Sistemas de manejo, atributos químicos do solo e dispersão de argila em áreas de microbacias

pab Pesquisa Agropecuária Brasileira Pesq. agropec. bras. 0100-204X 1678-3921 Embrapa Secretaria de Pesquisa e Desenvolvimento; Pesquisa Agropecuária Brasileira Resumo: O objetivo deste trabalho foi avaliar o efeito de diferentes sistemas de manejo sobre o grau de dispersão de argila e sua relação com os atributos químicos do solo e o índice de qualidade participativo (IQP) do sistema plantio direto, em áreas de microbacias do Oeste do Paraná. Os sistemas de manejo avaliados foram: sistema plantio direto; plantio direto com sucessão de culturas; plantio direto com revolvimento do solo; e sistema convencional. Além disso, os sistemas de manejo foram avaliados quanto ao seu IQP. Amostras de solo foram coletadas a 0,0-0,20 m de profundidade do solo, em 40 áreas agrícolas e em 6 matas nativas tidas como referências. Foram avaliados grau de dispersão de argila, carbono orgânico total, pH (em CaCl2), potássio trocável (K+), fósforo disponível (P), cálcio e magnésio trocáveis (Ca2++Mg2+), e acidez potencial (H+Al3+). Ajustou-se um modelo de regressão linear múltipla pelo método dos mínimos quadrados. Realizou-se a comparação de médias do grau de dispersão de argila, por microbacia, a 5% de probabilidade. Os sistemas de manejo foram comparados pelo teste de Scott-Knott. Os atributos químicos do solo apresentaram maior influência sobre a dispersão da argila do que os diferentes sistemas de manejo avaliados. O sistema plantio direto integral apresentou o maior teor de carbono orgânico, que foi semelhante ao das áreas nativas. O sistema convencional e o plantio direto com revolvimento do solo apresentaram menor IQP e maiores taxas de dispersão de argila do que as áreas sob sistema plantio direto integral. O IQP permite diferenciar os sistemas de manejo convencional e plantio direto. Introduction The use of the soil and its management change the agricultural productivity and sustainability. The quality of the farm systems and of anthropic actions can be assessed by the alterations caused to soil physical, chemical, and biological properties (Matias et al., 2012; Cardoso et al., 2013). Erosion is the main negative environmental impact from inadequate soil use, which results in particle loss, watercourse silting, and reduction of soil fertility and agricultural productivity (Demarchi & Zimback, 2014). Together with other soil properties, water-dispersible clay is used to understand the stability of the soil microstructure and its relationship with erosive processes (Igwe & Obalum, 2013), since the released particles can clog the pores, reducing the water flows and gases (Chaves et al., 2001; Nguetnkam & Dultz, 2014). In addition, these particles are easily transported in flowing streams to waterbodies (Demarchi & Zimback, 2014), favoring their contamination (Martin et al., 2015). Conservation systems, such as the no-tillage system (NTS), have been adopted as an alternative to ensure soil conservation. In the NTS, the continuous input of organic wastes is essential to the maintenance of the soil structure (Silva et al., 2014) and to increase the stability of the aggregates (Silva et al., 2011). The NTS has as its three main principles the minimum soil disturbance, crop rotation, and permanent soil cover, either by straw or living plants (Nunes et al., 2020). However, it is not always completely implemented, which results in the reduction of its benefits as a conservation practice (Silva et al., 2014). To monitor the quality of management systems and reduce the degradation risks of the agricultural production systems, the no-tillage system participatory quality index (IQP) was proposed by the Federação Brasileira de Plantio Direto e Irrigação (FEBRAPDP) (Metodologia…, 2011), in partnership with Itaipu Binacional, to predict the potential impacts of future scenarios in a qualitative or demonstrative way (Roloff et al., 2011). In addition, the tool results in the recommendation of improvements for management practices, with the differential feature of the active participation of producers themselves