Seasonal effect of land use type on soil absolute and specific enzyme activities in a Brazilian semi-arid region

Catena 172 (2019) 397–407

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Seasonal effect of land use type on soil absolute and specific enzyme activities in a Brazilian semi-arid region

T

Érica de Oliveira Silvaa, Erika Valente de Medeirosa, Gustavo Pereira Dudaa, ⁎ Mario Andrade Lira Juniorb, , Michel Brossardc, Julyana Braga de Oliveiraa, Uemeson José dos Santosa, Claude Hammeckerc a

Federal Rural University of Pernambuco, Bom Pastor Av. s/n, 55292-270 Garanhuns, Brazil Federal Rural University of Pernambuco, Rua Dom Manuel de Medeiros, s/n, Dois Irmãos, 52171-900 Recife, Brazil c IRD/UMR Eco & Sol, place Pierre Viala, 2, 34060 Montpellier, France b

A R T I C LE I N FO

A B S T R A C T

Keywords: Semiarid β-Glucosidase Urease Phosphatase Enzyme per unit of soil organic carbon Enzyme per unit of microbial biomass carbon

Tropical environments are considerable contributors to overall soil carbon loss to the atmosphere. Land use effects on soil chemical attributes have been well documented mainly in humid environments; however, less attention has been paid to the changes in soil enzymatic activities in dry ecosystems that is a sensitive indicator in ecological processes, due to its importance in soil dynamics and microbial activity. The present study is part of interdisciplinary project that investigated the effect of land cover type and seasonal variation on absolute and specific enzymatic activities per unit of soil organic carbon (SOC) and per microbial biomass carbon (MBC). We assessed five different land use type (Tropical dry forest-TDF, protected area with Angico –ANA, protected area with Ipê-TAB, Scrub area-SCR and agricultural area with maize-M) and five areas of each land use in three layers: 0–0.05, 0.05–0.10 and 0.10–0.20 m. The samples were collected at rainy season 1 in April 2014 (RS1), dry season October 2015 (DS) and rainy season 1 April 2016 (RS2). The conversion of the preserved area provided a reduction in absolute enzymatic activities, especially in the SCR and M. The reductions were of 76% for βglucosidase, 95% for urease and 72% for acid phosphatase. The specific enzymatic activities per unit of MBC increased with the change of soil use, except in M. The enzymatic activity per unit of SOC in the TDF area was higher in relation to the other areas evaluated, except for specific activity of acid phosphatase. The land use type influenced the absolute and specific soil enzyme activities, but not show a clear trend of seasonal effect.

1. Introduction The conversion from natural ecosystem to agricultural usage contributes approximately 17% of global greenhouse gas (GHG) emissions (Lybbert and Sumner, 2012). In this sense, tropical environments have been one of the major contributor to carbon emission to the atmosphere (Earles et al., 2012), and understanding the effects of land use changes becomes critical. However land cover changes effects are much less understood in seasonally dry tropical ecosystems, (Blackie et al., 2014; Hoekstra et al., 2005). These ecosystems are among the most endangered forests (Lepers et al., 2005) with few areas under protected legal status (FAO, 2010; Green et al., 2013). South America accounts for more than half (54.2%) of the remaining Tropical dry forests, including the two most extensive contiguous areas, one in southeastern Bolivia, Paraguay and northern Argentina (Särkinen et al., 2011) and one in the northeast Brazil, which

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covers most of the semiarid area and is the largest remaining area of dry tropical forest in the world (de Almeida-Cortez et al., 2016) Studies that help clarify soil C and N dynamics in semi-arid regions are highly important since they affect GHG emissions and are affected by anthropogenic changes in soil cover, as well as the climatic conditions (Sampaio et al., 2012; Sousa et al., 2012). Nevertheless there is limited knowledge of the extent and magnitude of these impacts in relation to the management systems practiced and the seasonal variations on the dynamics of the biogeochemical cycles (Campo and Merino, 2016), although. Some studies have indicated seasonal effects (Campo and Merino, 2016; Cuevas et al., 2013; Marín-Spiotta and Sharma, 2013; Ribeiro et al., 2016). Enzymatic activities can play a significant role in the influence of soil dynamics and microbial functions (de Medeiros et al., 2017)and changes in microbial community through human intervention are known to affect soil enzymatic activities, since the former are the main

Corresponding author at: Microbiology Laboratory, Federal Rural University of Pernambuco, CEP, Recife, Brazil. E-mail addresses: [email protected] (M.A.L. Junior), [email protected] (M. Brossard).

https://doi.org/10.1016/j.catena.2018.09.007 Received 19 February 2018; Received in revised form 27 August 2018; Accepted 4 September 2018 0341-8162/ © 2018 Elsevier B.V. All rights reserved.

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drivers for the latter (Bowles et al., 2014; de Medeiros et al., 2015; Smith et al., 2015). On the other hand, as extracellular enzymes are immobilized in the humic and mineral fractions of the soil, their activity is also conditioned by factors such as vegetation composition, management practices, soil pH, moisture content and soil temperature, aggregate stability and compaction (Benitez et al., 2005; Raiesi and Beheshti, 2014). Thus, the absolute enzymatic activity does not allow to verify if the observed effects are due to soil microbial biomass and organic matter contents or enzymatic activity per se (Raiesi and Beheshti, 2014; Wang et al., 2012) Thus, several studies have used specific enzymatic activities (global enzymatic activity in relation to soil microbial biomass (CBM) or soil organic carbon (SOC) per unit of C to compare biochemical activity in soils with different organic C levels (Trasar-Cepeda et al., 2008). These variables have been shown to be a good indicator of microbial change in tropical forests, including depth (Weintraub et al., 2013) as shown by several studies (de Medeiros et al., 2015; de Medeiros et al., 2017; Raiesi and Beheshti, 2014; Wang et al., 2012). For example, in dry tropical environments, specific enzyme activity was more sensitive to conversion of forests to agriculture than to global (de Medeiros et al., 2015). Many studies evaluate the conversion of natural ecosystems especially in humid environments. However, there is still a gap on the understanding of the conversion of drier areas using sensitive tools. This is especially true for the Brazilian semiarid, which is important both due to its extension and to it strong seasonal leaf loss, in response to the very irregular rainfall (Maia et al., 2007). Thus, the objective of the present study was to evaluate the impact of conversion of dry tropical forests to anthropic areas with different types of land cover and seasonal variation on absolute and specific enzymatic activities in tropical areas of the Brazilian semi-arid region.

•

white jurema (Piptadenia stipulacea), quince (Croton sonderianus), mallow (Waltheria indica), arboreal, Juá (Zizyphus joazeiro) and herbaceous plants. Conventional farming (M - 7°57′15.4″S, 38°23′49.1″W): Cultivated with maize (Zea mays) conventionally from 2005 to 2015, but under fallow due to the severe drought from 2011 to 2013. The farmer has applied an unknown amount of sheep dung over the cultivated time.

