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27 pages, 5221 KiB  
Article
The Social and Ecological Dimension of Ecosystem Service Enhancement in Post-Mining Forest Rehabilitation: Integrating Stakeholder Perspectives
by Bohwi Lee, Dawou Joung, Wonho Kim, Juin Ko and Hakjun Rhee
Forests 2025, 16(1), 7; https://doi.org/10.3390/f16010007 (registering DOI) - 24 Dec 2024
Abstract
Mining activities lead to significant environmental degradation, including soil erosion, water pollution, and biodiversity loss. In South Korea, abandoned coal mines cause considerable ecological disturbances in mountainous regions. Forest rehabilitation has been proposed as a strategy to mitigate these impacts, but its effectiveness [...] Read more.
Mining activities lead to significant environmental degradation, including soil erosion, water pollution, and biodiversity loss. In South Korea, abandoned coal mines cause considerable ecological disturbances in mountainous regions. Forest rehabilitation has been proposed as a strategy to mitigate these impacts, but its effectiveness depends on successfully integrating ecosystem services (ES). This study assesses the social value of ES in post-mining rehabilitation by incorporating perspectives from local communities and experts in forestry and mining sectors. A mixed-methods approach involving surveys and interviews was employed to gather stakeholder views on 18 ES, including provisioning, regulating, cultural, and habitat services. Results indicate that local communities prioritize cultural and regulating services, such as mental health, aesthetic value, and climate regulation, while experts emphasize regulating services like soil erosion control and carbon sequestration. This divergence highlights the need for a balanced approach that integrates both ecological and socio-cultural benefits, suggesting that community needs have not been adequately reflected in current practices. The study findings underscore the importance of incorporating community input into forest management to ensure both ecological outcomes and social value, offering a framework for adaptive management that aligns ecological goals with community needs, ultimately promoting sustainability and resilience in post-mining landscapes. Full article
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<p>Conceptual framework linking mining activities and forest rehabilitation to ES (adapted from [<a href="#B29-forests-16-00007" class="html-bibr">29</a>,<a href="#B44-forests-16-00007" class="html-bibr">44</a>] under Creative Commons License). The solid lines in the framework illustrate the direct impacts of mining activities on biodiversity and natural capital, whereas the dotted lines represent the interactions among the supply, demand, and flow of ES. The decline in the supply of ES due to mining activities adversely affects human well-being and social demand, which subsequently influences social capital.</p>
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<p>Study area for local community perception survey.</p>
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<p>Provisioning services perceptions between mining and forest experts (<span class="html-italic">p</span> &lt; 0.05) *. Note: The * indicates statistically significant differences between groups.</p>
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<p>Regulating services perceptions between mining and forest experts (<span class="html-italic">p</span> &lt; 0.05) *. Note: The * indicates statistically significant differences between groups.</p>
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<p>Cultural services perceptions between mining and forest experts (<span class="html-italic">p</span> &lt; 0.05) *. Note: The * indicates statistically significant differences between groups.</p>
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<p>Habitat services perceptions between mining and forest experts (<span class="html-italic">p</span> &lt; 0.05) *. Note: The * indicates statistically significant differences between groups.</p>
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<p>Mean values and standard errors for perceived importance of ES in post-mining forest rehabilitation, assessed on a 11-point Likert scale from 0 (unknown) to 10 (very strongly positive benefit) (<span class="html-italic">n</span> = 87).</p>
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<p>Typology of ES for post-mining forest rehabilitation from expert and community survey (<span class="html-fig-inline" id="forests-16-00007-i001"><img alt="Forests 16 00007 i001" src="/forests/forests-16-00007/article_deploy/html/images/forests-16-00007-i001.png"/></span> = top 5 community services).</p>
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17 pages, 6973 KiB  
Article
Active Moss Biomonitoring of Mercury in the Mine-Polluted Area of Abbadia San Salvatore (Mt. Amiata, Central Italy)
by Federica Meloni, Sergio Calabrese, Orlando Vaselli, Francesco Capecchiacci, Francesco Ciani, Lorenzo Brusca, Sergio Bellomo, Walter D’Alessandro, Kyriaki Daskalopoulou, Stefania Venturi, Barbara Nisi, Daniele Rappuoli, Franco Tassi and Jacopo Cabassi
Toxics 2025, 13(1), 2; https://doi.org/10.3390/toxics13010002 (registering DOI) - 24 Dec 2024
Abstract
Active biomonitoring of mercury (Hg) using non-indigenous moss bags was performed for the first time within and around the former Hg mining area of Abbadia San Salvatore (Mt. Amiata, central Italy). The purpose was to discern the Hg spatial distribution, identify the most [...] Read more.
Active biomonitoring of mercury (Hg) using non-indigenous moss bags was performed for the first time within and around the former Hg mining area of Abbadia San Salvatore (Mt. Amiata, central Italy). The purpose was to discern the Hg spatial distribution, identify the most polluted areas, and evaluate the impacts of dry and wet deposition on mosses. The exposed moss bags consisted of a mixture of Sphagnum fuscum and Sphagnum tenellum from an external uncontaminated area. In each site, two different types of moss bags, one uncovered (to account for the wet + dry deposition) and one covered (to evaluate the dry deposition), were exposed. The behavior of arsenic (As) and antimony (Sb) in the mosses was investigated to assess the potential relationship with Hg. GEM (Gaseous Elemental Mercury) concentrations were also measured at the same sites where the mosses were exposed, although only as a reference in the initial stages of biomonitoring. The results revealed that the main Hg emissions sources were associated with the former mining area of Abbadia San Salvatore, in agreement with the measured GEM concentrations, while arsenic and antimony were related to soil enriched in As-Sb waste material. The three elements registered higher concentrations in uncovered mosses with respect to the covered ones, i.e., wet deposition was the key factor for their accumulation on the uncovered mosses, while dry deposition was especially important for the covered samples in the mining area. Hg was accumulated in the mosses via GEM adsorption, uptake of particulate Hg, and precipitation via raindrops/snowfall, with almost no loss and without post-deposition volatilization. The results testified that the chosen biomonitoring technique was an extremely useful tool for understanding Hg transport and fate in a contaminated area. Full article
(This article belongs to the Special Issue Monitoring and Assessment of Mercury Pollution)
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Graphical abstract
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<p>Study area and sampling sites location.</p>
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<p>Boxplot of As, Sb, and Hg (in ng/g) in covered (C) and uncovered (U) mosses.</p>
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<p>The average temperature (°C) and precipitation (mm) trend from the ASS weather station TOS07000001 (Tuscany Regional Hydrological Service, <a href="http://www.sir.toscana.it" target="_blank">www.sir.toscana.it</a>) during the study period. The dashed line indicates the temperature of 0 °C.</p>
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<p>Correlation matrix of As, Sb, and Hg in covered (C) and uncovered (U) mosses. The number inside each cell represents Spearman’s correlation coefficient. See the text for further details.</p>
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<p>Bar diagram of the Enrichment Factor (EF) values of As, Sb, and Hg in covered (C) and uncovered (U) mosses divided according to the sampling sites. The black line represents EF = 2, i.e., the value above which enrichment in heavy metals occurs.</p>
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<p>Median of RAF Hg values in covered and uncovered mosses and median of GEM (in ng/m<sup>3</sup>) in the 10 sampling sites. See the text for further details.</p>
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15 pages, 5443 KiB  
Article
Conservation Tillage Mitigates Soil Organic Carbon Losses While Maintaining Maize Yield Stability Under Future Climate Change Scenarios in Northeast China: A Simulation of the Agricultural Production Systems Simulator Model
by Hongrun Liu, Baocai Su, Rui Liu, Jiajie Wang, Ting Wang, Yijia Lian, Zhenzong Lu, Xue Yuan, Zhenwei Song and Runzhi Li
Agronomy 2025, 15(1), 1; https://doi.org/10.3390/agronomy15010001 (registering DOI) - 24 Dec 2024
Abstract
Global warming may reduce maize yields and soil organic carbon (SOC), potentially threatening global food security and soil health. To address this concern in Northeast China, one of the world’s major maize production areas, the maize Agricultural Production Systems Simulator Model (APSIM) was [...] Read more.
