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Search Results (2,099)

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27 pages, 2666 KiB  
Review
Farming Practice Variability and Its Implications for Soil Health in Agriculture: A Review
by Elsadig Omer, Dora Szlatenyi, Sándor Csenki, Jomana Alrwashdeh, Ivan Czako and Vince Láng
Agriculture 2024, 14(12), 2114; https://doi.org/10.3390/agriculture14122114 - 22 Nov 2024
Viewed by 278
Abstract
Soil health is essential for sustainable agricultural operations, as it supports farm production and ecosystem services. The adoption of sustainable agriculture practices such as conservation tillage, cover cropping, and crop rotation provides significant benefits for both crop productivity and environmental sustainability. These practices [...] Read more.
Soil health is essential for sustainable agricultural operations, as it supports farm production and ecosystem services. The adoption of sustainable agriculture practices such as conservation tillage, cover cropping, and crop rotation provides significant benefits for both crop productivity and environmental sustainability. These practices can increase soil biodiversity, nutrient cycling, and organic matter, which increase the resilience of agroecosystems. This narrative review synthesizes the insights of the soil health practices adoption literature, with a focus on common farming practices that can improve soil health and enhance crop yields, reviewing the results of various approaches and pointing out the challenges and opportunities for implementing sustainable agriculture on a larger scale. This paper discusses the effects of various tillage and cropping system approaches on soil health, including no-till and conventional tillage systems, crop rotation, cover cropping, cultivator combinations, and fertilizer application. This study found that conservation tillage is more beneficial to soil health than conventional tillage—which is still debated among scientists and farmers—and that different tillage methods interact differently. In contrast, agricultural yields increase more with intercropping, crop rotation, and cover crops than monocropping. For maintaining soil fertility, this study shows that agricultural yields could be increased by implementing zero tillage. This review identifies the most suitable farming practices for improving soil health while boosting crop production with minimal negative impact on the soil. It also highlights the benefits of these practices in maintaining soil quality. Full article
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Figure 1
<p>Principles of soil health recommended by USDA-NRCS.</p>
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<p>Linking soil health to ecosystem services.</p>
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<p>Soil management techniques or control measures for sustainable agriculture.</p>
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<p>Optimal physical, biological, and chemical properties promote soil health.</p>
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<p>Pumping and cycling of nutrients through the building of a “safety net” by enhancing organic matter inputs, accessing deep soil nutrients, and improving soil structure, through carful management is required to balance resource competition in agroforestry system, by [<a href="#B189-agriculture-14-02114" class="html-bibr">189</a>].</p>
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<p>Knowledge gaps exist in our current understanding of soil health.</p>
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17 pages, 2845 KiB  
Article
Sulfur Induces As Tolerance in Barley Plants
by Mar Gil-Díaz, Juan Alonso, Carolina Mancho, Pilar García-Gonzalo and M. Carmen Lobo
Agriculture 2024, 14(12), 2110; https://doi.org/10.3390/agriculture14122110 - 22 Nov 2024
Viewed by 177
Abstract
The use of sulfur (S) in polluted soils can reduce metal(loid) toxicity and enhance phytoremediation effectiveness. Here we studied the response of barley plants to As in soil amended with sulfate or elemental sulfur throughout the growing cycle. A greenhouse experiment was carried [...] Read more.
The use of sulfur (S) in polluted soils can reduce metal(loid) toxicity and enhance phytoremediation effectiveness. Here we studied the response of barley plants to As in soil amended with sulfate or elemental sulfur throughout the growing cycle. A greenhouse experiment was carried out using 4-L pots filled with clay-loam soil spiked with 60 mg kg−1 As (Na2HAsO4·7H2O). Two chemical forms of sulfur (elemental sulfur (S0) or sulfate (CaSO4·2H2O)) were applied at a dose of 1 and 3 Mg ha−1, respectively, and two previously seeded barley plants were transplanted in each pot, using eight pots per treatment. At the end of the growing cycle, the biomass, nutrients, and metal(loid) content, as well as several physiological and biochemical parameters of the plants were analyzed. Moreover, the effect of the treatments on soil characteristics was also evaluated, including soil pore water. The treatment with sulfur promoted the growth of barley plants through their vegetative cycle, enhancing photosynthesis, although biomass did not significantly increase. Both sources of S promoted the accumulation of As in the root, thereby limiting its translocation to the aerial part of the plant, sulfate being more effective (an increase of 300%) than elemental S (an increase of 82%). The addition of S decreased soil pH. Furthermore, both treatments, but particularly sulfate, increased soluble sulfate and stimulated soil biological properties. In conclusion, the application of sulfate to As-polluted soil can enhance As phytostabilization by barley plants while simultaneously improving the biological properties of the soil. Full article
(This article belongs to the Special Issue Risk Assessment and Remediation of Agricultural Soil Pollution)
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<p>Arsenic mean concentration in the TCLP extract and standard deviation at two sampling times: at tillering (t1) and at the end of the experiment (t2). Bars with the same letter do not differ significantly (<span class="html-italic">p</span> &lt; 0.05); lower letters for t1 and upper letters for t2.</p>
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<p>Characteristics of pore water (mean values and standard deviation): (<b>A</b>) As concentration, (<b>B</b>) pH, and (<b>C</b>) electrical conductivity. Bars with the same letter do not differ significantly (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Effect of the treatments on soil respiration and enzyme activities in rhizosphere soil samples. Bars with the same letter do not differ significantly (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Effect of the treatments on soil respiration and enzyme activities in rhizosphere soil samples. Bars with the same letter do not differ significantly (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Mean values and standard deviation of height, SPAD index, chlorophyll fluorescence, malondialdehyde content, and biomass of the barley plants in the different treatments. Bars with the same letter do not differ significantly (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Arsenic concentration in the root, stem, and grain of barley plants (<b>A</b>), and translocation factor (<b>B</b>). Bars with the same letter do not differ significantly (<span class="html-italic">p</span> &lt; 0.05), lower letters for root, upper letters for stem, and Greek letters for grain.</p>
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<p>Arsenic concentration in the root, stem, and grain of barley plants (<b>A</b>), and translocation factor (<b>B</b>). Bars with the same letter do not differ significantly (<span class="html-italic">p</span> &lt; 0.05), lower letters for root, upper letters for stem, and Greek letters for grain.</p>
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<p>Sulfur concentration in the root, stem, and grain of barley plants. Bars with the same letter do not differ significantly (<span class="html-italic">p</span> &lt; 0.05).</p>
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18 pages, 3634 KiB  
Article
Insights into the Driving Factors of Methane Emission from Double-Season Rice Field Under Different Fertilization Practices in South China
by Jin Zheng, Yusheng Lu, Peizhi Xu, Kaizhi Xie, Changmin Zhou, Yaying Li, Haoyang Geng, Qianyuan Wang and Wenjie Gu
Agronomy 2024, 14(12), 2767; https://doi.org/10.3390/agronomy14122767 - 21 Nov 2024
Viewed by 304
Abstract
Paddy fields are the main agricultural source of greenhouse gas methane (CH4) emissions. To enhance rice yield, various fertilization practices have been employed in rice paddies. However, the key microbial and abiotic factors driving CH4 emissions under different fertilization practices [...] Read more.
