[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
You seem to have javascript disabled. Please note that many of the page functionalities won't work as expected without javascript enabled.
 
 
Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Article Types

Countries / Regions

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Search Results (4,105)

Search Parameters:
Keywords = soil layer

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
21 pages, 1835 KiB  
Article
Leaf to Root Morphological and Anatomical Indicators of Drought Resistance in Coffea canephora After Two Stress Cycles
by Guilherme A. R. de Souza, Danilo F. Baroni, Wallace de P. Bernado, Anne R. Santos, Larissa C. de S. Barcellos, Letícia F. T. Barcelos, Laísa Z. Correia, Claudio M. de Almeida, Abraão C. Verdin Filho, Weverton P. Rodrigues, José C. Ramalho, Miroslava Rakočević and Eliemar Campostrini
Agriculture 2025, 15(6), 574; https://doi.org/10.3390/agriculture15060574 - 7 Mar 2025
Abstract
Coffea canephora genotypes adopt distinct strategies to cope with drought and rehydration. We hypothesized that the greater drought tolerance of genotype ‘3V’ compared to ‘A1’, previously reflected in physiological and anatomical leaf traits after two water-stress (WS) cycles, could also be observed in [...] Read more.
Coffea canephora genotypes adopt distinct strategies to cope with drought and rehydration. We hypothesized that the greater drought tolerance of genotype ‘3V’ compared to ‘A1’, previously reflected in physiological and anatomical leaf traits after two water-stress (WS) cycles, could also be observed in P–V curve responses, root and branch anatomy, leaf midrib elongation (CVL), and root distribution. The ‘3V’ and ‘A1’ plants were grown under well-watered (WW) conditions and two cycles of water stress (WS). The ‘3V’ was more sensitive to WS, with reduced branch xylem vessel density (BXVD), while ‘A1’ demonstrated increased BXVD. Root xylem vessel area (RXVA) decreased to a greater extent in ‘3V’ than in ‘A1’, and both genotypes showed increased bulk elastic modulus. Regardless of water conditions, ‘A1’ maintained a higher relative leaf water content at the turgor loss point (RWCTLP). Morphological acclimation did not occur in the second WS cycle. The ‘3V’ plants developed greater root mass in deeper soil layers than ‘A1’ under the WS condition. These findings suggest that ‘A1’ follows a conservative drought-avoidance strategy with lower physio-morphological plasticity, while ‘3V’ exhibits greater drought tolerance. Such responses highlighted coordinated physiological, morphological, and anatomical adaptations of the above- and below-ground organs for resource acquisition and conservation under WS. Full article
(This article belongs to the Section Crop Production)
27 pages, 10829 KiB  
Article
Potentiality Delineation of Groundwater Recharge in Arid Regions Using Multi-Criteria Analysis
by Heba El-Bagoury, Mahmoud H. Darwish, Sedky H. A. Hassan, Sang-Eun Oh, Kotb A. Attia and Hanaa A. Megahed
Water 2025, 17(5), 766; https://doi.org/10.3390/w17050766 - 6 Mar 2025
Viewed by 119
Abstract
This study integrates morphometric analysis, remote sensing, and GIS with the analytical hierarchical process (AHP) to identify high potential groundwater recharge areas in Wadi Abadi, Egyptian Eastern Desert, supporting sustainable water resource management. Groundwater recharge primarily comes from rainfall and Nile River water, [...] Read more.
This study integrates morphometric analysis, remote sensing, and GIS with the analytical hierarchical process (AHP) to identify high potential groundwater recharge areas in Wadi Abadi, Egyptian Eastern Desert, supporting sustainable water resource management. Groundwater recharge primarily comes from rainfall and Nile River water, particularly for Quaternary aquifers. The analysis focused on the Quaternary and Nubian Sandstone aquifers, evaluating 16 influencing parameters, including elevation, slope, rainfall, lithology, soil type, and land use/land cover (LULC). The drainage network was derived from a 30 m-resolution Digital Elevation Model (DEM). ArcGIS 10.8 was used to classify the basin into 13 sub-basins, with layers reclassified and weighted using a raster calculator. The groundwater potential map revealed that 24.95% and 29.87% of the area fall into very low and moderate potential categories, respectively, while low, high, and very high potential zones account for 18.62%, 17.65%, and 8.91%. Data from 41 observation wells were used to verify the potential groundwater resources. In this study, the ROC curve was applied to assess the accuracy of the GWPZ models generated through different methods. The validation results indicated that approximately 87% of the wells corresponded accurately with the designated zones on the GWPZ map, confirming its reliability. Over-pumping in the southwest has significantly lowered water levels in the Quaternary aquifer. This study provides a systematic approach for identifying groundwater recharge zones, offering insights that can support resource allocation, well placement, and aquifer sustainability in arid regions. This study also underscores the importance of recharge assessment for shallow aquifers, even in hyper-arid environments. Full article
(This article belongs to the Special Issue Advance in Groundwater in Arid Areas)
Show Figures

Figure 1

Figure 1
<p>(<b>a</b>) Egypt Landsat satellite image; (<b>b</b>) study area by Google Earth image, 2024.</p>
Full article ">Figure 2
<p>Geological map of Wadi Abadi Basin (after CONCO, 1987 [<a href="#B63-water-17-00766" class="html-bibr">63</a>]).</p>
Full article ">Figure 3
<p>The flowchart of approaches and methodology.</p>
Full article ">Figure 4
<p>Geographical distribution of the main groundwater aquifers and forty-two of drilled wells of the Wadi Abadi basin.</p>
Full article ">Figure 5
<p>Hydrogeological cross-section (A–A′) of the Nubia Sandstone aquifer at the study area.</p>
Full article ">Figure 6
<p>(<b>a</b>) Digital elevation model; (<b>b</b>) slope; (<b>c</b>) aspect; (<b>d</b>) rainfall distribution; (<b>e</b>) lithology; (<b>f</b>) soil types; and (<b>g</b>) LULC.</p>
Full article ">Figure 7
<p>(<b>a</b>) Stream order and number; (<b>b</b>) stream length; (<b>c</b>) bifurcation ratio; (<b>d</b>) drainage density; (<b>e</b>) length of overland flow; (<b>f</b>) stream frequency; (<b>g</b>) drainage texture; (<b>h</b>) elongation ratio; and (<b>i</b>) relief ratio.</p>
Full article ">Figure 8
<p>A groundwater potential zone map (GWPZ) associated with observation wells illustrating the classes of potential recharge zoning at Wadi Abadi.</p>
Full article ">
16 pages, 7121 KiB  
Article
Aridification Inhibits the Release of Dissolved Organic Carbon from Alpine Soils in Southwest China
by Yanmei Li, Jihong Qin, Yuwen Chen, Hui Sun and Xinyue Hu
Soil Syst. 2025, 9(1), 24; https://doi.org/10.3390/soilsystems9010024 - 6 Mar 2025
Viewed by 119
Abstract
The alpine peatlands in western Sichuan Province are currently experiencing aridification. To understand the effects of aridification on the characteristics of organic carbon release from alpine soils, the soil in the northwest Sichuan Plateau was investigated. Soil columns were incubated under different moisture [...] Read more.
The alpine peatlands in western Sichuan Province are currently experiencing aridification. To understand the effects of aridification on the characteristics of organic carbon release from alpine soils, the soil in the northwest Sichuan Plateau was investigated. Soil columns were incubated under different moisture conditions in situ and in the laboratory, and ultraviolet-visible absorption spectroscopy and three-dimensional fluorescence spectroscopy were used to assess the soil dissolved organic carbon (DOC) levels. The results revealed that (1) the cumulative release of DOC from alpine soil in the northwest Sichuan Plateau decreased with decreasing moisture content. The cumulative release of soil DOC in the laboratory (0–5 cm soil reached 1.93 ± 0.43 g/kg) was greater than that from soil incubated in situ (0–5 cm soil reached 1.40 ± 0.13 g/kg); (2) the cumulative release of DOC in 0–5 cm soil exhibited the greatest response to changes in water content, and the cumulative release of DOC from the 0–5 cm soil layer (1.40 ± 0.13 g/kg) was greater than that from the 5–15 cm soil layer (1.25 ± 0.03 g/kg); and (3) UV-visible absorption spectra and 3D fluorescence spectral characteristics indicated that aridification increases the content of chromophoric dissolved organic matter (CDOM) components with strong hydrophobicity, especially tyrosine components (surface soil increased 39.59~63.31%), in alpine soil DOC. This increase in hydrophobic CDOM components enhances the aromaticity and degree of humification of DOC. Our results revealed that drought inhibits the release of soil DOC, which is unfavorable for the sequestration of organic carbon in alpine soils, potentially resulting in the loss of soil carbon pools and further degradation of alpine ecosystem functions. Full article
Show Figures