in the monitoring of the NTS quality (Nunes et al., 2020). The objective of this work was to evaluate the effect of the different farm systems on clay dispersion, and its relationship with soil chemical properties and IQP index in watersheds areas, in the state of Paraná, Brazil. Materials and Methods The study areas are located in the Paraná 3 hydrographic watershed, in the west Paraná mesoregion, between 24º01'S and 25º35'S and 53º26'W and 54º37'W, at 420 m altitude, composed by 28 municipalities, in the state of Paraná, Brazil. The predominant class of soil in the areas is Latossolo Vermelho distrófico (Santos et al., 2013), Ferralsol (FAO, 1988) or Oxisol (Soil Survey Staff, 2014), followed by Ultisol (FAO, 1988). The climate of the region is Cfa (subtropical with hot summers), according to the classification of Köppen-Geiger. The soil sampling was performed between June and July 2015, in the municipalities of Mercedes, Toledo, Itaipulândia, Santa Helena, Entre Rios do Oeste, and Marechal Cândido Rondon, in the state of Paraná. The areas were grouped according to the municipality: micro watersheds of Sanga Mineira (2 areas); Toledo (7 areas); Buriti (3 areas); Pacuri (3 areas); Facão Torto (4 areas); Arroio Fundo, Ajuricaba, and Curvado (21 areas). Forty areas with different levels of management quality were sampled, and six native forests (NF) were used as reference, corresponding each NF to each municipality in the cluster (Table 1). The farm management quality was assessed, using the IQP, which is composed of eight indicators (crop rotation intensity, crop rotation diversity, persistence of straw, soil-tillage frequency, correct terracing, soil conservation evaluation, balanced soil fertilization, and producer’s commitment (time of adoption of NTS) (Table 2). The IQP score was obtained between June and July, 2015, from farmers’ questionnaire responses, and from a local visit by the teams of Itaipu Binacional, Parque Tecnológico Itaipu (PTI), and FEBRAPDP, using the method described by FEBRAPDP (Metodologia…, 2011). The farm systems were divided into the following types: no-tillage system (NTS); no-tillage with crop succession (NT); no-tillage with soil disturbance (NTD); and conventional system (CS). The NTS met the basic assumptions of permanent soil cover, crop rotation, and minimum soil disturbance (Nunes et al., 2020). The other farm systems grouped as the following descriptions. NT was characterized by minimum soil disturbance and succession of crops (only two different crop species in a year). NTD was subjected to soil tillage for the control of soil compaction and weed. CS was characterized by periodic soil disturbance, by means of plowing and harrowing, at the moment of each crop planting. Table 1. History data of the farms: surface (in hectares), years under no-tillage system (t NTS), contour farming (yes or no), soil disturbance (if yes, periodicity), succession of crops, and IQP score. Area Surface (ha) t NTS (year) Contour farming Soil disturbance Succession of crops IQP 1 15.00 19 Yes No Soybean/maize/fallow 8.0 2 13.44 12 Yes Each 2 years Soybean/maize/oat 7.5 3 15.55 22 Yes No Soybean/maize/fallow 5.3 4 23.39 26 Yes No Soybean/maize/oat 8.3 5 26.23 19 Yes No Soybean/maize/fallow 8.6 6 19.81 24 Yes No Soybean/maize/fallow 8.4 7 75.34 21 Yes No Soybean/maize/fallow 8.6 8 48.51 26 No No Soybean/maize/fallow 7.6 9 33.91 20 No No Soybean/maize/wheat 7.8 10 15.11 20 Yes Each 6 years Soybean/maize/oat 5.6 11 9.71 20 Yes No Soybean/maize/fallow 7.8 12 3.85 22 Yes No Soybean/maize/oat 8.4 13 72.39 18 Yes No Soybean/maize/fallow 5.5 14 4.84 22 Yes Each 3 years Soybean/maize/fallow 8.5 15 195.04 18 Yes Each 6 years Soybean/maize/fallow 7.3 16 26.36 15 No No Soybean/soybean/wheat 3.3 17 10.02 12 Yes Each 2 years Soybean/maize/fallow 6.9 18 0.97 CS(1) Yes Each 2 years Cassava/pasture 4.1 19 54.02 19 Yes Each 5 years Soybean/maize/fallow 6.1 20 6.73 24 Yes No Soybean/maize/fallow 7.8 21 5.56 24 Yes No Soybean/maize/fallow 7.2 22 2.14 25 Yes No Soybean/maize/fallow 5.8 23 38.95 15 Yes No Soybean/maize/fallow 4.4 24 1.10 12 Yes No Soybean/maize/fallow 7.0 25 2.05 19 Yes