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Search Results (4,837)

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Keywords = soil health

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15 pages, 294 KiB  
Review
Biochar-Induced Microbial Shifts: Advancing Soil Sustainability
by Meesha Sharma, Rishabh Kaushik, Maharaj K. Pandit and Yi-Hsuan Lee
Sustainability 2025, 17(4), 1748; https://doi.org/10.3390/su17041748 - 19 Feb 2025
Abstract
Biochar utilisation as a soil enhancer has gathered considerable interest owing to its notable capacity to boost soil productivity, enhance carbon sequestration, and improve agricultural sustainability. Nonetheless, how biochar affects the soil microbiome, a key to soil health and ecological functioning, remains a [...] Read more.
Biochar utilisation as a soil enhancer has gathered considerable interest owing to its notable capacity to boost soil productivity, enhance carbon sequestration, and improve agricultural sustainability. Nonetheless, how biochar affects the soil microbiome, a key to soil health and ecological functioning, remains a contested subject. Given the critical role microbial communities play in maintaining soil health and functioning, variations in soil microbiota may have a substantial impact on soil fertility and stability. Despite a wealth of studies on the effects of biochar on soil microbial communities, the results demonstrate that the reaction of the microbiome to biochar varies greatly depending on the edaphic and biochar properties and other factors such as the experimental conditions and agricultural practices. Notably, different components of the soil microbiome may respond to soil/biochar properties in a unique way, which makes generalising the impacts of biochar on the soil microbiome a difficult task. In this review, we comprehensively examine the factors governing the impacts of biochar on the soil microbiome, especially in terms of its repercussions on microbial diversity, community structure, and functional dynamics, and the potential ramifications for agricultural productivity and environmental sustainability. Full article
24 pages, 4942 KiB  
Article
Cross-Effect Between Cover Crops and Glyphosate-Based Herbicide Application on Microbiote Communities in Field Crops Soils
by Jérôme Bernier Brillon, Marc Lucotte, Blandine Giusti, Gilles Tremblay and Matthieu Moingt
Agriculture 2025, 15(4), 432; https://doi.org/10.3390/agriculture15040432 - 19 Feb 2025
Abstract
This study investigates how cover crops (CC) and different application rates of glyphosate-based herbicide (GBH) may affect soil microbial communities. Our hypothesis was that the use of CC would promote the presence of certain microbial communities in soils and mitigate the potential impact [...] Read more.
This study investigates how cover crops (CC) and different application rates of glyphosate-based herbicide (GBH) may affect soil microbial communities. Our hypothesis was that the use of CC would promote the presence of certain microbial communities in soils and mitigate the potential impact of GBH on these communities. CC can promote biodiversity by increasing plant diversity in fields, while GBH may have non-target effects on species that utilize the shikimate pathway. Crop managements in an experimental field in Southern Québec (Canada) consisted in Glyphosate-based Herbicide (GBH) applications rates at 0.84, 1.67 and 3.33 L ha−1 in corn, soybean and wheat fields cultivated with Direct Seeding along with CC (DSCC) and at 3.33 L ha−1 in similar crops cultivated with direct seeding but without CC (DS). DSCC did not significantly impact microbial richness compared to DS, but did alter specific abundance among prokaryotes and eukaryotes. A permutational multivariate analysis revealed that the type of crop (soybean, wheat, maize) significantly influenced the composition of eukaryotic communities in 2018 and 2019, but not prokaryotic communities. Importantly, the study identifies a cross-effect between CC and GBH application rates suggesting that herbicide use in soybean plots can influence Anaeromyxobacter populations. Also, higher abundance of Enoplea and Maxilopoda were observed in plots with the lower application rate of GBH. Both eukaryotes group are known to be sensitive to crop management. These findings emphasize the need for a holistic approach to agricultural practices, considering the combined effects of both CC and GBH application rates on soil microbial health. Ultimately, the study calls for sustainable agricultural practices that preserve microbial diversity, which is essential for maintaining ecosystem services and soil health. Full article
(This article belongs to the Special Issue Benefits and Challenges of Cover Crops in Agricultural Systems)
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<p>Soil texture, soil elementary content, the cultivars and the cover crops used in the experimental design. Elementary contents were obtained for phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), aluminium (Al), bore (B), copper (Cu), iron (Fe), manganese (Mn), zinc (Zn), sodium (Na), nickel (Ni), cadmium (Cd), chrome (Cr), cobalt (Co) and lead (Pb) are presented as means ± standard error on the mean.</p>
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<p>Principal coordinates analysis (PCoA) using Bray-Curtis dissimilarity test on procaryotes and eucaryotes composition in soil between the different crop managements in corn, soy and wheat. An analysis with contrast was performed to assess significant difference between DS and DSCC plots. Also, a univariate analysis was performed to assess significant difference between all crops managements. A threshold of 0.05 was used to assessed statistical significance for all statistical analyses. A post-hoc letters test was performed when statistical significances were observed. Then, relative abundance of all taxonomic group was represented by phyloseq bar plots for each crop for 2018 and 2019.</p>
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<p>Values of eukaryotic (<b>A</b>,<b>B</b>) and prokaryotic (<b>C</b>,<b>D</b>) richness index in soil (n = 96) between crop managements (DS 3.33, DSCC 0.84, DSCC 1.67 and DSCC 3.33).</p>
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<p>Values of eukaryotic (<b>A</b>,<b>B</b>) and prokaryotic (<b>C</b>,<b>D</b>) richness index in soil (n = 96) between crop managements (DS 3.33, DSCC 0.84, DSCC 1.67 and DSCC 3.33).</p>
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<p>Eukaryotic and prokaryotic evenness between crop managements in 2018 and 2019.</p>
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<p>Abundance of eukaryotes between crop managements in 2018 and 2019.</p>
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<p>Abundance of prokaryotes between crop managements in 2018 and 2019.</p>
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<p>Relative abundance of eukaryotes (<b>A</b>) and prokaryotes (<b>B</b>) taxa and comparison between crops managements.</p>
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26 pages, 6025 KiB  
Article
Remote Sensing and Soil Moisture Sensors for Irrigation Management in Avocado Orchards: A Practical Approach for Water Stress Assessment in Remote Agricultural Areas
by Emmanuel Torres-Quezada, Fernando Fuentes-Peñailillo, Karen Gutter, Félix Rondón, Jorge Mancebo Marmolejos, Willy Maurer and Arturo Bisono
Remote Sens. 2025, 17(4), 708; https://doi.org/10.3390/rs17040708 - 19 Feb 2025
Abstract
Water scarcity significantly challenges agricultural systems worldwide, especially in tropical areas such as the Dominican Republic. This study explores integrating satellite-based remote sensing technologies and field-based soil moisture sensors to assess water stress and optimize irrigation management in avocado orchards in Puerto Escondido, [...] Read more.
