Impact of Land Cover and Meteorological Attributes on Soil Fertility, Temperature, and Moisture in the Itacaiúnas River Watershed, Eastern Amazon
by
, , , , , , and
Renato Oliveira da Silva Júnior
,
Tatiane Barbarelly Serra Souza Morais
,
Wendel Valter da Silveira Pereira
,
Gabriel Caixeta Martins
,
Paula Godinho Ribeiro
,
Adayana Maria Queiroz de Melo
,
Marcio Sousa da Silva
and
Sílvio Junio Ramos
*
Instituto Tecnológico Vale—Desenvolvimento Sustentável, Belém 66055-090, PA, Brazil
*
Author to whom correspondence should be addressed.
Environments 2025, 12(4), 98; https://doi.org/10.3390/environments12040098
Submission received: 30 January 2025
/
Revised: 13 March 2025
/
Accepted: 19 March 2025
/
Published: 24 March 2025
(This article belongs to the Special Issue New Insights in Soil Quality and Management, 2nd Edition)
Abstract
:The Amazon has undergone significant changes in the landscape with the expansion of human activities. The objective of this study was to characterize the relationship between soil temperature (ST) and moisture (SM) with meteorological data and soil attributes in pasture, forest, and transition areas in the Itacaiúnas River Watershed (IRW), Eastern Amazon. Soil samples were analyzed to determine chemical and granulometric attributes. SM and ST were measured up to 40 cm deep using sensors, and the meteorological variables were quantified by hydrometeorological stations. The chemical characteristics and granulometry indicated greater limitations in the Forest soil, with lower levels of organic carbon and higher contents of sand. In Pasture A, Pasture B, and Transition areas, with some exceptions, there was a progressive increase in ST from July to September. In general, SM was positively correlated with rainfall and negatively correlated with ST, air temperature, wind speed, and solar radiation. Linear models for ST (10–20 cm depth) in Pasture B and Forest areas indicate positive relationships with air temperature and wind speed and negative relationships with solar radiation. The findings of this study can be useful in decision-making regarding the management of ecosystems in the IRW.
1. Introduction
Anthropogenic activities have caused severe environmental changes in the Amazon, especially deforestation associated with agriculture, livestock, and logging [1]. These changes can significantly impact the ecosystem, leading to increased carbon emissions and climate change [2,3], increased runoff [4], reduced evapotranspiration, and increased sensible heat flux [5]. Additionally, alterations in soil chemical and physical properties have been reported, including changes in organic matter contents, level of aggregation, degree of acidity, concentrations of exchangeable bases, and cation retention capacity [6,7,8,9].
Soil moisture (SM) and soil temperature (ST) can also be strongly affected by changes in vegetation cover [10,11]. SM plays a fundamental role in land–atmosphere interactions, as it acts on hydrological processes, energy conversion, climate change, and ecology [12]. It is directly influenced by rainfall and temperature, considering that rainfall is the main source of moisture for the soil and temperature controls evapotranspiration.
Changes in vegetation can influence SM by affecting water infiltration and field capacity [13]. At the same time, soil water availability affects the revegetation of altered areas [14]. For example, a recent meta-analysis of 66 studies revealed significant reductions in SM across three ecological zones following changes in land cover [15]. Similarly, in a study comparing different land covers, smaller reductions in SM in forests compared to pastures were observed, mainly due to the mitigation of ST beneath tree canopies [16]. These findings highlight the importance of monitoring SM and assessing the impacts of vegetation changes on soil water dynamics.
Changes in ST commonly affect processes such as soil respiration, storage of organic matter, establishment of vegetation, and microbial decomposition [17]. ST depends on several environmental factors, such as weather conditions, topography, soil water content, and vegetation cover [18]. The vegetation influences the temporal and spatial variations of heat in the soil because of its role in the interception and reflection of solar radiation, which reduces the ST and adjacent air [19]. For instance, when comparing areas with different degrees of vegetation cover (exposed area, 40%, and 80%) in China, less seasonal variation was found in ST for both locations with vegetation in relation to the exposed area. In the current scenario of climate change and global warming, monitoring ST deserves special attention because of the potential impacts of high temperatures on ecosystem stability [11].
The Itacaiúnas River Watershed (IRW) in the Brazilian Amazon has undergone marked changes in land use and vegetation in recent years [20,21,22,23], especially associated with activities such as mining, urbanization, construction of roads and railways, livestock, and deforestation [20,22]. While most studies in the region have focused on broader environmental changes, the specific dynamics of ST and SM in relation to land use and vegetation alterations remain largely unexplored. Additionally, the interaction between meteorological variables and soil properties is not well understood, particularly in the context of the region’s rapid development. Expanding knowledge of these dynamics is essential for improving land management strategies and supporting sustainable development. Therefore, the objectives of this study were to evaluate the behavior of SM and ST and understand the relationships of these variables with soil properties and meteorological conditions in forest, pasture, and transition areas in the IRW.
2. Material and Methods
2.1. Study Area
The IRW is located in the Tocantins-Araguaia hydrographic region, Eastern Amazon. The territorial extension of this region is approximately 42,000 km2, with about 700,000 inhabitants. The climate of the watershed is typical of monsoon (Am), which corresponds to tropical rainy (hot and humid) [24]; the average annual rainfall is approximately 1790 mm, with 87% of the total rainfall occurring during the rainy season (November to May) and 13% occurring during the dry season (June to October) [4]. The average air temperature is approximately 26 °C.
The Cateté, Aquiri, Cinzento, Tapirapé, Preto, Parauapebas, Vermelho, and Sororó rivers represent the main tributaries of the Itacaiúnas River. The relief is accentuated with an altitude that varies between 80 m and 900 m. The predominant vegetation is tropical forest and mountainous savannah, but there are extensive pastures that surround a mosaic of forest remnants. The predominant soil classes include Argisols and Litholic Neosols, in addition to Haplic Gleisols, Oxisols, and Nitisols, which occur in a smaller proportion. In the IRW, there are indigenous lands and conservation units protected by law that occupy 11,700 km2, corresponding to approximately a quarter of the watershed area [25].
Four areas with different land uses and covers were studied: (i) Pasture A, (ii) Transition (secondary forest/pasture), (iii) Forest, and (iv) Pasture B (Figure 1). Pasture A, Transition, and Forest are located in the municipality of Marabá, and Pasture B is in the municipality of Água Azul do Norte, both in the state of Pará, Brazil. Pasture A is located on a hilltop, consisting of an open field with low grass and animal traffic. Pasture B is very close to the Itacaiúnas River and also has grass and animal traffic. Transition is located on a hilltop subject to a rise in the water table because of its proximity to the Sororó River. Forest, in turn, is located in the National Forest of Tapirapé, which represents a Conservation Unity with the native ombrophilous forest [26].
2.2. Studied Variables
2.2.1. Meteorological Variables
The meteorological variables studied were rainfall, solar radiation, air temperature, and relative moisture [26], which were quantified from July 2019 to January 2020. These variables were measured by hydrometeorological stations from the company Campbell Scientific of Brazil that were installed in each studied area. Every hour, the stations measure and transmit data via satellite, including the following parameters: rainfall, river levels, air temperature, solar radiation, relative air moisture, wind direction and speed, and barometric pressure. All stations have a standard model, consisting of a 10 m tower, where most of the sensors are installed, a plastic box with equipment, a metal box with a power supply system and antennas, and a 1 m high support height, where the rain gauge is installed. Daily averages were calculated for the evaluation of meteorological data.
2.2.2. Soil Variables
SM and ST data were collected using sensors (Drill & Drop; Sentek Technologies, Stepney South, Australia) at the meteorological stations present in each area from April 2019 to January 2020. The probe was 90 cm long, with sensors every 10 cm along the probe length, which allowed obtaining data for several soil depths. The devices were connected to a data logger and provided average, maximum, and minimum values of SM and ST at nine depths every hour [26]. In this study, only data obtained up to a depth of 40 cm were evaluated. These soil depths were chosen because they normally present the main variations in ST and SM data [27]. The daily averages of both attributes were calculated for further analysis.