Global warming may reduce maize yields and soil organic carbon (SOC), potentially threatening global food security and soil health. To address this concern in Northeast China, one of the world’s major maize production areas, the maize Agricultural Production Systems Simulator Model (APSIM) was used to evaluate how different tillage methods and straw return practices affect maize yields and SOC under future climate scenarios. The purpose of this study is to deal with the threat of global warming to the yield and SOC in the northeastern maize-producing areas, explore sustainable agricultural management strategies to stabilize the yield, enhance the soil carbon pool, counter the impact of climate change, and seek ways to ensure regional food and soil health. This study explored three tillage methods—plowing tillage (PT), rotary tillage (RT), and no-tillage (NT)—and two straw return methods—straw return (SR) and no straw return (SN)—under two Representative Concentration Pathway (RCP) scenarios: RCP4.5 and RCP8.5. The results showed that under the climate change scenarios: (1) For different tillage methods, no-tillage (NT) management showed the greatest increase in crop yield at 6.2%. SOC is highest under NT in the 0–20 cm soil layer under both straw return methods and climate scenarios. (2) For different straw return methods, SOC decreases when the straw is removed (SN) but increases when the straw is returned (SR) in both scenarios. Soil organic carbon density (SOCD) declines but can be mitigated by straw return. (3) Overall, tillage and straw return practices can significantly impact SOC under RCP4.5 but not under RCP8.5. Tillage and straw return practices together explain more than 50% yield changes under climate change scenarios. Through the modeling approach, this study revealed the potential benefits of integrating tillage and straw management practices to sustain maize yields and SOC. These practices can mitigate long-term climate change impacts on crop yields and soil health. Full article
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<p>The Gongzhuling experimental station of the Chinese Academy of Agricultural Sciences.</p>
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<p>A comparison of the changes in mean annual precipitation (<b>A</b>) and mean annual temperature (<b>B</b>) under two typical concentration paths based on future climate models. The dashed line is the fitted linear trend line.</p>
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<p>The variation in maize yield over time under different tillage practices and straw return methods under two climate scenarios (RCP4.5 and RCP8.5). The dashed line is the fitted linear trend line.</p>
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<p>Effects of three tillage practices (PT, NT, RT) and two straw return practices (SN, SR) and their interactions on maize yield under RCP4.5 (<b>A</b>) and RCP8.5 (<b>B</b>) climate scenarios. ns: <span class="html-italic">p</span> &gt; 0.05.</p>
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<p>The variation in SOC content within the 0–20 cm soil tillage layer over time under different tillage practices and straw return methods under two climate scenarios (RCP4.5 and RCP8.5). The dashed line is the fitted linear trend line.</p>
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<p>The variation in SOC content within the 20–40 cm soil tillage layer over time under different tillage practices and straw return methods under two climate scenarios (RCP4.5 and RCP8.5). The dashed line is the fitted linear trend line.</p>
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<p>The effects of different tillage practices and different straw return methods on SOC in different tillage layers of the soil. Among them, (<b>A</b>,<b>C</b>) indicate the changes in SOC content of straw under RCP4.5 climate scenario with different return methods and different tillage methods, respectively, and (<b>B</b>,<b>D</b>) indicate the changes in SOC content of straw under RCP8.5 climate scenario with different return methods and different tillage methods, respectively. *** indicates the significant correlation at <span class="html-italic">p</span> &lt; 0.01. ns: <span class="html-italic">p</span> &gt; 0.05.</p>
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<p>Changes in SOCD in 0–40 cm of farmland soils from 1980 to 2100 at Gongzhuling Experimental Station.</p>
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<p>PLS-PM analysis of the combined effects of tillage and straw returning on yield under future climate scenarios. Single-headed arrows indicate the hypothesized direction of causation. The indicated values are the path coefficients. Red arrows indicate a positive effect, whereas blue arrows indicate a negative effect. The arrow width is proportional to the strength of the relationship. R<sup>2</sup> on the parameters indicates the percentage of the variance explained by other variables. *, <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. L1 is the first soil layer (0–20 cm), and the L2 is the second soil layer (20–40 cm). (<b>A</b>) Path analysis in RCP4.5 scenario; (<b>B</b>) Path analysis in RCP8.5 scenario.</p>
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17 pages, 3153 KiB  
Article
Influence of Biochar Feedstocks on Nitrate Adsorption Capacity
by Riad Eissa, Lordwin Jeyakumar, David B. McKenzie and Jianghua Wu
Earth 2024, 5(4), 1080-1096; https://doi.org/10.3390/earth5040055 (registering DOI) - 23 Dec 2024
Abstract
The demand for intensive agriculture to boost food and crop production has increased. High nitrogen (N) fertilizer use is crucial for increasing agricultural productivity but often leads to significant nitrate losses, posing risks to surface and groundwater quality. This study examines the role [...] Read more.
The demand for intensive agriculture to boost food and crop production has increased. High nitrogen (N) fertilizer use is crucial for increasing agricultural productivity but often leads to significant nitrate losses, posing risks to surface and groundwater quality. This study examines the role of biochar as a soil amendment to enhance nutrient retention and mitigate nitrate leaching. By improving nitrogen efficiency, biochar offers a sustainable strategy to reduce the environmental impacts of intensive agriculture while maintaining soil fertility. An incubation study investigated four biochar feedstocks: spruce bark biochar at 550 °C (SB550), hardwood biochar (75% sugar maple) at 500 °C (HW500), sawdust (fir/spruce) biochar at 427 °C (FS427), and softwood biochar at 500 °C (SW500), to identify the most effective nitrate adsorbent. Scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FT-IR) were employed to analyze biochar morphology and surface functional groups. Adsorption isotherms were modeled using the Langmuir and Freundlich equations. The results indicated that surface functional groups, such as aromatic C=C stretching and bending, aromatic C–H bending, and phenolic O–H bending, play crucial roles in enhancing electrostatic attraction and, consequently, the nitrate adsorption capacity of biochar. The equilibrium adsorption data from this study fit well with both the Langmuir and Freundlich isotherm models. Among the four biochar types tested, SB550 exhibited the highest nitrate adsorption capacity, with a maximum of 184 mg/g. The adsorption data showed excellent conformity to the Langmuir and Freundlich models, with correlation coefficients (R2) exceeding 0.987 for all biochar types. These findings highlight the high accuracy of these models in predicting nitrate adsorption capacities. Full article
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<p>SEM images of the biochars, including (<b>a</b>) SB550, (<b>b</b>) HW500, (<b>c</b>) FS427, and (<b>d</b>) SW500.</p>
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<p>FT-IR spectra of biochars before and after nitrate adsorption highlight key functional groups, such as aromatic C=C stretching and phenolic O–H bending, which contribute to nitrate adsorption efficiency. The spectra include the following biochars: (<b>a</b>) SB550, (<b>b</b>) HW500, (<b>c</b>) FS427, and (<b>d</b>) SW500.</p>
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<p>Langmuir and Freundlich isotherms models for nitrate adsorption onto biochar, including (<b>a</b>) SB550, (<b>b</b>) HW500, (<b>c</b>) S427, and (<b>d</b>) SW500.</p>
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<p>Nitrate removal rates from aqueous solutions by different biochar types, including (<b>a</b>) SB550, (<b>b</b>) HW500, (<b>c</b>) FS427, and (<b>d</b>) SW500.</p>
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<p>Effect of initial nitrate concentration on nitrate adsorption (mg/g) and nitrate removal rate (%) by biochar (<b>a</b>) SB550, (<b>b</b>) HW500, (<b>c</b>) FS427, and (<b>d</b>) SW500.</p>
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<p>Effect of initial solution pH and the equilibrium solution pH on nitrate removal rate, (<b>a</b>) SB550, (<b>b</b>) HW500, (<b>c</b>) FS427, and (<b>d</b>) SW500.</p>
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27 pages, 11781 KiB  
Article
Exploring the Interaction Between Landslides and Carbon Stocks in Italy
by Jibran Qadri and Francesca Ceccato
Sustainability 2024, 16(24), 11273; https://doi.org/10.3390/su162411273 - 23 Dec 2024
Abstract
Landslides, as natural hazards, have far-reaching impacts beyond their immediate effects on human lives and infrastructure; landslides disrupt both carbon storage and ecosystem stability, and their role in the global carbon cycle cannot be underestimated. This study delves into the complex relationship between [...] Read more.