Paddy fields are the main agricultural source of greenhouse gas methane (CH4) emissions. To enhance rice yield, various fertilization practices have been employed in rice paddies. However, the key microbial and abiotic factors driving CH4 emissions under different fertilization practices in paddy fields remain largely uncharted. This study conducted field experiments in a traditional double-cropping rice area in South China, utilizing five different fertilization practices to investigate the key factors influencing CH4 emissions. High-throughput sequencing and PICRUSt2 functional prediction were employed to investigate the contributions of soil physicochemical properties, CH4-metabolizing microorganisms (methanogens and methanotrophs), and key genes (mcrA and pmoA) on CH4 emissions. The results showed that CH4 emission fluxes exhibited seasonal variations, with consistent patterns of change observed across all treatments for both early- and late-season rice. Compared to the no-fertilization (NF) treatment, cumulative CH4 emissions were lower in early-season rice with green manure (GM) and straw returning (SR) treatments, as well as in late-season rice with GM treatment, while rice yields were maintained at higher levels. High-throughput sequencing analysis revealed that potential methanogens were primarily distributed among four orders: Methanobacteriales, Methanocellales, Methanomicrobiales, and Methanosarcinales. Furthermore, there was a significant positive correlation between the relative abundance of the CH4-related key gene mcrA and these microorganisms. Functional analysis indicated that these potential methanogens primarily produce methane through the acetoclastic and hydrogenotrophic pathways. Aerobic CH4-oxidizing bacteria, predominantly from the genus Methylocystis, were detected in all the treatments, while the CH4 anaerobic-oxidizing archaea ANME-1b was only detected in chemical fertilization (CF) and cow manure (CM) treatments. Our random forest analysis revealed that the relative abundance of two methanogens (Methanocellales and Methanosarcinales) and two environmental factors (pH and DOC) had significant impacts on the cumulative CH4 emissions. The variance decomposition analysis highlighted the CH4-metabolizing microorganisms explained 50% of the variance in the cumulative CH4 emissions, suggesting that they are the key microbial factors driving CH4 emissions. These findings provide guidance for the development of rational measures to reduce CH4 emissions in paddy fields. Full article
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<p>Rice yields, seasonal variations in CH<sub>4</sub> flux and the cumulative CH<sub>4</sub> emissions of different fertilization treatments: (<b>a</b>,<b>b</b>) Rice yields of early- and late-season rice in different fertilization treatments. (<b>c</b>,<b>d</b>) Seasonal variations in CH<sub>4</sub> flux of early- and late-season rice in different growth cycles. (<b>e</b>,<b>f</b>) Cumulative CH<sub>4</sub> emissions of early- and late-season rice in different fertilization treatments. Different letters (a, b, c) indicate significant differences in the mean value of each chemical property among different treatments (Duncan’s multiple range test, <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Differences in microbial communities in the paddy soils with different fertilization treatments: (<b>a</b>,<b>b</b>) The relative abundance (%) of the main phyla taxa (top 10) from different fertilization soils in early- and late-season rice. (<b>c</b>–<b>h</b>) Alpha-diversity indices of microbial communities in different fertilization soils in early- and late-season rice. Different letters (a, b) indicate significant differences in the mean value of each chemical property among different treatments (Duncan’s multiple range test, <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Differences in CH<sub>4</sub>-metabolizing microorganisms in the paddy soils with different fertilization treatments: (<b>a</b>,<b>b</b>) The relative abundance (%) of CH<sub>4</sub>-metabolizing microorganisms from different fertilization soils in early- and late-season rice. (<b>c</b>–<b>f</b>) The relative abundance (%) of CH<sub>4</sub>-metabolizing genes <span class="html-italic">mcrA</span> and <span class="html-italic">pmoA</span> from different fertilization soils in early- and late-season rice. Different letters (a, b, c) indicate significant differences in the mean value of each chemical property among different treatments (Duncan’s multiple range test, <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>The relationship between CH<sub>4</sub>-metabolizing microorganisms and soil properties in all samples: (<b>a</b>) Spearman’s correlations between the relative abundances of methanogenic and methanotrophic microorganisms and <span class="html-italic">mcrA</span> and <span class="html-italic">pmoA</span> gene and soil properties. (<b>b</b>) Spearman’s correlations between the relative abundances of methanogenic and methanotrophic microorganisms and the <span class="html-italic">mcrA</span> and <span class="html-italic">pmoA</span> genes. Spearman’s correlation coefficient, R &gt; 0.7. significant correlation; * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01. (<b>c</b>) Distance-based redundancy analysis (RDA) diagram showing the correlations between methanogenic and methanotrophic microorganisms and <span class="html-italic">mcrA</span> and <span class="html-italic">pmoA</span> genes and soil properties. The soil property abbreviations are given in <a href="#agronomy-14-02767-t001" class="html-table">Table 1</a>. A Mantel test was conducted to evaluate the correlations between methanogenic and methanotrophic microorganisms and <span class="html-italic">mcrA</span> and <span class="html-italic">pmoA</span> genes and soil properties.</p>
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<p>Influences of biotic and abiotic factors on cumulative CH<sub>4</sub> emissions: (<b>a</b>) Relative importance of microbial abundances, gene abundances, and environmental factors in explaining cumulative CH<sub>4</sub> emissions according to the random forest analysis. (<b>b</b>) Relative importance of microbial abundances, gene abundances, and environmental factors in explaining cumulative CH<sub>4</sub> emissions according to the variance decomposition analysis. <span class="html-italic">p</span> &lt; 0.001 indicates that the whole model exhibits an extremely significant level. <span class="html-italic">p</span> &lt; 0.05, represented by the asterisks, indicates the factors that have a significant contribution to the cumulative CH<sub>4</sub> emissions.</p>
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<p>Distribution of genes responsible for methanogenesis and CH<sub>4</sub>-oxidation varies in soils treated with different fertilization regimes for early- and late-season rice. The heatmap was scaled according to rows within each group. Apart from the CH<sub>4</sub>-metabolizing pathways depicted in the figure, genes including <span class="html-italic">mcr</span>, <span class="html-italic">mtr</span>, <span class="html-italic">mer</span>, <span class="html-italic">mch</span>, <span class="html-italic">ftr</span>, and <span class="html-italic">fwd</span> play crucial roles in the archaeal anaerobic CH<sub>4</sub>-oxidation.</p>
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20 pages, 10852 KiB  
Article
Impact of Grazing Tibetan Pigs on Soil Quality
by Guoxin Wu, Haoqi Wang, Mengqi Duan, Licuo Ze, Shixiong Dong, Huimin Zhang, Kejun Wang, Zhankun Tan and Peng Shang
Agriculture 2024, 14(11), 2096; https://doi.org/10.3390/agriculture14112096 - 20 Nov 2024
Viewed by 216
Abstract
Pig manure, as an organic fertilizer, can significantly affect soil nutrient content, pH, and electrical conductivity. Moreover, the accumulation of heavy metals in pig manure and their potential ecological risks are also important concerns in soil management. Additionally, grazing systems may influence soil [...] Read more.
