Soil aggregation and associated organic matter under management systems in sandy-textured soils, subtropical region of Brazil

Increasing the diversity of plant species in agricultural production areas favors the maintenance or improvement of soil quality, particularly for soils with a sandy texture. This beneficial effect is related to the formation of aggregates of different origins. This study aimed to (i) verify whether soil use and management affect the proportion of biogenic (Bio) and physicogenic (Phy) aggregates and (ii) verify whether biogenic aggregation is more likely to lead to soil improvement than physicogenic aggregation. Three management systems were evaluated (permanent pasture, PP; no-tillage system, NT; and no-tillage + Brachiaria system, NT + B) as well as a reference area (Atlantic Forest biome vegetation, NF). According to their origin or formation pathway, the aggregates were separated, identified, and classified as Bio (formed by biological processes) and Phy (resulting from chemical and physical actions). The differentiation between Bio and Phy aggregates was performed based on the visualization of morphological features, such as shape, size, presence of roots, porosity, and subunit arrangements, and junctions. Only the PP area was able to promote greater aggregate formation of biological origin, with greater amounts of Bio aggregates. The highest total organic carbon (TOC) contents and the least negative δ13C values were also quantified in the aggregates of the PP area. The NT + B system provided an increase in the TOC content of its aggregates in comparison with aggregates in the NT and NF areas. Among the formation pathways, the Bio aggregates had the highest TOC and soil organic matter fractions contents and the most negative δ13C values. Perennial forage grasses vegetation was more important than the plant species diversity in favoring Bio aggregate formation. The beneficial effect of Brachiaria can be observed when incorporated as part of intercropping with corn in grain production systems. The biogenic aggregates favored the concentration of more labile soil organic matter fractions. The results of this study can provide important theoretical information for future studies focused on the combination of different plant species in agricultural food production areas on sandy-textured soils.


Introduction
Sandy textured soils cover approximately 9.0 million km 2 (900 million ha) of the Earth's surface (Hartemink & Huting, 2007;Silva et al., 2020), and represent approximately 8.0% of the Brazilian territory (Donagemma et al., 2016). The main characteristic of these soils is the high content of the sand fraction to the detriment of the silt and clay fractions. Quartz in the sand fraction is the main mineral found in the soil surface horizons. This explains its high textural fragility, high permeability, low water, and nutrient storage capacity (Šimanský et al., 2019), as well as its high susceptibility to erosion and groundwater contamination (Donagemma et al., 2016). However, these soils are favorable for mechanization. When managed with sustainable practices, they can help maintain or improve the quality of agricultural land. Despite these restrictive characteristics, the possibilities for the use of these soils in agriculture can vary, according to the practices adopted and technologies used.
Owing to the advances in research and the use of new practices and technologies, agricultural areas with sandy texture soils have been successfully incorporated into the Brazilian production chain (Fontana et al., 2020). This has included the production of grains, fibers, energy materials, sugarcane, forestry, and pasture. The adoption of management systems such as permanent pasture (PP) and conservationist systems such as no-tillage (NT) systems can increase the stability of agricultural production on these soils . Pasture systems (cultivated permanently or temporarily) are fundamental for erosion control on sandy textured soils because they increase the soil organic matter (SOM) content (Martins et al., 2020). Implementation of these management systems also promotes associations with mycorrhizal fungi that contribute to the improvement of soil aggregation (Donagemma et al., 2016), predominantly in the superficial layers.
The correct choice of soil management system with crop rotation and the inclusion of forage grasses, such as Brachiaria, for animal feed or soil protection under NT systems can improve the sustainability of food production in regions with high agroclimatic risk. This was investigated by Silva et al. (2020) in sandy textured soil areas of Brazil. The corn-Brachiaria intercropping is a technology where two grass species are grown together (grain crop + tropical forage), with the objective of producing corn grain and straw; and, in the off-season, Brachiaria forage (animal feed) or straw (soil coverage in quantity and quality for the NT). This practice allows the consolidation of the NT system in agrifood areas, with positive effects on the subsequent crops, including soybeans, and the conservation of natural resources (Ceccon et al., 2013).
One of the most efficient ways to verify the impact of soil management systems, with an emphasis on more fragile soils, is to assess the dynamics of aggregation and SOM. This is particularly the case for SOM-associated aggregates of different origins or formation pathways (Ferreira et al., 2020). Soil aggregation protects and conserves SOM, acting as a reservoir of nutrients for plants, and contributes to reducing the rate of increase of CO 2 concentration in the atmosphere and the associated global warming (Bronick & Lal, 2005). The use of the visual separation method based on the morphological characteristics of aggregates (Batista et al., 2013;Bullock et al., 1985;Ferreira et al., 2020;Loss et al., 2014;Pulleman et al., 2005;Velasquez et al., 2007) has been a practical, simple, effective, and an economical way to assess soil structure and aggregation (Lavelle et al., 2014;Pereira et al., 2021). It can be used successfully to determine and monitor the structural quality of the edaphic environment under different management strategies (Suárez et al., 2019).