Landslides, as natural hazards, have far-reaching impacts beyond their immediate effects on human lives and infrastructure; landslides disrupt both carbon storage and ecosystem stability, and their role in the global carbon cycle cannot be underestimated. This study delves into the complex relationship between landslides and carbon stocks such as, in particular, soil organic carbon (SOC) and above-ground biomass (AGB), and outlines the spatial relationship between different types of landslides, soil organic carbon (SOC), and the carbon cycle, underscoring the importance of understanding these interconnections for environmental sustainability and climate change mitigation efforts. By employing machine learning algorithms on the Google Earth Engine platform, landslide susceptibility maps were created for different landslide types across Italy, and their spatial patterns with SOC accumulation were analyzed using the Python environment. The findings reveal a nuanced relationship between landslide hazard levels and SOC dynamics, with varying trends observed for different landslide types. In addition, this study investigates the potential impact of large-scale landslide events on carbon sequestration in the short term via a case study of the May 2023 landslide event in the Emilia Romagna region of Italy. The analysis reveals a substantial reduction in above-ground biomass by 35%, which approximately accounts for the loss of 0.133 MtC, and a decrease in SOC accumulation in 72% of the affected areas, indicating that landslides can transform carbon sinks into carbon sources, at least in the short term, and suggested that carbon released from extreme landslide events at a larger scale needs to be accounted for in regional or national carbon emissions. This research underscores the importance of considering landslides in carbon cycle assessments and emphasizes the need for sustainable land management strategies to protect and enhance carbon sinks, such as forests and healthy soils, in the face of increasing natural hazards and climate change impacts. Full article
(This article belongs to the Special Issue Sustainable Environmental Analysis of Soil and Water)
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<p>Schematic diagram of LSM framework and specific processing steps for each part (<a href="#sec2dot1-sustainability-16-11273" class="html-sec">Section 2.1</a>, <a href="#sec2dot2-sustainability-16-11273" class="html-sec">Section 2.2</a> and <a href="#sec2dot3-sustainability-16-11273" class="html-sec">Section 2.3</a>).</p>
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<p>(<b>a</b>) Landslide susceptibility map for all, R, F, SH, and C landslide types. (<b>b</b>) Landslide susceptibility map for RT, DF, SL, DSGSD, and TR landslide type.</p>
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<p>(<b>a</b>) Landslide susceptibility map for all, R, F, SH, and C landslide types. (<b>b</b>) Landslide susceptibility map for RT, DF, SL, DSGSD, and TR landslide type.</p>
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<p>Areas (km<sup>2</sup>) susceptible to different classifications of landslide types obtained from landslide susceptibility maps.</p>
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<p>AUC-ROC for all, R, F, SH, C, RT, DF, SL, DSGSD, and TR landslide types.</p>
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<p>Feature importance for all, R, F, SH, C, RT, DF, SL, DSGSD, and TR landslide types.</p>
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<p>Feature importance for all, R, F, SH, C, RT, DF, SL, DSGSD, and TR landslide types.</p>
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<p>Feature importance for all, R, F, SH, C, RT, DF, SL, DSGSD, and TR landslide types.</p>
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<p>SOC accumulation trends and landslide type hazard level for all, R, F, SH, C, RT, DF, SL, DSGSD, and TR landslide types.</p>
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<p>Landslide-affected region.</p>
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<p>Impact of landslides on vegetation and carbon sequestration by comparing the NDVI (Normalized Difference Vegetation Index) and biomass levels before and after the landslides in the region.</p>
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<p>Above-ground biomass density difference between 2022 and 2023.</p>
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16 pages, 6153 KiB  
Article
Precipitation Controls Topsoil Nutrient Buildup in Arid and Semiarid Ecosystems
by Eduardo Medina-Roldán, Meixin Wang, Takafumi Miyasaka, Yueming Pan, Xiang Li, Bing Liu and Hao Qu
Agriculture 2024, 14(12), 2364; https://doi.org/10.3390/agriculture14122364 - 23 Dec 2024
Abstract
Soil nutrient buildup is a key process in nutrient-poor arid and semiarid regions. However, our knowledge of the factors that control soil nutrient buildup in these systems is still limited. An experiment was set up and carried out for five and a half [...] Read more.
Soil nutrient buildup is a key process in nutrient-poor arid and semiarid regions. However, our knowledge of the factors that control soil nutrient buildup in these systems is still limited. An experiment was set up and carried out for five and a half years in order to investigate how precipitation and other site factors control soil nutrient buildup. Topsoil carbon (C), nitrogen (N), phosphorus (P), and potassium (K) derived from litter (soil nutrient buildup) were tracked twice a year at two sites differing in terms of climate and soils (Urat: arid and Naiman: semiarid, both in Inner Mongolia). Precipitation was manipulated at both sites to include seven precipitation levels: three reduced levels (−20, −40, and −60% with respect to the background), background (control), and three enhanced levels (+20, +40, and +60% with respect to the background). The dynamic buildup (i.e., amount of nutrients released among consecutive samplings) for all nutrients was controlled by precipitation (nonlinearly), site effects (lower buildup at the site dominated by aeolian pedogenesis), and seasonality (higher under warm conditions). However, the considered nutrients differed in the factor that most determined their buildup. Through studying the concurrent dynamics of litter decomposition and soil nutrient buildup, we can foresee that changes in precipitation and land degradation are most likely to affect the soil nutrient pools in these ecosystems. Full article
(This article belongs to the Special Issue Soil Microbial Community and Ecological Function in Agriculture)
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<p>Litter (ΔC litter) and dynamic soil C buildup (ΔC soil) along a five and a half year decomposition experiment that manipulated precipitation inputs at two sites (arid Urat and semiarid Naiman) in Inner Mongolia, China. The dynamic soil C buildup was calculated by subtracting the soil C content (gC kg<sup>−1</sup> of dry soil) on each consecutive sampling date (soil ΔC = soil C masst + 1 − soil C masst). Litter C release was calculated similarly, taking into account litter C content and litter mass. Precipitation treatments included precipitation background (cont); precipitation decreases by 20, 40, and 60%; and precipitation surpluses of 20, 40, and 60%.</p>
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<p>Litter (ΔN litter) and dynamic soil N buildup (ΔN soil) over a five and a half year decomposition experiment that manipulated precipitation inputs at two sites (arid Urat and semiarid Naiman) in Inner Mongolia, China. Explanations of calculations and precipitation treatment labels are provided in <a href="#agriculture-14-02364-f001" class="html-fig">Figure 1</a> caption.</p>
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<p>Litter (ΔP litter) and dynamic soil P buildup (ΔP soil) over a five and a half year decomposition experiment that manipulated precipitation inputs at two sites (arid Urat and semiarid Naiman) in Inner Mongolia, China. Explanations of calculations and precipitation treatment labels are provided in <a href="#agriculture-14-02364-f001" class="html-fig">Figure 1</a> caption.</p>
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<p>Litter (ΔK litter) and dynamic soil K buildup (ΔK soil) over a five and a half year decomposition experiment that manipulated precipitation inputs at two sites (arid Urat and semiarid Naiman) in Inner Mongolia, China. Explanations of calculations and precipitation treatment labels are provided in <a href="#agriculture-14-02364-f001" class="html-fig">Figure 1</a> caption. Note the difference in the orders of magnitude of the K mass values between the main and insert panels.</p>
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<p>Total soil C (<b>a</b>), N (<b>b</b>), P (<b>c</b>) and K (<b>d</b>) buildup (soil ΣΔi) at the end of a five and a half year decomposition experiment that manipulated precipitation inputs at two sites (arid Urat and semi-arid Naiman) in Inner Mongolia, China. ΣΔi calculated as the overall sum of Δi. Precipitation treatment labels are the same as in <a href="#agriculture-14-02364-f001" class="html-fig">Figure 1</a>. Note the difference in units for the different nutrients.</p>
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<p>Block distribution of the experiments at each site (from [<a href="#B19-agriculture-14-02364" class="html-bibr">19</a>]; reused with permission from Springer−Nature).</p>
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<p>Total soil contents of C, N, P, and K along a five and a half year decomposition experiment that manipulated precipitation inputs at two sites (arid Urat and semiarid Naiman) in Inner Mongolia, China. Precipitation treatments included precipitation background (cont); precipitation decreases by 20, 40, and 60%; and precipitation surpluses of 20, 40, and 60%.</p>
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19 pages, 3714 KiB  
Article
Sequoia Groves of Yosemite: Visitor Use and Impact Monitoring
by Sheri A. Shiflett, Jeffrey S. Jenkins, Rachel F. Mattos, Peter C. Ibsen and Nicole D. Athearn
Forests 2024, 15(12), 2256; https://doi.org/10.3390/f15122256 - 22 Dec 2024
Viewed by 256
Abstract
Despite being long-lived and massive, giant sequoias (Sequoiadendron giganteum (Lindl.) J. Bucholz) are susceptible to erosion given their relatively shallow root structure. Human-caused soil compaction and vegetation loss through social trails are primary drivers of erosion in giant sequoia groves, particularly for [...] Read more.