Water scarcity significantly challenges agricultural systems worldwide, especially in tropical areas such as the Dominican Republic. This study explores integrating satellite-based remote sensing technologies and field-based soil moisture sensors to assess water stress and optimize irrigation management in avocado orchards in Puerto Escondido, Dominican Republic. Using multispectral imagery from the Landsat 8 and 9 satellites, key vegetation indices (NDVI and SAVI) and NDWI, a water-related index that specifically indicates changes in crop water contents, rather than vegetation vigor, were derived to monitor vegetation health, growth stages, and soil water contents. Crop coefficient (Kc) values were calculated from these vegetation indices and combined with reference evapotranspiration (ETo) estimates derived from three meteorological models (Hargreaves–Samani, Priestley–Taylor, and Blaney–Criddle) to assess crop water requirements. The results revealed that soil moisture data from sensors at 30 cm depth strongly correlated with satellite-derived estimates, reflecting avocado trees’ critical root zone dynamics. Additionally, seasonal patterns in the vegetation indices showed that NDVI and SAVI effectively tracked vegetative growth stages, while NDWI indicated changes in the canopy water content, particularly during periods of water stress. Integrating these satellite-derived indices with field measurements allowed a comprehensive assessment of crop water requirements and stress, providing valuable insights for improving irrigation practices. Finally, this study demonstrates the potential of remote sensing technologies for large-scale water stress assessment, offering a scalable and cost-effective solution for optimizing irrigation practices in water-limited regions. These findings advance precision agriculture, especially in tropical environments, and provide a foundation for future research aimed at enhancing data accuracy and optimizing water management practices. Full article
(This article belongs to the Special Issue Remote Sensing for Eco-Hydro-Environment)
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<p>(<b>a</b>) The geographical location of the study site; (<b>b</b>) a high-resolution satellite image of the orchard. Both maps include latitude and longitude references in degrees (WGS 84/EPSG:4326) to ensure spatial accuracy.</p>
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<p>Spectral reflectance curves of avocado orchards derived from Landsat 8 and 9 satellite data. The figure shows the distinct spectral bands (blue, green, red, near-infrared, and shortwave infrared) used to calculate the vegetation and water indices. The variation in reflectance values across these bands provides insights into plant health, water contents, and stress conditions. Seasonal changes in reflectance highlight the impact of varying water availability on vegetation indices, illustrating how water stress influences plant vitality throughout the growing season.</p>
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<p>Three-dimensional spatial distribution of sensors in the field. The horizontal plane (X and Y coordinates) represents the layout of the field, where the distances in meters are illustrative and do not reflect the actual distance between sensors. In contrast, the vertical axis (Z coordinate) corresponds to the sensor placement depth in centimeters.</p>
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<p>Daily variations in temperature, precipitation, and solar radiation for 2021 and 2022.</p>
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<p>Temperature, precipitation, and solar radiation variability for 2021 and 2022.</p>
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<p>Top- and bottom-performing sensors: correlation analysis with satellite data (2021–2022).</p>
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<p>Top- and bottom-performing sensors: correlation analysis with satellite data (2021–2022).</p>
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<p>Satellite-based soil moisture and precipitation by season (2021–2022).</p>
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<p>Seasonal evolution of vegetative expression using NDVI, NDWI, and SAVI (2021–2022).</p>
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<p>Kc values calculated using the Kc-NDVI relation for the three indices.</p>
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<p>Seasonal evolution of ETo using three models (2021–2022).</p>
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30 pages, 450 KiB  
Review
Impact of Lead Pollution from Vehicular Traffic on Highway-Side Grazing Areas: Challenges and Mitigation Policies
by Tareq A. Al-Sabbagh and Sheikh Shreaz
Int. J. Environ. Res. Public Health 2025, 22(2), 311; https://doi.org/10.3390/ijerph22020311 - 19 Feb 2025
Viewed by 79
Abstract
One major environmental concern is the lead (Pb) pollution from automobile traffic, especially in highway-side grazing areas. Sheep grazing in Pb-contaminated areas are particularly vulnerable because Pb exposure from soil, water, and feed can have harmful effects that impair their general health, reproductive [...] Read more.
One major environmental concern is the lead (Pb) pollution from automobile traffic, especially in highway-side grazing areas. Sheep grazing in Pb-contaminated areas are particularly vulnerable because Pb exposure from soil, water, and feed can have harmful effects that impair their general health, reproductive capability, and immune systems. Long-term hazards to cattle from persistent Pb exposure include neurotoxicity, hematological abnormalities, reproductive health problems, and immunosuppression. These can have serious consequences, such as reduced productivity and even mortality. Additionally, through the food chain, Pb bioaccumulation in lamb tissues directly endangers human health. Pb poisoning is caused by a variety of intricate mechanisms, including disturbances in calcium-dependent processes, oxidative stress, and enzyme inhibition. To mitigate these risks, an interdisciplinary approach is essential, combining expertise in environmental science, toxicology, animal husbandry, and public health. Effective strategies include rotational grazing, alternative foraging options, mineral supplementation, and soil remediation techniques like phytoremediation. Additionally, the implementation of stringent regulatory measures, continuous monitoring, and community-based initiatives are vital. This review emphasizes the need for comprehensive and multidisciplinary methodologies to address the ecological, agricultural, and public health impacts of Pb pollution. By integrating scientific expertise and policy measures, it is possible to ensure the long-term sustainability of grazing systems, protect livestock and human health, and maintain ecosystem integrity. Full article
15 pages, 5695 KiB  
Article
Microbial Community Composition of Explosive-Contaminated Soils: A Metataxonomic Analysis
by Francisco J. Flores, Esteban Mena, Silvana Granda and Jéssica Duchicela
Microorganisms 2025, 13(2), 453; https://doi.org/10.3390/microorganisms13020453 - 19 Feb 2025
Viewed by 125
Abstract
Munition disposal practices have significant effects on microbial composition and overall soil health. Explosive soil contamination can disrupt microbial communities, leading to microbial abundance and richness changes. This study investigates the microbial diversity of soils and roots from sites with a history of [...] Read more.