Pig manure, as an organic fertilizer, can significantly affect soil nutrient content, pH, and electrical conductivity. Moreover, the accumulation of heavy metals in pig manure and their potential ecological risks are also important concerns in soil management. Additionally, grazing systems may influence soil health and ecological balance by altering the soil microbial community structure. Therefore, this study investigates the impact of grazing Tibetan pigs on soil quality, focusing on the physicochemical properties, heavy metal accumulation, and microbial diversity. In the surface soil after grazing (GS0), pH, EC, AP, and AK were significantly higher than before grazing (NS0) (p < 0.05), while AN showed no significant difference. In the 10 cm soil layer, pH, EC, AK, and AN in GS10 were significantly higher than in NS10 (p < 0.05), whereas AP was significantly lower (p < 0.05). At the 20 cm depth, pH, EC, AP, and AK in GS20 were significantly higher than in NS20 (p < 0.05), but AN was significantly lower (p < 0.05). Overall, AN, AP, and AK decreased with increasing soil depth, while pH and EC showed no significant changes between the 10 cm and 20 cm layers (p > 0.05). In GS0 soil, the contents of Cd(II) and Zn(II) were significantly lower than those in NS0 (p < 0.05), while Pb(II) content was significantly higher (p < 0.05). There were no significant differences in Cu(II), Ni(II), Cr(VI), As(V), and Hg(II) (p > 0.05). In GS10 soil, Ni and Pb(II) contents were higher, whereas Cu(II), Zn(II), and Hg(II) contents were lower. In GS20 soil, Pb(II) and Cr(VI) contents were higher, while Cu(II) and Zn(II) contents were lower. Overall, GS had consistently lower Cd(II), Cu(II), Zn(II), and Hg(II) contents at all depths compared to NS, while Pb(II) and Cr(VI) contents were higher, showing depth-related variation trends, possibly due to plant absorption and heavy metal leaching. Probiotics such as Firmicutes, Bacteroidetes, and Acinetobacter increased significantly in soil, resulting in changes in the soil bacterial community. Full article
(This article belongs to the Section Agricultural Soils)
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<p>Aerial view of sample collection site.</p>
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<p>Changes in physical and chemical properties of soil in different soil layers. (<b>A</b>) The depth of the soil is 0 cm. (<b>B</b>) The depth of the soil is 10 cm. (<b>C</b>) The depth of the soil is 20 cm. (<b>D</b>) Line chart of content change in physical and chemical properties in different soil layers. * <span class="html-italic">p</span> &lt; 0.05; *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Changes in heavy metal content in soil. (<b>A</b>) The depth of the soil is 0 cm. (<b>B</b>) The depth of the soil is 10 cm. (<b>C</b>) The depth of the soil is 20 cm. (<b>D</b>) Line chart of heavy metal content change in different soil layers. * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Venn diagrams and sample viability analysis. (<b>A</b>–<b>D</b>) Overlaps of Venn diagrams indicate shared OTUs in different groups. (<b>E</b>,<b>F</b>) Sparse curves were used to assess the adequacy of sequencing for each sample, with each curve representing an example. One sample is shown for each curve. (<b>G</b>) Hierarchical abundance curves assess the homogeneity and abundance of species contained in the samples, with each curve representing one sample.</p>
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<p>The alpha diversity indices of soil microbial communities across different groups. Good’s Coverage (<b>A</b>) reflects the sequencing saturation of the samples. The PD whole tree index (<b>B</b>) assesses phylogenetic diversity based on the evolutionary characteristics of OTU sequences. Chao1 and Ace indices (<b>C</b>,<b>D</b>) focus on species richness within the samples. Simpson and Shannon indices (<b>E</b>,<b>F</b>) combine species richness and evenness.</p>
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<p>Assessing the beta diversity of soil microbiota. (<b>A</b>) PCA analysis based on species abundance information from OTU lists to study the relationship between bacterial community structure between samples. The soil microflora structure at the phylum level (<b>B</b>) and genus level (<b>C</b>) was analyzed by PCoA scatter plots based on the Bray–Curtis distance algorithm.</p>
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<p>Stacked maps of the relative abundance of soil microbiota at the phylum and genus levels. (<b>A</b>,<b>B</b>) represent taxon assignments at the phylum and genus levels, respectively.</p>
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<p>Heatmaps displaying the top 20 shared phyla (<b>A</b>) and genera (<b>B</b>) across different groups. In each heatmap, rows represent species and columns represent samples. The color of each block indicates the species’ relative abundance in a sample.</p>
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<p>Differences in soil microbiota abundance between groups at the phylum level. Results were assessed by one-way ANOVA. All data are expressed as mean ± SD. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Differences in soil microbiota abundance between groups at the genus level. Results were assessed by one-way ANOVA. All data are expressed as mean ± SD. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>The UPGMA clustering tree of soil microorganisms (<b>A</b>) and the functional difference analyses between NS0 and GS0 (<b>B</b>), NS10 and GS10 (<b>C</b>), and NS20 and GS20 (<b>D</b>) based on the KEGG functional hierarchy. Differences in gene distribution between groups were determined using ANOVA with <span class="html-italic">p</span> ≤ 0.05.</p>
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16 pages, 4595 KiB  
Article
Effects of Two Trichoderma Strains on Apple Replant Disease Suppression and Plant Growth Stimulation
by Wen Du, Pengbo Dai, Mingyi Zhang, Guangzhu Yang, Wenjing Huang, Kuijing Liang, Bo Li, Keqiang Cao, Tongle Hu, Yanan Wang, Xianglong Meng and Shutong Wang
J. Fungi 2024, 10(11), 804; https://doi.org/10.3390/jof10110804 - 20 Nov 2024
Viewed by 339
Abstract
Fusarium oxysporum, the pathogen responsible for apple replant disease (ARD), is seriously threatening the apple industry globally. We investigated the antagonistic properties of Trichoderma strains against F. oxysporum HS2, aiming to find a biological control solution to minimize the dependence on chemical [...] Read more.
Fusarium oxysporum, the pathogen responsible for apple replant disease (ARD), is seriously threatening the apple industry globally. We investigated the antagonistic properties of Trichoderma strains against F. oxysporum HS2, aiming to find a biological control solution to minimize the dependence on chemical pesticides. Two of the thirty-one Trichoderma strains assessed through plate confrontation assays, L7 (Trichoderma atroviride) and M19 (T. longibrachiatum), markedly inhibited = F. oxysporum, with inhibition rates of 86.02% and 86.72%, respectively. Applying 1 × 106 spores/mL suspensions of these strains notably increased the disease resistance in embryonic mung bean roots. Strains L7 and M19 substantially protected Malus robusta Rehd apple rootstock from ARD; the plant height, stem diameter, leaf number, chlorophyll content, and defense enzyme activity were higher in the treated plants than in the controls in both greenhouse and field trials. The results of fluorescent labeling confirmed the effective colonization of these strains of the root soil, with the number of spores stabilizing over time. At 56 days after inoculation, the M19 and L7 spore counts in various soils confirmed their persistence. These results underscore the biocontrol potential of L7 and M19 against HS2, offering valuable insights into developing sustainable ARD management practices. Full article
(This article belongs to the Section Fungal Pathogenesis and Disease Control)
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<p>Antagonistic observation of biocontrol <span class="html-italic">Trichoderma</span> strains L7 and M19 against <span class="html-italic">F. oxysporum</span> HS2. The morphology of L7 and M19 Petri dishes is observed on the (<b>left</b>), showing a distinct light yellow antagonistic zone forming at the mycelial intersection, with <span class="html-italic">Trichoderma</span> gradually covering <span class="html-italic">F. oxysporum</span> HS2. The red boxes indicate the confrontation observation zones. In the (<b>middle</b>), microscopic observation reveals that the test strains cause twisting, collapsing, and rupturing of HS2 mycelia during the parasitism process. On the (<b>right</b>), scanning electron microscope images show <span class="html-italic">Trichoderma</span> strains coiling around the mycelia of HS2.</p>
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<p>Identification of the tested <span class="html-italic">Trichoderma</span> isolates. (<b>A</b>) Colony morphology and microscopic observation of the tested <span class="html-italic">Trichoderma</span> isolates. L7 has circular and velvety colonies with light green conidia and slender mycelia. The phialides are slender, and the conidia are nearly spherical or ovoid, measuring 3.0–4.5 μm and 2.5–4.0 μm. M19 exhibits light green conidia with colonies radiating outward from the center, showing high sporulation rates centrally. The mycelia are tree-like, and the oval-shaped conidia measure 2.0–3.0 μm and 2.0–6.0 μm. (<b>B</b>) Phylogenetic trees of two <span class="html-italic">Trichoderma</span> strains constructed based on ITS sequences. Phylogenetic tree constructed by the neighbor-joining method based on ITS sequences. The percentage of replicate trees in which the associated taxa clustered together in the bootstrap test (1000 replicates) is shown next to the branches. The tree is drawn to scale, with branch lengths in the same units as those of the evolutionary distances used to infer the phylogenetic tree. The evolutionary distances were computed using the Poisson correction method and are in the units of the number of amino acid substitutions per site. Based on the tree, strain M19 clustered within the <span class="html-italic">T. longibrachiatum</span> branch, while L7 clustered within the <span class="html-italic">T. atroviride</span> branch. All positions containing gaps and missing data were eliminated. Evolutionary analyses were conducted in MEGA6.</p>