Figure 1

Figure 1
<p>Study area on the northwestern plateau of Sichuan with the approximate locations of the sampling sites. (<b>a</b>) Location of the study site in China, (<b>b</b>) location of the actual sampling positions. A corresponds to the edge of the alpine lake, where the water level is high and the soil is basically saturated year round; B corresponds to the alpine grassland in the middle of the slope, where the soil is relatively moist; and C corresponds to a higher slope position, where the soil is relatively dry. The image was generated with ArcGIS 10.8.</p>
Full article ">Figure 2
<p>Cumulative release of soil DOC under different water contents in the laboratory and during in situ incubation. (<b>a1</b>) 0–5 cm of soil in situ, (<b>b1</b>) 5–10 cm of soil in situ, (<b>c1</b>) 10–15 cm of soil in situ, (<b>a2</b>) 0–5 cm of soil in the laboratory, (<b>b2</b>) 5–10 cm of soil in the laboratory, and(<b>c2</b>) 10–15 cm of soil in the laboratory. A-A, soil from position A was incubated at position A; A-B, soil from position A was incubated at position B; A-C, soil from position A was incubated at position C; FC, field capacity; SWC, saturated water content.</p>
Full article ">Figure 2 Cont.
<p>Cumulative release of soil DOC under different water contents in the laboratory and during in situ incubation. (<b>a1</b>) 0–5 cm of soil in situ, (<b>b1</b>) 5–10 cm of soil in situ, (<b>c1</b>) 10–15 cm of soil in situ, (<b>a2</b>) 0–5 cm of soil in the laboratory, (<b>b2</b>) 5–10 cm of soil in the laboratory, and(<b>c2</b>) 10–15 cm of soil in the laboratory. A-A, soil from position A was incubated at position A; A-B, soil from position A was incubated at position B; A-C, soil from position A was incubated at position C; FC, field capacity; SWC, saturated water content.</p>
Full article ">Figure 3
<p>Characteristics of ultraviolet-visible absorption spectra of soil DOC under different moisture conditions. (<b>a1</b>) The 0–5 cm soil layer in situ, (<b>b1</b>) the 5–10 cm soil layer in situ, (<b>c1</b>) the 10–15 cm soil layer in situ, (<b>a2</b>) the 0–5 cm soil layer in the laboratory, (<b>b2</b>) the 5–10 cm soil layer in the laboratory, and (<b>c2</b>) the 10–15 cm soil layer in the laboratory. Pre(A), the preincubation soil layer at position A; A-A, the soil from position A was incubated at position A; A-B, the soil from position A was incubated at position B; A-C, the soil from position A was incubated at position C; FC, field capacity; SWC, saturated water content. The lowercase letters “a”, “b”, “c”, and “d” indicate significant (<span class="html-italic">p</span> &lt; 0.05) differences among the different moisture conditions.</p>
Full article ">Figure 4
<p>Characteristics of three-dimensional fluorescence regional integration of soil DOC under different moisture conditions. (<b>a1</b>) The 0–5 cm soil layer in situ, (<b>b1</b>) the 5–10 cm soil layer in situ, (<b>c1</b>) the 10–15 cm soil layer in situ, (<b>a2</b>) the 0–5 cm soil layer in the laboratory, (<b>b2</b>) the 5–10 cm soil layer in the laboratory, and (<b>c2</b>) the 10–15 cm soil layer in the laboratory. Pre(A), the preincubation soil layer at position A; A-A, the soil from position A was incubated at position A; A-B, the soil from position A was incubated at position B; A-C, the soil from position A was incubated at position C; FC, field capacity; SWC, saturated water content. Peak I, associated with aromatic proteins such as tyrosine; Peak II, linked to aromatic proteins and biochemical oxygen demand (BOD<sub>5</sub>); Peak III, resembling fulvic acid; Peak IV, indicative of microbial byproducts such as tryptophan; and Peak V, akin to humic substances, specifically macromolecular humic acids.</p>
Full article ">Figure 4 Cont.
<p>Characteristics of three-dimensional fluorescence regional integration of soil DOC under different moisture conditions. (<b>a1</b>) The 0–5 cm soil layer in situ, (<b>b1</b>) the 5–10 cm soil layer in situ, (<b>c1</b>) the 10–15 cm soil layer in situ, (<b>a2</b>) the 0–5 cm soil layer in the laboratory, (<b>b2</b>) the 5–10 cm soil layer in the laboratory, and (<b>c2</b>) the 10–15 cm soil layer in the laboratory. Pre(A), the preincubation soil layer at position A; A-A, the soil from position A was incubated at position A; A-B, the soil from position A was incubated at position B; A-C, the soil from position A was incubated at position C; FC, field capacity; SWC, saturated water content. Peak I, associated with aromatic proteins such as tyrosine; Peak II, linked to aromatic proteins and biochemical oxygen demand (BOD<sub>5</sub>); Peak III, resembling fulvic acid; Peak IV, indicative of microbial byproducts such as tryptophan; and Peak V, akin to humic substances, specifically macromolecular humic acids.</p>
Full article ">
24 pages, 1668 KiB  
Review
Progress and Prospects of Research on Physical Soil Crust
by Huiyun Xu, Xuchao Zhu and Meixia Mi
Soil Syst. 2025, 9(1), 23; https://doi.org/10.3390/soilsystems9010023 - 4 Mar 2025
Viewed by 174
Abstract
Physical soil crust (PSC) is a dense structural layer formed on the surface of bare or very low-cover land due to raindrop splashes or runoff. The formation of crust changes the properties of the soil and strongly affects water infiltration and runoff and [...] Read more.
Physical soil crust (PSC) is a dense structural layer formed on the surface of bare or very low-cover land due to raindrop splashes or runoff. The formation of crust changes the properties of the soil and strongly affects water infiltration and runoff and sediment production processes on slopes. The irrational use of soil and water resources and frequent human production activity under the influence of urbanization increase the possibility of inducing erosion. Studying the formation and structural characteristics of PSC to predict terrestrial hydrological processes and improve models for predicting erosion is very important. Many studies of PSC have been carried out in China and abroad, but they are mainly unilateral discussions of the basic properties and characteristics of crust and its effects on runoff and sediment yield on slopes. Studies systematically analyzing and synthesizing the progress of crust research, however, are lacking. By reading the literature and analyzing the developmental history of PSC, we provide a comprehensive review of the following: (1) the meaning, main types, and classification of PSC, (2) the mechanism of formation and the characteristics and dynamic development of crust, (3) the factors affecting the formation of crust, including natural and anthropogenic factors and comprehensive effects, and (4) the development and formation of crust in the soil environment, i.e., hydrological processes and erosion. We also summarize the potential directions for future research on PSC: (1) studying the dynamics of soil structure during the development of crust, (2) developing an objective and standardized quantitative method for studying crust formation, (3) using models of erosion influenced by crust development, (4) improving the scale of the degree of crust development and structural characteristics, and (5) rationalizing the management of crust to optimize land structure and increase crop yield. Full article
Show Figures

