Advertisement

Biological and chemical attributes of soils under forest species in Northeast Brazil

  • Olmar Baller Weber
  • Maria Catia Barroso da Silva
  • Cristiane Figueira da SilvaEmail author
  • João Alencar de Sousa
  • Carlos Alberto Kenji Taniguch
  • Deborah dos Santos Garruti
  • Ricardo Espindola Romero
Open Access
Original Paper
  • 111 Downloads

Abstract

Timber forests contribute to the sustainable development of the biomes in tropical regions. The aim of this study was to evaluate the biological and chemical properties of the soil as a consequence of the cover with native and non-native species in the Acaraú basin, a transition area from the coast to the Brazilian semi-arid region. Areas planted with four native species (Anadenanthera colubrina, Astronium fraxinifolium, Handroanthus impetiginosus, Colubrina glandulosa) and three exotic species (Acacia mangium, Casuarina equisetifolia, Eucalyptus urophylla) plus a non-forested agricultural area were evaluated for organic carbon contents, and microbial and chemical soil properties. The levels of soil organic carbon were highest in A. colubrina and C. equisetifolia plantations. Low basal soil respiration was observed but the microbial biomass was particularly low in the non-forested area. In the C. equisetifolia, E. urophylla, and H. impetiginosus plantations, elevated soil metabolic quotients were found. The A. colubrina and H. impetiginosus plantations had the highest levels of easily extracted-glomalin related soil protein. Tree species affect concentrations of essential nutrients and the biological quality of the soil in different ways. They can also improve the biological and chemical properties of the soil in the coastal plains of tropical regions.

Keywords

Soil quality Brazilian timber species Eucalyptus Acacia Casuarina 

Introduction

Brazil has large areas of forest and the greatest diversity of plants (Beech et al. 2017). Reforestation has recently reached 7.8 million hectares (Moreira et al. 2017), the wood from which is used in the pulp and paper and energy industries. The plantations are concentrated in the Atlantic Forest region and in the Cerrado biome, where species of Eucalyptus (5.7 million ha), Pinus (1.6 million ha) and other genera (0.6 million ha) (IBÁ 2017) have been planted. There was practically little use of native species. The success of these plantations is linked to favorable environmental conditions for intensive forestry, the adaptability of the introduced species and the abundance of available land.

In the Brazilian northeast where the Caatinga biome (dry tropical forest) is found, there is significant anthropogenic pressure to transform land into agriculturally productive, non-forested areas (Silva et al. 2018). The region has low soil organic carbon stocks (Bernoux et al. 2002), making these productive areas vulnerable to soil fertility loss (Ferreira et al. 2014). This has led to recommendations that conservation practices should be adopted to aid regeneration (Medeiros et al. 2017) using tree plantations, including native species such as Handroanthus impetiginosus (Mart. ex DC.) Mattos (Fonseca Filho et al. 2017), Anadenanthera colubrina (Vell.) Brenan (Monteiro et al. 2006), and other hardwood species (Lucena et al. 2007; Ramos et al. 2008; Fernandes et al. 2017). Forest coverage enables changes in the physical (Martinkoski et al. 2017), chemical and biological attributes of soil (Gomes et al. 2012; Silva et al. 2015; Chandra et al. 2016; Medeiros et al. 2017), and can modify microenvironments (Joly et al. 2017), thus affecting the sustainability of forest ecosystems.

Chemical indicators and soil organic carbon (SOC) (Li et al. 2018a), in addition to biological indicators such as microbial biomass activity (Cheng et al. 2013; Liu et al. 2018), and glomalin-related soil protein (GRSP) (Silva et al. 2014), have been successfully used in the evaluation of soil quality in forest ecosystems. Soil microbial biomass (SMB) plays an important role in biogeochemical cycles, as it is a significant source of enzymes responsible for the transformation of soil organic matter (SOM) (Medeiros et al. 2017). Although it is only a small proportion of SOM, it constitutes an important labile fraction functioning as an early indicator of changes in soil organic carbon stability resulting from alterations in land use and management (Li et al. 2018b). It is believed that modifications in soil microbial biomass could be the result of alterations in organic matter inputs and the immobilization of C and N during the decomposition process (Plaza et al. 2004; Li et al. 2018a).

Glomalin is a glycoprotein produced by arbuscular mycorrhizal (AM) fungi, (components of cellular walls of hyphae and spores) (Wright et al. 1996; Rillig 2004; Driver et al. 2005), liberated in the soil after the decomposition and senescence of these fungi (Driver et al. 2005). There is a direct relationship between GRSP, organic carbon content, and the stability of soil aggregates (Gispert et al. 2013; Rotter et al. 2017), and this affects nutrient cycles, water flow, microbial activities and plant development (Six and Paustian 2014) contributing to the sustainability of agroecosystems.

In light of the interest of public and private reforestation institutions, and the need for sustainable management of plantations on coastal plains in the semiarid tropics, four native species and three introduced species were evaluated in this study to determine their impact on soil biological and chemical attributes in the Acaraú basin, a transition area from the coast to the Brazilian semiarid region. Productive areas in the coastal zone are often vulnerable to soil degradation (Cunha et al. 2010; Mota and Valladares 2011), and forest plantations could be a viable option for agribusinesses in the region.

Materials and methods

Study area and description of the species

The study of the seven species was carried out on an experimental area (3.6 ha) in the Lower Acaraú basin, approximately 30 km from the Atlantic coast (coordinates 3°27′06″S and 40°08′48″W). According to the Köppen climate classification system, the climate is Aw (tropical, with a dry winter) (Alvares et al. 2014). Annual rainfall reaches nearly 950 mm, with more intense rainfall between the months of February and June; annual evaporation may exceed 1500 mm. Due to irregular rain distribution and high temperatures during the year (Fig. 1), plants may easily experience hydric stress.
Fig. 1

Average air temperatures and monthly rainfall recorded in the Lower Acaraú Basin, State of Ceara (Brazil), during the forest soil evaluation period

Analysis of the soil profile under Acacia mangium Willd. shows Dystrophic Sandy Gray Argisol developed from the ‘Formação Barreiras’ (Tertiary-Quaternary) formed by pre-littoral tableland sediments, with a Master Bt (iluvial mineral horizon with concentration of clay translocated from the upper horizons) below 1.3 m (Table 1). It may be assumed that this type of soil occurs under the other canopies evaluated here, and it is notable that Grayish Argisol has been reported in the coastal tablelands of Brazil’s northeast (Bezerra et al. 2015), as well as some agricultural areas of the Acaraú basin.
Table 1

Morphological description of horizons, granulometric aspects and chemical analyzes of the Dystrophic Sandy Gray Argisol

Horizons

Depth (m)

Morphological descriptions

Munsell chart color

Observation

Ap1

0–0.10

Very dark grayish brown

(2.5Y 3/2)

Sand, very friable, slightly plastic and non-sticky, smooth and clear transition

Ap2

0.10–0.27

Dark grayish brown

(2.5Y 4/2)

Sand, very friable, non-plastic and non-sticky, smooth and gradual transition

EA

0.27–0.49

Light olive brown

(2.5Y 5/3)

Sand, very friable, non-plastic and non-sticky, smooth and gradual transition

E1

0.49–0.87

Light olive brown

(2.5Y 5/3)

Sand, very friable, non-plastic and non-sticky, smooth and gradual transition

E2

0.87–1.15

Light yellowish brown

(2.5Y 6/3)

Loamy sand, very friable, non-plastic and non-sticky, wavy and gradual transition

BE

1.15–1.29

Pale yellow

(2.5Y 7/3)

Sandy loam, very friable, slightly plastic and slightly sticky, irregular and abrupt transition

Bt1

1.29–1.84

Light gray

(2.5Y 7/2)

Sandy loam, very friable, slightly plastic and slightly sticky, smooth and gradual transition

Bt2

1.84–2.05+

Light gray

(2.5Y 7/2)

Sandy loam, very friable, slightly plastic and slightly sticky

 

Granulometry (%)

Clay dispersed in water (%)

Sand

Silt

Clay

Ap1

0–0.1

92.44

1.87

5.69

3.47

Ap2

0.1–0.27

93.54

0.57

5.89

5.19

EA

0.27–0.49

90.75

2.09

7.16

7.24

E1

0.49–0.87

89.08

1.58

9.33

8.57

E2

0.87–1.15

85.75

2.25

12.00

11.56

BE

1.15–1.29

79.43

2.25

18.32

17.40

Bt1

1.29–1.84

79.58

1.86

18.56

16.84

Bt2

1.84–2.05+

77.72

2.91

19.37

17.02

  

pH in water (ratio 1:2.5)

Sorption complex (mmolc kg−1)

SOC (g kg−1)

Ca2+

Mg2+

K+

Na+

Ap1

0–0.1

5.4

15.1

6.3

0.5

0.5

9.1

Ap2

0.1–0.27

5.8

6.3

3.3

0.5

0.4

2.9

EA

0.27–0.49

6.3

5.2

2.8

0.8

0.6

1.8

E1

0.49–0.87

6.1

3.6

3.9

0.4

0.5

1.1

E2

0.87–1.15

5.2

2.2

3.4

0.6

0.7

1.4

BE

1.15–1.29

4.8

1.3

1.2

1.2

1.8

5.4

Bt1

1.29–1.84

4.6

0.7

0.6

0.6

1.4

2.5

Bt2

1.84–2.05+

4.4

0.8

1.0

0.8

0.6

4.8

Planting of all the species occurred between October 2010 and March 2011; the saplings were 6 years old at the time of soil sampling. Each species with about thirty trees occupied 270 m2 (9 m by 30 m), and rectangular spacing was used, making 3 m between rows and 2 m between trees in the same row. At the time of planting, saplings were fertilized with 120 g NPK (10:28:20) and 30 g of FTE Br-12 (composition per kg: 18 g of B, 8.0 g of Cu, 30 g of Fe, 30 g of Mn, 1.0 g of Mo and 90 g of Zn) per plant. At six months, all plants received a supplement of NPK (50 g plant−1) with the aim of promoting establishment. The plants were not provided additional fertilizer in order to ensure that the conditions were those of the natural fertility of the soil. At 3 years, some of the smaller trees were removed, leaving 30 trees in each plot.

