Physical and chemical soil quality and litter stock in agroforestry systems in the Eastern Amazon

doi:10.21203/rs.3.rs-4228306/v1

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Physical and chemical soil quality and litter stock in agroforestry systems in the Eastern Amazon

https://doi.org/10.21203/rs.3.rs-4228306/v1

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The influence of biodiversity and age of agroforestry systems (AFS) on the provision of ecosystem services, such as nutrient cycling, needs to be better understood to support management practices that promote such services. This study aimed to quantify and compare litter stock and the physical and chemical attributes of soil in four AFSs with different ages and arrangements to a secondary forest (FLO) in the Eastern Amazon. Litter stock did not differ among the AFSs, but the youngest AFS was lower (5.73 ± 1.04 Mg ha^− 1) than in FLO (11.42 ± 2.44 Mg ha^− 1). Similarities were found between FLO and the oldest AFS for most of the soil chemical attributes. The soil pH in AFSs was higher than in FLO in the surface layer, and the organic matter content of FLO did not differ from 2 and 51-year-old AFSs. The Al content and aluminum saturation of younger and 26 years-old AFSs were lower than in FLO. Particle density and total porosity did not differ among ecosystems, while soil density in the two younger AFSs was higher than in FLO. According to PCA results, variables such as organic matter, CEC_pH7, H + Al, Al content, and m % tended to be higher in FLO and oldest AFS. It was evident that the maturity and diversity of AFSs are relevant factors for Amazonian agroforests, as they offer positive impacts on ecosystem functionality, such as nutrient cycling and water retention.

shifting cultivation

multistrata system

soil fertility

environmental restoration

In recent years, the expansion of the agricultural frontier in the Amazon has led to drastic reductions in the extent of forest ecosystems (Viteri-Salazar and Toledo 2020; Pokorny et al. 2021), as well as excessive soil loss and consequently, a decline in natural resources (Rosati et al. 2021; Centeno-Alvarado et al. 2023), establishing itself as one of the activities responsible for forest ecosystem degradation in the last 50 years (Nobre et al. 2016). The inherent impacts of agricultural activities are related to disruptions in carbon and nutrient cycles, resulting in long-term consequences for the region's soil quality (Han and Zhu 2020). In addition to impairing soil quality, the release of greenhouse gases into the atmosphere exacerbates the impacts of climate change and leads to decreased agricultural and forestry productivity, especially due to the frequency of dry periods and extreme weather events such as heatwaves and intense rainfall within short intervals (IPCC 2023).

A promising alternative for restoring degraded areas and enhancing production inputs is the implementation of agroforestry systems (AFS) (Wu et al. 2019), which stand out as strategies for alternative land use, combining agricultural, forestry, and, in some cases, animal production, providing environmental and socioeconomic benefits (Goncalves et al. 2021; Castle et al. 2022). Other benefits are related to the reduced use of chemical pesticides due to higher concentrations of soil organic carbon and nitrogen (Fahad et al. 2022), as well as the greater capacity to provide ecosystem services compared to monocultures (Duran-Bautista et al. 2020; Mulyoutami et al. 2023). In the tropics, AFSs play a crucial role in climate regulation, being able to recover 46% of above-ground carbon in 30 years, surpassing the 35% recovery observed in secondary forests over the same period (Cardozo et al. 2022).

The maintenance and/or improvement of biogeochemical cycles are among the main characteristics and benefits provided by AFSs, as plant species, especially trees, drive nutrient cycling through high litter production and decomposition, resulting in the subsequent mineralization of these nutrients by soil biota (Mortimer et al. 2018; Koutika et al. 2021; Sari et al. 2022; Kim and Isaac 2022). Tree species also contribute to soil protection and conservation by reducing erosive processes, as vegetative cover provides greater protection against direct rainfall, reducing soil compaction and surface sealing. Roots provide greater soil structural stabilization, preventing its loss through erosion, as well as nutrient loss, by creating a root depletion zone, causing nutrients to migrate closer to the rhizosphere through transpiratory flow. These processes occur in AFSs in general but can be intensified by factors such as species quantity (biodiversity). Furthermore, some tree species may provide these benefits more intensely than others.

In this context, the careful selection of species for the composition of these AFSs plays a fundamental role in ensuring maximum efficiency in promoting ecosystem services, which will also have repercussions on the establishment of more productive sites in these systems. The strategic presence of species, such as those belonging to the prominent Fabaceae family, plays a vital role in atmospheric nitrogen fixation in symbiosis with bacteria, providing an additional nutrient input and further stimulating soil microbial activity (Pires et al. 2018). The biodiversity of AFSs, in turn, besides driving these ecosystem processes, can promote connectivity between different habitats, improving water quality, acting as natural filters, and reducing pollutant leaching (Rosati et al. 2021). However, further investigations are imperative to understand the specific influence of different AFS arrangements and ages on soil physical properties and fertility. This analysis becomes even more urgent given the low natural fertility and high acidity characteristic of most tropical soils (Pavlidis and Tsihrintzis 2018).