Figure 1

Figure 1
<p>Methods for measuring the thickness of physical crust. (<b>a</b>). Vernier calipers. (<b>b</b>). Observation of microscope sections. (<b>c</b>). CT scanning and porosity thresholding.</p>
Full article ">Figure 2
<p>Four stages of quantifying physical crust. (<b>a</b>). Qualitative description. (<b>b</b>). Semi-quantitative representation. (<b>c</b>). Preliminary quantification. (<b>d</b>). Quantitative expression.</p>
Full article ">Figure 3
<p>Simulation of the processes and trends in the development of physical crust in Quaternary red clay in southern China.</p>
Full article ">
17 pages, 1585 KiB  
Article
Effect of Clay Amendment and Strategic Deep Tillage on Soil Water Dynamics and Plant Growth Under Controlled Environments
by Kanchana Wickramarachchi, Giacomo Betti and Gaus Azam
Plants 2025, 14(5), 799; https://doi.org/10.3390/plants14050799 - 4 Mar 2025
Viewed by 259
Abstract
Strategic deep tillage (SDT) practices, such as soil mixing following the application of soil amendments, are promising approaches to alleviate topsoil water repellence and other subsoil constraints and improve crop productivity. However, there is a lack of knowledge on the effect of SDT [...] Read more.
Strategic deep tillage (SDT) practices, such as soil mixing following the application of soil amendments, are promising approaches to alleviate topsoil water repellence and other subsoil constraints and improve crop productivity. However, there is a lack of knowledge on the effect of SDT on soil water dynamics, especially under water-limited environments. This study evaluates the effects of clay incorporation, soil inversion and deep soil mixing on soil water infiltration, surface evaporation rates, soil water storage and subsequent impacts on the below and aboveground growth of wheat (Triticum aestivum L. var Scepter) in controlled environments. Results show that soil mixing significantly improved water infiltration compared to an untreated control. Clay incorporation exhibited the highest bare soil surface evaporation rates immediately and two years post-tillage, leading to substantial water losses under warm and dry ambient conditions. Despite improving soil water storage in deeper layers, high evaporation rates in clay-incorporated soils negatively impacted wheat growth, with reduced shoot biomass and root length density. Conversely, soil inversion and mixing-only treatments demonstrated balanced improvements in water infiltration, soil water use, and wheat shoot biomass. These findings underscore the trade-offs associated with SDT practices, particularly in managing soil water loss and crop productivity in water-limited environments. This study also highlights the need for the careful selection of SDT for soil amelioration strategies tailored to soil types and climatic conditions to enhance agricultural productivity and sustainability. Full article
Show Figures

Figure 1

Figure 1
<p>Infiltration of water (mm) from (<b>a</b>) the pond of the permeameter, (<b>b</b>) initial infiltration rate (<span class="html-italic">f</span>, mm/h), (<b>c</b>) infiltration rate (<span class="html-italic">f</span>, mm/h) over the duration of the experiment, and (<b>d</b>) steady state <span class="html-italic">f</span> (mm/h) for different soil amelioration treatments. Vertical error bars represent the standard error of the mean value. Bar graphs with different letters are significantly different at <span class="html-italic">p</span> ≤ 0.05. Note that the <span class="html-italic">Y</span>-axis scale differs between the figures.</p>
Full article ">Figure 2
<p>Daily loss of water through evaporation (E) from bare soil (<b>a</b>–<b>c</b>) immediately after tillage in 2019 at 30 °C and 80% relative humidity in a growth chamber and (<b>d</b>–<b>f</b>) two years after tillage in 2021 at 30 °C and 45% relative humidity in a glasshouse. DAW = days after watering. Vertical error bars represent the standard error of the mean value. Bar graphs with different letters for a given DAW are significantly different at <span class="html-italic">p</span> ≤ 0.05 (<b>a</b>,<b>b</b>,<b>d</b>,<b>e</b>). Note that the <span class="html-italic">Y</span>-axis scale differs between the figures.</p>
Full article ">Figure 3
<p>Effect of tillage treatments on (<b>a</b>) shoot dry biomass (SDB) and (<b>b</b>) root length density (RLD). Relationship between the wheat (<span class="html-italic">Triticum aestivum</span> L. var Scepter) shoot dry biomass and (<b>c</b>) total RLD, 0–30 cm, and (<b>d</b>) cumulative evaporative loss from the bare soils. Bar graphs with different letters are significantly different at <span class="html-italic">p</span> ≤ 0.05 and the absence of letters refers to non-significance (for 10–20 and 20–30 cm depths) at <span class="html-italic">p</span> ≤ 0.05. Note that the <span class="html-italic">Y</span>-axis scale differs between the figures.</p>
Full article ">Figure 4
<p>Soil volumetric water content (VWC, cm<sup>3</sup>/cm<sup>3</sup>) at (<b>a</b>) 0–10 cm, (<b>b</b>) 10–20 cm, and (<b>c</b>) 20–30 cm depths under four tillage treatments over 38 days after seeding (DAS). Vertical error bars represent the standard error of the mean value. Note that the <span class="html-italic">Y</span>-axis scale differs between the figures.</p>
Full article ">Figure 5
<p>(<b>a</b>) A typical sandy soil profile in southwestern Australia and (<b>b</b>) a schematic of the four tillage treatments.</p>
Full article ">Figure 6
<p>(<b>left</b>) CSIRO disc permeameter, and (<b>right</b>) a soil column with the evaporation dome.</p>
Full article ">
13 pages, 1191 KiB  
Article
Soil Organic Carbon Turnover Following Afforestation of a Savanna Revealed by Particle-Size Fractionation and Natural 13C Measurements in Ivory Coast
by Thierry Desjardins, Thierry Henry Des Tureaux, Magloire Mandeng-Yogo and Fethiye Cetin
Land 2025, 14(3), 535; https://doi.org/10.3390/land14030535 - 4 Mar 2025
Viewed by 86
Abstract
Soil organic matter plays a crucial role in the global carbon cycle, yet the magnitude and direction of changes in soil carbon content following vegetation shifts in the tropics remain highly debated. Most studies have focused on short-term changes, typically spanning only a [...] Read more.
Soil organic matter plays a crucial role in the global carbon cycle, yet the magnitude and direction of changes in soil carbon content following vegetation shifts in the tropics remain highly debated. Most studies have focused on short-term changes, typically spanning only a few months or years. In this study, we investigated the medium-term dynamics of organic matter at a site where savanna, protected from fire for 58 years, has gradually transitioned to woodland vegetation. Natural 13C abundance analysis combined with particle-size fractionation was used to characterize the changes in SOM over time. While carbon content remains relatively stable, δ13C exhibits a distinct shift, particularly in the surface layers, reflecting the gradual replacement of savanna-derived carbon with tree-derived carbon. All fractions were influenced by the inputs and outputs of carbon from both savanna and tree sources. In the coarse fractions, most of the carbon originates from trees; however, a significant proportion of savanna-derived carbon (ranging from 10% to 40%, depending on the fraction, depth, and patch) persists, likely in the form of black carbon. In the fine fractions, nearly half of the carbon (40% to 50%) remains derived from the savanna, highlighting the greater stability of organic matter that is physically bound to clays and protected within microaggregates. Full article
(This article belongs to the Section Land, Soil and Water)
Show Figures