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<p>Effect of <span class="html-italic">Trichoderma</span> on the growth of <span class="html-italic">M. robusta</span> Rehd seedlings. (<b>A</b>) Determination of growth indexes of <span class="html-italic">Trichoderma</span> on <span class="html-italic">M. robusta</span> Rehd. Labels (<b>a</b>–<b>f</b>) represent seedling height, root length, fresh weight, root fresh weight, leaf number, and chlorophyll content, respectively. Values with superscript letters a and b are significanty diferent across columns (<span class="html-italic">p</span> &lt; 0.05). Results showed significant improvements in <span class="html-italic">M. robusta</span> Rehd seedling parameters after treatment with strains M19 and L7 compared to the control (CK). (<b>B</b>) The effect of <span class="html-italic">Trichoderma</span> on the growth of <span class="html-italic">M. robusta</span> Rehd. CK represents <span class="html-italic">M. robusta</span> Rehd seedlings treated with only water, L7 represents seedlings treated with L7 spore suspension, and M19 represents seedlings treated with M19 spore suspension.</p>
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<p>The effect of <span class="html-italic">Trichoderma</span> on the activity of defense enzymes in the roots of <span class="html-italic">M. robusta</span> seedlings. (<b>a</b>) SOD activity, (<b>b</b>) CAT activity, (<b>c</b>) PAL activity, and (<b>d</b>) root activity. CAT activity, SOD activity, PAL activity, and root vitality were all higher in <span class="html-italic">M. robusta</span> Rehd seedlings treated with the two <span class="html-italic">Trichoderma</span> strains compared to CK. Values with superscript letters a and b are significanty diferent across columns (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Effect of <span class="html-italic">Trichoderma</span> on <span class="html-italic">M. robusta</span> Rehd seedlings in normal cropping soil (60 days). (<b>A</b>) Growth of <span class="html-italic">M. robusta</span> Rehd seedlings in normal cropping soil for 60 days. CK represents <span class="html-italic">M. robusta</span> Rehd seedlings treated with only water, L7 represents seedlings treated with L7 spore suspension, and M19 represents seedlings treated with M19 spore suspension. The treatment of <span class="html-italic">Trichoderma</span> spore suspension in normal cropping soil significantly increased seedling height and demonstrated a strong growth-promoting effect. (<b>B</b>) Determination of physiological indexes of <span class="html-italic">M. robusta</span> Rehd seedlings growing in normal cropping soil for 60 days. Labels (<b>a</b>–<b>d</b>) represent seedling height, stem diameter, chlorophyll content, and leaf number, respectively. Values with superscript letters a and b are significanty diferent across columns (<span class="html-italic">p</span> &lt; 0.05). Significant enhancements in seedling height, leaf number, chlorophyll content, and root health were noted, indicating a strong growth-promoting effect.</p>
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<p>Effect of <span class="html-italic">Trichoderma</span> on <span class="html-italic">M. robusta</span> Rehd seedlings in continuous cropping soil (60 days). (<b>A</b>) Growth of <span class="html-italic">M. robusta</span> Rehd plants in continuous cropping soil for 60 days. CK represents <span class="html-italic">M. robusta</span> Rehd seedlings treated with only water, L7 represents seedlings treated with L7 spore suspension, and M19 represents seedlings treated with M19 spore suspension. The treatment of <span class="html-italic">Trichoderma</span> spore suspension in continuous cropping soil significantly increased seedling height and demonstrated a strong growth-promoting effect. (<b>B</b>) Determination of physiological indexes of <span class="html-italic">M. robusta</span> Rehd seedlings growing in continuous cropping soil for 60 days. Labels (<b>a</b>–<b>d</b>) represent seedling height, stem diameter, chlorophyll content, and leaf number, respectively. Values with superscript letters a, b and c are significanty diferent across columns (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Fluorescence observation and colonization status of two transformants in soil suspension and <span class="html-italic">M. robusta</span> Rehd root soil. (<b>A</b>) Fluorescence observed by two transformants in soil suspension. (<b>a</b>) Represents normal cropping soil; (<b>b</b>) represents continuous cropping soil; L7, M19, and MOCK are fluorescence of L7 transformant in soil, fluorescence of M19 transformant in soil, and CK of soil. Samples were taken after root drenching treatment, diluted 100 times, and fluorescence was observed under a fluorescence microscope. (<b>B</b>) Colonization status of two transformants in the soil of <span class="html-italic">M. robusta</span> Rehd root. Over time, the spore counts of the marked strains fluctuated before stabilizing. Notably, the colonization spore count of strain M19 was higher than that of strain L7 in both soil types.</p>
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23 pages, 2426 KiB  
Review
Biorefinery and Bioremediation Strategies for Efficient Management of Recalcitrant Pollutants Using Termites as an Obscure yet Promising Source of Bacterial Gut Symbionts: A Review
by Rongrong Xie, Blessing Danso, Jianzhong Sun, Majid Al-Zahrani, Mudasir A. Dar, Rania Al-Tohamy and Sameh S. Ali
Insects 2024, 15(11), 908; https://doi.org/10.3390/insects15110908 - 20 Nov 2024
Viewed by 374
Abstract
Lignocellulosic biomass (LCB) in the form of agricultural, forestry, and agro-industrial wastes is globally generated in large volumes every year. The chemical components of LCB render them a substrate valuable for biofuel production. It is hard to dissolve LCB resources for biofuel production [...] Read more.
Lignocellulosic biomass (LCB) in the form of agricultural, forestry, and agro-industrial wastes is globally generated in large volumes every year. The chemical components of LCB render them a substrate valuable for biofuel production. It is hard to dissolve LCB resources for biofuel production because the lignin, cellulose, and hemicellulose parts stick together rigidly. This makes the structure complex, hierarchical, and resistant. Owing to these restrictions, the junk production of LCB waste has recently become a significant worldwide environmental problem resulting from inefficient disposal techniques and increased persistence. In addition, burning LCB waste, such as paddy straws, is a widespread practice that causes considerable air pollution and endangers the environment and human existence. Besides environmental pollution from LCB waste, increasing industrialization has resulted in the production of billions of tons of dyeing wastewater from several industries, including textiles, pharmaceuticals, tanneries, and food processing units. The massive use of synthetic dyes in various industries can be detrimental to the environment due to the recalcitrant aromatic structure of synthetic dyes, similar to the polymeric phenol lignin in LCB structure, and their persistent color. Synthetic dyes have been described as possessing carcinogenic and toxic properties that could be harmful to public health. Environmental pollution emanating from LCB wastes and dyeing wastewater is of great concern and should be carefully handled to mitigate its catastrophic effects. An effective strategy to curtail these problems is to learn from analogous systems in nature, such as termites, where woody lignocellulose is digested by wood-feeding termites and humus-recalcitrant aromatic compounds are decomposed by soil-feeding termites. The termite gut system acts as a unique bioresource consisting of distinct bacterial species valued for the processing of lignocellulosic materials and the degradation of synthetic dyes, which can be integrated into modern biorefineries for processing LCB waste and bioremediation applications for the treatment of dyeing wastewaters to help resolve environmental issues arising from LCB waste and dyeing wastewaters. This review paper provides a new strategy for efficient management of recalcitrant pollutants by exploring the potential application of termite gut bacteria in biorefinery and bioremediation processing. Full article
(This article belongs to the Special Issue Ecologically Important Symbioses in Insects)
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<p>Volume of lignocellulose biomass waste generated in China.</p>
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<p>Structure of lignocellulose biomass.</p>
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<p>Quantity of wastewater produced by textile industries.</p>
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<p>Representation of natural lignocellulolytic systems and their conversion efficiency for the three constituted polymers of lignocellulose.</p>
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<p>Representation of natural lignocellulolytic systems and their conversion efficiency for the three constituted polymers of lignocellulose.</p>
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<p>Termite guts system of lower and higher termites. Foregut (F) and midgut (M), the hindgut of higher termites is increasingly elongated compared to lower termites and may be differentiated into a mixed segment (ms) and several proctodeal compartments (P1–P5).</p>
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18 pages, 6181 KiB  
Article
The Colonization of Synthetic Microbial Communities Carried by Bio-Organic Fertilizers in Continuous Cropping Soil for Potato Plants
by Wenming Zhang, Shiqing Li, Pingliang Zhang, Xuyan Han, Yanhong Xing and Chenxu Yu
Microorganisms 2024, 12(11), 2371; https://doi.org/10.3390/microorganisms12112371 - 20 Nov 2024
Viewed by 278
Abstract
Synthetic microbial communities (SynComs) play significant roles in soil health and sustainable agriculture. In this study, bacterial SynComs (SCBs) and fungal SynComs (SCFs) were constructed by selecting microbial species that could degrade the potato root exudates associated with continuous cropping obstacles. SCBs, SCFs, [...] Read more.