It was assumed that the soil profile under Acacia mangium would be similar for the other species evaluated. Fine roots were common along the surface horizons (Ap) but rarer in the Master Bt horizon. The E horizon represents the eluviation of clay material, while the master Bt is an alluvial clay horizon. Analytical procedures used to determine pH, exchangeable nutrients and SOC contents are described by Silva (2009). Based on growth and uniform size, seven experimental plots were selected, consisting of Brazilian timber species: (1) Anadenanthera colubrina, (2) Astronium fraxinifolium, (3) Handroanthus impetiginosus, and (4) Colubrina glandulosa; and three exot species: (5) Acacia mangium Willd., (6) Casuarina equisetifolia L., and (7) Eucalyptus urophylla S. T. Blake (clone GG-702). The control area was not forested here, and had been previously planted to food crops; grasses native to the Acaraú basin grew there during the rainy season.

Soil sampling

Twelve to fifteen soil samples were taken from the upper layer (up to 10 cm deep) at a minimum of one meter from the tree trunk in the plot. The samples were collected following a zig-zag path. Soil samples were collected between the 8th and 10th of August and November 2016, and February and May 2017. The samples were sieved (2 mm mesh) to remove leaf litter and large aggregates. One portion of the sample was refrigerated (± 4 °C) and then subjected to an analysis of respiratory activity and of microbial biomass carbon. Other portions of the samples were air-dried and subjected to chemical analyses.

Measurement of organic carbon and microbial properties

The soil organic carbon (SOC) content was measured using the wet dichromate oxidation method (Silva 2009). Basal soil respiration (BSR) was measured by incubating 50 g of dry soil in jars containing NaOH for the capture of CO2, followed by titration with HCl (Silva et al. 2007). The rate of respiration was measured after ten days of incubation in 2 L jars kept at room temperature (22–25 °C) in the dark. Carbon from the soil microbial biomass (SMB) was measured using the fumigation-extraction method (Vance et al. 1987) in which the differences in carbon concentration between fumigated and non-fumigated extracts are converted into microbial carbon (C-mic) using the Kc of 0.33 as suggested by Sparling and West (1988). Using the microbial properties and the SOC, the following were measured: metabolic quotient (qCO2, relationship between BSR and SMB) (Silva et al. 2007), microbial quotient of the soil (qMic ratio between SMB and SOC) (Tótola and Chaer 2002), and the mineralization quotient of the soil organic matter (qMin ratio between BSR and SOC) (Rasid et al. 2016).

Other fractions of dry soil were used for the analyses of easily extractable-glomalin related soil protein (EE-GRSP) and total-glomalin related soil protein (T-GRSP) according to Wright and Upadhyaya (1998) and Rillig (2004), with modifications in the extraction process (1 g dry soil) with sodium citrate (20 mM at pH 7.0 for EE-GRSP and 50 mM at pH 8.0 for T- GRSP). The Coomassie Brilliant Blue Bradford Assay (Wright et al. 1996) was used to measure the two protein fractions.

Chemical analyses of the soil

Fractions of dry soil were used for the analysis of chemical properties except for the analysis of the hydrogen ionic potential (pH) of the soil. The procedures of Silva (2009) were used to determine the of pH in water (proportion 1:2.5) and the extraction of the elements P, K, Na, Fe, Cu, Mn and Zn (extractor Mehlich-1), Ca and Mg (extractable by KCl at 1.0 mol L−1). A spectrophotometer (λ = 660 nm) determined available phosphorus while exchangeable K and Na contents were determined in a flame photometer. The Ca, Mg, Fe, Cu, Mn and Zn contents were measured by atomic absorption spectroscopy. Total nitrogen content was determined using the Dumas Nitrogen Analyzer (NDA 701) apparatus, in accordance with Ribeiro (2010).

Statistical analysis

Soil property data were subjected to a descriptive statistical analysis, measuring averages, maximums and minimums (95%), asymmetric measures, kurtosis, and W values on the Shapiro–Wilk test (p ≤ 0.05), which indicated abnormal data distribution. In addition, for a better understanding of the soil attributes data, a multivariate analysis was carried out. Hierarchical clustering was analyzed (Agglomerative Hierarchical Clustering, AHC) according to dissimilarities or similarities. The Spearman dissimilarity (matrix) was used to examine properties with non-normalized data, and as an agglomeration technique of unweighted pair-groups (Unweighted Pair-Group Average, UPGM) with centralized and reduced data. The principal components analysis (PCA) was from Spearman analysis (Abdi and Williams 2010), enabling atypical properties to be identified through their average values and the separation of forest cover and the non-forested area among the principal components. The calculations and the graphics were generated by the XLSTAT © program version 2016.1 (Addinsoft Inc., Brooklyn, NY, USA).

Results and discussion

From the descriptive statistical analysis data (Tables 2 and 3), it was inferred that timber species (A. colubrina, A. fraxinifolium, H. impetiginosus, C. glandulosa, A. mangium, C. equisetifolia, E. urophylla), cultivated for 6 years, changed the biological and chemical attributes of the soil in different ways. This Argisol soil, which is predominantly sand (ranging from 93% sand near the surface to less than 78% along the horizon at 2 m deep), can be advantageous for forest ecosystems. Tree roots may reach considerable depths, enabling a more efficient capture of water when rainfall is scarce, a situation favored by the presence of a horizon with an accumulation of clay (Bt—Table 1) which retains moisture in the soil. Argisols are common in Brazil’s northeast (Cunha et al. 2010), particularly in the coastal tablelands (Bezerra et al. 2015). Plantations for timber production could help producers reduce or even end deforestation in the dry tropical forest. This would, however, require adaptation of soil and plant management practices, as well as the definition of objectives specifically for planted forests (Chazdon et al. 2016) and also considering the naturally low fertility of this type of soil (Table 1).
Table 2

Biological attributes of soils under trees and non-forested control in the Lower Acaraú Basin, State of Ceara (Brazil)

Forestry area

Soil organic carbon (SOC, g kg−1)

Mean

Lower 95%

Upper 95%

Variance

Skewness

Kurtosis

W

C. V. (%)

Acacia mangium

10.56

9.79

11.36

0.24

− 0.06

− 2.86

0.96

4.7

Anadenanthera colubrina

11.57

10.39

12.75

0.55

− 0.32

− 3.3

0.93

6.4

Casuarina equisetifolia

11.9

8.18

15.61

5.45

1.54

2.62

0.87

19.6

Eucalyptus urophylla

8.83

7.19

10.48

1.06

− 0.15

− 5.01

0.84

11.6

Astronium fraxinifolium

8.64

8.2

9.08

0.08

0.92

0.59

0.96

3.21

Handroanthus impetiginosus

10.95

9.28

12.62

1.1

0.12

1.1

0.98

9.6

Colubrina glandulosa

11.1

9.46

12.73

1.06

0.54

1. 21

0.97

9.3

Control

8.69

8.49

8.89

0.02

0.84

0.93

0.96

1.5

Forestry area

Total-glomalin related soil protein(T-GRSP, mg g−1)

Mean

Lower 95%

Upper 95%

Variance

Skewness

Kurtosis

W

C. V. (%)

Acacia mangium

1.90

1.85

1.94

< 0.01

0.64

− 2.31

0.89

1.52

Anadenanthera colubrina

1.99

1.41

2.57

0.13

− 0.02

− 0.69

0.99

18.24

Casuarina equisetifolia

2.03

1.61

2.39

0.06

− 0.14

0.23

0.99

12.32

Eucalyptus urophylla

1.54

1.33

1.74

0.02

− 0.68

1.75

0.93

8.35

Astronium fraxinifolium

1.48

0.78

2.18

0.19

− 1.64

2.62

0.82

29.76

Handroanthus impetiginosus

2.04

1.48

2.6

0.12

1.99

3.99

0.63

17.24

Colubrina glandulosa

2.17

1.98

2.36

0.01

0.96

1.79

0.94

5.05

Control

1.48

1.39

1.58

< 0.01

− 0.64

1.72

0.93

4.04

Forestry area

Easily extracted-glomalin related soil protein (EE-GRSP, mg g−1)