Figure 1

Figure 1
<p>δ<sup>13</sup>C profiles of SOM under the different types of vegetation: gallery forest (GF), savannas (GSs), and fire-protected savannas (FPSs).</p>
Full article ">Figure 2
<p>C content (in mgC·g<sup>−1</sup> soil of the soil layer) of the particle-size organic fractions in the two upper layers of the soil under the different types of vegetation: gallery forest (GF), savannas (GSs), and fire-protected savannas (FPSs). The numbers beside the bars indicate the C content expressed as % of total carbon for each particle-size fraction.</p>
Full article ">Figure 3
<p>δ<sup>13</sup>C of particle-size organic fractions of the two upper layers (0–10 and 10–20 cm) of the soil under the different types of vegetation: gallery forest (GF), savannas (GSs), and fire-protected savannas (FPSs).</p>
Full article ">
17 pages, 2106 KiB  
Article
Different Soil Properties, Wolfberry Yields, and Quality Responses to Organic Fertilizer Levels in Two Fields with Varying Fertility Levels in Qaidam
by Congcong Li, Yajun Xin, Tingting Xu, Youliang Wang, Shouzhong Xie, Tahir Shah, Chi Zhang, Hangle Ren, Chongpeng Zheng, Rong Zhang, Haiyan Sheng and Yajun Gao
Soil Syst. 2025, 9(1), 21; https://doi.org/10.3390/soilsystems9010021 - 4 Mar 2025
Viewed by 188
Abstract
(1) Background: This study aimed to evaluate the effects of organic fertilizer dose on soil nutrients, wolfberry fruit nutrient compositions, and fruit yields. (2) Methods: We conducted a two-year field trial in two typical fields with different fertility levels in the Qaidam area. [...] Read more.
(1) Background: This study aimed to evaluate the effects of organic fertilizer dose on soil nutrients, wolfberry fruit nutrient compositions, and fruit yields. (2) Methods: We conducted a two-year field trial in two typical fields with different fertility levels in the Qaidam area. Six treatments were applied to each field, including CK, M2 M4, M6, M8, and M10 (representing 0, 2, 4, 6, 8, and 10 kg organic fertilizer/plant, respectively) in the high-fertility field and CK, M3, M6, M9, M12, and M15 (representing 0, 3, 6, 9, 12, and 15 kg organic fertilizer/plant, respectively) in the low-fertility field. An ANOVA was used to determine the significant difference between treatments, and the LSD method was used for multiple comparisons of analysis of variance. (3) Results: In the high-fertility field, the application of organic fertilizer significantly affected the total nitrogen (N) content, mineral N storage, and soil organic matter content. The application of too much organic fertilizer significantly increased the soil’s EC value. In the low-fertility field, the effect of organic fertilizer application on soil nutrient enhancement differed significantly among soil layers but significantly increased the contents of total phenols, flavonoids, and amino acids in wolfberry fruit, and there was a significant trend of increasing wolfberry yield with increasing organic fertilizer application. (4) Conclusions: In the Qaidam area of the Tibetan Plateau, it is recommended to apply 2–4 kg commercial organic fertilizer/plant in the high-fertility wolfberry orchards while 9–12 kg in the low-fertility wolfberry orchards is recommended. Full article
Show Figures

Figure 1

Figure 1
<p>Effect of different organic fertilizer levels on dry fruit yield of wolfberries in high-fertility fields. The first means the first harvest (wolfberry fruits harvested in late July), the second means the second harvest (wolfberry fruits harvested in mid-August), and the third means the third harvest (wolfberry fruits harvested in mid-September). Different uppercase and lowercase letters represent significant differences between treatments (<span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 2
<p>Effects of different organic fertilizer levels on dry fruit yield of wolfberry in low-fertility fields. The first means the first harvest (wolfberry fruits harvested in early August), and the second means the second harvest (wolfberry fruits harvested in late August). Different uppercase and lowercase letters represent significant differences between treatments (<span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 3
<p>Redundancy analysis (RDA) of wolfberry fruit quality and soil properties in the high-fertility fields. Soil physical and chemical properties: TN, total soil nitrogen; OM, soil organic matter; pH, soil pH; EC, soil EC; Nox.-N, soil nitrate nitrogen storage; Fruit quality: FC, flavonoid; TPC, total phenols; PC, polysaccharide; SSC, soluble solid; ABTS, ABTS radical scavenging power; CUPRAC, copper ion reducing power; DPPH, DPPH scavenging power; PFRAC, potassium ferricyanide reducing power; AAC, total amino acids. The red lines refer to soil properties, the blue line refer to wolfberry fruit quality index.</p>
Full article ">Figure 4
<p>Redundancy analysis (RDA) of wolfberry fruit quality and soil properties in the low-fertility fields. Soil physical and chemical properties: TN, total soil nitrogen; OM, soil organic matter; pH, soil pH; EC, soil EC; Nox.-N, soil nitrate nitrogen storage; Fruit quality: FC, flavonoid; TPC, total phenol; PC, polysaccharide; SSC, soluble solid; ABTS, ABTS radical scavenging power; CUPRAC, copper ion reducing power; DPPH, DPPH scavenging power; PFRAC, potassium ferricyanide reducing power; AAC, total amino acids. The red lines refer to soil properties, the blue line refer to wolfberry fruit quality index.</p>
Full article ">Figure 5
<p>Analysis of the relative importance of fruit yield, appearance and soil physical and chemical properties in high-fertility fields. Yield, dried wolfberry fruit yield; HGW, hundred grain weight; L/D, fruit shape index.</p>
Full article ">Figure 6
<p>Analysis of the relative importance of fruit yield, appearance and soil physical and chemical properties in the low-fertility fields. Yield, dried wolfberry fruit yield; HGW, hundred grain weight; L/D, fruit shape index.</p>
Full article ">
20 pages, 2761 KiB  
Article
Impact of Aggregate-Associated Carbon on Soil Mechanical Properties: Stability and Compaction Indices in Pomegranate Orchards of Different Ages
by Ahmed Ali Abdelrhman, Yasser A. Sayed, Mohamed E. Fadl, Cristiano Casucci, Marios Drosos, Antonio Scopa and Hussein Moftah
Soil Syst. 2025, 9(1), 20; https://doi.org/10.3390/soilsystems9010020 - 4 Mar 2025
Viewed by 164
Abstract
The relationships between soil aggregates, aggregate-associated carbon (C), and soil compaction indices in pomegranate orchards of varying ages (0–30 years) in Assiut, Egypt, were investigated. Soil bulk density (Bd) and organic carbon (OC) content increased with orchard age in both the surface (0.00–0.20 [...] Read more.
The relationships between soil aggregates, aggregate-associated carbon (C), and soil compaction indices in pomegranate orchards of varying ages (0–30 years) in Assiut, Egypt, were investigated. Soil bulk density (Bd) and organic carbon (OC) content increased with orchard age in both the surface (0.00–0.20 m) and subsurface (0.20–0.40 m) layers 0.20–0.40 m). The percentage of macroaggregates (R0.25) and their OC content in the aggregate fraction > 0.250 mm increased as the pomegranate orchard ages increased in the surface layer (0.00–0.20 m). Older pomegranate orchards show improved soil structure, indicated by higher mean weight diameter (MWD) and geometric mean diameter (GMD), alongside reduced fractal dimension (D) and erodibility (K). As orchard ages increased, maximum bulk density (BMax) decreased due to an increase in OC, while the degree of compactness (DC) increased, reaching a maximum at both soil layers for the 30 Y orchards. Soil organic carbon and aggregate-associated C significantly influenced BMax, which led to reducing the soil compaction risk. Multivariate analyses identified the >2 mm aggregate fraction as the most critical factor influencing the DC, soil compaction, and K indices in pomegranate orchards. The OC content in the >2 mm aggregates negatively correlated with BMax, DC, and K but was positively associated with MWD and GMD. Moreover, DC and Bd decreased with higher proportions of >2 mm aggregates, whereas DC increased with a higher fraction of 2–0.250 mm aggregation. These findings highlight the role of aggregate size fractions and their associated C in enhancing soil structure stability, mitigating compaction, and reducing erosion risks in pomegranate orchards. Full article
Show Figures