Synthetic microbial communities (SynComs) play significant roles in soil health and sustainable agriculture. In this study, bacterial SynComs (SCBs) and fungal SynComs (SCFs) were constructed by selecting microbial species that could degrade the potato root exudates associated with continuous cropping obstacles. SCBs, SCFs, and SCB + SCF combinations were then inoculated into organic fertilizers (OFs, made from sheep manure) to produce three bio-organic fertilizers (BOFs), denoted by SBFs (BOFs of inoculated SCBs), SFFs (BOFs of inoculated SCFs), and SBFFs (BOFs of inoculated SCB + SCF combinations), respectively. The OF and three BOFs, with a chemical fertilizer (CK) as the control, were then used in pot experiments involving potato growth with soil from a 4-year continuous cropping field. Microbial diversity sequencing was used to investigate the colonization of SCBs and SCFs into the rhizosphere soil and the bulk soil, and their effects on soil microbial diversity were evaluated. Source Tracker analysis showed that SCBs increased bacterial colonization from the SBFs into the rhizosphere soil, but at a relatively low level of 1% of the total soil bacteria, while SCFs increased fungi colonization from the SFF into the bulk soil at a much higher level of 5–18% of the total soil fungi. In combination, SCB + SCF significantly increased fungi colonization from the SBFF into both the bulk soil and the rhizosphere soil. Overall, the soil fungi were more susceptible to the influence of the BOFs than the bacteria. In general, the application of BOFs did not significantly change the soil microbial alpha diversity. Correlation network analysis showed that key species of bacteria were stable in the soils of the different groups, especially in the rhizosphere soil, while the key species of fungi significantly changed among the different groups. LEfSe analysis showed that the application of BOFs activated some rare species, which were correlated with improvements in the function categories of the tolerance of stress, nitrogen fixation, and saprotroph functions. Mantel test analysis showed that the BOFs significantly affected soil physicochemical properties, influencing bacterial key species, and core bacteria, promoting potato growth. It was also noted that the presence of SynCom-inoculated BOFs may lead to a slight increase in plant pathogens, which needs to be considered in the optimization of SynCom applications to overcome continuous cropping obstacles in potato production. Full article
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<p>Source Tracker analysis of SynCom colonization into the BOF (SBF: BOF inoculated with SCBs; SFF: BOF inoculated with SCFs; SBFF: BOF inoculated with SCB + SCF).</p>
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<p>Source Tracker analysis of microbial colonization from BOF into bulk soil (B) and rhizosphere soil (R): (<b>a</b>). soil bacteria; (<b>b</b>). soil fungi (OF: organic fertilize; SBF: BOF inoculated with SCBs; SFF: BOF inoculated with SCFs; SBFF: BOF inoculated with SCB + SCF).</p>
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<p>Effect of application of BOFs on soil microbial alpha diversity and species composition (B: bulk soil; R: rhizosphere soil: (<b>a</b>), bacteria, (<b>b</b>): fungi). CK: no organic fertilizers applied; F: organic fertilizer applied; FSB: SBF applied; FSF: SFF applied; FSBF: SBFF applied.</p>
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<p>Effect of bio-organic fertilizer implication on soil microbial beta diversity (B: bulk soil; R: rhizosphere soil: (<b>a</b>): bacteria, (<b>b</b>): fungi). Ellipses represent a 95% confidence interval. CK: no organic fertilizers applied; F: organic fertilizer applied; FSB: SBF applied; FSF: SFF applied; FSBF: SBFF applied.</p>
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<p>The correlation network diagram of each group (B: bulk soil; R: rhizosphere soil: (<b>a</b>), bacteria, (<b>b</b>): fungi). The correlation network was constructed as Spearman’s correlation, with a genera relative abundance of &gt;0.1%, r &gt; 0.8, and <span class="html-italic">p</span> &lt; 0.05. Red lines represent significant positive relationships, and green lines denote negative relationships. The genera shown on these figures are the module hub (key species) in each network diagram (ZI (within-module connectivity), with &gt;2.5 and PI (among-module connectivity) &lt; 0.62. CK: no organic fertilizers applied; F: organic fertilizer applied; FSB: SBF applied; FSF: SFF applied; FSBF: SBFF applied.</p>
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<p>Line discriminant analysis (LDA) effect size (LEfSe) analysis (B: bulk soil; R: rhizosphere soil; (<b>a</b>), bacteria, (<b>b</b>): fungi). CK: no organic fertilizers applied; F: organic fertilizer applied; FSB: SBF applied; FSF: SFF applied; FSBF: SBFF applied. The figures show the genera with LDA scores greater than 3.0.</p>
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<p>Mantel test analysis of plant growth, environmental factors, and microbial community (biomarkers were analyzed according to <a href="#microorganisms-12-02371-f006" class="html-fig">Figure 6</a>, key species were analyzed according to <a href="#microorganisms-12-02371-f005" class="html-fig">Figure 5</a>, and the fraction of the core bacterial/fungal species analyzed with a Veen plot are shown in <a href="#microorganisms-12-02371-f003" class="html-fig">Figure 3</a>). *, **, and *** represent <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, respectively; a dark green square represents significant positive relationships, and a pink square represents negative relationships. The darker the color or the larger the square area, the greater the absolute value of the correlation coefficient; red lines represent <span class="html-italic">p</span> &lt; 0.01, green lines represent 0.01 &lt; <span class="html-italic">p</span> &lt; 0.05, blue lines represent <span class="html-italic">p</span> ≥  0.05, — represents a positive relationship, --- represents a negative relationship, and the width of the line represents the magnitude of the correlation coefficient.</p>
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<p>Function prediction of microbial community (R: rhizosphere soil; B: bulk soil). CK: no organic fertilizers applied; F: organic fertilizer applied; FSB: SBF applied; FSF: SFF applied; FSBF: SBFF applied.</p>
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14 pages, 8709 KiB  
Article
Effect of Flax By-Products on the Mechanical and Cracking Behaviors of Expansive Soil
by Georgy Lazorenko, Anton Kasprzhitskii, Vasilii Mischinenko, Alexandr Fedotov and Ekaterina Kravchenko
Materials 2024, 17(22), 5659; https://doi.org/10.3390/ma17225659 - 20 Nov 2024
Viewed by 263
Abstract
Expansive soils, prone to significant volume changes with moisture fluctuations, challenge engineering infrastructure due to their swelling and shrinking. Traditional stabilization methods, including mechanical and chemical treatments, often have high material and environmental costs. This study explores fibrous by-products of flax processing, a [...] Read more.