2.2. Soil physical attributes The soil texture analysis (sand, clay and silt contents) was performed in a hydrometer using sodium hexametaphosphate as the dispersing agent, according to Loveland and Whaley (1991) (Table 1). 2.3. Soil chemical attributes, organic carbon content (SOC) and microbial biomass carbon (MBC) The following chemical attributes were determined: pH in water (1:2.5), available P, exchangeable K+, Al3+, Ca2+ and Mg2+. The P and K+ were extracted through Mehlich−1, and K+ was quantified by flame photometry while P by the colorimetry method. The nitrogen was measured with the combustion method at a temperature of 925 °C in an elemental CHNS-O analyzer (Perkin Elmer PE-2400). The SOC content was determined through hot oxidation with potassium dichromate, according to Yeomans and Bremner (1988) (Table 2). The soil microbial biomass carbon (MBC) content was determined through the irradiation method (Mendonça, 2005), followed by extraction with 0.5 M K2SO4 and the carbon content in the extracts was determined through the colorimetric method (Bartlett and Ross, 1988) (Table 2). 2.4. Absolute and specific enzyme activities

2. Materials and methods The soil urease (URE EC. 3.5.1.5) activity was determined using urea as substrate, according to Kandeler and Gerber (1988), β-glucosidase (Beta EC 3.2.1.21) in ρ-nitrophenyl-β-D-glucoside substrate according to Eivazi and Tabatabai (1988) and acid phosphatase (Pac EC. 3.1.3) in ρ-nitrophenyl phosphate according to Eivazi and Tabatabai (1977). All product absorbances were measured in spectrophotometer (Libra S22, Biochrom, Cambridge, England). The specific activities were obtained by the division by SOC (AcostaMartínez et al., 2003) and MBC (Raiesi and Beheshti, 2014).

2.1. Study area and soil samples The study area is in Serra Talhada - Pernambuco State, Northeastern Brazil (7°59′31″S and 38°17′59″W), with an altitude of 430 m (Fig. 1). The climate in the region is BSh Köeppen (Alvares et al., 2013) semiarid hot climate, with average annual temperature of 28 °C, average yearly rainfall precipitation of 600 mm, concentrated from January to April. According to the Brazilian classification system the soil is Luvisol chromic (Solos, 2013). Soil samples were collected in April 2014 (rainy season 1, RS1), October 2015 (Dry season, DS) and April 2016 (rainy season 2, RS2) in areas with different land cover type, namely: Tropical dry forest (TDF), protected area with Angico (ANA), protected area with Ipê (TAB), Scrub area (SCR) and agricultural area with maize (M). Prior to soil samples five repetition plots of 0.4 ha were established under each land cover and soil samples were collected from each plot in the layers: 0–0.05, 0.05–0.10 and 0.10–0.20 m. Triplicate soil samples were collected from each plot and layer under each land cover typ. The all areas are located next to each other. Five areas were selected:

2.5. Data analysis Data were analyzed through ANOVA followed by the Student Newman-Keul's test at 5% probability when appropriate. The Pearson correlations between measured soil variables were determined across the land uses and soil depths. 3. Results 3.1. Absolute enzyme activities The TDF area showed the highest absolute enzymatic activities in the first two layers (0–0.05 and 0.05–0.10 m) and in rainy periods, except for acid phosphatase activity. The lowest absolute activities of Beta and URE were observed in the conventional farming area (M). The TDF area showed the highest enzymatic activity of Beta in the first two layers, in all evaluated periods, except in the dry period (Fig. 2). In the last layer evaluated (0.10–0.20 m) the TDF area did not present significant difference in relation to the other systems of land use. Overall all layers that the largest reductions in Beta activity occurred in the SCR and M areas. The area of Maize presented a reduction in Beta activity of 48, 54 and 64% in RS1, DS and RS2, respectively,

• Tropical dry forest - Caatinga (TDF – 7°57′47.0″S, 38°23′01.5″W): reasonably preserved, but used for uncontrolled grazing; • Angico Forest (ANA - 7°57′07.5″S, 38°23′56.1″W): it has been cov• •

ered with Angico (Anadenanthera sp.) since 1978. Before 1978 it was cultivated with cotton (Gossypium hirsutum) and palm (Opuntia ficusindica); Ipê Forest (TAB - 7°57′10.1″S, 38°23′45.5″W): cultivated with buffel grass (Cenchrus ciliaris) and cotton (Gossypium hirsutum). It underwent natural regeneration from 1998 by Ipê (Tabebuia chrysotricha); Scrub (SCR - 7°57′16.2″S, 38°23′45.4″W): covered with Scrub for > 20 years. Predominance of black jurema (Mimosa tenuiflora), 398

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Fig. 1. Map showing the limits of Pernambuco State, Brazil and location of soil samples collected.

for RS1, DS and RS2 higher in the TDF than in M, respectively. Nevertheless, there is a reversal of results in the DS, that is, the area of M presented the highest activity of URE for the 0.10–0.20 m layer. Overall, though URE activity was highest for TDF, followed by TAB > ANA > SCR > M. Compared to the TDF area, all use systems present reductions of 21, 29, 42 and 59% for TAB, ANA, SCR and M, respectively. The absolute Pac activity was between 1.3 and 7.7; 2.0–3.8; and 1.5–4.8 μg PNP g−1 soil h−1, in layers 0–0.05, 0.05–0.10 and 0.10–0.20 m, respectively (Fig. 4), with the lower values at the most superficial layer, in RS1 and RS2, in the areas of SCR and M, presenting a reduction in relation to the TDF area of 83 and 72% in RS1; and 40 and 46% in RS2, respectively. In the DS usage systems of use did not influence Pac activity. The absolute activities of all enzymes evaluated were sensitive to seasonal variations, but it was not possible to perceive a clear seasonal tendency except for Beta activity up to 0.10 m from the surface (Fig. 2), where it is observed that the smallest activities in the areas in succession (ANA, TAB and SCR) occurred in RS2. While for URE only in the area of M it is possible to observe that the smaller activities occurred in the rainy periods (Fig. 3). Pac showed very unstable activities performance in the evaluated periods (Fig. 4).

Table 1 Granulometric composition and textural class of soils under different usage histories in Serra Talhada, Pernambuco, Brazil. Areas

Sand (%)

Silt (%)

Clay (%)

Texture

TDF ANA SCR TAB M

0–0.05 m 59.0 76.5 68.6 59.8 53.8

25.4 15.4 17.9 24.9 30.5

15.6 8.1 13.5 15.3 15.7

Sandy Sandy Sandy Sandy Sandy

loam loam loam loam loam

TDF ANA SCR TAB M

0.05–0.10 m 58.4 72.1 62.3 56.4 51.7

23.2 17.1 16.5 26.2 30.6

18.4 10.8 21.2 17.4 17.7

Sandy Sandy Sandy Sandy Sandy

loam loam clay loam loam loam

TDF ANA SCR TAB M

0.10–0.20 m 56.2 71.0 61.7 54.7 55.1

20.6 16.8 16.9 27.9 25.8

23.2 12.2 21.4 17.4 19.1

Sandy Sandy Sandy Sandy Sandy

clay loam loam clay loam loam loam

*TDF - Caatinga; ANA - Angico Forest; SCR - Scrub; TAB - Ipê Forest; M conventional farming.