Soil sampling for chemical and granulometric characterization was carried out in August 2022. The meteorological station in each area was used as a reference to define the sampling points, which were geographically delimited according to the access conditions. Soil samples (0.5 kg) were collected using a stainless steel Dutch auger at depths of 0–20 cm and 30–50 cm in 4 positions—east, west, north, and south of the sensor. These samples were air-dried, sieved to a fraction of 2 mm, and analyzed regarding chemical and physical attributes at the Brazilian Environmental and Agricultural Analysis Laboratory (LABRAS) according to the methodology described by [28,29]. Briefly, the pH was measured in a soil–water suspension ratio of 1:2.5; soil organic carbon (OC) was determined by the potassium dichromate (K2Cr2O7) method; sulfur (S) was determined by turbidimetry; exchangeable calcium (Ca2+), magnesium (Mg2+), and aluminum (Al3+) were extracted with 1 M KCl; phosphorus (P) and potassium (K+) were extracted by Mehlich-1 solution (HCl 0.05 M + H2SO4 0.0125 M) and quantified by ICP-OES. The concentration of boron (B) was extracted in hot water, and copper (Cu), iron (Fe), manganese (Mn), and zinc (Zn) were extracted by DTPA solution. These nutrients were determined by atomic absorption spectrophotometry (AAS). From these results, the sum of bases (SB), cation exchange capacity (CEC), Al saturation (m%), and base saturation (V%) were calculated.
2.3. Statistical Analysis
The averages of physical and chemical attributes were compared by the Tukey test (p < 0.05) after two-way analysis of variance (ANOVA). In addition, differences in ST and SM medians were evaluated using the Kruskal–Wallis test, followed by the Dunn test (p < 0.05).
Pearson correlation analysis with Holm correction was performed to evaluate the correlations between soil and meteorological attributes. Furthermore, to study the relationship between meteorological variables and temperature in the 10–20 cm depth, linear stepwise regression was used. In this analysis, only Pasture B and Forest areas were considered. The models were validated considering the normality of the residuals and the homogeneity of variance. All statistical analyses and graphs were generated using the R software, 4.4.3 version [30].
3. Results and Discussion
3.1. Soil Physical and Chemical Properties
The clay content was higher in the surface soil compared to the subsurface soil in Pasture A (Figure 2). The attributes clay, sand, silt, pH, OC, CEC, Cu, and Mn varied between areas, while no statistical differences were observed for P, SB, V%, B, Fe, and Zn. In general, the soil from the Forest area presented the highest values of pH and sand, as well as the lowest values of clay, silt, OC, CEC, Cu, and Mn. According to the classification by [31], acidity was medium in the Forest area and high in Pasture A, Pasture B, and Transition, and the contents of OC were low in Pasture A and Forest and medium in Pasture B and Transition.
The acidity results may be directly related to V%, which was medium in the Forest area and low in the other areas [31], indicating greater occupation of cations in the soil exchange complex without significant anthropogenic impact. Under tropical conditions, as observed in the Amazon, heavy rains favor the leaching of bases and the maintenance of H+ and Al3+ ions, which reduces soil pH [32,33] and may explain the greater acidity in pasture and transition areas.
The higher levels of OC in pasture and transition areas may be related to the addition of organic materials from the presence of cattle in livestock [34]. Grasses may also be incorporating significant amounts of plant materials from both shoots and roots [35,36]. In addition, in the case of the Forest area, the higher sand contents may be hindering the formation of aggregates in the soil [37]. The texture was classified as sandy clay loam in Pasture A, sandy clay loam in Pasture B and Transition, and sandy loam in Forest [38]. In the Amazon, natural forest soils typically have a predominance of sand [39].
The OC contents may also have a direct relationship with CEC, which was high in the pasture and transition areas and medium in the forest area [31]. This attribute was higher in the Pasture and Transition areas (Figure 2), which may contribute to the retention of cationic nutrients such as Cu and Mn in the soil, leading to higher concentrations in these areas compared to the Forest. Similarly, a study in Porto Velho (southeastern Brazilian Amazon) also found higher CEC in the surface layer (0–10 cm) of pasture soils compared to forest areas [32].
A Pearson correlation analysis was carried out considering soil fertility attributes in all areas (Supplementary Table S1). There were positive correlations between the OC content and CEC (r = 0.70), clay (r = 0.35), Fe (r = 0.72), SB (r = 0.61), Cu (r = 0.43), Mn (r = 0.73), and Zn (r = 0.61). Furthermore, a positive correlation was observed between the CEC and the clay content (r = 0.57). These correlations can be explained by the direct relationship of the CEC with the clay fraction and mainly the organic matter (OM), which is the main source of negative charges for the retention of cations in tropical soils [40]. In addition, OM tends to form stable complexes with Fe, Cu, Mn, and Zn due to its high sorption capacity, reducing its decomposition by microorganisms [41,42].
3.2. Soil Temperature and Soil Moisture and Meteorological Attributes
ST tended to be higher in Pasture A and lower in Forest (Table 1). In general, the ST in the 0–10 cm depth of the Pasture A soils was higher than that of the deeper depths, regardless of the month studied. In addition, ST in the 10–40 cm depth did not differ statistically, except in December and January, when the deeper depths had slightly higher temperatures. In the Transition and Pasture B areas, the STs in the 0–10 cm depth were also slightly lower than that of the 30–40 cm depth in December and in December and January, respectively (rainy season). The higher upper ST may be due to heat transport from the surface, and the opposite pattern in the rainy season may be due to surface radiative cooling [43].
In Pasture A, Pasture B, and Transition areas, with some exceptions, there was a progressive increase in ST from July to September (Table 1, Figure 3), with the highest ST observed in August and September. No clear pattern was observed regarding ST variation in the other months. The results are consistent with the dry season (June to October) in the IRW, with high temperatures and lower pluviometric levels, which may explain the higher ST [26].
The Forest area presented lower ST values than the other areas, which can be associated with the greater vegetation cover and less solar radiation that can reach the soil surface [17,44]. Regardless of the month evaluated, this area showed similar STs in the 0–10 and 10–20 cm depths. Furthermore, there was an increase in ST up to October, followed by a slight reduction up to January (Figure 3). The greatest difference in ST in the Forest soil was 2.75 °C, observed in the 0–10 cm depth between July and September. This area presented the smallest variation in ST values when compared to the other areas, which is related to the vegetation cover that reduces temperature fluctuations.
In most months and depths, Pasture A had higher SM (Table 2). In general, there was an increase in SM from the most superficial to the deepest depths in the Pasture A, Transition, and Forest areas. In all areas, there was a progressive decrease in SM from July to September, followed by a progressive increase (Table 2, Figure 3), which is consistent with the rainy season in IRW.
SM values may have a direct relationship with climatic seasonality in the IRW since the rainfall values were higher in the wettest months and lower in the less rainy months (Figure 3). In Pasture B and Forest, rainfall was lower than expected at the beginning of the rainy season, which can be explained by the low-intensity El Niño that occurred in that period in the region. During this phenomenon, the warm phase of the Pacific Decadal Oscillation weakens the ascending branch of the Hadley cell, decreasing the intensity of the Intertropical Convergence Zone and weakening the convective activity in the Amazon, which decreases the rainfall and increases incident radiation [45,46].
Regarding the values of air moisture, it is noted that they are also directly proportional to rainfall: in the rainy season, there is a significant increase in this variable, while there is a decrease in the dry season. For air temperature values, there are small variations between Pasture A and the Forest and greater variations in Pasture B.
Solar radiation values had patterns similar to those of ST and different from those of SM (Figure 3). According to [47], evaporation and evapotranspiration rates increase in humid forests during the dry season, coinciding with increased incident radiation and sensible heat flux. Comparative studies show that radiation over forest areas is greater than over pasture areas because of differences in the reflection of solar radiation (albedo) and in the long wave of the radiation balance [48]. In this study, only short-wave radiation (incident) was evaluated, which explains the higher radiation values observed in the pasture and transition areas.