Munition disposal practices have significant effects on microbial composition and overall soil health. Explosive soil contamination can disrupt microbial communities, leading to microbial abundance and richness changes. This study investigates the microbial diversity of soils and roots from sites with a history of ammunition disposal, aiming to identify organisms that may play a role in bioremediation. Soil and root samples were collected from two types of ammunition disposal (through open burning and open detonation) and unpolluted sites in Machachi, Ecuador, over two years (2022 and 2023). High-throughput sequencing of the 16S rRNA gene (for bacteria) and the ITS region (for fungi and plants) was conducted to obtain taxonomic profiles. There were significant variations in the composition of bacteria, fungi, and plant communities between polluted and unpolluted sites. Bacterial genera such as Pseudarthrobacter, Pseudomonas, and Rhizobium were more abundant in roots, while Candidatus Udaeobacter dominated unpolluted soils. Fungal classes Dothideomycetes and Sordariomycetes were prevalent across most samples, while Leotiomycetes and Agaricomycetes were also highly abundant in unpolluted samples. Plant-associated reads showed a higher abundance of Poa and Trifolium in root samples, particularly at contaminated sites, and Alchemilla, Vaccinium, and Hypericum were abundant in unpolluted sites. Alpha diversity analysis indicated that bacterial diversity was significantly higher in unpolluted root and soil samples, whereas fungal diversity was not significantly different among sites. Redundancy analysis of beta diversity showed that site, year, and sample type significantly influenced microbial community structure, with the site being the most influential factor. Differentially abundant microbial taxa, including bacteria such as Pseudarthrobacter and fungi such as Paraleptosphaeria and Talaromyces, may contribute to natural attenuation processes in explosive-contaminated soils. This research highlights the potential of certain microbial taxa to restore environments contaminated by explosives. Full article
(This article belongs to the Section Environmental Microbiology)
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<p>Abundance at the class taxonomic rank level for 16S and ITS barcodes: (<b>a</b>) Bacteria, (<b>b</b>) fungi, (<b>c</b>) plants.</p>
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<p>Abundance at the class taxonomic rank level for 16S and ITS barcodes: (<b>a</b>) Bacteria, (<b>b</b>) fungi, (<b>c</b>) plants.</p>
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<p>Heatmap representing the most abundant genera in all treatments: (<b>a</b>) Bacteria, (<b>b</b>) fungi, (<b>c</b>) plants.</p>
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<p>Heatmap representing the most abundant genera in all treatments: (<b>a</b>) Bacteria, (<b>b</b>) fungi, (<b>c</b>) plants.</p>
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<p>Shannon, Simpson, Observed, and Chao1 diversity indices for bacteria and fungi. A Shapiro–Wilk normality test was conducted separately on soil and root data for both bacteria and fungi. To assess whether alpha diversity differed significantly among treatments, ANOVA and Duncan tests were applied to normally distributed data, while the Kruskal–Wallis test was used for data that did not meet normality assumptions. Different letters (a, b) indicate statistically significant differences between groups (<span class="html-italic">p</span> &lt; 0.05), while ’ns’ denotes non-significant differences.</p>
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<p>Redundancy analysis (RDA) ordination plot for bacterial communities based on Bray–Curtis dissimilarity. The vectors indicate the direction and strength of environmental variables influencing community composition, such as Site, Year, and Reads. The length of the vectors reflects the degree of correlation with the ordination axes.</p>
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<p>Redundancy analysis (RDA) ordination plot for fungal communities based on Bray–Curtis dissimilarity. The vectors indicate the direction and strength of environmental variables influencing community composition, such as Site, Year, and Reads. The length of the vectors reflects the degree of correlation with the ordination axes.</p>
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<p>The differential abundance of bacterial and fungal taxa across root and soil samples from unpolluted, incineration, and detonation sites. Error bars represent standard deviations.</p>
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16 pages, 2059 KiB  
Article
Screening of Antagonistic Trichoderma Strains to Enhance Soybean Growth
by Na Yu, Yijia Gao, Feng Chang, Wenting Liu, Changhong Guo and Hongsheng Cai
J. Fungi 2025, 11(2), 159; https://doi.org/10.3390/jof11020159 - 19 Feb 2025
Viewed by 132
Abstract
This study investigates the isolation and screening of Trichoderma strains that exhibit antagonistic properties against soybean root-infecting Fusarium species, particularly F. oxysporum. From soybean rhizosphere soil, 37 antagonistic Trichoderma strains were identified using the plate confrontation method, demonstrating inhibitory effects ranging from [...] Read more.
This study investigates the isolation and screening of Trichoderma strains that exhibit antagonistic properties against soybean root-infecting Fusarium species, particularly F. oxysporum. From soybean rhizosphere soil, 37 antagonistic Trichoderma strains were identified using the plate confrontation method, demonstrating inhibitory effects ranging from 47.57% to 72.86% against F. oxysporum. Strain 235T4 exhibited the highest inhibition rate at 72.86%. Molecular identification confirmed that the strains belonged to eight species within the Trichoderma genus, with notable strains promoting soybean growth in greenhouse tests. In pot experiments, the application of Trichoderma significantly reduced the disease index of soybean plants inoculated with F. oxysporum, particularly with strain 223H16, which achieved an 83.78% control efficiency. Field applications further indicated enhanced soybean growth metrics, including increased pod numbers and plant height, when treated with specific Trichoderma strains. Additionally, Trichoderma application enriched the fungal diversity in the soybean rhizosphere, resulting in a significant reduction of Fusarium populations by approximately 50%. This study highlights the potential of Trichoderma species as biological control agents to enhance soybean health and productivity while improving soil fungal diversity. Full article
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<p>The effects of <span class="html-italic">Trichoderma</span> treatment on the phenotype and biomass of potted soybean plants. (<b>A</b>) Phenotype of soybeans after Trichoderma treatment. (<b>B</b>) plant height (cm). (<b>C</b>) root length (cm). (<b>D</b>) fresh weight (g). (<b>E</b>) dry weight (g). Data are present as means ± standard deviation; Different letters in the same column indicate significant differences between different treatments (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Differences in fungal community compositions among <span class="html-italic">Trichoderma</span> treatments. This column chart displays the relative abundances of species at the phylum level.</p>
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<p>Relative abundance of <span class="html-italic">Fusarium</span> species in soybean rhizosphere soil in the field. Data are present as means ± standard deviation; Different letters in the same column indicate significant differences between different treatments (<span class="html-italic">p</span> &lt; 0.05).</p>
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21 pages, 27128 KiB  
Article
Spatiotemporal Dynamics of PM2.5-Related Premature Deaths and the Role of Greening Improvement in Sustainable Urban Health Governance
by Peng Tang, Tianshu Liu, Xiandi Zheng and Jie Zheng
Atmosphere 2025, 16(2), 232; https://doi.org/10.3390/atmos16020232 - 18 Feb 2025
Viewed by 82
Abstract
Environmental particulate pollution is a major global environmental health risk factor, which is associated with numerous adverse health outcomes, negatively impacting public health in many countries, including China. Despite the implementation of strict air quality management policies in China and a significant reduction [...] Read more.
Environmental particulate pollution is a major global environmental health risk factor, which is associated with numerous adverse health outcomes, negatively impacting public health in many countries, including China. Despite the implementation of strict air quality management policies in China and a significant reduction in PM2.5 concentrations in recent years, the health burden caused by PM2.5 pollution has not decreased as expected. Therefore, a comprehensive analysis of the health burden caused by PM2.5 is necessary for more effective air quality management. This study makes an innovative contribution by integrating the Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI), and Soil-Adjusted Vegetation Index (SAVI), providing a comprehensive framework to assess the health impacts of green space coverage, promoting healthy urban environments and sustainable development. Using Nanjing, China, as a case study, we constructed a health impact assessment system based on PM2.5 concentrations and quantitatively analyzed the spatiotemporal evolution of premature deaths caused by PM2.5 from 2000 to 2020. Using Multiscale Geographically Weighted Regression (MGWR), we explored the impact of greening improvement on premature deaths attributed to PM2.5 and proposed relevant sustainable governance strategies. The results showed that (1) premature deaths caused by PM2.5 in Nanjing could be divided into two stages: 2000–2015 and 2015–2020. During the second stage, deaths due to respiratory and cardiovascular diseases decreased by 3105 and 1714, respectively. (2) The spatial variation process was slow, with the overall evolution direction predominantly from the southeast to northwest, and the spatial distribution center gradually shifted southward. On a global scale, the Moran’s I index increased from 0.247251 and 0.240792 in 2000 to 0.472201 and 0.468193 in 2020. The hotspot analysis revealed that high–high correlations slowly gathered toward central Nanjing, while the proportion of cold spots increased. (3) The MGWR results indicated a significant negative correlation between changes in green spaces and PM2.5-related premature deaths, especially in densely vegetated areas. This study comprehensively considered the spatiotemporal changes in PM2.5-related premature deaths and examined the health benefits of green space improvement, providing valuable references for promoting healthy and sustainable urban environmental governance and air quality management. Full article
(This article belongs to the Section Air Quality)
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<p>Study Area. (<b>A</b>) the map of China. (<b>B</b>) the administrative boundary of Jiangsu Province. (<b>C</b>) the administrative boundary and population distribution of Nanjing City.</p>
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<p>Spatial distribution of premature deaths from pM<sub>2.5</sub>-related diseases (2000–2020). (<b>A</b>) is the spatial distribution of premature deaths caused by respiratory diseases due to PM2.5; (<b>B</b>) is the spatial distribution of premature deaths caused by cardiovascular diseases due to PM2.5.</p>
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<p>Changes in number of premature deaths from PM<sub>2.5</sub>-related diseases.</p>
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<p>Standard deviation ellipses (<b>A1</b>): respiratory diseases; (<b>B1</b>): cardiovascular diseases. (<b>A`</b>,<b>B`</b>) the change of the center of the standard deviation ellipse.</p>
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<p>Cold and hot spot analysis (<b>A</b>–<b>E</b>): spatial distributions of cold and hot spots for PM<sub>2.5</sub>-induced premature deaths from respiratory diseases; (<b>F</b>–<b>J</b>): spatial distributions of cold and hot spots for PM<sub>2.5</sub>-induced premature deaths from cardiovascular diseases.The numbers in the image represent Z-scores. Positive values indicate high-value clusters, while negative values indicate low-value clusters.</p>
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<p>Change in proportions of cold and hot spot areas. R means the area proportion of respiratory system diseases; C means the area proportion of cardiovascular diseases.</p>
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<p>Multiscale Geographically Weighted Regression analysis of vegetation indices and HIA results. (<b>A</b>–<b>C</b>) the regression results of respiratory diseases caused by PM2.5 and vegetation index. (<b>D</b>–<b>F</b>) the regression results of cardiovascular diseases caused by PM2.5 and vegetation index.</p>
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35 pages, 13972 KiB  
Review
Environmental Challenges in Southern Brazil: Impacts of Pollution and Extreme Weather Events on Biodiversity and Human Health
by Joel Henrique Ellwanger, Marina Ziliotto, Bruna Kulmann-Leal and José Artur Bogo Chies
Int. J. Environ. Res. Public Health 2025, 22(2), 305; https://doi.org/10.3390/ijerph22020305 - 18 Feb 2025
Viewed by 161
Abstract
The Amazon rainforest plays a fundamental role in regulating the global climate and therefore receives special attention when Brazilian environmental issues gain prominence on the global stage. However, other Brazilian biomes, such as the Pampa and the Atlantic Forest in southern Brazil, have [...] Read more.