Despite being long-lived and massive, giant sequoias (Sequoiadendron giganteum (Lindl.) J. Bucholz) are susceptible to erosion given their relatively shallow root structure. Human-caused soil compaction and vegetation loss through social trails are primary drivers of erosion in giant sequoia groves, particularly for trees that are near formal trails and access roads. We develop a method to observe and quantify the near-tree impacts from park visitors and to relate the overall amount of use with ground cover impact parameters to assess whether the desired conditions of each grove are being met for the park to maintain a spectrum of recreational opportunities. We collected data on visitation, ground cover, soil compaction, and social trailing using a combination of targeted surveys and observations at the three giant sequoia groves in Yosemite National Park. The Mariposa Grove receives the most visitation, and use levels among groves were consistent with relative size and facilities available. Selected parameters for ground cover data were analyzed by comparing values within undisturbed versus trampling-disturbed subplots at both 0–2 m and 2–8 m. Exposed soil cover and compaction were generally higher in anthropogenically disturbed subplots versus undisturbed subplots, and vegetation cover was reduced in some disturbed subplots. Each grove had one surveyed tree where average soil compaction was ≥2.2 kg/cm2, which may limit root growth and impact seedling regeneration. Each of the three groves had some trees with social trail presence, yet less than 7% of mature trees within any grove were impacted by social trails, and most social trails were rated as having low impairment. Coupling soil compaction measurements and estimates of trampling-disturbed areas with mapping of social trail conditions within groves provides a general assessment of visitor-associated impacts to sequoia groves and can facilitate a relatively rapid way to track hotspot (i.e., increasingly impacted) trees over time. Full article
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<p>Location of giant sequoia (<span class="html-italic">Sequoiadendron giganteum</span> (Lindl.) J. Bucholz) groves within Yosemite National Park (<b>top left</b>), with inset maps of MARI (<b>bottom left</b>), TUOL (<b>top right</b>), and MERC (<b>bottom right</b>), which each include names and locations of trees sampled and formal trails within the groves.</p>
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<p>Plot design for 2 m and 8 m radius tree plots with four subplots at each radius.</p>
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<p>(<b>A</b>) Trail counter results showing hourly trail use within each giant sequoia (<span class="html-italic">Sequoiadendron giganteum</span> (Lindl.) J. Bucholz) grove at Yosemite National Park. All three groves (MARI, TUOL, MERC) had significantly different trail usage (<span class="html-italic">p</span> &lt; 0.5). (<b>B</b>) Trail counter results showing daily total trail usage within each grove for weekdays (blue) and weekends (red, Fri–Sun). (<b>C</b>) Trail counter results showing hourly average trail use within each grove for weekends compared to weekdays. Points are means ± 1 SD. Significant differences between weekday and weekend use are denoted by an *.</p>
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<p>Exposed soil percent cover data (<b>A</b>), vegetation percent cover (<b>B</b>), and soil compaction (<b>C</b>) for trampling undisturbed and trampling disturbed plots at 2 m and 8 m distances ± 1SD within giant sequoia (<span class="html-italic">Sequoiadendron giganteum</span> (Lindl.) J. Bucholz) groves (MARI, TUOL, MERC). For MARI and MERC, plots were considered disturbed if anthropogenic disturbance percent cover was estimated at 5% or greater, whereas in TUOL, plots were considered disturbed if anthropogenic disturbance percent cover was estimated at 2.5% or greater. There were too few disturbed plots in TUOL when using the threshold of greater than 5% anthropogenically disturbed area. Significant differences between pairs (i.e., 2 m or 8 m) are represented by * such that * &lt; 0.05, ** ≤ 0.01, *** ≤ 0.001.</p>
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<p>Average soil compaction ± 1 SD (kg/cm<sup>2</sup>) across all 8 subplots around a given tree within each giant sequoia (<span class="html-italic">Sequoiadendron giganteum</span> (Lindl.) J. Bucholz) grove (MARI, TUOL, MERC, <span class="html-italic">n</span> = 24).</p>
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<p>Mapped extent of social trails around five selected trees within giant sequoia (<span class="html-italic">Sequoiadendron giganteum</span> (Lindl.) J. Bucholz) groves (<b>A</b>) MARI, (<b>B</b>) TUOL, and (<b>C</b>) MERC. Digitized drawings include the name of the tree, social trail condition (<a href="#forests-15-02256-t002" class="html-table">Table 2</a>), average soil compaction, and also show formal trail locations mapped by researchers (black outline) and as an existing National Park Service base layer (brown lines). N.B. The inner ring around each tree occurs at 2 m distance, whereas the outer ring occurs at 8 m distance. Spatial arrangement of individual trees in this figure has no bearing on physical distances among them, with the exception of MERC 30 and MERC 34 (<a href="#forests-15-02256-f001" class="html-fig">Figure 1</a>).</p>
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<p>Number of trees impacted by social trails within each giant sequoia (<span class="html-italic">Sequoiadendron giganteum</span> (Lindl.) J. Bucholz, SEGI) grove, including the total number impacted, those with a well-defined social trail (ST), and those where the width of the ST was greater than 0.5 m at any point. These data are an aggregate of sampling conducted by National Park Service staff of five trees per grove and data collected by SEGI inventory volunteers. The percentage of mature trees impacted by social trails within each grove is displayed to the right of the dashed line.</p>
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20 pages, 5108 KiB  
Review
Physio-Biochemical Mechanisms of Arbuscular Mycorrhizal Fungi Enhancing Plant Resistance to Abiotic Stress
by Dandi Sun, Xiaoqian Shang, Hanwen Cao, Soon-Jae Lee, Li Wang, Yantai Gan and Shoujiang Feng
Agriculture 2024, 14(12), 2361; https://doi.org/10.3390/agriculture14122361 - 22 Dec 2024
Viewed by 420
Abstract
Agricultural innovations in the past decades have addressed the mounting challenges of food, feed, and biofuel security. However, the overreliance on synthetic fertilizers and pesticides in agriculture has exacerbated biodiversity loss, environmental degradation, and soil health deterioration. Leveraging beneficial soil microorganisms, particularly arbuscular [...] Read more.