3.2. Specific enzymatic activities per unit of MBC (EA/MBC) and SOC (EA/SOC) comparing to the TDF area. The SCR area showed reductions in Beta enzyme activity of 37, 45 and 76% in RS1, DS and RS2, respectively, for the same comparison. In general, the absolute enzymatic activity of urease was significantly higher (P b 0.05) in the TDF area, especially in the 0–0.05 m layer (Fig. 3), with a similar patter to those of BETA, being 74, 35 and 95% in the 0–0.05 m layer and 68, 12 and 64% in the 0.05–0.10 m layer

The specific activity of β-glucosidase per unit of MBC was significantly affected by soil use and seasonality systems (Fig. 5A, B and C), with succession areas generally having greater Beta specific activity in relation to both TDF and M. The highest Beta specific activity from microbial biomass was 56% in the ANA than in TDF, indicating a higher physiological efficiency of the microorganisms present in. Most areas showed a higher activity in the second rainy season (RS2). 399

400

pH (H2O) P (mg dm−3) Ca2+ Mg2+ K+ Na+

pH (H2O) P (mg dm−3) Ca2+ Mg2+ K+ Na+ H + Al SB CTC SOC (g kg−1) N (g kg−1) CBM (μg g−1)

pH (H2O) P (mg dm−3) Ca2+ Mg2+ K+ Na+ H + Al SB CTC SOC (g kg−1) N (g kg−1) CBM (μg g−1)

pH (H2O) P (mg dm−3) Ca2+ Mg2+ K+ Na+ H + Al SB CTC SOC (g kg−1) N (g kg−1) CBM (μg g−1)

7.3 ± 0.2 34.6 ± 8.4 10.1 ± 1.8 2.1 ± 0.2 0.7 ± 0.3 0.1 ± 0.0

6.7 ± 0.2 46.8 ± 13.2 8.3 ± 1.0 3.5 ± 0.6 0.7 ± 0.2 0.2 ± 0.0

0–0.5 m 7.1 ± 0.2 79.8 ± 8.7 8.5 ± 1.3 2.5 ± 0.7 0.5 ± 0.2 0.1 ± 0.0

TDF RS2

M

6.7 (0.1) 3.9 (8.0) 5.7 (2.0) 4.1 (1.0) 0.3 (0.1) 0.0 (0.0) 1.4 (0.3) 10.1 (2.0) 11.5 (2.3) 9.2 (3.1) 2.1 (0.4) 245.4 ± 70.8

6.3 ± 0.6 105.8 ± 14.6 6.6 ± 1.4 2.0 ± 0.3 0.6 ± 0.2 0.1 ± 0.0

ANA

7.0 ± 0.4 14.4 ± 8.5 6.5 ± 0.8 3.9 ± 0.6 0.3 ± 0.1 0.1 ± 0.0 1.2 ± 0.3 10.7 ± 0.7 12.0 ± 0.6 11.2 ± 2.6 1.3 ± 0.3 283.8 ± 49.6

6.9 ± 0.1 28.5 ± 11.1 5.4 ± 1.0 2.0 ± 0.4 0.6 ± 0.1 0.2 ± 0.3

SCR

6.8 ± 0.13 18.2 ± 7.4 6.2 ± 1.4 4.7 ± 91.0 0.3 ± 0.1 0.0 ± 0.0 1.1 ± 0.4 11.1 ± 2.0 12.2 ± 2.1 9.0 ± 0.5 0.9 ± 0.2 281.3 ± 53.8

7.2 ± 0.7 24.3 ± 14.8 6.0 ± 1.4 4.8 ± 0.8 0.3 ± 0.1 0.2 ± 0.3 1.2 ± 0.5 11.3 ± 1.7 12.5 ± 1.5 12.2 ± 3.2 2.3 ± 0.5 356.2 ± 80.3

6.8 ± 0.2 11.2 ± 4.7 6.7 ± 0.4 3.2 ± 0.4 0.6 ± 0.2 0.2 ± 0.0 1.9 ± 0.3 10.7 ± 0.6 12.6 ± 0.5 9.3 ± 0.8 1.3 ± 0.16 458.2 ± 71.6

7.3 ± 0.2 15.5 ± 4.8 6.8 ± 0.7 2.9 ± 0.5 0.7 ± 0.3 0.3 ± 0.0 2.0 ± 0.4 10.8 ± 1.4 12.8 ± 1.3 12.0 ± 1.3 1.4 ± 0.3 572.6 ± 55.4

6.5 ± 0.1 29.9 ± 17.2 4.2 ± 1.6 3.9 ± 0.7 0.2 ± 0.1 0.0 ± 0.0 1.6 ± 0.4 8.3 ± 1.1 10.0 ± 1.4 13.7 ± 1.5 1.8 ± 0.5 412.2 ± 78.3

7.0 ± 0.5 32.7 ± 24.2 6.5 ± 0.4 3.7 ± 0.6 0.4 ± 0.1 0.1 ± 0.0 1.2 ± 0.2 10.7 ± 0.8 12.0 ± 1.0 13.4 ± 3.6 1.4 ± 0.5 174.4 ± 62.1

0.10–0.20 m 7.1 ± 0.2 0.3 ± 0.2 5.0 ± 2.5 2.9 ± 0.9 0.2 ± 0.1 0.0 ± 0.0 1.2 ± 0.2 8.2 ± 2.5 9.4 ± 2.7 12.8 ± 4.3 1.9 ± 0.3 300.6 ± 55.9

6.7 (0.2) 2.0 (1.5) 5.0 (1.8) 3.4 (1.1) 0.3 (0.1) 0.0 (0.0) 1.4 (0.4) 8.8 (1.6) 10.2 (2.0) 10.6 (4.9) 1.9 (0.2) 257.3 ± 53.3

6.4 ± 0.2 32.5 ± 13.7 3.8 ± 0.7 3.4 ± 0.4 0.2 ± 0.0 0.0 ± 0.0 1.3 ± 0.3 7.5 ± 0.9 8.8 ± 1.2 14.7 ± 2.0 1.6 ± 0.2 297.8 ± 81.9

7.2 ± 0.8 21.8 ± 10.0 6.0 ± 0.8 4.8 ± 0.7 0.5 ± 0.2 0.1 ± 0.2 1.0 ± 0.4 11.4 ± 2.8 12.4 ± 2.7 11.9 ± 3.5 2.0 ± 0.4 395.1 ± 71.3

0.05–0.10 m 7.3 ± 0.1 2.5 ± 3.1 5.2 ± 2.6 2.4 ± 0.9 0.3 ± 0.1 0.0 ± 0.0 0.7 ± 0.3 7.9 ± 2.4 8.6 ± 2.7 13.6 ± 2.7 2.3 ± 0.4 258.5 ± 46.4

6.9 ± 0.4 30.3 ± 17.4 5.7 ± 0.8 3.7 ± 0.5 0.4 ± 0.1 0.0 ± 0.0 1.4 ± 0.2 9.8 ± 1.2 11.2 ± 1.3 13.9 ± 3.2 1.4 ± 0.4 366.1 ± 53.3

TDF

7.7 ± 0.3 30.1 ± 13.4 8.3 ± 0.9 3.0 ± 0.3 1.3 ± 0.2 0.3 ± 0.1 1.7 ± 0.3 12.8 ± 1.1 14.5 ± 1.2 20.0 ± 2.3 2.6 ± 0.7 576.2 ± 70.3

6.8 ± 0.1 14.2 ± 3.2 4.7 ± 1.3 3.1 ± 0.4 0.4 ± 0.0 0.0 ± 0.0 1.3 ± 0.3 8.1 ± 2.2 9.5 ± 2.3 15.0 ± 4.0 2.2 ± 0.5 259.2 ± 45.6

M

6.6 ± 0.1 34.5 ± 9.3 3.6 ± 0.7 3.5 ± 0.2 0.3 ± 0.1 0.0 ± 0.0 1.5 ± 0.3 7.4 ± 0.5 8.9 ± 0.6 18.3 ± 2.2 1.0 ± 0.1 281.3 ± 43.0

TAB

0–0.5 m 7.4 ± 0.2 7.3 ± 4.6 6.3 ± 1.6 2.9 ± 0.7 0.3 ± 0.1 0.0 ± 0.0 0.7 ± 0.1 9.5 ± 2.0 10.2 ± 2.0 29.9 ± 8.5 3.1 ± 0.9 804.7 ± 135.8

DS

TAB

Cmolc·dm−3

Cmolc·dm−3

Cmolc·dm−3

SCR DS

ANA

RS1

TDF

Table 2 Chemical and microbial biomass attributes of Luvisol of studied areas in Serra Talhada, Pernambuco, Brazil.