The Amazon rainforest plays a fundamental role in regulating the global climate and therefore receives special attention when Brazilian environmental issues gain prominence on the global stage. However, other Brazilian biomes, such as the Pampa and the Atlantic Forest in southern Brazil, have been facing significant environmental challenges, either independently or under the influence of ecological changes observed in the Amazon region. The state of Rio Grande do Sul is located in the extreme south of Brazil and in 2024 was hit by major rainfalls that caused devastating floods. The Pampa is a non-forest biome found in Brazil only in Rio Grande do Sul. This biome is seriously threatened by loss of vegetation cover and many classes of pollutants, including pesticides and plastics. Mining ventures are also important sources of soil, water and air pollution by potentially toxic elements in Rio Grande do Sul, threatening both the Pampa and the Atlantic Forest. Furthermore, southern Brazil is often affected by pollution caused by smoke coming from fires observed in distant biomes such as the Pantanal and the Amazon. Considering the significant environmental challenges observed in southern Brazil, this article revisits the historical participation of Rio Grande do Sul in Brazilian environmentalism and highlights the main environmental challenges currently observed in the state, followed by an in-depth analysis of the effects of pollution and extreme weather events on biodiversity and human health in the region. This review encompassed specifically the following categories of pollutants: potentially toxic elements (e.g., arsenic, cadmium, chromium, cobalt, copper, lead, mercury, titanium), air pollutants, plastics, and pesticides. Pathogen-related pollution in the context of extreme weather events is also addressed. This article emphasizes the critical importance of often-overlooked biomes in Brazilian conservation efforts, such as the Pampa biome, while also underscoring the interconnectedness of climate change, pollution, their shared influence on human well-being and ecological balance, using Rio Grande do Sul as a case study. Full article
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<p>Brazil’s map showing the distribution of the terrestrial Brazilian biomes. Rio Grande do Sul state is highlighted on the edge in bold. In orange: coverage of the Pampa biome. In light green: coverage of the Atlantic Forest biome. In yellow: coverage of the Cerrado biome. In red: coverage of the Pantanal biome. In pink: coverage of the Caatinga biome. In dark green: coverage of the Amazon biome. Brazil is located in Latin America and shares borders with the following countries: French Guiana (GUF), Suriname (SUR), Guyana (GUY), Venezuela (VEN), Colombia (COL), Peru (PER), Bolivia (BOL), Paraguay (PAR), Argentina (ARG), and Uruguay (URU), as shown on the map. Chile (CHL) is also visible. Coordinates obtained using SIRGAS2000.</p>
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<p>Representative images of Rio Grande do Sul landscapes. (<b>A</b>) Pampa biome in São Gabriel City, showing predominant grassy vegetation in the foreground and forestry activity in the background, one of the biggest current threats to the Pampa (photo credit: Alexandre Copês). (<b>B</b>) Ecotone zone near Porto Alegre City, showing the transition between the Pampa and Atlantic Forest biomes (photo credit: Joel H. Ellwanger). (<b>C</b>,<b>D</b>) Mountainous region of Rio Grande do Sul, Canela City, showing mixed ombrophilous forest belonging to the Atlantic Forest biome (photo credits: Joel H. Ellwanger).</p>
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<p>Map of Rio Grande do Sul. The state border is highlighted on the edge in bold. In green: distribution of forests. In yellow: distribution of grasslands. In pink: distribution of agriculture. In red: distribution of areas without vegetation (composed of urban areas, mining, beaches, dunes, sand spots, and other regions without vegetation). In blue: water bodies. Images from Google Satellite and data from MapBiomas.</p>
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<p>Main anthropogenic activities and pollution classes observed in Rio Grande do Sul. Atmospheric pollution fuels climate change, which exacerbates the impacts of other pollution classes. PTEs—potentially toxic elements. CO<sub>2</sub>—carbon dioxide.</p>
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<p>Rio Grande do Sul 2024 flood. (<b>A</b>) Central region of Porto Alegre City. (<b>B</b>,<b>C</b>) The Guaíba Lake shore. (<b>D</b>) Vegetation on the Marinha Park shore (Porto Alegre) severely impacted after being submerged for several days. (Photo credits: Alexandre Copês).</p>
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<p>Sanitation-related problems observed in Porto Alegre City. A and C: Ipanema Beach (freshwater beach) in Porto Alegre showing a sign indicating that the water is unfit for swimming during the 2023 summer season (<b>A</b>) and the release of domestic sewage into the water at the beach (<b>B</b>). (<b>C</b>) Presence of domestic sewage in a stream located in a public park in Porto Alegre. (<b>D</b>) Presence of accumulated garbage in a bridge repair structure located in Dilúvio Stream, which flows into the Guaíba Lake. The Dilúvio Stream is a habitat for varied fauna, but it presents several classes of pollutants, including toxic metals and biological contamination, thus fueling pathogen pollution and other health issues that affect humans and animals (photo credits: Joel H. Ellwanger).</p>
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<p>Combined consequences of pollution and climate change.</p>
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<p>Health problems observed in the human population of Rio Grande do Sul, which may be exacerbated by climate change. PTEs—potentially toxic elements.</p>
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20 pages, 2298 KiB  
Article
Effects of Land Use Changes on CO2 Emission Dynamics in the Amazon
by Adriano Maltezo da Rocha, Mauricio Franceschi, Alan Rodrigo Panosso, Marco Antonio Camillo de Carvalho, Mara Regina Moitinho, Marcílio Vieira Martins Filho, Dener Marcio da Silva Oliveira, Diego Antonio França de Freitas, Oscar Mitsuo Yamashita and Newton La Scala Jr.
