), confirming their role as reference systems for soil organic matter accumulation. In contrast, pasture and eucalyptus plantations exhibited the lowest stocks (≈ 20–30 t C ha⁻¹ and ≈ 2–3 t N ha⁻¹), reflecting long-term depletion of organic matter and nutrient cycling following forest conversion (Fig. 3 ). Agroforestry systems, however, had intermediate to high values of both C and N (≈ 35–45 t C ha⁻¹ and ≈ 3–4 t N ha⁻¹), approaching forest levels, particularly at 700 m. When expressed as relative stocks compared to the secondary forest, pasture and eucalyptus retained only 40–55% of total C and N, whereas agroforestry systems recovered up to 80% of soil C and more than 90% of soil N at 100 m (Fig. 4 ). 3.4. Litter add-on and incubation kinetics The mean CO₂ mineralization values demonstrated a clear and consistent effect of both litter addition and land use across altitudes. Treatments with litter released substantially more CO₂ (6.0–10.5 g C/ kg − 1 soil) than those without litter (2.3–3.1 g C/ kg − 1 soil) (Fig. 5 ). At 100 m altitude, the agroforestry system showed the highest CO₂ mineralization (10.5 g C/ kg − 1 soil), followed by pasture (9.64 g C/ kg − 1 soil) and eucalyptus (9.22 g C/ kg − 1 soil), whereas secondary forest exhibited the lowest (7.66 g C/ kg − 1 soil). At 700 m, the same pattern persisted, though overall CO₂ release was slightly lower, with agroforestry again outperforming other systems (9.43 g C/ kg − 1 soil), followed by pasture (8.18 g C/ kg − 1 soil), secondary forest (6.45 g C/ kg − 1 soil), and eucalyptus (4.94 g C/ kg − 1 soil) (Fig. 5 ). In contrast, under absence of litter, CO₂ mineralization remained low and relatively uniform among systems (2.3–3.0 g C/ kg − 1 soil), indicating that surface organic inputs were the key determinant of microbial activity. These findings align with the conceptual framework proposed, where the availability of labile organic matter regulates microbial responsiveness and soil biological recovery. Overall, the results indicate that (i) litter input strongly stimulates microbial activity, (ii) the agroforestry system maintains the highest biological performance across altitudes, and (iii) lower CO₂ release in secondary forest and eucalyptus systems reflects a predominance of more stable, recalcitrant organic matter pools typical of mature or structurally simplified systems. 3.5. Logistic modeling of CO₂ evolution Cumulative CO₂ production fitted the logistic growth model for all land use systems and altitudes (r² = 0.99) (Table 3 ). The saturation parameter ( a ), representing the total CO₂ evolved, was consistently higher in treatments with litter. At 100 m altitude, the value ranged from 7.19 in secondary forest to 9.98 in the agroforestry system, and at 700 m from 5.10 in eucalyptus to 9.02 in the agroforestry system. Without litter, a value dropped substantially (2.24–2.89). The b coefficient, related to the inflection point of the logistic curve, varied between − 1.46 and − 2.67, with the largest negative values in the agroforestry system at 100 m (-2.67) and in eucalyptus at 700 m (-2.37), suggesting faster stabilization of CO₂ evolution in these systems. The c coefficient (rate of CO₂ increase) ranged narrowly (-0.10 to -0.20), indicating similar mineralization kinetics across systems (Table 3 ). The estimated half-time (t 1/2 = –b/c) ranged from 7.3 to 19.0 days, depending on land use and litter presence. Shorter t 1/2 values, such as those in the agroforestry system with litter at 700 m (7.3 days) and eucalyptus without litter at 100 m (13.2 days), indicate faster mineralization and greater microbial turnover. Conversely, longer t 1/2 values under agroforestry and pasture without litter (≈ 19 days) reveal slower CO₂ accumulation. Table 3 Coefficients of the logistic equation, coefficients of determination and estimated time to reach half of the maximum production of CO 2 (t 1/2 = -b/c) for the different land use systems and altitudes Land uses Absense of litter Presence of litter a b c t 1/2 r² a b c t 1/2 r² Altitude 100 m Secondary forest 2.89 -2.17 -0.13 16.7 0.99 7.19 -1.69 -0.10 16.9 0.99 Pasture 2.77 -2.08 -0.14 14.9 0.99 9.12 -1.90 -0.10 19.0 0.99 Eucalyptus 2.28 -2.25 -0.17 13.2 0.99 9.00 -1.47 -0.10 14.7 0.99 Agroforestry system 2.55 -2.67 -0.14 19.0 0.99 9.98 -1.81 -0.11 16.5 0.99 Altitude 700 m Secondary forest 2.68 -2.00 -0.15 13.3 0.99 6.30 -1.52 -0.16 9.5 0.99 Pasture 2.88 -2.36 -0.15 15.7 0.99 7.54 -1.57 -0.10 15.2 0.99 Eucalyptus 2.24 -2.25 -0.16 14.0 0.99 5.10 -2.37 -0.14 16.9 0.99 Agroforestry system 2.25 -1.96 -0.15 13.0 0.99 9.02 -1.46 -0.20 7.3 0.99 These patterns confirm that the addition of litter not only increases the total amount of CO₂ released (a) but also alters the mineralization dynamics ( c and t 1/2 ), particularly in diversified systems such as agroforestry (Table 3 ). 3.5. Multivariate Analisys Table 4 summarizes the principal component analysis (PCA) of soil chemical, physical, and microbial attributes across land-use systems and altitudes. The first four principal components explained 87.5% of the total variance, with PC1, PC2, PC3, and PC4 accounting for 35.9%, 27.3%, 16.2%, and 8.1%, respectively. PC1 represented the main soil fertility gradient, positively correlated with K, Ca 2+ , Mg 2+ , CEC, BS, and P, and negatively with Al 3+ and H + Al, indicating a transition from acidic and nutrient-poor soils (pasture and eucalyptus) to more fertile conditions under agroforestry systems and secondary forests. PC2 captured variability related to microbial and organic matter dynamics, with strong positive correlations for SOC, N–MB, and H + Al, contrasting soils with higher biological activity (forest and agroforestry) against degraded pastures (Table 4 ). PC3 was associated with nutrient cycling, mainly total N, P–MB, and SOC, while PC4 represented secondary variability linked to microbial C (C–MB) and texture (clay and silt fractions). The PCA biplot (PC1 × PC2) explained 63.2% of the total variance (PC1 = 35.9%, PC2 = 27.3%), clearly separating the land-use systems according to their soil chemical and biological attributes. Three distinct groups emerged along the decline in several soil quality indicators causing degradation to recovery gradient. Group 1 (Baseline), represented by secondary forests at both altitudes, was associated with higher SOC, N–MB, and moderate fertility, reflecting natural reference conditions. Group 2 (Low-input systems), including pastures and eucalyptus systems, was characterized by higher Al 3+ and H + Al and lower base cations (Ca 2+ , Mg 2+ , K), indicating nutrient depletion and soil acidification typical of degraded soils. Group 3 (More favorable soil attributes), composed of agroforestry systems, showed higher K, Ca 2+ , Mg 2+ , CEC, and BS, suggesting chemical and biological recovery driven by tree–crop interactions and organic inputs (Fig. 6 ). Table 4 Principal component analysis of the chemical attributes in four land uses systems (forest, pasture, eucalyptus and agroforestry), two altitudes (100m and 700m) at the depth of 0.00-0.10 m Explained variance ratio (%) PC 1 PC 2 PC 3 PC 4 35,85 27,31 16,19 8,15 Cumulative variance (%) 35,85 63,17 79,35 87,5 Soil Attributes Correlation with major components Sand 0,14 0,102 -0,465 0,066 Silt 0,059 -0,004 0,39 -0,452 Clay -0,243 -0,123 0,133 0,438 Bd -0,171 0,125 -0,161 0,16 pH 0,299 -0,135 0,049 -0,107 P 0,34 0,052 0,129 -0,024 K 0,358 -0,028 -0,054 0,059 Ca 2+ 0,332 0,032 -0,154 0,186 Mg 2+ 0,342 -0,021 -0,04 0,015 Al 3+ -0,205 0,259 -0,256 -0,049 H + Al -0,211 0,336 -0,157 -0,01 SOC 0,045 0,388 0,259 -0,109 NT -0,083 0,179 0,473 0,193 CEC 0,308 0,224 -0,143 0,164 BS 0,344 -0,078 0,103 0,166 C–MB -0,101 0,029 0,228 0,602 N–MB 0,061 0,42 0,142 -0,041 P–MB 0,019 -0,04 0,366 0,11 q Mic 0,041 -0,353 -0,259 0,108 Carbon stocks 0–40 cm 0,223 0,21 0,041 0,101 Nitrogen stocks 0–40 cm 0,226 0,184 0,077 0,09 Along PC1, agroforestry systems occupied the positive axis, contrasting with the negative scores of pastures and eucalyptus, which reflect lower fertility and higher acidity (Fig. 6 ). PC2 captured differences in microbial and organic matter activity, distinguishing forests and agroforestry (higher SOC and N–MB) from degraded pastures with reduced biological functionality. Discussion Within the landscape studied, the sequence of land-use change from secondary forest to pasture and subsequently to eucalyptus was associated with a decline in several soil quality indicators, including losses in chemical fertility, nutrient stocks, and microbial functionality. However, as our sampling design lacks a temporal baseline and true replication, we cannot infer a causal trajectory of degradation in the soils of Atlantic Forest. Conversely, the regenerative agroforestry system effectively showed higher chemical and biological attributes compared to the pasture and eucalyptus systems, suggesting a potential for soil quality recovery under this management. The lower pH, base saturation (BS%), and exchangeable Ca²⁺ and Mg²⁺ in the pasture and eucalyptus systems of this study are consistent with the typical pattern of nutrient mining and soil acidification (Sandoval López et al. 2018 ; Silva et al., 2023 ). Such degradation results from the combined effects of base cation removal by harvest, low residue return, and aluminum mobilization (Behera & Sahani 2003 ; Cook et al. 2016 ; Sandoval López et al. 2018 ). In contrast, the agroforestry system was associated with higher levels of K, Ca 2+ , Mg 2+ , CEC, and BS, particularly at 700 m. This recovery may reflect the cumulative effects of litterfall, biological N fixation, and root exudation that enhance nutrient recycling and reduce soil acidity (Jácome et al. 2020 ; Souza et al. 2024 ). The contribution of Inga sessilis and other leguminous shade trees likely explains the enrichment of total N and CEC at high altitude. Although SOC and total N at the 0–10 cm layer were not significantly different among systems, the C and N stocks (0–40 cm) revealed strong contrasts, with agroforestry recovering up to 80–90% of forest C and N pools. This pattern indicates that soil restoration occurs progressively from surface to subsoil horizons, where aggregate stabilization protects organic matter from decomposition (Hanke et al. 2024 ; Tonucci et al. 2023 ). The lower bulk density under agroforestry (≈ 1.02–1.10 kg dm⁻³) compared to pasture (up to 1.60 kg dm⁻³) indicates better physical conditions associated with this system also demonstrates physical improvement and greater pore continuity, which favor root proliferation and organic matter incorporation (Perreira et al. 2025). Microbial indicators effectively demonstrated the degradation recovery gradient across land uses. The decline in microbial phosphorus (P–MB) under pasture and eucalyptus reflects nutrient stress and the depletion of labile C sources conditions known to suppress microbial activity and diversity (Rocha Junior et al. 2018 ). Similarly, the reduction in microbial biomass C (C–MB) at 700 m compared with forest areas, and the higher C–MB values in the agroforestry system at 100 m, emphasize the responsiveness of this indicator to land-use change. Elevated microbial quotient (qMic) values in low-input pasture and eucalyptus systems evaluated in this study indicate a smaller, stressed microbial community with reduced metabolic efficiency, whereas the agroforestry system maintained intermediate to high C–MB and low qMic values, consistent with a larger and more functionally stable community. The high C–MB observed under eucalyptus at 100 m may not indicate true ecological recovery but rather a transient microbial flush associated with the accumulation of a thick litter layer. According to Brinkman et al. ( 2017 ), eucalyptus plantations tend to accumulate substantially more litter than secondary forests; however, this material decomposes slowly due to its chemical composition and the reduced density and diversity of decomposer organisms. Consequently, increases in microbial biomass under eucalyptus may reflect short-term microbial responses to litter inputs rather than sustained improvements in soil quality. The sharp decrease in P–MB under low-input (pasture and eucalyptus systems) reinforces its role as an early and sensitive indicator of nutrient limitation, warranting its inclusion as a key metric in soil health assessments (Awoonor et al. 2023 ). The CO 2 mineralization kinetics confirmed that litter input strongly stimulates microbial respiration, particularly under agroforestry. The higher cumulative CO 2 evolution and faster half-life (t 1/2 ) indicate that regenerative management not only enhances carbon storage but also sustains an active C turnover. Such balanced dynamics are critical for nutrient cycling and soil resilience (Carvalho et al. 2016 ; Henrique et al. 2022 ). The greater responsiveness to litter addition under agroforestry reinforces the idea that biological activity depends on continuous organic inputs which is a defining feature of regenerative systems as implemented here. Altitude modulated the intensity of soil recovery. The amplified restoration of C and N stocks at 700 m can be attributed to cooler and more humid microclimates that reduce decomposition rates and favor microbial growth (Siles et al. 2016 ; Tashi et al. 2016 ; Shedayi et al. 2016 ). This aligns with evidence that montane agroforestry systems accumulate more organic matter due to greater root biomass and reduced soil disturbance. Thus, environmental heterogeneity must be considered when scaling regenerative practices across tropical landscapes. The PCA results captured the multidimensional nature of soil degradation and