7.1 ± 0.3 102.2 ± 4.4 8.3 ± 1.4 2.5 ± 0.3 0.8 ± 0.1 0.2 ± 0.0

TAB

6.5 ± 0.2 82.5 ± 14.3 5.7 ± 1.3 2.6 ± 0.3 0.9 ± 0.2 0.3 ± 0.1 2.5 ± 0.4 9.5 ± 1.9 11.9 ± 2.1 8.5 ± 1.1 1.8 ± 0.2 357.9 ± 77.7

6.5 ± 0.1 108.3 ± 24.0 6.4 ± 1.8 2.7 ± 0.5 0.9 ± 0.3 0.2 ± 0.1 2.2 ± 0.5 10.2 ± 2.3 12.3 ± 2.1 12.2 ± 1.0 1.5 ± 0.5 386.5 ± 72.9

6.7 ± 0.2 90.4 ± 12.5 6.6 ± 1.6 2.5 ± 0.4 1.9 ± 0.5 0.3 ± 0.2 3.4 ± 0.3 11.3 ± 1.9 14.7 ± 1.9 19.6 ± 5.7 1.8 ± 0.6 296.3 ± 106.1

ANA

6.6 ± 0.1 79.6 ± 9.2 8.0 ± 0.5 2.9 ± 0.1 0.7 ± 0.2 0.3 ± 0.4

M

6.9 ± 0.2 8.4 ± 2.2 6.8 ± 1.0 3.1 ± 0.7 0.4 ± 0.2 0.1 ± 0.0 2.9 ± 0.4 10.4 ± 1.8 13.3 ± 1.8 5.4 ± 0.8 0.5 ± 0.1 283.4 ± 109.4

6.9 ± 0.1 13.8 ± 6.8 6.0 ± 1.1 2.9 ± 0.6 0.4 ± 0.2 0.1 ± 0.0 1.4 ± 0.8 9.4 ± 1.9 10.7 ± 1.4 6.3 ± 1.5 0.5 0.0 208.1 ± 43.7

7.0 ± 0.2 36.0 ± 16.3 4.6 ± 0.8 2.4 ± 0.5 0.7 ± 0.3 0.2 ± 0.1 1.0 ± 0.3 7.8 ± 1.5 8.8 ± 1.3 11.7 ± 2.7 0.5 ± 0.1 327.7 ± 68.2

SCR

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6.7 ± 0.1 54.2 ± 16.1 9.2 ± 1.0 3.6 ± 0.8 0.5 ± 0.2 0.1 ± 0.0 1.9 ± 0.7 13.5 ± 1.5 15.3 ± 2.0 12.0 ± 1.4 1.1 ± 0.2 382.4 ± 89.0 6.8 ± 0.1 23.3 ± 6.4 10.0 ± 0.9 3.8 ± 0.5 0.4 ± 0.1 0.2 ± 0.0 2.7 ± 0.7 14.4 ± 1.1 17.1 ± 1.5 7.1 ± 1.0 1.2 ± 0.2 296.8 ± 74.9

0.05–0.10 m 7.0 ± 0.3 51.3 ± 26.2 8.8 ± 1.8 2.4 ± 0.8 0.7 ± 0.2 0.2 ± 0.0 3.1 ± 0.5 12.1 ± 2.4 15.1 ± 2.9 14.1 ± 2.7 1.7 ± 0.5 267.0 ± 27.1

0.10–0.20 m 7.0 ± 0.1 70.1 ± 20.8 11.1 ± 0.0 2.2 ± 0.4 0.7 ± 0.1 0.3 ± 0.1 4.1 ± 0.4 14.3 ± 0.4 18.3 ± 0.6 8.6 ± 0.8 1.6 ± 0.2 369.0 ± 43.9

pH (H2O) P (mg dm−3) Ca2+ Mg2+ K+ Na+ H + Al SB CTC SOC (g kg−1) N (g kg−1) CBM (μg g−1)

pH (H2O) P (mg dm−3) Ca2+ Mg2+ K+ Na+ H + Al SB CTC SOC (g kg−1) N (g kg−1) CBM (μg g−1) 7.1 ± 0.1 11.6 ± 6.8 7.4 ± 1.2 2.1 ± 0.5 0.5 ± 0.2 0.1 ± 0.0 1.9 ± 0.7 10.1 ± 1.4 11.9 ± 1.8 8.3 ± 2.0 1.7 ± 0.3 243.1 ± 45.0

7.2 ± 0.3 48.4 ± 6.1 8.0 ± 1.1 2.5 ± 1.3 0.8 ± 0.3 0.2 ± 0.0 2.1 ± 0.3 11.5 ± 2.1 15.1 ± 1.8 14.0 ± 2.3 2.3 ± 0.9 326.2 ± 58.4

2.1 ± 0.3 13.0 ± 1.7 15.0 ± 1.7 24.8 ± 5.0 2.6 ± 0.4 604.7 ± 138.2

RS2

TDF

6.6 ± 0.3 95.7 ± 10.0 6.0 ± 0.5 2.4 ± 0.4 0.4 ± 0.1 0.1 ± 0.0 1.6 ± 0.6 9.0 ± 0.8 10.5 ± 0.7 9.7 ± 1.6 2.1 ± 0.3 51.4 ± 17.2

6.6 ± 0.2 112.8 ± 15.0 5.5 ± 1.0 1.9 ± 0.3 0.6 ± 0.2 0.1 ± 0.0 2.5 ± 0.7 8.2 ± 1.3 10.6 ± 1.3 8.6 ± 0.3 1.8 ± 0.3 263.5 ± 56.2

1.6 ± 0.3 9.3 ± 1.6 11.0 ± 1.7 15.0 ± 3.2 1.7 ± 0.31 294.8 ± 25.3

ANA

6.2 ± 0.6 8.7 ± 2.6 7.4 ± 1.5 2.8 ± 0.4 0.3 ± 0.1 0.1 ± 0.0 2.6 ± 0.6 10.6 ± 1.8 13.2 ± 1.3 4.6 ± 0.8 0.7 ± 0.1 68.3 ± 15.5

6.7 ± 0.2 13.7 ± 6.7 6.0 ± 1.2 2.2 ± 0.3 0.6 ± 0.2 0.1 ± 0.0 3.4 ± 0.4 9.0 ± 1.4 12.3 ± 1.5 5.3 ± 0.8 0.7 ± 0.1 60.6 ± 12.6