Soil aggregates are defined as natural secondary units that are composed of primary particles (sand, silt, and clay) and SOM that are bound together by organic substances, iron and aluminum oxides, carbonates, silica, and the clay itself (Santos et al., 2015). When present in the soil structure, soil aggregates can regulate several ecological functions, such as water and air dynamics, susceptibility to erosion, and nutrient cycling, as well as SOM protection and accumulation. These units can be classified, based on their origin or formation pathway, into biogenic or biogenic origin (formed by biotic processes) and physicogenic or physicogenic origin (resulting from abiotic factors) (Batista et al., 2013;Loss et al., 2014;Pulleman et al., 2005;Velasquez et al., 2007). They can also be classified as intermediates when there is no definite evidence of a specific origin (Batista et al., 2013;Ferreira et al., 2020;Pulleman et al., 2005). Due to the importance of soil aggregates as an indicator of the management system applied in agricultural production areas, Pereira et al. (2021) gathered a set of images, data, and studies on different soil and environmental conditions in Brazil that point to biogenic aggregation being a more sensitive and reliable soil quality indicator than physicogenic aggregation. SOM transformations are closely linked to aggregation dynamics, as suggested by Lavelle et al. (2020) in one of the steps integrating their conceptual model of biogenic and physicogenic macroaggregate formation. The incorporation of organic matter into the soil matrix is primarily a biological process associated with root growth, rhizodeposition, comminution processes, feeding, and bioturbation of invertebrates. The study of these transformations involves the knowledge and understanding of the different SOM fractions and their respective stabilization mechanisms. These include molecular recalcitrance, structural composition and arrangement, physical protection by occlusion, and formation of organo-mineral complexes (Conceição et al., 2008).
Measuring and monitoring the response of such indicators (aggregation and SOM) to changes in land use and management are valuable for providing information on the ability of the anthropic systems to improve soil quality by minimizing or reversing environmental degradation processes (Webster et al., 2019). However, there are few studies in this field encompassing soils with sandy texture. One of the main challenges in the sustainable management of these environments is the implementation of production systems that include new species combinations over time. This should be undertaken with the aim of promoting benefits related to increasing crop productivity and improving soil quality (Donagemma et al., 2016).
The following hypotheses were therefore formulated. (H 1 ) Increasing plant species diversity in agricultural production areas favors the maintenance or improvement of soil quality. (H 2 ) This beneficial effect is related to biogenic aggregate formation (e.g., improved fertility; increased nutrient cycling; decreased erodibility; improved soil water movement and retention; and increased crop productivity). (H 3 ) The biogenic pathway alters SOM-associated dynamics (mechanisms related to the deposition, stabilization, and accumulation of organic carbon in the soil). To test these three hypotheses, the study aimed to (i) verify whether soil use and management affect the proportion of biogenic and physicogenic aggregates and (ii) verify whether biogenic aggregation leads to soil improvement when compared with physicogenic aggregation with respect to the sequestration and storage of total organic carbon and its organic fractions.

Material and methods
Location, climate, and soil of the study area The study was conducted in the municipality of Terra Roxa, located towards the west of the state of Paraná (South of Brazil), at coordinates 24° 11′ 34″ S and 54° 06′ 62″ W, with an average altitude of 319 m (Fig. 1A). The region's climate is humid subtropical with hot summers (Cfa), according to the Köppen classification (Alvares et al., 2013). The soils in the study area were classified as Argissolo Vermelho-Amarelo Distrófico, which have a sandy texture at the superficial horizons (Santos et al., 2018). The classification corresponded with Paleudalfs in the USA Soil Taxonomy (Soil Survey Staff, 2014) or the Acrisols in the FAO classification system (IUSS Working Group WRB, 2015).

History of the evaluated areas
Three areas with different management systems and an associated reference area were evaluated, comprising four sample areas in total. The management systems were permanent pasture (PP), a no-tillage (soybean/maize succession) (NT) system, and a notillage + Brachiaria system (B), encompassing corn and Brachiaria ruziziensis intercropping and succession with soybeans (NT + B). The reference area corresponded with a forest fragment in the Atlantic Forest biome (FN) (Fig. 1B-E) (Table 1). The chemical and physical attributes of the sample areas from the 0-0.10-m soil layer are shown in Table 2. Soil sampling was conducted in August 2020, with corn (Zea mays L.) being the annual crop that preceded the collection period. Soybeans (Glycine max L.) were sown in the managed areas during October 2020. In the NT + B area, the collection was performed 35 days after Brachiaria desiccation. All sample areas belonged to the same farm and were subjected to the same conditions of relief and climate and belonged to the same soil class, and within a radius of 150 m of each other, whose coordinates and altitudes are shown in Table 1.
Collecting the samples and separating the aggregates In each sample area, five 400 m 2 plots were randomly demarcated. Within each plot, undeformed samples (clods) were randomly collected to form a composite sample, totaling five composite samples per sampled area. Each composite sample corresponds to one pseudo repetition, collected from the 0-0.05-m to 0.05-0.10-m layers, totaling 40 sample units (four areas × five pseudo repetitions × two layers). After collection, the samples were air dried and then sieved using a set of 9.7-m and 8.0-mm mesh. Only the aggregates retained between the meshes (9.7 > Ø ≥ 8.0 mm) were selected for study.