recovery. PC1, explaining 35.9% of total variance, represented the chemical fertility gradient positively associated with K, Ca 2+ , Mg 2+ , CEC, BS, and P, and negatively with Al 3+ and H + Al. PC2, explaining 27.3%, reflected biological functionality, positively correlated with SOC, N–MB, and microbial activity. Together, these axes visually summarized the soil restoration trajectory from secondary forest to agroforestry, eucalyptus, and pasture, confirming that agroforestry systems occupy an intermediate to advanced position. The PCA biplot also distinguished three ecological states: baseline (secondary forest), characterized by high SOC and microbial activity; low-input pasture and eucalyptus systems, marked by low fertility and biological stress; and the more favorable soil attributes (agroforestry), showing improved nutrient status and microbial function. This multivariate structure complements the univariate results, reinforcing the concept that regenerative agroforestry simultaneously restores chemical fertility and biological resilience, a synergy fundamental to long-term soil multifunctionality and climate mitigation in tropical agroecosystems. Conclusion This study demonstrates that land-use change in the Atlantic Forest region may cause a degradation gradient, in which the conversion from secondary forest to pasture and eucalyptus is associated with marked chemical, physical, and biological soil deterioration. Importantly, these impacts reflect the management conditions of the pasture and eucalyptus systems evaluated, which did not follow adequate technical management guidelines, rather than inherent limitations of these land-use systems. These low-input pasture and eucalyptus systems exhibited higher bulk density, soil acidity, and aluminum saturation, along with depleted base cations, reduced microbial biomass, and lower C and N stocks. In contrast, the regenerative agroforestry system reversed much of this degradation, recovering up to 80–90% of forest-level carbon and nitrogen stocks and restoring key indicators of fertility and microbial functionality. Higher base saturation, cation exchange capacity, and microbial biomass, coupled with lower bulk density and aluminum toxicity, demonstrate that tree crop integration enhances both the storage and the cycling of nutrients. The CO 2 mineralization assays confirmed that litter input and continuous organic matter turnover are critical drivers of the more favorable soil attributes. Multivariate analysis (PCA) clearly separated land-use systems along chemical and biological gradients, confirming that agroforestry occupies intermediate to advanced more favorable soil attributes between low-input pasture and eucalyptus systems and forest reference conditions. The convergence of agroforestry and secondary forest soils at higher altitudes further emphasizes the synergistic effect of cooler and moister microclimates on carbon stabilization and microbial activity. Collectively, these findings confirm that regenerative agroforestry fosters soil restoration by simultaneously improving chemical fertility, biological efficiency, and organic matter dynamics in tropical environment. These multifunctional highlights agroforestry as a viable strategy for climate mitigation, carbon sequestration, and long-term ecosystem resilience in tropical agricultural landscapes. Declarations Funding No funding was received to assist with the preparation of this manuscript. Competing interests The authors have no competing interests to declare that are relevant to the content of this article. Ethics approval Not applicable. Consent to participate Not applicable. Consent for publication Not applicable. Data availability The datasets generated and analysed during the current study are available from the corresponding author on reasonable request. Author contributions: Conceptualization: Paulo Roberto da Rocha Júnior, Felipe Vaz Andrade, Anna Carolyna Fernandes Ferreira; Methodology: Paulo Roberto da Rocha Júnior, Felipe Vaz Andrade, Anna Carolyna Fernandes Ferreira, Eduardo de Sá Mendonça; Formal analysis and investigation: Paulo Roberto da Rocha Júnior, Anna Carolyna Fernandes Ferreira, Felipe Vaz Andrade, Querubim Máximo de Siqueira Neto, Fabio Ribeiro Pires; Data curation: Paulo Roberto da Rocha Júnior, Querubim Máximo de Siqueira Neto; Writing – original draft: Paulo Roberto da Rocha Júnior, Querubim Máximo de Siqueira Neto; Writing – review and editing: Paulo Roberto da Rocha Júnior, Fabio Ribeiro Pires, Eduardo de Sá Mendonça, Anna Carolyna Fernandes Ferreira, Felipe Vaz Andrade, Querubim Máximo de Siqueira Neto; Supervision: Felipe Vaz Andrade, Fabio Ribeiro Pires, Eduardo de Sá Mendonça. Additional information An AI-assisted tool (ChatGPT, OpenAI) was used to improve clarity and language of the manuscript; all scientific content, interpretation, and final wording were reviewed and approved by the authors. Acknowledgements The authors acknowledge the financial support provided by CAPES ( Coordenação de Aperfeiçoamento de Pessoal de Nível Superior ) and FAPES ( Fundação de Amparo à Pesquisa e Inovação do Espírito Santo ) through research scholarships, which made this study possible. The authors also thank the farmers for granting access to their farms and for allowing the use of their agricultural areas for the development of this research. References Assad, E. D., Pinto, H. S., Martins, S. C., Groppo, J. D., Salgado, P. R., Evangelista, B., Vasconcellos, E., Sano, E. E., Pavão, E., Luna, R., Camargo, P. B. & Martinelli, L. A. (2013). Changes in soil carbon stocks in Brazil due to land use: paired site comparisons and a regional pasture soil survey. 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P., & Nascimento, A. L. (2024). Soil physical, chemical and biological properties in Conilon coffee intercropping systems. Revista Brasileira de Ciência do Solo , 48 , e0230056. https://doi.org/10.36783/18069657rbcs20230056 Sousa Neto, E., Carmo, J. B., Keller, M., Martins, S. C., Alves, L. F., Vieira, S. A., Piccolo, M. C., Camargo, P., Couto, H. T. Z., Joly, C. A., and Martinelli, L. A. (2011) Soil-atmosphere exchange of nitrous oxide, methane and carbon dioxide in a gradient of elevation in the coastal Brazilian Atlantic forest, Biogeosciences, 8, 733–742. https://doi.org/10.5194/bg-8-733-2011 Sparling, G.P. (1992). Ratio of microbial biomass carbon to soil organic carbon as a sensitive indicator of changes in soil organic matter. Aust. J. Soil Res. 30, 195–207. https://doi.org/10.1071/sr9920195 Tavares, P. D., Silva, C. F., Pereira, M. G., Freo, V. A., Bieluczyk, W., & Silva, E. M. R. (2018). Soil quality under agroforestry systems and traditional agriculture in the Atlantic Forest biome. Revista Caatinga, 31(4), 954–962. https://doi.org/10.1590/1983-21252018v31n418rc Tashi, S., Singh, B., Keitel, C., & Adams, M. (2016). Soil carbon and nitrogen stocks in forests along an altitudinal gradient in the eastern Himalayas and a meta-analysis of global data. Global Change Biology, 22(6), 2255–2268. https://doi.org/10.1111/gcb.13234 Tedesco, M. J., Gianello, C., Bissani, C. A., Bohnen, H. & Volkweiss, S. J. (1995). Análise de solo, plantas e outros materiais. 2. ed. rev. e ampl. Porto Alegre: UFRGS, (Boletim Técnico, 5). Teixeira, P. C., Donagemma, G. K., Fontana, A., & Teixeira, W. G. (Eds.). (2017). Manual de métodos de análise de solo (3rd ed., rev. & expanded). Brasília, DF: Embrapa. Thomazini, A., Mendonça, E. S., Cardoso, I.M. & Garbin, M.L. (2015). SOC dynamics and soil quality index of agroforestry systems in the Atlantic rainforest of Brazil. Geoderma Regional , 5 , 104–113. https://doi.org/10.1016/j.geodrs.2015.04.002 Tonucci, R. G., Vogado, R. F., Silva, R. D., Pompeu, R. C. F. F., Oda-Souza, M., & Souza, H. A. D. (2023). Agroforestry system improves soil carbon and nitrogen stocks in depth after land-use changes in the Brazilian semi-arid region. Revista Brasileira de Ciência do Solo, 47, e0220124. https://doi.org/10.36783/18069657rbcs20220124 Uruhahy, J.C.C., Collares, J.E.R., Santos, M.M., Barreto, R.A.A., 1983. Vegetação: as regiões fitoecólogicas, sua natureza e seus recursos econômicos estudo fitogeográfico. In: Folhas São Paulo; Rio de Janeiro/Vitória. Geologia, geomorfologia, pedologia, vegetação e uso potencial da terra. Projeto RADAMBRASIL, Rio de Janeiro 780 p. Vance, E.D., Brookes, P.C. & Jenkinson, D.S. (1987). An extraction method for measuring soil microbial biomass C. Soil Biology & Biochemistry , 19, 703–707. https://doi.org/10.1016/0038-0717(87)90052-6 Vieira, S.A., Alves, L.F., Duarte-Neto, P.J., Martins, S.C., Veiga, L.G., Scaranello, M.A., Picollo, M.C., Camargo, P.B., do Carmo, J.B., Neto, E.S., Santos, F.A.M., Joly, C.A. and Martinelli, L.A. (2011). Stocks of carbon and nitrogen and partitioning between above- and belowground pools in the Brazilian coastal Atlantic Forest elevation range. Ecology and Evolution , 1 , 421–434. https://doi.org/10.1002/ece3.41 Vigo, C. N., Oclocho-Garcia, F. E., Trigoso, D. I., & Oliva-Cruz, M. (2024). Influence of Eucalyptus globulus plantations on soil characteristics at different altitudinal levels. Trees, Forests and People, 18, 100677. https://doi.org/10.1016/j.tfp.2024.100677 Yeomans, J.C., Bremner, J.M., 1988. A rapid and precise method for routine determination of organic carbon in soil. Communications in Soil Science and Plant Analysis. 19, 1467–76. https://doi.org/10.1080/00103628809368027 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 18 May, 2026 Reviewers agreed at journal 07 May, 2026 Reviewers agreed at journal 06 May, 2026 Reviewers agreed at journal 04 May, 2026 Reviewers invited by journal 04 May, 2026 Editor assigned by journal 04 May, 2026 Submission checks completed at journal 29 Apr, 2026 First submitted to journal 27 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9545474","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":635949739,"identity":"2854e29e-eefb-4f5b-9753-daf1425d7708","order_by":0,"name":"Paulo Roberto da Rocha Junior","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYHACNhDBzMDA2MDAUAFiMjeQouUMiMlInBYIYGwDk/i1mLc3P3vwg+EOu8Ht5sbHlfNqo/nbgVp+VGzDqUXmzDFzwx6GZ8wGdw42G57ddjx3xmHGBsaeM7dxapGQyGGT4GE4zGxwI7FNsnHbsdwGoBZmxjb8WiT/wLXMOZY7nxgt0ghbGmpyNxDUwnPMTFrG4Bmz5I3EZsOGYwdyNwK1HMTrF/bmZ5JvKu4k891If/iwoaYud975wwcf/KjArQUCDA4kQ1mHweQBAurBauygjDoiFI+CUTAKRsFIAwBZDFncfgfe3wAAAABJRU5ErkJggg==","orcid":"","institution":"Universidade Federal do Espírito Santo (UFES) Centro Universitário Norte do Espírito Santo (CEUNES)","correspondingAuthor":true,"prefix":"","firstName":"Paulo","middleName":"Roberto da Rocha","lastName":"Junior","suffix":""},{"id":635949740,"identity":"f8d59266-8d27-4b85-a578-43ae0c659b1e","order_by":1,"name":"Anna Carolyna Fernandes","email":"","orcid":"","institution":"Universidade Federal do Espírito Santo (UFES) Centro de Ciências Agrárias e Engenharias (CCAE)","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"Carolyna","lastName":"Fernandes","suffix":""},{"id":635949741,"identity":"aeb2d90b-edac-420f-99f5-84b98ff064ec","order_by":2,"name":"Querubim Máximo de Siqueira Neto","email":"","orcid":"","institution":"Universidade Federal do Espírito Santo (UFES) Centro Universitário Norte do Espírito Santo (CEUNES)","correspondingAuthor":false,"prefix":"","firstName":"Querubim","middleName":"Máximo de Siqueira","lastName":"Neto","suffix":""},{"id":635949742,"identity":"eb18b75b-d428-4afe-a432-c94a13119083","order_by":3,"name":"Fabio Ribeiro Pires","email":"","orcid":"","institution":"Universidade Federal do Espírito Santo (UFES) Centro Universitário Norte do Espírito Santo (CEUNES)","correspondingAuthor":false,"prefix":"","firstName":"Fabio","middleName":"Ribeiro","lastName":"Pires","suffix":""},{"id":635949743,"identity":"2065f5bc-a677-4e7e-8f5f-a643495b3e93","order_by":4,"name":"Eduardo Sá Mendonça","email":"","orcid":"","institution":"Universidade Federal do Espírito Santo (UFES) Centro de Ciências Agrárias e Engenharias (CCAE)","correspondingAuthor":false,"prefix":"","firstName":"Eduardo","middleName":"Sá","lastName":"Mendonça","suffix":""},{"id":635949744,"identity":"8a61015b-1119-40c5-93a3-adaa20e170c8","order_by":5,"name":"Felipe Vaz Andrade","email":"","orcid":"","institution":"Universidade Federal do Espírito Santo (UFES) Centro de Ciências Agrárias e Engenharias (CCAE)","correspondingAuthor":false,"prefix":"","firstName":"Felipe","middleName":"Vaz","lastName":"Andrade","suffix":""}],"badges":[],"createdAt":"2026-04-27 19:09:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9545474/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9545474/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109197286,"identity":"e2d874de-15e7-468b-8b46-a9dc15042f13","added_by":"auto","created_at":"2026-05-13 13:13:59","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1649995,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of the municipality of \u003cem\u003eAlegre\u003c/em\u003e-ES, in which 100 m \u003cem\u003eRive \u003c/em\u003eand 700 m \u003cem\u003eLagoa Seca\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9545474/v1/dbd4a9e8287e7391833dc465.jpeg"},{"id":109197273,"identity":"cbcba623-9db5-474c-8277-c50268e8d911","added_by":"auto","created_at":"2026-05-13 13:13:50","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":717996,"visible":true,"origin":"","legend":"\u003cp\u003eLand-use transitions from forest to agriculture in the Atlantic Forest region.