1.3 ± 0.3 8.2 ± 1.6 9.8 ± 1.7 9.9 ± 1.6 0.7 ± 0.1 145.3 ± 16.0

SCR

7.2 ± 0.2 65.7 ± 4.5 9.3 ± 2.7 2.7 ± 0.4 0.6 ± 0.1 0.2 ± 0.0 1.7 ± 0.3 12.7 ± 3.1 14.4 ± 3.4 8.1 ± 3.2 1.6 ± 0.5 332.3 ± 81.1

7.0 ± 0.2 72.5 ± 11.0 9.4 ± 2.4 2.9 ± 1.5 0.7 ± 0.1 0.2 ± 0.0 2.1 ± 0.5 13.1 ± 4.0 15.1 ± 4.1 12.5 ± 2.0 2.0 ± 0.4 271.7 ± 48.1

1.7 ± 0.4 11.7 ± 1.5 13.4 ± 1.7 11.9 ± 3.8 2.2 ± 0.8 252.1 ± 33.4

TAB

6.7 ± 0.3 66.8 ± 15.3 10.3 ± 2.2 3.3 ± 0.7 0.4 ± 0.2 0.2 ± 0.1 1.7 ± 0.2 14.3 ± 2.7 16.0 ± 2.6 6.2 ± 0.7 1.1 ± 0.3 127.3 ± 22.7

6.7 ± 0.2 70.5 ± 17.3 9.5 ± 0.8 3.2 ± 0.2 0.6 ± 0.3 0.2 ± 0.0 2.0 ± 0.5 13.5 ± 0.9 15.5 ± 1.1 8.6 ± 1.8 0.9 ± 0.2 128.0 ± 20.4

1.4 ± 0.5 11.9 ± 0.7 13.2 ± 0.6 7.7 ± 0.8 0.6 ± 0.1 161.8 ± 28.6

M

*SB - sum of bases; CEC - cation exchange capacity; SOC - soil organic carbon; TN – total nitrogen; MBC - microbial biomass carbon. Means followed by ± numbers represent standard deviation (SD). TDF - Caatinga; ANA Angico Forest; SCR - Scrub; TAB - Ipê Forest; M - conventional farming. RS1 - rainy season (April/2014), DS - dry period (October/2015); and RS2 - rainy season (April/2016).

2.2 ± 0.2 12.6 ± 1.5 14.8 ± 1.5 13.9 ± 0.8 1.1 ± 0.1 281.3 ± 71.0

M

2.9 ± 0.6 11.6 ± 1.8 14.5 ± 2.3 16.7 ± 3.3 4.0 ± 2.2 322.8 ± 78.6

DS

TAB

H + Al SB CTC SOC (g kg−1) N (g kg−1) CBM (μg g−1)

Table 2 (continued)

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Fig. 2. Enzymatic activity of β-glucosidase in the soil in the 0–0.05 m (A), 0.05–0.10 m (B) and 0.10–0.20 m (C) layer, as a function of soil use and seasonality. Vertical bars indicate variation. Means followed by the same capital letter do not differ between use systems and averages followed by the same lowercase letter do not differ between the sampling periods, by Student Newman-Keuls test at 5% significance. TDF – Tropical dry forest; ANA - Angico Forest; SCR - Scrub; TAB - Ipê Forest; M - conventional farming. RS1 - rainy season (April/2014), DS - dry period (October/2015) and RS2 - rainy season (April/2016).

Fig. 3. Enzymatic activity of urease in soil in the 0–0.05 m (A), 0.05–0.10 m (B) and 0.10–0.20 m (C) layer, due to soil use and seasonality. Vertical bars indicate variation. Means followed by the same capital letter do not differ between use systems and averages followed by the same lowercase letter do not differ between the sampling periods, by Student Newman-Keuls test at 5% significance. TDF – Tropical dry forest; ANA - Angico Forest; SCR - Scrub; TAB - Ipê Forest; M - conventional farming. RS1 - rainy season (April/2014), DS - dry period (October/2015) and RS2 - rainy season (April/2016).

Fig. 4. Enzymatic activity of acid phosphatase in the soil in the 0–0.5 m (A), 0.05–0.10 m (B) and 0.10–0.20 m (C) layer, due to soil use and seasonality. Vertical bars indicate variation. Means followed by the same capital letter do not differ between use systems and averages followed by the same lowercase letter do not differ between the sampling periods, by Student Newman-Keuls test at 5% significance. TDF – Tropical dry forest; ANA - Angico Forest; SCR - Scrub; TAB - Ipê Forest; M - conventional farming. RS1 - rainy season (April/2014), DS - dry period (October/2015) and RS2 - rainy season (April/2016).

The enzymatic activity of β-glucosidase per unit of SOC was little affected by changes in soil use and seasonality. Beta-specific activity in the TDF area was significantly higher only in the dry period (DS) (Fig. 6A, B and C). Evaluating the 0–0.20 m layer in a general way, the specific activity per unit of Beta SOC in the native vegetation area (TDF) was 18% higher than in the M area. In relation to the areas of succession TDF area was 16 and 13% larger than the areas of TAB and SCR. The ANA area showed a specific activity per unit of Beta SOC very close to the native area, being 4% higher. Changes in function of the evaluated periods show little or no variation. The specific activity per unit of SOC of urease was influenced by changes in soil use and seasonality (Fig. 6D, E and F). The area of native vegetation presented in general greater activity in relation to the area

The specific enzymatic activity of urease per unit of MBC was generally more expressive in the succession areas (Fig. 5D, E and F). Considering the 0–0.20 m layer and all the periods sampled, the preserved area showed an EA/MBC ratio of 19, 30 and 86% lower than the ANA, TAB and SCR areas, respectively, and 21% than the Conventional farming (M) area, with the highest values found in RS2 for most of the evaluated areas. The specific activity of the acid phosphatase per MBC unit of the natural preservation area in relation to the other soil use systems presented lower or near activity (Fig. 5G, H and I). Our results indicate that conversion of forest to cullable areas did not reduce the proportion of acid phosphatase activity per MBC unit. The TDF area presented 66, 68, 77 and 122% lower results compared to ANA, M, TAB and SCR areas. 402

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Fig. 5. Specific β-glucosidase activity per unit of MBC (μg product g−1 MBC) in the soil in the 0–0.05 m (A), 0.05–0.10 m (B) and 0.10–0.20 m (C); Specific urease activity per unit of MBC (μg product g−1 MBC) in soil in the 0–0.05 cm (D), 0.05–0.10 m (E) and 0.10–0.20 m (F); specific acid phosphatase activity per unit of MBC (μg product g−1 MBC) in the soil in the 0–0.05 m (G), 0.05–0.10 m (H) and 0.10–0.20 m (I) layer as a function of soil use and seasonality. Vertical bars indicate variation. Means followed by the same capital letter do not differ between use systems and averages followed by the same lowercase letter do not differ between the sampling periods, by Student Newman-Keuls test at 5% significance. TDF – Tropical dry forest; ANA - Angico Forest; SCR - Scrub; TAB - Ipê Forest; M - conventional farming. RS1 - rainy season (April/2014), DS - dry period (October/2015) and RS2 - rainy season (April/2016).

activity on a SOC basis for the two deepest layers. There were no correlations between pH ou P and Pac activities.