Expansive soils, prone to significant volume changes with moisture fluctuations, challenge engineering infrastructure due to their swelling and shrinking. Traditional stabilization methods, including mechanical and chemical treatments, often have high material and environmental costs. This study explores fibrous by-products of flax processing, a sustainable alternative, for reinforcing expansive clay soil. Derived from the Linum usitatissimum plant, flax fibers offer favorable mechanical properties and environmental benefits. The research evaluates the impact of flax tow (FT) reinforcement on enhancing soil strength and reducing cracking. The results reveal that incorporating up to 0.6% randomly distributed FTs, consisting of technical flax fibers and shives, significantly improves soil properties. The unconfined compressive strength (UCS) increased by 29%, with 0.6% FT content, reaching 525 kPa, compared to unreinforced soil and further flax tow additions, which led to a decrease in UCS. This reduction is attributed to diminished soil–fiber interactions and increased fiber clustering. Additionally, flax tows effectively reduce soil cracking. The crack length density (CLD) decreased by 6% with 0.4% FTs, and higher concentrations led to increased cracking. The crack index factor (CIF) decreased by 71% with 0.4% flax tows but increased with higher FT concentrations. Flax tows enhance soil strength and reduce cracking while maintaining economic and environmental efficiency, offering a viable solution for stabilizing expansive clays in geotechnical applications. Full article
(This article belongs to the Section Green Materials)
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<p>Grain size distribution curve of the selected soil.</p>
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<p>Preparation and application of scutched flax fiber [<a href="#B31-materials-17-05659" class="html-bibr">31</a>].</p>
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<p>Raw/untreated flax tows used in this study (<b>a</b>), SEM image (<b>b</b>) and EDS spectrum of fiber surface (<b>c</b>).</p>
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<p>Graphical procedure of measuring the cracks of the clay surface.</p>
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<p>Variation in shear stress with horizontal displacement for FT-reinforced soil.</p>
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<p>Effect of flax tow content on soil shear strength.</p>
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<p>Failure mode of (<b>a</b>) unreinforced and (<b>b</b>) FT-reinforced expansive soil samples in the simple shear.</p>
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<p>Axial stress–strain behavior of unreinforced and FT-reinforced clay.</p>
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<p>Effect of flax tow reinforcement on the unconfined compressive strength.</p>
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<p>Failure mode of (<b>a</b>) unreinforced and (<b>b</b>) FT-reinforced expansive soil samples in UCS test.</p>
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<p>The impact of flax tow inclusion on clay crack parameters.</p>
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<p>Final crack patterns of (<b>a</b>) unreinforced and (<b>b</b>) FT-reinforced expansive soil samples in desiccation tests.</p>
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13 pages, 4506 KiB  
Article
Identification of Key Soil Quality Indicators for Predicting Mean Annual Increment in Pinus patula Forest Plantations in Tanzania
by Joshua Maguzu, Salim M. Maliondo, Ilstedt Ulrik and Josiah Zephaniah Katani
Forests 2024, 15(11), 2042; https://doi.org/10.3390/f15112042 - 19 Nov 2024
Viewed by 298
Abstract
There is an unexplored knowledge gap regarding the relationship between soil quality and mean annual increment (MAI) in forest plantations in Tanzania. Therefore, this study aimed to identify soil quality indicators and their impact on the mean annual increment (MAI) of Pinus patula [...] Read more.
There is an unexplored knowledge gap regarding the relationship between soil quality and mean annual increment (MAI) in forest plantations in Tanzania. Therefore, this study aimed to identify soil quality indicators and their impact on the mean annual increment (MAI) of Pinus patula at Sao Hill (SHFP) and Shume forest plantations (SFP) in Tanzania. The forests were stratified into four site classes based on management records. Tree growth data were collected from 3 quadrat plots at each site, resulting in 12 plots in each plantation, while soil samples were taken from 0 to 40 cm soil depth. Analysis of variance examined the variation in soil quality indicators between site classes at two P. patula plantation sites. Covariance analysis assessed the differences in MAI and stand variables across various site classes, taking into account the differing ages of some stands, with stand age serving as a covariate. Linear regression models explored the relationship between soil quality indicators and MAI, while partial least squares regression predicted MAI using soil quality indicators. The results showed that, at SHFP, sand, organic carbon (OC), cation exchange capacity, calcium (Ca), magnesium (Mg), and available P varied significantly between site classes, while silt, clay, and available P varied significantly at SFP. At SHFP, sand and clay content were positively correlated with MAI, while at SFP, silt content, available P (Avail P), potassium (K), Ca, and Mg showed significant positive correlations. Soil quality indicators, including physical and chemical properties (porosity, clay percentages, sand content, and OC) and only chemical (K, Mg, Avail P, and soil pH) properties were better predictors of the forest mean annual increment at SHFP and SFP, respectively. This study underscores the importance of monitoring the quality of soils in enhancing MAI and developing soil management strategies for long-term sustainability in forests production. Full article
(This article belongs to the Special Issue Forest Soil Physical, Chemical, and Biological Properties)
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<p>Locations of soil sampling plots at Sao Hill and Shume Forest Plantations in Tanzania.</p>
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<p>Variation of soil physical properties among the site classes. Plotted above the error line, distinct lowercase letters signify significant differences among site classes at Sao Hill and Shume Forest Plantations.</p>
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<p>Variation of soil chemical properties among the site classes. Plotted above the error line, distinct lowercase letters signify significant differences among site classes at Sao Hill and Shume Forest Plantations in Tanzania.</p>
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<p>Distribution difference of mean annual increment and other stand attributes in different site classes. Above the error line, different small letters indicate that there are significant differences among different site classes.</p>
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<p>Illustrates the correlations between soil physical properties and mean annual increment. The fitted linear relationships reflect the potential relationship of MAI-physical properties interactions at Sao Hill and Shume Forest Plantations in Tanzania.</p>
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<p>Illustrates the correlations between soil chemical properties and mean annual increment. The fitted linear relationships reflect the potential relationship of MAI-chemical properties interactions at Sao Hill and Shume Forest Plantations in Tanzania.</p>
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14 pages, 4131 KiB  
Article
Utilizing Marble Waste for Soil Acidity Correction in Colombian Caribbean Agriculture: A Sustainability Assessment
by Johnny Oliver Corcho Puche, Brian William Bodah, Karen Esther Muñoz Salas, Hugo Hernández Palma, Suzi Huff Theodoro, Alcindo Neckel, Andrea Liliana Moreno-Ríos, Giana Mores, Caliane Christie Oliveira de Almeida Silva, Leila Dal Moro, Grace Tibério Cardoso and Claudete Gindri Ramos
Sustainability 2024, 16(22), 10076; https://doi.org/10.3390/su162210076 - 19 Nov 2024
Viewed by 442
Abstract
Agricultural industrial waste has demonstrated potential as a soil acidity corrector and fertilizer, in addition to reducing environmental impacts caused by inadequate waste disposal. Ornamental rock waste is a sustainable alternative as it contains essential elements for plant growth. (1) Background: this study [...] Read more.
Agricultural industrial waste has demonstrated potential as a soil acidity corrector and fertilizer, in addition to reducing environmental impacts caused by inadequate waste disposal. Ornamental rock waste is a sustainable alternative as it contains essential elements for plant growth. (1) Background: this study aims to evaluate using marble waste in SENA and the Gallo Crudo Quarry in Colombia as an acidity mitigator in soils cultivated with maize (Zea mays) in a greenhouse. (2) Method: four treatments were applied: T0: without marble dust—MD; three doses of MD (T1: 1.1 Mg of MD ha−1; T2: 2.2 Mg of MD ha−1; and T3: 3.3 Mg of MD ha−1). After 70 days, soil fertility analyses were carried out. (3) Results: The results show that the chemical properties of the soil improved with all treatments, mainly with T2, influencing the calcium (Ca), carbon (C), sulfur (S), and magnesium (Mg) contents. MD’s pH and Al + H values were higher than conventional treatments. The T2 treatment reduced soil acidity from 0.2 cmol + kg−1 to 0.0 cmol + kg−1 and increased pH to 7.91 compared to the control (5.4). The maize plants in the T2 treatment developed better, indicating that the dose of 2.2 Mg of MD ha−1 can replace commercial limestone. (4) Conclusions: This agroecological technique is an innovative alternative in Colombia, replicable in areas with ornamental rock reserves, benefiting the agricultural economy and contributing to target the Sustainable Development Goals, which promote sustainability, responsible management of natural resources, and a reduction in environmental impacts. Full article
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<p>(<b>A</b>) El Porvenir Agricultural and Biotechnology Center—SENA; (<b>B</b>) Gallo Crudo Quarry.</p>
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<p>(<b>A</b>) Marble exploration front; (<b>B</b>) marble sample.</p>
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<p>Greenhouse experiments.</p>
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<p>X-ray diffractogram of the MD used in the presented experiment.</p>
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<p>Nutrients are available in soils with different marble dust treatments. Notes: C: percentages (%), S and P concentrations measured in mg kg<sup>−1</sup>, and Ca, Mg, K, Al + H, and cation exchange capacity in cmol + kg<sup>−1</sup>, (* <span class="html-italic">p</span> &lt; 0.05). Standard errors of three replications are represented by vertical bars (I).</p>
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<p>Effects of treatments on the height of maize plants. Note: vertical bars (I) represent the standard error of three replications.</p>
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<p>Comparison of the size of maize plants in different treatments.</p>
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16 pages, 1395 KiB  
Article
Effects of Rubber Plantation Restoration in National Parks on Plant Diversity and Soil Chemical Properties
by Chunyan Du, Donghai Li, Weifeng Wang, Xiaobo Yang, Zhixiang Wu, Chuan Yang, Yingying Zhang, Qingmao Fu and Dongling Qi
Diversity 2024, 16(11), 701; https://doi.org/10.3390/d16110701 - 18 Nov 2024
Viewed by 349
Abstract
Plantations left for natural succession play a significant role in Tropical Rainforest National Parks. Studying the succession and restoration of plantations is crucial for achieving a park’s authenticity and integrity, as well as for maximizing its ecological functions. However, the changes in vegetation [...] Read more.