Detailed studies on the physical and chemical properties of soil, considering variability in AFS arrangements and ages, are essential for optimizing sustainable management of these systems and enhancing their economic and environmental benefits. Understanding these attributes will assist in strategies for restoration, particularly in the Amazon, reducing the use of higher-impact activities, mitigating climate change, and aiding in Brazil's goal of restoring 12 million hectares by 2030 (Brazil 2017). In this sense, we seek to answer the following question: Do older and more biodiverse AFSs provide better soil physical and chemical conditions? Thus, the objectives of this study were (a) to quantify soil physical and chemical attributes and litter production in AFSs with different ages and arrangements and (b) to compare them with a secondary forest in the Eastern Amazon.

2.1. Study site

The research was conducted on a rural property situated in the municipality of Irituia, in the state of Pará, Brazil, located in the Eastern Amazon region (Fig. 1), from July to December 2022, covering an area of 20 hectares (01° 46´12” S and 48° 26´ 21” W). The local climate is classified as humid equatorial (Am) according to the Köppen-Geiger climate classification system (Alvares et al. 2013). The average air temperature is 25°C, with annual precipitation ranging between 2,250 and 2,500 mm, and an average relative humidity of 85% (INMET 2024). The predominant soils in the municipality are Yellow Latosols and Gleisols, characterized by medium to clayey texture.

2.2. Characterization of the studied ecosystems

The study was conducted in four agroforestry systems (AFS) with different ages, components (species), and spatial arrangements (planting forms and spacing) (Table 1 Sup.). All AFSs were established in areas previously used for shifting cultivation, and currently do not employ irrigation systems or agrochemicals, with weed control carried out through manual slashing and/or pruning. Additionally, we evaluated a naturally regenerated successional forest used as a reference ecosystem (FLO). In all ecosystems, the collected soil (0–10 cm and 10–20 cm) was characterized for texture and particle size distribution (Table 2).

Table 2

Granulometric characterization of soil (0–10 and 10–20 cm) in agroforestry systems (AFS) of different ages and a reference forest (FLO) assessed in the municipality of Irituia - Pará, Eastern Amazon. AFS 1 = AFS with 7 months of age; AFS 2 = AFS with 5 years of age; AFS 3 = AFS with 26 years of age, and AFS 4 = AFS with 51 years of age
System	Depth	Sand	Silt	Clay	Textural class
System	(cm)	----------------- (%) ------------------			Textural class
AFS 1	0–10	73.90	6.00	20.10	Sandy clay loam
AFS 1	10–20	75.20	4.00	20.80	Sandy clay loam
AFS 2	0–10	74.25	4.00	21.75	Sandy clay loam
AFS 2	10–20	76.80	5.00	18.20	Sandy loam
AFS 3	0–10	77.90	5.00	17.10	Sandy loam
AFS 3	10–20	74.70	5.00	20.30	Sandy clay loam
AFS 4	0–10	77.35	6.00	16.65	Sandy loam
AFS 4	10–20	72.50	6.00	21.50	Sandy clay loam
FLO	0–10	74.25	6.00	19.75	Sandy loam
FLO	10–20	75.80	5.00	19.20	Sandy loam

2.3. Sampling and data collection

The soil and litter sampling in the evaluated systems followed a random sampling system with a fixed area method, where six plots of 5 m x 5 m (25 m²) were allocated in each system. For the collection of litter and soil stock, within each plot (replication), five collection points were established, equidistant from each other, in a cross shape. For litter collection, a collector square of 0.25 m x 0.25 m x 0.10 m (width, length, and height) was used (Fig. Sup. 1a-b). Near the litter collection points, five simple soil samples (disturbed samples) were also collected using a Dutch auger at depths of 0–10 and 10–20 cm, which were mixed and homogenized to form a composite sample per plot (Fig. Sup. 2b). The undisturbed soil samples were collected using a volumetric ring, with one sample collected per plot and per soil depth. The litter and soil samples were processed at the Forest Studies Center of the Federal Rural University of the Amazon, Capitão Poço - PA.

2.3.1. Litter stock

The collected samples were taken to the laboratory for pre-drying at room temperature for two days. Then, the samples were dried in a forced air oven at 65°C for 48 hours (Fig. Sup. 1c) and finally weighed on a precision balance of 0.01 g (BEL® model S3102) to obtain the dry mass (Fig. Sup. 1d). The total dry mass was converted to Mg ha^− 1 (Eq. 1).

$${L}_{s}=\frac{(Dm/\text{1.000.000})}{{C}_{A}} (Eq. 1)$$

Where: L_s = Litter stock (Mg ha^− 1); Dm = Litter dry mass (g); C_A = Colletor area (ha).