Annual Soybean/maize/oat 7.1 26 2.12 CS(1) Yes Each 2 years Cassava 5.7 27 16.52 19 Yes No Soybean/maize/oat 6.3 28 5.96 14 Yes No Soybean/maize/fallow 6.6 29 3.62 CS(1) No Annual Soybean/maize/fallow 4.0 30 25.36 24 Yes No Soybean/maize/fallow 8.3 31 14.87 24 Yes Annual Soybean/maize/oat 7.5 32 6.58 19 Yes Annual Soybean/maize/oat 6.8 33 5.28 9 No No Soybean/maize/fallow 6.4 34 4.27 7 Yes No Soybean/maize/fallow 7.0 35 2.03 CS(1) Yes Annual Cassava/soybean 7.4 36 11.92 19 Yes No Soybean/maize/fallow 7.8 37 4.47 18 No Each 4 years Soybean/maize/fallow 7.4 38 8.18 12 Yes Each 3 years Soybean/maize/fallow 5.0 39 20.5 24 Yes No Soybean/maize/fallow 6.3 40 6.13 13 Yes Each 6 years Soybean/maize/fallow 5.4 (1) CS, conventional system; IQP, participatory quality index. Table 2. Weighting factors and formula for component indicators of the no-tillage system (NTS) participatory quality index (IQP), 2015, according to Nunes et al. (2020). Indicator Weighting factor Calculation Crop rotation intensity (RI) 1.5 Number of crops in 3 years/9(1) Crop rotation diversity (RD) 1.5 Different vegetal species in the rotation/4(2) Persistence of straw (PS) 1.5 Number of grasses in the rotation/6(3) Soil-tillage frequency (TF) 1.5 Years between tillage or (if no-tillage) base x 0.8(4) Correct terracing (CT) 1.0 Overflow < 2, SC= 1.0 2 < Overflow < 3, SC= 0.5 Overflow > 3, SC = 0(5) Soil conservation evaluation (SC) 1.0 If: Countour-line operations/ No erosion signals/ No surround soil compaction/ No soil compaction, +1 for each. ∑ / 4(6) Balanced soil fertilization (BF) 1.0 If: if based on soil chemical analysis (chemical fertilization/ liming,+0.5 for each)(7) Producer’s commitment (PC) 1.0 Years under NTS/25(8) (1) Except for fallow; 9 is considered the maximum number of different crops in the South of Brazil. (2)Ideal number of different vegetal species in the South of Brazil. (3)Except for grasses destined for hay and silage; 6 is considered the ideal number of grasses in the South of Brazil. (4)Base x 0.8, assuming that 80% of the area under NTS and 20% are tilled by terracing. (5)In 5 years. (6)4 is the maximum sum of the conservation compounds. (7)If not based on soil chemical analysis, zero. (8)25 is the maximum time of adoption of the NTS identified on the study region. For the chemical and physical analyses, five points spaced 30 m apart were sampled in each area, at 0-0.20 m soil depth, arranged in a transect according to the methodology described by Bartz et al. (2013). The samples were air dried, ground, and sieved to 2 mm. The chemical analyses were performed according to Tedesco et al. (1995). Determinations for pH in CaCl2, H+Al3+, Ca2++Mg2+, P, K+, and the granulometry and clay dispersed in water were performed according to Claessen (1997); and total organic carbon (TOC) was determined according to Walkley & Black (1934). The granulometry determination was carried out by the pipette method, with slow stir, using sodium hydroxide solution (1 N NaOH). For the water-dispersible clay, the same granulometry procedure was applied without the use of NaOH. The degree of clay dispersion (CD) was calculated by the ratio of water-dispersible clay to total clay. A multiple linear regression model was fitted using the method of least squares and the data collected from the 46 areas (40 agricultural areas and 6 native forests). The model’s normality was verified by the quantile-quantile plot, and Shapiro-Wilk’s test, at 5% of probability. To negate the effect of the unit of variables, the regression metric coefficients were standardized according to the formula β’k = βk × Sxk / Sy, in which: β’k is the normalized coefficient of the explanatory attribute K; βk is the metric coefficient of the explanatory attribute K; Sxk is the standard deviation of the explanatory attribute K; and Sy is the standard deviation of the attribute response. Based on the standardized coefficients, the contributions of explanatory attributes were ranked and compared according to confidence intervals of 95%. Forty agricultural areas were sampled, and the CD was compared in the 29 most contrasting ones in relation to the