Differences in energy balance in forest and pasture areas were evaluated by [49] in the southwestern Amazon. These authors found that the reduction in moisture, mainly in rainfall, has an impact on the behavior of water storage in the soil. During dry seasons, although rainfall and specific moisture are quite low, SM storage profiles (up to 3.4 m depth) indicate that vegetation continues to draw water from deep depths of forest soil, which is not observed in the pasture. Furthermore, at the beginning of the dry season, the change in moisture storage in the forest is faster compared to that in the pasture because the profile is losing water through root absorption (to supply transpiration) and through lateral drainage. Data for both forest and pasture show a very pronounced seasonal cycle, which explains the large variation in SM between the dry and wet seasons in Pasture A, unlike Forest. In Pasture B and Transition areas, SM values also did not vary much, possibly due to the greater proximity of these areas to rivers.
3.3. Relationship Between Attributes
3.3.1. Soil Temperature and Moisture and Physical-Chemical Attributes
SM in the 0–10 cm depth positively correlated with soil P content (Supplementary Table S2). This is important for agricultural management since water is required for the diffusion of P into the topsoil depth [50]. These results reinforce the potential for higher temperatures to contribute to soil nutrient availability, which is related to the increase in biological activity and consequent decomposition of OM, as well as the acceleration of chemical reactions [51,52].
In addition, clay was positively correlated with ST in the 0–10 cm depth (r = 0.96). Texture has a great influence on the physical, water, and chemical behavior of the soil. Therefore, its evaluation is of great importance for soil use and management. A previous study found that sandy soil in bare conditions showed the highest surface temperature, followed by clay soils [44]. Thus, it can be inferred that sandy soils presented the highest temperature, which was different from the results observed in this study.
3.3.2. Soil Temperature and Moisture Between Depths
In all studied areas, there were strong positive correlations between the four evaluated depths, both for ST and SM (Table 3). In addition, these variables were correlated. An exception is observed for Pasture B, considering the correlations between T4 and SM and for Forest between T1, T2, and T3 and SM, which were not significant.
In Pasture and Transition areas, negative correlations between ST and SM were found for all depths, which corroborates the study by [53]. It is known that if SM increases, the ST tends to decrease, while if SM is low, the change in ST is more pronounced [53]. On the other hand, a positive correlation between SM and ST was found in Forest areas, which deserves further investigation.
3.3.3. Soil Temperature and Moisture and Meteorological Variables
In Pasture and Forest areas, ST showed positive correlations with air temperature, wind speed, and solar radiation, while negative correlations with SM, air moisture, and rainfall were found in Pasture areas (Table 3). Similar trends were observed for the Transition area; however, correlations with temperature and air moisture were not analyzed. Previous studies in Bangladesh and several other countries also found a positive correlation between air temperature and ST [44,54].
In general, SM was positively correlated with rainfall and negatively correlated with ST, except for Pasture B (0–10 cm). Pasture A, Transition (except air temperature), and Forest areas had SM in the 0–10 cm depth negatively correlated with air temperature, wind speed, and solar radiation and positively with air moisture. The same behavior was observed in a study evaluating different vegetation types in Karst areas of Southwest China [55]. Another study found that the annual air temperature and SM were also negatively related in the Mississippi River Watershed from 1950 to 2010, indicating that the dry and wet soils lead to warm and cool weather in the watershed [56].
To further investigate the relationships between ST (10–20 cm) and meteorological attributes, linear models were adjusted for Pasture B and Forest areas. All parameters of the linear equations were statistically significant, and the retained variables showed low to moderate collinearity (VIF < 10). Positive relationships with air temperature and wind speed and negative relationships with solar radiation were evidenced (Table 4). The rainy season in the IRW is well characterized by the behavior of the ascending attributes of rainfall, SM, and air moisture, as well as by the decrease in radiation, air, and ST [57]. Thus, the negative effect of solar radiation on ST can be explained by the presence of clouds, decreasing radiation. In addition, the Forest area is under or close to the Tapirapé forest, which concentrates a lot of air and soil moisture, with a downward trend in ST and radiation.
The relationships observed between ST, SM, and meteorological variables such as air temperature, wind speed, and solar radiation can provide valuable insights for hydrological modeling [58]. The positive correlation between ST and air temperature, as well as the negative relationship between ST and SM, suggests that changes in SM can be used as a predictor for ST dynamics [59]. This understanding is crucial for assessing the effects of rainfall and air moisture on ST variations, which influence evaporation and infiltration rates in hydrological models. The use of these relationships in hydrological models can improve predictions of water availability and SM distribution, supporting agricultural and environmental management in the IRW [60].
4. Conclusions
The Forest area presents trends toward lower soil fertility results compared to other areas. The lowest soil temperatures observed in the Forest area were due to the greater vegetation cover that protects the soil surface against the effects of solar radiation. In general, soil moisture was positively correlated with rainfall and negatively correlated with soil temperature, air temperature, wind speed, and solar radiation. The clay contents had positive correlations with ST and the available P concentration with SM. For temperature, the linear model indicates positive relationships with air temperature and wind speed and negative relationships with solar radiation. Incorporating these findings into hydrological models is suggested to improve predictions of water balance dynamics in the IRW.
Information on the interactions between ST, SM, and meteorological conditions can strengthen adaptive strategies for climate change, guiding sustainable land and water management practices. This information is also critical for assessing the impact of climate variability on nutrient cycling and carbon sequestration, which will be important for contributing to the sustainability of human activities in the Amazon.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/environments12040098/s1, Table S1: Pearson’s correlation coefficient between soil attributes from different meteorological stations (n = 40); Table S2: Pearson’s correlation coefficient between soil attributes and meteorological variables in all evaluated areas (n = 4; mean value).
Author Contributions
Conceptualization, R.O.d.S.J., M.S.d.S. and S.J.R.; methodology, R.O.d.S.J., W.V.d.S.P., G.C.M., P.G.R., A.M.Q.d.M. and M.S.d.S.; validation, R.O.d.S.J. and S.J.R.; formal analysis, W.V.d.S.P., G.C.M. and P.G.R.; investigation, R.O.d.S.J. and S.J.R.; resources, R.O.d.S.J. and S.J.R.; data curation, W.V.d.S.P., G.C.M., P.G.R., A.M.Q.d.M. and M.S.d.S.; writing—original draft preparation, R.O.d.S.J., T.B.S.S.M., W.V.d.S.P., G.C.M., P.G.R., A.M.Q.d.M. and M.S.d.S.; writing—review and editing, R.O.d.S.J., T.B.S.S.M., W.V.d.S.P., G.C.M., P.G.R., A.M.Q.d.M., M.S.d.S. and S.J.R.; visualization, R.O.d.S.J. and S.J.R.; supervision, R.O.d.S.J. and S.J.R.; project administration, R.O.d.S.J., M.S.d.S. and S.J.R.; funding acquisition, R.O.d.S.J., M.S.d.S. and S.J.R. All authors have read and agreed to the published version of the manuscript.
Funding
The authors would like to thank the Instituto Tecnológico Vale for the financial support in the development of this study.
Data Availability Statement
The data that support this study are available upon request.
Acknowledgments
S.J.R thanks the National Council for Scientific and Technological Development (CNPq) for the research productivity scholarship (grant number 304560/2023-5).
Conflicts of Interest
The authors have no relevant financial or non-financial interests to disclose.