2.3.2. Chemical soil properties

The disturbed soil samples were air-dried for 72 hours and sieved through a 2 mm mesh (Fig. Sup. 2c-e) (10 mesh) for determination of water pH and soil organic matter (SOM) through potentiometry and programmed heating combustion, respectively. Additionally, the chemical elements P, K, and Na were extracted using double-acid Mehlich 1 solution. Al, Ca, and Mg were extracted in a 1 mol L^− 1 KCl solution. Potential acidity (H + Al) was extracted with calcium acetate buffered at pH 7 through neutralization volumetry, and subsequently, the CEC at pH 7, effective CEC, base saturation (V %), and aluminum saturation (m %) were calculated following the IUSS WRB Working Group (2015).

2.3.4. Physical soil properties

With the disturbed soil samples, we also determined the particle density (Dp). The undisturbed samples were used to assess soil density (Sd), total porosity (TP), soil moisture (θ), gravimetric moisture content (Gm), volumetric moisture content (Vm), and soil water retention. Soil moisture data (θ) were fitted using the Van Genuchten exponential model to determine the soil water retention curve (Van Genuchten, 1980).

2.4. Data analysis

The variables of litter and soil physical and chemical attributes underwent the Shapiro-Wilk normality test and Levene's test for homogeneity of variance, both at a significance level of 5%. For variables that did not meet the assumptions of normality and/or homogeneity of variance (e.g., Al and m), a transformation was applied using the Box-Cox adjustment. If the assumptions were still not met after transformation, the Kruskal-Wallis test (p < 0.05) was utilized, and post-hoc comparisons of medians were conducted using the Dunn test (p < 0.05). In cases where parametric assumptions were satisfied, the variables were analyzed using analysis of variance (ANOVA) with the F test (p < 0.05), and if significance was found, means were compared using the Tukey test (p < 0.05). Additionally, soil physical and chemical attributes underwent principal component analysis (PCA) to assess potential correlations between ecosystems. All statistical analyses were performed using R software version 4.2.3 (R Development Core Team 2023).

3.1. Litter stock

For litter stock, the mean of FLO (11.42 ± 2.44 Mg ha^− 1) was only higher than AFS 1 (5.73 ± 1.04 Mg ha^− 1), however, there was no statistical difference among the AFSs (F_[4;25] = 3.58; p > 0.05. Figure 2). AFSs 2, 3, and 4 did not differ from AFS 1 and FLO (Fig. 2).

3.2. Soil chemical attributes

The AFSs showed higher water pH than observed in the FLO (0–10 cm layer), with AF 3 presenting the highest value, averaging 66.72% higher than FLO (Fig. 3a). The same pattern was observed in the 10–20 cm layer, where the lowest water pH was observed in FLO (Fig. 3a). Regarding Ca, at both evaluated depths, its levels in AFS soils 1 and 3 were higher than in FLO (Fig. 3b). For Mg content, there was a significant difference only in the 0–10 cm layer, with AFS 3 having a higher value compared to AFS 2 and 4 (Fig. 3c). K levels showed different behaviors in AFSs and FLO, where the highest content was observed in FLO, not differing from AFS 2 and 4 (Fig. 3d).

The highest mean of P was observed in FLO, regardless of the analyzed layer (Fig. 3d-e). As for H + Al, the lowest value was obtained in AFS 3 (0–10 cm) and in AFSs 1 and 3, in the 10–20 cm layer (Fig. 3f). For effective CEC, AFS 3 showed a superior result to AFS 4 (0–10 cm), but the ecosystems did not differ from each other at a depth of 10–20 cm (Fig. 3g). CEC at pH 7 was similar among ecosystems in the surface soil layer, and FLO had a higher average at the 10–20 cm depth (Fig. 3h). Regarding organic matter (OM) content, the average obtained in FLO was higher than in AFSs 1 and 3 (0–10 cm), but there was no significant difference for this nutrient among ecosystems at the second depth (Fig. 3i). For base saturation (V %), it was found that AFS 3 and 1 had the highest averages at the 0–10 cm depth, with AFS 3 being 21.41% higher than AFS 4, while at the 10–20 cm depth, AFSs 1 and 3 presented the highest averages (Fig. 3j).

AFSs 1 and 3 showed the lowest medians for aluminum content and aluminum saturation (m %) at both evaluated depths when compared to FLO, while AFSs 2 and 4 did not differ statistically from the reference ecosystem (Fig. 4).

3.3. Soil physical attributes

In the surface soil layer (0–10 cm), the highest Sd was observed in AFS 1 and the lowest in FLO; however, in the 10–20 cm depth, there was no significant difference between systems (Fig. 5a). As for TP and Pd, there were no statistically significant differences between ecosystems at both depths (Fig. 5b-c). For Gm, the lowest results were observed in AFS 1 and AFS 3, in the 0–10 and 10–20 cm layers, respectively (Fig. 5d). Vm was statistically similar among ecosystems in the first layer, and followed a similar pattern to Gm in the second layer, with a lower value in AFS 3 (Fig. 5e).

In the surface layer, AFSs 1, 2, and 3 showed a greater difference in field capacity and permanent wilting point, indicating a higher water storage in the soil of these systems (Fig. 6a), while in the 10–20 cm layer, this behavior was observed in AFSs 1 and 2 (Fig. 6b). At both depths, AFS 4 exhibited a water retention curve with less variation along the increase in matric potential, similar to FLO (Fig. 6).

