Topsoil carbon and nitrogen stocks, microbial community and soil aggregation under long-term organic horticulture in tropical Entisols

ABSTRACT

Under climate change, adapting agricultural systems is essential to balance food production with soil conservation, thereby sustaining key ecosystem services. This study investigated the transition from conventional to organic horticulture on Entisols (Neossolo Quartzarênico Órtico típico) in the Brazilian Northeast (0.00-1.00 m soil profile), using natural vegetation (NV - primary forest) as a reference. Three systems were evaluated: organic horticulture 1 (OH1 - intensive vegetable crops with green manure and compost), organic horticulture 2 (OH2 - banana cultivation since 2009), and conventional horticulture (CH – mineral nitrogen, phosphorus, and potassium (NPK) and chicken manure). The study investigated how these systems influence carbon (C) and nitrogen (N) stocks in soil organic matter (SOM) fractions and their impact on soil structure and the microbial community (phospholipid fatty acids (PLFAs) biomarkers). Although OH1 increased soil organic carbon (SOC) and carbon of mineral-associated organic matter (C-MAOM) stocks in the surface layer (0.00-0.15 m), its accumulated stocks in the 0.00-1.00 m profile were 9.6 % lower than those of the NV, whilst CH and OH2 showed reductions of only 0.7 and 2.4 %, respectively. A carbon saturation deficit and lower stocks in the subsoil of the agricultural systems explain this discrepancy. Regarding soil structure, OH1 maintained surface aggregation (0.00-0.15 m) similar to the NV, supported by a higher proportion of fungal biomarkers and carbon of particulate organic matter (C-POM) contents. Conversely, the OH2 system exhibited lower aggregate stability and a higher gram-positive to gram-negative (G+/G-) bacterial ratio, reflecting the interaction between management practices and the crop species (banana and Panicum residues) with lower diversity. Unexpectedly, CH exhibited higher C-POM stocks and subsoil macroaggregation than the organic systems. Although intensive organic horticulture improves SOM and microbial diversity at the surface, it fails to increase carbon in the subsoil due to low mineral protection and insufficient organic matter added at depth. It highlights the need for strategies, such as deep-rooted cover crops, to mitigate the subsoil carbon deficit in tropical sandy soils.

Keywords
organic farming; soil management; microbial communities; sustainability

INTRODUCTION

Adapting agricultural systems to climate change is essential for balancing food production with soil conservation, thereby ensuring efficient ecosystem services (Smith et al., 2015). Organic farming is expanding globally as a sustainable alternative (Lorenz and Lal, 2016). However, the various practices employed in these systems require further investigation to understand their impacts and strengthen scientific knowledge (Rigolot and Quantin, 2022).

Soil organic matter (SOM) serves as a critical reservoir for carbon (C), nitrogen (N), phosphorus (P), and sulfur (S), supporting crop growth (Sarker et al., 2018). It also enhances soil structure (Chaney and Swift, 1984) and is a vital resource for microbiota (Delgado-Baquerizo et al., 2018). Soil organic matter can be classified into various functional compartments, reflecting different cycling times and soil functions (von Lützow et al., 2007). Physical fractionation separates SOM into mineral-associated organic matter (MAOM <0.053 mm), particulate organic matter (POM, 2 mm > POM > 0.053 mm), and dissolved organic matter (DOM <0.45 µm) (Lavallee et al., 2020).

Organic farming has been shown to increase C and N within POM and MAOM fractions in the 0.00–0.15 m layer of sandy soils (Atoloye et al., 2024). However, carbon stock benefits with organic management may not extend beyond 0.15 m (Blanco-Canqui et al., 2017). Dissolved organic matter is particularly responsive to soil management within agroecosystems, deriving from leached compounds, litter, rhizodeposition, and microbial exudates (Gmach et al., 2020). While the “cascade model” suggests that older C may be relocated deeper into the profile (Scott and Rothstein, 2014), sandy soils characterized by low SOM levels and limited mineral protection are highly susceptible to C loss when disturbed (Salviano et al., 2016; Souza et al., 2017).

In horticulture, intensive land use practices are employed to maintain soil fertility (Madeira et al., 2013). However, in the absence of organic inputs, such management can threaten carbon stocks, thereby reducing productivity and the provision of ecosystem services. As organic farming and the agricultural use of sandy soils expand globally, recognizing their potential for sustainable production is crucial (Huang and Hartemink, 2020).

Agricultural practices in organic farming, such as organic inputs and green manure, enhance soil aggregate stability, although these effects are often limited to the surface layers (Crittenden et al., 2015). Microbial communities are affected by management; for instance, residue removal alters both fungal and bacterial populations (Maillard et al., 2023). Both organic and inorganic inputs alter these communities, impacting soil functions (Li et al., 2021). Long-term organic systems particularly influence microbial communities and functionality (Hartmann et al., 2015). Conservation practices in sandy soils have been shown to enhance soil C by more than 10 % in topsoil over 15 years (Colunga et al., 2025), with organic farming improving microorganism biomass (Araújo and Melo, 2010; Willekens et al., 2014) and soil structure (Ren et al., 2014).