Figure 1

Figure 1
<p>Location of sampling sites for pomegranate orchards.</p>
Full article ">Figure 2
<p>The first two principal coordinates of the dataset are affected by aggregate stability, aggregate-associated OC concentrations, and soil compaction; (<b>a</b>) (0.00–0.20 m); (<b>b</b>) (0.20–0.40 m).</p>
Full article ">
20 pages, 2678 KiB  
Article
Low-Temperature Slow Pyrolysis: Exploring Biomass-Specific Biochar Characteristics and Potential for Soil Applications
by Matheus Antonio da Silva, Adibe Luiz Abdalla Filho, Ruan Carnier, Juliana de Oliveira Santos Marcatto, Marcelo Saldanha, Aline Renee Coscione, Thaís Alves de Carvalho, Gabriel Rodrigo Merlotto and Cristiano Alberto de Andrade
Technologies 2025, 13(3), 100; https://doi.org/10.3390/technologies13030100 - 3 Mar 2025
Viewed by 295
Abstract
The pyrolysis process of residues has emerged as a sustainable method for managing organic waste, producing biochars that offer significant benefits for agriculture and the environment. These benefits depend on the properties of the raw biomass and the pyrolysis conditions, such as washing [...] Read more.
The pyrolysis process of residues has emerged as a sustainable method for managing organic waste, producing biochars that offer significant benefits for agriculture and the environment. These benefits depend on the properties of the raw biomass and the pyrolysis conditions, such as washing and drying. This study investigated biochar production through slow pyrolysis at 300 °C, using eight biomass types, four being plant residues (PBR)—sugarcane bagasse, filter cake, sawdust, and stranded algae—and four non-plant-based residues (NPBR)—poultry litter, sheep manure, layer chicken manure, and sewage sludge. The physicochemical properties assessed included yield, carbon (C) and nitrogen (N) content, electrical conductivity, pH, macro- and micronutrients, and potentially toxic metals. Pyrolysis generally increased pH and concentrated C, N, phosphorus (P), and other nutrients while reducing electrical conductivity, C/N ratio, potassium (K), and sulfur (S) contents. The increases in the pH of the biochars in relation to the respective biomasses were between 0.3 and 1.9, with the greatest differences observed for the NPBR biochars. Biochars from sugarcane bagasse and sawdust exhibited high C content (74.57–77.67%), highlighting their potential use for C sequestration. Filter cake biochar excelled in P (14.28 g kg⁻1) and micronutrients, while algae biochar showed elevated N, calcium (Ca), and boron (B) levels. NPBR biochars were rich in N (2.28–3.67%) and P (20.7–43.4 g kg⁻1), making them ideal fertilizers. Although sewage sludge biochar contained higher levels of potentially toxic metals, these remained within regulatory limits. This research highlights variations in the composition of biochars depending on the characteristics of the original biomass and the pyrolysis process, to contribute to the production of customized biochars for the purposes of their application in the soil. Biochars derived from exclusively plant biomasses showed important aspects related to the recovery of carbon from biomass and can be preferred as biochar used to sequester carbon in the soil. On the other hand, biochars obtained from residues with some animal contributions are more enriched in nutrients and should be directed to the management of soil fertility. Full article
(This article belongs to the Special Issue Recent Advances in Applied Activated Carbon Research)
Show Figures

Figure 1

Figure 1
<p>Total C results in the biomasses and their respective biochars, before and after washing. (<b>A</b>) total C content and (<b>B</b>) mass yield and C recovery. The vertical bars represent the 5% confidence interval.</p>
Full article ">Figure 2
<p>Organic C results in the biomasses and their respective biochars. (<b>A</b>) organic C content and (<b>B</b>) recovery percentage of organic C. The vertical bars represent the 5% confidence interval.</p>
Full article ">Figure 3
<p>Total N results in the biomasses and their respective biochars, before and after washing. (<b>A</b>) total N content and (<b>B</b>) total N recoverey. The vertical bars represent the 5% confidence interval.</p>
Full article ">Figure 4
<p>Extractable N results in the biomasses and their respective biochars. (<b>A</b>) extractable N content and (<b>B</b>) extractable N recovery. The vertical bars represent the 5% confidence interval.</p>
Full article ">Figure 5
<p>C/N ratio for biomasses and the respective biochars, (<b>A</b>) from PBR and (<b>B</b>) from NPBR.</p>
Full article ">Figure 6
<p>S and K results for all biochar and its respective biomasses. (<b>A</b>) Total S content, (<b>B</b>) Recovery of S, (<b>C</b>) Total K content and (<b>D</b>) Recovery of K. The vertical bars represent the 5% confidence interval.</p>
Full article ">
20 pages, 2560 KiB  
Article
Grazing Intensity Accelerates Surface Soil C and N Cycling in Alpine Pastures as Revealed by Soil Genes and δ15N Ratio
by Salvatore Raniolo, Luca Da Ros, Laura Maretto, Damiano Gianelle, Federica Camin, Luana Bontempo, Piergiorgio Stevanato, Enrico Sturaro, Andrea Squartini and Mirco Rodeghiero
Sustainability 2025, 17(5), 2165; https://doi.org/10.3390/su17052165 - 3 Mar 2025
Viewed by 183
Abstract
European grasslands are vital carbon (C) sinks, contributing to climate change mitigation. Grazing intensity significantly influences soil C and nitrogen (N) cycles through effects on soil conditions and microbial communities. While heavy grazing is linked to soil C loss and altered N processes, [...] Read more.
European grasslands are vital carbon (C) sinks, contributing to climate change mitigation. Grazing intensity significantly influences soil C and nitrogen (N) cycles through effects on soil conditions and microbial communities. While heavy grazing is linked to soil C loss and altered N processes, existing studies show conflicting outcomes. This study examines the impact of cattle grazing on soil C and N cycles in a historical alpine pasture in the eastern Italian Alps (1868 m a.s.l.). The following three grazing intensities were analyzed: heavy (8.19 LU ha−1), moderate (0.59 LU ha−1), and light (0.06 LU ha−1). Soil was sampled from two depth layers (0–5 cm, 5–10 cm) and analyzed for bulk density, C and N content, C/N ratio, exchangeable N, δ15N, and microbial genes targeting general abundance (16S), N fixation (nifH), nitrification (amoA), and denitrification (nirK, nosZ) using real-time PCR. The results revealed decreased C and N concentrations with increasing grazing intensity, exclusively in the 0–5 cm soil layer. Higher δ15N and enhanced nitrification and denitrification suggest a more open N cycle under heavy grazing. These findings highlight the potential of microbial gene markers and δ15N isotopic ratios to monitor N cycle dynamics in alpine pastures, informing sustainable grazing management. Full article
Show Figures