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe land use change pathway in Espírito Santo began during colonization, when smallholders cleared native forests through slash-and-burn practices to establish croplands. Following this initial phase, land use dynamics in the region likely progressed through a sequence characterized by deforestation and the establishment of pastures and small-scale croplands. Many of these areas were subsequently converted into extensive pastures maintained by recurrent burning, which in some cases led to severe soil degradation. To restore productivity, portions of these lands were later converted into eucalyptus plantations, while others underwent natural regeneration into secondary forests. More recently, part of these agricultural areas—both former croplands and, in some cases, directly degraded pastures—have been transitioning into agroforestry systems, particularly regenerative coffee-based systems. Today, the landscape reflects a mosaic of land uses, including degraded pastures, eucalyptus plantations, secondary forests, and a growing share of regenerative agroforestry systems.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9545474/v1/96fca8c618a1de5337eb5cb3.jpeg"},{"id":109197285,"identity":"667832c0-242c-480e-bdb2-8de03b1668c0","added_by":"auto","created_at":"2026-05-13 13:13:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":491121,"visible":true,"origin":"","legend":"\u003cp\u003eCarbon and Nitrogen stocks at 0.00-0.40 m in two altitudes 100 m and 700 m for each land use systems in the southern region of \u003cem\u003eEspírito Santo\u003c/em\u003e state.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9545474/v1/efc8e4e2e9658ddc6931c26c.png"},{"id":109197270,"identity":"f0ab3a38-71c1-4b1a-9912-5efcba6369c5","added_by":"auto","created_at":"2026-05-13 13:13:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":489884,"visible":true,"origin":"","legend":"\u003cp\u003eRelative soil carbon and nitrogen stocks (0.00–0.40 m) compared to secondary forest at two altitudes (100 m and 700 m) across different land-use systems in the southern region of Espírito Santo, Brazil.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9545474/v1/d5c3eb8012d44dc247c92058.png"},{"id":109197274,"identity":"bec0fa0c-5e3b-48eb-9eb1-4edb59c1faa4","added_by":"auto","created_at":"2026-05-13 13:13:50","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":72863,"visible":true,"origin":"","legend":"\u003cp\u003eMean values of accumulated CO\u003csub\u003e2 \u003c/sub\u003e(g C/kg soil) after 60 days of incubation in different uses, altitudes and presence and absence of litter.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-9545474/v1/e524ea92f7046a9892678be1.png"},{"id":109197275,"identity":"fde7d34a-52b4-4381-9354-cce6d6d05652","added_by":"auto","created_at":"2026-05-13 13:13:51","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":308918,"visible":true,"origin":"","legend":"\u003cp\u003ePCA biplot (PC1 × PC2) of soil attributes across land-use systems and altitudes.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9545474/v1/f9b52d97b1992a173b727779.jpeg"},{"id":109197292,"identity":"c4a9b43c-e223-403e-a7d9-eca12a39e646","added_by":"auto","created_at":"2026-05-13 13:14:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5831086,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9545474/v1/eb107cd2-298e-4cd0-a7fd-c51f51a92a11.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Regenerative agroforestry is associated with improved soil fertility and carbon stocks compared to pasture and eucalyptus in Atlantic Forest soils","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLand-use change is a major driver of soil organic carbon (SOC) loss and ecosystem degradation in tropical landscapes, reducing soil fertility, microbial activity, and carbon (C) and nitrogen (N) stocks (Dias et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Menezes et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In the Atlantic Forest an altitudinally heterogeneous and highly fragmented biome recent evidence demonstrates that soil C and N and related properties are highly sensitive to land use change and altitude (Martins et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Pinto et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Vieira et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Understanding this sensitivity is critical, as the region has experienced extensive conversion of native vegetation to pasture systems and subsequent monocultures can fundamentally alter soil quality and ecosystem function depending on management practices.\u003c/p\u003e \u003cp\u003eThe conversion of native Atlantic Forest vegetation to extensive pasture systems may represents the first major step in soil degradation. Following deforestation and pasture establishment, soil microbial biomass and basal respiration decline sharply and continue to decrease with pasture age, indicating a progressive loss of biological functionality (Burak et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This biological decline is accompanied by significant chemical losses: studies replacing native vegetation with agricultural systems, including pastures, documented reduced soil chemical fertility, particularly in exchangeable base levels (Silva et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). At the landscape scale, forest-to-pasture conversion in tropical regions significantly alters soil chemical properties, especially under poor and extensive management resulting in ongoing fertility losses (organic matter, bases and phosphorus) and progressive pasture soil physical degradation (Pinto et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe magnitude of C loss during this initial conversion is substantial. Across 17 paired sites in Brazilian biomes, native-to-pasture conversion reduced soil C by an average of 7.5 Mg C ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (0\u0026ndash;10 cm) and 11.0 Mg C ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (0\u0026ndash;30 cm) (Assad et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In some cases, degraded pastures show even more severe losses, with C depletion reaching 47 Mg C ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in the most degraded systems (Braz et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), demonstrating the substantial impact of extensive pasture degradation on soil C storage and ecosystem function. However, it is important to note that these impacts are context dependent and strongly influenced by soil type, topography, and management history.\u003c/p\u003e \u003cp\u003eOne strategy adopted to mitigate land degradation in the Atlantic Forest region has been the widespread establishment of eucalyptus plantations over degraded pastures. However, converting degraded pastureland into eucalyptus plantations does not necessarily reverse the degradation trajectory. Across Brazil, successive eucalyptus rotations on former pasture and agricultural lands have consistently resulted in declines in soil C stocks (Cook et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Sandoval L\u0026oacute;pez et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Moreover, silvopastoral systems incorporating eucalyptus trees often exhibit lower SOC levels than open pastures (Pinheiro et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In addition, eucalyptus plantations may lead to a decline in soil microbial quality following land-use conversion, indicating adverse effects on the overall biological functioning of the soil (Behera \u0026amp; Sahani, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurther evidence shows that long-established \u003cem\u003eEucalyptus grandis\u003c/em\u003e plantations on former pasturelands experience losses of soil C, nitrogen, and other nutrients confirming that tree planting alone does not guarantee soil restoration (Sandoval L\u0026oacute;pez et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The magnitude of these negative effects, however, varies with species, rotation length, and silvicultural practices. Collectively, these findings indicate that the transition from native forest to pasture and subsequently to eucalyptus plantations can represent a continued trajectory of reduction of soil quality rather than ecological recovery.\u003c/p\u003e \u003cp\u003eIt is important to note that pasture systems and eucalyptus plantations are not inherently degrading land uses. These systems can maintain soil structure and organic matter (Camargo et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Freitas et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Vigo et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In the present study, the pasture and eucalyptus systems were managed under low external inputs (i.e., no fertilization, liming, or erosion control) on steep slopes typical of the \u003cem\u003e'Mar de Morros\u003c/em\u003e' landscape. Under these conditions, both systems exhibited signs of soil degradation relative to the secondary forest reference. In this context, limited fertilization, absence of liming, soil compaction, and insufficient ground cover likely accelerated nutrient depletion and soil acidification.\u003c/p\u003e \u003cp\u003eHowever, in regions characterized by steep relief, highly weathered soils, and intense rainfall, such as the \u0026lsquo;\u003cem\u003eMar de Morros\u003c/em\u003e\u0026rsquo; domain of the Atlantic Forest, pasture and eucalyptus systems established and maintained under low technological input and minimal soil management frequently exacerbates soil physical, chemical, and biological degradation. In these contexts, limited fertilization, absence of liming, soil compaction, and insufficient ground cover accelerate nutrient depletion, acidification, and loss of microbial functionality\u003c/p\u003e \u003cp\u003eIn contrast, systems combining trees, pastures, and perennial crops such as coffee with continuous organic inputs under regenerative agriculture principles can restore SOC and microbial functions compared with conventional systems (Hanke et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In a Brazilian coffee agroforestry system with \u003cem\u003eGrevillea robusta\u003c/em\u003e, microbial biomass and enzyme activities (β-glucosidase and arylsulfatase) were maintained at levels comparable to those of an adjacent native semi-deciduous tropical forest, indicating that well-designed agroforestry can preserve soil microbial functioning to near-natural levels (dos Santos Bastos et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), suggesting that agroforestry systems can partially reverse the degradation caused by pasture and monoculture conversion.\u003c/p\u003e \u003cp\u003eStudies conducted in agroforestry coffee systems within the Atlantic Forest biome in Brazil indicate that these systems can significantly enhance soil biological attributes compared with full-sun coffee systems. Higher levels of total organic carbon and microbial biomass have been reported, along with lower soil CO₂-C losses, suggesting improved biological functioning and greater soil stability under agroforestry management (Thomazini et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In Santo Ant\u0026ocirc;nio do Amparo (Minas Gerais), coffee agroforestry systems intercropped with trees increased soil organic matter and available phosphorus compared with conventional monoculture, while exchangeable bases (K 138 mg dm⁻\u0026sup3;; Ca\u0026sup2;⁺ 4.9 cmolc dm⁻\u0026sup3;; Mg\u0026sup2;⁺ 1.2 cmol\u003csub\u003ec\u003c/sub\u003e dm⁻\u0026sup3;) remained similar between systems (J\u0026aacute;come et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In a Conilon system, agroforestry coffee systems improved key soil chemical and organic attributes compared to full-sun production. Shaded systems increased soil organic matter and cation availability, raising pH and enhancing the contents of exchangeable bases particularly calcium and magnesium while also improving phosphorus availability. These changes indicate that agroforestry promotes a more balanced and biologically favorable soil environment relative to conventional monoculture systems (Souza et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These consistent improvements across multiple regions suggest that regenerative coffee agroforestry can substantially recover soil quality degraded by the pasture-to-monoculture conversion pathway.\u003c/p\u003e \u003cp\u003eWe propose that regenerative coffee agroforestry systems promote more favorable soil attributes through four interconnected mechanisms. First, greater litter inputs, shading, and microclimate stability increase microbial biomass (MBC), basal respiration, and the microbial quotient (qMIC), restoring biological functionality lost during pasture and monoculture phases. Second, reduced soil disturbance and permanent vegetative cover improve soil fertility by raising pH, enhancing base saturation (V%), and increasing cation exchange capacity (CEC) properties critically depleted during the degradation pathway. Third, improved stabilization of organic matter promotes higher SOC, total N, and profile-level C and N stocks (CS/NS), partially recovering the C and N losses incurred during forest-to-pasture conversion. Finally, litter inputs alter incubation kinetics, increasing the availability of labile C and microbial responsiveness, which supports the biological recovery observed in regenerative systems.\u003c/p\u003e \u003cp\u003eBecause temperature and moisture vary sharply along short altitudinal gradients in the Atlantic Forest, these regenerative benefits are expected to be amplified at higher elevations (Burak et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Souza Neto et al. 2011). The combination of cooler temperatures and higher moisture availability at elevated sites should enhance organic matter accumulation, reduce decomposition rates, and support greater microbial biomass and activity compared to low-altitude sites. This altitude dependent response is particularly relevant in the Atlantic Forest, where elevation driven environmental heterogeneity may create distinct windows of opportunity for soil recovery.