Agricultural innovations in the past decades have addressed the mounting challenges of food, feed, and biofuel security. However, the overreliance on synthetic fertilizers and pesticides in agriculture has exacerbated biodiversity loss, environmental degradation, and soil health deterioration. Leveraging beneficial soil microorganisms, particularly arbuscular mycorrhizal (AM) fungi, offers an emerging solution to reduce dependence on synthetic agrochemicals in crop production. Understanding the mechanisms can help maximize AM fungi’s benefits in response to abiotic stresses. In this review, we explore the main mechanisms of AM fungi in promoting soil nutrient mobilization and uptake, increasing water absorption, stimulating antioxidative enzyme activities, altering morphophysiological structure, and performing hormonal crosstalk when mycorrhizal plants face an abiotic stressor. Also, we highlight the necessity of innovating practical ways to cope with variations in AM fungal species, diversity in host species, soil, and environmental conditions, as well as difficulties in mass multiplication for commercialization. Understanding the mechanisms and limitations may help explore the biofertilizer potential of AM fungal symbiosis, benefiting crop production while addressing the environment and soil health issues. Full article
(This article belongs to the Special Issue Mycorrhizal Symbiosis in Agricultural Production)
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<p>Arbuscular mycorrhizal fungi colonize root cortical cells through germinating spores, forming hyphal branches, and developing an extraradical mycelium that forms an extensive network in the soil. The initial colonization involves hyphal contact on root surfaces through the outer cortex, the formation of a dense hyphal sheath surrounding the colonized surface (ectomycorrhizas), or the penetration of fungal hyphae into host tissues (endomycorrhizas), establishing the mutual-benefit symbiosis.</p>
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<p>Arbuscular mycorrhizal fungi colonize roots to form a mutually beneficial symbiosis that induces upward nutrient flow and downward carbon flow in the plant–soil–rhizosphere continuum. AM fungi require C sources from host plants for spore propagation and extraradical hyphal development. In contrast, AM fungi produce a network of extraradical mycelium, spreading from host roots into the surrounding soil and establishing belowground interconnections to supply plant-required nutrients like P, N, Zn, Cu, and Mg. These functioning features occur in the hyphosphere with hyphal exudates and other enzymes involved in the nutrient flow process. While the plant–AM fungi symbiotic relationship is a nutrient trade-off, the mutually beneficial association can enhance plant ability to acquire essential minerals, which is crucial for enhancing soil health through the contribution of hyphosphere microbiomes to nutrient cycling, carbon sequestration, and soil aggregation.</p>
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<p>Physiological mechanisms of osmotic adjustment at the (a) cellular, (b) whole plant, and (c) system scale in a typical dryland agroecosystem. It is typical that a water–carbon linear correlation occurs in plant scale, whereas at the ecosystem scale water–carbon correlation becomes nonlinear. Abbreviations: intercellular CO<sub>2</sub> concentration (ci), ambient stomatal CO<sub>2</sub> concentration (ca), conductance (gs), seasonal air temperature (Ta), relative humidity (Rh), vapor pressure deficit (VPD), soil water content (SWC), leaf area index (LAI), water use efficiency (WUE), and seasonal total evapotranspiration (ET), transpiration (T), soil evaporation (E), net ecosystem exchange (NEE), and gross primary productivity (GPP).</p>
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<p>Arbuscular mycorrhizal fungi enhance plant resistance to abiotic stress, which is through increasing photosynthesis, improving root-to-stem ratio, increasing nutrient uptake, increasing root surface areas, promoting metabolisms, balancing hormones, stimulating enzyme activities, and interacting with other microbiomes in the hyphosphere.</p>
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14 pages, 1630 KiB  
Article
Insights into Orris (Iris pallida Lam.) In Vivo Acclimatization and Response to Salt Stress via Exogenous Melatonin Application
by Annalisa Meucci, Cristina Ghelardi, Rita Maggini, Fernando Malorgio, Beatrice Pezzarossa, Irene Rosellini and Anna Mensuali
Agriculture 2024, 14(12), 2353; https://doi.org/10.3390/agriculture14122353 - 21 Dec 2024
Viewed by 237
Abstract
The loss of agricultural land is one of the main problems facing the global agricultural sector, and it is related to multiple phenomena; one of the main causes is soil salinization, induced both by natural processes and human activities. Among the strategies adopted [...] Read more.
The loss of agricultural land is one of the main problems facing the global agricultural sector, and it is related to multiple phenomena; one of the main causes is soil salinization, induced both by natural processes and human activities. Among the strategies adopted to deal with soil salinization and its mitigation, the cultivation of species able to survive in saline soils seems to be an effective way of making salt-compromised lands usable. Iris pallida is a rustic plant and a species of high economic value that is mostly cultivated for perfume production. Consequently, the application of I. pallida to cover soils not suitable for crops traditionally cultivated for human and livestock nutrition could be considered; therefore, a preliminary test on the capacity of I. pallida to tolerate salinity during the acclimatization phase of micropropagated plants was conducted. Plantlets were treated with exogenous melatonin during the in vitro phase by adding it to the culture medium; therefore, during the acclimatization phase, crescent salt doses (150, 300, and 400 mM) were added to the soil every 14 days, administering melatonin to plants by a spray solution 24 h before each salt addition. At the end of the experiment, biometric measurements, chlorophylls, carotenoids, and macro-element contents were measured, and the relative water content (RWC) was determined in each salt addition. The results showed that orris plants can survive soil salt concentrations of up to 400 mM, and that the 50 µM melatonin spray treatment can protect orris rhizomes from salt side effects. Full article
(This article belongs to the Section Crop Production)
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<p>Root development (cm) monitored during the different steps of the treatment: plants transferring to Magenta<sup>®</sup> vessels with ventilated caps (T0), transferring to perlite (T1), to peat and perlite (T2), and their length at the end of the acclimatization phase (TF). Data, reported as mean values ± S.E., were subjected to analysis of variance (ANOVA), the different letters indicate significant differences among means (Tukey post-test, <span class="html-italic">p</span> ≤ 0.05) and ns indicates no significance among the treatments.</p>
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<p>Leaf development (cm) monitored during the different steps of the treatment: plants transferred to containers with ventilated caps (T0), transferred to perlite (T1), and transferred to peat and perlite (T2), and their length at the end of the acclimatization phase (TF). The data, reported as mean values ± S.E., were subjected to analysis of variance (ANOVA), the different letters indicate significant differences among means (Tukey post-test, <span class="html-italic">p</span> ≤ 0.05) and ns indicates no significance among the treatments.</p>
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<p>Number of roots (<b>a</b>) and new leaves (<b>b</b>) of <span class="html-italic">I. pallida</span> plants treated with 400 mM of salt (Ctr + salt) and with both melatonin and salt and (50–100 µM + salt) compared to control plants (Ctr). Data, reported as mean values ± S.E., were subjected to analysis of variance (ANOVA), and the different letters indicate significant differences among means (Tukey post-test, <span class="html-italic">p</span> ≤ 0.05).</p>
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<p>Dry weight (g plant<sub>dw</sub> <sup>−1</sup>) of rhizomes (<b>a</b>) and leaves (<b>b</b>) of <span class="html-italic">I. pallida</span> plants treated with 400 mM of salt (Ctr + salt) and with both melatonin and salt (50–100 µM + salt) compared to control plants (Ctr). The data, reported as mean values ± S.E., were subjected to analysis of variance (ANOVA), and the different letters indicate significant differences among means (Tukey post-test, <span class="html-italic">p</span> ≤ 0.05).</p>
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<p>Relative water content (RWC) of leaves of <span class="html-italic">I. pallida</span> plants treated with 400 mM of salt (Ctr + salt) and with both melatonin and salt (50–100 µM + salt) compared to control plants (Ctr). The data, reported as mean values ± S.E., were subjected to analysis of variance (ANOVA), and the different letters indicate significant differences among means (Tukey post-test, <span class="html-italic">p</span> ≤ 0.05).</p>
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<p>Total chlorophyll (ChlTOT) and carotenoid content (µg g<sub>fw</sub><sup>−1</sup>) in leaves of <span class="html-italic">I. pallida</span> plants treated with 400 mM of salt (Ctr + salt) and with both melatonin and salt (50–100 µM + salt) compared to control plants (Ctr). The data, reported as mean values ± S.E., were subjected to analysis of variance (ANOVA), and the different letters indicate significant differences among means (Tukey post-test, <span class="html-italic">p</span> ≤ 0.05).</p>
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<p>Macro-elements content (Ca<sup>2+</sup>, Mg<sup>2+</sup>, Na<sup>+</sup>, K<sup>+</sup> g kg<sub>dw</sub><sup>−1</sup>) in roots (<b>a</b>), rhizomes (<b>b</b>), and leaves (<b>c</b>) of <span class="html-italic">I. pallida</span> plants treated only with 400 mM of salt and with both melatonin and salt (50–100 µM + salt) compared to control. The data, reported as mean values ± S.E., were subjected to analysis of variance (ANOVA), the different letters indicate significant differences among means (Tukey post-test, <span class="html-italic">p</span> ≤ 0.05) and ns indicates no significance among the treatments.</p>
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45 pages, 6788 KiB  
Article
Biomass Refined: 99% of Organic Carbon in Soils
by Robert J. Blakemore
Biomass 2024, 4(4), 1257-1300; https://doi.org/10.3390/biomass4040070 (registering DOI) - 20 Dec 2024
Viewed by 221
Abstract
Basic inventory is required for proper understanding and utilization of Earth’s natural resources, especially with increasing soil degradation and species loss. Soil carbon is newly refined at >30,000 Gt C (gigatonnes C), ten times above prior totals. Soil organic carbon (SOC) is up [...] Read more.