3.4. Principal Component Analysis (PCA)

It was found that the first two components of the PCA explained 62.7% and 60.7% of the new variables formed from all axes, for depths 0–10 cm and 10–20 cm, respectively (Fig. 7). For the surface soil layer, a positive correlation was observed between the sum of bases (SB), V %, Ca, Mg, and effective CEC, with AFSs 1 and 3 showing the highest means for these attributes at this depth (Fig. 7a). In the 10–20 cm depth, PCA showed that, in addition to these attributes, the aforementioned AFSs tend to have higher means of Ug and Uv (Fig. 7b). The variables OM, CEC at pH 7, H + Al, Al content, and m % tend to be higher in FLO and AFS 4, at both depths (Fig. 7)

4.1. Litter stock

The higher litter stock observed in the FLO ecosystem compared to AFS 1 is likely linked to the maturity and diversity of this ecosystem, considering not only the lower litter deposition but also the reduced interaction of this litter with soil microfauna in younger ecosystems (Saj et al., 2021). Analogously, the litter stock of FLO compared to AFSs 2, 3, and 4 reveals a trend of increasing nutrient cycling efficiency along the temporal gradient due to the gradual increase in favorable environmental conditions such as humidity, temperature, oxygen, and pH (Froufe et al., 2020). In this case, the quality and composition of the litter are variables that directly influence nutrient availability for the soil-plant system.

Our results also highlight the importance of species selection to compose AFSs. The high quantity of reproductive material quantified in AFS 2, for example, suggests a possible association with the predominance of A. magium, a species that blooms and fruits multiple times a year (Matos et al., 2023). Although this predominance benefits soil protection due to the considerable increase in stored litter layer, ecosystems dominated by this species generally also exhibit lower decomposition rates, leading to slow release of nutrients for the soil-plant system (Jambul et al., 2020).

Species composition also plays a crucial role in litter quality and, consequently, decomposition rates, as it directly interacts with soil biota (Sena et al., 2020). For example, leaves of Theobroma cacao, present in AFSs 2 and 3, despite high lignin content, decompose rapidly due to notable concentrations of cellulose (Firmino et al., 2021). On the other hand, leaves considered hard (hardness > 372 g), such as those of Mangifera indica and Musa x paradisiaca found in AFSs 1 and 3, respectively, are characterized by lower palatability and longer decomposition time, although they may constitute a stable substrate (Matos et al., 2022).

4.2. Soil chemical attributes

We found similarities between the FLO and AFS 4 regarding most chemical attributes at both depths. This resemblance can be attributed to the presence of shading tree species in both environments, which contribute to the formation of a sub-canopy environment with high biomass production (Fujii et al. 2018). This microclimate favors the proliferation and activity of microorganisms, which decompose biomass and mineralize nutrients from this organic matter, highlighting the importance of this environment for nutrient biogeochemical cycling (Sadeghi et al. 2023; Bastos et al. 2023).

Overall, soils in the Amazon region are characterized by high acidity and low natural chemical fertility, so we can infer that despite AFS 4 and the FLO being older, the low levels of Ca, Mg, and V % in these ecosystems may be associated with the absence of liming, since Ca and Mg are the main inputs for this soil preparation stage (Siepel et al. 2019), consequently influencing the V % (Macedo et al. 2023).

The higher CEC_pH7 in the AFSs and FLO areas in the 0–10 cm layer may be justified by the lower OM content present in AFS 1 and 2 in the superficial layer, as expected due to the youth of the plants in these ecosystems and their vegetative structure in full development. OM presents OH groups in its aromatic groups, with the potential to generate negative charges at pH values above 3.5, justifying the lower soil pH value in the FLO (Chien et al. 2018). The lower pH in the reference ecosystem may reflect the higher values of potential acidity in this system, as these two factors have a directly proportional relationship.

Our results are aligned with previous research on the impacts of prolonged fire use in forest ecosystems (Cecilio Rebola et al. 2021; Mukul et al. 2022; Primo et al. 2023), highlighting the potential of AFSs to restore functional aspects in the Amazon. The practice of burning plant residues and the use of mineral fertilizers may have contributed to the increase in soil acidity over time, as these practices reduce soil pH and favor the release of hydrogen ions (H+) and aluminum (Al + 3) (Chaves et al. 2020). This continuous acidification process may be intensified due to base losses by leaching, dissolution reactions of CO₂ in the soil solution, dissociation of H + ions from organic and inorganic radicals by microbial activity (Ontman et al. 2023).

Considering that soil acidity negatively affects nutrient availability to plants (Malavolta, 2006; Matschullat et al. 2019, 2020), our results emphasize the importance of litter stock for maintaining forest ecosystems. Despite the more acidic pH, the FLO showed higher OM levels in the superficial layer compared to the other evaluated ecosystems, which may be justified by the high litter stock. However, soil acidity and Al^+ 3 toxicity have little effect on native tree species, such as those cultivated in AFSs, because the high concentration of Al and m % in Amazonian soils, mainly in Oxisols, is a natural condition resulting from the high degree of weathering and abundant presence of aluminum and iron oxides, associated with the region's climatic conditions, with high rainfall and temperatures (Quesada et al. 2011).