Figure 1

Figure 1
<p>Location of the study area in the north-eastern Italian Alps (<b>left panel</b>) and a map of pasture as a function of grazing intensity (<b>right panel</b>). Different shades of blue indicate the level of grazing intensity according to the position density of the animals in a square area of 625 m<sup>2</sup>, determined with GPS collars (from July to September in the years 2019 and 2020). The soil sampling plots (diamonds) are also reported for the three grazing intensities (H—heavy, M—moderate, L—light).</p>
Full article ">Figure 2
<p>Soil bulk density mean values (g cm<sup>−3</sup>) in relation to soil depth (panel (<b>A</b>); S = superficial layer; D = deep layer) and grazing intensity (panel (<b>B</b>); H = heavy; M = moderate; L = light) in 2018. Soil mean content of NH<sub>4</sub><sup>+</sup> (mg NH<sub>4</sub><sup>+</sup> kg<sup>−1</sup>) as a function of soil depth (panel (<b>C</b>)) and soil mean content of NO<sub>3</sub><sup>−</sup> (mg NO<sub>3</sub><sup>−</sup> kg<sup>−1</sup>) in a function of the 2-way interaction of grazing intensity and soil depth (panel (<b>D</b>)) in 2018. Means not sharing any letter are significantly different by ANOVA based on the permutation test (“aovp” R function) at the specified level of significance. For details, see <a href="#app1-sustainability-17-02165" class="html-app">Table S1</a>.</p>
Full article ">Figure 3
<p>Mean values of total soil N (%) (panel (<b>A</b>)), C (%) (panel (<b>B</b>)), C/N ratio (panel (<b>C</b>)), and δ<sup>15</sup>N (panel (<b>D</b>)) as a function of the 2-way interactions between grazing intensity and soil depth (grazing intensity: H = high; M = moderate; L = low; soil depth: S = superficial layer; D = deep layer) in 2018 and 2020. Means not sharing any letter are significantly different by ANOVA based on the permutation test (“aovp” R function) at the specified level of significance. For details, see <a href="#app1-sustainability-17-02165" class="html-app">Table S2</a>.</p>
Full article ">Figure 4
<p>Correlation plot with Kendall rank’s coefficient between copies of selected genes (16S, <span class="html-italic">nifH</span>, <span class="html-italic">nosZ</span>, <span class="html-italic">nirK</span>, ratio <span class="html-italic">nosZ</span>/<span class="html-italic">nirK</span>, and <span class="html-italic">amoA</span>) and pedological variables. The copies of genes were expressed on a logarithmic scale. The level of statistical significance is marked with asterisks (* = <span class="html-italic">p</span> &lt; 0.05, ** = <span class="html-italic">p</span> &lt; 0.01, *** = <span class="html-italic">p</span> &lt; 0.001).</p>
Full article ">Figure 5
<p>Boxplots showing log mean abundance of the number of copies per gram of soil dry weight for the following genes: 16S (panel (<b>A</b>)), <span class="html-italic">nifH</span> (panel (<b>B</b>)), <span class="html-italic">nirK</span> (panel (<b>C</b>)), <span class="html-italic">nosZ</span> (panel (<b>D</b>)), <span class="html-italic">nosZ</span>/<span class="html-italic">nirK</span> (panel (<b>E</b>)), and <span class="html-italic">amoA</span> (sum of AOA and AOB, panel (<b>F</b>)) in relation to grazing intensity (H = heavy grazing; M = moderate grazing; L = light grazing). Means not sharing any letter are significantly different by the ANOVA based on the permutation test (“aovp” R function) at the specified level of significance. For details, see <a href="#app1-sustainability-17-02165" class="html-app">Table S4</a>.</p>
Full article ">Figure 6
<p>Effects of grazing intensity (H = heavy grazing; M = moderate grazing; L = light grazing) on AOB presence (panel (<b>A</b>)) and ratio AOB/<span class="html-italic">amoA</span> (panel (<b>B</b>)). Means sharing the same letter are not significantly different for <span class="html-italic">p</span> &lt; 0.05 by the general linear model based on binomial distribution (panel (<b>A</b>)) and general additive model based on beta distribution (panel (<b>B</b>)) at the specified level of significance. For details, see <a href="#app1-sustainability-17-02165" class="html-app">Table S5</a>.</p>
Full article ">
18 pages, 1316 KiB  
Article
Impact of Agricultural Land Use on Organic Carbon Content in the Surface Layer of Fluvisols in the Vistula River Floodplains, Poland
by Miroslaw Kobierski, Krystyna Kondratowicz-Maciejewska and Beata Labaz
Agronomy 2025, 15(3), 628; https://doi.org/10.3390/agronomy15030628 - 28 Feb 2025
Viewed by 275
Abstract
Floodplains with fluvisols in Poland are crucial areas for both agriculture and environmental relevance. The largest areas of fluvisols are located in the floodplains of the Vistula River and have been identified as significant reservoirs of organic carbon. Humic substances were determined using [...] Read more.
Floodplains with fluvisols in Poland are crucial areas for both agriculture and environmental relevance. The largest areas of fluvisols are located in the floodplains of the Vistula River and have been identified as significant reservoirs of organic carbon. Humic substances were determined using the following procedure: Cdec—carbon after decalcification, CHA+CFA—carbon of humic and fulvic acids (extracted with 0.5 M NaOH solution), CFA—carbon of fulvic acids (extracted with 2 M HCl solution), CHumin—proportion of carbon in humins. The extraction of soluble organic matter (DOC and DON) was also determined. In the surface layer of grasslands, significantly higher mean contents of total organic carbon (TOC) and total nitrogen (Nt) were found compared with arable soils. In fluvisols used as grasslands, compared to the arable soils, significantly higher contents of Cdec, CHA, CFA, Chumin, DOC, DON, and C-stock were observed. The study results indicate that the agricultural use of environmentally valuable lands, such as floodplains, affected the stock of organic carbon and the properties of the humic substances. Grasslands stored significantly more SOC (10.9 kg m−2) than arable soils (6.7 kg m−2), emphasizing their role as organic carbon resevoirs. Agricultural practices such as limiting plowing and introducing grasslands can support carbon sequestration. Therefore, the role of fluvisols in floodplains in carbon sequestration should be emphasized in climate change mitigation strategies. Full article
(This article belongs to the Section Soil and Plant Nutrition)
Show Figures

Figure 1

Figure 1
<p>Schemes of the study location. Localization of the areas and soil profiles under investigation. Schematic maps of Europe (<b>A</b>) and Poland (<b>B</b>). Study location—Grudziadz Basin, Lower Vistula River (<b>C</b>).</p>
Full article ">Figure 2
<p>Arable soil after a flood episode in spring (<b>A</b>); grassland in summer (<b>B</b>).</p>
Full article ">Figure 3
<p>Soil parameters and the significance levels (ANOVA, Tukey test). Description: (<b>a</b>) bulk density, (<b>b</b>) stock of TOC.</p>
Full article ">Figure 4
<p>Soil parameters and the significance levels (ANOVA, Tukey test). Description: (<b>a</b>) total organic carbon, (<b>b</b>) total nitrogen.</p>
Full article ">Figure 5
<p>Average values of ratios and the significance levels (ANOVA, Tukey test). Description: (<b>a</b>) carbon to nitrogen content, (<b>b</b>) carbon content of humic acids to fulvic acids.</p>
Full article ">
16 pages, 3642 KiB  
Article
Soil Carbohydrates and Glomalin-Related Soil Proteins Affect Aggregate Characteristics in Chinese Fir Plantations with Different Stand Types
by Zhiyao Wang, Lei Du, Xianyu Yao, Yili Guo, Shaoming Ye and Shengqiang Wang
Forests 2025, 16(3), 444; https://doi.org/10.3390/f16030444 - 28 Feb 2025
Viewed by 324
Abstract
Soil carbohydrates and glomalin-related soil proteins (GRSPs), as important components of soil organic matter, are the essential basis for maintaining soil aggregate stability. They interact with each other and influence each other. Exploring the relationships and mechanisms of action between these two components [...] Read more.
Soil carbohydrates and glomalin-related soil proteins (GRSPs), as important components of soil organic matter, are the essential basis for maintaining soil aggregate stability. They interact with each other and influence each other. Exploring the relationships and mechanisms of action between these two components and soil aggregates is of great significance for improving soil quality and promoting the sustainable development of forest stands. This study focused on investigating soil aggregate composition (including >2, 2–1, 1–0.25, and <0.25 mm fractions) and stability (as indicated by the mean weight diameter (MWD) and geometric mean diameter (GMD)) as well as aggregate-associated carbohydrates and GRSP components in Chinese fir plantations with different stand types, including Chinese fir × Michelia macclurei (stand I), Chinese fir × Mytilaria laosensis (stand II), and pure Chinese fir (stand III). The results indicated that in the 0–20 cm and 20–40 cm soil layer, the MWD and GMD of the two mixed Chinese fir stands were significantly (p < 0.05) higher than that of the pure Chinese fir stand. The contents of carbohydrates and GRSP in the soil also showed similar trends. This suggests that mixed Chinese fir stands (especially the Chinese fir × Michelia macclurei) enhance soil aggregate stability as well as the contents of carbohydrates and GRSP in the soil. The results also revealed that although both carbohydrates and GRSP significantly contribute to the formation and stability of large soil aggregates, PLS-PM analysis showed that in the 0–20 cm and 20–40 cm soil layer, the path coefficient of GRSP to aggregate stability was 0.840 and 0.576, while that of carbohydrates was 0.134 and 0.398. Therefore, compared with carbohydrates, GRSP (especially the easily extractable fraction of GRSP) has a more pronounced effect on soil aggregate stability. This finding provides a scientific basis and practical guidance for enhancing the productivity of Chinese fir plantations. Full article
Show Figures