However, recent evidence in Entisols suggests that, although organic management improves the molecular quality of SOM at the surface (0.00-0.15 m), which is also associated with C and N contents, this qualitative change did not occur in deep layers (>0.35 m), where both organic and conventional management have limitations (Silva et al., 2025). Furthermore, because carbon saturation and physical protection are primarily governed by soil matrix capacity rather than biochemical recalcitrance, there is a knowledge gap about how intensive horticulture affects aggregate formation and the magnitude of C and N stocks in soils with limited mineral protection.

We hypothesized that long-term organic management enhances C and N stocks within organic fractions in the surface layer, but the limited mineral protection of Entisols restricts these benefits to deeper layers, resulting in subsoil stocks comparable to those under conventional management; intensive organic management (OH1) promotes superior physical and biological properties, and increases the soil organic matter content compared with the less intensive system (OH2); and, although organic systems maintain higher microbial biomass and nutrient stocks than conventional systems, soil aggregate stability remains inferior to that of natural vegetation, indicating that organic inputs do not fully offset the physical degradation caused by intensive horticulture in sandy soils. This study aimed to evaluate the influence of long-term management systems on C and N stocks, soil structure, and microbial communities in Entisols. Two organic systems with over 20 years of history were assessed: organic horticulture 1 (OH1, intensive) and organic horticulture 2 (OH2, less intensive), and compared with conventional horticulture (CH), using natural vegetation (NV) as a reference.

MATERIALS AND METHODS

Description of study areas and historical land use

The study was conducted in the Limoeiro dos Pompeus district, Guaraciaba do Norte municipality, Ceará, Brazil (Figure S1). According to the Köppen classification system, the region exhibits a tropical rainy climate with dry summers (As), characterized by an average annual temperature of 24 °C and precipitation of 1,200 mm (Figure S2a and Figure S2b). The periods with the highest rainfall and temperatures are observed between January and May and from September to December (Figure S2a). All experimental areas are adjacent, situated on the same pedological unit [Entisols (Soil Survey Staff, 2014) or Neossolo Quartzarênico Órtico típico (Santos et al., 2018)], and share identical topographic and climatic conditions.

Four study areas were defined based on their long-term management history:

(i) organic horticulture 1 (OH1): Managed under intensive organic principles since 1997. This system involves bi-annual green manure (cocktail of species) incorporated at flowering (45-70 Mg ha^-1), regular application of organic compost (3-8 kg m^-2), bokashi, and strict crop rotation in vegetable beds.

(ii) organic horticulture 2 (OH2): Followed the OH1 management protocol until 2009, when it was converted to organic banana cultivation (Musa paradisiaca L.). Management is currently based on annual mowing of Panicum sp. between rows, with manual harvesting and machine traffic limited to the 4 m spacing between rows.

(iii) conventional horticulture (CH): Represents a conventional production system using mineral fertilizers (NPK 20-10-20) and organic inputs (poultry manure). It has been cultivated with vegetables (tomatoes and peppers) and transitioned to conventional banana since 2018, including the application of contact herbicides and pesticides as required.

(iv) natural vegetation (NV): A preserved reference area consisting of primary forest (Carrasco and or Sub-evergreen Tropical Pluvio-Nebular Forest). This area is characterized by an irregular canopy and sparse emergent trees, with no history of grazing or wood extraction.

Detailed descriptions of historical land use, along with soil chemical and physical properties, are available in the supplementary materials (Table S1 and Table S2).

Soil and litter sampling

Disturbed and undisturbed soil samples were collected at layers of 0.00-0.15, 0.15-0.35, 0.35-0.55, and 0.55-1.00 m following the opening of trenches (1.5 × 1 × 1 m), with four replicates in each area. Morphopedogenetic criteria such as soil color, structure, and penetration resistance were assessed using a pointed instrument. For organic horticulture 2 (OH2) and conventional horticulture (CH), trenches were positioned between rows of plants, with samples taken adjacent to the plants. In organic horticulture 1 (OH1), trenches were opened in vegetable beds. Locations were randomly selected within each crop and natural vegetation area. During sampling, OH1 had been fallow for four months with senescent spontaneous plants and avocado trees (Persea americana Mill.) present. Litter was collected using a 1 m² wooden frame, dried at 60 °C in a forced-circulation oven, and weighed.

Physical fractionation of soil organic matter (SOM)

Disturbed soil samples (<2 mm) underwent physical size fractionation, based on Cambardella and Elliott (1992). Samples were dispersed in sodium hexametaphosphate solution and wet sieved to separate particulate organic matter (POM) associated with the sand fraction (>53 µm) and mineral-associated organic matter (MAOM) (silt + clay, <53 µm). Fractions were dried to constant mass at 60 °C.