Mean

Lower 95%

Upper 95%

Variance

Skewness

Kurtosis

W

C. V. (%)

Acacia mangium

0.81

0.68

0.93

0.01

− 0.25

− 3.82

0.91

9.82

Anadenanthera colubrina

0.91

0.77

1.06

0.01

1.61

2.75

0.85

9.76

Casuarina equisetifolia

0.75

0.62

0.88

0.01

− 0.76

− 0.14

0.96

10.75

Eucalyptus urophylla

0.79

0.59

0.99

0.02

1.14

0.47

0.9

16.06

Astronium fraxinifolium

0.61

0.42

0.8

0.01

− 1.2

0.84

0.9

19.29

Handroanthus impetiginosus

0.85

0.65

1.06

0.02

− 0.45

1.08

0.98

14.75

Colubrina glandulosa

0.64

0.53

0.76

< 0.01

− 0.23

− 0.48

0.99

11.71

Control

0.75

0.65

0.85

< 0.01

− 0.16

− 2.72

0.96

8.14

Forestry area

T-GRSP:SOC ratio (/%)

Mean

Lower 95%

Upper 95%

Variance

Skewness

Kurtosis

W

C. V. (%)

Acacia mangium

18

16.61

19.38

0.76

− 0.02

− 5.9

0.76

4.84

Anadenanthera colubrina

17.33

11.03

23.64

15.69

0.62

1.61

0.95

22.85

Casuarina equisetifolia

17.29

11.31

23.28

14.14

− 0.45

− 2.91

0.92

21.74

Eucalyptus urophylla

17.51

15.61

19.41

1.42

0.22

0.7

0.99

6.81

Astronium fraxinifolium

17.07

9.5

24.65

22.65

− 1.79

3.18

0.76

27.88

Handroanthus impetiginosus

18.76

13.38

24.14

11.43

0.76

0.47

0.97

18.01

Colubrina glandulosa

19.73

16.15

23.31

5.06

− 1.56

2.44

0.85

11.39

Control

17.12

15.88

18.36

0.6

0.75

1.05

0.97

4.53

Forestry area

EE-GRSP:SOC ratio (%)

Mean

Lower 95%

Upper 95%

Variance

Skewness

Kurtosis

W

C. V. (%)

Acacia mangium

7.62

6.45

8.81

0.55

− 1.13

1.53

0.93

9.72

Anadenanthera colubrina

7.96

5.93

10.01

1.64

1.32

1.1

0.85

16.07

Casuarina equisetifolia

6.48

4.33

8.64

1.83

1.21

1.95

0.92

20.9

Eucalyptus urophylla

8.97

7.77

10.17

0.56

0.64

− 2.41

0.86

8.36

Astronium fraxinifolium

7.06

5.17

8.96

1.42

− 1.47

1.75

0.82

16.85

Handroanthus impetiginosus

7.82

6.46

9.2

0.73

1.33

1.18

0.86

10.97

Colubrina glandulosa

5.82

5.01

6.63

0.26

− 1.42

2.31

0.89

8.7

Control

8.63

7.58

9.68

0.43

− 0.35

− 3.97

0.87

7.64

Forestry area

Basal soil respiration (BSR, µg C–CO2 g−1 h−1)

Mean

Lower 95%

Upper 95%

Variance

Skewness

Kurtosis

W

C. V. (%)

Acacia mangium

0.32

0.22

0.42

< 0.01

0.65

− 2.17

0.9

20.1

Anadenanthera colubrina

0.23

0.13

0.33

< 0.01

− 1.71

2.55

0.85

27.4

Casuarina equisetifolia

0.26

0.12

0.41

0.01

− 0.37

− 3.9

0.85

33.9

Eucalyptus urophylla

0.4

0.15

0.65

0.03

1.44

2.39

0.88

39.7

Astronium fraxinifolium

0.25

0.05

0.45

0.02

0.08

− 5.69

0.82

49.6

Handroanthus impetiginosus

0.23

0.19

0.28

< 0.01

1.09

− 0.05

0.85

14.1

Colubrina glandulosa

0.38

0.15

0.61

0.02

− 0.93

− 0.85

0.87

38

Control

0.32

0.09

0.54

0.02

0.56

− 2.67

0.9

45.1

Forestry area

Soil microbial biomass (SMB, µg C-mic/g)

Mean

Lower 95%

Upper 95%

Variance

Skewness

Kurtosis

W

C. V. (%)

Acacia mangium

107.8

30.52

185.08

2359.5

1.98

3.93

0.68

45.1

Anadenanthera colubrina

117.81

17.71

217.91

3957

0.8

− 1.54

0.88

53.4

Casuarina equisetifolia

99.47

75.76

123.19

222.13

1.86

3.59

0.76

14.9

Eucalyptus urophylla

98.53

57.69

139.37

658.62

0.49

− 2.33

0.94

26.1

Astronium fraxinifolium

112.11

40.16

184.61

2045

0.2

1

0.98

40.3

Handroanthus impetiginosus

58.99

41.38

76.59

122.41

0.39

− 2.54

0.94

18.7

Colubrina glandulosa

142.29

61.38

223.2

2585

0.81

1.23

0. 96

35.7

Control

97.99

59.47

136.51

586.06

− 0.16

1.24

0.97

24.7

Forestry area

Metabolic quotient (µg C–CO2 µg−1 C-mic h−1)

Mean

Lower 95%

Upper 95%

Variance

Skewness

Kurtosis

W

C. V. (%)

Acacia mangium

3.36

1.21

5.52

1.83

− 1.08

1.21

0.66

40.2

Anadenanthera colubrina

2.53

0.19

4.86

2.16

− 0.53

− 1.68

0.96

58.2

Casuarina equisetifolia

2.66

1.25

4.07

0.78

0.45

0.75

0.99

33.3

Eucalyptus urophylla

4.13

1.92

6.35

1.94

0.2

− 0.73

0.99

33.6

Astronium fraxinifolium

2.77

− 1.1

6.64

5.92

1.88

3.57

0.75

87.9

Handroanthus impetiginosus

4.16

2.25

6.05

1.42

− 0.05

− 5.36

0.85

28.7

Colubrina glandulosa

3.16

0.09

6.24

3.74

0.22

0.96

0.98

61.1

Control

3.52

0.43

6.6

3.75

0.01

− 4.72

0.89

55.1

Forestry area

Microbial quotient (%)

Mean

Lower 95%

Upper 95%

Variance

Skewness

Kurtosis

W

C. V. (%)

Acacia mangium

10.09

3.55

16.62

16.86

1.96

3.88

0.69

40.7

Anadenanthera colubrina

10.35

0.75

19.94

36.34

1.16

0.36

0.88

58.2

Casuarina equisetifolia

8.43

7.18

9.68

0.78

0.88

− 0.46

0.94

9.3

Eucalyptus urophylla

11.03

8.35

13.7

2.82

1.09

0.43

0.92

15.2

Astronium fraxinifolium

12.95

4.58

21.32

27.69

0.51

1.39

0.96

40.6

Handroanthus impetiginosus

5.45

3.38

7.54

1.07

− 0.42

− 3.63

0.86

23.9

Colubrina glandulosa

13.13

3.93

22.32

33.36

1.21

1.2

0.91

43.9

Control

11.28

6.76

15.81

8.07

− 0.01

0.84

0.99

25.1

Forestry area

Mineralization quotient (%)

Mean

Median

Lower 95%

Upper 95%

Variance

Skewness

Kurtosis

W

C. V. (%)

Acacia mangium

3.02

2.96

2.04

3.99

0.38

0.23

− 4.43

0.87

20.4

Anadenanthera colubrina

1.96

2.15

1.24

2.68

0.2

− 1.91

3.69

0.74

22.9

Casuarina equisetifolia

2.12

2.14

1.31

3.08

0.31

0.59

1.4

0.97

25.4

Eucalyptus urophylla

4.49

4.01

2.15

6.83

2.16

1.27

0.82

0.84

32.7

Astronium fraxinifolium

2.96

2.87

0.54

5.38

2.31

0.07

− 5.56

0.81

51.4

Handroanthus impetiginosus

2.16

2.08

1.7

2.62

0.08

1.31

1.17

0.87

13.2

Colubrina glandulosa

3.42

3.62

1.34

5.5

1.71

− 0.38

− 3.79

0.87

38.2

Control

3.66

3.36

1.05

6.28

2.7

0.57

− 2.71

0.89

44.8

Underlined W-values were significant in the Shapiro–Wilk test (p < 0.05), indicating that the hypothesis for a normal distribution was rejected. C.V. (Coefficient of variation). Soil samplings: August and November 2016, February and May 2017