cultivated with maize in the rainy periods. Considering the 0–0.20 m layer, this difference reaches about 60% in both RS1 and RS2. Acid phosphatase activity per SOC unit indicates that conversion of the native vegetation area did not inhibit acid phosphatase production per SOC unit. In general, successive areas, ANA, SCR, TAB and M presented specific enzymatic activity of acid phosphatase per SOC unit, 3, 52, 38 and 44% more than the area under native vegetation, respectively (Fig. 6G, H and I). Seasonality variations did not show a clear trend, however, it is possible to observe that for most areas, as observed in the specific activity per unit of MBC, the second rainy season (RS2) also provided better conditions for acid phosphatase activity per SOC unit. More significant correlations were found for the layer-specific, rather than overall, correlations for both absolute and specific enzymatic activities (Table 3). Absolute enzymatic activities had positive correlations with SOC and MBC, except for the deepest layer for which there was negative correlation between SOC and URE. Most specific activities on SOC and MBC basis had negative correlations with SOC and MBC for all soil layers. N contents had significant correlation with PAC specific and Beta absolute activities, but not with URE, except for URE specific

4. Discussion 4.1. Absolute enzyme activities The conversion of areas under native vegetation into cultivated areas commonly promotes the reduction of enzymatic activities (Raiesi and Beheshti, 2015). The results obtained in this study indicated, in general, that the absolute enzymatic activities of the areas that underwent the soil conversion process showed a reduction. In a tropical dry environment, Vinhal-Freitas et al. (2017) evaluated the impacts of land use systems in areas under native vegetation, pasture and sugarcane cultivation (Saccharum officinarum) and found that the conversion of native area to area under cultivation promoted the reduction of absolute enzymatic activity of β-glucosidase, corroborating the present study. This reduction can be related to the lower concentration of C, common in areas that underwent the conversion process, thus causing a lower microbial activity and consequently of the enzymes, since these

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Fig. 6. Specific β-glucosidase activity per unit of SOC (μg product g−1 SOC) in the soil in the 0–0.05 m (A), 0.05–0.10 m (B) and 0.10–0.20 m (C); specific urease activity per unit of SOC (μg product g−1 SOC) in soil in the 0–0.05 m (D), 0.05–0.10 m (E) and 0.10–0.20 m (F); specific acid phosphatase activity per unit of SOC (μg product g−1 SOC) in the soil in the 0–0.05 m (G), 0.05–0.10 m (H) and 0.10–0.20 cm (I) layer as a function of soil use and seasonality. Vertical bars indicate variation. Means followed by the same capital letter do not differ between use systems and averages followed by the same lowercase letter do not differ between the sampling periods, by Student Newman-Keuls test at 5% significance. TDF – Tropical dry forest; ANA - Angico Forest; SCR - Scrub; TAB - Ipê Forest; M - conventional farming. RS1 - rainy season (April/2014), DS - dry period (October/2015) and RS2 - rainy season (April/2016).

that the area under cultivation, especially the area under cultivation of Opuntia oleracea showed absolute urease activity superior to the area under native vegetation, in contrast to the data mentioned above. The higher activities of the URE in areas with higher organic matter input are expected, since their activity is closely related to organic matter decomposition (Vinhal-Freitas et al., 2017). According to Bowles et al. (2014) the high activity of the URE is related to the supply of C to the microbiota, since this can cause a limitation of N to the microbial community and consequently increase the production of enzymes to mineralize N. It was expected that the soils with the lowest concentration of P available would have had higher acid phosphatase activity. However, considering all the data there is no clear relation between Pac activity and P levels present in soils. The results suggest that Pac activity may have been more influenced by other factors such as nutrient availability, microbial community structure, microbial biomass physiology, root exudates, moisture and temperature (Burns et al., 2013; Hendriksen et al., 2016). The results found in this study for Pac differ from the study by de Medeiros et al. (2017) in areas of natural regeneration in an area of Caatinga, where they recorded the highest acid phosphatase activity in the late regeneration area, attributing this result to the greater plant

are mainly of microbial origin (Raiesi and Beheshti, 2015; Stone et al., 2014). de Medeiros et al. (2015) evaluating different systems of land use observed that the area under monoculture showed absolute activity of the Beta superior to the area under native vegetation and this result resulted in the presence of higher SOM content and the management adopted in the area. The stability of the Beta activity in the evaluated periods, in the 0.10–0.20 m layer, may be related to the lower availability of C in this layer, since this enzyme is closely linked to SOC (de Medeiros et al., 2015) levels and these tend to decrease in depth due to the lower SOM contribution (Moscatelli et al., 2012; Tischer et al., 2015). As for absolute urease activity, Vinhal-Freitas et al. (2017) found higher URE activity in the soil of the native vegetation area compared to the area planted with sugarcane, the reduction was approximately 60%. Raiesi and Beheshti (2015) in a study in the northeastern region of Iran also found that conversion of native forest to agricultural areas led to a reduction of 28% in absolute urease activity. de Medeiros et al. (2017) evaluating stages of natural regeneration in a tropical dry environment in the northeast of Brazil found that URE activity was 100% higher in the late regeneration area compared to the early regeneration area, in the 0–0.05 layer m. On the other hand, de Medeiros et al. (2015) study in a tropical dry region of Pernambuco, Brazil, verified 404

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Table 3 Pearson (r) correlation coefficients between different soil properties and enzymatic activities of Luvisol of studied areas in Serra Talhada, Pernambuco, Brazil. pH

P

Ca2+

Mg2+

K+

Na+

H + Al

SB

CEC

SOC

N

MBC

Clay

Sand

Silt

0–0.20 m Pac Pac (EA/MBC) Pac (EA/SOC) Beta Beta (EA/MBC) Beta (EA/SOC) URE URE (EA/MBC) URE (EA/SOC)

0,25 −0,31 −0,17 0,31 −0,15 0,12 0,26 −0,21 −0,02

−0,08 0,08 −0,15 0,17 0,38 0,33 0,18 0,09 0,07

0,27 −0,04 0,18 0,03 −0,20 −0,12 0,20 −0,07 0,09

0,14 −0,02 0,36 −0,24 −0,30 −0,14 −0,31 −0,31 −0,16

−0,07 −0,13 −0,23 0,50 0,36 0,59 0,31 0,07 0,16

−0,25 −0,09 −0,10 0,13 0,11 0,31 0,15 0,05 0,20

−0,22 0,12 0,14 −0,05 0,04 0,01 0,25 0,38 0,47

0,08 −0,04 0,21 −0,01 −0,17 −0,02 0,19 −0,04 0,19

0,00 0,00 0,22 −0,02 −0,13 −0,01 0,24 0,09 0,31

0,46 −0,35 −0,55 0,84 0,28 0,47 0,44 −0,20 −0,28

0,19 −0,26 −0,46 0,50 0,18 0,26 0,22 −0,24 −0,28

0,45 −0,62 −0,32 0,56 −0,26 0,28 0,27 −0,51 −0,21

−0,04 0,13 0,46 −0,51 −0,50 −0,50 −0,20 0,04 0,20

−0,12 0,05 −0,25 0,31 0,46 0,36 0,12 0,18 0,02

0,20 −0,17 0,00 −0,05 −0,26 −0,12 −0,02 −0,28 −0,19

0–0.05 m Pac Pac (EA/MBC) Pac (EA/SOC) Beta Beta (EA/MBC) Beta (EA/SOC) URE URE (EA/MBC) URE (EA/SOC)

