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Article

Does the Process of Passive Forest Restoration Affect the Hydrophysical Attributes of the Soil Superficial Horizon?

by
Nayana Alves Pereira
1,
Simone Di Prima
2,3,
Renata Cristina Bovi
1,
Laura Fernanda Simões da Silva
1,
Gustavo de Godoy
1,
Rafaela Pereira Naves
1 and
Miguel Cooper
1,*
1
Luiz de Queiroz School of Agriculture, University of São Paulo, Av. Pádua Dias 11, Piracicaba 13418-900, Brazil
2
Agricultural Department, University of Sassari, Viale Italia, 39, 07100 Sassari, Italy
3
Univ Lyon, Université Claude Bernard Lyon 1, CNRS, ENTPE, UMR 5023 LEHNA, F-69518 Vaulx-en-Velin, France
*
Author to whom correspondence should be addressed.
Water 2020, 12(6), 1689; https://doi.org/10.3390/w12061689
Submission received: 17 April 2020 / Revised: 8 June 2020 / Accepted: 10 June 2020 / Published: 12 June 2020
(This article belongs to the Special Issue Advances in Ecohydrology for Water Resources Optimization in Arid and Semi-arid Areas)
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Abstract

:
There has been an increase in the area of secondary tropical forests in recent years due to forest restoration in degraded areas. Recent analyses suggest that the success of passive forest restoration is highly uncertain and needs to be better understood. This study aimed to investigate the behavior of saturated hydraulic conductivity (Ks) and some hydrophysical soil attributes between agricultural land uses, restored forests, and a degraded forest fragment. The areas evaluated are located in the municipality of Rio Claro, São Paulo, Brazil, under different types of land use: (i) two areas in the process of passive forest restoration: one of 18 and another of 42 years (NR18 and NR42); (ii) a degraded forest fragment (FFD); (iii) pasture (P), and (iv) sugarcane (SC). The hydraulic soil conductivity characterization was performed using the Beerkan method. Dry soil bulk density (BD), total porosity (Pt), macroporosity (Mac), microporosity (Mic), penetration resistance (PR), mean aggregate diameter (MWD), and soil organic carbon (OC) were also determined. The comparative analysis of the hydrophysical attributes of the soil superficial horizon in agricultural land uses (P and SC), restored forests (NR18 and NR42), and a degraded forest (DFF) confirms that the recovery of soil hydrological functioning in ongoing forest restoration processes can be a relatively slow process. In addition, the intensity of previous land use leaves footprints that can affect passive restoration areas for decades after agriculture abandonment, increasing the time for the recovery of Ks and soil hydrophysical attributes.

Graphical Abstract

1. Introduction

A substantial increase in the area of secondary tropical forests has been occurring in the last 20 years [1,2]. There is promising evidence that these areas have been increasing as a result of the ambitious goals implemented worldwide for the recovery of degraded and deforested land, such as the Bonn Challenge (2011) and the New York Declaration on Forest (2014). Many degraded areas, especially those that have not been subjected to many years of monoculture, are recovered using passive restoration techniques. One of these techniques is natural forest regeneration, which takes advantage of the resilience of degraded areas by stimulating the germination of local seed banks, seed dispersal processes, regrowth of trunks, or seedlings that resisted the disturbances. This restoration approach is economically advantageous because it reduces implementation costs, such as purchase of seedlings, inputs, application of silvicultural techniques, maintenance of plantations, and labor [3].
The potential of passive restoration to recover some groups of biodiversity in tropical regions (such as plants, birds, and invertebrates) and structural parameters of vegetation (density, biomass, and height) is well reported in the literature [4,5,6]. Furthermore, large-scale forest cover, either natural or restored, has impacts on climate, soil, and hydrology [7]. Soil is the substrate for forest restoration, and the knowledge of its functioning is essential for understanding and managing restoration processes. Thus, the success of forest restoration processes depends on the presence of a healthy soil environment in which seedlings and trees can develop successfully. The recovery of soil hydrophysical attributes is fundamental to maximize the success of restoration efforts [8]; however, it is still not fully understood in forest restoration because soil is still under-investigated in these environments. Monitoring soil hydrophysical attributes in forest restoration environments, such as soil bulk density, total porosity, macro and microporosity, and aggregate stability, can supply data on soil-related functions, such as root growth, water infiltration and drainage, gas exchange, biological activity, water retention, and carbon stock supply [9].
The hydraulic conductivity of saturated soil (Ks) is also a key parameter that describes the movement of water in the soil and exerts a dominating influence on the partitioning of rainfall into vertical and lateral flow paths. Therefore, estimates of Ks are essential for describing and modeling hydrological processes [10]. Moreover, Ks is dependent on the soil structure, an attribute deeply affected by soil management [11,12]. In forest ecosystems, it is expected that there will be greater input and decomposition of the litter, which is the main supply of organic matter to the soil and regulates the cycling of nutrients. Higher concentrations of organic matter in the soil also favor microbial and soil fauna activities and provide more suitable conditions for soil protection [13]. The formation and evolution of soil aggregates are driven by biological and microbial activities, and the supply of organic matter under forests helps to maintain the stability of soil aggregates [14]. The literature reports some studies that evaluated areas in different stages of passive restoration in tropical forests [4,7,15,16,17,18,19]. In some areas, improvements were observed in the contribution of soil biomass and biodiversity [4,17], lower soil bulk density, and greater total soil porosity [17,19], which probably influenced the formation of more stable aggregates in the soil and higher Ks. In general, the results reinforce the importance of expanding knowledge about the hydrophysical attributes in forest restoration areas, since the forest’s age alone is not sufficient to restore soil functioning. Besides, different stages of forest restoration may show distinct signs of soil recovery.
Among Brazilian biomes, the Atlantic Forest suffered the most significant degradation, due to the conversion of primary forests to sugarcane and coffee plantations [20]. Currently, it has a high number of degraded fragments and only 12% of the original coverage [21]. In this biome, the current forest cover is a heterogeneous forest mosaic of different ages, located in different landscape conditions and, therefore, with varying levels of disturbance [22]. Understanding the degree of tropical forest disturbances and their recovery by restoration processes is challenging. In addition, the time that natural regeneration takes to recover the hydrophysical soil attributes disturbed by intense previous land use after deforestation is very variable [19]. These processes need to be better understood [18,23,24,25,26].
Considering that the soil structure and functioning are essential for the improvement of Ks [8,17,18] and that the impact of passive restoration on the soil hydrophysical functioning is still little known, this study aimed to answer the following question: Is passive restoration capable of improving soil Ks, as well as the physical attributes of the soil superficial horizon? To answer this question, we investigated the behavior of Ks and some soil physical attributes of the soil superficial horizon in forests of different ages undergoing passive restoration by natural regeneration, a degraded forest fragment, a pasture, and a sugarcane field.