The aggregates were then taken to the laboratory, examined under a binocular magnifying glass, and were classified according to their origin or formation pathways. For this study, two classes (biogenic and physicogenic) were identified and evaluated from the morphological patterns established by Bullock et al. (1985). This was done using a protocol adapted by Pulleman et al. (2005) and validated by the studies gathered by Pereira et al. (2021). The differentiation between the aggregates was performed based on the visualization of morphological features, such as shape, size, presence of roots, porosity (Bullock et al., 1985;Pulleman et al., 2005;Batista et al., 2013;Melo et al., 2019;Pinto et al., 2021a, b;Pereira et al., 2021), and subunit arrangements and junctions (Pereira et al., 2021). The biogenic classes were those in which it was possible to visualize rounded shapes. These were produced in the intestinal tract of soil macrofauna, mainly Oligochaeta (earthworms). The biogenic classes also include samples where it was possible to visualize the presence and activity of roots. The physicogenic class were defined as being those that had angular shapes resulting from the interaction between carbon, clay, cations, and soil wetting and drying cycles (Fig. 2). After identification, the relative contribution (% by mass) of each aggregate class from each sample area was determined.

Analysis of SOM in aggregates
Aggregates from each class (biogenic and physicogenic) were crushed and passed through a 2.0-mm mesh sieve (Teixeira et al., 2017). This material was then used for organic matter analyses. Total organic carbon (TOC) was determined via wet oxidation of organic matter using potassium dichromate (K 2 Cr 2 O 7 ) at a concentration of 0.167 mol −1 in sulfuric medium. TOC was then quantified by titration using ammoniacal ferrous sulfate solution ((NH 4 ) 2 Fe (SO 4 ) 2 ) 6H 2 O 0.2 mol L −1 ) as a titrant with ferroin as an indicator (Yeomans & Bremner, 1988).  The isotopic abundance of δ 13 C ( 13 C/ 12 C) was determined by crushing ± 300 mg of each aggregate sample in a mortar. Samples were then passed through a 100 mesh (149 μm) sieve. The samples were evaluated using a Delta V Advantage mass spectrometer (Thermo Fisher Scientific, Bremen, Germany) at the Research Laboratory of Biotransformations of C and N (LABCEN), Santa Maria, RS. The results were expressed in the form of δ 13 C (‰), and the carbon was relative to the international standard Pee Dee Belemnite (PDB) (Faure & Mensing, 2005), as shown in Eq. 1.
where δ 13 C is the isotopic abundance, R sample is the 13 C/ 12 C isotope ratio, and R PDB is the international standard PDB ratio.
For the granulometric physical fractionation, the method proposed by Cambardella and Elliott (1993) was used. Aliquots of 20 g of material were homogenized with 60 mL of a 5.0 g L −1 sodium hexametaphosphate solution for 15 h on a horizontal shaker. The suspension was sieved through a 53-μm sieve under a jet of water. The organic matter retained in the sieve (particulate organic matter -Supplementary material) was oven dried at 50 °C and crushed using porcelain mortar and pestle. Particulate organic matter content was determined according to Yeomans and Bremner (1988), through wet oxidation of organic matter with 0.167 mol (1) δ 13 C = R sample − R PDB R PDB × 10 3 0 ∕ 00 L −1 K 2 Cr 2 O 7 in sulfuric medium and external heating (particulate organic carbon, POC). The other fraction corresponds to the mineral-associated organic matter and represents the fraction of organic matter associated with clay and minerals and at the most advanced stage of decomposition. Mineral-associated organic matter content was obtained by the difference between TOC and POC (mineral-associated organic carbon, MAOC), considering the difference the weight percentages of fractions. Densimetric physical fractionation (Machado, 2002;Sohi et al., 2001) was used to obtain the free light fraction and intra-aggregate light fraction of the SOM extracted using sodium iodide solution (NaI) 1.80 Mg m −3 (± 0.02). For this, aliquots of 5.0 g of material were weighed into 50-mL centrifuge flasks and 35 mL of NaI was added. The flasks were shaken manually for 30 sec so that the less dense organic fractions remained on the surface of the solution. Next, the samples were centrifuged at 18,000 rpm for 15 min at a temperature of 18 °C to promote sedimentation of the soil mineral particles. The supernatant organic fraction present in the solution (free light fraction) was aspirated together with the NaI solution, and immediately separated by vacuum filtration (Sterifil Aseptic System, 47 mm -Millipore) with previously weighed glass fiber filters (47-mm diameter; 2 microns-Whatman type GF/A). The collected fractions were washed with distilled water to remove excess NaI from the fraction and filter. The organic fraction, together with the filter, Fig. 2 Representative images of soil aggregates in the 9.7-8.0-mm fraction. A-C Biogenic aggregates. D-F Physicogenic aggregates of areas under different management systems in subtropical region of Brazil was subsequently dried at 65 °C, weighed, and macerated in mortar. After removing the free light fraction (FLF -Supplementary material), the intra-aggregate or occluded light fraction (ILF) was extracted by applying vibration using a Hielscher device (model UP400S) for 90 sec at an energy of 1000 J mL −1 in the NaI solution. This device has a manufacturer's specified power of 400 W, and was used with 0.1 cycle and 100% amplitude, with the probe inserted at 3 cm into the solution. For the remaining soil in the centrifuge tube, this operation was performed in an ice bath, in order to avoid a sudden rise in temperature and keep it below 40 °C. After treatment with ultrasound, the samples were again centrifuged at 18,000 rpm for 15 min, and ILF was collected on filters, dried, weighed, and ground, in the same way as was done for the FLF. The organic carbon contents of the free light (FLFC) and intra-aggregate light (ILFC) fractions of SOM were also analyzed according to Yeomans and Bremner (1988).