Dissolved organic matter (DOM) was assessed in refrigerated samples (~4 °C) stored in dark conditions to prevent photooxidation up until the moment of extraction. The extraction was carried out in deionized water (1:1, m:v) with gentle stirring. The solution was centrifuged at 3,450 rpm for 10 min, and the supernatant was filtered through polycarbonate membranes (<0.4 µm) and stored at -18 °C before freeze-drying for elemental analysis (Chantigny et al., 2007).

Carbon and nitrogen stocks in POM, MAOM, DOM, and total fractions

Carbon (C) content in POM and MAOM was measured by wet oxidation with external heating (Walkley and Black, 1934; Yeomans and Bremner, 1988). Nitrogen (N) content was determined by Kjeldahl distillation (Bremner and Mulvaney, 1982). Carbon and N content in DOM was assessed from lyophilized filtrate using a CHNS elemental analyzer (Flash EA 1112, Thermo Finnigan).

Carbon and N stocks in POM, MAOM, and DOM were calculated using specific soil mass (Ellert and Bettany, 1995), which considers equivalent soil mass across land use areas. Thickness correction for agroecosystems was based on natural vegetation density. Soil mass at each layer was determined using equation 1.

Eq. 1 $Msoil=T\times ha\times BD$

in which: Msoil is soil mass per hectare in each evaluated layer (Mg ha^-1); BD is soil bulk density in each sampled layer (Mg m^-3); T is soil thickness for each layer in meters (m); ha is the area (1 ha = 10,000 m²). Then, the soil thickness layer to be subtracted from or added to agroecosystems in relation to natural vegetation was determined by equation 2.

Eq. 2 $T\left(ad│sub\right)=(Mref-Msoil)\times fha/D$

in which: T_{(ad | sub)} is the soil thickness layer to be added to (+) or subtracted (-) from the agroecosystem layer (m); Mref is the average equivalent mass of the reference treatment (Mg ha^-1); Msoil is the equivalent soil mass of each treatment (Mg ha^-1); fha is the conversion factor from ha to m² (0.0001 ha m^-2); D_s is soil density for the sampled layer area (Mg m^-3). After determining the thickness, C and N stocks were calculated by equation 3.

Eq. 3 $ST=(C\times BD\times T\pm T(ad\left|sub\right))\times 10$

in which: ST is C or N stock in SOM fractions (Mg ha^-1); T is the soil layer thickness (m); BD is soil bulk density of the cropping system in the chosen layer (Mg m^-3); C is C or N content in SOM fraction (g kg^-1); and 10 is the conversion factor from kg m^-2 to Mg ha^-1.

Stocks in the reference area were calculated by equation 4.

Eq. 4 $ST=C\times BD\times T\times 10$

in which: BD is soil bulk density of the layer in the reference area.

Calculation of carbon saturation deficit (CSD)

Carbon saturation deficit (CSD) was calculated for each soil layer according to the methodology of Souza et al. (2017). This calculation was based on the carbon saturation level (CSL), following the models proposed by Six et al. (2002), in which CSL (g kg^-1) represents the maximum theoretical C capacity of the mineral phase (silt + clay). For this study, we applied the specific model developed for 1:1 clay soils dominated by phyllosilicates, which is the predominant mineralogy in these Entisols as previously identified by Alencar (2005), using equation 5.

Eq. 5 $CSL=5.5+0.26\times (silt+clay)$

in which: CSL is C saturation level (g of C per kg^-1 of MAOM); (silt + clay) is silt + clay content (%).

The CSL value was used to calculate CSD (%) using equation 6.

Eq. 6 $CSD=\left[\frac{CSL-C}{CSL}\right]\times 100$

in which: CSD is C saturation deficit (%); and C is MAOM organic C content of the land use systems (g kg^-1).

Physical soil analyses

Undisturbed samples were used to determine bulk density via the volumetric ring method (Teixeira et al., 2017), and granulometry was assessed by the Pipette Method (Teixeira et al., 2017). Aggregate stability was evaluated using air-dried aggregates subjected to wet sieving to classify diameters and quantify primary particles (Amaro Filho et al., 2008). Residual moisture was determined gravimetrically.

Phospholipid fatty acid (PLFA) - profile analysis of microbial community structure

Samples for PLFA analysis were collected using sterile techniques, with 70 % alcohol for instrument sterilization, talc-free nitrile gloves, and storage in sterile 50 mL Falcon tubes. Samples were kept refrigerated (~4 °C) and then freeze-dried using the FreeZone LABCONCO® 6 L system. The modified PLFA analysis procedures followed Bligh and Dyer (1959); Frostegard and Baath (1996), and Quideau et al. (2016).