management and to the IQP analyzed per watershed, at 5% probability. The t-test (2 areas), Tukey’s test (3 to 9 areas), and Scott-Knott’s test (≥10 areas compared) were used to compare the degree of dispersion. The IQP scores, CD grades, and TOC contents of the different farm systems and native forests contrasted by analysis of variance (Anova) with unbalanced data, and, the means were compared by Scott-Knott’s test, at 5% probability. All analyses were performed in the R software (R Core Team, 2018). Results and Discussion All evaluated areas have their soil with heavy clay, except for one area, which fits into a clay textural class (FAO, 1988). The values of clay varied from 585 to 832.5 g kg-1 (Figure 1), which is a relatively small variation that exists in the region, as a consequence of the homogeneity of the parent material (basalt, from Serra Geral formation). There is a significant difference for the degree of clay dispersion (CD) between the Sanga Mineira, Facão Torto, and Arroio Fundo, Ajuricaba and Curvado watersheds (AAC) (Table 3). There was a significant difference for CD in the two areas evaluated in the Sanga Mineira watershed between the no-tillage with crop succession (NT) and no-tillage with soil disturbance (NTD) farm systems. The area with NT showed the highest IQP, the highest CD and K+, and even the lowest Ca2++Mg2+. In contrast, the area with NTD had the lowest CD and K+ and the highest Ca2++Mg2+ content. Figure 1. Textural triangle of the different evaluated areas from 0.0 to 0.20 m soil depth, in the West of Paraná state, Brazil, June and July, 2015. Table 3. No-tillage system participatory quality index (IQP), the degree of clay dispersion (CD), and soil chemical composition of the 29 evaluated areas and native forests (NF) of the different farm systems (FS), at 0.0-0.20 m soil depth, in the West of Paraná state, Brazil, June and July, 2015(1). Watershed Area FS(2) IQP* CD TOC pH H+Al3+ Ca2++Mg2+ K+ P % (g kg-1) (cmolc dm3) Sanga Mineira 1 NT 7.9 87a 11.96a 5.3a 3.65a 9.47b 0.64a 7.48a 2 NTD 7.5 73b 11.18a 5.6a 3.24a 11.39a 0.27b 4.99a NF 78 22.04 5.9 2.98 12.22 0.60 2.69 Toledo 3 NT 5.3 79a 15.11a 5.6a 3.86b 7.41b 0.50a 19.66a 4 NTS 8.1 78a 18.73a 5.3b 5.14a 7.34b 0.48a 9.30a 5 NT 8.9 72a 17.00a 5.7a 3.70b 7.97a 0.44a 22.22a 6 NT 8.6 84a 18.73a 5.2b 5.48a 7.26b 0.45a 8.31a 7 NT 8.9 77a 12.59a 5.0b 4.72a 5.85b 0.51a 18.72a 8 NT 7.6 78a 14.48a 4.7b 6.22a 4.81b 0.37a 18.64a 9 NT 7.8 73a 17.63a 5.2b 5.35a 8.14a 0.53a 22.15a NF 53 25.34 4.3 1.69 6.93 0.18 2.39 Buriti 10 NTD 8.3 86a 9.60b 5.1a 3.97a 7.80a 0.60a 16.28a 11 NT 7.4 79a 14.64ab 5.2a 4.43a 7.83a 0.76a 23.05a 12 NTS 8.4 77a 19.05a 5.4a 3.44a 8.37a 0.74a 12.48a NF 81 14.43 5.2 3.95 8.00 0.70 17.27 Pacuri 13 NT 7.0 80a 10.86a 5.3a 3.92a 8.30a 0.57a 26.87a 14 NT 8.5 85a 13.06a 5.1a 4.28a 8.25a 0.65a 9.16a 15 NTD 7.3 82a 10.39a 5.0a 4.68a 7.39a 0.41a 23.24a NF 82 11.96 5.2 4.30 7.82 0.61 18.02 Facão Torto 16 NT 6.7 80ab 11.49ab 5.4a 3.64b 8.47b 0.61a 12.00a 17 NT 6.9 78b 15.11a 6.1a 2.81b 10.68a 0.71a 25.97a 18 CS 4.1 88a 9.13b 4.6b 6.14a 6.00c 0.76a 26.39a 19 NT 6.1 84ab 10.07ab 5.5a 3.23b 7.75b 0.58a 18.79a NF 82 11.45 5.4 3.96 8.23 0.66 20.79 Arroio Fundo, Ajuricaba, and Curvado (AAC) 20 NT 7.8 65d 16.84a 4.6c 6.40b 6.82e 0.24d 25.42a 21 NT 7.6 78b 14.17a 5.3b 3.88c 10.25b 0.43c 5.30b 22 NT 5.8 70d 13.85a 5.3b 4.11c 9.71b 0.51c 5.63b 23 NT 4.4 78b 12.91a 4.6c 4.71c 6.06e 0.51c 8.54b 24 NT 7.0 73c 11.18b 4.0d 8.84a 6.15e 0.54c 33.61a 25 NTD 7.1 84a 14.01a 5.5b 3.64c 8.43c 0.96a 21.97a 26 CS 5.7 82a 9.44b 4.9c 4.42c 8.09c 0.08d 2.75b 27 NTS 6.3 76c 15.58a 5.2b 3.88c 9.14b 0.47c 28.82a 28 NT 5.5 89a 12.43b 5.2b 3.74c 7.75d 0.37c 14.75b 29 CS 4.7 83a 15.11a 5.9a 3.23c 17.23a 0.68b 2.97b NF 77 13.59 5.1 4.69 8.96 0.48 14.97 (1) Means followed by equal letters, in the columns, do not differ by the t-test (Sanga Mineira), Tukey’s test (Toledo, Buriti, Pacuri, and Facão Torto), and Scott-Knott’s test (AAC), at 5% probability. (2)NTS, no-tillage system; NT, no-tillage with crop succession; NTD, no-tillage with soil disturbance; and CS, conventional system. The multiple linear regression model was