0,43 −0,16 0,17 0,35 −0,38 0,02 0,17 −0,25 −0,03

−0,18 0,39 0,11 0,01 0,48 0,37 0,03 0,19 0,20

0,40 0,00 0,18 0,32 −0,24 −0,02 0,29 −0,21 0,01

−0,04 −0,08 0,21 −0,34 −0,45 −0,25 −0,50 −0,63 −0,55

−0,11 0,06 −0,11 0,43 0,58 0,58 −0,02 −0,10 −0,07

−0,35 0,01 −0,18 0,04 0,29 0,27 −0,12 −0,06 −0,06

−0,24 0,06 −0,29 0,35 0,62 0,35 0,30 0,30 0,26

0,00 −0,07 0,01 0,19 −0,10 0,08 0,16 −0,24 −0,03

−0,07 −0,04 −0,08 0,27 0,10 0,17 0,22 −0,11 0,05

0,60 −0,24 −0,11 0,80 0,11 0,22 0,47 −0,03 0,05

0,35 −0,19 −0,05 0,51 0,04 0,20 0,28 −0,10 0,03

0,68 −0,29 0,08 0,63 −0,26 0,09 0,25 −0,36 −0,17

0,21 −0,01 0,31 −0,26 −0,69 −0,38 −0,15 −0,38 −0,18

−0,19 0,01 −0,30 0,30 0,70 0,43 0,20 0,46 0,28

0,16 −0,01 0,27 −0,30 −0,66 −0,43 −0,21 −0,48 −0,32

0.05–0.10 m Pac Pac (EA/MBC) Pac (EA/SOC) Beta Beta (EA/MBC) Beta (EA/SOC) URE URE (EA/MBC) URE (EA/SOC)

−0,26 −0,26 −0,39 0,36 0,03 0,19 0,29 −0,05 0,05

−0,06 −0,23 −0,27 0,10 −0,12 0,05 0,31 −0,04 0,11

0,18 −0,07 −0,30 0,13 0,21 −0,10 0,31 0,03 −0,04

0,32 −0,14 0,07 0,18 −0,05 0,29 −0,08 −0,25 −0,14

−0,17 −0,09 −0,16 0,28 0,01 0,27 0,76 0,29 0,50

−0,25 −0,23 −0,21 0,29 −0,19 0,34 0,49 0,07 0,33

−0,14 0,42 0,32 −0,34 0,06 −0,23 0,47 0,66 0,55

0,17 −0,12 −0,15 0,13 0,01 0,06 0,40 0,05 0,12

0,10 0,03 −0,03 0,01 0,03 −0,02 0,50 0,25 0,28

0,45 −0,55 −0,77 0,53 −0,28 −0,12 0,02 −0,60 −0,60

0,02 −0,53 −0,71 0,42 −0,29 −0,08 −0,02 −0,49 −0,52

0,15 −0,74 −0,54 0,60 −0,64 0,32 0,14 −0,63 −0,25

−0,16 0,33 0,42 −0,20 0,26 0,04 0,12 0,39 0,32

−0,01 −0,01 0,09 −0,10 −0,22 −0,07 −0,09 −0,04 0,05

0,12 −0,20 −0,37 0,26 0,12 0,07 0,04 −0,19 −0,26

0.10–0.20 m Pac Pac (EA/MBC) Pac (EA/SOC) Beta Beta (EA/MBC) Beta (EA/SOC) URE URE (EA/MBC) URE (EA/SOC)

0,09 −0,47 −0,14 −0,12 −0,34 −0,32 0,20 −0,53 −0,08

−0,04 0,38 −0,06 0,39 0,52 0,48 0,05 0,14 -0,06

0,25 −0,08 0,37 −0,43 −0,29 −0,17 0,33 −0,02 0,29

0,56 0,00 0,57 −0,27 −0,24 −0,01 0,03 −0,10 0,12

−0,22 −0,15 −0,05 0,37 0,00 0,67 0,51 0,02 0,34

−0,16 −0,01 0,07 0,28 0,05 0,62 0,47 0,11 0,35

−0,23 −0,13 0,15 −0,05 −0,13 0,35 0,51 0,21 0,55

0,26 −0,04 0,49 −0,38 −0,26 0,05 0,48 0,05 0,44

0,14 −0,07 0,44 −0,32 −0,25 0,15 0,55 0,11 0,53

−0,08 −0,31 −0,71 0,52 0,01 −0,24 −0,69 −0,64 -0,85

−0,50 −0,09 −0,83 0,66 0,35 0,23 −0,31 −0,30 -0,56

−0,04 −0,88 −0,38 0,30 −0,66 −0,03 0,00 −0,81 -0,22

−0,04 −0,24 0,22 −0,49 −0,42 −0,30 0,29 0,05 0,32

−0,26 0,30 −0,29 0,53 0,52 0,42 −0,30 0,19 -0,21

0,39 −0,21 0,21 −0,32 −0,35 −0,32 0,17 −0,30 0,01

Correlation coefficients (r) with bold-face are significant at P ≤ 0.05. SB - sum of bases; CEC - cation exchange capacity; SOC - soil organic carbon; TN – total nitrogen; MBC - microbial biomass carbon; Pac – acid phosphatase; Pac (EA/MBC) – acid phosphatase specific enzymatic activities per unit of MBC; Pac (EA/SOC) – acid phosphatase specific enzymatic activities per unit of SOC; Beta – β-glucosidase; Beta (EA/MBC) – β-glucosidase specific enzymatic activities per unit of MBC; Beta (EA/SOC) – β-glucosidase specific enzymatic activities per unit of SOC; URE – urease; URE (EA/MBC) – urease specific enzymatic activities per unit of MBC; URE (EA/SOC) – urease specific enzymatic activities per unit of SOC.

4.2. Specific enzymatic activities per unit of MBC (EA/MBC) and (EA/ SOC)

diversity and longer regeneration time that provided greater input of organic residues, stimulating biomass microbial. In another study by de Medeiros et al. (2015), evaluating the conversion of dry tropical forests to agricultural areas, verified that conversion did not reduce phosphatase activity with monoculture or crop consortium. Raiesi and Beheshti (2015) observed that the activity of acid phosphatase in an area of native vegetation in Iran was similar to agricultural crop area, relating this result to the narrow range of pH values of the evaluated soils (7.28–7.57). The lower activity of Beta observed in rainy periods suggests that there may be a lower SOC decomposition in that period, since this enzymatic activity is an indicator of organic matter decomposition (Burns et al., 2013; Singh et al., 2017). Lebrun et al. (2012) evaluating the temporal variations of the enzymatic activities in Luvisol under different systems of use observed that only the activity of the URE varied a lot over time.