2. Material and Methods

2.1. Study Area

The study was carried out in the county of Rio Claro (22°20′ S, 47°34′ W), São Paulo State, Southeast Brazil (Figure 1). The area is inserted in the Corumbataí River hydrographic basin, and has more than 1700 km2 and more than 200 years of land-use change [22], with an altitude between 562 and 643 m a.s.l. The climate is classified as Cwa, according to Köppen classification, characterized by dry winters and rainy and hot summers [27]. The wettest period is from December to February, while the driest, from June to August. The average annual precipitation is 1344 mm, with an average annual temperature of 20.5 °C [27]. According to the geomorphological map of São Paulo state, the area is located in the Peripheral Depression of the São Paulo state [28]. The native forest cover is classified as semi-deciduous rainforest [29], belonging to the Atlantic Forest biome [30].
The exploitation of the Brazilian Atlantic Forest was accentuated in the 20th century, resulting in severe changes to the ecosystems, primarily by reducing and increasing the pressure on biodiversity [31]. After almost 500 years of land-use changes, the Atlantic Forest, which covered about 150 million hectares in Brazil, currently has less than 12% of its original forest cover (1.2 million km2) [21]. Nowadays, more than 80% of forest fragments are smaller than 50 ha [21], isolated, unprotected, and severely altered [32]. The few fragments larger than 100 ha are located on steep terrain, where human occupation is difficult [33].
The intense deforestation in the study area resulted from the exploitation and expansion of coffee, cotton, and pastures [32] during the first half of the twentieth century [31]. According to 1962 satellite images [22], the actual restored forest cover in the study area was previously occupied by pastures. Restoration by natural regeneration processes occurred due to two main reasons: (1) the abandonment of areas, which had low agricultural suitability, due to the predominance of steep slopes, and the presence of sandy and rocky soils [22]; and (2) environmental planning efforts to comply with the Brazilian Forest Code [22,30]. Currently, land use in this area is dominated by Urochloa decumbens Stapf pastures (43.68%), sugar cane (25.57%), fragments of native forest (12.36%), and other types of land uses (14.5%) [34].
For the development of this study, we selected five different land uses: (a) pasture (P); (b) sugarcane (SC); (c) 18-year-old passive forest restoration by natural regeneration (NR18); (d) 42-year-old passive forest restoration by natural regeneration (NR42), and (e) degraded forest fragment (DFF) (Figure 1 and Figure 2). The DFF area is currently isolated, but before this measure, there were intense cattle invasions associated with frequent burning in the contiguous sugar cane fields during the dry season that invaded the remnant fragment. Oxisols and Ultisols represent the soils of the study area, according to Soil Taxonomy, 2014 [35] with sandy loam and loamy, sandy textures (Table 1). The soil texture of the superficial horizon did not show important variations among the study sites being considered homogeneous.

2.2. Soil Sampling and Analytical Procedures

The field campaign and soil sampling were performed during July 2018. Within each study area, five sampling points were selected, distant 5.5 m from each other. The soil water infiltration was measured using the Beerkan method [36]. In each study area, ten water infiltration measurements were performed (two infiltration measurements at each sampling point), using a steel cylinder with an internal diameter of 0.16 m. The sampled infiltration surfaces were assumed to be large enough to allow the determination of representative Ks values that accounted for soil heterogeneity and the contribution of biopores [37]. The location of the insertion of the steel cylinder was previously prepared by removing the litter and other materials to expose the soil surface. Subsequently, the steel ring was inserted 0.01 m into the soil. For each measurement, a volume of 150 mL of water was added repeatedly into the steel cylinder. The time for complete infiltration of each poured volume was logged. This procedure was repeated until the difference between the infiltration times of two or three consecutive tests underwent minimal variation. In total, 50 infiltration runs (10 runs × 5 areas) of cumulative infiltration (I) as a function of time (t) were performed.
The values of Ks were estimated by the simplified Beerkan infiltration method (SSBI—Steady version of the Simplified method based on a Beerkan Infiltration run) [38]. The SSBI method estimates Ks as follows:
K s = i s γ γ w r α * + 1
where is (L T−1) is the slope of the linear regression fitted to the final portion of the cumulative infiltration time series, r (L) is the ring radius, γw is a dimensionless constant related to the shape of the wetting front often fixed at 1.818 [39], γ is an infiltration constant, often fixed at 0.75 [40,41], and α* (L−1) is the sorptive number, that expresses the relative importance of the capillary over gravity forces during water movement in unsaturated soil [42,43]. According to a previous investigation carried out on similar soils in the Atlantic forest [44], we assumed a value of α* = 0.0012 mm−1, also taking into account that it represents the suggested first approximation value for most field soils [45].
At each sampling point, disturbed and undisturbed soil samples were collected at 0–0.10 m depth. The disturbed samples were collected to determine the gravimetric moisture content of the saturated soil (Ug, %), the organic carbon (OC, g kg−1), and the soil particle density (ρs, g cm−3). Undisturbed samples were collected using Kopeck metal rings with cutting edges measuring 0.05 × 0.05 m (diameter × height). These samples were used to determine: (a) the initial volumetric moisture content (θi, cm3 cm−3), (b) total porosity (Tp, cm3 cm−3), (c) macroporosity (Mac, cm3 cm−3), (d) microporosity (Mic, cm3 cm−3), (e) dry soil bulk density (Pt, g cm−3) and (f) penetration resistance (PR, MPa). Bulk density (BD) was determined by the ratio of the dry soil mass and the cylinder volume [46]. The volumetric moisture content of the saturated soil (θs, cm3 cm−3) was obtained using the measure of BD (θs = Ug × BD). We also computed the depths of wetting fronts for all infiltration experiments at the end of the experiment, Zwf (L). For this purpose, we considered a piston flow width displacement of the volume of water in the soil porosity [47].
In the laboratory, each undisturbed soil sample was gradually saturated with water by capillary action and then weighed. The total porosity (Tp) was calculated by the ratio between BD and ρs [48]. The soil particle density (ρs) was determined by a helium gas pycnometer (ACCUPYC 1330, Micromeritics Instrument Corporation®, Norcross, GA, USA). Mic was estimated using the tension table with the soil matric potential of −6 kPa (Ψ = −0.006), and Mac was determined by the difference between Tp and Mic.
PR was determined using an electronic benchtop penetrometer (CT3 Texture Analyzer, Brookfield, Middlebore, MA, USA), equipped with a 25 kg load cell and a metal rod with a 30° semi-angle cone tip, a basal diameter of 3.81 mm, and penetration velocity of 10 mm min−1. PR measurements were obtained through an automated data acquisition system. Such measures were collected at the center of each undisturbed soil sample with soil moisture equivalent to a water pressure of 10 kPa. For all samples, the measurements obtained from the upper (1 cm) and lower (1 cm) were discarded, following the procedures of Imhoff [49].
The determination of the aggregate means weight diameter (MWD) was performed using the dry methodology [50]. For this, 50 g of soil were collected from the 0–0.10 m depth. These samples were separated into size classes by sieving in a dry medium through a Solotest vibratory mechanical stirrer containing a set of mesh sieves: 4.0, 2.0, 1.0, 0.50, and 0.25 mm. The calculations of (MWD) were obtained using the equation of Kemper [51]:
M W D = ∑ X i s . W i s
where MWD = mean weight diameter of the soil aggregates (mm); Xis = mean diameter of each class (mm); Wis = proportion of aggregates in each sieve class (i) (%).