Agronomy 2025, 15(2), 488; https://doi.org/10.3390/agronomy15020488 - 18 Feb 2025
Viewed by 168
Abstract
Global climate change is closely tied to CO2 emissions, and implementing conservation-agricultural systems can help mitigate emissions in the Amazon. By maintaining forest cover and integrating sustainable agricultural practices in pasture, these systems help mitigate climate change and preserve the carbon stocks [...] Read more.
Global climate change is closely tied to CO2 emissions, and implementing conservation-agricultural systems can help mitigate emissions in the Amazon. By maintaining forest cover and integrating sustainable agricultural practices in pasture, these systems help mitigate climate change and preserve the carbon stocks in Amazon forest soils. In addition, these systems improve soil health, microclimate regulation, and promote sustainable agricultural practices in the Amazon region. This study aimed to evaluate the CO2 emission dynamics and its relationship with soil attributes under different uses in the Amazon. The experiment consisted of four treatments (Degraded Pasture—DP; Managed Pasture—MP; Native Forest—NF; and Livestock Forest Integration—LF), with 25 replications. Soil CO2 emission (FCO2), soil temperature, and soil moisture were evaluated over a period of 114 days, and the chemical, physical, and biological attributes of the soil were measured at the end of this period. The mean FCO2 reached values of 4.44, 3.88, 3.80, and 3.14 µmol m−2 s−1 in DP, MP, NF, and LF, respectively. In addition to the direct relationship between soil CO2 emissions and soil temperature for all land uses, soil bulk density indirectly influenced emissions in NF. The amount of humic acid induced the highest emission in DP. Soil organic carbon and carbon stock were higher in MP and LF. These values demonstrate that FCO2 was influenced by the Amazon land uses and highlight LF as a low CO2 emission system with a higher potential for carbon stock in the soil. Full article
(This article belongs to the Section Farming Sustainability)
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<p>Experimental areas. (<b>A</b>) DP—Degraded Pasture, (<b>B</b>) MP—Managed Pasture, (<b>C</b>) LF—Livestock–Forest Integration, and (<b>D</b>) NF—Native Forest. Paranaíta, MT, Brazil.</p>
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<p>Daily means and mean standard error bars of soil CO<sub>2</sub> emission (<b>A</b>), soil moisture (<b>B</b>), and soil temperature (<b>C</b>) in different land uses, Paranaíta, MT, Brazil, 2018 to 2019.</p>
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<p>Linear regression between soil CO<sub>2</sub> emission and soil temperature in different land use typologies.</p>
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<p>Biplot graph with soil attributes, management systems, and confidence ellipses (95% confidence). FCO<sub>2</sub>: soil CO<sub>2</sub> emission, Ts: soil temperature, Ms: soil moisture. pH: potential of hydrogen, H + Al: potential acidity, Cstock: soil carbon stock, CEC: cation exchange capacity, Macro: macroporosity, Micro: microporosity, BD: soil bulk density, FA: fulvic acid, HA: humic acid, MBC: soil microbial biomass carbon, BSR: basal soil respiration.</p>
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16 pages, 2933 KiB  
Perspective
New Approach to Experimental Soil Health Definition Using Thermogravimetric Fingerprinting
by Ina Krahl, David Tokarski, Jiri Kučerík, Elisabeth Schwitzky and Christian Siewert
Agronomy 2025, 15(2), 487; https://doi.org/10.3390/agronomy15020487 - 18 Feb 2025
Viewed by 200
Abstract
Degradation and sealing are still frequent in soil management today despite intensive research. An unsatisfactory assessment of soil key components and soil health still limits sustainable land use. For the future evaluation of soil health, soils under productive use have been compared with [...] Read more.
Degradation and sealing are still frequent in soil management today despite intensive research. An unsatisfactory assessment of soil key components and soil health still limits sustainable land use. For the future evaluation of soil health, soils under productive use have been compared with natural and semi-natural soils using thermogravimetric fingerprinting of air-dried soil samples. This approach has led to a more precise quantification of known relationships and the discovery of several new ones between soil components that have evolved over thousands of years of soil formation without human intervention, each changing in a specific way due to land use. The use-related deviations from the natural soil condition allow a distinction between natural soils, disturbed soils, and soil-like carbon-containing mineral mixtures (e.g., compost, horticultural substrates). Carbon added to soils with fresh organic residues or from anthropogenic (soot, slag) or geological (coal) sources can be distinguished from soil organic matter (humus) accumulated during soil genesis, regardless of extreme chemical heterogeneity. The degree of carbon sequestration in soils is easy to quantify. Using near-natural soils as a reference, considering bound water seems to be a suitable starting point for the experimental definition of soil health. An elucidation of the causal relationships between the soil components used should accompany it. Full article
(This article belongs to the Special Issue Soil Health and Properties in a Changing Environment)
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<p>Example of natural eutrophication with tall herbaceous vegetation in highly productive watershed forests of the Salair Mountains (Western Siberia) without any human influence on apparently low-fertility soils (retisols) in a temperate climate.</p>
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<p>Origin of soil samples with different human impacts from lowland (green) and mountainous areas (brown) during different study periods.</p>
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<p>Mean dynamics of mass losses of air-dried soil samples conditioned at 76% relative air humidity with selected temperature areas of mass losses closely related to clay and soil organic carbon contents (SOC), Siewert 2004 [<a href="#B28-agronomy-15-00487" class="html-bibr">28</a>].</p>
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<p>Relationship between clay-dependent thermal mass losses in natural soils and deviations caused by different amendments (based on data from Siewert and Kučerík 2015 [<a href="#B29-agronomy-15-00487" class="html-bibr">29</a>]).</p>
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<p>Predictability of thermal mass losses (TMLs) between 110 °C and 550 °C using mass losses in two 10 °C temperature increase intervals correlating with organic carbon and clay contents in near-natural soil samples from different climatic regions (Siewert and Kučerík 2015 [<a href="#B29-agronomy-15-00487" class="html-bibr">29</a>]).</p>
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29 pages, 5116 KiB  
Article
Biodegradable Microplastics from Agricultural Mulch Films: Implications for Plant Growth-Promoting Bacteria and Plant’s Oxidative Stress
by Bruno Carneiro, Paula Marques, Tiago Lopes and Etelvina Figueira
Antioxidants 2025, 14(2), 230; https://doi.org/10.3390/antiox14020230 - 18 Feb 2025
Viewed by 305
Abstract
This study explores the interactions between biodegradable (BIO) microplastics and plant growth-promoting bacteria (PGPB), assessing their effects on soil health and crop productivity. Five bacterial strains, Bacillus, Enterobacter, Kosakonia, Rhizobium, and Pseudomonas, were exposed to BIO microplastics to [...] Read more.