Soil subsamples (3 g of lyophilized soil) from layers of 0.00-0.15, 0.15-0.35, 0.35-0.55, and 0.55-1.00 m were extracted by stirring in 3 mL of 0.05 mol L^-1 phosphate buffer, 4 mL of chloroform, and 8 mL of methanol for 16 h at 140 rpm. Samples were centrifuged at 2500 rpm for 12 min, and supernatant was filtered. This extraction was repeated twice, with phase separation facilitated by 12 mL of 2 mol L^-1 NaCl. Total lipids were collected from the denser phase, dried under N₂ at 38 °C, resuspended in chloroform, and fractionated using silica columns with chloroform and methanol. Phospholipids were retrieved with methanol, dried, and subjected to transesterification.

Transesterification involved resuspension in methanol and toluene, adding KOH and methyl nonadecanoate as the internal standard, and heating at 37 °C. Post-reaction, samples were diluted with water, acidified, extracted with hexane, and dried. Methyl esters were analyzed using a Shimadzu QP 2010SE GC-MS with conditions specified for optimal retention and separation.

The PLFA biomarkers were identified using the National Institute of Standards and Technology library (NIST 2011) with an 85 % similarity index. The PLFAs biomarkers of gram-positive bacteria (G+) corresponded to i14:0, a15:0, i17:0, a17:0. For gram-negative (G-) they were: 16:1ω7z, cy17:0, 18:1ω8e, 18:1ω7e, 19:1ω9z, 19:1ω9e. Actinobacterias: 18:10Me and 19:10Me. Universal bacteria: 15:0, 16:0, 17:0, and 18:0; fungal biomarkers (F) through the biomarkers 18:2ω6,9, 18:1ω9e and 18:1ω9z (Frostegard and Baath, 1996; Myers et al., 2001; Veum et al., 2019; Liu et al., 2022).

Statistical analyses

Statistical comparisons were performed between land-use systems (OH1, OH2, CH, and NV) in each soil layer (0.00-0.15, 0.15-0.35, 0.35-0.55, and 0.55-1.00 m). Due to the nature of long-term field areas, a sampling with randomization constraints was adopted, where four replicates (trenches) were distributed within each cultivation area, characterizing an observational study (Carvalho, 2024). One-way analysis of variance (ANOVA) was performed for each layer, and means were compared using Tukey's test at a 10 % probability level (p<0.10). The choice of p<0.10 aims to reduce the probability of Type II errors (false negatives), ensuring that significant long-term changes in nutrient stocks and soil properties are not overlooked.

An integrative Principal Component Analysis (PCA) was conducted to evaluate the relationship between microbial community structure (PLFA biomarkers), soil organic matter fractions (C and N stocks in POM, DOM, and MAOM), and soil physical structure (aggregate diameter classes). This approach provided an integrated assessment of the drivers of soil health across treatment areas. Statistical analyses were performed using SISVAR and R software (version 4.3.1) with the packages ExpDes.pt, vegan, and factoextra. Eigenvector values and ANOVA tables are available in the supplementary material (Table S4).

RESULTS

Stocks of organic carbon and total nitrogen in soil organic matter fractions across different horticultural systems

Carbon within organic matter associated with minerals (C-MAOM) was identified as holding the highest stocks across different horticultural systems, with a general average of 72.91 Mg ha^-1, followed by particulate organic matter (C-POM) at 13.59 Mg ha^-1 and dissolved organic matter (C-DOM) at 0.19 Mg ha^-1, over a 0.00-1.00 m soil profile (Figure 1).

Figure 1
Soil organic carbon stocks (SOC) (a) and mineral-associated (C-MAOM) (c), particulate (C-POM) (b) and dissolved (C-DOM) (d) organic matter, in Entisols under organic horticulture 1 (OH1), organic horticulture 2 (OH2), conventional horticulture (CH) and natural vegetation (NV) at 0.00-0.15, 0.15-0.35, 0.35-0.55 and 0.55-1.00 m soil layer, in the municipality in Guaraciaba do Norte – CE, Brazil. Means followed by the same letter do not differ by the Tukey test at 10 % probability. Capital letters compare systems of use for the same depth and lowercase letters compare layers in the same system of use. Horizontal bars indicate the standard error of the mean.

Significant variations in carbon stocks were observed between fractions and land use systems within the analyzed profile. Stocks of soil organic carbon (SOC) and C-MAOM (0.00-1.00 m) were significantly lower in organic horticulture 1 (OH1) at 80.44 and 68.04 Mg ha^-1, respectively, when compared with organic horticulture 2 (OH2) at 86.87 and 75.18 Mg ha^-1 and natural vegetation (NV) at 89.03 and 76.32 Mg ha^-1. Conventional horticulture (CH) displayed SOC and C-MAOM stocks of 89.64 and 72.08 Mg ha^-1, respectively, aligning more closely with OH2 and NV only in terms of C-MAOM stocks. Regarding the particulate fraction, C-POM stocks in CH (17.57 Mg ha^-1) were significantly higher than those in OH1 (12.40 Mg ha^-1) and OH2 (11.70 Mg ha^-1), while NV (12.71 Mg ha^-1) showed no statistical difference from the other systems. Conversely, C-DOM stocks were significantly higher in OH1 (0.26 Mg ha^-1) than in CH (0.11 Mg ha^-1), with NV and OH2 yielding intermediate values that did not differ statistically from the other treatments (Figure 1).