2.3. Data Analysis

For each studied variable (Ks, BD, Tp, Mac, Mic, PR, MWD, and OC) analysis of variance (ANOVA) was performed, considering the type of land use as an explanatory variable (P, SC, NR18, NR42, and DFF), after having checked that the assumptions of normality of residues and homogeneity of variances were met, through the Shapiro–Wilk test and the Barllet test, respectively. In the case of significance at 10%, the Tukey test was applied, which compares the means two by two. For the variables Ks and PR, normality was determined using the natural logarithm of the value obtained initially, due to the high variability of the data [52]. For the Mic, the assumptions of ANOVA were not met, even after data transformation. A Kruskal–Wallis non-parametric test was then performed, also at a 10% significance level. To simultaneously compare the hydrophysical attributes of the soil between the different uses, a principal component analysis (PCA) was performed for standardized data. All analyses were performed in the R software (R Core Team, Vienna, Austria, 2018).

3. Results

Soil Hydrophysical Attributes of the Study Areas

The soils in the study area did not present soil particle distribution differences down to the depth of 0.30 m (Table 1). Among the different types of land use studied in this investigation, there were statistically significant differences for the variables Ks, Mic, PR, MWD, and OC (Table 2 and Figure 3). The BD, Tp, and Mac variables did not show statistically significant differences among the studied land uses (Table 2).
Considering soil Mic, SC only differed from NR18. For this attribute, NR42 presented a medium value of the coefficient of variance (CV) and the rest a low CV. Significant differences were observed for PR between the agricultural (P and SC) and the passive restoration areas (NR18 and NR42). PR values for P and SC were higher than NR18, NR42, and DFF (Figure 3). NR18 and DFF had PR values three times lower than P, and NR42 had PR values four times lower than P. When compared to SC, the passive restoration areas and DFF also presented lower PR values. High CV values for PR were found for SC, NR18, and NR42.
The land use with the highest passive restoration age—NR42—presented similar MWD values to DFF and P and differed from all the other land uses. We noted that the youngest passive restoration area—NR18–presented MWD values similar to both agricultural land uses (P and SC). For this attribute, CV values were low for P and NR42 and medium for SC, NR18, and DFF (Table 2; Figure 3).
Considering the soil OC content, P had the highest values and was similar to NR42, while SC, NR18, and DFF presented lower values. All land uses had medium CV’s (Table 2).
The mean values of the wetting front depths ranged from 219 to 319 mm, depending on the site. Therefore, Ks data may be considered representative of the studied upper layers (Table 1). In addition, we did not detect any restrictive layer that may have affected Ks predictions [54].
Ks was moderately high for P and presented significant differences with both passive restoration areas. NR18 and NR42 had high Ks values, approximately double and four times the Ks value of P, respectively. SC and DFF had similar values of Ks. All land uses presented high CV values for Ks, especially NR42, followed by P (Table 2).
Although no significant differences among land uses were observed for BD, a tendency of decrease in this attribute could be observed in the forest land uses when compared to the agricultural land uses (Table 2). The same was valid for Tp, where the restoration age showed a tendency to improve, presenting similar values to DFF. The behavior of Ks also showed a tendency to increase according to restoration age, considering the mean and absolute values of this attribute (Figure 3).
A PCA was performed to investigate and relate the soil hydrophysical attributes between the different land uses (Table 3 and Figure 4).
Component 1 represents 48.39% of the data variation, and the variables most related to this Axis were BD (positively) and Tp (negatively) (Table 3 and Figure 4). In addition, BD was positively correlated to PR, while Tp was mainly correlated to Mac and, to a lesser extent, to Mic. Ks remained almost perpendicular to component 1; this demonstrated that there was no correlation between this attribute with BD, considering all land uses.
The higher values of component 1 indicate higher levels of soil degradation, while the lower values indicate the opposite. On this Axis, the forest areas are positioned to the left, while the non-forest areas are on the right. In this case, the highest values of BD and lower values of Tp were associated with agricultural land uses (P and SC), while the lower BD values were associated with DFF. Therefore, the sequence of the most degraded to the least degraded areas was P, SC, NR18, NR42, and DFF, with a remarkable tendency for less degradation of the two areas in the process of restoration and DFF.
Component 2 explained 26.8% of the data variation. The variables most correlated to this Axis were Mac (positively) and Mic (negatively), and Ks had a low relationship with Mac, Tp, and BD. Considering the land use, it is possible to notice that the forest areas were distributed along the entire Axis, while P was below, and SC was above. Besides that, the highest values of Mac were more associated with SC, while Mic and PR were related to P. As for the restoration areas (NR18 and NR42), we noticed a tendency to decrease Mic and increase Ks, according to the ages of passive restoration.