applied, since CD phenomena is affected by many factors, such as the charge sparsity of the cations in the exchange complex (Melo et al., 2020), pH, and point of zero charge (Chorom & Rengasamy, 1995), organic matter content, mineralogy (Melo et al., 2019), and phosphate adsorption. The analyses by multiple regression evidenced that TOC, Ca2++Mg2+ and H+Al3+ were negatively associated with CD, while K+ was positively associated with CD. Phosphorus and pH associations with CD were not significant (Figure 2). Figure 2. Standardized coefficients (axis x) of soil chemical properties for the degree of clay dispersion in 46 areas (40 farm systems and 6 native forests), in the West of Paraná state, Brazil, June and July, 2015. One area under NT and the area under CS in Facão Torto showed a significant difference regarding CD. The highest CD was observed in the area with CS, which showed the lowest IQP, besides the highest H+Al3+ and the lowest Ca2++Mg2+ and TOC levels. In contrast, the lowest CD was observed in the area with NT, which showed the highest IQP, Ca2++Mg2+ and TOC, and the lowest H+Al3+. Despite the low H+Al3+ in the NT area 17, Ca2+ and Mg2+ were high and probably enough to neutralize the particles’ electric field. In highly weathered soils, Ca and Mg have similar capacity to induce clay flocculation, despite their distinct charge sparsity, probably because the charge density of kaolinite (the main clay mineral in these soils) is low, according to Melo et al. (2020). These authors also show that this fact does not necessarily happen in soils with predominance of clay minerals with high-charge density. In the watersheds of Ajuricaba, Arroio Fundo, and Curvado (AAC), the highest CD in all the evaluated areas was observed in area 28 with NT, which showed 5.5 IQP and the lowest levels of TOC, H+Al3+, and Ca2++Mg2+ (Table 3). In areas 26 and 29 with CS, higher CD values than in other CS areas were observed. However, area 26 shows lower levels of TOC, Ca2++Mg2+, and K+ than area 29. In this case, despite the highest content of flocculant cations in area 26 (Ca2++Mg2+), the K+ content was higher than the area 20. Consequently, it was not possible to verify difference for CD between these areas. The lowest CD of watershed AAC was verified in area 20, with NT and with the highest values of IQP and H+Al3+, besides the lowest K+ content, which corroborates Nguetnkam & Dultz (2014), since these ions are considered as the main flocculating agents in the soil (Basga et al., 2018). H+ is a potential-determining ion and, consequently, it can favor the balance of charges in these soils, which results in higher-clay flocculation (Melo et al., 2020). As a trivalent cation, Al3+ enables the thickness reduction of the double electric layer of soil clay, decreasing the electrostatic repulsion of the particles (Chaves et al., 2001). Our results corroborate those reported by Igwe & Udegbunam (2008), who found that Ca2+ and H+Al3+ were the factors that most influenced CD (Figure 2). Despite the reduction of clay dispersion, high levels of potential acidity and Al3+ are considered negative for nutrient availability and root development in the soil and should be neutralized. Therefore, chemical correction should be adequately planned, especially for the dose to be applied. In the analyses of the 40 areas, a positive correlation between CD and K+, and a negative one with Ca2++Mg2+, H+Al3+, and TOC (p≤0.05) were observed (Figure 2). These results explain the obtained ones from CD for NF, which were close to or even above to those of some agricultural areas, a fact that hinders its adoption as an isolated conclusive indicator. The Ca2++Mg2+ contents were the most influential factors in the reduction of CD, followed by H+Al3+ and TOC. K+ is the factor that contributed most to the increase of CD. Phosphorus and pH did not contribute statistically with CD. In the watersheds assessed in the present study, the lowest CD values were verified in the areas with the highest levels of Ca2++Mg2+ and TOC, except for the AAC watershed. In this watershed, the lowest value of CD was observed in the area with the highest-TOC