Carbon and N dynamics showed depth-dependent variations across the various systems assessed. In the surface layer (0.00-0.15 m), OH1 and OH2 had significantly higher SOC and C-MAOM stocks compared with CH and NV. With regard to the particulate fraction (C-POM), CH stocks were significantly higher than those of OH1 and OH2, whilst NV showed intermediate values that did not differ statistically from the other treatments. At a layer of 0.15-0.35 m, CH had the highest SOC and C-MAOM stocks compared with OH1, although only SOC was significantly higher than NV. This trend persisted in the 0.35-0.55 m layer, where CH had SOC and C-POM stocks higher than those of both organic systems. In contrast, the NV showed no significant difference from OH2 for SOC, remaining statistically comparable to both organic systems with regard to C-POM (Figure 1).

Significant differences in SOC stocks were observed between agricultural systems and NV at various layers, particularly in the 0.55-1.00 m layer. Regarding C-MAOM, stocks in CH were significantly lower than those in NV and OH2, whereas OH1 exhibited intermediate values and showed no significant difference from the other treatments. For C-POM, the organic systems displayed notably lower stocks than CH and NV, which were statistically similar. Conversely, C-DOM followed the opposite trend, with higher values recorded in the organic systems than in CH (Figure 1).

Total stocks of nitrogen (N) and N associated with minerals (N-MAOM) were generally lower in CH systems within the 0.00-1.00 m layer, while organic systems demonstrated similarities to NV. However, N-MAOM stocks were significantly elevated in OH2. The N-POM stocks of OH1 and NV were comparable to each other yet higher than CH, which aligned with OH2. The highest N-DOM stocks were found in OH1 when compared with other systems, in the order OH1>NV=OH2=CH (p<0.10) (Figure 2).

Figure 2
Total-nitrogen tocks (N-total) (a) and mineral-associated organic matter (N-MAOM) (b), particulate (N-POM) (c) and dissolved (N-DOM) (d), in Entisols under organic horticulture 1 (OH1), organic horticulture 2 (OH2), conventional horticulture (CH) and natural vegetation (NV) at 0.00-0.15, 0.15-0.35, 0.35-0.55 and 0.55-1.00 m soil layer, in the municipality in Guaraciaba do Norte – CE, Brazil. Means followed by the same letter do not differ by the Tukey test at 10 % probability. Capital letters compare systems of use for the same layer and lowercase letters compare layers in the same system of use. Horizontal bars indicate the standard error of the mean. Absence of a bar at depth 0.55 to 1.00 m in OH1 indicates that N could not be detected in any repetition.

Further analysis demonstrated depth-specific variations in each system. At 0.00-0.15 m soil layer, higher stocks of N-total and N-MAOM were detected in organic systems compared with CH and NV. At 0.15-0.35 m depth, NV exhibited higher N-total stocks than agricultural systems. Both N-total and N-MAOM stocks at the 0.55-1.00 m soil layer were higher in NV than in CH. Additionally, OH1 exhibited higher N-POM stocks at 0.00-0.15 m than CH. The N-DOM demonstrated disparities at 0.00-0.15 and 0.55-1.00 m, where OH1 and NV outperformed CH at 0.00-0.15 m, but OH1 surpassed NV at 0.55-1.00 m (Figure 2). At a layer of 0.55-1.00 m in OH1, C/N ratio estimation was hindered by low N levels, below the Kjeldahl method detection limit (Figure 3).

Figure 3
Ratio carbon/nitrogen in soil C/N – Total (a) and mineral-associated organic matter (C/N-MAOM) (b), particulate (C/N-POM) (c) and dissolved (C/N-DOM) (d), in Entisols under organic horticulture 1 (OH1), organic horticulture 2 (OH2), conventional horticulture (CH) and natural vegetation (NV) at 0.00-0.15, 0.15-0.35, 0.35-0.55 and 0.55-1.00 m soil layers, in the municipality in Guaraciaba do Norte – CE, Brazil. Means followed by the same letter do not differ by the Tukey test at 10 % probability. Capital letters compare systems of use for the same depth and lowercase letters compare depths in the same system of use. Horizontal bars indicate the standard error of the mean. Absence of a bar at depth 0.55 to 1.00 m in OH1 indicates that N could not be detected in any repetition.

The carbon saturation deficit (CSD) was notably lower in OH1 at 0-15 cm, achieving near saturation with a value of 1.44 ± 3.48 %. OH2 and CH exhibited higher CSD values of 33.48 % and 38.93 %, respectively, exceeding NV's 20 %. At 35-55 cm, organic systems showed higher deficits compared with NV. Meanwhile, CH displayed the highest CSD at 0.55-1.00 m, notably higher than NV (Table 1).