4. Discussion

According to the Ks classification of the Soil Survey Staff [55], the Ks values obtained in the studied areas varied between high (36 < 360 mm h−1) and moderately high (3.6 < 36 mm h−1). At the P site, Ks was moderately high due to soil compaction, evidenced by the high PR value, high Mic (Figure 3 and Figure 4), and low MWD (Table 2). PR integrates the effects of soil bulk density and moisture, affecting the soil physical conditions for root growth [56]. The PR value for P was above 2 Mpa, which is considered a critical limit for root growth in the literature [57,58]. This result reports the degradation of the area, as demonstrated in the PCA (Figure 4). The high content of OC is explained by the accumulation of biomass in pastures, promoted by the root system of grasses that are abundant during the year and by the organic matter added by the animals [57,59], but its effect on soil aggregation was not observed due to the high level of compaction in this area. The decrease in Ks has been similarly reported in several studies after the establishment of pastures in tropical soils and occurred due to changes in the soil pore distribution caused by animal trampling. A study at the watershed scale in the Atlantic Forest biome showed moderately high values of Ks (22 mm h−1) in the surface 0.15 m soil layer in pasture when compared to eucalyptus (40 mm h−1) and a 44-year-old secondary forest (61 mmh−1) [60]. Another study, carried out in the Amazon, also reported a decrease in soil water infiltration after conversion from forest to pasture, from 1258 to 100 mm [61]. The reduction in soil water infiltration and, consequently, lower Ks, promote lateral water fluxes [10,62,63,64], higher runoff and, consequently, higher soil erosion [65].
The absence of soil cover combined with agricultural machinery used in the SC area promoted the lowest values of MWD and OC, which probably influenced the lower value of Ks when compared to NR18 and NR42. Even though Ks in SC did not differ statistically from NR18 and NR42, the latter have higher absolute values. This land use was highly associated with soil Mac (Figure 4), which was probably associated with soil tillage operations carried out during sugarcane replanting and in the period between ratoon-crop harvests. These soil management practices can have positive effects, in the short term, on the physical quality of the soil, due to the modifications provided to the soil porosity, mainly in the superficial soil horizons [66,67].
The values obtained for Ks in the NR18 and NR42 areas were classified as high, according to Soil Survey Staff [55]. Indeed, these Ks values indicated a good ability of the soil to infiltrate and percolate plant-available water to the root zone and to drain excess water out of the root zone [64]. These areas showed a tendency to improve the MWD and OC variables, where the latter is one of the primary agents of formation and stabilization of soil aggregates in the soil superficial horizons, with a direct influence on porosity [68,69]. The MWD indicates the largest stable aggregates in the soil in terms of structural organization. Aggregates with MWD greater than 0.25 mm are considered stable [68]. The addition of OC (root activity, growth, and functioning; death of plant tissues; among others) provides substrate for microbial growth and the production of secondary organic compounds that act as cementing agents. These agents help aggregate formation [70] and stabilization that increase their resistance when submitted to external forces [71]. A tendency for higher soil aggregation was evident in the NR18 and NR42 areas, improving soil structure and, consequently, Ks (Figure 3). Indeed, soils with a high degree of aggregate stability usually also have good soil physical quality, with good soil porosity, aeration, water retention and infiltration [72]. In this sense, soils with greater aggregate stability are considered structurally superior to similar soils with weaker aggregation [73].
The high variability of Ks obtained in NR42 (Figure 3 and Figure 4) suggested the occurrence of preferential flows in this area, which can occur due to several factors, among them, the presence of continuous biopores created by large roots of decomposed trees or channels created by macrofauna [25,43,74]. This hypothesis corroborates studies developed in forests under tropical climates [18,64,75,76]. However, although preferential flows are considered a common phenomenon in forested soils, future studies are needed to understand better how they work in areas of passive restoration and their effects in space and time, considering that they are the main cause of groundwater pollution and contamination [77].
The set of soil attributes of the soil superficial horizon analyzed in this study demonstrated that the DFF land use was the least degraded (Figure 4). The lower value of BD and higher values of Tp, Mac, and MWD (Table 2) show that, although the forest fragment has suffered some degradation in the past, it still maintains its functionality, even though Ks presented lower values when compared to NR18 and NR42. The fact that DFF presented lower Ks values when compared to the restored areas can be possibly due to other factors that affect this attribute in natural environments, such as plant density and diversity, canopy cover [3,19], geological and topographic variations [78], soil water repellency [44,77], among others.
The joint analysis of the hydrophysical attributes evaluated here, through the PCA, showed that areas in the process of passive restoration had a lower degree of degradation when compared to areas of agricultural use (Figure 4). This lower degree of degradation in restored areas can be interpreted as a general tendency of improvement in the hydrophysical attributes after restoration initiation. High values of Ks, aggregate stability, and the improvement tendencies in the other evaluated attributes evidence the soil hydrophysical recovery in the restored forests (Table 2; Figure 3 and Figure 4) [79].
The time scale for the recovery of the hydrophysical attributes in restoration areas in our study was longer than other studies that observed positive changes in these attributes in shorter periods after restoration implementation. In a study in Mexico, areas with passive restoration times of 0, 4, 10, and 20 years after pasture abandonment showed a significant BD improvement associated with an increase in water infiltration rates with restoration age [23]. On the other hand, OC contents were lower in the 10- and 20-year-old restored forests (3.94 and 3.71%, respectively) when compared to the actual pasturelands (4.79%). Higher water infiltration rates and OC contents were observed with increasing age of forest restoration (15 and 20 years) with values varying between 493 and 2.462 mm h−1 when compared to pastures in Costa Rica [75]. Other studies also showed improvements in hydrophysical attributes in shorter periods (8 to 12 years) after restoration initiation [76,80]. The longer time needed to recover the hydrophysical attributes in our study could be explained by the longer period this area was previously occupied by pastures and also by the legacy effect of intense degradation processes that occurred before restoration. This legacy effect phenomenon was observed by Piché and Kelting [14] in soils under 55- to 60-year-old secondary forests. These previous land-use effects could explain the slow recovery of Mac, TP, and BD (Table 2) in NR18 and NR42.

5. Conclusions

The comparative analysis of the soil superficial horizon’s hydrophysical attributes in agricultural land uses (P and SC), forests of different ages undergoing passive restoration by natural regeneration (NR18 and NR42), and a degraded forest fragment (DFF) confirms that the evolution of different soil hydrological functions in the ongoing restoration of natural ecosystems can be a relatively slow process.
The observed trend of increasing Ks and recovery of soil physical attributes in passive forest restoration of different ages leads us to conclude that the time that the secondary succession takes to recover the soil hydrological properties, disturbed by the previous deforestation and the use of pastures, are not governed just by the restoration time.
The intensity of previous land use leaves footprints that can affect areas submitted to natural regeneration processes for decades after agriculture abandonment and is one of the main critical factors that affect soil physical attributes and Ks over time. This is alarming because it is well known that the soil hydrophysical attributes affect fluid flow and transport, and can, consequently, influence runoff generation and water table recharge.
Important ecosystem services, such as water regulation and provision, depend on the recovery of soil hydrological functions in degraded environments. Knowledge of how forest restoration processes, either active or passive, affect soil function, is essential. The data presented and discussed in this study adds to this knowledge and shows that the recovery of soil hydrophysical functioning is a slow process and varies according to the type of soil attribute and the previous land use and its degradation legacy.

Author Contributions

N.A.P. carried out the data collection and wrote the initial draft; M.C. participated in the design of the study and helped to draft and edit the manuscript; L.F.S.d.S. and R.C.B. provided important advice on the concept of the structuring of the manuscript; S.D.P., R.C.B., L.F.S.d.S., G.d.G. and R.P.N. analyzed the data and made great contributions to writing the manuscript. All authors reviewed and approved the final manuscript.

Funding

This research was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil—(CAPES), Finance Code 001.