References
- Baudena, M.; Tuinenburg, O.A.; Ferdinand, P.A.; Staal, A. Effects of Land-use Change in the Amazon on Precipitation Are Likely Underestimated. Glob. Change Biol. 2021, 27, 5580–5587. [Google Scholar] [CrossRef]
- Feitosa, T.B.; Fernandes, M.M.; Santos, C.A.G.; Silva, R.M.D.; Garcia, J.R.; Araujo Filho, R.N.D.; Fernandes, M.R.D.M.; da Cunha, E.R. Assessing Economic and Ecological Impacts of Carbon Stock and Land Use Changes in Brazil’s Amazon Forest: A 2050 Projection. Sustain. Prod. Consum. 2023, 41, 64–74. [Google Scholar] [CrossRef]
- Doblas-Rodrigo, Á.; Gallejones, P.; Artetxe, A.; Rosa, E.; del Hierro, Ó.; Merino, P. Grassland Contribution to Soil Organic Carbon Stock under Climate Change Scenarios in Basque Country (Spain). Reg. Environ. Chang. 2022, 22, 34. [Google Scholar] [CrossRef]
- Cavalcante, R.B.L.; Pontes, P.R.M.; Souza-Filho, P.W.M.; de Souza, E.B. Opposite Effects of Climate and Land Use Changes on the Annual Water Balance in the Amazon Arc of Deforestation. Water Resour. Res. 2019, 55, 3092–3106. [Google Scholar] [CrossRef]
- Rizzo, R.; Garcia, A.S.; Vilela, V.M.D.F.N.; Ballester, M.V.R.; Neill, C.; Victoria, D.C.; da Rocha, H.R.; Coe, M.T. Land Use Changes in Southeastern Amazon and Trends in Rainfall and Water Yield of the Xingu River during 1976–2015. Clim. Change 2020, 162, 1419–1436. [Google Scholar] [CrossRef]
- Melo, V.F.; Barros, L.S.; Silva, M.C.S.; Veloso, T.G.R.; Senwo, Z.N.; Matos, K.S.; Nunes, T.K.O. Soil Bacterial Diversities and Response to Deforestation, Land Use and Burning in North Amazon, Brazil. Appl. Soil Ecol. 2021, 158, 103775. [Google Scholar] [CrossRef]
- Silva-Olaya, A.M.; Mora-Motta, D.A.; Cherubin, M.R.; Grados, D.; Somenahally, A.; Ortiz-Morea, F.A. Soil Enzyme Responses to Land Use Change in the Tropical Rainforest of the Colombian Amazon Region. PLoS One 2021, 16, e0255669. [Google Scholar] [CrossRef]
- Soares, M.D.R.; de Souza, Z.M.; Campos, M.C.C.; da Silva, R.B.; Esteban, D.A.A.; Noronha, R.L.; dos Santos Gomes, M.G.; da Cunha, J.M. Land-Use Change and Its Impact on Physical and Mechanical Properties of Archaeological Black Earth in the Amazon Rainforest. CATENA 2021, 202, 105266. [Google Scholar] [CrossRef]
- Damian, J.M.; Durigan, M.R.; Cherubin, M.R.; Maia, S.M.F.; Ogle, S.M.; de Camargo, P.B.; Ferreira, J.N.; de Oliveira Júnior, R.C.; Cerri, C.E.P. Deforestation and Land Use Change Mediate Soil Carbon Changes in the Eastern Brazilian Amazon. Reg. Environ. Chang. 2021, 21, 64. [Google Scholar] [CrossRef]
- Feng, H. Individual Contributions of Climate and Vegetation Change to Soil Moisture Trends across Multiple Spatial Scales. Sci. Rep. 2016, 6, 32782. [Google Scholar] [CrossRef]
- Wang, X.; Chen, R.; Han, C.; Yang, Y.; Liu, J.; Liu, Z.; Guo, S.; Song, Y. Soil Temperature Change and Its Regional Differences under Different Vegetation Regions across China. Int. J. Climatol. 2021, 41. [Google Scholar] [CrossRef]
- Deng, Y.; Wang, S.; Bai, X.; Luo, G.; Wu, L.; Chen, F.; Wang, J.; Li, C.; Yang, Y.; Hu, Z.; et al. Vegetation Greening Intensified Soil Drying in Some Semi-Arid and Arid Areas of the World. Agric. For. Meteorol. 2020, 292–293, 108103. [Google Scholar] [CrossRef]
- Wang, Y.; Yang, J.; Chen, Y.; Fang, G.; Duan, W.; Li, Y.; De Maeyer, P. Quantifying the Effects of Climate and Vegetation on Soil Moisture in an Arid Area, China. Water 2019, 11, 767. [Google Scholar] [CrossRef]
- Lozano-Parra, J.; Schnabel, S.; Pulido, M.; Gómez-Gutiérrez, Á.; Lavado-Contador, F. Effects of Soil Moisture and Vegetation Cover on Biomass Growth in Water-limited Environments. L. Degrad. Dev. 2018, 29, 4405–4414. [Google Scholar] [CrossRef]
- Su, B.; Shangguan, Z. Decline in Soil Moisture Due to Vegetation Restoration on the Loess Plateau of China. L. Degrad. Dev. 2019, 30, 290–299. [Google Scholar] [CrossRef]
- Lozano-Parra, J.; Pulido, M.; Lozano-Fondón, C.; Schnabel, S. How Do Soil Moisture and Vegetation Covers Influence Soil Temperature in Drylands of Mediterranean Regions? Water 2018, 10, 1747. [Google Scholar] [CrossRef]
- Yu, L.; Liu, Y.; Bu, K.; Wang, W.J.; Zhang, S. Soil Temperature Mitigation Due to Vegetation Biophysical Feedbacks. Glob. Planet. Change 2022, 218, 103971. [Google Scholar] [CrossRef]
- Ma, T.; Nan, X.; Wu, R.; Yan, H.; Wu, N.; She, J.; Bao, Z. Quantifying the Impact of Canopy Structural Characteristics on Soil Temperature Variations in Different Bamboo Communities. Atmosphere 2023, 14, 445. [Google Scholar] [CrossRef]
- Ni, J.; Cheng, Y.; Wang, Q.; Ng, C.W.W.; Garg, A. Effects of Vegetation on Soil Temperature and Water Content: Field Monitoring and Numerical Modelling. J. Hydrol. 2019, 571, 494–502. [Google Scholar] [CrossRef]
- Lima, M.W.D.; Pereira, W.V.D.S.; Souza, E.S.D.; Teixeira, R.A.; Palheta, D.D.C.; Faial, K.D.C.F.; Costa, H.F.; Fernandes, A.R. Bioaccumulation and Human Health Risks of Potentially Toxic Elements in Fish Species from the Southeastern Carajás Mineral Province, Brazil. Environ. Res. 2022, 204, 112024. [Google Scholar] [CrossRef]
- Silva-Júnior, R.O.D.; Souza-Filho, P.W.M.E.; Salomão, G.N.; Tavares, A.L.; Santos, J.F.D.; Santos, D.C.; Dias, L.C.; Silva, M.S.D.; Melo, A.M.Q.D.; Costa, C.E.A.D.S.; et al. Response of Water Balance Components to Changes in Soil Use and Vegetation Cover Over Three Decades in the Eastern Amazon. Front. Water 2021, 3. [Google Scholar] [CrossRef]
- Souza-Filho, P.W.M.; de Souza, E.B.; Silva Júnior, R.O.; Nascimento, W.R.; Versiani de Mendonça, B.R.; Guimarães, J.T.F.; Dall’Agnol, R.; Siqueira, J.O. Four Decades of Land-Cover, Land-Use and Hydroclimatology Changes in the Itacaiúnas River Watershed, Southeastern Amazon. J. Environ. Manage. 2016, 167, 175–184. [Google Scholar] [CrossRef]
- Tavares, A.L.; Da Silva, R.O.; Do Carmo, A.M.C.; Martins, G.C.; Nascimento, W.D.R.; Ferreira, D.B.D.S.; Da Silva, M.S.; Costa, C.E.A.D.S.; Pontes, P.R.; Tedeschi, R.G. Analysis of the Wind Regime at High Spatial-Temporal Resolution in the Itacaiúnas River Watershed, Eastern Amazon. Front. Earth Sci. 2023, 10. [Google Scholar] [CrossRef]
- Alvares, C.A.; Stape, J.L.; Sentelhas, P.C.; de Moraes Gonçalves, J.L.; Sparovek, G. Köppen’s Climate Classification Map for Brazil. Meteorol. Zeitschrift 2013, 22, 711–728. [Google Scholar] [CrossRef]
- Sahoo, P.K.; Dall’Agnol, R.; Salomão, G.N.; da Silva Ferreira Junior, J.; Silva, M.S.; e Souza Filho, P.W.M.; Powell, M.A.; Angélica, R.S.; Pontes, P.R.; da Costa, M.F.; et al. High Resolution Hydrogeochemical Survey and Estimation of Baseline Concentrations of Trace Elements in Surface Water of the Itacaiúnas River Basin, Southeastern Amazonia: Implication for Environmental Studies. J. Geochemical Explor. 2019, 205, 106321. [Google Scholar] [CrossRef]
- Silva Junior, R.O.D.; Tavares, A.L.; Silva, M.S.D.; Martins, G.C.; Costa, C.E.A.D.S.; Melo, A.M.Q.D.; Nascimento Júnior, W.D.R. Installation and Calibration of Sensors for Analysis of Soil Humidity and Temperature in Eastern Amazon Areas. J. Environ. Anal. Prog. 2023, 8, 086–098. [Google Scholar] [CrossRef]
- Alizamir, M.; Kisi, O.; Ahmed, A.N.; Mert, C.; Fai, C.M.; Kim, S.; Kim, N.W.; El-Shafie, A. Advanced Machine Learning Model for Better Prediction Accuracy of Soil Temperature at Different Depths. PLoS One 2020, 15, e0231055. [Google Scholar] [CrossRef] [PubMed]
- Manual de Métodos de Análise de Solo, 3rd ed.; Teixeira, P.C., Donagemma, G.K., Fontana, A., Teixeira, W.G., Eds.; Embrapa Solos: Brasília, Brazil, 2017; ISBN 978-85-7035-771-7. [Google Scholar]
- Silva, C. Manual de Análises Químicas de Solos, Plantas e Fertilizantes, 2nd ed.; Embrapa Informação Tecnológica: Brasília, DF, Brazil, 2009. [Google Scholar]
- R Core Team R: A Language and Environment for Statistical Computing, Vienna, Australia. 2024. Available online: https://cran.r-project.org/doc/manuals/r-release/fullrefman.pdf (accessed on 29 January 2025).