The conversion of forest areas to cultivated areas increased the enzymatic activity per unit of MBC in the soil, except for the area of conventional farming that presented increase only for acid phosphatase. This result can be an indication that the enzymatic activity after the modifications of the soil use decreases at a lower rate than the microbial biomass or the production and release of enzymes by the microorganisms of the soil under cultivation is higher (Raiesi and Beheshti, 2014), and this microbial community is considered to be more metabolically active (Lagomarsino et al., 2011). de Medeiros et al. (2015) results, on the other hand, differ from ours since they found higher enzymatic activity per unit of MBC in forest area compared to cultivated areas, indicating a higher degree of microbial activity in this area. The enzymatic activity per unit of MBC reflects the structure and

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The absence of correlation between P content, pH and Pac activity differs from other papers (Acosta-Martinez et al., 2018; Wang et al., 2012), but the correlation found with SOC and MBC indicate these may be more important for Pac activity as also found by other authors (Bowles et al., 2014; Burns et al., 2013; Hendriksen et al., 2016).

composition of the microbial community and this is conditioned to the combined effects of different pH, quantity and quality of SOM, aggregation and agricultural management (Raiesi and Beheshti, 2015) or influenced by intracellular enzymatic activity (Nannipieri et al., 2002). Agreeing with Lagomarsino et al. (2011), the results suggest that in spite of the environmental disturbance occurring both in the succession areas (ANA, SCR and TAB) and in the area under cultivation (M), the microbial community has good physiological capacity and is more metabolically active, when compared to the soil of the forest area. The higher enzyme production per unit of MBC may also be related to the availability of soil nutrients (Raiesi and Beheshti, 2014), or the greater turnover of microbial biomass that may lead to the synthesis of more enzymes than in soils of forest areas (Beheshti et al., 2012; Raiesi and Beheshti, 2015), while a third possible explanation is a change in the composition of the microbial community, as suggested by Kivlin and Treseder (2014). In contrast to the observed results for EA/MBC, observing EA/SOC, it was observed that in general the TDF area had higher AE/SOC in relation to the other areas, except Pac EA/SOC, mainly in the layers of (0.05–0.10 and 0.10–0.20 m). These changes in soil enzyme activities can occur independently of changes in soil organic carbon content (Raiesi and Beheshti, 2014). Despite the a higher EA/SOC of the preserved area, it is possible to observe that in some periods and strata evaluated the areas in succession and under cultivation showed an increase in the enzymatic activity per unit of SOC, mainly acid phosphatase, indicating that the reduction of SOC is greater than in enzymatic activity (Raiesi and Beheshti, 2014; Raiesi and Beheshti, 2015). These results corroborate those found by de Medeiros et al. (2015), they verified that Pac EA/SOC in the cultivated area was almost triple the preserved area. These increases in the EA/SOC may be associated with the separation of soil aggregates after cultivation, thus releasing enzymes that were trapped and immobilized (Beheshti et al., 2012; Raiesi and Beheshti, 2014; Raiesi and Beheshti, 2015), increasing the contact between soil organic matter and soil enzymes, promoting SOC losses through the enzymatic action (Allison and Jastrow, 2006). According to (Raiesi and Beheshti, 2014) the SOC in cultivated soils is reduced at a higher rate than the enzymatic activity, thus raising the EA/SOC, due to SOC losses, mainly in the most labile manner. In addition, the conversion of forest areas into cultivated areas may lead to pressures in soil microorganisms due to disturbance and environmental stresses, leading to enzymatic enrichment (Sinsabaugh et al., 2008; Wang et al., 2012). Even with the increase of EA/SOC ratios in the areas in succession and under cultivation, the preserved area showed to be more balanced and with enzymatic activity per unit of more expressive enzymatic SOC, except for acid phosphatase, which can also be attributed to a humid fraction containing extracellular enzymes bound to the colloids (Raiesi and Beheshti, 2014; Raiesi and Beheshti, 2015).

5. Conclusions Our results indicate that the impact of conversion of dry tropical forests to anthropic areas with different land cover types on soil absolute enzyme activities and chemical and microbiological attributes is clear to different cover type, but not show a clear trend of seasonal effect. While absolute enzyme activities showed, as usually expected, reduction due to cultivation, when evaluate the specific activities per unit of SOC and MBC, the effects of land use type are less clear cut, and may indeed be higher than the found in the preserved area, indicating a physiologically active microbial community in anthropic areas of the Brazilian semi-arid region. Acknowledgements We thank fellowships and grants from CNPq (306401/2015-0, 483287/2013-0, 401896/2013-7, 306980/2013-4), CAPES and FACEPE (APQ-0223-5.01/15; APQ-0419-5.01/15, APQ-0453-5.01/15) which financed the research, as well as the owner of Buenos Aires Farm for allowing us to conduct the research in his property. Appendix A. Supplementary data Supplementary data associated with this article can be found in the online version, at https://doi.org/10.1016/j.catena.2018.09.007. These data include the Google map of the most important areas described in this article. References Acosta-Martínez, V., Zobeck, T., Gill, T., Kennedy, A., 2003. Enzyme activities and microbial community structure in semiarid agricultural soils. Biol. Fertil. Soils 38, 216–227. Acosta-Martinez, V., Cano, A., Johnson, J., 2018. Simultaneous determination of multiple soil enzyme activities for soil health-biogeochemical indices. Appl. Soil Ecol. 126, 121–128. Allison, S.D., Jastrow, J.D., 2006. Activities of extracellular enzymes in physically isolated fractions of restored grassland soils. Soil Biol. Biochem. 38, 3245–3256. de Almeida-Cortez, J.S., Tavares, F.M., Schulz, K., Pereira, R.d.C.A., Cierjacks, A., 2016. Floristic survey of the caatinga in areas with different grazing intensities, Pernambuco, Northeast Brazil. JEAP 1, 43–51. Alvares, C.A., et al., 2013. Köppen's climate classification map for Brazil. Meteorol. Z. 22, 711–728. Bartlett, R.J., Ross, D.S., 1988. Colorimetric determination of oxidizable carbon in acid soil solutions. Soil Sci. Soc. Am. J. 52, 1191–1192. Beheshti, A., Raiesi, F., Golchin, A., 2012. Soil properties, C fractions and their dynamics in land use conversion from native forests to croplands in northern Iran. Agric. Ecosyst. Environ. 148, 121–133. Benitez, E., Sainz, H., Nogales, R., 2005. Hydrolytic enzyme activities of extracted humic substances during the vermicomposting of a lignocellulosic olive waste. Bioresour. Technol. 96, 785–790. Blackie, R., et al., 2014. Tropical Dry Forests: The State of Global Knowledge and Recommendations for Future Research. CIFOR. Bowles, T.M., Acosta-Martínez, V., Calderón, F., Jackson, L.E., 2014. Soil enzyme activities, microbial communities, and carbon and nitrogen availability in organic agroecosystems across an intensively-managed agricultural landscape. Soil Biol. Biochem. 68, 252–262. Burns, R.G., et al., 2013. Soil enzymes in a changing environment: current knowledge and future directions. Soil Biol. Biochem. 58, 216–234. Campo, J., Merino, A., 2016. Variations in soil carbon sequestration and their determinants along a precipitation gradient in seasonally dry tropical forest ecosystems. Glob. Chang. Biol. 22, 1942–1956. Cuevas, R.M., Hidalgo, C., Payán, F., Etchevers, J.D., Campo, J., 2013. Precipitation influences on active fractions of soil organic matter in seasonally dry tropical forests of the Yucatan: regional and seasonal patterns. Eur. J. For. Res. 132, 667–677. Earles, J.M., Yeh, S., Skog, K.E., 2012. Timing of carbon emissions from global forest clearance. Nat. Clim. Chang. 2, 682. Eivazi, F., Tabatabai, M., 1977. Phosphatases in soils. Soil Biol. Biochem. 9, 167–172. Eivazi, F., Tabatabai, M., 1988. Glucosidases and galactosidases in soils. Soil Biol.

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