4.3. Soil physical attributes

The higher Sd recorded in AFS 1 compared to AFSs 3 and 4, as well as FLO, can be explained by the lower accumulation of soil organic matter, greater interference of anthropogenic activities, and consequently, greater soil compaction. On the other hand, the lower S_D observed in the 0–10 cm layer in FLO and AFSs 3 and 4 are related to the age and increased soil organic matter, as over time, the soil tends to accumulate organic matter from plant residues, animal debris, and microorganisms (Siqueira et al., 2020), facilitating gradual infiltration and percolation of water from the surface to subsurface (Cherubin et al., 2019).

The positive correlation of Gm and TP with K and Mg highlights crucial aspects in soil dynamics, as under adequate moisture conditions, these nutrients mobilize in the soil and are incorporated into the profile through pores, becoming accessible for root absorption by plants. In this regard, attention is drawn to soil decompaction before liming, as it assists in the process of water infiltration and percolation, reducing surface runoff and consequently, the transport of particles and nutrients in erosive processes (Borisov et al., 2022), which can contaminate downstream water bodies (Zhu et al., 2020).

In mature ecosystems with proper management, soils have higher organic matter content, better structure (Saputra et al., 2020), and higher moisture (Mutuku et al., 2021), with high water retention at lower matric potentials, as evidenced for FLO and AFS 4, especially at the 10–20 cm depth. Such characteristics are related to the age and degree of soil cover (Cherubin et al., 2019), where older ecosystems have a more developed root system, tending to have higher microporosity and reduced water loss through evaporation (Huang et al., 2022; Lichner et al., 2020). AFS 3, due to wider spacing between plants, may experience a more pronounced evaporation rate than other systems (Hernández-Ochoa et al., 2022). The discrepancies observed for AFSs along the matric potential indicate that areas with lower vegetation cover have reduced water availability, with a narrow range in water concentration.

The decrease from field capacity to permanent wilting point was extremely drastic for the younger AFSs (1 and 2), resulting in a much narrower optimal water range, especially in the 0–10 cm soil layer. Regarding field capacity (FC) and permanent wilting point (PWP), θ was higher in both soil depths in AFSs 1 and 2, indicating greater vulnerability of these systems to drought events due to higher exposure to high temperatures during these periods (Carvalho et al., 2021). In contrast, AFS 4 may be less impacted by water stress, as suggested by the decrease in CC among systems at the 10–20 cm depth. The similarity of these attributes in the 0–10 cm layer among these systems may be related to rapid water absorption in the soil and consequent evaporation due to high temperatures during drier periods of the year (Flammini et al., 2018). PWP is influenced by soil clay content concentrations, while FC is influenced by a complex interaction between clay content, structure, S_D, and organic carbon in the soil (Wiecheteck et al., 2020). Thus, in FC, changes in soil moisture can be partially compensated for by the sandy soil texture (Reynolds et al., 2002).

Older agroforestry systems promoted average values of litter stock and soil physical and chemical properties closer to those of a native forest ecosystem. From an ecological perspective, we also highlight that diversification provides positive impacts and in a shorter temporal scale for the return of ecosystem functionality, such as nutrient cycling and water retention. For this reason, agroforestry systems are characterized as promising alternatives for the recovery of degraded soils in the Amazon originating from shifting cultivation and even from other similar anthropic activities, such as degraded pasture areas and mining sites.

Competing interests: All authors declared no potential conflicts of interest with respect to the research, author-ship, and publication of this manuscript.

Author contributions

All authors contributed to the study conception and design. Material preparation and data collection were performed by Francisco Elves Duarte de Souza and Jesus de Nazaré dos Santos Data analysis was performed by Walmer Bruno Rocha Martins. The first draft of the manuscript was written by Francisco Elves Duarte de Souza, Jesus de Nazaré dos Santos, Walmer Bruno Rocha Martins and Cassio Rafael Costa dos Santos. Eric Victor de Oliveira Ferreita, Raimundo Thiago Lima da Silva, Manoel Tavares de Paula, José Darlon Nascimento Alves, José Sebastião Romano de Oliveira and Julia Isabella de Matos Rodrigues provided critical reviews of the intelectual content. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Funding

No funding was received to assist with the preparation of this manuscript.