Table 3

pH and chemical attributes of soils under trees and non-forested control in the Lower Acaraú Basin, State of Ceara (Brazil)

Forestry area

Soil pH (in water, ratio 1:2.5)

 

Mean

Lower 95%

Upper 95%

Variance

Skewness

Kurtosis

W

C. V. (%)

Acacia mangium

5.41

5.29

5.53

0.01

0.56

0.93

0.98

1.4

Anadenanthera colubrina

6.10

5.90

6.31

0.02

1.75

3.05

0.79

2.1

Casuarina equisetifolia

5.58

5.48

5.69

0.01

− 0.06

− 5.45

0.83

1.2

Eucalyptus urophylla

5.51

4.93

6.08

0.13

1.96

3.90

0.69

6.6

Astronium fraxinifolium

5.85

5.73

5.97

0.01

− 1.73

2.99

0.80

1.3

Handroanthus impetiginosus

6.05

6.61

6.48

0.07

0.77

0.01

0.96

4.5

Colubrina glandulosa

5.81

5.48

6.15

0.04

− 1.00

− 0.01

0.92

3.6

Control

6.29

6.24

6.33

< 0.01

− 1.81

3.47

0.77

0.4

Forestry area

N-total (mg g−1 soil)

Mean

Lower 95%

Upper 95%

Variance

Skewness

Kurtosis

W

C. V. (%)

Acacia mangium

0.48

0.41

0.54

0.02

− 1.76

3.23

0.81

8.5

Anadenanthera colubrina

0.57

0.27

0.88

0.03

0.0

− 3.96

0.93

33.4

Casuarina equisetifolia

0.46

0.30

0.62

0.01

0.37

− 3.53

0.90

21.5

Eucalyptus urophylla

0.32

0.18

0.45

0.01

0.98

1.98

0.92

27.4

Astronium fraxinifolium

0.43

0.26

0.59

0.01

− 0.41

0.88

0.98

23.9

Handroanthus impetiginosus

0.44

0.34

0.54

< 0.01

0.81

0.07

0.96

13.7

Colubrina glandulosa

0.47

0.38

0.56

< 0.01

0.10

− 5.42

0.81

12.4

Control

0.41

0.20

0.62

0.02

0.01

− 2.06

0.98

32.1

Forestry area

C:N ratio

Mean

Lower 95%

Upper 95%

Variance

Skewness

Kurtosis

W

C. V. (%)

Acacia mangium

22.12

19.67

24.66

2.32

− 0.21

− 0.47

0.99

6.5

Anadenanthera colubrina

21.64

11.89

31.39

37.52

0.38

3.14

0.92

28.3

Casuarina equisetifolia

26.07

19.59

32.54

16.53

0.21

1.33

0.97

15.6

Eucalyptus urophylla

28.68

19.07

38.29

36.47

0.04

− 2.34

0.97

21.5

Astronium fraxinifolium

20.85

12.79

28.95

25.80

1.06

2.06

0.92

24.3

Handroanthus impetiginosus

25.16

19.44

30.88

12.91

0.65

0.87

0.98

14.3

Colubrina glandulosa

23.90

17.55

30.25

15.91

1.51

1.98

0.83

16.7

Control

23.00

10.73

35.26

59.42

0.89

− 0.31

0.94

33.5

Forestry area

P (mg dm3l)

Mean

Lower 95%

Upper 95%

Variance

Skewness

Kurtosis

W

C. V. (%)

Acacia mangium

0.16

0.14

0.18

< 0.01

1.13

2.23

0.89

7.7

Anadenanthera colubrina

0.14

0.09

0.19

0.01

0.87

1.93

0.91

22.8

Casuarina equisetifolia

0.26

0.15

0.38

0.01

− 0.39

1.23

0.98

27.9

Eucalyptus urophylla

0.14

0.05

0.23

< 0.01

1.28

1.50

0.91

40.8

Astronium fraxinifolium

0.17

0.05

0.31

0.01

− 0.15

− 0.94

0.99

45.8

Handroanthus impetiginosus

0.21

0.08

0.34

0.01

− 0.18

1.44

0.96

38.5

Colubrina glandulosa

0.22

0.13

0.31

< 0.01

1.70

2.92

0.81

24.9

Control

0.14

0.08

0.19

0.01

− 1.19

1.98

0.92

24.7

Forestry area

K+ (mmolc.dm−3)

Mean

Lower 95%

Upper 95%

Variance

Skewness

Kurtosis

W

C. V. (%)

Acacia mangium

1.64

0.71

2.58

0.34

1.19

1.57

0.58

35.6

Anadenanthera colubrina

1.34

0.61

2.08

0.21

0.48

1.63

0.94

34.4

Casuarina equisetifolia

1.75

0.88

2.63

0.30

0.38

0.95

0.98

31.3

Eucalyptus urophylla

1.06

0.75

1.36

0.03

− 0.22

− 4.60

0.86

17.8

Astronium fraxinifolium

1.11

0.37

1.85

0.22

0.51

1.58

0.95

41.60

Handroanthus impetiginosus

1.65

0.91

2.39

0.22

0.92

0.25

0.95

28.2

Colubrina glandulosa

2.44

1.42

3.47

0.41

− 0.48

0.34

0.98

26.3

Control

2.06

1.19

2.93

0.29

− 0.94

0.52

0.73

26.4

Forestry area

Na+ (mmolc.dm−3)

Mean

Lower 95%

Upper 95%

Variance

Skewness

Kurtosis

W

C. V. (%)

Acacia mangium

1.88

1.06

2.69

0.26

− 0.10

− 0.10

0.99

27.3

Anadenanthera colubrina

0.97

0.23

1.72

0.22

1.47

2.50

0.88

48.1

Casuarina equisetifolia

1.60

0.77

2.42

0.27

− 0.11

0.99

0.99

32.5

Eucalyptus urophylla

1.32

0.57

2.08

0.22

0.91

0.35

0.95

35.6

Astronium fraxinifolium

0.77

0.39

1.55

0.06

0.89

− 0.89

0.91

30.8

Handroanthus impetiginosus

1.09

0.64

1.54

0.07

1.19

1.50

0.93

12.3

Colubrina glandulosa

1.32

0.23

2.42

0.47

0.72

− 0.53

0.96

51.7

Control

1.13

0.85

1.40

0.03

0.01

− 5.48

0.84

15.5

Forestry area

Ca2+ (mmolc.dm−3)

Mean

Lower 95%

Upper 95%

Variance

Skewness

Kurtosis

W

C. V. (%)

Acacia mangium

19.88

16.45

23.31

4.63

0.34

− 3.59

0.47

10.83

Anadenanthera colubrina

23.59

20.59

26.59

3.55

− 0.19

− 0.84

0.99

7.99

Casuarina equisetifolia

25.28

21.32

29.25

6.21

− 0.12

− 2.01

0.97

9.8

Eucalyptus urophylla

17.54

13.46

21.61

6.54

0.08

− 2.29

0.97

14.6

Astronium fraxinifolium

19.19

15.09

23.29

6.63

− 0.39

− 0.01

0.99

13.4

Handroanthus impetiginosus

26.10

20.99

31.22

10.31

− 0.44

1.59

0.95

12.3

Colubrina glandulosa

23.80

19.16

28.45

8.52

0.98

− 0.63

0.87

12.3

Control

17.79

15.65

19.94

1.81

0.06

0.84

0.99

7.6

Forestry area

Mg2+ (mmolc.dm−3)

Mean

Lower 95%

Upper 95%

Variance

Skewness

Kurtosis

W

C. V. (%)

Acacia mangium

7.61

6.13

9.09

0.86

0.75

− 1.79

0.33

12.2

Anadenanthera colubrina

9.88

9.03

10.74

0.28

− 1.71

2.89

0.81

0.5

Casuarina equisetifolia

8.56

5.85

11.33

2.96

0.31

− 1.40

0.98

20.1

Eucalyptus urophylla

6.86

5.01

8.72

1.36

− 0.47

1.46

0.96

17.1

Astronium fraxinifolium

7.84

6.66

9.01

0.54

− 1.65

2.61

0.80

9.4

Handroanthus impetiginosus

9.26

6.85

11.66

2.28

− 1.21

1.91

0.92

16.3

Colubrina glandulosa

8.92

6.46

11.39

2.39

0.12

− 3.66

0.93

17.3

Control

7.49

2.01

12.96

11.82

− 1.37

2.54

0.88

45.9

Forestry area

Cu (mg dm3)