This study explores the interactions between biodegradable (BIO) microplastics and plant growth-promoting bacteria (PGPB), assessing their effects on soil health and crop productivity. Five bacterial strains, Bacillus, Enterobacter, Kosakonia, Rhizobium, and Pseudomonas, were exposed to BIO microplastics to examine strain-specific responses. This study revealed that while most bacteria experienced growth inhibition, Kosakonia sp. O21 was poorly affected by BIO microplastics, indicating a potential for microplastic degradation. This study further investigated the effect of these microplastics on plant growth and biochemistry. Results showed that exposure to BIO microplastics significatively reduced plant growth and caused oxidative stress, affecting membranes and proteins and inducing the activity of glutathione S-transferases (GSTs), catalase (CAT), and superoxide dismutase (SOD) as antioxidant responses. Bacterial inoculation alleviated plant oxidative stress, especially at lower concentrations of microplastics. These findings emphasize the critical role of oxidative stress in mediating the negative effects of BIO microplastics on plants and the relevance of bacterial strains that can tolerate BIO microplastics to protect plants from BIO microplastics’ effects. Results also highlight the importance of extending research to assess the long-term implications of biodegradable microplastics for soil PGPBs and plant health and crop productivity. This study contributes to sustainable agricultural practices by offering insights into mitigating the risks of microplastic pollution through microbial-based interventions. Full article
(This article belongs to the Section Antioxidant Enzyme Systems)
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<p>Proportion of biodegradable (BIO) microparticles with different sizes after production. Microparticles were separated by size in 6 fractions: &gt;2.8 mm; 2.8–2 mm; 2–1 mm; 1–0.5 mm; 0.5–0.25 mm; and &lt;0.25 mm.</p>
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<p>SEM images of biodegradable (BIO) microplastics; (<b>a</b>) BIO 2–1 mm (50×); (<b>a.1</b>) BIO 2–1 mm (1000×); (<b>a.2</b>) BIO 2–1 mm (1000×)—View with protuberances; (<b>b</b>) BIO 1–0.5mm (150×); (<b>b.1</b>) BIO 1–0.5 mm (600×)—Detail of a protuberance; (<b>c</b>) BIO 0.5–0.25 mm (40×); (<b>c.1</b>) BIO 0.5–0.25 mm (1000×)—Detail of the edge; (<b>c.2</b>) BIO 0.5–0.25mm (1000×)—detail of the surface; (<b>d</b>) BIO &lt; 0.25 mm (600×); (<b>d.1</b>) BIO &lt; 0.25 mm (1500×); (<b>e</b>) BIO UV (250×); (<b>e.1</b>) BIO UV (1200×)—Edge of the particle; (<b>e.2</b>) BIO UV (1000×)—Surface of the particle. White arrows indicate silicate particles (confirmed by EDS); black arrows indicate plastic protuberances.</p>
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<p>ATR-FTIR spectra of BIO microplastics and weathered BIO microplastics.</p>
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<p>Thermogravimetric analysis of BIO microplastics and weathered BIO microplastics.</p>
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<p>Bacterial growth. Effect of BIO microplastics exposure on protein content of distinct bacterial strains. <span class="html-italic">Bacillus</span> sp. J25 (blue circles), <span class="html-italic">Enterobacter ludwigii</span> sp. C11 (orange squares), <span class="html-italic">Kosakonia</span> sp. O21 (green triangles), <span class="html-italic">Rhizobium</span> E-20-8 (teal inverted triangles), and <span class="html-italic">Pseudomonas</span> sp. S22 (purple diamonds). BIO concentration of 1% (<span class="html-italic">w</span>/<span class="html-italic">v</span>). Particle sizes are grouped by the intervals of 2.8–2 mm; 2–1 mm; 1–0.5 mm; 0.5–0.25 mm; and &lt;0.25 mm. Values are means of five replicates + standard error. Growths significantly different (<span class="html-italic">p</span> &lt; 0.05) from the respective control (same bacterial strain not exposed to BIO microplastics) are marked with an asterisk.</p>
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<p>Bacterial growth with BIO microplastics as sole source of carbon. Growth estimated by protein content of <span class="html-italic">Bacillus</span> sp. J25 (blue), <span class="html-italic">Enterobacter ludwigii</span> sp. C11 (orange), and <span class="html-italic">Kosakonia</span> sp. O21 (Green) exposed to minimal medium (C) or supplemented with 0.1% BIO particles with size &lt;0.25 mm (BIO). Values are means of five replicates + standard error. Growths significantly different (<span class="html-italic">p</span> &lt; 0.05) from the respective control (same bacterial strain not exposed to BIO) are marked with an asterisk.</p>
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<p>Plant growth. Shoot (<b>a</b>) and root (<b>b</b>) growth of lettuce plants not inoculated or inoculated with <span class="html-italic">Kosakonia</span> sp. O21 grown at different concentrations of biodegradable (BIO) microplastics. Ni—no bacterial inoculation; B—inoculation with bacteria. Values are means of at least five replicates ± standard error. Different uppercase letters indicate significant differences (<span class="html-italic">p</span> &lt; 0.05) among conditions in plants inoculated with bacteria, different lowercase letters indicate significant differences among conditions in plants not inoculated, and asterisks indicate significant differences between inoculated and not inoculated plants for the same condition.</p>
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<p>Biochemical parameters of the roots of not inoculated (dark bars) or inoculated (green bars) lettuce plants grown with biodegradable (BIO) microplastics at different concentrations (0% to 5%) in different inoculation conditions (Ni—no bacterial inoculation; B—bacterial inoculation with <span class="html-italic">Kosakonia</span> sp. O21); (<b>a</b>) Protein content; (<b>b</b>) Protein Carbonylation; (<b>c</b>) Superoxide dismutase activity (SOD); (<b>d</b>) Catalase activity (CAT); (<b>e</b>) Soluble carbohydrates; (<b>f</b>) Lipid peroxidation (LPO); (<b>g</b>) electron transport system activity (ETS); (<b>h</b>) Glutathione S-transferase (GST); (<b>i</b>) Principal coordinates ordination of biochemical parameters in the roots of inoculated and non-inoculated plants. Values are means of five replicates + standard error. Different lowercase letters indicate significant differences (<span class="html-italic">p</span> &lt; 0.05) among conditions in non-inoculated plants, different uppercase letters indicate significant differences among conditions in inoculated plants, and asterisks indicate significant differences between inoculated and non-inoculated plants for the same BIO concentration.</p>
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<p>Photosynthetic pigments of the shoots of not inoculated (dark bars) or inoculated (green bars) lettuce plants grown with biodegradable microplastics (BIO) at different concentrations (0% to 5%) in different inoculation conditions (Ni—no bacterial inoculation; B—bacterial inoculation with <span class="html-italic">Kosakonia</span> sp. O21); (<b>a</b>) Chlorophyll a; (<b>b</b>) Chlorophyll b; (<b>c</b>) Carotenoids. Values are means of five replicates + standard error. Different lowercase letters indicate significant differences (<span class="html-italic">p</span> &lt; 0.05) among conditions in non-inoculated plants, different uppercase letters indicate significant differences among conditions in inoculated plants, and asterisks indicate significant differences between inoculated and non-inoculated plants for the same BIO concentration.</p>
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<p>Biochemical parameters of the shoots of not inoculated (dark bars) or inoculated (green bars) lettuce plants grown with biodegradable microplastics (BIO) at different concentrations (0% to 5%) in different inoculation conditions (Ni—no bacterial inoculation; B—bacterial inoculation with <span class="html-italic">Kosakonia</span> sp. O21); (<b>a</b>) Protein content; (<b>b</b>) Protein Carbonylation; (<b>c</b>) Superoxide dismutase activity (SOD); (<b>d</b>) Catalase activity (CAT); (<b>e</b>) Soluble carbohydrates; (<b>f</b>) Lipid peroxidation (LPO); (<b>g</b>) electron transport system activity (ETS); (<b>h</b>) Glutathione S-transferase (GST); (<b>i</b>) Principal coordinates ordination of biochemical parameters in the roots of inoculated and non-inoculated plants. Values are means of five replicates + standard error. Different lowercase letters indicate significant differences (<span class="html-italic">p</span> &lt; 0.05) among conditions in non-inoculated plants, different uppercase letters indicate significant differences among conditions in inoculated plants, and asterisks indicate significant differences between inoculated and non-inoculated plants for the same BIO concentration.</p>
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14 pages, 882 KiB  
Article
The Effects of Reducing Nitrogen and Increasing Density in the Main Crop on Yield and Cadmium Accumulation of Ratoon Rice
by Qinqin Tian, Dechao Zheng, Pingping Chen, Shuai Yuan and Zhenxie Yi
Agronomy 2025, 15(2), 485; https://doi.org/10.3390/agronomy15020485 - 17 Feb 2025
Viewed by 136
Abstract
Rice cultivated in cadmium (Cd)-polluted acidic paddy soil poses important health risks in China. Mitigating Cd accumulation in rice is of crucial importance for food safety and human health. In this study, using Chuangliangyou 669 as the ratoon rice variety, a field experiment [...] Read more.