Plantations left for natural succession play a significant role in Tropical Rainforest National Parks. Studying the succession and restoration of plantations is crucial for achieving a park’s authenticity and integrity, as well as for maximizing its ecological functions. However, the changes in vegetation and soil properties during the natural succession of these decommissioned plantations remain unclear. In this study, we examined rubber [(Hevea brasiliensis (Willd. Ex A. Juss.) Muell. Arg] plantations in the Yinggeling area of the National Park of Hainan Tropical Rainforest. We used community surveys, field sampling, and soil property analyses to investigate the species richness, diversity, and species composition of the aboveground plant communities during three succession periods of rubber plantations left for natural succession, including 0 years (ZY), 3 years (TY), and 7 years (SY). The soil pH, total organic carbon, total nitrogen, total phosphorus, available phosphorus, nitrate nitrogen, ammonium nitrogen, and total potassium contents in the three succession periods were analyzed. These results showed that there were 92 species of understory plants in the decommissioned rubber plantations, belonging to 72 genera in 39 families. The highest number of understory plant species was found in the plantations with 3 years of natural succession, totaling 66 species from 49 genera in 29 families. The number of families, genera, and species followed the pattern TY > SY > ZY. The Margalef richness index (F), Simpson index (D), and Shannon–Wiener index (H) of understory plants in the 0-year succession plantations were significantly lower than those in the 3-year and 7-year succession plantations. However, there was no significant difference in the Pielou (EH) index among the succession gradients. The soil pH, nitrate nitrogen (NO3--N), and available phosphorus (AP) in the 0-year succession plantations were significantly higher than those in the 3-year and 7-year succession plantations. There were no significant differences in soil total nitrogen (TN), total phosphorus (TP), total organic carbon (TOC), and ammonium nitrogen (NH4+-N) across the three succession gradients. The soil total potassium (TK) in the 3-year succession plantations was significantly higher than that in the 0-year and 7-year succession plantations. Soil available phosphorus and total phosphorus (TP) were positively correlated with the Margalef index, Simpson index, Shannon–Wiener index, and Pielou index. The recovery rate of understory vegetation in decommissioned rubber plantations was faster than that of the soil. This indicates that the construction of the National Park of Hainan Tropical Rainforest has significantly promoted the recovery of the number of plant species and plant species diversity that have been left from rubber plantation operations. These findings not only deepen our understanding of soil property changes during the vegetation succession of artificial forests, particularly rubber plantations, but they also hold significant implications for guiding tropical forest management and sustainable development. Full article
(This article belongs to the Special Issue Biodiversity Conservation Planning and Assessment)
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<p>Species diversity of understory plants in rubber plantations with different succession times. Note: “ZY” represents a rubber plantation with 0 years of natural succession; “TY” represents a rubber plantation with 3 years of natural succession; “SY” represents a rubber plantation with 7 years of natural succession. Different letters (a, b) indicated significant difference at 0.05 level.</p>
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<p>Analysis of soil chemical properties of rubber plantations at different successional times. Note: “ZY”, “TY”, and “SY are the variables, and soil properties as explanatory variables. “ZY” represents a rubber plantation with 0 years of natural succession; “TY” represents a rubber plantation with 3 years of natural succession; “SY” represents a rubber plantation with 7 years of natural succession. Different letters (a, b) indicated significant difference at 0.05 level.</p>
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<p>RDA ranking of plant diversity and soil physicochemical properties in rubber plantation understory at different succession times. Note: “Pielou”, “Shannon–Wiener”, “Simpson”, and “Marglef” are the variables, and soil properties as explanatory variables.</p>
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<p>Comparison of the recovery rate of understory plants and soil chemical properties of rubber plantation at different succession times. Note: “ZY” represents a rubber plantation with 0 years of natural succession; “TY” represents a rubber plantation with 3 years of natural succession; “SY” represents a rubber plantation with 7 years of natural succession.</p>
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17 pages, 6741 KiB  
Article
Comprehensive Assessment of the Correlation Between Ancient Tea Garden Soil Chemical Properties and Tea Quality
by Houqiao Wang, Wenxia Yuan, Qiaomei Wang, Yuxin Xia, Wang Chun, Haoran Li, Guochen Peng, Wei Huang and Baijuan Wang
Horticulturae 2024, 10(11), 1207; https://doi.org/10.3390/horticulturae10111207 - 15 Nov 2024
Viewed by 386
Abstract
Understanding the correlation between soil chemical properties and tea quality is essential for the comprehensive management of ancient tea gardens. However, the specific links between these factors in ancient tea gardens remain underexplored. This study analyzes the soil chemical properties of four distinct [...] Read more.
Understanding the correlation between soil chemical properties and tea quality is essential for the comprehensive management of ancient tea gardens. However, the specific links between these factors in ancient tea gardens remain underexplored. This study analyzes the soil chemical properties of four distinct research regions in Nanhua County to explore their effects on key chemical components in ancient tea garden teas, providing a scientific basis for improving the quality of ancient tea garden teas through soil management. Employing high performance liquid chromatography (HPLC) and inductively coupled plasma mass spectrometry (ICP-MS), the chemical components of tea and the chemical properties of the soil were meticulously quantified. Following these measurements, the integrated fertility index (IFI) and the potential ecological risk index (PERI) were evaluated and correlation analysis was conducted. The results revealed that ancient tea garden tea quality is closely linked to soil chemical properties. Soil’s total nitrogen (TN), total sulfur (TS), and available potassium (AK) negatively correlate with tea’s catechin gallate (CG) component and AK also with polyphenols. Most other soil properties show positive correlations with tea components. The research also evaluated soil heavy metals’ IFI and PERI. IFI varied significantly among regions. Hg’s high pollution index indicates ecological risks; Cd in Xiaochun (XC) region poses a moderate risk. PERI suggests moderate risk for XC and Banpo (BP), with other areas classified as low risk. Implementing reasonable fertilization and soil amelioration measures to enhance soil fertility and ensure adequate supply of key nutrients will improve the quality of ancient tea gardens. At the same time, soil management measures should effectively control heavy metal pollution to ensure the quality and safety of tea products. Insights from this study are crucial for optimizing soil management in ancient tea gardens, potentially improving tea quality and sustainability. Full article
(This article belongs to the Special Issue Tea Tree: Cultivation, Breeding and Their Processing Innovation)
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<p>Representative ancient tea trees from four regions. In (<b>a</b>): represents the typical tea tree of the CLZ research region; In (<b>b</b>): represents the typical tea tree of the BP research region; In (<b>c</b>): represents the typical tea tree of the XC research region; In (<b>d</b>): represents the typical tea tree of the GLT research region.</p>
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<p>Analysis of the components of epicatechin distribution in four research regions. Note: ECG-epicatechin gallate. In (<b>a</b>): Percentage stacked bar chart of catechin composition; In (<b>b</b>): Radar chart of catechin composition.</p>
