Soil quality indicators under management systems in a Quilombola community in the Brazilian Cerrado

Abstract

Soil management systems exert different effects on soil attributes, especially on the organic matter content, and, consequently, the soil aggregation. The aim of this study was to evaluate the impact of different land uses practiced by quilombola family farmers on water stable aggregates, glomalin and organic carbon in soil aggregates. Soil samples were collected at depths of 0-10 and 10-20 cm from areas cultivated under the following management systems: 1) conventional corn plantation (MA), 2) cultivation of citrus trees intercropped with annual crops (AC) (a conservationist approach), 3) pasture of Brachiaria (Urochloa spp.) (PA), and, as reference, an area of the Cerrado (CR) free of any anthropogenic interference. The studied areas were evaluated in a completely randomized design, with five replications, in a subplot scheme. The plots were the management systems and the subplots the depths. Soil macro-aggregates were predominant at both depths and the aggregate stability indices were higher than 90 % for all management systems. Total organic carbon in the two aggregate classes (micro and macroaggregates) correlated with the MWD (mean weight-diameter), but not with the easily extractable glomalin (EEG) related soil protein. Soil micro and macro-aggregates, EEG and MWD discriminated management systems and are important soil quality indicators. The carbon content in both micro-aggregates (C-MIC) and macro-aggregates (C-MAC) of the intercropped system (AC) was higher than in the CR. The soil attributes that best separated the areas were C-MIC, MWD and EEG in macro-aggregates for the depth of 0-10 cm, and EEG in micro-aggregates, together with MWD and C-MAC for the depth of 10-20 cm.

Evaluation relationship of glomalin and aggregate morphometry in mined areas in the recovery process brasilian Amazon (RO)

“…In most of the mines and reference areas, there was a tendency to increase the concentrations of GRSP-T and GRSP-EE in soil samples with larger diameter aggregates, and this same pattern can be observed when assessing the contents of GRSP in the aggregates, regardless of the development stages of the areas (Table 5), confirming that each type and size of aggregate has its own significance in terms of soil functioning and fertility (Peche Filho, 2018). This fact is consistent with previous studies (Fokom et al, 2012;Zhang et al, 2014;Zhu et al, 2019;Silva et al, 2019;Liu et al, 2020), in which macroaggregates have the greatest capacity for accumulation of glomalin and are highly correlated with aggregate stability due to the contribution of soil microbiota (Deng et al, 2018;Yao et al, 2019;Zhu et al, 2017;Liu et al, 2020). Silva et al, (2019) discuss that in macroaggregates, the action of fungal mycelium provides greater stability of aggregates in water (Zou et al, 2015), and this fact may be an indication of greater synthesis of PSRG (Kohler-Milleret et al, 2013).…”

“…This fact is consistent with previous studies (Fokom et al, 2012;Zhang et al, 2014;Zhu et al, 2019;Silva et al, 2019;Liu et al, 2020), in which macroaggregates have the greatest capacity for accumulation of glomalin and are highly correlated with aggregate stability due to the contribution of soil microbiota (Deng et al, 2018;Yao et al, 2019;Zhu et al, 2017;Liu et al, 2020). Silva et al, (2019) discuss that in macroaggregates, the action of fungal mycelium provides greater stability of aggregates in water (Zou et al, 2015), and this fact may be an indication of greater synthesis of PSRG (Kohler-Milleret et al, 2013). GRSP is reported as one of the main sources of C in the soil, playing a crucial role in the capture and COS (Singh et al, 2017;Kumar et al, 2018), as well as maintaining soil fertility (Preger et al 2007).…”

Drive Mechanisms of Soil Quality Changes in Peri-Urban Areas

“…First, the study on SQ and its driving mechanism has been explored a lot. On the one hand, some studies have proposed various calculation methods to quantitatively evaluate SQ, such as comprehensive SQ index [16][17][18], multi-variable index Kriging interpolation [19], and dynamic model method [20,21]. On the other hand, some researchers have uncovered the influencing factors of SQ from the aspects of land use pattern [22,23], farming system [24] and agricultural management measures [25].…”

Selection of Indicators to Discriminate Soil Tillage Systems and to Assess Soil Quality in a Red Latosol

“…This analysis has an advantage over the principal component analysis (PCA) because CDA maximizes the variation between and minimizes the variation within groups of individuals and PCA ignores the structure of groups of the dataset [17]. The CDA has been used to discriminating organic and conventional coffee production systems [18], find the indicators that most discriminate soil management systems [19], distinguish among pesticides which had a prominent role for the segregation of classes [20], and select indicators that discriminated well three agroecosystems [21].…”