Acknowledgments

Miguel Cooper acknowledges the National Council for Scientific and Technological Development (CNPq) for the fellowship. We would like to thank Aline Aparecida Fransozi, Sergio Esteban Lozano-Baez, Ricardo César Gomes, Silvio F. Barros, and Pedro H.S. Brancalion for support at the beginning of this study and the team “Cooper Trupe” for field support. We would like to thank Vittoria Giannini, who helped with the English revision of the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Chazdon, R. Beyond Deforestation: Restoring Forests and Ecosystem Services on Degraded Lands. Science 2008, 320, 1458–1460. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  2. Filoso, S.; Bezerra, M.O.; Weiss, K.C.B.; Palmer, M.A. Impacts of forest restoration on water yield: A systematic review. PLoS ONE 2017, 12, e0183210. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  3. Chazdon, R.L. Regeneração de florestas tropicais. Boletim Museu Paraense Emílio Goeldi Ciencias Naturais 2012, 7, 195–218. [Google Scholar]
  4. Crouzeilles, R.; Ferreira, M.S.; Chazdon, R.; Lindenmayer, D.B.; Sansevero, J.B.B.; Monteiro, L.; Iribarrem, A.; Latawiec, A.E.; Strassburg, B.B.N. Ecological restoration success is higher for natural regeneration than for active restoration in tropical forests. Sci. Adv. 2017, 3, e1701345. [Google Scholar] [CrossRef] [Green Version]
  5. Meli, P.; Holl, K.D.; Benayas, J.M.R.; Jones, H.P.; Jones, P.C.; Montoya, D.; Mateos, D.M. A global review of past land use, climate, and active vs. passive restoration effects on forest recovery. PLoS ONE 2017, 12, e0171368. [Google Scholar] [CrossRef]
  6. Rozendaal, D.M.A.; Bongers, F.; Aide, T.M.; Dávila, E.Á.; Ascarrunz, N.; Balvanera, P.; Becknell, J.M.; Bentos, T.V.; Brancalion, P.H.S.; Cabral, G.A.L.; et al. Biodiversity recovery of Neotropical secondary forests. Sci. Adv. 2019, 5, eaau3114. [Google Scholar] [CrossRef] [Green Version]
  7. Godsey, S.; Elsenbeer, H. The soil hydrologic response to forest regrowth: A case study from southwestern Amazonia. Hydrol. Process. 2002, 16, 1519–1522. [Google Scholar] [CrossRef]
  8. Giannini, V.; Bertacchi, A.; Bonari, E.; Silvestri, N. Rewetting in Mediterranean reclaimed peaty soils and its potential for phyto-treatment use. J. Environ. Manag. 2018, 208, 92–101. [Google Scholar] [CrossRef] [Green Version]
  9. Burger, J.A.; Kelting, D.L. Using soil quality indicators to assess forest stand management. For. Ecol. Manag. 1999, 122, 155–166. [Google Scholar] [CrossRef]
  10. Zimmermann, A.; Schinn, D.S.; Francke, T.; Elsenbeer, H.; Zimmermann, B. Uncovering patterns of near-surface saturated hydraulic conductivity in an overland flow-controlled landscape. Geoderma 2013, 195, 1–11. [Google Scholar] [CrossRef]
  11. Mesquita, M.G.B.; Moraes, S.O. The dependence of the saturated hydraulic conductivity on physical soil properties. Ciência Rural 2004, 34, 963–969. [Google Scholar] [CrossRef] [Green Version]
  12. Ahuja, L.R.; Naney, J.W.; Green, R.E.; Nielsen, D.R. Macroporosity to Characterize Spatial Variability of Hydraulic Conductivity and Effects of Land Management. Soil Sci. Soc. Am. J. 1984, 48, 699–702. [Google Scholar] [CrossRef]
  13. De Andrade, A.G.; Tavares, S.D.L.; Coutinho, H.D.C. Contribuição da serrapilheira para recuperação de áreas degradadas e para manutenção da sustentabilidade de sistemas agroecológicos. Embrapa Solos 2003, 24, 55–63. [Google Scholar]
  14. Piché, N.; Kelting, D.L. Recovery of soil productivity with forest succession on abandoned agricultural land. Restor. Ecol. 2015, 23, 645–654. [Google Scholar] [CrossRef]
  15. Ziegler, A.D.; Giambelluca, T.; Tran, L.T.; Vana, T.T.; Nullet, M.A.; Fox, J.; Vien, T.D.; Pinthong, J.; Maxwell, J.; Evett, S. Hydrological consequences of landscape fragmentation in mountainous northern Vietnam: Evidence of accelerated overland flow generation. J. Hydrol. 2004, 287, 124–146. [Google Scholar] [CrossRef]
  16. Paul, M.; Catterall, C.P.; Pollard, P.; Kanowski, J. Recovery of soil properties and functions in different rainforest restoration pathways. For. Ecol. Manag. 2010, 259, 2083–2092. [Google Scholar] [CrossRef]
  17. Nyberg, G.; Bargués-Tobella, A.; Kinyangi, J.; Ilstedt, U. Soil property changes over a 120-yr chronosequence from forest to agriculture in western Kenya. Hydrol. Earth Syst. Sci. 2012, 16, 2085–2094. [Google Scholar] [CrossRef] [Green Version]
  18. Gageler, R.; Bonner, M.; Kirchhof, G.; Amos, M.; Robinson, N.; Schmidt, S.; Shoo, L.P. Early Response of Soil Properties and Function to Riparian Rainforest Restoration. PLoS ONE 2014, 9, e104198. [Google Scholar] [CrossRef]
  19. Lozano, S.; Cooper, M.; Meli, P.; Ferraz, S.F.; Rodrigues, R.R.; Sauer, T.J. Land restoration by tree planting in the tropics and subtropics improves soil infiltration, but some critical gaps still hinder conclusive results. For. Ecol. Manag. 2019, 444, 89–95. [Google Scholar] [CrossRef]
  20. Sparovek, G.; Barretto, A.; Berndes, G.; Martins, S.; Maule, R. Environmental, land-use and economic implications of Brazilian sugarcane expansion 1996–2006. Mitig. Adapt. Strat. Glob. Chang. 2008, 14, 285–298. [Google Scholar] [CrossRef]
  21. Ribeiro, M.C.; Metzger, J.P.; Martensen, A.C.; Ponzoni, F.J.; Hirota, M.M. The Brazilian Atlantic Forest: How much is left, and how is the remaining forest distributed? Implications for conservation. Biol. Conserv. 2009, 142, 1141–1153. [Google Scholar] [CrossRef]
  22. Ferraz, S.F.; Ferraz, K.M.; Cassiano, C.C.; Brancalion, P.H.S.; da Luz, D.T.; Azevedo, T.N.; Metzger, J.P. How good are tropical forest patches for ecosystem services provisioning? Landsc. Ecol. 2014, 29, 187–200. [Google Scholar] [CrossRef]
  23. Peñuela, M.C.; Drew, A.P. A Model to Assess Restoration of Abandoned Pasture in Costa Rica Based on Soil Hydrologic Features and Forest Structure. Restor. Ecol. 2004, 12, 516–524. [Google Scholar] [CrossRef]
  24. Bruijnzeel, L.A. Hydrological functions of tropical forests: Not seeing the soil for the trees? Agric. Ecosyst. Environ. 2004, 104, 185–228. [Google Scholar] [CrossRef]
  25. Zwartendijk, B.; Meerveld, H.J.; Ghimire, C.; Bruijnzeel, L.; Ravelona, M.; Jones, J. Rebuilding soil hydrological functioning after swidden agriculture in eastern Madagascar. Agric. Ecosyst. Environ. 2017, 239, 101–111. [Google Scholar] [CrossRef] [Green Version]