Mean

Lower 95%

Upper 95%

Variance

Skewness

Kurtosis

W

C. V. (%)

Acacia mangium

0.27

0.23

0.32

< 0.01

− 2.00

4.00

0.63

9.0

Anadenanthera colubrina

0.03

0.01

0.05

< 0.01

− 2.00

4.00

0.63

46.1

Casuarina equisetifolia

0.24

0.04

0.43

0.01

0.54

− 2.94

0.86

51.1

Eucalyptus urophylla

0.04

< 0.01

nd

nd

nd

0

Astronium fraxinifolium

0.04

< 0.01

nd

nd

nd

0

Handroanthus impetiginosus

0.06

0.02

0.11

< 0.01

0.0

− 6.00

0.73

44.1

Colubrina glandulosa

0.17

0.10

0.25

< 0.01

0.85

− 1.29

0.86

26.9

Control

0.03

0.01

0.06

< 0.01

− 2.00

4.00

0.63

46.1

Forestry area

Fe (mg dm3)

Mean

Lower 95%

Upper 95%

Variance

Skewness

Kurtosis

W

C. V. (%)

Acacia mangium

2.04

1.91

2.18

0.01

− 0.75

0.34

0.85

4.2

Anadenanthera colubrina

1.29

0.89

1.68

0.06

− 1.10

− 5.02

0.86

19.2

Casuarina equisetifolia

1.94

1.20

2.68

0.21

0.59

− 1.31

0.96

23.9

Eucalyptus urophylla

2.31

1.86

2.77

0.08

− 0.37

− 3.59

0.90

12.3

Astronium fraxinifolium

1.51

1.08

1.94

0.07

− 0.57

− 1.71

0.95

17.7

Handroanthus impetiginosus

1.30

0.92

1.68

0.06

− 0.29

− 3.96

0.90

18.4

Colubrina glandulosa

1.59

0.92

2.26

0.17

1.17

2.22

0.90

26.4

Control

1.04

0.45

1.63

0.13

− 0.23

− 4.51

0.87

35.5

Forestry area

Mn (mg dm3 soil)

Mean

Lower 95%

Upper 95%

Variance

Skewness

Kurtosis

W

C. V. (%)

Acacia mangium

5.24

3.88

6.59

0.72

− 0.48

− 1.92

0.75

16.2

Anadenanthera colubrina

6.71

5.22

8.20

0.88

− 0.45

− 3.42

0.86

13.9

Casuarina equisetifolia

5.29

3.45

7.12

1.32

0.15

− 4.68

0.88

21.8

Eucalyptus urophylla

5.89

3.73

8.05

1.84

− 0.31

0.46

0.99

23.1

Astronium fraxinifolium

6.01

5.21

6.82

0.25

0.81

1.18

0.97

8.4

Handroanthus impetiginosus

6.07

4.81

7.33

0.62

− 1.17

0.39

0.86

13.0

Colubrina glandulosa

5.45

3.42

7.49

1.63

− 0.14

− 4.98

0.85

23.4

Control

4.86

2.50

7.22

2.20

0.45

− 3.44

0.86

30.5

Forestry area

Zn (mg dm3)

Mean

Lower 95%

Upper 95%

Variance

Skewness

Kurtosis

W

C. V. (%)

Acacia mangium

1.99

1.64

2.33

0.05

0.83

− 0.03

0.76

10.9

Anadenanthera colubrina

1.59

0.89

2.29

0.19

1.62

3.02

0.84

27.5

Casuarina equisetifolia

1.60

0.92

2.28

0.18

1.67

2.85

0.83

26.7

Eucalyptus urophylla

1.09

0.77

1.41

0.04

0.47

− 3.32

0.86

18.1

Astronium fraxinifolium

1.85

− 0.08

3.79

1.49

1.95

3.85

0.71

65.8

Handroanthus impetiginosus

1.56

0.90

2.23

0.17

1.76

3.11

0.79

26.6

Colubrina glandulosa

1.38

0.98

1.77

0.06

0.39

− 2.44

0.95

17.9

Control

1.30

0.35

2.25

0.35

1.13

1.78

0.93

45.6

The elements P, K, Na, Cu, Fe, Mn and Zn were extracted with Mehlich-1; Ca and Mg were extracted with KCl (mol L−1). C.V.: Coefficient of variation Underlined W-values were significant in the Shapiro–Wilk test (p < 0.05), indicating that the hypothesis for a normal distribution was rejected. Soil samplings: August and November 2016, February and May 2017

Based on measurements of dispersion, asymmetry and kurtosis, the majority of soil properties had a normal data distribution (insignificant W values (p ≤ 0.05) in the Shapiro–Wilk test) (Table 2). There were exceptions for the following parameters, which showed high values of kurtosis and significant W-values indicating an abnormal distribution of the data: pH (3.90) and qCO2 (3.57) under E. urophylla; SMB (3.93) and qMic (3.88) under A. mangium; qMin (3.69) planted with A. colubrina; and T-GRSP (3.99) in the area with H. impetiginosus. There were differences in the formation of leaf litter under these canopies (data not shown here), which possibly contributed to an increase in spatial variability of soil properties. It would, however, be a mistake to rule out that the variability of properties might possibly be due to periods of rainfall and of drought, with the formation of different microenvironments under the canopies and in the non-forested area. Seasonal variations have been observed in Caatinga vegetation (Holanda et al. 2017), and in agroforestry systems in the northeast region (Lima et al. 2010), as well as in Eucalyptus urophylla x Eucalyptus globulus populations in southern Brazil where there is a temperate climate (Vieira et al. 2014).

In this study, the vegetative cover indicated low BSR (basal soil respiration) ranging from 0.23 µg (H. impetiginosus; A. colubrina) to 0.40 µg C–CO2 g−1 h−1 (E. urophylla), which was reflection of low microbiota activity and the consequent immobilization of organic carbon. The SMB (soil microbial biomass) was equally low in the control area and in areas planted with C. equisetifolia, E. urophylla, H. impetiginosus. In these sites, there were high values of qCO2 indicating microbial stress. This could be due to low fertility, microenvironmental variations, variations in high temperatures, and/or hydric deficits in the soil. SMB is low in more arid environments (Xu et al. 2013) where there are large oscillations in topsoil temperatures without plant cover. In this study, SMB values were slightly higher under C. glandulosa (142. 29 µg C-mic g−1 soil), A. colubrina (117.81 µg C-mic g−1 soil), A. fraxinifolium (112.11 µg C-mic g−1 soil), and A mangium (107.8 µg C-mic g−1 soil), all sites with good leaf litter. These values should serve as the baseline for defining critical levels of the Argisol microbial biomass on tablelands planted with timber species, (without maintenance fertilization), as they are comparable to those observed in the Caatinga (Ferreira et al. 2014) and in plantations of Pinus tecunumanii and Eucalyptus grandis in the Cerrado biome (Silva 2009). On the other hand, in areas of the Cerrado where fertilizers and agronomic practices result in high crop productivity, the levels of edaphic respiration rates are equivalent to/or higher than 4.16 µg C–CO2 g−1 h−1 and the SMB is 375 µg C-mic g−1 (Lopes et al. 2013).

Sites with E. urophylla and A. fraxinifolium and the control area indicated low levels of glomalin-related soil protein (< 2.0 mg T-GRSP per gram of dry soil). The area with A. fraxinifolium also had low EE-GRSP levels, while the areas planted with A. colubrina and H. impetiginosus had the highest levels of this protein fraction. The leaf litter deposition and the microenvironments in the vegetation coverage under these last two species may be favorable to AM fungi activity, microorganisms responsible for the production and deposition of glomalin in the rhizosphere. It is also possible that the quality of the leaf litter affected the decomposition rates of the organic matter, corroborating Joly et al. (2017), as well as the glomalin content (Rotter et al. 2017) in the soil. Silva et al. (2014) noted some factors which could be related to the production or deposition of this protein by the AM fungi in plantations and in non-forested areas. These include climate, soil biota, concentrations of nutrients, the diversity of AM fungi, as well as the host and its productivity (Oliveira et al. 2009; Silva et al. 2012).

Some soil attributes such as the SOC and its relationship with N and GRSP fractions (Table 2), plus soil nutrient levels (N, P, K+, Ca2+, Mg2+, Na+ Fe, Mn, Zn; Table 3), showed a normal distribution, observed with insignificant W-values by the Shapiro–Wilk test (p < 0.05). There was evidence of contributions of A. colubrina and C. equisetifolia to the SOC increase, mainly in relation to areas under E. urophylla, A. fraxinifolium and the control. The litter under the canopies of these last two species may have affected the carbon cycling as there was a low soil metabolic quotient in these environments (Table 2). Behera and Sahani (2003) observed an inefficient use of organic substrate from Eucalyptus (30-year-old trees) compared to the leaf litter from a naturally regenerated forest. It should be noted that Eucalyptus litter often has high cellulose: N and lignin: N ratios (Barreto et al. 2008), indicating a certain recalcitrance on leaf litter matter.