Basic inventory is required for proper understanding and utilization of Earth’s natural resources, especially with increasing soil degradation and species loss. Soil carbon is newly refined at >30,000 Gt C (gigatonnes C), ten times above prior totals. Soil organic carbon (SOC) is up to 24,000 Gt C, plus plant stocks at ~2400 Gt C, both above- and below-ground, hold >99% of Earth’s biomass. On a topographic surface area of 25 Gha with mean 21 m depth, Soil has more organic carbon than all trees, seas, fossil fuels, or the Atmosphere combined. Soils are both the greatest biotic carbon store and the most active CO2 source. Values are raised considerably. Disparity is due to lack of full soil depth survey, neglect of terrain, and other omissions. Herein, totals for mineral soils, Permafrost, and Peat (of all forms and ages), are determined to full depth (easily doubling shallow values), then raised for terrain that is ignored in all terrestrial models (doubling most values again), plus SOC in recalcitrant glomalin (+25%) and friable saprock (+26%). Additional factors include soil inorganic carbon (SIC some of biotic origin), aquatic sediments (SeOC), and dissolved fractions (DIC/DOC). Soil biota (e.g., forests, fungi, bacteria, and earthworms) are similarly upgraded. Primary productivity is confirmed at >220 Gt C/yr on land supported by Barrow’s “bounce” flux, C/O isotopes, glomalin, and Rubisco. Priority issues of species extinction, humic topsoil loss, and atmospheric CO2 are remedied by SOC restoration and biomass recycling via (vermi-)compost for 100% organic husbandry under Permaculture principals, based upon the Scientific observation of Nature. Full article
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<p>Atmospheric CO<sub>2</sub> drawdown and O<sub>2</sub> release is from invasion and expansion of land plants. Ref. [<a href="#B7-biomass-04-00070" class="html-bibr">7</a>] extend this with “<span class="html-italic">plant evolution from fresh water to salt water and, at least 500 million years ago, to land</span>”. figure 5 in ref. [<a href="#B8-biomass-04-00070" class="html-bibr">8</a>], who stated “<span class="html-italic">The first land plants buried so much</span> [soil organic] <span class="html-italic">carbon that O<sub>2</sub> accumulated in the atmosphere to roughly present levels</span>”. Most biomass and organic matter are yet found in soils, especially with the most recent ecological studies including terrestrial plants that root or seed as being soil-based thus within a soil inventory.</p>
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<p>Interlinked exchange recycling between endosymbiotic plant Chloroplasts and eukaryote Mitochondria in both autotrophic and heterotrophic plants, fungi, or animals. (Source with permission: Cornell, B: <a href="https://old-ib.bioninja.com.au/higher-level/topic-8-metabolism-cell/untitled-2/photosynthesis-vs-respirati.html" target="_blank">https://old-ib.bioninja.com.au/higher-level/topic-8-metabolism-cell/untitled-2/photosynthesis-vs-respirati.html</a>, accessed 10 May 2024).</p>
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<p>Different models have different CO<sub>2</sub> values, but 550 Ma ago when submerged plants emerged, the (shaded/yellow) estimates range from 20,000 down to 2500 ppm and, as discussed later in Results, this implies &gt;5000–40,000 (median 23,000) Gt C active drawdown via living biomass into soils. (<a href="https://en.wikipedia.org/wiki/File:Phanerozoic_Carbon_Dioxide.png" target="_blank">https://en.wikipedia.org/wiki/File:Phanerozoic_Carbon_Dioxide.png</a> 27 May 2024. CC-BY, accessed on 11 November 2024).</p>
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<p>Countering a common misconception that Ocean supports most biomass and abundance (=productivity and biodiversity) is a recent summary (<a href="https://ourworldindata.org/grapher/biomass-vs-abundance-taxa" target="_blank">https://ourworldindata.org/grapher/biomass-vs-abundance-taxa</a>, accessed on 11 November 2024; CC-BY). Terrestrial soil data presented are wide underestimations lacking both full depth and 3D area; however, those taxa inventoried from registers, such as humans or livestock (possibly birds), are not subject to similar areal gains. Annelid counts are terrestrial earthworms (not marine worms). Cnidarians are mostly marine corals/jellies.</p>
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<p>Major global carbon stores [<a href="#B39-biomass-04-00070" class="html-bibr">39</a>] prior to current Biosphere and Soil carbon revisions. Lithosphere is the rocky mantle with calcitic or dolomitic rocks such as dolomite, limestone, chalk, or marble. Soil organic and inorganic carbon (SOC + SIC) total is ~3000 Gt C, cf., the current study concluding &gt;30,000 Gt C or ×10, approaching Oceans’ dissolved carbon (DOC + DIC) mostly eroded from soils or rocks. Should the 5000–10,000 Gt C in mainly terrestrial fossil fuel stocks (e.g., coal, oil, gas) be added, the Soil tally matches the Oceans’. Note: Pg C = Gt C.</p>
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<p>Figure 6 in ref. [<a href="#B40-biomass-04-00070" class="html-bibr">40</a>] with data taken from [<a href="#B42-biomass-04-00070" class="html-bibr">42</a>]. Conventional summary of carbon stocks and sources as reviewed in the current study. Note: Oceanic dissolved inorganic carbon (DIC) is shown, but neither soil inorganic carbon (SIC + DIC) nor the enormous inorganic carbon in Lithospheric rocks on land (as shown in <a href="#biomass-04-00070-f005" class="html-fig">Figure 5</a>). Another disparity example is in the misplaced priorities of online search of the GCP website with 102 hits for “<span class="html-italic">ocean</span>/<span class="html-italic">marine</span>” but only 26 for “<span class="html-italic">soil</span>”.</p>
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<p>Figure 9.16 in ref. [<a href="#B42-biomass-04-00070" class="html-bibr">42</a>]. Compared to [<a href="#B40-biomass-04-00070" class="html-bibr">40</a>], total soil (2900 Gt C) is less by 200 Gt C (in Permafrost) and NPP is a bit higher at (142/2 =) 71 Gt C/yr. Dissolved organic carbon (DOC) in the Ocean, amounting to about 660–680 Gt C, is spread throughout its depth, and may be relatively ancient and non-reactive [<a href="#B45-biomass-04-00070" class="html-bibr">45</a>]. Ref. [<a href="#B42-biomass-04-00070" class="html-bibr">42</a>] say vertical transfer of DOC creates a downward flux of organic carbon from upper ocean known as “<span class="html-italic">export production</span>” of roughly 11 Gt C that may better reflect Ocean NPP, cf., Land’s 142 Gt C/yr GPP, yet further diminishing marine relevance. An admission is that “<span class="html-italic">Ocean-atmosphere</span>” flux is (passive) “<span class="html-italic">gas exchange</span>”.</p>
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<p>AR5 (figure 10.5 in ref. [<a href="#B41-biomass-04-00070" class="html-bibr">41</a>]) wherein Ocean values are the same as AR6 (IPCC 2024: fig. 5.12) but all terrestrial values differ: Viz., Vegetation 450–650, median 550 vs. 450; Soils 1500–2400, median ~2000 vs. 1700; Permafrost ~1700(!) vs. 1200 Gt C. Fossil fuel reserve values differ too.</p>
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<p>Carbon cycle modified from figure 5 in ref. [<a href="#B87-biomass-04-00070" class="html-bibr">87</a>], updated as discussed.</p>
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<p>(<b>A</b>) Atmospheric CO<sub>2</sub> (log-scale ppm) and (<b>B</b>) O<sub>2</sub> (linear %) correlations modelled through time with black line medians and 95% confidence intervals shaded grey. Five prior extinction events are marked on a pink Era band. Fluctuations in atmospheric CO<sub>2</sub> and O<sub>2</sub> levels are from biotic, climatic, or mass extinction events altering global biomass stocks, then as now [<a href="#B91-biomass-04-00070" class="html-bibr">91</a>].</p>
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<p>From figure 12 in ref. [<a href="#B106-biomass-04-00070" class="html-bibr">106</a>] of SOC with abbreviations of MNC for Microbial Necromass-C; EE- and T-GRSP for easily extractable and total Glomalin-Related Soil Proteins; AMF for Arbuscular mycorrhiza; BRC and FRC for Bacterial and Fungal Residual carbon. GRSP made up 24% or 18% of 20.4 or 25.1 g/kg SOC stocks, respectively. Of note, outside of FRC and GRSP-C, bacterial BRC contributed about 15% of their absolute total SOC carbon across both study habitats. It is likely mistaken to claim increases from Crop to Woodland, as woodlands are cleared for crops.</p>
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<p>Soil carbon to &gt;2.5 m from figure 5 in ref. [<a href="#B50-biomass-04-00070" class="html-bibr">50</a>]. DIC (in blue) is for subsurface soils and, as average soil depth is now &gt;13–21 m, doubling for greater depth seems entirely justified.</p>
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<p>After NOAA (<a href="https://gml.noaa.gov/ccgg/trends/global.html" target="_blank">https://gml.noaa.gov/ccgg/trends/global.html</a>, accessed 11 November 2022) mean CO<sub>2</sub> globally averaged over marine surface sites (i.e., remote from immediate land influences), showing median (black) and seasonal (red) CO<sub>2</sub> fluxes mainly attributed to continual Soil Respiration (brown) or boreal spring/summer land plant Drawdown (green) factors. Note lack of any signal of COVID-19 transport reductions with industry shutdowns from 2020–2022. (Source: [<a href="#B154-biomass-04-00070" class="html-bibr">154</a>], 2022—<a href="https://vermecology.wordpress.com/2020/08/31/barrow/" target="_blank">https://vermecology.wordpress.com/2020/08/31/barrow/</a>, accessed 11 November 2024).</p>
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<p>NOAA’s Barrow site in Alaska is the northernmost monitoring station yielding seasonally fluctuating curves of 60–80 Gt C/yr flux (blue), what I call the “<span class="html-italic">Barrow bounce</span>”, being much higher than fossil fuel emissions and far in excess of any expensive Biomass Energy or Carbon Capture &amp; Storage (BECCS/CCS) schemes. Revised terrestrial NPP (green) vs. soil respiration SR (brown) fluxes just about balance out, more or less; being much greater than the prior guesstimates (black).</p>
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12 pages, 6447 KiB  
Article
A Novel K+ Slow-Release Cementitious Material Developed from Subway Tunnel Muck for Ecological Concrete Applications
by Daien Yang, Fushen Zhang, Leyang Lv, Zhiyuan Zhang, Ziyang Liu, Qianqian Liu and Yanjun Liu
Buildings 2024, 14(12), 4051; https://doi.org/10.3390/buildings14124051 - 20 Dec 2024
Viewed by 235
Abstract
This study explored a novel cementitious material developed from subway tunnel muck (STM) intended for ecological concrete (EC) preparation. The effects of three alkaline activators (NaOH, KOH, and CaO) on the properties of the cementitious materials were systematically examined. The results indicated that [...] Read more.