Alvares CA, Stape JL, Sentelhas PC, et al (2013) Köppen’s climate classification map for Brazil. Meteorol Zeitschrift 22:711–728. https://doi.org/10.1127/0941-2948/2013/0507
Bastos T dos S, Barreto-Garcia PAB, Mendes I de C, et al (2023) Response of soil microbial biomass and enzyme activity in coffee-based agroforestry systems in a high-altitude tropical climate region of Brazil. CATENA 230:107270. https://doi.org/10.1016/j.catena.2023.107270
Borisov BA, Efimov OE, Eliseeva O V. (2022) Organic Matter and Physical Properties of Postagrogenic Eroded Soddy-Podzolic Soil and Arable Soddy-Podzolic Soil. Eurasian Soil Sci 55:971–977. https://doi.org/10.1134/S1064229322070031
Brasil (2017) PLANAVEG: Plano Nacional de Recuperação da Vegetação Nativa. Brasília MMA 73p.
Cardozo EG, Celentano D, Rousseau GX, et al (2022) Agroforestry systems recover tree carbon stock faster than natural succession in Eastern Amazon, Brazil. Agrofor Syst 96:941–956. https://doi.org/10.1007/s10457-022-00754-7
Carvalho AF, Fernandes-Filho EI, Daher M, et al (2021) Microclimate and soil and water loss in shaded and unshaded agroforestry coffee systems. Agrofor Syst 95:119–134. https://doi.org/10.1007/s10457-020-00567-6
Castle SE, Miller DC, Merten N, et al (2022) Evidence for the impacts of agroforestry on ecosystem services and human well-being in high-income countries: a systematic map. Environ Evid 11:10. https://doi.org/10.1186/s13750-022-00260-4
Cecilio Rebola L, Pandolfo Paz C, Valenzuela Gamarra L, F.R.P. Burslem D (2021) Land use intensity determines soil properties and biomass recovery after abandonment of agricultural land in an Amazonian biodiversity hotspot. Sci Total Environ 801:149487. https://doi.org/10.1016/j.scitotenv.2021.149487
Centeno-Alvarado D, Lopes AV, Arnan X (2023) Fostering pollination through agroforestry: A global review. Agric Ecosyst Environ 351:108478. https://doi.org/10.1016/j.agee.2023.108478
Chaves SF da S, Gama MAP, Alves RM, et al (2020) Evaluation of physicochemical attributes of a yellow latosol under agroforestry system as compared to secondary forest in the Eastern Amazon. Agrofor Syst 94:1903–1912. https://doi.org/10.1007/s10457-020-00513-6
Cherubin MR, Chavarro-Bermeo JP, Silva-Olaya AM (2019) Agroforestry systems improve soil physical quality in northwestern Colombian Amazon. Agrofor Syst 93:1741–1753. https://doi.org/10.1007/s10457-018-0282-y
Chien S-WC, Chen S-H, Li C-J (2018) Effect of soil pH and organic matter on the adsorption and desorption of pentachlorophenol. Environ Sci Pollut Res 25:5269–5279. https://doi.org/10.1007/s11356-017-9822-7
Duran-Bautista EH, Armbrecht I, Serrão Acioli AN, et al (2020) Termites as indicators of soil ecosystem services in transformed amazon landscapes. Ecol Indic 117:106550. https://doi.org/10.1016/j.ecolind.2020.106550
Fahad S, Chavan SB, Chichaghare AR, et al (2022) Agroforestry Systems for Soil Health Improvement and Maintenance. Sustainability 14:14877. https://doi.org/10.3390/su142214877
Firmino VC, Brasil LS, Martins RT, et al (2021) Litter decomposition of exotic and native plant species of agricultural importance in Amazonian streams. Limnology 22:289–297. https://doi.org/10.1007/s10201-021-00655-1
Flammini A, Corradini C, Morbidelli R, et al (2018) Experimental Analyses of the Evaporation Dynamics in Bare Soils under Natural Conditions. Water Resour Manag 32:1153–1166. https://doi.org/10.1007/s11269-017-1860-x
Froufe LCM, Schwiderke DK, Castilhano AC, et al (2020) Nutrient cycling from leaf litter in multistrata successional agroforestry systems and natural regeneration at Brazilian Atlantic Rainforest Biome. Agrofor Syst 94:159–171. https://doi.org/10.1007/s10457-019-00377-5
Fujii K, Shibata M, Kitajima K, et al (2018) Plant–soil interactions maintain biodiversity and functions of tropical forest ecosystems. Ecol Res 33:149–160. https://doi.org/10.1007/s11284-017-1511-y
Goncalves N, Andrade D, Batista A, et al (2021) Potential economic impact of carbon sequestration in coffee agroforestry systems. Agrofor Syst 95:419–430. https://doi.org/10.1007/s10457-020-00569-4
Han M, Zhu B (2020) Changes in soil greenhouse gas fluxes by land use change from primary forest. Glob Chang Biol 26:2656–2667. https://doi.org/10.1111/gcb.14993
Hernández-Ochoa IM, Gaiser T, Kersebaum K-C, et al (2022) Model-based design of crop diversification through new field arrangements in spatially heterogeneous landscapes. A review. Agron Sustain Dev 42:74. https://doi.org/10.1007/s13593-022-00805-4