\u003c/p\u003e \u003cp\u003eBased on this conceptual framework, we evaluate the mechanisms expected to underpin soil functional improvements following a complete land-use sequence: secondary native forest (reference system - baseline), pasture (low-input systems first transition), eucalyptus plantation (low-input systems second transition), and regenerative agroforestry coffee (association with more favorable soil conditions). We test the hypothesis that regenerative coffee agroforestry enhances microbial biomass and activity, soil fertility, and C and N stocks relative to low-input pasture and eucalyptus systems evaluated in this study, and that these benefits increase with altitude. This process-based framing provides applied guidance for soil restoration and climate mitigation in Atlantic Forest agricultural landscapes (Marques et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Tavares et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), demonstrating that regenerative agroforestry represents a viable pathway to recover soil quality and ecosystem function in regions extensively degraded by conventional land-use practices.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Area and Environmental Context\u003c/h2\u003e \u003cp\u003eThe study was conducted in Alegre (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), southern Esp\u0026iacute;rito Santo, Brazil, within the Atlantic Forest biome a global biodiversity hotspot under intense land-use pressure. The Capara\u0026oacute; micro-region features strongly undulating relief ranging from 120 to 1,300 m a.s.l., creating distinct environmental conditions across short elevational gradients. Predominant soils are Red\u0026ndash;Yellow Latosols and Red\u0026ndash;Yellow Argisols (Ferralsols and Acrisols, WRB classification) derived from gneiss/granite parent materials (Cunha et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo capture environmental heterogeneity and test the hypothesis that regenerative benefits increase with altitude, we selected two contrasting elevations: low elevation (100 m a.s.l.) at Rive (20\u0026deg;44\u0026prime;01.4\u0026Prime;S, 41\u0026deg;25\u0026prime;52.6\u0026Prime;W) and high elevation (700 m a.s.l.) at Lagoa Seca (20\u0026deg;51\u0026prime;32.2\u0026Prime;S, 41\u0026deg;27\u0026prime;34.4\u0026Prime;W). Climate transitions from tropical monsoon (Am; mean\u0026thinsp;\u0026asymp;\u0026thinsp;23\u0026deg;C) at low elevation to humid subtropical with dry winter (Cwa; \u0026lt; 18\u0026deg;C in the coldest month, \u0026gt; 22\u0026deg;C in the warmest) at higher elevation. Annual rainfall ranges from 1,300\u0026ndash;1,500 mm, with 60\u0026ndash;70% concentrated between November and March, supporting seasonal dynamics that influence carbon mineralization and microbial activity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Land-Use Systems\u003c/h2\u003e \u003cp\u003eTo evaluate the land-use changes from native forest through low-input pasture and eucalyptus systems to regenerative agroforestry, we established a comparative framework encompassing four distinct land-use systems at both elevations. This design allows us to compare soil quality among the different land uses: native forest, pasture, eucalyptus and agroforestry, while accounting for altitude-dependent responses (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eThe land use change pathway in Esp\u0026iacute;rito Santo began during colonization, when smallholders cleared native forests through slash-and-burn practices to establish croplands. Following this initial phase, land use dynamics in the region likely progressed through a sequence characterized by deforestation and the establishment of pastures and small-scale croplands. Many of these areas were subsequently converted into extensive pastures maintained by recurrent burning, which in some cases led to severe soil degradation. To restore productivity, portions of these lands were later converted into eucalyptus plantations, while others underwent natural regeneration into secondary forests. More recently, part of these agricultural areas\u0026mdash;both former croplands and, in some cases, directly degraded pastures\u0026mdash;have been transitioning into agroforestry systems, particularly regenerative coffee-based systems. Today, the landscape reflects a mosaic of land uses, including degraded pastures, eucalyptus plantations, secondary forests, and a growing share of regenerative agroforestry systems.\u003c/em\u003e \u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Secondary Forest (SF): Baseline Reference System\u003c/h2\u003e \u003cp\u003eThe secondary forest represents the closest available proxy to native Atlantic Forest conditions. It comprises semideciduous seasonal forest with ~\u0026thinsp;20 years of prior selective logging history and is currently under natural regeneration without active management (PROBIO 2004; Uruhahy et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1983\u003c/span\u003e). This system serves as the baseline for evaluating degradation in converted systems and recovery potential in agroforestry.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Pasture (PAS)\u003c/h2\u003e \u003cp\u003ePastures were established\u0026thinsp;~\u0026thinsp;9 years prior to sampling following forest clearing and consist of \u003cem\u003eBrachiaria decumbens\u003c/em\u003e. They have never been renewed, except for occasional burning, show low dry-season cover, and exhibit progressive degradation under minimal management (manual weeding, occasional herbicide; no liming or fertilization), reflecting the common post-deforestation trajectory in the region.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Eucalyptus Plantations (EUC)\u003c/h2\u003e \u003cp\u003eEucalyptus plantations on former pasture represent a shift to monoculture that typically does not reverse soil degradation. Species were selected by altitude: \u003cem\u003eEucalyptus grandis\u003c/em\u003e (3 \u0026times; 3 m; 5 years) at 100 m and \u003cem\u003eCorymbia citriodora\u003c/em\u003e (3 \u0026times; 2 m; 9 years) at 700 m. Both received NPK only at planting. This management mirrors regional practice and is associated with declining soil quality over successive rotations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.2.4 Regenerative Agroforestry Coffee (AF)\u003c/h2\u003e \u003cp\u003eLow elevation (100 m a.s.l.): The agroforestry system consisted of \u003cem\u003eCoffea canephora\u003c/em\u003e (robusta coffee) intercropped with \u003cem\u003eCedrela fissilis\u003c/em\u003e (cedar) and \u003cem\u003eBrachiaria\u003c/em\u003e grass between coffee rows. \u003cem\u003eCedrela fissilis\u003c/em\u003e was established first at a spacing of 2.5 \u0026times; 1.0 m and pruned during the initial years to regulate canopy structure. Subsequently, \u003cem\u003eCoffea canephora\u003c/em\u003e was planted under the tree canopy at a spacing of 2.5 \u0026times; 1.0 m (row \u0026times; plant) and maintained for nine years prior to sampling. This system operated under minimal external inputs, with no mineral fertilization, soil acidity correction, or pest and disease control. Weed management relied on manual practices, and soil cover was maintained through grass and litter accumulation. Continuous litter input from cedar trees contributed to microclimate regulation and supported soil biological recovery, allowing substantial improvement of soil properties relative to the degraded pasture baseline.\u003c/p\u003e \u003cp\u003eHigh elevation (700 m a.s.l.): The agroforestry system comprised \u003cem\u003eCoffea arabica\u003c/em\u003e intercropped with \u003cem\u003eInga sessilis\u003c/em\u003e (inga) and banana (\u003cem\u003eMusa\u003c/em\u003e spp.) as shade components, established on former pastureland and maintained for eleven years prior to sampling. \u003cem\u003eInga sessilis\u003c/em\u003e was initially established and pruned after the first two years, after which coffee was planted at a spacing of 2.5 \u0026times; 1.2 m (row \u0026times; plant). Weed control was performed by manual hoeing. Liming and mineral fertilization were applied based on soil analysis, using an NPK formulation (20\u0026ndash;05\u0026ndash;20) at the onset of the rainy season to support coffee productivity while maintaining soil fertility. Periodic pruning of \u003cem\u003eInga sessilis\u003c/em\u003e was conducted to regulate light availability to the coffee plants, ensuring continuous soil cover and sustained organic matter inputs. The longer establishment period compared to the low-elevation system reflects the slower growth rates typical of higher-altitude conditions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.2.5. Experimental Design Context\u003c/h2\u003e \u003cp\u003eAs a result of the study being conducted in established, commercial production areas, the experiment did not follow a classical replicated plot design. Instead, it was structured as a comparative\u0026ndash;mensurative experiment, in which the four land-use systems (secondary forest, pasture, eucalyptus, and agroforestry) were evaluated in contrasting but internally homogeneous production areas at two altitudes. Within each land-use and altitude combination, three composite soil samples (pseudo replicates) were collected from randomized locations to characterize within-area variability. This experimental approach is consistent with field-based ecological and agricultural studies conducted under operational constraints, where true replication at the field scale is not feasible (Hurlbert, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1984\u003c/span\u003e). Similar designs have been successfully applied in studies assessing land-use and management impacts on soil properties (Arevalo et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). While this design limits statistical inference beyond the specific conditions studied (i.e., we cannot generalize about all pastures or eucalyptus plantations), it provides high ecological realism and allows for a robust comparison of the soil attributes associated with each land-use system under local management practices. Therefore, treatment effects should be interpreted as differences associated with the land-use systems as implemented in this specific landscape, not as inherent properties of the land-use types themselves.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Sampling Design and Analytical\u003c/h2\u003e \u003cp\u003eSoil sampling was conducted in February (late rainy season) to capture seasonal conditions when microbial activity and organic matter dynamics are most active. For chemical and biological analysis samples were collected in triplicate for each land-use and elevation combination at depth of 0\u0026ndash;0.10 m. For carbon stocks samples were also collected ate the depth of 0.10\u0026ndash;0.40 m.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Chemical and Physical Characterization\u003c/h2\u003e \u003cp\u003eSubsamples (0\u0026ndash;0.10 m) were air-dried and sieved (\u0026lt;\u0026thinsp;2 mm) for chemical analyses following Embrapa standards (Teixeira et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Measured attributes included pH (H₂O), available P and K, exchangeable Ca\u003csup\u003e2+\u003c/sup\u003e, Mg\u003csup\u003e2+\u003c/sup\u003e, and Al\u003csup\u003e3+\u003c/sup\u003e; derived attributes included potential acidity (H\u0026thinsp;+\u0026thinsp;Al), SOC, total N (TN), CEC, and base saturation (V%). Undisturbed cores at 0\u0026ndash;0.10 and 0.10\u0026ndash;0.40 m were collected for bulk density; particle size distribution was determined to contextualize edaphic variation (Teixeira et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 Microbial Biomass and Quotient\u003c/h2\u003e \u003cp\u003eMicrobial biomass C, N, and P (C-MB, N-MB, P-MB) were quantified by irradiation\u0026ndash;extraction on fresh soils, stored at ~\u0026thinsp;4\u0026deg;C, sieved (\u0026lt;\u0026thinsp;2 mm), subjected to 2.5 MeV gamma irradiation, and extracted with 0.5 M K₂SO₄ (Brookes et al. 1985; Mendon\u0026ccedil;a and Matos \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). C-MB was calculated with \u003cem\u003ek\u003c/em\u003eC\u0026thinsp;=\u0026thinsp;0.33 (Vance et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1987\u003c/span\u003e). The microbial quotient was computed as qMIC = (C-MB/SOC) \u0026times; 100, an indicator of microbial allocation and efficiency (Sparling \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1992\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3 Soil Organic Matter Mineralization and Lability\u003c/h2\u003e \u003cp\u003eA 4 \u0026times; 2 \u0026times; 2 factorial (four land uses \u0026times; two elevations \u0026times; litter presence/absence) with three replicates assessed substrate lability and microbial responsiveness. Fresh 0\u0026ndash;0.10 m soils (50 g) were incubated in closed systems for 60 days; cumulative CO₂ was periodically measured (Mendon\u0026ccedil;a and Matos \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). For litter treatments, 5 g of plant residue from each respective land use was added to simulate in-situ inputs (Grugiki et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Cumulative CO₂ (Y) was fitted to a logistic model:\u003c/p\u003e \u003cp\u003eY\u0026thinsp;=\u0026thinsp;a / [1\u0026thinsp;+\u0026thinsp;e^(\u0026minus;(b\u0026thinsp;+\u0026thinsp;cx))]\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003ea\u003c/em\u003e is the asymptotic maximum CO₂ (labile C pool), \u003cem\u003eb\u003c/em\u003e the horizontal displacement (lag), \u003cem\u003ec\u003c/em\u003e the growth rate (mineralization kinetics), and \u003cem\u003et\u003c/em\u003e\u0026frac12; = \u0026minus;\u003cem\u003eb\u003c/em\u003e/\u003cem\u003ec\u003c/em\u003e the time to half of maximum CO₂. Lower \u003cem\u003et\u003c/em\u003e\u0026frac12; indicates faster mineralization and higher microbial activity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e2.3.4 Carbon and Nitrogen Stocks\u003c/h2\u003e \u003cp\u003eSOC was determined by wet oxidation (Yeomans \u0026amp; Bremner \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1988\u003c/span\u003e) and TN by Kjeldahl digestion (Tedesco et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). Profile-level carbon and nitrogen stocks (CS/NS) were computed as:\u003c/p\u003e \u003cp\u003eCS/NS = [(SOC or TN) \u0026times; Bd \u0026times; d] / 10\u003c/p\u003e \u003cp\u003ewhere SOC or TN (g kg⁻\u0026sup1;), bulk density Bd (g cm⁻\u0026sup3;), and layer thickness \u003cem\u003ed\u003c/em\u003e (cm). Stocks to 0\u0026ndash;0.40 m evaluate recovery in AF relative to SF.