This study explored a novel cementitious material developed from subway tunnel muck (STM) intended for ecological concrete (EC) preparation. The effects of three alkaline activators (NaOH, KOH, and CaO) on the properties of the cementitious materials were systematically examined. The results indicated that NaOH exhibited the most effective activation performance, followed by KOH, with CaO being the least effective. The NaOH-activated materials exhibited the highest compressive strength (reaching up to 12.15 MPa), the densest microstructure (characterized by the lowest porosity and smallest average pore size), the most substantial gel formation (evidenced by the highest mass loss in thermogravimetric analysis), and the optimal gel structure (indicated by the pronounced peak sharpening in Fourier transform infrared spectroscopy) after a 28-day curing period. Moreover, the crystallization of potassium salts under KOH activation detrimentally impacted the microstructure of KOH-activated materials. To balance the need for structural strength and nutrient provision, NaOH + KOH-activated materials were selected for the preparation of EC. Notably, the application of NaOH + KOH-activated materials resulted in a significant increase in K+ concentration in the soil layer, compared to common soil. Furthermore, NaOH + KOH-activated materials exhibited a slow-release effect, thereby offering sustained nutrient support conducive to plant development. Full article
(This article belongs to the Section Building Materials, and Repair & Renovation)
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<p>The effects of MOH on STM: (<b>a</b>) XRD patterns of STM and STM after alkali treatment; (<b>b</b>) Si concentration in different alkali solutions versus time.</p>
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<p>Compressive strength versus different activators (<b>a</b>) and different OH<sup>-</sup> concentrations (<b>b</b>).</p>
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<p>SEM and EDS results of (<b>a</b>) NM12; (<b>b</b>) KM12; (<b>c</b>) NKM12.</p>
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<p>MIP results of NM12, KM12, NKM12, and CM: (<b>a</b>) cumulative intrusion; (<b>b</b>) incremental inrusion.</p>
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<p>NM12, KM12, NKM12, and CM at 28 d: (<b>a</b>) XRD patterns; (<b>b</b>) FTIR patterns.</p>
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<p>TG and DTG curves of NM12 and KM12.</p>
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<p>The concentration of K<sup>+</sup> and pH in the topsoil at different times.</p>
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23 pages, 935 KiB  
Article
The Influence of the Method of Use and Fertilization of Foothill Soil on the Concentration and Load of Trace Elements Leached into the Soil Profile by Percolating Water
by Piotr Kacorzyk, Jacek Strojny, Mirosław Kasperczyk and Barbara Wiśniowska-Kielian
Agronomy 2024, 14(12), 3047; https://doi.org/10.3390/agronomy14123047 - 20 Dec 2024
Viewed by 262
Abstract
The aim of this study was to assess the impact of the method of use and different fertilization of the foothill soil on the manganese (Mn), copper (Cu), cadmium (Cd) and lead (Pb) concentration in leachates and the loads of trace elements leached [...] Read more.
The aim of this study was to assess the impact of the method of use and different fertilization of the foothill soil on the manganese (Mn), copper (Cu), cadmium (Cd) and lead (Pb) concentration in leachates and the loads of trace elements leached from the soil profile. An experiment was carried out in Wiśnicz Foothills (Malopolska Province, Poland). In each plot, three lysimeters were installed, placed at a depth of 0–30 cm. The experiment included six variants, five on meadow, without fertilization (A—control); with mineral fertilization (B); with liquid manure (C) and with manure application (D); non-fertilized and non-mowed (E); and on arable land with mineral fertilization (F), in three repetitions each. Leachates were collected for three years in three periods: I—intensive growing, II—slow growing, and III—non-growing seasons. In general, the highest concentrations of Mn, Cu, Cd and Pb were recorded in leachates during period III. The lowest amounts of Cu and Cd were found in leachates in period II and Mn and Pb in period I. The exception were leachates from the following treatments: fertilized with liquid manure, which contained the most Mn and Cd in period II and the least Cd in period I; unused meadow, which contained the least Pb in period I; and leachates from arable land contained the least Cd in period I. The differences in the content of trace elements in the leachates were significant and amounted to 150–200% for Cd and Pb and about 20% for Mn and Cu. Mineral fertilization generally did not affect significantly Mn, Cu, Pb and Cd content in relation to the control, and the contents of Mn, Cu and Pb were even lower than in the leachates from the control. There was a significant increase in Mn concentration in leachates from unused meadow and arable land, Cu, Pb and Cd after both natural fertilizer applications and from arable land compared to other objects, in addition to Cd from unused meadow. Generally, the highest loads of trace elements were removed in period II and the smallest in period I of the study. Differences in leached loads during these periods were 2- to 8-fold and greater after liquid manure and manure application. The differences in Mn, Cd and Pb loads in subsequent years were 1.5- to 2-fold, and Cu loads in all years were similar. Natural fertilizers increased the trace element loads 1.5–4-fold compared to the control. Smaller differences concerned Mn and Cu and larger Pb loads. The method of land use significantly affected the quantity and quality of water percolates through the soil profile. Contrary to popular belief, the leachates from the unused meadow were not of the best quality, which resulted from their increased permeability into the soil under these conditions. Due to the quantity and quality of leachate waters and surface runoff in the foothill and mountain areas, it is advisable to limit tillage treatments, and the rational use of meadows and pastures with moderate fertilization is recommended. It is important to emphasize the importance of the proper management of the use of foothill and mountain areas for the optimal supply of plants with trace elements. Substantial losses of microelements necessary for optimal plant development may require their use in the form of fertilizers, which will result in increased agricultural production expenditures, reduce economic effects and slow down the pace of achieving sustainable agriculture. Full article
(This article belongs to the Section Soil and Plant Nutrition)
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<p>Factors determining relationships between trace element concentration in leachate water and variants of fertilization (Regression Decision Tree C&amp;RT). The experimental variants are marked with rectangles in different colors; the numerical values above the rectangle indicate the trace element concentration in the leachate; ID—the next element of the division; N—the number of observations in a given interval.</p>
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<p>Factors determining relationships between trace element loads moving through the soil profile in leachate water and variants of fertilization (Regression Decision Tree C&amp;RT). The experimental variants are marked with rectangles in different colors; the numerical values above the rectangle indicate the trace element load in the leachate; ID—the next element of the division; N—the number of observations in a given interval.</p>
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16 pages, 1973 KiB  
Article
Climate Factors Dominate the Spatial Distribution of Soil Nutrients in Desert Grassland
by Chunrong Guo, Ruixu Zhao, Hongtao Jiang and Wenjing Qu
Atmosphere 2024, 15(12), 1524; https://doi.org/10.3390/atmos15121524 - 20 Dec 2024
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Abstract
Soil nutrient distribution in desert grasslands is predominantly influenced by climatic factors, particularly precipitation and temperature. Siziwang Banner, situated within the desert grassland belt of Inner Mongolia, represents a typical arid zone where soil nutrient dynamics are shaped by the interplay of precipitation, [...] Read more.