Table 1
Carbon saturation deficit (CSD) in Entisols under organic horticulture 1 (OH1), organic horticulture 2 (OH2), conventional horticulture (CH) and natural vegetation (NV) at depths of 0.00-0.15, 0.15-0.35, 0.35-0.55 and 0.55-1.00 m, in the municipality of Guaraciaba do Norte CE, Brazil

Land use	Soil layer
	0.00-0.15 m	0.15-0.35 m	0.35-0.55 m	0.55-1.00 m
OH1	01.44±3.48 Cc	55.44±8.01 Ab	70.23±3.54 Aa	71.74±0.48 ABa
OH2	33.48±1.86 Ac	56.14±3.11 Ab	67.87±0.98 Aa	73.78±1.42 ABa
CH	38.93±1.63 Ac	55.01±0.35 Ab	63.39±1.58 ABb	77.87±0.79 Aa
NV	20.03±4.08 Bc	44.80±0.99 Ab	53.82±6.37 Bb	63.41±1.24 Ba

• Mean value of carbon saturation deficit (CSD), followed by the standard error of the mean (SE). Means followed by the same letter do not differ by the Tukey test at 10 % probability. Capital letters compare systems of use for the same depth and lowercase letters compare depths in the same system of use.

Aggregate stability in different horticultural systems

The stability of water-stable aggregates varied significantly across land use systems and depths. At 0.15-0.35 m, the highest aggregate stability was recorded in NV compared with other systems, with agricultural systems being comparable. At 0.35-0.55 m, NV excelled over OH1 and OH2, the latter presenting the lowest percentage of aggregates. The CH system displayed consistency across land uses. At deeper levels (0.55-1.00 m), OH2 exhibited reduced aggregate stability compared with other uses, with OH1 and CH showing similar stability (Figure 4).

Figure 4
Mass percentages of water-stable aggregates (WEA) total samples (a) and of the diameter classes of 4.76-2.00 mm (b), 2.00-1.00 mm (c), 1.00-0.50 mm (d), 0.50-0.25 mm (e), 0.25-1.00 mm (f) in the samples and Mean weight diameter (MWD) (g), in Entisols under organic horticulture 1 (OH1), organic horticulture 2 (OH2), conventional horticulture (CH) and natural vegetation (NV) at 0.00-0.15, 0.15-0.35, 0.35-0.55 and 0.55-1.00 m depth, in municipality in Guaraciaba do Norte – CE, Brazil. Means followed by the same letter do not differ by the Tukey test at 10 % probability. Capital letters compare systems of use for the same depth and lowercase letters compare depths in the same system of use. Horizontal bars indicate the standard error of the mean.

Analysis of aggregate classes revealed that aggregates with diameters between 4.76-2.00 mm constituted approximately 68 % on average, reflecting similar trends observed for total aggregation in agricultural systems. NV and OH2 exhibited extreme values, the former with the highest and the latter with the lowest stable aggregate percentages. NV maintained the highest stable aggregate percentages across various diameter classes (4.76-0.106 mm). For the 0.00-0.15 m depth, in the 4.76-2.00 mm class, OH1 paralleled NV in stability, whereas at 0.15-0.35 m, only CH achieved stability percentages akin to NV. At 0.35-0.55 and 0.55-1.00 m, OH1 and OH2 showed lower stability than NV, with CH being similar to NV. OH2 had the lowest percentage of stable aggregates, showing significant differences from CH (Figure 4).

For the 2.00-1.00 mm class, NV was surpassed by OH2 at the 0.35-0.55 m soil layer. In the 1-0.5 mm class, significant differences occurred at 0.35-0.55 m (OH1 > OH2 = CH = NV) and at 0.55-1.00 m, where NV had reduced aggregation compared with OH2 (Figure 4).

Stability of 0.5-0.25 mm and 0.25-0.11 mm aggregate classes displayed consistent behavior. The OH2 achieved higher stability than NV at all depths in the 0.25-0.11 mm class, and from 0.15 m in the 0.5-0.25 mm class. In the 0.25-0.11 mm class, NV consistently had lower stability than agricultural systems at depths beyond 0.15 m (Figure 4).

Mean Weight Diameter (MWD) findings indicated superior values for NV at shallower depths compared with agricultural systems, which were otherwise similar. However, depth-related variations were observed in subsequent layers. At 0.35-0.55 m, CH surpassed OH1, while at 0.55-1.00 m, no significant differences were found (Figure 4).

Structure of microbial communities in land-use systems

The microbial community structure, assessed via phospholipid fatty acid analysis (PLFA), revealed that a substantial proportion comprised universal markers, averaging 2.97 nmol g^-1. Bacteria accounted for an average of 2.25 nmol g^-1, constituting approximately 33 % of the total PLFAs (6.7 nmol g^-1 on average). Specifically, gram-positive bacteria (G+) represented 8.5 %, gram-negative bacteria (G-) 24 %, and Actinobacteria 13% of the total. Fungal presence was around 9.2 % of the total PLFAs (Figure 5).