content. Farm systems with the highest-TOC contents show the lowest values of CD, once organic matter (OM) contributes significantly to soil aggregation (Basga et al., 2018). Farm systems with the highest levels of K+ showed the highest CD (Figure 2), which corroborates the observations by Nguetnkam & Dultz (2014), as verified in the present work in Sanga Mineira and AAC for the similarity found in watersheds with the same K+ content (Toledo, Buriti, and Pacuri). Spera et al. (2008) state that the thickness of the double electric layer is altered by the cation concentration and nature. Low-valence cations such as Na+ and K+ have a low capacity to neutralize the electric field generated by the particles, which intensifies the repulsive forces and facilitates dispersion. The increase of CD as a function of the higher levels of K+ shows its deleterious effect on the soil structure - a fact that has received little attention (Paradelo et al., 2013). The clay dispersion was similar for all farm systems (Table 4). Nonetheless, the lowest-IQP score was verified in CS, which proves the sensitivity of IQP to assess the quality of management (Nunes et al., 2020). A greater TOC content was observed in NTS and in native forests than in the other systems with farm management that underwent more disturbance. The OM is directly related to carbon stock and nutrient availability, soil structure maintenance and the microbiological activity (Martinez-Salgado et al., 2010). Higher levels of OM and Ca2+, along with the presence of other cations in the soil promote the flocculation and aggregation of negatively charged clay particles (Tavares Filho et al., 2010). Table 4. No-tillage system participatory quality index (IQP), degree of clay dispersion (CD), and total organic carbon (TOC) in the different farm systems and native forests, at 0.0-0.20 m soil depth, in the West of the Paraná state, Brazil, June and July, 2015(1). Farm system IQP(2) CD (%) TOC (g kg-1) No-tillage system (NTS) 7.60a 77.00 17.79a No-tillage with crop succession (NT) 7.14a 78.37 13.90b No-tillage with soil disturbance (NTD) 7.55a 81.25 11.30b Conventional system (CS) 4.83b 84.33 11.23b Native forest (NF) - 75.50 16.47a CV (%) 18.2 8.7 22.9 (1) Means followed by equal letters, in the columns, do not differ from each other by Scott-Knott’s test, at 5% probability. (2)IQP is an index to evaluate farm systems, and it is not applicable to native areas. Matias et al. (2012) state that the soil management causes changes in OM and soil physical properties. The incorporation of plant residues increases the clay dispersion (Igwe & Udegbunam, 2008), in comparison to the residues left on the soil, as it affects the dynamics of the OM, reducing the aggregation of the soil. Cardoso et al. (2013) state that the acceleration of the oxidation and reduction of stable OM reduces the biological activity. OM made it possible to monitor the changes of management quality and, consequently, of soil quality, which corroborates the findings of Shukla et al. (2006). In our study, the CS and NTD evaluated areas had their conditions aggravated by soil disturbance, which results from their lower-TOC contents. The lowest-TOC content may be attributed to the low level of OM entering in the farm systems, as observed in the no-tillage with crop succession and in conventional systems in the Buriti, Facão Torto, and AAC watersheds. In general, the soil chemical management was more important for the CD changes than soil plowing. This finding can be inferred from the high number of significant associations between CD and soil chemical properties (Figure 2), and from the small number of statistical differences between NT and CT areas (Table 3). However, the mechanical effect from the soil disturbance had less influence than the chemical management, as it can be observed in the Sanga Mineira watershed. The mechanical effect is manifested mainly by the intense disturbance of the soil, as in the conventional system in the Facão Torto watershed. However, areas under NTS showed the greatest TOC content, the main factor for nutrient availability, soil structure