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical Analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics (mean, SD) were computed for each attribute within each land use and elevation, and overall means/SDs were also calculated. ANOVA was performed in SISVAR to test treatment effects, with significance at 5% and 1% by the F-test (Ferreira \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). For soil carbon stocks, only means were computed. A Principal Component Analysis (PCA) was performed to identify the main gradients of variation among physical, chemical, and biological soil attributes across land-use systems and altitudes. The analysis was conducted to the depth 0.00\u0026ndash;0.10 m using standardized data based on the correlation matrix. Variables with absolute correlations\u0026thinsp;\u0026ge;\u0026thinsp;0.30 with each principal component were considered significant contributors. The PCA allowed visualization of relationships between soil attributes and land-use systems through biplots. Analyses were performed using R software (L\u0026ecirc; \u0026amp; Husson \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Microbial functioning and CO\u003csub\u003e2\u003c/sub\u003e Mineralized\u003c/h2\u003e \u003cp\u003eAt the 100 m altitude, there is a significant difference among land uses for microbial variables (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). No clear pattern was detected for microbial biomass carbon (C\u0026ndash;MB), the highest values were found under eucalyptus management (576.13 \u0026micro;g kg⁻\u0026sup1;), followed by agroforestry (505.85 \u0026micro;g kg⁻\u0026sup1;) whereas secondary forest (464.86 \u0026micro;g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and pasture (434.37 \u0026micro;g kg⁻\u0026sup1;) showed lower values. N\u0026ndash;MB presented no significant variation across land uses (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), averaging 46.7 \u0026micro;g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. In contrast, P\u0026ndash;MB differed (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), with secondary forest exhibiting the highest values (23.44 \u0026micro;g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and pasture the lowest (9.13 \u0026micro;g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). The microbial coefficient (qMic) also varied among systems (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), ranging from 1.95 \u0026micro;g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in secondary forest to 6.70 \u0026micro;g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in pasture. At the 700 m altitude, overall higher C\u0026ndash;MB values were obtained compared to 100 m, and at this altitude, the secondary forest had the highest values (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Secondary forest had the highest values of the C\u0026ndash;MB (580.00 \u0026micro;g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), followed by pasture (475.20 \u0026micro;g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), eucalyptus (420.72 \u0026micro;g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and agroforestry (413.66 \u0026micro;g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). N\u0026ndash;MB remained unchanged (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), while P\u0026ndash;MB decreased significantly under eucalyptus (1.67 \u0026micro;g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and pasture (2.30 \u0026micro;g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) compared with secondary forest (7.96 \u0026micro;g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The qMic had moderate variation among systems (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with higher values under agroforestry (4.76) and pasture (3.57) compared with secondary forest (2.59).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCarbon, Nitrogen and Phosphorus of MB and microbial coefficient (\u003cem\u003eq\u003c/em\u003eMic) for different land use systems, at different altitudes 100 m and 700 m at depth 0.00\u0026ndash;0.10 m\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMicrobial functioning\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary forest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePasture\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEucaliptus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAgroforestry system\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eS.D.\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e \u003cp\u003e\u0026micro;g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e \u003cp\u003e------------------------------------------------100 m ------------------------------------------------\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u0026ndash;MB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e464,86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e434,37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e576,13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e505,85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e495,30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e45,69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u0026ndash;MB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55,70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45,06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41,08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e46,71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4,50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP\u0026ndash;MB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23,44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9,13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15,87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9,39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14,46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5,20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eq\u003c/em\u003eMic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5,75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4,28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e \u003cp\u003e-----------------------------------------------700 m -----------------------------------------------\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u0026ndash;MB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e580,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e475,20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e420,72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e413,66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e472,40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e55,21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u0026ndash;MB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56,08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49,13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41,80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51,27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e49,57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4,11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP\u0026ndash;MB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7,96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7,22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4,79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2,80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eq\u003c/em\u003eMic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4,76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3,55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003e1/\u003c/sup\u003eS.D. Standart of deviation; \u003csup\u003e2/\u003c/sup\u003eF test (ANOVA); *(p\u0026thinsp;\u0026lt;\u0026thinsp;0.05); **(p\u0026thinsp;\u0026lt;\u0026thinsp;0.01); \u003csup\u003ens\u003c/sup\u003e not significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Soil fertility and chemical functionality\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the chemical and physical attributes of soils (0.00\u0026ndash;0.10 m) under different land-use systems at two altitudes. At both altitudes, clear contrasts were obtained among land uses, overall secondary forest and agroforestry soils generally presented better quality, while the conversion to pasture and eucalyptus was associated with the decrease of the soil quality. In general, higher BD was found in the pasture management (1.10 kg dm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e at 100 m and 1.60 kg dm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e at 700 m), suggesting physical degradation following vegetation removal. Whereas agroforestry maintained lower bulk density values (1.02 and 1.10 kg dm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e) lower to the forest condition in both altitudes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eChemical and physical attributes of soil collected at 0.00-0.10 m depth of at two altitudes for each land use systems in the southern region of the state of \u003cem\u003eEsp\u0026iacute;rito Santo\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSoil attributes\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary forest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePasture\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEucaliptus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAgroforestry system\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSecondary forest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePasture\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eEucaliptus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAgroforestry system\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eS.D.\u003csup\u003e12\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003csup\u003e13\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e-------------------------100 m-------------------------\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003e-------------------------700 m-------------------------\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSand (%)\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e56.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e51.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e56.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e72.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e48.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e9.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSilt (%)\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e15.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e10.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClay (%)\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e33.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e42.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e38.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e20.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e35.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e6.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBd (kg dm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e)\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epH (H\u003csub\u003e2\u003c/sub\u003eO)\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e5.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP (mg dm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e)\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK (mg dm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e)\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e156.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e60.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e23.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e20.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e285.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e85.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e91.