Statistical analysis
For each soil layer, the data were initially tested for the analysis of variance (ANOVA), assumptions of normality of residuals, and homoscedasticity. In cases where the assumptions were not met, the variables were transformed according to the Box-Cox test, and the assumptions were then retested. In cases where the assumptions were met, including transformed or untransformed variables, the analysis of variance was undertaken using a 4 × 2 factorial scheme (evaluated areas × aggregate formation pathways). Some variables did not meet the assumptions of the analysis of variance even after transformation. In these cases, they were tested using the non-parametric Kruskal-Wallis test and Fisher's minimum significant difference. Principal component analysis (PCA), based on Pearson's correlation matrix, was also performed on the evaluated attributes. All analyses were performed using R software (R Core Team, 2013).

Results
Impacts of management systems on aggregate formation pathways Figure 3 shows the proportions of biogenic and physicogenic aggregates in the areas being studied. The impact of food production systems on aggregate formation pathways was most pronounced in the topsoil layer. Only the PP area was able to promote more biogenic aggregate formation. The PP area outperformed both the physicogenic (73% and 27%, respectively) and biogenic aggregates from the NT (14%) and NT + B (30%) areas, and particularly from the NF area (47%), which has a more stable and balanced environment ( Fig. 3; 0-0.05-m layer). Among the NT, Fig. 3 Aggregate formation pathways of areas under different management systems in subtropical region of Brazil. Measurement corresponds to 100 g of soil aggregates of size 9.7-8.0 mm before separation between physicogenic and biogenic. PP, permanent pasture; NT, no-tillage system; NT + B, no-tillage + brachiaria system; NF, vegetation of the Atlantic Forest biome; ① ANOVA + Tukey's test without data transformations; and ② Kruskal-Wallis test + Fisher's minimum significant difference NT + B, and NF areas, the corn-Brachiaria intercropping system was found to have favored the formation of aggregates of biogenic origin. These values were similar to those quantified from the reference area (NF) (Fig. 3; 0-0.05-m layer).
Effect of management systems and aggregation on maintenance and origin of stored carbon The results of the total organic carbon (TOC) and 13 C isotopic abundance (δ 13 C) are presented in Table 3. Results from the PP area were shown to have raised carbon content in the aggregates, from both formation pathways (0-0.05-m layer). This was also the case for the general averages between the different areas (0.05-0.10-m layer). Results showed that these aggregates had the highest TOC contents. In addition, the isotopic signal levels were affected, in which the least negative δ 13 C values were recorded. These values ranged from approximately − 13 to − 15‰, regardless of the aggregate class (Table 3; 0-0.10-m layer).
Results from the NT + B system showed an increase in the TOC content of its aggregates, in comparison with the aggregates in the NT and NF areas. In the biogenic aggregates, the increases were 17.9% and 38.2%, respectively. In the physicogenic aggregates, the increases were 35.1% and 44.4%, respectively (Table 3; 0-0.05-m layer). In the aggregates from these three areas, the δ 13 C values ranged from − 19 to − 22 ‰ (Table 3; 0-0.10-m layer).
Regarding the formation pathways, the biogenic samples showed potential for SOM maintenance and accumulation, as well as affecting the origin of the carbon stored in the aggregates from the topsoil layer. The aggregates of biogenic origin in the NT area had the highest TOC contents compared to physicogenic aggregation (17.99 × 13.77 g C kg −1 soil). In the biogenic aggregates of the NF, NT + B, and PP areas, increases of 19.2%, 14.0%,and 12.9% in TOC contents were quantified compared to the physicogenic (Table 3; 0-0.05-m layer). The overall mean δ 13 C values from the biogenic aggregates had increased Table 3 Total organic carbon (TOC) and 13 C isotopic abundance (δ13C) of biogenic (Bio) and physicogenic (Phy) aggregates from areas under different management systems in a subtropical region of Brazil Means followed by the same capital letter in the column do not indicate differences between management systems for the same type of aggregate. The same lowercase letter in the row indicates no differences between the aggregate types for the same system evaluated PP permanent pasture, NT no-tillage system, NT + B no-tillage + Brachiaria system, NF vegetation of the Atlantic Forest biome, X 1 overall average for the areas, X 2 overall average for the aggregates (a) ANOVA + Tukey's test without data transformations (b)  negative values than those from the physicogenic aggregates (Table 3; 0-0.05-m layer).

Influence of management systems and aggregation on SOM protection
The results of the analysis for particulate organic matter (granulometric fraction) and the free light fraction of SOM (densimetric fraction) are presented in Figs. S1 and S2 (Supplementary material), respectively. Tables 4 and 5 show the organic carbon results for the physical fractions of the SOM. The disparity between results that is often found in the literature emphasizes the need to consider the importance of multiple influencing factors in the SOM stabilization mechanisms.
The areas managed under pasture and grain production influenced the highest particulate organic carbon (POC) content. This was the case for both the biogenic aggregates from the PP area (Table 4; 0-0.05-m layer) and for the overall average across all the field sites (Table 4; 0.05-0.10-m layer). The highest POC contents were recorded here, as well as in the biogenic aggregates from the NT and NT + B areas (Table 4; 0-0.05-m layer). Results from the PP area also showed the presence of increased carbon content in the most recalcitrant SOM fraction (mineral-associated organic carbon, MAOC). This was reflected in the highest overall averages between sampling areas of 21.60 and 18.90 g C kg −1 soil (Table 4; 0-0.05 and 0.05-0.10-m layers, respectively).