Huang W, Lai H, Du J, et al (2022) Effect of polymer water retaining agent on physical properties of silty clay. Chem Biol Technol Agric 9:47. https://doi.org/10.1186/s40538-022-00309-z
INMET IN de M (2024) Catálogo de estações automáticas. https://tempo.inmet.gov.br/TabelaEstacoes/A248. Accessed 14 Mar 2024
IPCC IP on CC (2023) Climate Change 2021 – The Physical Science Basis. Cambridge University Press
IUSS Working Group WRB (2015) World reference base for soil resources 2014. International soil classification system for naming soils and creating legends for soil maps, 106th edn. Rome
Jambul R, Limin A, Ali AN, Slik F (2020) Invasive Acacia mangium dominance as an indicator for heath forest disturbance. Environ Sustain Indic 8:100059. https://doi.org/10.1016/j.indic.2020.100059
Kim D-G, Isaac ME (2022) Nitrogen dynamics in agroforestry systems. A review. Agron Sustain Dev 42:60. https://doi.org/10.1007/s13593-022-00791-7
Koutika L-S, Taba K, Ndongo M, Kaonga M (2021) Nitrogen-fixing trees increase organic carbon sequestration in forest and agroforestry ecosystems in the Congo basin. Reg Environ Chang 21:109. https://doi.org/10.1007/s10113-021-01816-9
Lichner Ľ, Alagna V, Iovino M, et al (2020) Evaporation from soils of different texture covered by layers of water repellent and wettable soils. Biologia (Bratisl) 75:865–872. https://doi.org/10.2478/s11756-020-00471-5
Macedo RS, Moro L, dos Santos Sousa C, et al (2023) Agroforestry can improve soil fertility and aggregate-associated carbon in highland soils in the Brazilian northeast. Agrofor Syst. https://doi.org/10.1007/s10457-023-00875-7
Matos FAR, Edwards DP, S. Magnago LF, et al (2023) Invasive alien acacias rapidly stock carbon, but threaten biodiversity recovery in young second-growth forests. Philos Trans R Soc B Biol Sci 378:. https://doi.org/10.1098/rstb.2021.0072
Matos TP, Dias-Silva K, Medeiros AO, et al (2022) Effects of exotic fruit plants on leaf decomposition in Amazon: a study in aquatic microcosm. Limnology 23:455–464. https://doi.org/10.1007/s10201-022-00699-x
Matschullat J, Lima RMB, Fromm SF von, et al (2019) Carbon, nitrogen and sulfur (CNS) status and dynamics in Amazon basin upland soils, Brazil. Soil Discuss Preprint:
Matschullat J, Martins GC, Enzweiler J, et al (2020) What influences upland soil chemistry in the Amazon basin, Brazil? Major, minor and trace elements in the upper rhizosphere. J Geochemical Explor 211:106433. https://doi.org/10.1016/j.gexplo.2019.106433
Mukul SA, Herbohn J, Ferraren A, Congdon R (2022) Limited role of shifting cultivation in soil carbon and nutrients recovery in regenerating tropical secondary forests. Front Environ Sci 10:. https://doi.org/10.3389/fenvs.2022.1076506
Mulyoutami E, Tata HL, Silvianingsih YA, van Noordwijk M (2023) Agroforests as the intersection of instrumental and relational values of nature: gendered, culture-dependent perspectives? Curr Opin Environ Sustain 62:101293. https://doi.org/10.1016/j.cosust.2023.101293
Mutuku EA, Vanlauwe B, Roobroeck D, et al (2021) Physico-chemical soil attributes under conservation agriculture and integrated soil fertility management. Nutr Cycl Agroecosystems 120:145–160. https://doi.org/10.1007/s10705-021-10132-x
Nobre CA, Sampaio G, Borma LS, et al (2016) Land-use and climate change risks in the Amazon and the need of a novel sustainable development paradigm. Proc Natl Acad Sci 113:10759–10768. https://doi.org/10.1073/pnas.1605516113
Ontman R, Groffman PM, Driscoll CT, Cheng Z (2023) Surprising relationships between soil pH and microbial biomass and activity in a northern hardwood forest. Biogeochemistry 163:265–277. https://doi.org/10.1007/s10533-023-01031-0
Pavlidis G, Tsihrintzis VA (2018) Environmental Benefits and Control of Pollution to Surface Water and Groundwater by Agroforestry Systems: a Review. Water Resour Manag 32:1–29. https://doi.org/10.1007/s11269-017-1805-4
Pires R de C, Reis Junior FB, Zilli JE, et al (2018) Soil characteristics determine the rhizobia in association with different species of Mimosa in central Brazil. Plant Soil 423:411–428. https://doi.org/10.1007/s11104-017-3521-5
Pokorny B, Robiglio V, Reyes M, et al (2021) The potential of agroforestry concessions to stabilize Amazonian forest frontiers: a case study on the economic and environmental robustness of informally settled small-scale cocoa farmers in Peru. Land use policy 102:105242. https://doi.org/10.1016/j.landusepol.2020.105242
Primo AA, Araújo Neto RA de, Zeferino LB, et al (2023) Slash and burn management and permanent or rotation agroforestry systems: A comparative study for C sequestration by century model simulation. J Environ Manage 336:117594. https://doi.org/10.1016/j.jenvman.2023.117594