Rice cultivated in cadmium (Cd)-polluted acidic paddy soil poses important health risks in China. Mitigating Cd accumulation in rice is of crucial importance for food safety and human health. In this study, using Chuangliangyou 669 as the ratoon rice variety, a field experiment was conducted in paddy fields with severe Cd pollution (Cd concentration > 1.0 mg kg−1). The aim was to explore the impacts of different nitrogen (N) fertilizer levels (N1-180 kg hm−2, N2-153 kg hm−2, N3-126 kg hm−2) and planting densities (D1-20 cm × 20 cm, D2-16.7 cm × 16.7 cm) in the main crop on the yield and Cd accumulation characteristics of ratoon rice. The results showed that reducing the amount of N fertilizer would lead to a decrease in the yield of ratoon rice, while increasing the planting density could increase the yield, mainly by increasing the effective panicle. Among the various combined treatments, the yields of N1M2 and N2M2 were relatively high. The planting density had no significant impact on the Cd concentration, translocation factor and bioaccumulation factor of ratoon rice. The Cd concentration in various tissues of ratoon rice decreased significantly with the reduction in N fertilizer application. Reducing N fertilizer application could increase the pH, reduce the concentration of available Cd in the soil and consequently reduce the Cd bioaccumulation factor of various tissues of ratoon rice and the Cd translocation factor from roots and stems to brown rice. Considering both the yield and the Cd concentration in brown rice, N2M2 was the optimal treatment of reducing N and increasing density, which could maintain a relatively high yield while significantly reducing the Cd concentration. Full article
(This article belongs to the Section Soil and Plant Nutrition)
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<p>Effects of N fertilizer and density on the soil available Cd concentration. N1: N application amount 180 kg hm<sup>−2</sup>; N2: N application amount 153 kg hm<sup>−2</sup>; N3: N application amount 126 kg hm<sup>−2</sup>; D1: planting density 20 cm × 20 cm; D2: planting density 16.7 cm × 16.7 cm. Different lowercase letters above boxes indicate significant differences among the same group under different treatments (<span class="html-italic">n</span> = 3, <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Effects of N fertilizer and density on the soil pH. N1: N application amount 180 kg hm<sup>−2</sup>; N2: N application amount 153 kg hm<sup>−2</sup>; N3: N application amount 126 kg hm<sup>−2</sup>; D1: planting density 20 cm × 20 cm; D2: planting density 16.7 cm × 16.7 cm. Different lowercase letters above boxes indicate significant differences among the same group under different treatments (<span class="html-italic">n</span> = 3, <span class="html-italic">p</span> &lt; 0.05).</p>
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20 pages, 2582 KiB  
Review
Recent Advances in Transcriptome Analysis Within the Realm of Low Arsenic Rice Breeding
by Guanrong Huang, Guoping Yu, Huijuan Li, Haipeng Yu, Zengying Huang, Lu Tang, Pengfei Yang, Zhengzheng Zhong, Guocheng Hu, Peng Zhang and Hanhua Tong
Plants 2025, 14(4), 606; https://doi.org/10.3390/plants14040606 - 17 Feb 2025
Viewed by 183
Abstract
Arsenic (As), a toxic element, is widely distributed in soil and irrigation water. Rice (Oryza sativa L.), the staple food in Southern China, exhibits a greater propensity for As uptake compared to other crops. Arsenic pollution in paddy fields not only impairs [...] Read more.
Arsenic (As), a toxic element, is widely distributed in soil and irrigation water. Rice (Oryza sativa L.), the staple food in Southern China, exhibits a greater propensity for As uptake compared to other crops. Arsenic pollution in paddy fields not only impairs rice growth but also poses a serious threat to food security and human health. Nevertheless, the molecular mechanism underlying the response to As toxicity has not been completely revealed until now. Transcriptome analysis represents a powerful tool for revealing the mechanisms conferring phenotype formation and is widely employed in crop breeding. Consequently, this review focuses on the recent advances in transcriptome analysis within the realm of low As breeding in rice. It particularly highlights the applications of transcriptome analysis in identifying genes responsive to As toxicity, revealing gene interaction regulatory modules and analyzing secondary metabolite biosynthesis pathways. Furthermore, the molecular mechanisms underlying rice As tolerance are updated, and the recent outcomes in low As breeding are summarized. Finally, the challenges associated with applying transcriptome analysis to low-As breeding are deliberated upon, and future research directions are envisioned, with the aim of providing references to expedite high-yield and low-arsenic breeding in rice. Full article
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<p>Molecular mechanisms of As uptake, transport and metabolism in rice. NIPs consist of proteins encoded by genes <span class="html-italic">NIP1;1</span>, <span class="html-italic">NIP3;1</span>, <span class="html-italic">NIP3;2</span>, <span class="html-italic">NIP3;3</span>, <span class="html-italic">NIP2;1</span> (<span class="html-italic">Lsi1</span>) and <span class="html-italic">NIP2;2</span> (<span class="html-italic">Lsi2</span>). PIPs consist of proteins encoded by genes <span class="html-italic">PIP1;2</span>, <span class="html-italic">PIP1;3</span>, <span class="html-italic">PIP2;4</span>, <span class="html-italic">PIP2;6</span> and <span class="html-italic">PIP2;7</span>. PTs consist of proteins encoded by genes <span class="html-italic">PT1</span>, <span class="html-italic">PT2</span>, <span class="html-italic">PT4</span> and <span class="html-italic">PT8</span>. ABCs consist of proteins encoded by genes <span class="html-italic">ABCC1</span>, <span class="html-italic">ABCC2</span> and <span class="html-italic">ABCC7</span>. PCSs consist of proteins encoded by genes PCS1 and PCS2. Grxs consist of proteins encoded by genes <span class="html-italic">Grx_C2.1</span> and <span class="html-italic">Grx_C7</span>. HACs consist of proteins encoded by genes <span class="html-italic">HAC1;1</span>, <span class="html-italic">HAC1;2</span> and <span class="html-italic">HAC4</span>. VOZs consist of proteins encoded by genes <span class="html-italic">VOZ1</span> and <span class="html-italic">VOZ2</span>. For details, please see <a href="#plants-14-00606-t002" class="html-table">Table 2</a>. As(V), As(III), MMA, DMA, TMA, PC, GSH, NIPs, PIPs, PTs, ABCs, PCSs, Grxs, HACs and VOZs are abbreviations for arsenite, arsenate, monomethyl arsenic acid, dimethyl arsenic acid, trimethyl arsenic acid, phytochelatin, glutathione, nodulin 26-like intrinsic proteins, plasma membrane intrinsic proteins, phosphate transporters, C-type ATP-binding cassette transporters, phytochelatin synthase, glutaredoxins, high As concentrations and transcription factor VOZs, respectively.</p>
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<p>The flow diagram of WGCNA.</p>
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<p>The application of transcriptome analysis in the realm of low-As rice breeding.</p>
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16 pages, 2397 KiB  
Article
Significant Enrichment of Potential Pathogenic Fungi in Soil Mediated by Flavonoids, Phenolic Acids, and Organic Acids
by Shaoguan Zhao, Yan Sun, Lanxi Su, Lin Yan, Xingjun Lin, Yuzhou Long, Ang Zhang and Qingyun Zhao
J. Fungi 2025, 11(2), 154; https://doi.org/10.3390/jof11020154 - 17 Feb 2025
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Abstract
It is well established that root exudates play a crucial role in shaping the assembly of plant rhizosphere microbial communities. Nonetheless, our understanding of how different types of exudates influence the abundance of potential pathogens in soil remains insufficient. Investigating the effects of [...] Read more.