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCa\u003csup\u003e+\u0026thinsp;2\u003c/sup\u003e (cmol\u003csub\u003ec\u003c/sub\u003e dm\u0026sup3;)\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMg\u003csup\u003e+\u0026thinsp;2\u003c/sup\u003e (cmol\u003csub\u003ec\u003c/sub\u003edm\u003csup\u003e\u0026minus;3\u003c/sup\u003e)\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAl\u003csup\u003e+\u0026thinsp;3\u003c/sup\u003e (cmol\u003csub\u003ec\u003c/sub\u003e dm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e)\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u0026thinsp;+\u0026thinsp;Al (cmol\u003csub\u003ec\u003c/sub\u003edm\u003csup\u003e3\u003c/sup\u003e)\u003csup\u003e7\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e5.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOC (dag kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003csup\u003e8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNT (dag kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003csup\u003e9\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEC (t)\u003csup\u003e10\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBS (%)\u003csup\u003e11\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e57.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e28.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e14.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003e1\u003c/sup\u003ePipette method (Slow stirring); \u0026sup2;Volumetric Ring Method (Teixeira et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e); \u003csup\u003e3\u003c/sup\u003epH in water (1:2.5 ratio); \u003csup\u003e4\u003c/sup\u003eMehlich-1 extractor and colorimetric determination; \u003csup\u003e5\u003c/sup\u003eMehlich-1 extraction and flame photometry determination; \u003csup\u003e6\u003c/sup\u003eextraction with 1 mol L\u003csup\u003e-1\u003c/sup\u003e potassium chloride and titration; \u003csup\u003e7\u003c/sup\u003eextraction with 0.5 mol L\u003csup\u003e-1\u003c/sup\u003e calcium acetate, pH 7.0 and titration (Teixeira et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e); \u003csup\u003e8\u003c/sup\u003eSoil organic carbon, wet oxidation with potassium dichromate and sulfuric acid (Yeomans \u0026amp; Bremner \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1988\u003c/span\u003e); \u003csup\u003e9\u003c/sup\u003eTotal nitrogen, determined by soil digestion with sulfuric acid and hydrogen peroxide, followed by steam distillation (Kjeldahl) (Tedesco et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1995\u003c/span\u003e); \u003csup\u003e10\u003c/sup\u003e Effective cation exchange capacity; \u003csup\u003e11\u003c/sup\u003e Base saturation; \u003csup\u003e12\u003c/sup\u003eStandard deviation;\u003csup\u003e13\u003c/sup\u003e F test (ANOVA).\u003c/p\u003e \u003cp\u003eSoil acidity was high across systems (pH 4.6\u0026ndash;6.3), with no significant differences (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, consistent declines in pH and nutrient availability were evident after forest conversion, particularly under pasture. Available P remained low in all systems (0.44\u0026ndash;2.47 mg dm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e) but tends to increase in agroforestry at both altitudes. Available K and exchangeable Ca\u003csup\u003e2+\u003c/sup\u003e, and Mg\u003csup\u003e2+\u003c/sup\u003e differed significantly (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), showing strong depletion in pasture and eucalyptus soils and comparatively higher levels under agroforestry within this study. At 700 m, agroforestry reached 285.03 mg dm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e K, 3.36 cmol\u003csub\u003ec\u003c/sub\u003e dm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e Ca\u003csup\u003e2+\u003c/sup\u003e, and 1.59 cmol\u003csub\u003ec\u003c/sub\u003e dm⁻\u0026sup3; Mg\u003csup\u003e2+\u003c/sup\u003e, compared to minimal levels in pasture (23.8 mg dm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e K, 0.66 cmol\u003csub\u003ec\u003c/sub\u003e dm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e Ca\u003csup\u003e2+\u003c/sup\u003e, 0.08 cmol\u003csub\u003ec\u003c/sub\u003e dm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e Mg\u003csup\u003e2+\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003eExchangeable Al\u003csup\u003e3+\u003c/sup\u003e also varied (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), being absent under agroforestry and eucalyptus and highest under forest and pasture at 700 m (1.02\u0026ndash;1.25 cmol\u003csub\u003ec\u003c/sub\u003e dm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e), reflecting higher acidity and base loss in degraded areas. The potential acidity (H\u0026thinsp;+\u0026thinsp;Al) did not differ significantly but tended to be lower in agroforestry (2.50\u0026ndash;4.27 cmol\u003csub\u003ec\u003c/sub\u003e dm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e) than in forest and pasture.\u003c/p\u003e \u003cp\u003eSOC and NT were not significantly affected (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), averaging 1.44 dag kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 0.14 dag kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively, however, at the altitude 100 m high numerical values were found in the forest and agroforestry systems and at 700 m in the pasture, forest and agroforestry. In contrast, CEC and BS presented strong responses to land use (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Agroforestry reached the highest values at both altitudes (CEC: 2.36\u0026ndash;5.68 cmol\u003csub\u003ec\u003c/sub\u003e dm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e; BS: 48.6\u0026ndash;57.1%), while pasture and eucalyptus exhibited the lowest.\u003c/p\u003e \u003cp\u003eOverall, soils under pasture and eucalyptus reflected the typical pattern of chemical and physical degradation following forest conversion, with compaction, nutrient depletion, and higher acidity. In contrast, agroforestry systems consistently improved soil bulk density and fertility indicators, recovering CEC, base saturation, and nutrient stocks toward levels observed in the secondary forest.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.3. C\u0026ndash;N sequestration (0\u0026ndash;40 cm)\u003c/h2\u003e \u003cp\u003eAcross both altitudes (100 m and 700 m), soil C and N stocks at 0\u0026ndash;40 cm depth were strongly influenced by land use. Secondary forests maintained the highest total C stocks (\u0026asymp;\u0026thinsp;45\u0026ndash;55 t C ha⁻\u0026sup1;) and N stocks (\u0026asymp;\u0026thinsp;4\u0026ndash;5 t N ha⁻\u0026sup1;), confirming their role as reference systems for soil organic matter accumulation. In contrast, pasture and eucalyptus plantations exhibited the lowest stocks (\u0026asymp;\u0026thinsp;20\u0026ndash;30 t C ha⁻\u0026sup1; and \u0026asymp;\u0026thinsp;2\u0026ndash;3 t N ha⁻\u0026sup1;), reflecting long-term depletion of organic matter and nutrient cycling following forest conversion (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAgroforestry systems, however, had intermediate to high values of both C and N (\u0026asymp;\u0026thinsp;35\u0026ndash;45 t C ha⁻\u0026sup1; and \u0026asymp;\u0026thinsp;3\u0026ndash;4 t N ha⁻\u0026sup1;), approaching forest levels, particularly at 700 m.\u003c/p\u003e \u003cp\u003eWhen expressed as relative stocks compared to the secondary forest, pasture and eucalyptus retained only 40\u0026ndash;55% of total C and N, whereas agroforestry systems recovered up to 80% of soil C and more than 90% of soil N at 100 m (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Litter add-on and incubation kinetics\u003c/h2\u003e \u003cp\u003eThe mean CO₂ mineralization values demonstrated a clear and consistent effect of both litter addition and land use across altitudes. Treatments with litter released substantially more CO₂ (6.0\u0026ndash;10.5 g C/ kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e soil) than those without litter (2.3\u0026ndash;3.1 g C/ kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e soil) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAt 100 m altitude, the agroforestry system showed the highest CO₂ mineralization (10.5 g C/ kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e soil), followed by pasture (9.64 g C/ kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e soil) and eucalyptus (9.22 g C/ kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e soil), whereas secondary forest exhibited the lowest (7.66 g C/ kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e soil). At 700 m, the same pattern persisted, though overall CO₂ release was slightly lower, with agroforestry again outperforming other systems (9.43 g C/ kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e soil), followed by pasture (8.18 g C/ kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e soil), secondary forest (6.45 g C/ kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e soil), and eucalyptus (4.94 g C/ kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e soil) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn contrast, under absence of litter, CO₂ mineralization remained low and relatively uniform among systems (2.3\u0026ndash;3.0 g C/ kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e soil), indicating that surface organic inputs were the key determinant of microbial activity. These findings align with the conceptual framework proposed, where the availability of labile organic matter regulates microbial responsiveness and soil biological recovery.\u003c/p\u003e \u003cp\u003eOverall, the results indicate that (i) litter input strongly stimulates microbial activity, (ii) the agroforestry system maintains the highest biological performance across altitudes, and (iii) lower CO₂ release in secondary forest and eucalyptus systems reflects a predominance of more stable, recalcitrant organic matter pools typical of mature or structurally simplified systems.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Logistic modeling of CO₂ evolution\u003c/h2\u003e \u003cp\u003eCumulative CO₂ production fitted the logistic growth model for all land use systems and altitudes (r\u0026sup2; = 0.99) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The saturation parameter (\u003cem\u003ea\u003c/em\u003e), representing the total CO₂ evolved, was consistently higher in treatments with litter. At 100 m altitude, the value ranged from 7.19 in secondary forest to 9.98 in the agroforestry system, and at 700 m from 5.10 in eucalyptus to 9.02 in the agroforestry system. Without litter, \u003cem\u003ea\u003c/em\u003e value dropped substantially (2.24\u0026ndash;2.89).\u003c/p\u003e \u003cp\u003eThe \u003cem\u003eb\u003c/em\u003e coefficient, related to the inflection point of the logistic curve, varied between \u0026minus;\u0026thinsp;1.46 and \u0026minus;\u0026thinsp;2.67, with the largest negative values in the agroforestry system at 100 m (-2.67) and in eucalyptus at 700 m (-2.37), suggesting faster stabilization of CO₂ evolution in these systems. The \u003cem\u003ec\u003c/em\u003e coefficient (rate of CO₂ increase) ranged narrowly (-0.10 to -0.20), indicating similar mineralization kinetics across systems (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe estimated half-time (t\u003csub\u003e1/2\u003c/sub\u003e = \u0026ndash;b/c) ranged from 7.3 to 19.0 days, depending on land use and litter presence. Shorter t\u003csub\u003e1/2\u003c/sub\u003e values, such as those in the agroforestry system with litter at 700 m (7.3 days) and eucalyptus without litter at 100 m (13.2 days), indicate faster mineralization and greater microbial turnover. Conversely, longer t\u003csub\u003e1/2\u003c/sub\u003e values under agroforestry and pasture without litter (\u0026asymp;\u0026thinsp;19 days) reveal slower CO₂ accumulation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCoefficients of the logistic equation, coefficients of determination and estimated time to reach half of the maximum production of CO\u003csub\u003e2\u003c/sub\u003e (t\u003csub\u003e1/2\u003c/sub\u003e = -b/c) for the different land use systems and altitudes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLand uses\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eAbsense of litter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c12\" namest=\"c8\"\u003e \u003cp\u003ePresence of litter\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ea\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eb\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ec\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003et\u003csub\u003e1/2\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003er\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003ea\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eb\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003ec\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003et\u003csub\u003e1/2\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003er\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"11\" nameend=\"c12\" namest=\"c2\"\u003e \u003cp\u003eAltitude 100 m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e16.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePasture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-1.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e19.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEucalyptus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e14.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgroforestry system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-1.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e16.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"11\" nameend=\"c12\" namest=\"c2\"\u003e \u003cp\u003eAltitude 700 m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePasture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e15.