  26. Mendes, M.S.; Latawiec, A.E.; Sansevero, J.B.B.; Crouzeilles, R.; Moraes, L.F.D.; Castro, A.; Alves-Pinto, H.N.; Brancalion, P.H.S.; Rodrigues, R.R.; Chazdon, R.L.; et al. Look down-there is a gap-the need to include soil data in Atlantic Forest restoration. Restor. Ecol. 2018, 27, 361–370. [Google Scholar] [CrossRef]
  27. Alvares, C.A.; Stape, J.L.; Sentelhas, P.C.; Gonçalves, J.L.M.; Sparovek, G. Köppen’s climate classification map for Brazil. Meteorologische Zeitschrift 2013, 22, 711–728. [Google Scholar] [CrossRef]
  28. De Oliveira, J.B.; Menk, J.R.F.; Barbieri, J.L.; Rotta, C.L.; Tremocoldi, W. Levantamento Pedologico Semidetalhado de Estado de São Paulo: Quadricula de Araras; Instituto Agronômico de Campinas: Campinas/São Paulo, Brasil, 1982.
  29. Koffler, N.F. Uso das terras da bacia do rio Corumbataí em 1990. Geografia 1993, 18, 135–150. [Google Scholar]
  30. Rodrigues, R.R. A vegetação de Piracicaba e municípios do entorno. Circular Técnica IPEF 1999, 189, 1–17. [Google Scholar]
  31. Pinto, L.P.; Bedê, L.; Paese, A.; Fonseca, M.; Paglia, A.; Lamas, I. Mata Atlântica Brasileira: Os desafios para conservação da biodiversidade de um hotspot mundial. In Biologia da Conservação: Essências; Rocha, C.F.D., Bergallo, H.G., Sluys, M.V., Alves, M.A.S., Eds.; RiMa Editora: Rio de Janeiro, Brazil, 2006; pp. 91–118. [Google Scholar]
  32. Da Fonseca, G.A. The vanishing Brazilian Atlantic forest. Biol. Conserv. 1985, 34, 17–34. [Google Scholar] [CrossRef]
  33. Silva, W.G.S.; Metzger, J.P.; Simões, S.; Simonetti, C. Relief influence on the spatial distribution of the Atlantic Forest cover on the Ibiúna Plateau, SP. Braz. J. Biol. 2007, 67, 403–411. [Google Scholar] [CrossRef] [PubMed]
  34. Valente, R.; Vettorazzi, C. Forest structure assessment in the Corumbataí river basin, SP. Sci. For. 2005, 68, 45–57. [Google Scholar]
  35. Soil Survey Staff. Soil Taxonomy: A Basic System of Soil Classification for Making and Interpreting Soil Surveys; Natural Resources Conservation Service: Whashington, DC, USA, 1999.
  36. Lassabatere, L.; Angulo-Jaramillo, R.; Ugalde, J.M.S.; Cuenca, R.; Braud, I.; Haverkamp, R. Beerkan Estimation of Soil Transfer Parameters through Infiltration Experiments-BEST. Soil Sci. Soc. Am. J. 2006, 70, 521–532. [Google Scholar] [CrossRef]
  37. Angulo-Jaramillo, R.; Bagarello, V.; Lovino, M.; Lassabatère, L. Infiltration Measurements for Soil Hydraulic Characterization; Springer International Publishing: Berlim, Germany, 2016. [Google Scholar] [CrossRef] [Green Version]
  38. Bagarello, V.; Di Prima, S.; Iovino, M. Estimating saturated soil hydraulic conductivity by the near steady-state phase of a Beerkan infiltration test. Geoderma 2017, 303, 70–77. [Google Scholar] [CrossRef]
  39. White, I.; Sully, M.J. Macroscopic and microscopic capillary length and time scales from field infiltration. Water Resour. Res. 1987, 23, 1514–1522. [Google Scholar] [CrossRef]
  40. Reynolds, W.D.; Elrick, D.E. Pressure infiltrometer. In Methods of Soil Analysis; Dane, J.H., Topp, G.C., Eds.; Part 4, Physical Methods, SSSA Book Series, No. 5; Soil Science Society of America: Madison, WI, USA, 2002; Volume 4, pp. 826–836. [Google Scholar]
  41. Haverkamp, R.; Ross, P.J.; Smettem, K.; Parlange, J.Y. Three-Dimensional analysis of infiltration from the disc infiltrometer: 2. Physically based infiltration equation. Water Resour. Res. 1994, 30, 2931–2935. [Google Scholar] [CrossRef] [Green Version]
  42. Raats, P.A.C. Analytical Solutions of a Simplified Flow Equation. Trans. ASAE 1976, 19, 683–689. [Google Scholar] [CrossRef]
  43. Di Prima, S.; Stewart, R.D.; Castellini, M.; Bagarello, V.; Abou Najm, M.R.; Pirastru, M.; Giadrossich, F.; Iovino, M.; Angulo-Jaramillo, R.; Lassabatere, L. Estimating the macroscopic capillary length from Beerkan infiltration experiments and its impact on saturated soil hydraulic conductivity predictions. J. Hydrol. 2020. [Google Scholar] [CrossRef]
  44. Lozano, S.; Cooper, M.; Ferraz, S.F.; Rodrigues, R.R.; Lassabatere, L.; Castellini, M.; Di Prima, S. Assessing Water Infiltration and Soil Water Repellency in Brazilian Atlantic Forest Soils. Appl. Sci. 2020, 10, 1950. [Google Scholar] [CrossRef] [Green Version]
  45. Elrick, D.E.; Reynolds, W.D. Methods for Analyzing Constant-Head Well Permeameter Data. Soil Sci. Soc. Am. J. 1992, 56, 320–323. [Google Scholar] [CrossRef]
  46. Grossman, R.B.; Reinsch, T. 2.1 Bulk Density and Linear Extensibility. In Methods of Soil Analysis: Part 4 Physical Methods; Dane, J.H., Topp, G.C., Eds.; Soil Science Society of America: Madison, WI, USA, 2002; Volume 5, pp. 201–228. [Google Scholar] [CrossRef]
  47. Lassabatere, L.; Di Prima, S.; Angulo-Jaramillo, R.; Keesstra, S.; Salesa, D. Beerkan multi-runs for characterizing water infiltration and spatial variability of soil hydraulic properties across scales. Hydrol. Sci. J. 2019, 64, 165–178. [Google Scholar] [CrossRef]
  48. Vomocil, J.A. Porosity. In Methods of Soil Analysis: Part 1 Physical and Mineralogical Properties, Including Statistics of Measurement and Sampling; Black, C.A., Ed.; Soil Science Society of America: Madison, WI, USA, 1965; Volume 9, pp. 299–314. [Google Scholar]
  49. Imhoff, S.; Da Silva, A.P.; Junior, M.D.S.D.; Tormena, C.A. Quantificação de pressões críticas para o crescimento das plantas. Revista Brasileira Ciência Solo 2001, 25, 11–18. [Google Scholar] [CrossRef] [Green Version]
  50. Salton, J.C.; Silva, W.M.; Tomazi, M.; Hernani, L.C. Determinação da Agregação do Solo—Metodologia em Uso na Embrapa Agropecuária Oeste; Embrapa Agropecuária Oeste. Comunicado Técnico, 184; Embrapa Agropecuária Oeste: Dourados, Brazil, 2012; 8p. [Google Scholar]
  51. Kemper, W.D.; Rosenau, R.C. Aggregate Stability and Size Distribution. In Methods of Soil Analysis; Klute, A., Ed.; Soil Science Society of America: Madison, WI, USA, 1986; pp. 425–442. [Google Scholar]
  52. Reynolds, W.; Drury, C.F.; Yang, X.; Tan, C. Optimal soil physical quality inferred through structural regression and parameter interactions. Geoderma 2008, 146, 466–474. [Google Scholar] [CrossRef]
  53. Gomes, F.P. Curso de Estatística Experimental, 12th ed.; Nobel: São Paulo, Brazil, 1990; 467p. [Google Scholar]