Figure 1

Figure 1
<p>Location of the experimental site.</p>
Full article ">Figure 2
<p>Soil aggregate-associated carbohydrates in different stand types of Chinese fir. Different lower-case letters represent significant differences (<span class="html-italic">p</span> &lt; 0.05) among different stand types. Different upper-case letters represent significant differences (<span class="html-italic">p</span> &lt; 0.05) among different aggregate sizes.</p>
Full article ">Figure 3
<p>Soil aggregate-associated GRSP in different stand types of Chinese fir. Different lower-case letters represent significant differences (<span class="html-italic">p</span> &lt; 0.05) among different stand types. Different upper-case letters represent significant differences (<span class="html-italic">p</span> &lt; 0.05) among different aggregate sizes.</p>
Full article ">Figure 4
<p>Correlation between soil carbohydrates, GRSP, and aggregates in different stand types of Chinese fir. *, **, and *** stand for significant differences at <span class="html-italic">p</span> &lt; 0.05, <span class="html-italic">p</span> &lt; 0.01, and <span class="html-italic">p</span> &lt; 0.001, respectively.</p>
Full article ">Figure 5
<p>Redundancy analysis showing the influence of soil carbohydrates and GRSP on aggregates in different stand types of Chinese fir.</p>
Full article ">Figure 6
<p>Partial least squares path model showing the influence of soil carbohydrates and GRSP on aggregates in different stand types of Chinese fir.</p>
Full article ">
19 pages, 3413 KiB  
Article
Coupled Influence of Magnetic Biochar and Solution Chemistries on Retention and Release of Nanoplastics in Porous Media
by Yan Qin, Yan Liang and Yongtao Peng
Int. J. Mol. Sci. 2025, 26(5), 2207; https://doi.org/10.3390/ijms26052207 - 28 Feb 2025
Viewed by 160
Abstract
Magnetic biochar (MBC), as an environmentally friendly material, has been extensively used for the remediation of soil and groundwater contamination. The retention and release of nanoplastics (NPs) with carboxyl (NPs-COOH) or amino functionalization (NPs- NH2) in saturated porous media were investigated [...] Read more.
Magnetic biochar (MBC), as an environmentally friendly material, has been extensively used for the remediation of soil and groundwater contamination. The retention and release of nanoplastics (NPs) with carboxyl (NPs-COOH) or amino functionalization (NPs- NH2) in saturated porous media were investigated under varying conditions of ionic strength (IS), MBC addition, humic acid (HA) concentration, and cation types. The reversible and irreversible retention of NPs was examined by altering the IS, increasing the solution pH, and inducing cation exchange. The results revealed that MBC enhanced the surface roughness of the media, thereby inhibiting NPs’ transport. The HA promoted NPs-NH2 transport more effectively than NPs-COOH due to electrostatic repulsion, steric hindrance, and competition for deposition sites. Under a reduced IS and increased pH, a portion of the retained NPs was released, with NPs-NH2 showing a greater release than NPs-COOH, indicating reversible retention. Additionally, the stronger charge-shielding and cation-bridging effects of Ca2+ significantly enhanced the retention of NPs. Cation exchange resulted in less NPs being released, as most were irreversibly retained in deeper primary minima. However, a small number of retained NPs were remobilized by electrical double layer expansion, surface deprotonation, and cation exchange, indicating reversible retention. These findings provide valuable insights into the fate of NPs in the environment. Full article
Show Figures