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEucalyptus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-2.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e16.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgroforestry system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThese patterns confirm that the addition of litter not only increases the total amount of CO₂ released (a) but also alters the mineralization dynamics (\u003cem\u003ec\u003c/em\u003e and t\u003csub\u003e1/2\u003c/sub\u003e), particularly in diversified systems such as agroforestry (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Multivariate Analisys\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e summarizes the principal component analysis (PCA) of soil chemical, physical, and microbial attributes across land-use systems and altitudes. The first four principal components explained 87.5% of the total variance, with PC1, PC2, PC3, and PC4 accounting for 35.9%, 27.3%, 16.2%, and 8.1%, respectively.\u003c/p\u003e \u003cp\u003ePC1 represented the main soil fertility gradient, positively correlated with K, Ca\u003csup\u003e2+\u003c/sup\u003e, Mg\u003csup\u003e2+\u003c/sup\u003e, CEC, BS, and P, and negatively with Al\u003csup\u003e3+\u003c/sup\u003e and H\u0026thinsp;+\u0026thinsp;Al, indicating a transition from acidic and nutrient-poor soils (pasture and eucalyptus) to more fertile conditions under agroforestry systems and secondary forests. PC2 captured variability related to microbial and organic matter dynamics, with strong positive correlations for SOC, N\u0026ndash;MB, and H\u0026thinsp;+\u0026thinsp;Al, contrasting soils with higher biological activity (forest and agroforestry) against degraded pastures (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePC3 was associated with nutrient cycling, mainly total N, P\u0026ndash;MB, and SOC, while PC4 represented secondary variability linked to microbial C (C\u0026ndash;MB) and texture (clay and silt fractions).\u003c/p\u003e \u003cp\u003eThe PCA biplot (PC1 \u0026times; PC2) explained 63.2% of the total variance (PC1\u0026thinsp;=\u0026thinsp;35.9%, PC2\u0026thinsp;=\u0026thinsp;27.3%), clearly separating the land-use systems according to their soil chemical and biological attributes. Three distinct groups emerged along the decline in several soil quality indicators causing degradation to recovery gradient. Group 1 (Baseline), represented by secondary forests at both altitudes, was associated with higher SOC, N\u0026ndash;MB, and moderate fertility, reflecting natural reference conditions. Group 2 (Low-input systems), including pastures and eucalyptus systems, was characterized by higher Al\u003csup\u003e3+\u003c/sup\u003e and H\u0026thinsp;+\u0026thinsp;Al and lower base cations (Ca\u003csup\u003e2+\u003c/sup\u003e, Mg\u003csup\u003e2+\u003c/sup\u003e, K), indicating nutrient depletion and soil acidification typical of degraded soils. Group 3 (More favorable soil attributes), composed of agroforestry systems, showed higher K, Ca\u003csup\u003e2+\u003c/sup\u003e, Mg\u003csup\u003e2+\u003c/sup\u003e, CEC, and BS, suggesting chemical and biological recovery driven by tree\u0026ndash;crop interactions and organic inputs (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrincipal component analysis of the chemical attributes in four land uses systems (forest, pasture, eucalyptus and agroforestry), two altitudes (100m and 700m) at the depth of 0.00-0.10 m\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eExplained variance ratio (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePC\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePC\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePC\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePC\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35,85\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27,31\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16,19\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8,15\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCumulative variance (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35,85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63,17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79,35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e87,5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoil Attributes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eCorrelation with major components\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0,465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,066\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSilt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0,004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0,452\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0,243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0,123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,438\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0,171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0,161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0,135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0,107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0,024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0,028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0,054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,059\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCa\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0,154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,186\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMg\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0,021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0,04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAl\u003csup\u003e3+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0,205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0,256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0,049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u0026thinsp;+\u0026thinsp;Al\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0,211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0,157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0,01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0,109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0,083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,193\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0,143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,164\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0,078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,166\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u0026ndash;MB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0,101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,602\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u0026ndash;MB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0,041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP\u0026ndash;MB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0,04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eq\u003c/em\u003eMic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0,353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0,259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,108\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarbon stocks 0\u0026ndash;40 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,101\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNitrogen stocks 0\u0026ndash;40 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAlong PC1, agroforestry systems occupied the positive axis, contrasting with the negative scores of pastures and eucalyptus, which reflect lower fertility and higher acidity (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). PC2 captured differences in microbial and organic matter activity, distinguishing forests and agroforestry (higher SOC and N\u0026ndash;MB) from degraded pastures with reduced biological functionality.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWithin the landscape studied, the sequence of land-use change from secondary forest to pasture and subsequently to eucalyptus was associated with a decline in several soil quality indicators, including losses in chemical fertility, nutrient stocks, and microbial functionality. However, as our sampling design lacks a temporal baseline and true replication, we cannot infer a causal trajectory of degradation in the soils of Atlantic Forest. Conversely, the regenerative agroforestry system effectively showed higher chemical and biological attributes compared to the pasture and eucalyptus systems, suggesting a potential for soil quality recovery under this management. The lower pH, base saturation (BS%), and exchangeable Ca\u0026sup2;⁺ and Mg\u0026sup2;⁺ in the pasture and eucalyptus systems of this study are consistent with the typical pattern of nutrient mining and soil acidification (Sandoval L\u0026oacute;pez et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Silva et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSuch degradation results from the combined effects of base cation removal by harvest, low residue return, and aluminum mobilization (Behera \u0026amp; Sahani \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Cook et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Sandoval L\u0026oacute;pez et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In contrast, the agroforestry system was associated with higher levels of K, Ca\u003csup\u003e2+\u003c/sup\u003e, Mg\u003csup\u003e2+\u003c/sup\u003e, CEC, and BS, particularly at 700 m. This recovery may reflect the cumulative effects of litterfall, biological N fixation, and root exudation that enhance nutrient recycling and reduce soil acidity (J\u0026aacute;come et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Souza et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The contribution of \u003cem\u003eInga sessilis\u003c/em\u003e and other leguminous shade trees likely explains the enrichment of total N and CEC at high altitude.\u003c/p\u003e \u003cp\u003eAlthough SOC and total N at the 0\u0026ndash;10 cm layer were not significantly different among systems, the C and N stocks (0\u0026ndash;40 cm) revealed strong contrasts, with agroforestry recovering up to 80\u0026ndash;90% of forest C and N pools. This pattern indicates that soil restoration occurs progressively from surface to subsoil horizons, where aggregate stabilization protects organic matter from decomposition (Hanke et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Tonucci et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The lower bulk density under agroforestry (\u0026asymp;\u0026thinsp;1.02\u0026ndash;1.10 kg dm⁻\u0026sup3;) compared to pasture (up to 1.60 kg dm⁻\u0026sup3;) indicates better physical conditions associated with this system also demonstrates physical improvement and greater pore continuity, which favor root proliferation and organic matter incorporation (Perreira et al. 2025).\u003c/p\u003e \u003cp\u003eMicrobial indicators effectively demonstrated the degradation recovery gradient across land uses. The decline in microbial phosphorus (P\u0026ndash;MB) under pasture and eucalyptus reflects nutrient stress and the depletion of labile C sources conditions known to suppress microbial activity and diversity (Rocha Junior et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Similarly, the reduction in microbial biomass C (C\u0026ndash;MB) at 700 m compared with forest areas, and the higher C\u0026ndash;MB values in the agroforestry system at 100 m, emphasize the responsiveness of this indicator to land-use change. Elevated microbial quotient (qMic) values in low-input pasture and eucalyptus systems evaluated in this study indicate a smaller, stressed microbial community with reduced metabolic efficiency, whereas the agroforestry system maintained intermediate to high C\u0026ndash;MB and low qMic values, consistent with a larger and more functionally stable community.\u003c/p\u003e \u003cp\u003eThe high C\u0026ndash;MB observed under eucalyptus at 100 m may not indicate true ecological recovery but rather a transient microbial flush associated with the accumulation of a thick litter layer. According to Brinkman et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), eucalyptus plantations tend to accumulate substantially more litter than secondary forests; however, this material decomposes slowly due to its chemical composition and the reduced density and diversity of decomposer organisms. Consequently, increases in microbial biomass under eucalyptus may reflect short-term microbial responses to litter inputs rather than sustained improvements in soil quality. The sharp decrease in P\u0026ndash;MB under low-input (pasture and eucalyptus systems) reinforces its role as an early and sensitive indicator of nutrient limitation, warranting its inclusion as a key metric in soil health assessments (Awoonor et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe CO\u003csub\u003e2\u003c/sub\u003e mineralization kinetics confirmed that litter input strongly stimulates microbial respiration, particularly under agroforestry. The higher cumulative CO\u003csub\u003e2\u003c/sub\u003e evolution and faster half-life (t\u003csub\u003e1/2\u003c/sub\u003e) indicate that regenerative management not only enhances carbon storage but also sustains an active C turnover. Such balanced dynamics are critical for nutrient cycling and soil resilience (Carvalho et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Henrique et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The greater responsiveness to litter addition under agroforestry reinforces the idea that biological activity depends on continuous organic inputs which is a defining feature of regenerative systems as implemented here. Altitude modulated the intensity of soil recovery. The amplified restoration of C and N stocks at 700 m can be attributed to cooler and more humid microclimates that reduce decomposition rates and favor microbial growth (Siles et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Tashi et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Shedayi et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This aligns with evidence that montane agroforestry systems accumulate more organic matter due to greater root biomass and reduced soil disturbance. Thus, environmental heterogeneity must be considered when scaling regenerative practices across tropical landscapes.