- Venegas, V.H.A.; Novais, R.F.; Barros, N.F.; Cantarutti, R.B.; LOPES, A.S. Interpretation of Soil Analysis Results. In Recommendations for the use of correctives and fertilizers in Minas Gerais; Comissão de Fertilidade do Solo do Estado de Minas Gerais: Viçosa, Brazil, 1999; pp. 25–32. [Google Scholar]
- de Lima, A.F.L.; Campos, M.C.C.; Martins, T.S.; Silva, G.A.; Brito, W.B.M.; dos Santos, L.A.C.; de Oliveira, I.A.; da Cunha, J.M. Soil Chemical Attributes in Areas under Conversion from Forest to Pasture in Southern Brazilian Amazon. Sci. Rep. 2022, 12, 22555. [Google Scholar] [CrossRef]
- Ramos, S.J.; Gastauer, M.; Martins, G.C.; Guedes, R.S.; Caldeira, C.F.; Souza-Filho, P.W.M.; Siqueira, J.O. Changes in Soil Properties during Iron Mining and in Rehabilitating Minelands in the Eastern Amazon. Environ. Monit. Assess. 2022, 194, 256. [Google Scholar] [CrossRef]
- Sekaran, U.; Kumar, S.; Luis Gonzalez-Hernandez, J. Integration of Crop and Livestock Enhanced Soil Biochemical Properties and Microbial Community Structure. Geoderma 2021, 381, 114686. [Google Scholar] [CrossRef]
- Ghosh, A.; Kumar, R.V.; Manna, M.C.; Singh, A.K.; Parihar, C.M.; Kumar, S.; Roy, A.K.; Koli, P. Eco-Restoration of Degraded Lands through Trees and Grasses Improves Soil Carbon Sequestration and Biological Activity in Tropical Climates. Ecol. Eng. 2021, 162, 106176. [Google Scholar] [CrossRef]
- de Oliveira, D.C.; Maia, S.M.F.; Freitas, R.D.C.A.; Cerri, C.E.P. Changes in Soil Carbon and Soil Carbon Sequestration Potential under Different Types of Pasture Management in Brazil. Reg. Environ. Change 2022, 22, 87. [Google Scholar] [CrossRef]
- Ge, N.; Wei, X.; Wang, X.; Liu, X.; Shao, M.; Jia, X.; Li, X.; Zhang, Q. Soil Texture Determines the Distribution of Aggregate-Associated Carbon, Nitrogen and Phosphorous under Two Contrasting Land Use Types in the Loess Plateau. CATENA 2019, 172, 148–157. [Google Scholar] [CrossRef]
- Santos, H.G.D.; Jacomine, P.K.T.; Anjos, L.H.C.D.; Oliveira, V.D.Á.; Lumbreras, J.F.; Coelho, M.R.; Almeida, J.A.D.; Araújo Filho, J.C.D.; Oliveira, J.B.D.; Cunha; et al. Sistema Brasileiro de Classificação de Solos, 5th ed.; Empresa Brasileira de Pesquisa Agropecuária-Embrapa Solos: Brasília, DF, Brazil, 2018. [Google Scholar]
- Souza, E.S.D.; Fernandes, A.R.; De Souza Braz, A.M.; Oliveira, F.J.D.; Alleoni, L.R.F.; Campos, M.C.C. Physical, Chemical, and Mineralogical Attributes of a Representative Group of Soils from the Eastern Amazon Region in Brazil. SOIL 2018, 4, 195–212. [Google Scholar] [CrossRef]
- Ramos, F.T.; Dores, E.F.G.D.C.; Weber, O.L.D.S.; Beber, D.C.; Campelo, J.H.; Maia, J.C.D.S. Soil Organic Matter Doubles the Cation Exchange Capacity of Tropical Soil under No-till Farming in Brazil. J. Sci. Food Agric. 2018, 98, 3595–3602. [Google Scholar] [CrossRef]
- Wagai, R.; Kajiura, M.; Asano, M. Iron and Aluminum Association with Microbially Processed Organic Matter via Meso-Density Aggregate Formation across Soils: Organo-Metallic Glue Hypothesis. SOIL 2020, 6, 597–627. [Google Scholar] [CrossRef]
- Chen, Y.; Shi, J.; Dong, J.; Wu, Y.; Li, C.; Ye, Y.; Tian, X.; Wang, Y. Synergistic Improvement of Soil Organic Carbon Storage and Wheat Grain Zinc Bioavailability by Straw Return in Combination with Zn Application on the Loess Plateau of China. CATENA 2021, 197, 104920. [Google Scholar] [CrossRef]
- Jin, M.; Mullens, T. A Study of the Relations between Soil Moisture, Soil Temperatures and Surface Temperatures Using ARM Observations and Offline CLM4 Simulations. Climate 2014, 2, 279–295. [Google Scholar] [CrossRef]
- Akter, M.; Miah, M.A.; Hassan, M.M.; Mobin, M.N.; Baten, M.A. Textural Influence on Surface and Subsurface Soil Temperatures under Various Conditions. J. Environ. Sci. Nat. Resour. 2015, 8, 149–153. [Google Scholar]
- Marengo, J.A. Interdecadal Variability and Trends of Rainfall across the Amazon Basin. Theor. Appl. Climatol. 2004, 78. [Google Scholar] [CrossRef]
- Ronchail, J.; Cochonneau, G.; Molinier, M.; Guyot, J.-L.; De Miranda Chaves, A.G.; Guimarães, V.; de Oliveira, E. Interannual Rainfall Variability in the Amazon Basin and Sea-Surface Temperatures in the Equatorial Pacific and the Tropical Atlantic Oceans. Int. J. Climatol. 2002, 22, 1663–1686. [Google Scholar] [CrossRef]
- da Rocha, H.R.; Manzi, A.O.; Cabral, O.M.; Miller, S.D.; Goulden, M.L.; Saleska, S.R.; R.-Coupe, N.; Wofsy, S.C.; Borma, L.S.; Artaxo, P.; et al. Patterns of Water and Heat Flux across a Biome Gradient from Tropical Forest to Savanna in Brazil. J. Geophys. Res. Biogeosciences 2009, 114, 1–8. [Google Scholar] [CrossRef]
- Bastable, H.G.; Shuttleworth, W.J.; Dallarosa, R.L.G.; Fisch, G.; Nobre, C.A. Observations of Climate, Albedo, and Surface Radiation over Cleared and Undisturbed Amazonian Forest. Int. J. Climatol. 1993, 13, 783–796. [Google Scholar] [CrossRef]
- von Randow, C.; Manzi, A.O.; Kruijt, B.; de Oliveira, P.J.; Zanchi, F.B.; Silva, R.L.; Hodnett, M.G.; Gash, J.H.C.; Elbers, J.A.; Waterloo, M.J.; et al. Comparative Measurements and Seasonal Variations in Energy and Carbon Exchange over Forest and Pasture in South West Amazonia. Theor. Appl. Climatol. 2004, 78, 5–26. [Google Scholar] [CrossRef]
- McBeath, T.M.; McLaughlin, M.J.; Kirby, J.K.; Armstrong, R.D. Dry Soil Reduces Fertilizer Phosphorus and Zinc Diffusion but Not Bioavailability. Soil Sci. Soc. Am. J. 2012, 76, 1301–1310. [Google Scholar] [CrossRef]
- Prendin, A.L.; Normand, S.; Carrer, M.; Bjerregaard Pedersen, N.; Matthiesen, H.; Westergaard-Nielsen, A.; Elberling, B.; Treier, U.A.; Hollesen, J. Influences of Summer Warming and Nutrient Availability on Salix Glauca L. Growth in Greenland along an Ice to Sea Gradient. Sci. Rep. 2022, 12, 3077. [Google Scholar] [CrossRef]
- Qian, Z.; Zhuang, S.; Gao, J.; Tang, L.; Harindintwali, J.D.; Wang, F. Aeration Increases Soil Bacterial Diversity and Nutrient Transformation under Mulching-Induced Hypoxic Conditions. Sci. Total Environ. 2022, 817, 153017. [Google Scholar] [CrossRef]