In this study, the highest levels of Mg2+ (9.88 mmolc dm−3) and soil N (0.57 g kg−1) occurred on sites with A. colubrina (Table 3), and differed from other coverages evaluated here. The highest levels of P (0.26 mg dm3), Fe (2.31 mg dm3), Ca2+ (26.10 mmolc dm−3) and Na+ (1.88 mmolc dm−3) were found in areas with C. equisetifolia, E. urophylla, H. impetiginosus and A. mangium, respectively, which shows that tree species affect the concentrations of nutrients and biological quality of soils in different ways. In addition, there might have been some microenvironmental influence due to soil sampling periods (Fig. 1) because arbuscular mycorrhizal activity and organic matter accumulation are generally higher during the driest periods of the year. Variations in communities of AM fungi have been observed in Costa Rican forests during the rainy and dry seasons (Lovelock et al. 2003) as well as a more intense fungal sporulation in the dry season. During the rainy season, there could also have been a leaching of nutrients and labile organic constituents, which indicates a vulnerability to soil degradation. Glomalin-related soil protein fractions (Singh et al. 2017) and soluble ions could have been leached out with the water.

Using agglomerative hierarchical clustering (AHC), three classes of vegetation cover were established using the averages of the soil properties analyzed (Fig. 2). The species of cluster 2 were very similar and consisted of two Brazilian timber species (H. impetiginosus and A. colubrina). Cluster 1 was formed of C. glandulosa, C. equisetifolia and A. mangium, while cluster 3 was made up of E. urophylla and A. fraxinifolium plus the control area. These latter two species of cluster 3 may require longer cultivation since natural regeneration takes at least 15 years for the organic carbon content to increase and for certain soil properties to improve (Medeiros et al. 2017). There may also have been variations in leaf litter formation which may have affected the microbial biomass (SMB) and the metabolic quotients and the mineralization of organic matter, not allowing the separation of forest species from the control (Table 2).
Fig. 2

Dendrogram constructed by UPGMA using the mean values of soil attributes as influenced by trees and non-forested area. Plant coverings separated by the cluster C 1 [Colubrina glandulosa (7), Casuarina equisetifolia (3), and Acacia mangium) (1)]; cluster C 2 [Handroanthus impetiginosus (6) and Anadenanthera colubrina (2)], and cluster C 3 [control (8), Eucalyptus urophylla (4), and Astronium fraxinifolium (5)]

Analyzing the Principal Components, F 1 explains 31.6%, F 2 25.2%, F 3 15.52%, F 4 11.05%; the other axes were irrelevant. However, only the first two components were retained, explaining almost 60% of the variance in the data set (Fig. 3). In the F 1 component, the more sensitive variables (with the largest square cosines) were: Ca2+ (0.842) > Mg2+ (0.756) > SOC (0.677) > N (0.646) > T-GRSP (0.603) > P (0.590), all with large positive factor loadings, and qMin (0.628) plus EE-GRSP:SOC (0.422) with negative loadings. In the F 2 component, the variables Na+ (0.805) > Fe (0.635) > BSR (0.632) > Cu (0.614) > T-GRSP:SOC ratio (0.437) > C:N ratio (0.429) showed large positive loadings, while pH (0.652) and Mn (0.312) were negative loadings. The other assessed soil properties were less sensitive to these major components.
Fig. 3

Principal component analysis (PCA) using the mean values of biological and chemical attributes of the soil under trees and non-forested area. Soil attributes: BSR (basal soil respiration), SOC (soil organic carbon), T-GRSP (total-glomalin related soil protein), EE-GRSP (easily extractable-glomalin related soil protein), SMB (soil microbial biomass), qCO2 (Metabolic quotient), qMic (Microbial quotient), qMin (Mineralization quotient), minerals

It should be noted that several properties were related to each other as observed in the significant correlations (p < 0.05) of Spearman. The positive relationships were associated with the one-way movement of the axis in component analysis (Fig. 3) such as: T-GRSP versus T-GRSP: SOC (r = 0.81); T-GRSP versus SOC (r = 0.81); T-GRSP versus Ca2+ (r = 0.83); SOC versus Ca2+ (r = 0.74); BSR versus qMic (r = 0.91); P versus Ca2+ (r = 0.81); Na+ versus Cu (r = 0.74); and, Ca2+ versus Mg2+ (r = 0.81). This direct relationship between the levels of glomalin and SOC has already been observed in soils under tree canopies (Vasconcellos et al. 2016; Rotter et al. 2017; Zang et al. 2017). The relationship between T-GRSP versus exchangeable Ca2+ could be associated with the activity of AM fungi in the rhizosphere of the plants as well as with the Ca cycle because of the litter of C. equisetifolia, A. colubrina, C. glandulosa and H. impetiginosus. In these same environments, higher values of SOC and T-GRSP were detected than under the other species. The variations in these biological properties, among other chemical characteristics, may affect the growth of the trees (Table 3).

Other significant correlations between soil properties were negative and the inverse relationship between EE-GRSP and qMic (r = − 0.74) may be associated with levels of labile fractions in the organic constituents and activity of the soil microbiota. The quality of the organic matter on the soil surface may have affected the availability of nutrients for fungi and other micro-organisms, as can be seen in the relationship between EE-GRSP:SOC versus P (r = − 0.88) and qMin versus Mg2+ (r = − 0.86). Links between pH values and the concentrations of some soil elements (pH versus Na+ (r = − 0.74); pH versus Cu (r = − 0.77); pH versus Fe (r = − 0.98) were also observed. An increase in pH could have favored the insolubility of Cu and Fe and the leaching of Na in the soil profile. This possibility should not be ruled out in the plantations of H. impetiginosus (pH = 6.1) and A. colubrina (pH = 6.1) (Table 3), which nearly reach the levels in the control (pH = 6.3), where a cover of native Paspalum was observed during the rainy season. We had expected to find a certain acidification of the soil in the forested areas as a consequence of the production of acids during the decomposition of organic matter. This could have favored the bioavailability of Cu, Fe, Mn and Zn. There might also have been an imbalance of ions in the rhizosphere due to their absorption and accumulation in the biomass of the trees, particularly under E. urophylla where low pH (5.5) and a low level of K+ (1.76 mmolc dm3) were detected (Table 3). These factors may have influenced the activity of microorganisms decomposing organic substrates. Moreover, it should be noted that the exudation and respiration of roots could contribute to the acidification of the rhizosphere (Hinsinger et al. 2003). More acidic environments inhibit the production of bacterial biomass but are compensated for this, in part by the growth and production of fungal biomass (Rousk et al. 2009).

Conclusions

Reforestation using Brazilian timber species such as Anadenanthera colubrina, Astronium fraxinifolium, Handroanthus impetiginosus, Colubrina glandulosa, and the non-native species Acacia mangium, Casuarina equisetifolia, Eucalyptus urophylla represents an alternative way to maintain or improve the biological and chemical properties of the Argisol in coastal tablelands of the tropics.

The cultivation of these species for 6 years increases organic matter, microbial biomass and glomalin-related soil protein, as well as the concentration of phosphorus, calcium, iron and manganese in the soil. The only exception was E. urophylla, which led to a reduction in pH and in the level of exchangeable potassium, reflecting a degree of stress among the soil microbiota, measured by metabolic and microbial quotients and the mineralization of the organic matter.

The effects of the species on soil biological and chemical properties indicates that H. impetiginosus and A. colubrina may be planted together, as can C. glandulosa, C. equisetifolia and A mangium, while plantations of E. urophylla and A. fraxinifolium cannot be differentiated from the non-forested control. These findings should be taken into account when devising reforestation programs with different tree species in the coastal plains of the tropics.

Notes

Acknowledgements

To SINDIMÓVEIS (Sindicato das Indústrias de Móveis do Ceará), ADECE (Agência de Desenvolvimento do Estado do Ceará) for financial support, and to the project´s support team at EMBRAPA.