  54. Di Prima, S.; Winiarski, T.; Angulo-Jaramillo, R.; Stewart, R.D.; Castellini, M.; Najm, M.R.A.; Ventrella, D.; Pirastru, M.; Giadrossich, F.; Capello, G.; et al. Detecting infiltrated water and preferential flow pathways through time-lapse ground-penetrating radar surveys. Sci. Total Environ. 2020, 726, 138511. [Google Scholar] [CrossRef] [PubMed]
  55. Soil Science Division Staff. Soil Survey Manual. In USDA Handbook 18; Ditzler, C., Scheffe, K., Monger, H.C., Eds.; Government Printing Office: Washington, DC, USA, 2017. [Google Scholar]
  56. Tormena, C.A.; Barbosa, M.C.; Da Costa, A.C.S.; Gonçalves, A.C.A. Densidade, porosidade e resistência à penetração em Latossolo cultivado sob diferentes sistemas de preparo do solo. Sci. Agric. 2002, 59, 795–801. [Google Scholar] [CrossRef] [Green Version]
  57. Taylor, H.M.; Roberson, G.M.; Parker, J.J. Soil strength-root penetration relations for medium-to coarse-textured soil materials. Soil Sci. 1966, 102, 18–22. [Google Scholar] [CrossRef]
  58. Taylor, H.M. Effects of soil strength on seedling emergence, root growth and crop yields. In Compaction of Agricultural Soils; Barnes, K.K., Ed.; ASAE: St. Joseph, MI, USA, 1971; pp. 292–304. [Google Scholar]
  59. Júnior, M.M.; Melo, W.J. Carbono, carbono da biomassa microbiana e atividade enzimática em um solo sob mata natural, pastagem e cultura do algodoeiro. Revista Brasileira Ciência Solo 1999, 23, 257–263. [Google Scholar] [CrossRef]
  60. Salemi, L.F.; Groppo, J.D.; Trevisan, R.; De Moraes, J.M.; Ferraz, S.F.; Villani, J.P.; Duarte-Neto, P.J.; Martinelli, L. Land-use change in the Atlantic rainforest region: Consequences for the hydrology of small catchments. J. Hydrol. 2013, 499, 100–109. [Google Scholar] [CrossRef]
  61. Scheffler, R.; Neill, C.; Krusche, A.; Elsenbeer, H. Soil hydraulic response to land-use change associated with the recent soybean expansion at the Amazon agricultural frontier. Agric. Ecosyst. Environ. 2011, 144, 281–289. [Google Scholar] [CrossRef] [Green Version]
  62. Biggs, T.; Dunne, T.; Muraoka, T. Transport of water, solutes and nutrients from a pasture hillslope, southwestern Brazilian Amazon. Hydrol. Process. 2006, 20, 2527–2547. [Google Scholar] [CrossRef]
  63. Chaves, J.; Neill, C.; Germer, S.; Neto, S.G.; Krusche, A.; Elsenbeer, H. Land management impacts on runoff sources in small Amazon watersheds. Hydrol. Process. 2008, 22, 1766–1775. [Google Scholar] [CrossRef]
  64. Zimmermann, B.; Elsenbeer, H.; De Moraes, J.M. The influence of land-use changes on soil hydraulic properties: Implications for runoff generation. For. Ecol. Manag. 2006, 222, 29–38. [Google Scholar] [CrossRef]
  65. Germer, S.; Neill, C.; Krusche, A.; Elsenbeer, H. Influence of land-use change on near-surface hydrological processes: Undisturbed forest to pasture. J. Hydrol. 2010, 380, 473–480. [Google Scholar] [CrossRef]
  66. Satiro, L.S.; Cherubin, M.R.; Safanelli, J.L.; Lisboa, I.P.; Da Rocha Junior, P.R.; Cerri, C.E.P.; Cerri, C.C. Sugarcane straw removal effects on Ultisols and Oxisols in south-central Brazil. Geoderma Reg. 2017, 11, 86–95. [Google Scholar] [CrossRef]
  67. Topp, G.C.; Reynolds, W.D.; Cook, F.J.; Kirby, J.M.; Carter, M.R. Physical attributes of soil quality. Soil quality for crop production and ecosystem health. In Development in Soil Science; Gregorich, E.G., Carter, M.R., Eds.; Elsevier: New York, NY, USA, 1997; Volume 25, pp. 21–58. [Google Scholar] [CrossRef]
  68. Six, J.; Conant, R.T.; Paul, E.A.; Paustian, K. Stabilization mechanisms of soil organic matter: Implications for C-saturation of soils. Plant Soil 2002, 241, 155–176. [Google Scholar] [CrossRef]
  69. Filho, C.C.; Muzilli, O.; Podanoschi, A.L. Estabilidade dos agregados e sua relação com o teor de carbono orgânico num Latossolo Roxo distrófico, em função de sistemas de plantio, rotações de culturas e métodos de preparo das amostras. Revista Brasileira Ciência Solo 1998, 22, 527–538. [Google Scholar] [CrossRef] [Green Version]
  70. Bronick, C.; Lal, R. Soil structure and management: A review. Geoderma 2005, 124, 3–22. [Google Scholar] [CrossRef]
  71. Salton, J.C.; Mielniczuk, J.; Bayer, C.; Boeni, M.; Conceição, P.C.; Fabrício, A.C.; Macedo, M.C.M.; Broch, D.L. Agregação e estabilidade de agregados do solo em sistemas agropecuários em Mato Grosso do Sul. Revista Brasileira Ciência Solo 2008, 32, 11–21. [Google Scholar] [CrossRef]
  72. An, S.; Mentler, A.; Mayer, H.; Blum, W.E. Soil aggregation, aggregate stability, organic carbon and nitrogen in different soil aggregate fractions under forest and shrub vegetation on the Loess Plateau, China. Catena 2010, 81, 226–233. [Google Scholar] [CrossRef]
  73. Vezzani, F.M. Qualidade do Sistema Solo na Produção Agrícola. 184 f. 2001. Tese de Doutorado. Tese (Doutorado em Ciência do Solo); Universidade Federal do Rio Grande do Sul: Porto Alegre, Brazil, 2001. [Google Scholar]
  74. Allaire, S.E.; Roulier, S.; Cessna, A.J. Quantifying preferential flow in soils: A review of different techniques. J. Hydrol. 2009, 378, 179–204. [Google Scholar] [CrossRef]
  75. Deuchars, S.; Townend, J.; Aitkenhead, M.J.; Fitzpatrick, E. Changes in soil structure and hydraulic properties in regenerating rain forest. Soil Use Manag. 2006, 15, 183–187. [Google Scholar] [CrossRef]
  76. Hassler, S.K.; Zimmermann, B.; Van Breugel, M.; Hall, J.S.; Elsenbeer, H. Recovery of saturated hydraulic conductivity under secondary succession on former pasture in the humid tropics. For. Ecol. Manag. 2011, 261, 1634–1642. [Google Scholar] [CrossRef]
  77. Doerr, S.H.; Ritsema, C.J.; Dekker, L.W.; Scott, D.F.; Carter, D. Water repellence of soils: New insights and emerging research needs. Hydrol. Process. 2007, 21, 2223–2228. [Google Scholar] [CrossRef]
  78. Cooper, M.; Medeiros, J.C.; Rosa, J.D.; Soria, J.E.; Toma, R.S. Soil functioning in a toposequence under rainforest in São Paulo, Brazil. Revista Brasileira Ciência Solo 2013, 37, 392–399. [Google Scholar] [CrossRef] [Green Version]
  79. Hillel, D. Soil structure and aggregation. In Introduction to Soil Physics; Academic Press: London, UK, 1980; pp. 40–52, 200–204. [Google Scholar]