Soil nutrient distribution in desert grasslands is predominantly influenced by climatic factors, particularly precipitation and temperature. Siziwang Banner, situated within the desert grassland belt of Inner Mongolia, represents a typical arid zone where soil nutrient dynamics are shaped by the interplay of precipitation, temperature, and topography. This study aims to investigate the spatial distribution of soil nutrients and assess the dominant role of climatic factors in this region, using geostatistical analyses and GIS techniques. The results reveal that soil nutrients exhibit higher concentrations in surface layers, gradually decreasing with depth. Horizontally, a pronounced gradient can be observed, with nutrient levels being higher in the southern regions and lower in the northern regions. Precipitation and temperature emerge as decisive factors driving these patterns; increased precipitation enhances the accumulation of soil organic matter and nitrogen, whereas elevated temperatures accelerate decomposition of organic matter, leading to nutrient losses. These findings underscore the critical role of climatic factors in governing soil nutrient distribution, offering valuable insights for soil management and ecological restoration efforts in arid ecosystems. Full article
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<p>Overview of the study area and sampling sites.</p>
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<p>Kriging interpolation map of soil organic matter.</p>
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<p>TN analysis of variance (ANOVA) plot.</p>
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<p>AK and AP analysis of variance (ANOVA) plot.</p>
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<p>Pearson correlation analysis: soil nutrients.</p>
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7 pages, 206 KiB  
Proceeding Paper
The Potential of Agroforestry to Enhance Rural Livelihoods in Punjab, Pakistan: A Socioeconomic Viewpoint
by Muhammad Bilal, Rabia Khan, Muhammad Tayyab, Muhammad Ikhlaq and Tahseen Aslam
Environ. Earth Sci. Proc. 2024, 31(1), 8; https://doi.org/10.3390/eesp2024031008 - 19 Dec 2024
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Abstract
Agroforestry improves the stability and productivity of agro-ecosystems and reduces environmental pressures, making it extremely flexible and useful in a variety of physical and social contexts. This practice is crucial to farmers’ livelihoods on both an ecological and economical level. Using an interview [...] Read more.
Agroforestry improves the stability and productivity of agro-ecosystems and reduces environmental pressures, making it extremely flexible and useful in a variety of physical and social contexts. This practice is crucial to farmers’ livelihoods on both an ecological and economical level. Using an interview schedule, data were gathered from 170 heads of rural families who were chosen at random. Of the responders, the majority (77.5%) were young (25 to 40 years old). Of those who had completed more than five years of schooling, only 46.7% were literate, while a sizable majority (53.3%) were illiterate. For the vast majority of responders (62.4%), farming was their primary source of income. Given that over 54% of the respondents only owned up to five acres of land, small farming was extremely common. The majority (61.3%) were considered poor with a monthly income of less than PKR 18,000. “good source of fuel wood” was placed at the top (mean = 3.1%) when it came to the effect of agroforestry on the food security of rural households. One of the main obstacles was having a small land holding (mean = 2.52). The majority of respondents believed that the primary benefit of agroforestry was a reduction in soil loss. The amount of land held, income source, and educational attainment all significantly correlated with the perception of poverty. The study found that the best way of sustainably assuring food security in the study area and satisfying rural residents’ needs for food for extended periods of time is to incorporate agroforestry into the current farming system. Full article
20 pages, 7291 KiB  
Article
Downscaling of Remote Sensing Soil Moisture Products That Integrate Microwave and Optical Data
by Jie Wang, Huazhu Xue, Guotao Dong, Qian Yuan, Ruirui Zhang and Runsheng Jing
Appl. Sci. 2024, 14(24), 11875; https://doi.org/10.3390/app142411875 - 19 Dec 2024
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Abstract
Soil moisture is a key variable that affects ecosystem carbon and water cycles and that can directly affect climate change. Remote sensing is the best way to obtain global soil moisture data. Currently, soil moisture remote sensing products have coarse spatial resolution, which [...] Read more.
Soil moisture is a key variable that affects ecosystem carbon and water cycles and that can directly affect climate change. Remote sensing is the best way to obtain global soil moisture data. Currently, soil moisture remote sensing products have coarse spatial resolution, which limits their application in agriculture, the ecological environment, and urban planning. Soil moisture downscaling methods rely mainly on optical data. Affected by weather, the spatial discontinuity of optical data has a greater impact on the downscaling results. The synthetic aperture radar (SAR) backscatter coefficient is strongly correlated with soil moisture. This study was based on the Google Earth Engine (GEE) platform, which integrated Moderate-Resolution Imaging Spectroradiometer (MODIS) optical and SAR backscattering coefficients and used machine learning methods to downscale the soil moisture product, reducing the original soil moisture with a resolution of 10 km to 1 km and 100 m. The downscaling results were verified using in situ observation data from the Shandian River and Wudaoliang. The results show that in the two study areas, the downscaling results after adding SAR backscattering coefficients are better than before. In the Shandian River, the R increases from 0.28 to 0.42. In Wudaoliang, the R value increases from 0.54 to 0.70. The RMSE value is 0.03 (cm3/cm3). The downscaled soil moisture products play an important role in water resource management, natural disaster monitoring, ecological and environmental protection, and other fields. In the monitoring and management of natural disasters, such as droughts and floods, it can provide key information support for decision-makers and help formulate more effective emergency response plans. During droughts, affected areas can be identified in a timely manner, and the allocation and scheduling of water resources can be optimized, thereby reducing agricultural losses. Full article
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<p>Study area. (<b>a</b>) is the surface coverage type and sites distribution of the Wudaoliang area; (<b>b</b>) is the surface coverage type and site distribution of the Shandian River.</p>
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<p>Flowchart for data processing and soil moisture downscaling. MODIS, SRTM, and SMAP are the abbreviations of the dataset that provides the data required for the experiment. NDVI, LST, SLOPE, and VV/VH are auxiliary data used to train the downscaling model. RF and XGB are the names of the models used for training.</p>
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<p>Heat map of R values between model data used for downscaling. (<b>a</b>) The Shandian River; (<b>b</b>) Wudaoliang. SMAP_10km is original soil moisture; NDVI, ALB, LST, LAI, SLOPE, VV, and VH are auxiliary data resampled to 10 km resolution.</p>
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<p>Various feature weights of RF. ALB, LAI, LST, NDVI, SLOPE, VV, and VH are auxiliary data used in building downscaling models using the random forest algorithm.</p>
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<p>Soil moisture distributions in the Shandian River before and after downscaling. SMAP_10km is the original soil moisture; SMAP_NOVV_1km is the downscaled soil moisture without SAR backscattering coefficient data; SMAP_1km and SMAP_100m are the downscaled soil moisture with added SAR backscattering coefficient data.</p>
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<p>Soil moisture distributions in the Shandian River before and after downscaling. SMAP_10km is the original soil moisture; SMAP_NOVV_1km is the downscaled soil moisture without SAR backscattering coefficient data; SMAP_1km and SMAP_100m are the downscaled soil moisture with added SAR backscattering coefficient data.</p>
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<p>Soil moisture distributions before and after downscaling in the Wudaoliang area. SMAP_10km is the original soil moisture; SMAP_NOVV_1km is the downscaled soil moisture without SAR backscattering coefficient data; SMAP_1km is the downscaled soil moisture with added SAR backscattering coefficient data.</p>
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<p>Scatter plot of before and after downscaling in the Shandian River. The red dotted line in the figure indicates the 1:1 line.</p>
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<p>Comparison of Taylor diagrams before and after downscaling in the Wudaoliang area. P1 represents 20/08/12, and P2 represents 20/08/20.</p>
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<p>Scatter plots of soil moisture and in situ SM before and after downscaling. (<b>a</b>) is the verification result before downscaling; (<b>b</b>) is the verification result after downscaling. The red dotted line in the figure indicates the 1:1 line.</p>
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