- Zhang, Z.; Pan, Z.; Pan, F.; Zhang, J.; Han, G.; Huang, N.; Wang, J.; Pan, Y.; Wang, Z.; Peng, R. The Change Characteristics and Interactions of Soil Moisture and Temperature in the Farmland in Wuchuan County, Inner Mongolia, China. Atmosphere (Basel) 2020, 11, 503. [Google Scholar] [CrossRef]
- Jian, J.; Steele, M.K.; Zhang, L.; Bailey, V.L.; Zheng, J.; Patel, K.F.; Bond-Lamberty, B.P. On the Use of Air Temperature and Precipitation as Surrogate Predictors in Soil Respiration Modelling. Eur. J. Soil Sci. 2022, 73. [Google Scholar] [CrossRef]
- Zhou, Q.; Sun, Z.; Liu, X.; Wei, X.; Peng, Z.; Yue, C.; Luo, Y. Temporal Soil Moisture Variations in Different Vegetation Cover Types in Karst Areas of Southwest China: A Plot Scale Case Study. Water 2019, 11, 1423. [Google Scholar] [CrossRef]
- Tang, C.; Chen, D. Interaction between Soil Moisture and Air Temperature in the Mississippi River Basin. J. Water Resour. Prot. 2017, 9, 1119–1131. [Google Scholar] [CrossRef] [PubMed]
- Silva Júnior, R.O.; Queiroz, J.C.B.; Ferreira, D.B.S.; Tavares, A.L.; Souza-Filho, P.W.M.; Guimarães, J.T.F.; Rocha, E.J.P. Estimativa de Precipitação e Vazões Médias Para a Bacia Hidrográfica Do Rio Itacaiúnas (BHRI), Amazônia Oriental, Brasil (Estimation of Precipitation and Average Flows for the Itacaiúnas River Watershed (IRW) - Eastern Amazonia, Brazil). Rev. Bras. Geogr. Física 2017, 10, 1638. [Google Scholar] [CrossRef]
- Gevaert, A.I.; Miralles, D.G.; de Jeu, R.A.M.; Schellekens, J.; Dolman, A.J. Soil Moisture-Temperature Coupling in a Set of Land Surface Models. J. Geophys. Res. Atmos. 2018, 123, 1481–1498. [Google Scholar] [CrossRef] [PubMed]
- Dembélé, M.; Schaefli, B.; van de Giesen, N.; Mariéthoz, G. Suitability of 17 Gridded Rainfall and Temperature Datasets for Large-Scale Hydrological Modelling in West Africa. Hydrol. Earth Syst. Sci. 2020, 24, 5379–5406. [Google Scholar] [CrossRef]
- Mao, Y.; Liu, S.; Nahar, J.; Liu, J.; Ding, F. Soil Moisture Regulation of Agro-Hydrological Systems Using Zone Model Predictive Control. Comput. Electron. Agric. 2018, 154, 239–247. [Google Scholar] [CrossRef]
Figure 1.
Description of the areas studied in the IRW.
Figure 2.
Soil attributes in the studied areas. Capital letters compare areas. For clay, capital letters compare the different areas within the same soil depth (dark cyan = superficial; light cyan = subsurface), and lowercase letters compare the different depths within the same area.
Figure 2.
Soil attributes in the studied areas. Capital letters compare areas. For clay, capital letters compare the different areas within the same soil depth (dark cyan = superficial; light cyan = subsurface), and lowercase letters compare the different depths within the same area.
Figure 3.
Soil temperature and moisture (10–20 cm depth) and meteorological attributes of different areas.
Figure 3.
Soil temperature and moisture (10–20 cm depth) and meteorological attributes of different areas.
Table 1.
Median values of soil temperature (°C) and median absolute deviations (in parentheses) obtained for the different areas, depths, and months.
Table 1.
Median values of soil temperature (°C) and median absolute deviations (in parentheses) obtained for the different areas, depths, and months.
| Area | Month | Depth | |||
|---|---|---|---|---|---|
| 0–10 cm | 10–20 cm | 20–30 cm | 30–40 cm | ||
| Pasture A | Jul | 30.53 (0.66) αAc | 29.41 (0.49) αBc | 29.39 (0.39) αBc | 29.52 (0.40) αBc |
| Aug | 32.44 (0.58) αAb | 31.08 (0.53) αBb | 31.04 (0.48) αβBb | 31.07 (0.51) αBb | |
| Sep | 33.70 (0.63) αAe | 32.22 (0.49) αBe | 32.05 (0.41) αBe | 32.10 (0.34) αBe | |
| Oct | 31.86 (0.61) αAb | 30.63 (0.46) αBb | 30.58 (0.41) αBb | 30.74 (0.37) αBb | |
| Nov | 29.90 (1.19) αAc | 28.71 (0.90) αBc | 28.91 (0.72) αBc | 29.22 (0.66) αBc | |
| Dec | 28.17 (0.62) αAd | 27.40 (0.44) αBd | 27.60 (0.43) αBd | 27.97 (0.36) αAd | |
| Jan | 26.59 (0.61) αAa | 25.88 (0.44) αBa | 26.13 (0.30) αCa | 26.60 (0.26) αAa | |
| Pasture B | Jul | 29.51 (0.60) βABa | 29.24 (0.43) αAb | 29.39 (0.33) αAb | 29.75 (0.25) αBb |
| Aug | 30.99 (1.14) βAc | 30.62 (0.99) αAd | 30.68 (0.94) αAd | 30.94 (0.95) αAde | |
| Sep | 31.16 (1.11) βAc | 31.11 (0.89) βAd | 31.23 (0.70) βAd | 31.57 (0.53) βAe | |
| Oct | 30.11 (1.61) βABab | 29.85 (1.36) βAac | 30.03 (1.03) βABac | 30.46 (0.87) αBac | |
| Nov | 30.23 (1.30) αβABb | 30.09 (0.99) βAc | 30.39 (0.81) βABc | 30.77 (0.64) βBcd | |
| Dec | 30.00 (0.78) βAab | 29.75 (0.78) βAac | 30.06 (0.54) βAc | 30.50 (0.51) βBc | |
| Jan | 29.64 (0.64) βAa | 29.45 (0.60) βAab | 29.68 (0.52) βAab | 30.17 (0.49) βBab | |
| Transition | Jul | 30.85 (0.55) αAc | 30.51 (0.42) βBc | 29.86 (0.31) βCd | 30.57 (0.22) βABd |
| Aug | 32.52 (0.59) αAb | 31.94 (0.46) βBb | 31.25 (0.45) βCb | 32.02 (0.39) βBb | |
| Sep | 33.69 (0.87) αAe | 33.11 (0.54) χBe | 32.35 (0.54) αCe | 33.13 (0.46) χABe | |
| Oct | 31.88 (0.92) αABb | 31.86 (0.43) χAb | 31.26 (0.36) χCb | 32.05 (0.34) βBb | |
| Nov | 31.15 (1.52) βAc | 31.12 (1.10) χABc | 30.63 (0.90) βBc | 31.51 (0.85) βAc | |
| Dec | 29.59 (0.47) βAd | 29.65 (0.42) βAd | 29.08 (0.32) χBd | 29.90 (0.28) χCd | |
| Jan | 28.40 (1.14) χABa | 28.22 (0.74) χAa | 27.72 (0.49) χCa | 28.55 (0.43) χBa | |
| Forest | Jul | 23.99 (0.48) χAd | 24.23 (0.41) χABd | 24.40 (0.33) χBc | 24.28 (0.34) χABc |
| Aug | 24.65 (1.05) χAd | 24.45 (0.85) χAd | 24.48 (0.64) χAc | 24.28 (0.47) χAc | |
| Sep | 26.74 (0.93) χAb | 26.38 (0.76) δABc | 26.23 (0.70) χBCb | 25.90 (0.68) δCb | |
| Oct | 26.32 (0.33) χAb | 26.21 (0.21) δAbc | 26.16 (0.15) δAb | 25.98 (0.12) χBb | |
| Nov | 26.14 (0.58) χAbc | 26.13 (0.34) δAbc | 26.12 (0.26) χAb | 25.93 (0.16) χBb | |
| Dec | 25.93 (0.32) χABc | 26.02 (0.34) χAb | 26.06 (0.26) δAb | 25.86 (0.21) δBb | |
| Jan | 25.36 (0.45) δAa | 25.53 (0.23) αABa | 25.57 (0.17) δBa | 25.52 (0.12) δBa | |
Equal letters indicate statistically equal medians. Greek letters compare areas within the same month and depth; capital letters compare depths within the same month and area; lowercase letters compare months within the same depth and area.