The grain production areas (NT and NT + B) also influenced the extensive increase in the organic carbon content for the free light fraction (FLFC) in the aggregates of the biogenic pathway in comparison with the same aggregate class in the pasture area. This was especially the case for the reference area (Table 5; 0-0.05-m layer). The results were superior at 118.8% and 151.7% in NT + B, and 229.7% and 279.2% in NT, respectively.

Table 4 Particulate organic carbon (POC) and mineral-associated organic carbon (MAOC) of biogenic (Bio) and physicogenic (Phy) aggregates from areas under different management systems in a subtropical region of Brazil
Means followed by the same capital letter in the column do not indicate differences between management systems for the same type of aggregate. The same lowercase letter in the row indicates no differences between the aggregate types for the same system evaluated PP permanent pasture, NT no-tillage system, NT + B no-tillage + Brachiaria system, NF vegetation of the Atlantic Forest biome, X 1 overall average for the areas, X 2 overall average for the aggregates (a) ANOVA + Tukey's test without data transformations (b)  From the point of view of SOM protection, the biogenic pathway has been demonstrated to be able to preserve and accumulate the most labile fractions. In the biogenic aggregates of the NT and NT + B systems, the highest contents of POC and FLFC were quantified in comparison with the physicogenic aggregates (Tables 4  and 5; 0-0.05-m layer). The increases were 33.0% and 55.9% for POC and 85.3% and 239.5% for FLFC, respectively. This pattern occurs repeatedly in the overall averages for biogenic versus physicogenic aggregates for POC (Table 4; 0.05-0.10-m layer) and the intra-aggregate light fraction organic carbon (Table 5; 0-0.05-m layer). Biogenic aggregation did not influence the carbon content of the most recalcitrant SOM fraction (MAOC) ( Table 4; 0-0.10-m layer).

Dissimilarity between the managed areas and the reference area
The principal component analyses (PCA) showed cumulative variance for principal components (PC) 1 and 2 of ± 82 and 78% (layers 0-0.05 m and 0.05-0.10 m, respectively). As shown in Fig. 4, the formation of three distinct groups was observed: (1) group comprised the aggregates (biogenic and physicogenic) from the PP area; (2) group comprised the biogenic aggregates from the NT and NT + B areas; and (3) group comprised the physicogenic aggregates from the NT, NT + B, and NF areas. In the case of Fig. 5, only two groups formed, with no clear separation of aggregate classes from the PP areas (1st group), and NT, NT + B, and NF (2nd group).
In both PCAs, the PP area was separated from the NT, NT + B, and NF areas along PC-1 (main axis) with ± 51% and 56%, with 13 C (0.50), TOC (0.57 and 0.53), and MAOC (0.52 and 0.50) as discriminant variables (correlation coefficient ≥ 0.50) (Figs. 4 and 5, respectively). In the 0-0.05-m layer, PC-2 with 31.63% (axis of least relevance), predominantly separated the biogenic aggregates from the physicogenic aggregates in the NT and NT + B Table 5 Organic carbon of the free light fraction (FLFC) and intra-aggregate light fraction (ILFC) of biogenic (Bio) and physicogenic (Phy) aggregates from areas under different management systems in a subtropical region of Brazil Means followed by the same capital letter in the column do not indicate differences between management systems for the same type of aggregate. The same lowercase letter in the row indicates no differences between the aggregate types for the same system evaluated PP permanent pasture, NT no-tillage system, NT + B no-tillage + Brachiaria system, NF vegetation of the Atlantic Forest biome, X 1 overall average for the areas, X 2 overall average for the aggregates (a) ANOVA + Tukey's test without data transformations; and areas. POC (− 0.50), FLFC (− 0.62), and ILFC (− 0.50) showed medium and negative correlations (≤ − 0.50) with PC-2, which was directly associated with biogenic aggregation (Fig. 4). The PCA showed that aggregation and SOM dynamics in sandy-textured soils were more closely related to agricultural production systems than to the vegetation of the biome.

Discussion
The pattern of biogenic aggregate proportion data observed in the study for the PP area ( Fig. 3; 0-0.05-m layer) is similar to that verified by other authors (Loss et al., 2014;Pinto et al., 2021b;Pulleman et al., 2005;Suárez et al., 2019). This may be related to the function of root system in grasses, especially those used for forage. During the initial formation of macroaggregates, roots bring together and trap soil mineral particles and microaggregates through mechanical processes and the production of root exudates, namely low-molecularweight and high-energy organic substances. These act as binding agents, which also stimulate microbial activity (Six et al., 2002). In northern Brazil, Velasquez et al. (2012) found that in experimental pasture areas, the presence of Brachiaria brizantha favored the formation of biogenic aggregates formed by the roots. The proportion of biogenic aggregates was higher in pasture areas with Brachiaria brizantha than in areas where it was absent. Forage grasses, especially Brachiaria, promote increased addition of carbon to the soil through renewal of the root system. This provides better conditions for the edaphic fauna and the formation of aggregates of biogenic origin (Loss et al., 2014;Mergen Junior et al., 2019;Pinto et al., 2021b). This favors the activity of soil invertebrates as ecosystem engineers, including earthworms, termites, ants, and other macroorganisms. They build biostructures of galleries, channels, or chambers via bioturbation. These soil invertebrates produce fecal pellets (coprolites) that can become highly stable macroaggregates, which comprise a large proportion of biogenic aggregates in the soil, mainly at the superficial horizons (Lavelle et al., 2020;Pereira et al., 2021;Zangerlé et al., 2011). According to Velasquez and Lavelle (2019), the presence of biogenic aggregates, as well as invertebrates and roots, demonstrates the presence of high biological activity. This likely indicates the occurrence of high-quality soil processes and optimal biological regulation for soil functioning.