References

  1. Abdi H, Williams LJ (2010) Principal component analysis. Wiley Interdisciplinary Reviews: Computational Statistics 2:433–459.  https://doi.org/10.1002/wics.101 CrossRefGoogle Scholar
  2. Alvares CA, Stape JL, Sentelhas PC, Gonçalves JLM, Sparovek G (2014) Köppen’s climate classification map for Brazil. Meteorol Z 22(6):711–728.  https://doi.org/10.1127/0941-2948/2013/0507 CrossRefGoogle Scholar
  3. Barreto PAB, Gama-Rodrigues EF, Gama-Rodrigues ACG, Barros NF, Fonseca S (2008) Activity, carbon and nitrogen of microbial biomass in eucalypt plantations in an age sequence (Text in Portuguese). R Bras Ci Solo 32:611–619.  https://doi.org/10.1590/S0100-06832008000200016 CrossRefGoogle Scholar
  4. Beech E, Rivers M, Oldfield S, Smith PP (2017) Global Tree Search: the first complete global database of tree species and country distributions. J Sustain Forest 36(5):454–489.  https://doi.org/10.1080/10549811.2017.1310049 CrossRefGoogle Scholar
  5. Behera N, Sahani U (2003) Soil microbial biomass and activity in response to eucalyptus plantation and natural regeneration in tropical soil. For Ecol Manag 174(1–3):1–11.  https://doi.org/10.1016/S0378-1127(02)00057-9 CrossRefGoogle Scholar
  6. Bernoux M, Carvalho MCS, Volkoff B, Cerri CC (2002) Brazil’s soil carbon stocks. Soil Sci Soc Am J 66(3):888–896.  https://doi.org/10.2136/sssaj2002.8880 CrossRefGoogle Scholar
  7. Bezerra CEE, Ferreira TO, Romero RE, Mota JCA, Vieira JM, Duarte LRS, Cooper M (2015) Genesis of cohesive soil horizons from north-east Brazil: role of argilluviation and sorting of sand. Soil Res 53:43–55.  https://doi.org/10.1071/SR13188 CrossRefGoogle Scholar
  8. Chandra LR, Gupta S, Pande V, Singh N (2016) Impact of forest vegetation on soil characteristics: a correlation between soil biological and physico-chemical properties. Biotech. 6(2):188.  https://doi.org/10.1007/s13205-016-0510-y Google Scholar
  9. Chazdon RL, Brancalion PHS, Laestadius L, Bennett-Curry A, Buckingham K, Kumar C, Moll-Rocek J, Vieira ICG, Wilson SJ (2016) When is a forest a forest? Forest concepts and definitions in the era of forest and landscape restoration. Ambio 45:538–550.  https://doi.org/10.1007/s13280-016-0772-y CrossRefGoogle Scholar
  10. Cheng F, Peng X, Zhao P, Yuan J, Zhong C, Cheng Y, Cui C, Zhang S (2013) Soil microbial biomass, basal respiration and enzyme activity of main forest types in the Qinling mountains. PLoS One 8:1–12.  https://doi.org/10.1371/journal.pone.0067353 Google Scholar
  11. Cunha TJF, Petrere VG, Silva DJ, Mendes MAS, Melo RF, Oliveira Neto MB, Silva MSL, Alvarez IA (2010) Principal soils of Brazilian tropical semiarid: characterization, potentialities, limitations, fertility and management. In: Sa IB, Silva PCG (eds) Brazilian semi-arid region: research, development and innovation (Text in Portuguese). Embrapa Semiárido, Petrolina, pp 49–87Google Scholar
  12. Driver JD, Holben WE, Rillig MC (2005) Characterization of glomalin as a hyphal wall component of arbuscular mycorrhizal fungi. Soil Biol Biochem 37:101–106.  https://doi.org/10.1016/j.soilbio.2004.06.011 CrossRefGoogle Scholar
  13. Fernandes MM, Oliveira TM, Fernandes MRM (2017) Natural regeneration of a forest fragment of Caatinga in the semi-arid region of Piauí (Text in Portuguese). Sci Plena 13(2):1–7.  https://doi.org/10.14808/sci.plena.2017.021701 Google Scholar
  14. Ferreira ACC, Leite LFC, Araújo ASF, Eisenhauer N (2014) Land-use type effects of soil organic carbon and microbial properties in a semi-arid region of Northeast Brazil. Land Degrad. Develop. 27(2):171–178.  https://doi.org/10.1002/ldr.2282 CrossRefGoogle Scholar
  15. Fonseca Filho IC, Bomfim BLS, Farias JC, Vieira FJ, Barros RFF (2017) Pau-d’arco-roxo (Handroanthus impetiginosus (Mart. Ex DC.) Mattos: knowledge and wood use in rural communities of Northeast Brazil (Text in Portuguese). Gaia Scientia 11(2):57–70.  https://doi.org/10.22478/ufpb.1981-1268.2017v11n2.34878 CrossRefGoogle Scholar
  16. Gispert M, Emran M, Pardini G, Doni S, Ceccanti B (2013) The impact of land management and abandonment on soil enzymatic activity, glomalin content and aggregate stability. Geoderma 202:51–61.  https://doi.org/10.1016/j.geoderma.2013.03.012 CrossRefGoogle Scholar
  17. Gomes JBV, Fernandes MF, Barreto AC, Araújo Filho JC, Curi N (2012) Soil attributes under agroecosystems and forest vegetation in the coastal tablelands of northeast Brazil. Ci Agrotec 36(6):649–664.  https://doi.org/10.1590/S1413-70542012000600007 CrossRefGoogle Scholar
  18. Hinsinger P, Plassard C, Tang C, Jaillard B (2003) Origins of root-mediated pH changes in the rhizosphere and their responses to environmental constraints: a review. Plant Soil 248:43–59.  https://doi.org/10.1023/A:1022371130939 CrossRefGoogle Scholar
  19. Holanda AC, Feliciano ALP, Freire FJ, Sousa FQ, Freire SRO, Alves AR (2017) Litter production and nutrients in area of caatinga biome (Text in Portuguese). Ciênc Florest 27(2):621–633.  https://doi.org/10.5902/1980509827747 CrossRefGoogle Scholar
  20. IBÁ (2017) (Indústria Brasileira de Árvores) Brazilian tree industry—Report 2017. http://iba.org/images/shared/Biblioteca/IBA_RelatorioAnual2017.pdf. Accessed 10 April 18
  21. Joly FX, Milcu A, Scherer-Lorenzen M, Jean LK, Bussotti F, Dawud SM, Müller S, Pollastrini M, Raulund-Rasmussen K, Vesterdal L, Hättenschwiler S (2017) Tree species diversity affects decomposition through modified micro-environmental conditions across European forests. New Phytol 214(3):1281–1293.  https://doi.org/10.1111/nph.14452 CrossRefGoogle Scholar
  22. Li J, Tong X, Awasthi MK, Wu F, Ha S, Ma J, Sun X, He C (2018a) Dynamics of soil microbial biomass and enzyme activities along a chronosequence of desertified land revegetation. Ecol Eng 111:22–30.  https://doi.org/10.1016/j.ecoleng.2017.11.006 CrossRefGoogle Scholar
  23. Li Y, Chang SX, Tian L, Zhang Q (2018b) Conservation agriculture practices increase soil microbial biomass carbon and nitrogen in agricultural soils: a global meta-analysis. Soil Biol Biochem 121:50–58.  https://doi.org/10.1016/j.soilbio.2018.02.024 CrossRefGoogle Scholar
  24. Lima SS, Leite LFC, Aquino AM, Oliveira FCO, Castro AAJF (2010) Litter and nutrient contents in argisol under different managements in Northern Piauí (Text in Portuguese). Rev Árvore 34(1):75–84.  https://doi.org/10.1590/S0100-67622010000100009 CrossRefGoogle Scholar
  25. Liu D, Huang Y, Sun H, An S (2018) The restoration age of Robinia pseudoacacia plantation impacts soil microbial biomass and microbial community structure in the Loess Plateau. CATENA 165:192–200.  https://doi.org/10.1016/j.catena.2018.02.001 CrossRefGoogle Scholar
  26. Lopes AAC, Sousa DMG, Chaer GM, Reis Junior FBR, Goedert WJ, Mendes IC (2013) Interpretation of microbial soil indicators as a function of crop yield and organic carbon. Soil Sci Soc Am J 77:461–472.  https://doi.org/10.2136/sssaj2012.0191 CrossRefGoogle Scholar
  27. Lovelock AE, Andersen K, Morton JB (2003) Arbuscular mycorrhizal communities in tropical forests are affected by host tree species and environment. Oecologia 135(2):268–279.  https://doi.org/10.1007/s00442-002-1166-3 CrossRefGoogle Scholar
  28. Lucena RFP, Albuquerque UP, Monteiro JM, Almeida CFCBR, Florentino ATN, Ferraz JSF (2007) Useful plants of the semi-arid Northeastern region of Brazil—a look at their conservation and sustainable use. Environ Monit Assess 125(1–3):281–290.  https://doi.org/10.1007/s10661-006-9521-1 CrossRefGoogle Scholar
  29. Martinkoski L, Vogel GF, Jadoski SO, Watzlawick LF (2017) Soil physical quality under silvopastoral management and secondary forest (Text in Portuguese). Floresta Ambient 24:e20160282.  https://doi.org/10.1590/2179-8087.028216 CrossRefGoogle Scholar
  30. Medeiros EV, Duda GP, Santos LAR, Lima JRS, Almeida-Cortês JS, Hammecker C (2017) Soil organic carbon, microbial biomass and enzyme activities responses to natural regeneration in a tropical dry region in Northeast Brazil. CATENA 151:137–146.  https://doi.org/10.1016/j.catena.2016.12.012 CrossRefGoogle Scholar