Table 2.
Median values of soil moisture and median absolute deviations (in parentheses) obtained for the different areas, depths, and months.
Table 2.
Median values of soil moisture and median absolute deviations (in parentheses) obtained for the different areas, depths, and months.
| Area | Month | Depth | |||
| 0–10 cm | 10–20 cm | 20–30 cm | 0–10 cm | ||
| Pasture A | Jul | 2.77 (0.78) αAc | 15.7 (1.19) αBd | 21.2 (0.40) αCd | 20.6 (0.16) αDd |
| Aug | 1.63 (0.16) αAd | 11.9 (0.16) αBe | 17.5 (0.54) αCe | 18.7 (0.33) αDe | |
| Sep | 1.46 (0.05) αAe | 9.66 (0.22) αBf | 16.6 (0.25) αCf | 18.3 (0.11) αDf | |
| Oct | 4.72 (0.29) αAb | 17.1 (0.31) αBb | 22.4 (0.27) αCb | 21.4 (0.21) αDb | |
| Nov | 5.05 (0.54) αAb | 17.8 (0.78) αBc | 23.5 (0.35) αCc | 22.2 (0.37) αDc | |
| Dec | 5.36 (0.60) αAb | 18.4 (0.47) αBc | 24.0 (0.34) αCc | 22.3 (0.29) αDc | |
| Jan | 7.08 (0.73) αAa | 19.9 (0.72) αBa | 25.0 (0.37) αCa | 23.1 (0.31) αDa | |
| Pasture B | Jul | 2.68 (0.31) αAc | 3.94 (0.10) βBc | 3.52 (0.22) βCc | 4.34 (0.39) βDc |
| Aug | 2.18 (0.14) βAd | 3.47 (0.10) βBd | 3.17 (0.05) βCd | 3.93 (0.03) βDd | |
| Sep | 7.95 (1.65) βABe | 9.78 (1.44) αCe | 7.50 (0.81) βAe | 8.77 (0.62) βBe | |
| Oct | 9.97 (0.99) βAb | 11.4 (0.95) βBb | 8.67 (0.72) βCab | 10.3 (0.88) βAab | |
| Nov | 10.4 (1.54) βAb | 11.4 (1.70) βBb | 8.12 (0.99) βCb | 9.25 (1.11) βAb | |
| Dec | 10.5 (1.20) βAab | 11.9 (1.08) βBab | 8.68 (0.41) βCab | 10.1 (0.47) βAab | |
| Jan | 11.3 (1.35) βAa | 12.7 (0.98) βχBa | 9.00 (0.46) βCa | 10.7 (0.54) βDa | |
| Transition | Jul | 1.39 (0.39) βAb | 7.75 (2.25) χBb | 9.74 (2.81) χCc | 10.9 (2.90) χCbc |
| Aug | 0.87 (0.09) χAd | 4.97 (0.38) χBd | 6.76 (0.24) χCe | 7.52 (0.21) χDe | |
| Sep | 0.77 (0.02) χAe | 4.56 (0.04) βBe | 6.57 (0.01) χCf | 7.35 (0.01) χDf | |
| Oct | 1.94 (0.50) χAb | 9.00 (1.90) χBb | 11.0 (1.77) χCbc | 11.6 (1.68) βCb | |
| Nov | 1.93 (1.26) χAb | 7.38 (3.52) χBbc | 9.70 (4.11) χCbd | 10.8 (4.23) βCc | |
| Dec | 3.15 (0.52) χAc | 10.7 (0.80) χBc | 13.0 (0.92) χCd | 14.2 (0.76) χDd | |
| Jan | 5.25 (1.10) χAa | 12.0 (0.83) βBa | 13.9 (0.69) χCa | 15.1 (0.58) χDa | |
| Forest | Jul | 4.44 (0.48) χAb | 6.50 (0.14) χBb | 14.4 (0.26) δCb | 19.0 (0.22) δDb |
| Aug | 3.53 (0.40) δAc | 5.45 (0.30) δBc | 12.6 (0.48) δCc | 17.4 (0.58) δDc | |
| Sep | 3.41 (0.44) δAc | 5.16 (0.09) χBd | 12.1 (0.06) δCd | 16.8 (0.05) δDd | |
| Oct | 5.14 (1.45) αAb | 6.66 (1.63) χBb | 13.9 (1.75) δCb | 18.4 (1.34) χDb | |
| Nov | 4.50 (0.76) αAb | 5.35 (0.21) δBc | 12.5 (0.23) χCc | 17.2 (0.21) χDc | |
| Dec | 11.4 (1.85) βAa | 12.6 (1.47) βAa | 21.2 (1.35) δBa | 26.1 (1.36) δCa | |
| Jan | 13.6 (2.46) βAa | 14.1 (1.44) χAa | 23.3 (1.34) δBa | 28.0 (1.05) δCa | |
Equal letters indicate statistically equal medians. Greek letters compare areas within the same month and depth; capital letters compare depths within the same month and area; lowercase letters compare months within the same depth and area.
Table 3.
Pearson’s correlation coefficient between temperature and soil moisture and meteorological variables for each assessed area.
Table 3.