The pattern observed in the PP area was not seen in the NT + B area during the study between aggregate classes (Fig. 3; 0-0.05-m layer). This may be associated with the timing of the adoption of the corn-Brachiaria (winter) intercropping. This is because forage grass (Brachiaria ruziziensis) was introduced around 6 years ago (from 2014 to 2020) to the area. In the same region as the current study, Rosset et al. (2019) identified that over 4 years (from 2008 to 2012), a corn-Brachiaria intercropping was efficient in improving soil aggregation under Ferritic Ferralsol (Latossolo Vermelho). The analysis of the data presented an improvement in the soil's structural quality. This was likely due to the more abundant and aggressive root system of Brachiaria when it is intercropped with corn. The corn-Brachiaria intercropping is an agricultural practice that can be adopted in different production systems. Implementation of this practice minimizes soil-related problems by coverage with plants. This improves productive capacity and supports the establishment of new pastures (Ceccon et al., 2013). This also justifies the similar values for the biogenic aggregate proportion of the NT + B system in comparison with those of the non-anthropized area (NF) (Fig. 3; 0-0.05-m layer).
The soil carbon pool represents a large and important part of the structure of the total carbon pool in the terrestrial environment. It also plays a key role in the global carbon cycle (Cheng et al., 2015;Paul et al., 2008). This carbon pool is more important in sandy textured soils because of their fragile nature and difficulty in increasing SOM concentrations (Reichert et al., 2016). In these soils, SOM is one of the most important cementing agents for aggregate formation and stabilization (Bronick & Lal, 2005;Šimanský et al., 2019). The TOC results in aggregates of the PP area (Table 3; 0-0.10-m layer) are due to the interaction of intrinsic and extrinsic factors, such as low levels of soil disturbance, maintenance of root biomass throughout the year returning a large percentage of organic residues, regular cattle defecation (Lal et al., 2003), and a less sandy soil texture (Table 2; 0-0.10-m layer).
Proper cattle management on pasture can also favor an increase in the soil carbon content through the constant addition of manure, as verified by Moncada et al. (2014). However, the same authors also found that this increase was more significant in pasture with silty loam soil than for sandy loam soil (27.8 and 13.4 g C kg −1 soil, respectively). During the current study, the PP area had a different soil texture of sandy loam when compared to the NT and NT + B areas, which had loamy sandy soils (Table 2; 0-0.10 m-layer). The higher clay content found in this area may contribute to improved conservation of SOM, likely through associations between transformed organic matter and the mineral particles. This leads to the formation of organo-mineral complexes (Six et al., 2004). This mechanism of chemical stabilization of SOM may be facilitated by the formation of aggregates, which enable the protection and accumulation of TOC and its more stable fractions.
The δ 13 C values identified in the aggregates in the PP area (Table 3; 0-0.10-m layer) indicated a predominance of carbon that has originated from C 4 photosynthetic cycle plants (− 6 to − 19‰;Smith & Epstein, 1971). This pattern has resulted from plant species fixing CO 2 through the PEPcase enzyme. Loss et al. (2014) observed δ 13 C values in grassland areas ranging from approximately − 16 to − 17‰ for biogenic aggregates and − 16 to − 18‰ for physicogenic aggregates. The levels of δ 13 C identified in the aggregates of the PP area during the study were closer to zero than those observed by Loss et al. (2014) in the same region. This may be related to the grass species used in the pasture system in which the PP area was established. Cynodon dactylon was cultivated there for approximately 45 years. In the area where the work of Loss et al. (2014) was undertaken, Axonopus compressus was grown for the pasture for approximately 30 years. In addition to increasing the carbon content in the aggregates, the PP area also affected the δ 13 C levels. The combination of the higher TOC contents and fewer negative δ 13 C values suggests a possible direct relationship between these two indicators in this system, and the same was also verified using the principal component analysis (PCA) data (Figs. 4 and 5).
When the TOC results on aggregates in the NT + B area are compared to those of the NT and NF areas (Table 3; layer 0-0.05-m), a distinct pattern was documented by Rosset et al. (2019). The authors found that after 4 years of corn-Brachiaria intercropping (from 2008 to 2012), the TOC contents were not significantly higher than those quantified in the NT system areas with different installation times for succession of soybeans in summer and corn in winter. This suggested that a longer study duration was necessary for significant changes to be observed. In the current study, the evaluated corn-Brachiaria intercropping was around 6 years old (from 2014 to 2020). This may be influencing the increase in TOC values observed in the aggregates of this system when compared with the aggregates of the reference area and corn area without the Brachiaria. The use of grasses, especially Brachiaria, has been a key factor in the viability of grain and fiber production systems, and for the sustainability of farming on sandy-textured soils (Donagemma et al., 2016).