Quesada CA, Lloyd J, Anderson LO, et al (2011) Soils of Amazonia with particular reference to the RAINFOR sites. Biogeosciences 8:1415–1440. https://doi.org/10.5194/bg-8-1415-2011
R Development Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. v. 4.2.3
Reynolds W., Bowman B., Drury C., et al (2002) Indicators of good soil physical quality: density and storage parameters. Geoderma 110:131–146. https://doi.org/10.1016/S0016-7061(02)00228-8
Rosati A, Borek R, Canali S (2021) Agroforestry and organic agriculture. Agrofor Syst 95:805–821. https://doi.org/10.1007/s10457-020-00559-6
Sadeghi S, Petermann BJ, Steffan JJ, et al (2023) Predicting microbial responses to changes in soil physical and chemical properties under different land management. Appl Soil Ecol 188:104878. https://doi.org/10.1016/j.apsoil.2023.104878
Saj S, Nijmeijer A, Nieboukaho JDE, et al (2021) Litterfall seasonal dynamics and leaf-litter turnover in cocoa agroforests established on past forest lands or savannah. Agrofor Syst 95:583–597. https://doi.org/10.1007/s10457-021-00602-0
Saputra DD, Sari RR, Hairiah K, et al (2020) Can cocoa agroforestry restore degraded soil structure following conversion from forest to agricultural use? Agrofor Syst 94:2261–2276. https://doi.org/10.1007/s10457-020-00548-9
Sari RR, Rozendaal DMA, Saputra DD, et al (2022) Balancing litterfall and decomposition in cacao agroforestry systems. Plant Soil 473:251–271. https://doi.org/10.1007/s11104-021-05279-z
Sena G, Francisco Gonçalves Júnior J, Tavares Martins R, et al (2020) Leaf litter quality drives the feeding by invertebrate shredders in tropical streams. Ecol Evol 10:8563–8570. https://doi.org/10.1002/ece3.6169
Siepel H, Bobbink R, van de Riet BP, et al (2019) Long-term effects of liming on soil physico-chemical properties and micro-arthropod communities in Scotch pine forest. Biol Fertil Soils 55:675–683. https://doi.org/10.1007/s00374-019-01378-3
Siqueira CCZ, Chiba MK, Moreira RS, Abdo MTVN (2020) Carbon stocks of a degraded soil recovered with agroforestry systems. Agrofor Syst 94:1059–1069. https://doi.org/10.1007/s10457-019-00470-9
Steinfeld JP, J.J.A. Bianchi F, Luiz Locatelli J, et al (2023) Increasing complexity of agroforestry systems benefits nutrient cycling and mineral-associated organic carbon storage, in south-eastern Brazil. Geoderma 440:116726. https://doi.org/10.1016/j.geoderma.2023.116726
van Genuchten MT (1980) A Closed‐form Equation for Predicting the Hydraulic Conductivity of Unsaturated Soils. Soil Sci Soc Am J 44:892–898. https://doi.org/10.2136/sssaj1980.03615995004400050002x
Viteri-Salazar O, Toledo L (2020) The expansion of the agricultural frontier in the northern Amazon region of Ecuador, 2000–2011: Process, causes, and impact. Land use policy 99:104986. https://doi.org/10.1016/j.landusepol.2020.104986
Wiecheteck LH, Giarola NFB, de Lima RP, et al (2020) Comparing the classical permanent wilting point concept of soil (−15,000 hPa) to biological wilting of wheat and barley plants under contrasting soil textures. Agric Water Manag 230:105965. https://doi.org/10.1016/j.agwat.2019.105965
Wu Q, Liang H, Xiong K, Li R (2019) Eco-benefits coupling of agroforestry and soil and water conservation under KRD environment: frontier theories and outlook. Agrofor Syst 93:1927–1938. https://doi.org/10.1007/s10457-018-0301-z
Zhu X, Liu W, Chen J, et al (2020) Reductions in water, soil and nutrient losses and pesticide pollution in agroforestry practices: a review of evidence and processes. Plant Soil 453:45–86. https://doi.org/10.1007/s11104-019-04377-3

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Physical and chemical soil quality and litter stock in agroforestry systems in the Eastern Amazon

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Abstract

Figures

1. Introduction

2. Material and Methods

2.1. Study site

2.2. Characterization of the studied ecosystems

2.3. Sampling and data collection

2.3.1. Litter stock

2.3.2. Chemical soil properties

2.3.4. Physical soil properties

2.4. Data analysis

3. Results

3.1. Litter stock

3.2. Soil chemical attributes

3.3. Soil physical attributes

3.4. Principal Component Analysis (PCA)

4. Discussion

4.1. Litter stock

4.2. Soil chemical attributes

4.3. Soil physical attributes

5. Conclusion

Declarations

References

Additional Declarations

Supplementary Files

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