maintenance, and soil health sustainability (Cardoso et al., 2013). The aggregation results from the rearrangement, flocculation, and cementation of particles by inorganic and organic substances (Bronick & Lal, 2005). Aggregation is affected by OM due to the nature of the cations present in the soil and their charge sparsity (Melo et al., 2020), as well as to the interaction of polyvalent cations with humic OM and clay, soil mineralogy, the presence of organic acids, and the behavior of aluminum, depending on the pH of the soil solution (Rengasamy, 2018). Therefore, the negative mechanical effect of the soil disturbance on the clay dispersion can be partially neutralized by the chemical management of the soil and the adequate fertilization of the production system. However, Melo et al. (2019) have shown that the reduction of CD does not imply necessarily that soil structural stability was improved. Floccules formed by electrostatic attraction are ephemerous and can be easily disrupted. It was possible to verify the OM importance by the similarity between the NTS and NF, reflected in their greatest IQP score and lowest CD among the farm systems assessed. In order to ensure the agricultural sustainability, conservation practices should be prioritized to increase and maintain the OM and a minimum soil disturbance. As an essential component of soil fertility, OM contributes positively to soil chemical, physical, and biological properties, improving the productivity and quality of production systems (Kaschuk et al., 2010). The soil chemical properties had a greater influence on the CD than on the quality of the soil management system assessed by IQP, since, in fact, the intensive soil use interferes negatively with the soil chemical properties. Nevertheless, the effects of farm systems are more complex, depending on several factors. Even so, chemical management requires as much attention as soil tillage and crop rotation systems. Therefore, all factors capable to interfere with clay dispersion should be monitored because the greater the CD, the greater the risks of erosion and compaction processes, causing damage to soil quality and agricultural sustainability. In addition, it has been endorsed that the NTS, when fully adopted, can mitigate the negative impact of management on soil quality, making the system more balanced and sustainable (Cardoso et al., 2013). Conclusions The soil chemical properties have a greater influence on the clay dispersion than the different farm systems assessed. No-tillage system used in full shows the highest organic carbon content, which is similar to that of the evaluated native areas. The areas managed with conventional system and no-tillage with soil disturbance show the highest levels of clay dispersion and the lowest no-tillage system participatory quality index (IQP), in relation to the areas with no-tillage system used in full. The IQP was effective to distinguish the conventional system from the no-tillage system; this index agreed with extreme values of clay dispersion and total organic carbon; therefore, this tool can help farmers to monitor the management quality of their agricultural areas. Acknowledgments To Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), for financial support (Edital Universal 461484/2014); to Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Capes), for support, and master and doctoral scholarships (Finance code 001) to the first author; to Federação Brasileira de Plantio Direto e Irrigação (FEBRAPDP), Itaipu Binacional, and Parque Tecnológico Itaipu, for permitting the use of facilities that provided the conditions for this research; to the farmers, for the permission to obtain soil data from their properties; to the field team composed by Ana Carolina Polinarski Coqueiro, Tatiane Gorte, Caroline Laurini Tonetti, Alessandra Santos, Guilherme Cardoso, and Herlon Nadolny, who made this work possible. References BARTZ, M.L.C.; PASINI, A.; BROWN, G.G. Earthworms as soil quality indicators in Brazilian no-tillage systems. Applied Soil Ecology, v.69, p.39-48, 2013. 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