Pearson’s correlation coefficient between temperature and soil moisture and meteorological variables for each assessed area.
| Area | Variable | T1 | T2 | T3 | T4 | U1 | U2 | U3 | U4 | Tair | Uair | WS | SR |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Pasture A (n = 276–281) | T2 | 0.99 | |||||||||||
| T3 | 0.98 | 1 | |||||||||||
| T4 | 0.97 | 0.99 | 1 | ||||||||||
| U1 | −0.8 | −0.8 | −0.8 | −0.8 | |||||||||
| U2 | −0.8 | −0.8 | −0.8 | −0.8 | 0.92 | ||||||||
| U3 | −0.9 | −0.9 | −0.9 | −0.9 | 0.9 | 0.96 | |||||||
| U4 | −0.9 | −0.9 | −0.9 | −0.9 | 0.93 | 0.96 | 0.98 | ||||||
| Tair | 0.67 | 0.6 | 0.56 | 0.53 | −0.5 | −0.5 | −0.5 | −0.6 | |||||
| Uair | −0.8 | −0.8 | −0.8 | −0.8 | 0.78 | 0.81 | 0.86 | 0.86 | −0.6 | ||||
| WS | 0.37 | 0.38 | 0.39 | 0.39 | −0.2 | −0.4 | −0.4 | −0.3 | 0.18 | −0.4 | |||
| SR | 0.45 | 0.37 | 0.31 | 0.28 | −0.4 | −0.4 | −0.4 | −0.5 | 0.64 | −0.6 | 0.2 | ||
| Rainfall | −0.3 | −0.3 | −0.3 | −0.3 | 0.55 | 0.4 | 0.36 | 0.45 | −0.4 | 0.34 | - | −0.3 | |
| Pasture B (n = 177–211) | T2 | 0.97 | |||||||||||
| T3 | 0.91 | 0.98 | |||||||||||
| T4 | 0.84 | 0.94 | 0.99 | ||||||||||
| U1 | −0.3 | −0.2 | −0.2 | - | |||||||||
| U2 | −0.2 | −0.2 | −0.1 | - | 0.99 | ||||||||
| U3 | −0.2 | −0.2 | −0.1 | - | 0.98 | 0.99 | |||||||
| U4 | −0.3 | −0.2 | −0.2 | - | 0.96 | 0.98 | 0.99 | ||||||
| Tair | 0.42 | 0.41 | 0.41 | 0.42 | 0.36 | 0.37 | 0.39 | 0.37 | |||||
| Uair | −0.5 | −0.4 | −0.3 | −0.2 | 0.86 | 0.84 | 0.84 | 0.84 | - | ||||
| WS | 0.2 | 0.23 | 0.25 | 0.25 | 0.17 | 0.17 | 0.17 | 0.15 | - | - | |||
| SR | 0.58 | 0.42 | 0.3 | 0.23 | −0.2 | −0.2 | −0.2 | −0.2 | 0.25 | −0.5 | - | ||
| Rainfall | −0.3 | −0.2 | - | - | 0.41 | 0.39 | 0.36 | 0.34 | - | 0.44 | 0.2 | −0.4 | |
| Transition (n = 257–488) | T2 | 0.98 | |||||||||||
| T3 | 0.96 | 1 | |||||||||||
| T4 | 0.95 | 0.99 | 1 | ||||||||||
| U1 | −0.9 | −0.9 | −0.9 | −0.9 | |||||||||
| U2 | −0.9 | −0.9 | −0.9 | −0.9 | 0.91 | ||||||||
| U3 | −0.9 | −0.9 | −0.9 | −0.9 | 0.9 | 1 | |||||||
| U4 | −0.9 | −0.9 | −0.9 | −0.9 | 0.9 | 0.99 | 1 | ||||||
| Tair | nd | nd | nd | nd | nd | nd | nd | nd | |||||
| Uair | nd | nd | nd | nd | nd | nd | nd | nd | nd | ||||
| WS | 0.47 | 0.48 | 0.48 | 0.49 | −0.3 | −0.3 | −0.3 | −0.3 | nd | nd | |||
| SR | 0.51 | 0.41 | 0.35 | 0.3 | −0.4 | −0.4 | −0.4 | −0.4 | nd | nd | 0.1 | ||
| Rainfall | −0.4 | −0.4 | −0.4 | −0.3 | 0.56 | 0.47 | 0.45 | 0.43 | nd | nd | 0.1 | −0.5 | |
| Forest (n = 261–265) | T2 | 0.96 | |||||||||||
| T3 | 0.92 | 0.99 | |||||||||||
| T4 | 0.88 | 0.98 | 1 | ||||||||||
| U1 | - | - | 0.14 | 0.2 | |||||||||
| U2 | - | - | 0.13 | 0.18 | 0.98 | ||||||||
| U3 | - | - | - | 0.16 | 0.97 | 0.99 | |||||||
| U4 | - | - | - | 0.17 | 0.96 | 0.98 | 1 | ||||||
| Tair | 0.82 | 0.66 | 0.58 | 0.52 | −0.3 | −0.2 | −0.2 | −0.2 | |||||
| Uair | - | 0.18 | 0.25 | 0.31 | 0.82 | 0.8 | 0.8 | 0.79 | −0.4 | ||||
| WS | 0.2 | - | - | - | −0.8 | −0.8 | −0.8 | −0.8 | 0.31 | −0.7 | |||
| SR | 0.38 | 0.18 | - | - | −0.3 | −0.3 | −0.3 | −0.3 | 0.68 | −0.6 | 0.4 | ||
| Rainfall | - | - | - | - | 0.58 | 0.57 | 0.48 | 0.43 | −0.3 | 0.43 | −0.3 | −0.4 |
T1–T4—Soil temperature in depths 1 (0–10 cm), 2 (10–20 cm), 3 (20–30 cm), and 4 (30–40 cm); U1–U4—soil moisture in depths 1 to 4; Tair—air temperature; Uair—maximum air moisture; WS—maximum wind speed; SR—solar radiation; Rainfall—accumulated rainfall.
Table 4.
Linear model for soil temperature (0–20 cm) as a function of soil attributes and meteorological variables.
Table 4.
Linear model for soil temperature (0–20 cm) as a function of soil attributes and meteorological variables.
| Area | Equation | R2 |
|---|---|---|
| Pasture B | T2 = 18.2 + 0.71Tair–0.08U2 + 0.02Rainfall + 0.71WS − 0.06Uair–0.007SR | 0.62 |
| Forest | T2 = −3.13 + 0.82Tair + 0.08Uair + 0.97WS–0.003SR | 0.73 |
T2—average daily soil temperature in the 10–20 cm depth; WS—average daily maximum wind speed; Tair—average daily value of average air temperature; SR—mean daily value of mean solar radiation; Uair—average daily value of maximum air moisture; U2—average daily value of soil moisture in the 10–20 cm depth.
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Silva Júnior, R.O.d.; Morais, T.B.S.S.; Pereira, W.V.d.S.; Martins, G.C.; Ribeiro, P.G.; Melo, A.M.Q.d.; Silva, M.S.d.; Ramos, S.J. Impact of Land Cover and Meteorological Attributes on Soil Fertility, Temperature, and Moisture in the Itacaiúnas River Watershed, Eastern Amazon. Environments 2025, 12, 98. https://doi.org/10.3390/environments12040098
AMA Style
Silva Júnior ROd, Morais TBSS, Pereira WVdS, Martins GC, Ribeiro PG, Melo AMQd, Silva MSd, Ramos SJ. Impact of Land Cover and Meteorological Attributes on Soil Fertility, Temperature, and Moisture in the Itacaiúnas River Watershed, Eastern Amazon. Environments. 2025; 12(4):98. https://doi.org/10.3390/environments12040098
Chicago/Turabian StyleSilva Júnior, Renato Oliveira da, Tatiane Barbarelly Serra Souza Morais, Wendel Valter da Silveira Pereira, Gabriel Caixeta Martins, Paula Godinho Ribeiro, Adayana Maria Queiroz de Melo, Marcio Sousa da Silva, and Sílvio Junio Ramos. 2025. "Impact of Land Cover and Meteorological Attributes on Soil Fertility, Temperature, and Moisture in the Itacaiúnas River Watershed, Eastern Amazon" Environments 12, no. 4: 98. https://doi.org/10.3390/environments12040098
APA StyleSilva Júnior, R. O. d., Morais, T. B. S. S., Pereira, W. V. d. S., Martins, G. C., Ribeiro, P. G., Melo, A. M. Q. d., Silva, M. S. d., & Ramos, S. J. (2025). Impact of Land Cover and Meteorological Attributes on Soil Fertility, Temperature, and Moisture in the Itacaiúnas River Watershed, Eastern Amazon. Environments, 12(4), 98. https://doi.org/10.3390/environments12040098
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