Figure 5
Phospholipid fatty acid methyl esters (PLFA) of microbial origin extracted from Entisols under organic cultivation system with bananas (OH2) and vegetables (OH1), conventional (CH) and under natural vegetation (NV) at layers of 0.00-0.15, 0.15-0.35, 0.35-0.55 and 0.55-1.00 m, in the municipality of Guaraciaba do Norte, CE, Brazil. Averages followed by the same letter do not differ according to the Tukey test at 10% probability. Uppercase letters compare use systems for the same depth and lowercase letters compare depths in the same use system.

Significant differences were observed among the land-use systems for fungi, actinomycetes, and the G+/G- ratio, indicating that different land-use practices influence microbial community structure. OH1 exhibited a higher abundance of fungi than OH2 and CH, but it was similar to NV in the 0.00-0.15 m soil layer. Conversely, actinobacteria levels were lower in NV than in OH1. The G+/G- ratio was higher in OH2 at the 0.00-0.15 m layer than in CH and NV, with NV showing a lower ratio than OH1. Similar patterns were observed at the 0.15-0.35 m soil layer, with OH2 exhibiting higher ratios compared with CH and NV (Figure 5).

The biplot showed that individual microbial biomarkers, C, N, and soil aggregates accounted for 66.4 % of the total variance, with 55.5 % explained by Principal Component 1 (PC1) and 14.9 % by Principal Component 2 (PC2). The analysis revealed a distinctive microbial composition for NV, characterized in the upper layers by the biomarkers C18:1w 9e, 19:1w9z, and C15:0a, and macroaggregates in the 4.76-2.00 mm range in the surface layers. The surface layers of the agricultural systems, particularly OH1, were positively influenced by PC1, which was associated with C and N contents in the soil fractions and various microbial biomarkers (Figure 6).

Figure 6
Scatterplot of the abundance variables of the individual biomarkers of the microbial groups and aggregates and organic matter of a Entisol under an organic farming system with vegetables (OH1) and banana (OH2), conventional (CH) and native vegetation (NV) at 0.00-0.15, 0.15-0.35, 0.35-0.55 and 0.55-1.00 m soil layer, in Guaraciaba do Norte, CE, Brazil.

DISCUSSION

Organic and conventional horticulture in Entisols: system management and physical stability

The higher stocks of C and N in the topsoil (0.00-0.15 m) of the organic systems (OH1) compared with the conventional system (CH) reflect the efficient integration between biological inputs and initial physical protection. The crop diversity and the application of organic compost increased microbial biomass (both bacterial and fungal), which directly contributed to the formation of macroaggregates (Kumar and Nath, 2019). In these Entisols, where clay is scarce (Alencar, 2005), organic matter and microbial exudates are the primary effective cementing agents for soil stability (Šimanský et al., 2019).

However, the confounding effect between management and the cultivated species must be considered. While OH1 benefits from intensive rotation with legumes, OH2 is dominated by banana (M. paradisiaca) and grasses (Panicum sp.). Our previous qualitative results demonstrated that banana residues in OH2 have lower lignin content than OH1 compost, resulting in lower fungal biomass (PLFA) and reduced aggregate stability at the surface (Silva et al., 2025). In addition, the G+/G- ratio, indicating group prevalence, was lower in CH and NV, higher in organic farming, notably at OH1's surface (0.00-0.15 m) and OH2 at 0.15-0.35 m. Higher index values suggest decomposers of more recalcitrant materials, showing organic farming potential to enhance G+ bacteria on the surface, unlike findings by Ge et al. (2013). This suggests that the biochemical quality of the cultivated species may be as decisive for soil health as the absence of synthetic inputs (Rana et al., 2018).

The failure of organic systems to increase carbon at depth (>0.35 m) is explained by low mineral protection and high aeration in these sandy soils (Salviano et al., 2016). Even with the translocation of DOM, the low clay content limits adsorption, causing C reaching the subsoil to be rapidly mineralized by active microbiota rather than stabilized (Blanco-Canqui et al., 2017).

Organic systems and soil quality in natural vegetation

The highest stocks of soil organic carbon (SOC) and carbon associated with mineral-associated organic matter (C-MAOM) in natural vegetation (NV) areas are attributed to the lack of anthropogenic activity and the continuous input of organic residues (Medeiros et al., 2022). On average, soil carbon and nitrogen stocks (0.00-0.35 m) in NV exceed those in lower-altitude regions by 40 and 29%, respectively, within the same soil class (0.00-0.40 m) in the Brazilian semi-arid zone (Bernardi et al., 2004). The study areas are situated in a humid enclave, characterized by elevated precipitation and reduced temperatures compared to adjacent regions (Bastos et al., 2017), promoting primary productivity and carbon accumulation in soils and vegetation (Tashi et al., 2016; Oliveira Filho et al., 2017).