Figure 1

Figure 1
<p>Characteristics of MBC. (<b>a</b>) XRD patterns; (<b>b</b>) FTIR spectra; (<b>c</b>) the vibrating-adsorbent magnetometer of MBC; (<b>d</b>) N<sub>2</sub> adsorption–desorption isotherms of MBC.</p>
Full article ">Figure 2
<p>SEM images: (<b>a</b>) energy-dispersive X-ray spectrum and (<b>b</b>) elemental distribution maps for C, O, and Fe (<b>c</b>) in MBC.</p>
Full article ">Figure 3
<p>SEM images of NPs. (<b>a</b>) NPs adsorbed onto MBC; (<b>b</b>) retained NPs on the quartz sand in the presence of MBC; (<b>c</b>) retained NPs on the quartz sand; (<b>d</b>) retained NPs in the presence of HA.</p>
Full article ">Figure 4
<p>Adsorption kinetics of the MBC for NPs-COOH (<b>a</b>) and NPs-NH<sub>2</sub> (<b>b</b>). Adsorption isotherm of the MBC with Langmuir isotherm (red), and Freundlich isotherm (blue) for NPs-COOH (<b>c</b>) and NPs-NH<sub>2</sub> (<b>d</b>).</p>
Full article ">Figure 5
<p>Breakthrough and release curves of NPs-COOH (<b>a</b>) and NPs-NH<sub>2</sub> (<b>b</b>) with different ISs at pH 7. The release of NPs was initiated by eluting with ultrapure water under pH 7 (phase I) and pH 10 (phase II), respectively, and the input concentration of NPs was 10 mg L<sup>−1</sup>.</p>
Full article ">Figure 6
<p>Breakthrough and release curves of NPs-COOH in 1 (<b>a</b>) and 5 (<b>b</b>) mM NaCl, and breakthrough and release curves of NPs-NH<sub>2</sub> in 1 (<b>c</b>) and 5 (<b>d</b>) mM NaCl at different concentrations of MBC. The release of NPs was initiated by eluting with ultrapure water under pH 7 (phase I) and pH 10 (phase II), respectively, and the input concentration of NPs was 10 mg L<sup>−1</sup>.</p>
Full article ">Figure 7
<p>Breakthrough and release curves of NPs-COOH in the absence (<b>a</b>) and presence (<b>b</b>) of MBC, and breakthrough and release curves of NPs-NH<sub>2</sub> in the absence (<b>c</b>) and presence (<b>d</b>) of MBC at different HA concentrations. The release of NPs was initiated by eluting with ultrapure water under pH 7 (phase I) and pH 10 (phase II), respectively; the input concentration of NPs was 10 mg L<sup>−1</sup> and the IS was 5 mM NaCl.</p>
Full article ">Figure 8
<p>Breakthrough and release curves of NPs-COOH (<b>a</b>–<b>c</b>) and NPs-NH<sub>2</sub> (<b>d</b>–<b>f</b>) in CaCl<sub>2</sub>: (<b>a</b>,<b>d</b>) under different IS; (<b>b</b>,<b>e</b>) at different concentrations of MBC and absence of HA under 1 mM CaCl<sub>2</sub>; (<b>c</b>,<b>f</b>) at different concentrations of HA and absence of MBC under 1 mM CaCl<sub>2</sub>. Released NP was initiated by eluting with H<sub>2</sub>O, 1 mM NaCl, H<sub>2</sub>O, 100 mM NaCl, and H<sub>2</sub>O in release phases I–V, respectively.</p>
Full article ">
17 pages, 3397 KiB  
Article
Effect of No-Tillage on Soil Bacterial Community Structure in the Black Soil Region of Northeast China
by Chuan Liu, Gang Liu, Hui Gao and Yun Xie
Sustainability 2025, 17(5), 2114; https://doi.org/10.3390/su17052114 - 28 Feb 2025
Viewed by 227
Abstract
To assess the effects of prolonged no-tillage practices on soil health and crop output, an 18-year field study was carried out in the black soil region of Northeast China. We investigated the variations in soil physicochemical properties, bacterial community structure, and soybean yield [...] Read more.
To assess the effects of prolonged no-tillage practices on soil health and crop output, an 18-year field study was carried out in the black soil region of Northeast China. We investigated the variations in soil physicochemical properties, bacterial community structure, and soybean yield under different no-tillage (NT) durations from year 10 to 18 and conventional tillage (CT) treatments for 18 years. The findings indicated that the 18-year no-tillage (NT18) treatment resulted in significantly greater levels of soil organic matter, total nitrogen, and available phosphorus—18.3%, 30.4%, and 65.8% higher, respectively (p < 0.05)—compared to the traditional tillage (CT18) treatment. In the 0–30 cm soil layer, the relative abundance of Acidobacteriota had risen with the duration of no-tillage, whereas Proteobacteria, Gemmatimonadota, and Verrucomicrobiota had shown a decline. In addition, no-tillage treatments increased network complexity, with longer durations of no-tillage leading to higher levels of complexity. Soybean yield increased by 8.5% under NT18 compared to CT18 (p < 0.05). These findings provide insights into the interaction between no-tillage treatments and soil bacterial microbial communities within the black soil region, thereby establishing a solid foundation for developing efficient, ecological, and sustainable conservation tillage systems in Northeast China. Full article
(This article belongs to the Section Soil Conservation and Sustainability)
Show Figures

Figure 1

Figure 1
<p>Location of the study area.</p>
Full article ">Figure 2
<p>Bacterial chao1 and observed species indices in the 0–30 cm soil layer of no-till plots with different durations. The numbers represent the duration in years. NT: no-tillage; CT: conventional tillage. Different letters are used to indicate significant differences, while the same letters indicate no significant differences. A black dot typically represents the mean. The horizontal line may represent the median.</p>
Full article ">Figure 3
<p>PCoA of soil bacterial communities with different no-tillage durations. NT: no-tillage; CT: conventional tillage. The numbers represent the duration in years.</p>
Full article ">Figure 4
<p>Species composition and relative abundance of soil microbial communities under Different no-tillage durations. NT: no-tillage; CT: conventional tillage. The numbers represent the duration in years.</p>
Full article ">Figure 4 Cont.
<p>Species composition and relative abundance of soil microbial communities under Different no-tillage durations. NT: no-tillage; CT: conventional tillage. The numbers represent the duration in years.</p>
Full article ">Figure 5
<p>Changes in relative abundance of soil microbial communities under different no-tillage Durations. NT: no-tillage; CT: conventional tillage. The numbers represent the duration in years. The ratio represents the relative abundance under no-tillage compared to that under conventional tillage. Different letters are used to indicate significant differences, while the same letters indicate no significant differences.</p>
Full article ">Figure 6
<p>Visualization of bacterial co-occurrence network modules under different no-tillage durations. Nodes represent individual taxonomic units (OTUs), whereas edges denote significant correlations between OTUs. The size of each node corresponds to the relative abundance of the OTUs, and the width of each edge reflects the Spearman correlation coefficient.</p>
Full article ">Figure 6 Cont.
<p>Visualization of bacterial co-occurrence network modules under different no-tillage durations. Nodes represent individual taxonomic units (OTUs), whereas edges denote significant correlations between OTUs. The size of each node corresponds to the relative abundance of the OTUs, and the width of each edge reflects the Spearman correlation coefficient.</p>
Full article ">
15 pages, 3250 KiB  
Article
Response Characteristics of Soil Water in Vegetated Slopes to Spring Rainfall Under Different Covers
by Xinlong Zhou, Zhengquan Yang, Lifei Zheng and Yunfeng Shi
Sustainability 2025, 17(5), 2079; https://doi.org/10.3390/su17052079 - 27 Feb 2025
Viewed by 222
Abstract
Spring is the optimal season for the ecological restoration of slopes. Addressing the response of soil water to spring rainfall is crucial to constructing a suitable hydrothermal environment for plant growth. In this study, three model slopes under different vegetation covers were constructed [...] Read more.
Spring is the optimal season for the ecological restoration of slopes. Addressing the response of soil water to spring rainfall is crucial to constructing a suitable hydrothermal environment for plant growth. In this study, three model slopes under different vegetation covers were constructed to measure soil water content during the spring. The accumulated increment in soil water (AISW), the growth rate of the soil water content rate (GRSW), the soil water recharge amount (∆SW), and the response time (Tr) of soil water were introduced to analyze its response to different spring rainfall events. The effects of vegetation and rainfall intensity were discussed. The results indicate that Cynodon dactylon mainly regulates surface soil water (0–20 cm), with a rapid and significant response in shallow soil. Magnolia multiflora is more effective in regulating deeper soil water (40–100 cm), especially during heavy rainfall, where shrubs enhance water infiltration into deeper layers. This study further demonstrates that increased rainfall intensity exacerbates the differences in water distribution between vegetation types. The combined effect of the canopy and root structure is crucial for water redistribution. Full article
Show Figures

Figure 1

Figure 1
<p>Design of model slope and layout of soil water sensors.</p>
Full article ">Figure 2
<p>Model slopes and weather station: (<b>a</b>) bare slope—BS; (<b>b</b>) grass slope—GS; (<b>c</b>) shrub slope—SS; (<b>d</b>) weather station.</p>
Full article ">Figure 3
<p>Measured rainfall events during experiment period.</p>
Full article ">Figure 4
<p>Variation in SWC in different slopes: (<b>a</b>) BS; (<b>b</b>) GS; (<b>c</b>) SS.</p>
Full article ">Figure 5
<p>Spatial distribution of soil water in three slopes under different rainfall levels: (<b>a</b>) light rainfall; (<b>b</b>) moderate rainfall; (<b>c</b>) heavy rainfall.</p>
Full article ">Figure 6
<p>Accumulated increments of SWC of slopes in different depths under three rainfall levels: (<b>a</b>) Level I; (<b>b</b>) Level II; (<b>c</b>) Level III. Note: Capital letters indicate significant differences in different soil layer data of the same slope (<span class="html-italic">p</span> &lt; 0.05), and lowercase letters indicate significant differences in the same soil layer data of different slopes (<span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 7
<p>Variations of GRSW in three slopes under different rainfall levels: (<b>a</b>) Level I; (<b>b</b>) Level II; (<b>c</b>) Level III.</p>
Full article ">
Back to TopTop