It is well established that root exudates play a crucial role in shaping the assembly of plant rhizosphere microbial communities. Nonetheless, our understanding of how different types of exudates influence the abundance of potential pathogens in soil remains insufficient. Investigating the effects of root exudates on soil-dwelling pathogenic fungi is imperative for a comprehensive understanding of plant–fungal interactions within soil ecosystems and for maintaining soil health. This study aimed to elucidate the effects of the principal components of root exudates—flavonoids (FLA), phenolic acids (PA), and organic acids (OA)—on soil microbial communities and soil properties, as well as to investigate their mechanisms of action on soil potential pathogenic fungi. The results demonstrated that the addition of these components significantly modified the composition and diversity of soil microbial communities, with OA treatment notably altering the composition of dominant microbial taxa. Furthermore, the introduction of these substances facilitated the proliferation of saprophytic fungi. Additionally, the incorporation of flavonoids, phenolic acids, and organic acids led to an increased abundance of potential pathogenic fungi in the soil, particularly in the FLA and PA treatments. It was observed that the addition of these substances enhanced soil fertility, pH, and antioxidant enzyme activity. Specifically, FLA and PA treatments reduced the abundance of dominant microbial taxa, whereas OA treatment altered the composition of these taxa. These findings suggest that the inclusion of flavonoids, phenolic acids, and organic acids could potentially augment the enrichment of soil potential pathogenic fungi by modulating soil properties and enzymatic activities. These results offer valuable insights into the interactions between plants and fungal communities in soil ecosystems and provide a scientific foundation for the management and maintenance of soil health. Full article
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<p>Based on the OTU level, the composition of fungal community in different treatments was analyzed. (<b>a</b>) The Venn diagram of different treatments was constructed according to the OTU number. (<b>b</b>) The difference in fungal community diversity among different treatments was analyzed based on the OTU level. (<b>c</b>) PCoA analysis was conducted to analyze the composition of fungal communities in different treatments. (<b>d</b>) The main classification of fungal communities at the genus level in different treatments was analyzed. “*” means <span class="html-italic">p</span> &lt; 0.05 and “**” means <span class="html-italic">p</span> &lt; 0.01 by Kruskal–Wallis rank-sum test. CK: 20% sterile methanol solution, FLA: flavonoids; OA: organic acids, PA: phenolic acids.</p>
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<p>The genus level composition of fungal communities in different treatments was analyzed using heatmap visualization and ternary analysis. (<b>a</b>) The top 20 genera of each treatment were depicted in the heatmap. (<b>b</b>) Ternary analysis was also employed to evaluate and compare the fungal communities between different treatments at the genus level. (<b>c</b>) Significant changes in four major pathogenic fungal genera across different treatments. Different small letters represent significant differences between different treatments. CK: 20% sterile methanol solution, FLA: flavonoids; OA: organic acids, PA: phenolic acids.</p>
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<p>Changes in the main pathogenic fungi genera in fungal communities under different treatments and their relationship with soil factors. (<b>a</b>) Network heatmap showing the association between four major pathogenic fungal genera and soil enzyme activity. (<b>b</b>,<b>c</b>) are redundancy analysis (RDA) plots, showing the distribution of fungal genera and chemical indices on the RDA1 and RDA2 axes. Arrows represent the direction and intensity of chemical indices, dots represent different fungal genera, and the significance level is <span class="html-italic">p</span> = 0.004. “*” indicates <span class="html-italic">p</span> &lt; 0.05, “**” indicates <span class="html-italic">p</span> &lt; 0.01, and “***” indicates <span class="html-italic">p</span> &lt; 0.001, for both the Pearson correlation and Mantel test.</p>
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<p>Functional predictions of fungal communities under different treatments were analyzed. (<b>a</b>) Clustering analysis was used to determine the functional groups in fungal communities under different treatments. (<b>b</b>) Principal Coordinate Analysis (PCoA) was used to evaluate the functional diversity and composition of fungal communities under different treatments. (<b>c</b>) The differences among the three functional groups across different treatments. Different small letters represent significant differences between different treatments. CK: 20% sterile methanol solution, FLA: flavonoids, OA: organic acids, PA: phenolic acids.</p>
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28 pages, 2501 KiB  
Review
Algae and Cyanobacteria Fatty Acids and Bioactive Metabolites: Natural Antifungal Alternative Against Fusarium sp.
by Miguel E. López-Arellanes, Lizbeth Denisse López-Pacheco, Joel H. Elizondo-Luevano and Georgia María González-Meza
Microorganisms 2025, 13(2), 439; https://doi.org/10.3390/microorganisms13020439 - 17 Feb 2025
Viewed by 229
Abstract
Fungal diseases caused by Fusarium spp. significantly threaten food security and sustainable agriculture. One of the traditional strategies for eradicating Fusarium spp. incidents is the use of chemical and synthetic fungicides. The excessive use of these products generates environmental damage and has negative [...] Read more.
Fungal diseases caused by Fusarium spp. significantly threaten food security and sustainable agriculture. One of the traditional strategies for eradicating Fusarium spp. incidents is the use of chemical and synthetic fungicides. The excessive use of these products generates environmental damage and has negative effects on crop yield. It puts plants in stressful conditions, kills the natural soil microbiome, and makes phytopathogenic fungi resistant. Finally, it also causes health problems in farmers. This drives the search for and selection of natural alternatives, such as bio-fungicides. Among natural products, algae and cyanobacteria are promising sources of antifungal bio-compounds. These organisms can synthesize different bioactive molecules, such as fatty acids, phenolic acids, and some volatile organic compounds with antifungal activity, which can damage the fungal cell membrane that surrounds the hyphae and spores, either by solubilization or by making them porous and disrupted. Research in this area is still developing, but significant progress has been made in the identification of the compounds with potential for controlling this important pathogen. Therefore, this review focuses on the knowledge about the mechanisms of action of the fatty acids from macroalgae, microalgae, and cyanobacteria as principal biomolecules with antifungal activity, as well as on the benefits and challenges of applying these natural metabolites against Fusarium spp. to achieve sustainable agriculture. Full article
(This article belongs to the Section Antimicrobial Agents and Resistance)
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<p>(<b>a</b>,<b>b</b>) Microscopic views of Mexican corn isolates. (<b>c</b>) <span class="html-italic">F. oxysporum</span> at 40×.</p>
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<p>Fusarium life cycle: from soil to plant wilting.</p>
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<p>Diagram with the steps of primary recovery of bioactive molecules of macro-, micro-, and cyanobacteria biomass.</p>
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<p>Antifungal mechanism of unsaturated fatty acids from macro-, micro-, and cyanobacteria in the cell membrane of <span class="html-italic">Fusarium</span> spp.</p>
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