  31. Monteiro JM, Almeida CFCB, Albuquerque UP, Lucena RFP, Florentino ATN, Oliveira RIC (2006) Use of traditional management of Anadenanthera colubrina (Vell) Brenan in semi-arid region of northeastern Brazil. J Ethnobiol Ethnomed 2(6):1–7.  https://doi.org/10.1186/1746-4269-2-6 Google Scholar
  32. Moreira JMMA, Simioni FJ, Oliveira EB (2017) Importance and performance of planted forests in the context of Brazilian agribusiness (Text in Portuguese). Floresta 47(1):85–94.  https://doi.org/10.5380/rf.v47i1.47687 CrossRefGoogle Scholar
  33. Mota LHSO, Valladares CS (2011) Vulnerability to soil degradation in the Acaraú Basin, State of Ceara (Text in Portuguese). Ver Ciên Agron 42(1):39–50.  https://doi.org/10.1590/S1806-66902011000100006 CrossRefGoogle Scholar
  34. Oliveira JRG, Souza RG, Silva FSB, Mendes ASM, Yano-Melo AM (2009) Role of autoctone community of arbuscular mycorrhizal fungi (AMF) on the development of native plant species in revegetated restinga dunes from coastal region of Paraíba State (Text in Portuguese). Rev Bras Bot 32(4):663–670.  https://doi.org/10.1590/S0100-84042009000400005 CrossRefGoogle Scholar
  35. Plaza C, Hernández D, Garcia-Gil JC, Polo A (2004) Microbial activity in pig slurry amended soils under semiarid conditions. Soil Biol Biochem 36:1577–1585.  https://doi.org/10.1016/j.soilbio.2004.07.017 CrossRefGoogle Scholar
  36. Ramos MA, Medeiros PM, Almeida ALS, Feliciano ALP, Albuquerque UP (2008) Can wood quality justify local preferences for firewood in area of Caatinga (dryland) vegetation? Biomass Bioenergy 32:503–509.  https://doi.org/10.1016/j.biombioe.2007.11.010 CrossRefGoogle Scholar
  37. Rasid MM, Chowdhury N, Osman KT (2016) Effects of microbial biomass and activity on carbon sequestration in soils under different planted forests in Chittagong, Bangladesh. Int J Agric For 6(6):197–205.  https://doi.org/10.5923/j.ijaf.20160606.01 Google Scholar
  38. Ribeiro PEA (2010) Implementation of total nitrogen analysis in soil by the Dumas method (Text in Portuguese). Sete Lagoas, Embrapa Milho e Sorgo, p 26Google Scholar
  39. Rillig MC (2004) Arbuscular mycorrhizae, glomalin, and soil aggregation. Can J Soil Sci 84:355–363.  https://doi.org/10.4141/S04-003 CrossRefGoogle Scholar
  40. Rotter P, Malý S, Sánka O, Sánka M, Cismár D, Zbíral J, Cechmánková J, Kalábová T (2017) Is glomalin an appropriate indicator of forest soil reactive nitrogen status? J Plant Nutr Soil Sci 180:694–704.  https://doi.org/10.1002/jpln.201700046 CrossRefGoogle Scholar
  41. Rousk J, Brookes P, Baath E (2009) Contrasting soil pH effects on fungal and bacterial growth suggest functional redundancy in carbon mineralization. Appl Environ Microbiol 75(6):1589–1596.  https://doi.org/10.1128/AEM.02775-08 CrossRefGoogle Scholar
  42. Silva FC (2009) Manual of chemical analyzes of soils, plants, and fertilizers. Brasília, Embrapa Informação Tecnológica, p 627Google Scholar
  43. Silva EE, Azevedo PHS, De-Polli H (2007) Determination of soil microbial biomass carbon (BMS-C). https://ainfo.cnptia.embrapa.br/digital/bitstream/CNPAB-2010/34389/1/cot098.pdf. Accessed 10 Jan 18 (Text in Portuguese)
  44. Silva CF, Simões-Araújo JL, Silva EMR, Pereira MG, Freitas MSM, Saggin Júnior OJ, Martins MA (2012) Arbuscular mycorrhizal fungi and glomalin-soil related protein in degraded areas and revegetated with eucalypt and wattle. Ciênc Florest 22(4):749–761.  https://doi.org/10.5902/198050987556 Google Scholar
  45. Silva CF, Araújo JLS, Silva EMR, Pereira MG, Schiavo JA, Freitas MSM, Saggin-Junior OJ, Martins MA (2014) Arbuscular mycorrhizal fungi: diversity, composition and glomalin area and degraded revegetated with sesbania (Text in Portuguese). R Bras Ciên Solo 38:423–431.  https://doi.org/10.1590/S0100-06832014000200007 CrossRefGoogle Scholar
  46. Silva GF, Santos D, Silva AP, Souza JM (2015) Soil quality indicators under different land use systems in the agreste region of paraiba, Brazil (Text in Portuguese). Rev Caatinga 28(3):25–35CrossRefGoogle Scholar
  47. Silva UBT, Delgado-Jaramillo M, Aguiar LMAS, Bernard E (2018) Species richness, geographic distribution, pressures, and threats to bats in the Caatinga dryland of Brazil. Biol Conserv 221:312–322.  https://doi.org/10.1016/j.biocon.2018.03.028 CrossRefGoogle Scholar
  48. Singh AK, Raij A, Pandey V, Singh N (2017) Contribution of glomalin to dissolve organic carbon under different land uses and seasonality in dry tropics. J Environ Manage 192:142–149.  https://doi.org/10.1016/j.jenvman.2017.01.041 CrossRefGoogle Scholar
  49. Six J, Paustian K (2014) Aggregate-associated soil organic matter as an ecosystem property and a measurement tool. Soil Biol Biochem 68:A4–A9.  https://doi.org/10.1016/j.soilbio.2013.06.014 CrossRefGoogle Scholar
  50. Sparling GP, West AW (1988) A direct extraction method to estimate soil microbial C: calibration in situ using microbial respiration and 14C-labeled cells. Soil Biol Biochem 20(3):337–343.  https://doi.org/10.1016/0038-0717(88)90014-4 CrossRefGoogle Scholar
  51. Tótola MR, Chaer GM (2002) Microbiological and microbiological processes as indicators of soil quality. In: Alvares-Venegas VM, Schaefer CEGR, Barros NF, Melo JWV, Costa LM (eds) Tópicos em ciência do solo (Text in Portuguese). Sociedade Brasileira de Ciência do Solo, Viçosa, pp 195–276Google Scholar
  52. Vance ED, Brookes PC, Jenkinson DS (1987) An extraction method for measuring soil microbial biomass C. Soil Biol Biochem 19(6):703–707.  https://doi.org/10.1016/0038-0717(87)90052-6 CrossRefGoogle Scholar
  53. Vasconcellos RLF, Bonfim JA, Baretta D, Cardoso EJBN (2016) Arbuscular mycorrhizal fungi and glomalin-related soil protein as potential indicators of soil quality in a recuperation gradient of Atlantic forest. Land Degrad Dev 27:325–334.  https://doi.org/10.1002/ldr.2228 CrossRefGoogle Scholar
  54. Vieira M, Schumacher MV, Araújo EF, Corrêa RS, Caldeira MVW (2014) Deposition of litter and nutrients in planting of Eucalyptus urophylla × E globulus (Text in Portuguese). Floresta Ambient 21(3):327–338.  https://doi.org/10.1590/2179-8087.053913 CrossRefGoogle Scholar
  55. Wright SF, Upadhyaya A (1998) A survey of soils for aggregate stability and glomalin, a glycoprotein produced by hyphae of arbuscular mycorrhizal fungi. Plant Soil 198(1):97–107.  https://doi.org/10.1023/A:1004347701584 CrossRefGoogle Scholar
  56. Wright SF, Franke-Snyder M, Morton JB, Upadhyaya A (1996) Time-course study and partial characterization of a protein on hyphae of arbuscular mycorrhizal fungi during active colonization of roots. Plant Soil 181:193–203.  https://doi.org/10.1007/BF00012053 CrossRefGoogle Scholar
  57. Xu X, Thornton PE, Posto WM (2013) A global analysis of soil microbial biomass carbon, nitrogen and phosphorus in terrestrial ecosystems. Glob Ecol Biogeogr 22:737–749.  https://doi.org/10.1111/geb.12029 CrossRefGoogle Scholar
  58. Zang J, Tang X, Zhong S, Yin G, Gao Y, He X (2017) Recalcitrant carbon components in glomalin-related soil protein facilitates soil organic carbon preservation in tropical forests. Sci Rep 7:2391.  https://doi.org/10.1038/s41598-017-02486-6 CrossRefGoogle Scholar

Copyright information

© The Author(s) 2019

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  • Olmar Baller Weber
    • 1
  • Maria Catia Barroso da Silva
    • 2
  • Cristiane Figueira da Silva
    • 3
    Email author
  • João Alencar de Sousa
    • 1
  • Carlos Alberto Kenji Taniguch
    • 1
  • Deborah dos Santos Garruti
    • 1
  • Ricardo Espindola Romero
    • 4
  1. 1.Embrapa Tropical AgroindustryFortalezaBrazil
  2. 2.Centro de Ciências e TecnologiaUniversidade Estadual do CearáFortalezaBrazil
  3. 3.Universidade Federal Rural do Rio de JaneiroRio De JaneiroBrazil
  4. 4.Depto Ciência do SoloUniversidade Federal do CearáFortalezaBrazil

Personalised recommendations