Comparison with natural vegetation (NV) reveals that the organic systems have not reached the original ecosystem equilibrium. Natural vegetation maintains higher C and N stocks in the subsoil due to the presence of deep roots and the input of stable polymers such as suberin, which offer greater resistance to mineralization (Silva et al., 2025). In agroecosystems, mechanical tillage, even under organic management, disrupts aggregates and exposes organic matter to degradation (Six et al., 2004). Thus, while inputs, crop rotation, and cover crops provide organic matter that acts as a cementing agent for stable aggregates and enhances C and N protection, continuous tillage acts in the opposite direction. In the case of OH2, because these practices have not occurred for some time, these beneficial effects were less conclusive.

Integration of PLFA data indicates that the microbial community structure in NV is composed of exclusive biomarkers that favor persistent aggregation, especially of fungi. Although OH1 manages to equal NV in surface aggregation through the input of C-POM, this structure is unstable and depends on continuous residue entry. The superiority of NV lies in the protection of carbon within microaggregates that are physically preserved over time, and the MAOM is protect for clay association (Cotrufo et al., 2015). Therefore, organic management in sandy soils mitigates the structural degradation of the conventional system but does not fully restore the biological and physical functionality observed in natural conditions (Silva et al., 2025).

Practices such as crop rotation, green manuring, and organic compost application are known to promote soil aggregation (Kumar and Nath, 2019). Consequently, OH1 showed an aggregation state similar to NV due to high C-POM content and dissolved carbon compounds in this layer, functioning as stabilizing agents (Guhra et al., 2022). Deeper soil layers exhibited reduced organic matter, leading to decreased macroaggregation with depth. NV, with the highest organic matter content at greater depths, correspondingly had the highest percentage of macroaggregates.

The study's strengths lie in its comprehensive assessment of various agricultural practices on soil health. By employing established methodologies and rigorous statistical analyses, it offers robust data that enhance our understanding of C and N dynamics in sandy soils. These insights are valuable for improving soil management strategies globally. However, the weaknesses include the challenge of effectively increasing organic carbon stocks at deeper soil levels within organic systems. This limitation points to the need for further research and development of techniques that can support deeper carbon sequestration.

On the opportunity front, the research opens avenues for enhancing agricultural practices. There is significant potential to apply these findings to similar environments worldwide, fostering more sustainable farming systems and improving soil health and resilience. Lastly, the study identifies potential threats to the broader application of its findings, such as climate variability and economic constraints. These factors may affect the feasibility and sustainability of adopting extensive organic practices in different contexts.

CONCLUSIONS

Long-term organic horticulture in Entisols effectively increases carbon and nitrogen stocks in surface layers, primarily within the organic fractions; however, these benefits do not extend to the subsoil, where stocks remain comparable with conventional management. Intensive organic management (OH1) promotes superior soil aggregation and microbial activity compared to less intensive systems (OH2), confirming that the diversity and intensity of organic inputs are decisive for surface soil health.

Despite these biological and nutritional gains over conventional practices, organic horticulture does not fully compensate for the physical degradation caused by intensive land use, as aggregate stability remains significantly lower than that observed in natural vegetation. These findings imply that for sandy tropical soils, sustainable management must evolve beyond surface amendments. Future strategies should integrate deep-rooting species and reduced tillage to enhance subsoil carbon sequestration.

ACKNOWLEDGMENTS

We also thank the Programa de Pós-Graduação em Ecologia e Recursos Naturais (PPGERN) and the support of the Soil Science Department from Federal University of Ceará (UFC), especially the soil chemistry and soil physics laboratories, as well as the Department of Soils of the Federal University of Viçosa (UFV) for the support of the Soil Physics and Organic Matter and Residues Laboratories. We are also grateful to the farmers who kindly allowed us to carry out the assessments on their land.: Nazareno O. Barbosa e Ricardo O. Neto.

How to cite:

Silva HF, Sousa PVF, Escobar MEO, Oliveira TS. Topsoil carbon and nitrogen stocks, microbial community and soil aggregation under long-term organic horticulture in tropical Entisols. Rev Bras Cienc Solo. 2026;50:e0250180. https://doi.org/10.36783/18069657rbcs20250180

FUNDING

This study was partially funded by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001. We are grateful to the CNPq (Brazilian National Council of Scientific and Technological Development) for the scholarship granted to the last author.

DATA AVAILABILITY

The data will be provided upon request.

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*

Corresponding author: e-mail: [e-mail]

Editor:

José Miguel Reichert https://orcid.org/0000-0001-9943-2898 and Jeferson Dieckow https://orcid.org/0000-0002-3025-4402

Publication Dates

• Publication in this collection
  09 Nov 2026
• Date of issue
  2026

History

• Received
  30 Oct 2025
• Accepted
  30 Mar 2026