The δ 13 C levels identified in the aggregates from the NT, NT + B, and NF areas (Table 3; 0-0.10-m layer) characterize surface C 3 cycle plants (− 24 to − 34‰; Smith & Epstein, 1971). These results are similar to those found by Assunção et al. (2019) in soil samples from areas of native forest, such as in the alluvial semideciduous seasonal forest fragment, the NT system with soybean/maize succession, and conventional management with oat/bean succession under Ferritic Ferralsol (Latossolo Vermelho) in the same subtropical region. The authors justified their data on the basis of plant species diversity in these areas, where the isotopic signal has changed from C 4 photosynthetic cycle plants to that of C 3 plants, with depletion of the stable 13 C isotope.
For the TOC results among aggregate classes, the key pattern revealed by this study reinforces the hypothesis that conservationist systems tend to increase carbon stabilization. This is due to more extensive formation and maintenance of biogenic aggregates (Brussaard et al., 2007;Suárez et al., 2019). This pattern is confirmed by the higher values of this attribute in the biogenic aggregates in the NT area; and by the increased TOC values in the biogenic aggregates of the NF, NT + B, and PP areas compared with the physicogenic aggregates (Table 3; 0-0.05-m layer). The data verified in this study corroborates those found by other authors (Batista et al., 2013;Loss et al., 2014Loss et al., , 2017Pulleman et al., 2005). Aggregates of biological origin contribute more effectively to protecting SOM by reducing decomposition rates and raising the soil's carbon sequestration potential, as reported by Silva Neto et al. (2010).
In biogenic aggregates from agroforestry systems, Suárez et al. (2019) found higher carbon contents in comparison with aggregates from other areas. The authors related these results to the presence of trees and burlap, which favored macrofauna activity. The high carbon content in biogenic aggregates was also examined by Pinto et al. (2021a) in areas with conservationist management systems in comparison with more conventional systems. The authors emphasized the importance of biogenic aggregation in preserving carbon in agricultural areas with different management systems. Similar results were observed by Vol:. (1234567890) Melo et al. (2019) and Ferreira et al. (2020) in the same study region. According to Melo et al. (2019), the greater the carbon input, the more extensive the formation of biogenic aggregates, while Ferreira et al. (2020) reported that biogenic aggregates indicated both greater biological activity and favored the maintenance and accumulation of carbon in the soil.
Regarding the increased number of negative δ 13 C values identified in aggregates of biogenic origin (Table 3; 0-0.05-m layer), other authors also identified a similar pattern. This included the study by Pinto et al. (2021b) which examined areas of 6-and 18-year-old NT systems with Brachiaria and Crotalaria spectabilis as cover plants, respectively. Loss et al. (2014) reported similar results in areas with NT systems and in secondary forests. However, Loss et al. (2017) reported no differences in the 13 C isotopic signals between the aggregate formation pathways. Linear correlations between δ 13 C and carbon accumulation in corn monocrops or corn rotated with soybean or wheat (Triticum aestivum L.) as reported by Qiao et al. (2015). This suggests that the amount of contributed carbon is important in determining the δ 13 C changes in the soil.
Permanent or temporary (cultivated or improved) pastures can provide a continuous deposition of organic residues, via plant residues and root biomass, via rhizodeposition. This favors inputs of the more labile carbon fractions, stimulating microbial activity and nutrient cycling (Geraei et al., 2016). This is in line with the POC results from the biogenic aggregates (Table 4; 0-0.05-m layer) and the overall average for the PP area (Table 4; 0.05-0.10-m layer). For these reasons, SOM fractions, such as POC and permanganate oxidizable carbon, are more sensitive indicators for assessing the effects of pasture management on organic matter dynamics (Webster et al., 2019). The PP area also influenced the increase in MAOC content, as shown by the overall mean values across the study areas (Table 4; 0-0.10-m layer). The MAOC, TOC, and 13 C variables contributed the most in the identification of areas from the 0-0.10 m layer in the PCA evaluation (Figs. 4 and 5). This has reinforced the hypothesis of a possible direct relationship between these indicators in the pasture system, which may be associated with mechanisms of stabilization and accumulation of SOM from an organic material enriched with the stable isotope 13 C.
During this study, special emphasis was placed on the practices used in the grain production systems (NT and NT + B). This included rotation, succession, and intercropping of plant species such as soybean/ corn and corn-Brachiaria/soybean, lower levels of topsoil disturbance, increased contribution, retention, and maintenance of biomass at the soil surface, as well as the quality and frequency of these contributed organic residues. This has predominantly influenced the most labile SOM fractions, including POC and FLFC (Tables 4 and 5; 0-0.05-m layer). This enrichment may originate from organic material from dead thick and thin roots, root exudates from rhizospheric activities; organic detritus of root origin occasionally ingested by invertebrates, such as earthworms, included in their coprolites; and/or organic material derived from transformed burlap by natural comminution and composting processes, progressively incorporated into the soil by digestion and bioturbation processes associated with macroinvertebrate feeding activities and the loss of dissolved organic matter (Lavelle et al., 2020).