  80. Muñoz-Villers, L.E.; Holwerda, F.; Alvarado-Barrientos, M.S.; Geissert, D.; Marín-Castro, B.; Gómez-Tagle, A.; McDonnell, J.J.; Asbjornsen, H.; Dawson, T.; Bruijnzeel, L.A. Efectos hidrológicos de la conversión del bosque de niebla en el centro de Veracruz, México. Bosque 2015, 36, 395–407. [Google Scholar] [CrossRef] [Green Version]
Water 12 01689 g001 550
Figure 1. Location of the study areas in the state of São Paulo, Southeast Brazil. NR18: Natural regeneration 18 years; NR42: Natural regeneration 42 years.
Figure 1. Location of the study areas in the state of São Paulo, Southeast Brazil. NR18: Natural regeneration 18 years; NR42: Natural regeneration 42 years.
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Water 12 01689 g002 550
Figure 2. Image of the study areas. P: Pasture; SC: Sugarcane; NR18: Passive restoration by natural regeneration 18 years; NR42: Passive restoration by natural regeneration 42 years; DFF: Degraded forest fragment.
Figure 2. Image of the study areas. P: Pasture; SC: Sugarcane; NR18: Passive restoration by natural regeneration 18 years; NR42: Passive restoration by natural regeneration 42 years; DFF: Degraded forest fragment.
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Water 12 01689 g003 550
Figure 3. Boxplot of hydrophysical attributes for the different land uses. Line within the box is the median. The bars represent standard error. Note: For this figure, we only considered the soil physical attributes that presented significant differences, according to Table 2. Nat.Reg = natural regeneration.
Figure 3. Boxplot of hydrophysical attributes for the different land uses. Line within the box is the median. The bars represent standard error. Note: For this figure, we only considered the soil physical attributes that presented significant differences, according to Table 2. Nat.Reg = natural regeneration.
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Water 12 01689 g004 550
Figure 4. Principal Component Analysis (PCA) biplot based on soil attributes: Log Ks (mm h−1), BD (g cm−3), Tp (cm3 cm−3), Mic (cm3 cm−3), Mac (cm3 cm−3), PR (Mpa). Symbols represent plot sites for each land-cover type: Nat.Reg = natural regeneration.
Figure 4. Principal Component Analysis (PCA) biplot based on soil attributes: Log Ks (mm h−1), BD (g cm−3), Tp (cm3 cm−3), Mic (cm3 cm−3), Mac (cm3 cm−3), PR (Mpa). Symbols represent plot sites for each land-cover type: Nat.Reg = natural regeneration.
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Table
Table 1. Soil classification, soil particle density (ρs), particle size distribution, and textural classes for the soil superficial horizon (0.10 m) of the study areas.
Table 1. Soil classification, soil particle density (ρs), particle size distribution, and textural classes for the soil superficial horizon (0.10 m) of the study areas.
Study AreaClay
(%)		Silt
(%)		Sand
(%)		ρs
g cm−3		Textural
Classes		Soil
Classification
P184782.63Sandy loamOxisols
SC195762.66Sandy loamOxisols
NR18116832.67Loamy sandOxisols
NR421415712.64Sandy loamUltisols
DFF1113762.65Sandy loamOxisols
P: Pasture; SC: Sugarcane; NR18: Natural regeneration 18 years; NR42: Natural regeneration 42 years; DFF: Degraded forest fragment.
Table
Table 2. Average values of the hydrophysical attributes of the soils in the study areas. BD: bulk density (g cm−3); Tp: total porosity (cm3 cm−3); Mac: macroporosity (cm3 cm−3); Mic: microporosity (cm3 cm−3); PR: penetration resistance (Mpa); MWD: mean weight diameter of the soil aggregates (mm); OC: soil organic carbon content (g kg−1), and Ks: saturated soil hydraulic conductivity (mm h−1).
Table 2. Average values of the hydrophysical attributes of the soils in the study areas. BD: bulk density (g cm−3); Tp: total porosity (cm3 cm−3); Mac: macroporosity (cm3 cm−3); Mic: microporosity (cm3 cm−3); PR: penetration resistance (Mpa); MWD: mean weight diameter of the soil aggregates (mm); OC: soil organic carbon content (g kg−1), and Ks: saturated soil hydraulic conductivity (mm h−1).
Land Uses
AttributesStatisticPSCNR18NR42DFF
BDMean1.51 a1.53 a1.47 a1.48 a1.39 a
CV2.984.464.409.545.97
TpMean0.42 a0.42 a0.44 a0.45 a0.47 a
CV3.516.205.0710.356.61
MacMean0.13 a0.16 a0.13 a0.16 a0.18 a
CV19.1614.3912.9817.0023.28
MicMean0.29 ab0.25 b0.30 a0.28 ab0.29 ab
CV6.306.215.2617.555.37
PRMean2.64 a1.60 a0.83 b0.66 b0.68 b
CV23.9232.3738.3033.5316.95
MWDMean0.99 bc0.76 c0.75 c1.34 ab1.68 a
CV2.9816.1712.866.8411.06
OCMean14.00 a7.33 c8.66 bc11.33 ab9.33 bc
CV14.2815.7413.3210.1816.36
KsMean28.46 b39.93 ab70.30 a124.22 a36.28 ab
CV104.8959.6080.50115.0873.28
The letters refer to the comparison test of means two by two in the Tukey test at the 90% confidence level. Averages followed by the same letter do not differ statistically. CV: coefficient of variance (%). Reference values of CV: Low: <10%; medium: between 10 and 20%; high: between 20 and 30%, and very high: >30% [53]. Note: For BD, Tp, Mac, Mic, and PR, the number of soil samples = 5; for Ks = 10; for MWD and OC = 3.
Table
Table 3. Principal component analysis (PCA) loadings for the first and second components of the principal component analysis.
Table 3. Principal component analysis (PCA) loadings for the first and second components of the principal component analysis.
VariablesComponent 1Component 2
Ks 0.49
BD0.5560.109
Tp−0.578
Mac−0.3530.512
Mic−0.262−0.618
PR0.391−0.316

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Pereira, N.A.; Di Prima, S.; Bovi, R.C.; da Silva, L.F.S.; de Godoy, G.; Naves, R.P.; Cooper, M. Does the Process of Passive Forest Restoration Affect the Hydrophysical Attributes of the Soil Superficial Horizon? Water 2020, 12, 1689. https://doi.org/10.3390/w12061689

AMA Style

Pereira NA, Di Prima S, Bovi RC, da Silva LFS, de Godoy G, Naves RP, Cooper M. Does the Process of Passive Forest Restoration Affect the Hydrophysical Attributes of the Soil Superficial Horizon? Water. 2020; 12(6):1689. https://doi.org/10.3390/w12061689

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Pereira, Nayana Alves, Simone Di Prima, Renata Cristina Bovi, Laura Fernanda Simões da Silva, Gustavo de Godoy, Rafaela Pereira Naves, and Miguel Cooper. 2020. "Does the Process of Passive Forest Restoration Affect the Hydrophysical Attributes of the Soil Superficial Horizon?" Water 12, no. 6: 1689. https://doi.org/10.3390/w12061689

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