\u003c/p\u003e \u003cp\u003eThe PCA results captured the multidimensional nature of soil degradation and recovery. PC1, explaining 35.9% of total variance, represented the chemical fertility gradient positively associated with K, Ca\u003csup\u003e2+\u003c/sup\u003e, Mg\u003csup\u003e2+\u003c/sup\u003e, CEC, BS, and P, and negatively with Al\u003csup\u003e3+\u003c/sup\u003e and H\u0026thinsp;+\u0026thinsp;Al. PC2, explaining 27.3%, reflected biological functionality, positively correlated with SOC, N\u0026ndash;MB, and microbial activity. Together, these axes visually summarized the soil restoration trajectory from secondary forest to agroforestry, eucalyptus, and pasture, confirming that agroforestry systems occupy an intermediate to advanced position. The PCA biplot also distinguished three ecological states: baseline (secondary forest), characterized by high SOC and microbial activity; low-input pasture and eucalyptus systems, marked by low fertility and biological stress; and the more favorable soil attributes (agroforestry), showing improved nutrient status and microbial function. This multivariate structure complements the univariate results, reinforcing the concept that regenerative agroforestry simultaneously restores chemical fertility and biological resilience, a synergy fundamental to long-term soil multifunctionality and climate mitigation in tropical agroecosystems.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrates that land-use change in the Atlantic Forest region may cause a degradation gradient, in which the conversion from secondary forest to pasture and eucalyptus is associated with marked chemical, physical, and biological soil deterioration. Importantly, these impacts reflect the management conditions of the pasture and eucalyptus systems evaluated, which did not follow adequate technical management guidelines, rather than inherent limitations of these land-use systems. These low-input pasture and eucalyptus systems exhibited higher bulk density, soil acidity, and aluminum saturation, along with depleted base cations, reduced microbial biomass, and lower C and N stocks.\u003c/p\u003e \u003cp\u003eIn contrast, the regenerative agroforestry system reversed much of this degradation, recovering up to 80\u0026ndash;90% of forest-level carbon and nitrogen stocks and restoring key indicators of fertility and microbial functionality. Higher base saturation, cation exchange capacity, and microbial biomass, coupled with lower bulk density and aluminum toxicity, demonstrate that tree crop integration enhances both the storage and the cycling of nutrients. The CO\u003csub\u003e2\u003c/sub\u003e mineralization assays confirmed that litter input and continuous organic matter turnover are critical drivers of the more favorable soil attributes.\u003c/p\u003e \u003cp\u003eMultivariate analysis (PCA) clearly separated land-use systems along chemical and biological gradients, confirming that agroforestry occupies intermediate to advanced more favorable soil attributes between low-input pasture and eucalyptus systems and forest reference conditions. The convergence of agroforestry and secondary forest soils at higher altitudes further emphasizes the synergistic effect of cooler and moister microclimates on carbon stabilization and microbial activity.\u003c/p\u003e \u003cp\u003eCollectively, these findings confirm that regenerative agroforestry fosters soil restoration by simultaneously improving chemical fertility, biological efficiency, and organic matter dynamics in tropical environment. These multifunctional highlights agroforestry as a viable strategy for climate mitigation, carbon sequestration, and long-term ecosystem resilience in tropical agricultural landscapes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received to assist with the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no competing interests to declare that are relevant to the content of this article.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEthics approval\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent to participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eData availability\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthor contributions:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: Paulo Roberto da Rocha J\u0026uacute;nior, Felipe Vaz Andrade, Anna Carolyna Fernandes Ferreira;\u003c/p\u003e\n\u003cp\u003eMethodology: Paulo Roberto da Rocha J\u0026uacute;nior, Felipe Vaz Andrade, Anna Carolyna Fernandes Ferreira, Eduardo de S\u0026aacute; Mendon\u0026ccedil;a;\u003c/p\u003e\n\u003cp\u003eFormal analysis and investigation: Paulo Roberto da Rocha J\u0026uacute;nior, Anna Carolyna Fernandes Ferreira, Felipe Vaz Andrade, Querubim M\u0026aacute;ximo de Siqueira Neto, Fabio Ribeiro Pires;\u003c/p\u003e\n\u003cp\u003eData curation: Paulo Roberto da Rocha J\u0026uacute;nior, Querubim M\u0026aacute;ximo de Siqueira Neto;\u003c/p\u003e\n\u003cp\u003eWriting \u0026ndash; original draft: Paulo Roberto da Rocha J\u0026uacute;nior, Querubim M\u0026aacute;ximo de Siqueira Neto;\u003c/p\u003e\n\u003cp\u003eWriting \u0026ndash; review and editing: Paulo Roberto da Rocha J\u0026uacute;nior, Fabio Ribeiro Pires, Eduardo de S\u0026aacute; Mendon\u0026ccedil;a, Anna Carolyna Fernandes Ferreira, Felipe Vaz Andrade, Querubim M\u0026aacute;ximo de Siqueira Neto;\u003c/p\u003e\n\u003cp\u003eSupervision: Felipe Vaz Andrade, Fabio Ribeiro Pires, Eduardo de S\u0026aacute; Mendon\u0026ccedil;a.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAdditional information\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAn AI-assisted tool (ChatGPT, OpenAI) was used to improve clarity and language of the manuscript; all scientific content, interpretation, and final wording were reviewed and approved by the authors.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge the financial support provided by CAPES (\u003cem\u003eCoordena\u0026ccedil;\u0026atilde;o de Aperfei\u0026ccedil;oamento de Pessoal de N\u0026iacute;vel Superior\u003c/em\u003e) and FAPES (\u003cem\u003eFunda\u0026ccedil;\u0026atilde;o de Amparo \u0026agrave; Pesquisa e Inova\u0026ccedil;\u0026atilde;o do Esp\u0026iacute;rito Santo\u003c/em\u003e) through research scholarships, which made this study possible. The authors also thank the farmers for granting access to their farms and for allowing the use of their agricultural areas for the development of this research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cspan\u003eAssad, E. D., Pinto, H. S., Martins, S. C., Groppo, J. D., Salgado, P. R., Evangelista, B., Vasconcellos, E., Sano, E. E., Pav\u0026atilde;o, E., Luna, R., Camargo, P. B. \u0026amp; Martinelli, L. A. (2013). Changes in soil carbon stocks in Brazil due to land use: paired site comparisons and a regional pasture soil survey. \u003cem\u003eBiogeosciences\u003c/em\u003e, 10, 6141\u0026ndash;6160.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eArevalo, C. B. M., Bhatti, J. S., Chang, S. X., \u0026amp; Sidders, D. (2009). Ecosystem carbon stocks and distribution under different land-uses in north central Alberta, Canada. 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A rapid and precise method for routine determination of organic carbon in soil. Communications in Soil Science and Plant Analysis. 19, 1467\u0026ndash;76. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/00103628809368027\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan\u003e\u003c/span\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"agroforestry-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"agfo","sideBox":"Learn more about [Agroforestry Systems](http://link.springer.com/journal/10457)","snPcode":"10457","submissionUrl":"https://submission.nature.com/new-submission/10457/3","title":"Agroforestry Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Agroforestry, Soil microbial biomass, Carbon Sequestration, Altitudinal gradient, Atlantic Forest, Soil fertility","lastPublishedDoi":"10.21203/rs.3.rs-9545474/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9545474/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLand-use change in tropical landscapes drives severe soil degradation, particularly in the Atlantic Forest of Brazil, where conversion from native vegetation to pasture and eucalyptus plantations may depletes fertility, microbial activity, and carbon (C) and nitrogen (N) stocks. This study evaluated a land-use transition sequence secondary forest \u0026rarr; pasture \u0026rarr; eucalyptus \u0026rarr; regenerative coffee agroforestry across two altitudes (100 and 700 m a.s.l.) in \u003cem\u003eEsp\u0026iacute;rito Santo State\u003c/em\u003e to assess whether land-use changes in the Atlantic Forest led to soil depletion. Soil chemical, physical, and microbial attributes, as well as C and N stocks, were evaluated to identify contrasting soil responses patterns. Pasture and eucalyptus showed signs of degradation within this study, exhibiting elevated bulk density (up to 1.60 kg dm⁻\u0026sup3;), low base saturation (\u0026lt;\u0026thinsp;10%), high Al\u0026sup3;⁺, and depleted microbial phosphorus (\u0026lt;\u0026thinsp;2.5 \u0026micro;g kg⁻\u0026sup1;). In contrast, regenerative agroforestry was associated with reducing compaction (~\u0026thinsp;1.10 kg dm⁻\u0026sup3;) and showed eliminating Al\u0026sup3;⁺, and increasing base saturation to \u0026gt;\u0026thinsp;50%, with substantial recovery of Ca\u0026sup2;⁺, Mg\u0026sup2;⁺, and microbial phosphorus to levels comparable to secondary forest. Microbial biomass and mineralization kinetics confirmed improved biological efficiency and faster C turnover. C and N stocks in agroforestry systems recovered up to 80\u0026ndash;90% of the forest reference, particularly at higher altitudes, where cooler and moister conditions enhanced organic matter stabilization. Principal component analysis (PCA) distinguished clear more favorable soil attributes gradients, positioning agroforestry between low-input pasture and eucalyptus systems and reference systems. Overall, regenerative coffee agroforestry restored chemical fertility, microbial activity, and organic matter pools, supporting its potential role as an effective strategy for soil for soil quality recovery restoration, C sequestration, and climate mitigation in the studied region.\u003c/p\u003e","manuscriptTitle":"Regenerative agroforestry is associated with improved soil fertility and carbon stocks compared to pasture and eucalyptus in Atlantic Forest soils","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-13 13:12:20","doi":"10.21203/rs.3.rs-9545474/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"13740722878558627899135252655722689237","date":"2026-05-18T11:23:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"285095281204407059984725625167839920453","date":"2026-05-07T06:27:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"7518943824460635391867626516903467076","date":"2026-05-06T14:42:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"255967857292390949248209735937323681992","date":"2026-05-04T16:49:18+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-04T14:23:04+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-04T13:59:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-29T05:27:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"Agroforestry Systems","date":"2026-04-27T18:52:15+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"agroforestry-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"agfo","sideBox":"Learn more about [Agroforestry Systems](http://link.springer.com/journal/10457)","snPcode":"10457","submissionUrl":"https://submission.nature.com/new-submission/10457/3","title":"Agroforestry Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"316f3c06-652f-4c79-9874-43e08b5b34e1","owner":[],"postedDate":"May 13th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"13740722878558627899135252655722689237","date":"2026-05-18T11:23:10+00:00","index":18,"fulltext":""},{"type":"reviewerAgreed","content":"285095281204407059984725625167839920453","date":"2026-05-07T06:27:19+00:00","index":15,"fulltext":""},{"type":"reviewerAgreed","content":"7518943824460635391867626516903467076","date":"2026-05-06T14:42:20+00:00","index":14,"fulltext":""},{"type":"reviewerAgreed","content":"255967857292390949248209735937323681992","date":"2026-05-04T16:49:18+00:00","index":13,"fulltext":""},{"type":"reviewersInvited","content":"8","date":"2026-05-04T14:23:04+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-04T13:59:21+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-13T13:12:20+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-13 13:12:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9545474","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9545474","identity":"rs-9545474","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}