The diversity and spatio-temporal arrangement of plant species in agricultural production systems, such a no-tillage systems, crop-livestock, or crop-livestockforest integrations, can promote several benefits for the edaphic environment. It can lead to an increase in the diversity of arbuscular mycorrhizal fungi and soil invertebrate macrofauna groups (Vilela et al., 2011). Crop rotation is a recommended practice, which should be conducted in a no-tillage system with cover plants with high biomass production that are intercropped with corn, as reported by Silva et al. (2020). The lower mobilization of the arable layer preserves the aggregates, decreasing the exposure of SOM to microbial processes and enzymatic reactions (Lal et al., 2003). The dynamics of crop residues on the soil surface in no-tillage systems, namely deposition, accumulation, preservation, and transformation, are closely linked to biotic factors, including SOM inputs and activity of soil organisms, and abiotic factors, such as straw fractionation and uniform distribution, water, soil texture, and organo-mineral interactions. Straw management under no-tillage is related to two system practices, including those which are chemically derived and carried out with herbicides or desiccants, and those that are mechanically derived, where machines and cultivation implements are used to grind and throw the organic material over the soil surface. Such factors, in association with soil biological agents, favored the extensive enrichment of biogenic aggregates with FLFC (Table 5; 0-0.05-m layer).
Management systems that promote environmental restoration or sustainability, as well as biogenic aggregates from these environments, may provide greater protection for the more labile SOM fractions within them after organic material has been encapsulated. The POC, FLFC, and ILFC data in biogenic aggregates, when compared to the corresponding data of physicogenic aggregates (Tables 4  and 5; 0-0.05-m layer), showed the predominance of organic material with higher lability (higher bioavailability). The incorporation and maintenance of this material are favored in biogenic aggregates by virtue of the soil fauna and the root system of plants. This is especially the case for no-tillage systems (Loss et al., 2014;Mergen Junior et al., 2019;Pinto et al., 2021a). Other authors have observed similar results when evaluating the same attributes, including Pulleman et al. (2005) and Loss et al. (2014) for POC, and Pinto et al. (2021a) for POC and FLFC. In the PCA study, the variables POC, FLFC, and ILFC contributed the most to the separation of biogenic and physicogenic aggregates in the 0-0.05-m layer (Fig. 4). This has corroborated the results of the statistical tests applied.
In terms of lability and protection, the free light fraction of SOM (or FLFC) ( Fig. S2 and Table 5) is considered more labile and less protected in comparison with particulate organic matter (POC) (Fig. S1 and Table 4) and the intra-aggregate light fraction of SOM (or ILFC) ( Table 5). POC is a significant fraction of SOM, which corresponds to all organic materials with a particle size of 53-2000 μm (Cambardella & Elliott, 1993). According to Lavallee et al. (2019), POC is more readily available, but its utility or quality to decomposers can vary depending on its chemical and nutritional composition. This generally follows the quality of the plant inputs. The FLFC has a composition that is compatible with that of plant materials with a high degree of lability, with stabilization being directly related to the time of deposition or intrinsic recalcitrance of the organic molecule. This is unlike in the case of ILFC, which comprises a diverse set of organic compounds. They have a more advanced degree of decomposition and stability due to the recalcitrance of the biomolecule, mainly from the physical protection resulting from its occlusion inside the aggregates, as described by Conceição et al. (2008).
This suggests that the labile SOM fractions are more sensitive and efficient indicators associated with soil aggregation, and that such fractions are important for the maintenance and regulation of various processes in the soil. High POC contents in biogenic aggregates may favor the formation of smaller aggregates, known as microaggregates. They, therefore, promote the stabilization of newly incorporated carbon (Loss et al., 2014;Pulleman & Marinissen, 2004;Six et al., 2004). Once they are physically protected after encapsulation into larger aggregates, the labile fractions of SOM can eventually become substrates for soil microfauna. The POC, MAOC, FLFC, and ILFC results verified in the study (Tables 4 and 5) highlight that the carbon contents in these fractions originate from the interactions between (i) the stabilization mechanisms of SOM, such as molecular recalcitrance, occlusion within aggregates, and adsorption of minerals; (ii) different forms of land use and management, such as non-anthropized environment and agroalimentary systems; and (iii) the chemical, physical, and biological processes in the soil that are responsible for the formation of different types of aggregates.
Finally, the study highlights the importance of evaluating the origin of aggregates, as well as the compartmentalization of SOM in measuring the edaphic quality of anthropized environments, especially when installed on soils of a more fragile nature. Degradation of these soils is triggered when the resistance of the system to anthropic disturbance is exceeded (Reichert et al., 2016). This makes it vital to monitor them using more practical, simple, efficient, and low-cost edaphic indicators.

Conclusions
Perennial forage grasses vegetation (root system functionality and organic residue contribution) was more important than the plant species diversity in favoring biogenic aggregate formation in the topsoil layer. This is because the pasture showed a higher proportion of these aggregates in comparison with grain production systems and the vegetation of the Atlantic Forest biome, which is a more diverse environment in terms of the number of species.
Despite the lower levels of total organic carbon in relation to pasture, the beneficial effect of Brachiaria can be observed when incorporated as part of intercropping with corn in grain production systems, in which the plant remains for less time in the system. Grain production systems influenced the formation of biogenic aggregates with a higher lability of organic carbon.
The biogenic aggregates favored the concentration of more labile soil organic matter fractions. This suggests an improvement in edaphic quality, with the differences being observed mostly in the topmost soil layer (0-0.05 m). The results of this study can provide important theoretical information for future studies focused on the combination of different plant species in agricultural food production areas on sandy-textured soils.