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<p>Box plots of ten soil properties at four research regions. In (<b>a</b>): Box plot of soil pH distribution in four research regions; In (<b>b</b>): Box plot of Soil Organic Matter distribution in four research regions; In (<b>c</b>): Box plot of soil Total Nitrogen distribution in four research regions; In (<b>d</b>): Box plot of soil Total Phosphorus distribution in four research regions; In (<b>e</b>): Box plot of soil Total Potassium distribution in four research regions; In (<b>f</b>): Box plot of soil Alkali-hydrolyzable nitrogen distribution in four research regions; In (<b>g</b>): Box plot of soil Available Phosphorus distribution in four research regions; In (<b>h</b>): Box plot of soil Available Potassium distribution in four research regions; In (<b>i</b>): Box plot of soil Cation Exchange Capacity distribution in four research regions; In (<b>j</b>): Box plot of soil Total Sulfur distribution in four research regions; In (<b>k</b>): Box plot of soil Exchanged magnesium distribution in four research regions; In (<b>l</b>): Box plot of soil Fluoride distribution in four research regions.</p>
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<p>The linear correlation between individual soil fertility indicators and the comprehensive IFI. In (<b>a</b>): Linear correlation between soil pH and Integrated fertility index in four research regions; In (<b>b</b>): Linear correlation between Soil Organic Matter and Integrated fertility index in four research regions; In (<b>c</b>): Linear correlation between Total Nitrogen and Integrated fertility index in four research regions; In (<b>d</b>): Linear correlation between Total Phosphorus and Integrated fertility index in four research regions; In (<b>e</b>): Linear correlation between Total Potassium and Integrated fertility index in four research regions; In (<b>f</b>): Linear correlation between Alkali-hydrolyzable nitrogen and Integrated fertility index in four research regions; In (<b>g</b>): Linear correlation between Available Phosphorus and Integrated fertility index in four research regions; In (<b>h</b>): Linear correlation between Available Potassium and Integrated fertility index in four research regions; In (<b>i</b>): Linear correlation between Cation Exchange Capacity and Integrated fertility index in four research regions; In (<b>j</b>): Linear correlation between Total Sulfur and Integrated fertility index in four research regions.</p>
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<p>Heavy metal element distribution analysis of four research regions soil layers including topsoil (0–20 cm, top) and subsoil (20–40 cm, sub). Soil heavy metal elements included As, Cu, Hg, Cd, Cr, Ni, Pb, and Zn. Note: Significant differences are indicated by different letters at the 0.05 level. In (<b>a</b>): Heavy metal As content in the 0–20cm and 20–40cm soil layers of four regions; In (<b>b</b>): Heavy metal Cd content in the 0–20cm and 20–40cm soil layers of four regions; In (<b>c</b>): Heavy metal Cu content in the 0–20cm and 20–40cm soil layers of four regions; In (<b>d</b>): Heavy metal Hg content in the 0–20cm and 20–40cm soil layers of four regions; In (<b>e</b>): Heavy metal Cr content in the 0–20cm and 20–40cm soil layers of four regions; In (<b>f</b>): Heavy metal Ni content in the 0–20cm and 20–40cm soil layers of four regions; In (<b>g</b>): Heavy metal Pb content in the 0–20cm and 20–40cm soil layers of four regions; In (<b>h</b>): Heavy metal Zn content in the 0–20cm and 20–40cm soil layers of four regions.</p>
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<p>PCA of soil properties and tea quality parameters across different soil layers. In (<b>a</b>): Principal component analysis of soil chemical properties and tea quality parameters in the 0–20 cm layer across four research regions; In (<b>b</b>): Principal component analysis of soil chemical properties and tea quality parameters in the 20–40 cm layer across four research regions.</p>
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18 pages, 4511 KiB  
Article
Spatial Variability of Soil CO2 Emissions and Microbial Communities in a Mediterranean Holm Oak Forest
by Claudia Di Bene, Loredana Canfora, Melania Migliore, Rosa Francaviglia and Roberta Farina
Forests 2024, 15(11), 2018; https://doi.org/10.3390/f15112018 - 15 Nov 2024
Viewed by 348
Abstract
Forests play a key role in the global carbon (C) cycle through multiple interactions between above-ground and soil microbial communities. Deeper insights into the soil microbial composition and diversity at different spatial scales and soil depths are of paramount importance. We hypothesized that [...] Read more.
Forests play a key role in the global carbon (C) cycle through multiple interactions between above-ground and soil microbial communities. Deeper insights into the soil microbial composition and diversity at different spatial scales and soil depths are of paramount importance. We hypothesized that in a homogeneous above-ground tree cover, the heterogeneous distribution of soil microbial functional diversity and processes at the small scale is correlated with the soil’s chemical properties. From this perspective, in a typical Mediterranean holm oak (Quercus ilex L.) peri-urban forest, soil carbon dioxide (CO2) emissions were measured with soil chambers in three different plots. In each plot, to test the linkage between above-ground and below-ground communities, soil was randomly sampled along six vertical transects (0–100 cm) to investigate soil physico-chemical parameters; microbial processes, measured using Barometric Process Separation (BaPS); and structural and functional diversity, assessed using T-RFLP and qPCR Real Time analyses. The results highlighted that the high spatial variability of CO2 emissions—confirmed by the BaPS analysis—was associated with the microbial communities’ abundance (dominated by bacteria) and structural diversity (decreasing with soil depth), measured by H′ index. Bacteria showed higher variability than fungi and archaea at all depths examined. Such an insight showed the clear ecological and environmental implications of soil in the overall sustainability of the peri-urban forest system. Full article
(This article belongs to the Special Issue Soil Organic Carbon and Nutrient Cycling in the Forest Ecosystems)
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<p>Daily mean air temperature monitored in the 0–24 h (°C) (shown as black continuous line), daily mean air temperature monitored at the time of soil CO<sub>2</sub> emissions measurements between 10 and 14 h (°C) (shown as black dotted line), and daily total rainfall (mm) (shown as gray column) over the soil CO<sub>2</sub> emissions monitoring period at the Castelporziano Reserve (Rome, Italy). A period was considered “dry” when the rainfall was equal to or less than twice the mean temperature (<b>a</b>). Daily mean soil temperature (°C) (shown as black dashed line) and daily mean soil water content (%, <span class="html-italic">v</span>:<span class="html-italic">v</span>) (shown as black continuous line) measured at 10 cm and 100 cm soil depth, respectively, over the soil CO<sub>2</sub> emissions monitoring period. Black and white circles represent daily mean soil temperature and daily mean water content, respectively, monitored at the time of soil CO<sub>2</sub> emissions measurements between 10 and 14 h (°C). (<b>b</b>) Soil CO<sub>2</sub> emissions (µmol CO<sub>2</sub> m<sup>−2</sup> s<sup>−1</sup>) were measured at the site in three plots (shown as plot 1: black triangle plot 2: white circle and plot 3: white diamond) during the period 6 June to 20 November 2013 with weekly or monthly soil CO<sub>2</sub> emissions monitoring (<span class="html-italic">n</span> = 12). Values are means ± SE (showed as vertical bars) of three replicates for each plot. For each measuring date, statistically significant differences among plots are shown by asterisks according to ANOVA (<span class="html-italic">* p</span> &lt; 0.05) (<b>c</b>).</p>
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<p>BaPS parameters measured at 0–20 cm layer: soil respiration rate (<span class="html-italic">RS</span>; mg C kg<sup>−1</sup> h<sup>−1</sup>) (<b>a</b>), gross denitrification rate (<span class="html-italic">Denitr</span>; µg N kg<sup>−1</sup> h<sup>−1</sup>) (<b>b</b>), and gross nitrification rate (<span class="html-italic">Nitr</span>; µg N kg<sup>−1</sup> h<sup>−1</sup>) (<b>c</b>). Values are means ± SE (showed as vertical bars) of three replicates for each plot. <span class="html-italic">Denitr</span> rate was detected in plots 1–2, while <span class="html-italic">Nitr</span> rate was only detected in plot 3. Values not followed by the same small letter are significantly different among plots within the same soil depth, according to ANOVA (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>BaPS parameters measured at 0–20 cm layer: soil respiration rate (<span class="html-italic">RS</span>; mg C kg<sup>−1</sup> h<sup>−1</sup>) (<b>a</b>), gross denitrification rate (<span class="html-italic">Denitr</span>; µg N kg<sup>−1</sup> h<sup>−1</sup>) (<b>b</b>), and gross nitrification rate (<span class="html-italic">Nitr</span>; µg N kg<sup>−1</sup> h<sup>−1</sup>) (<b>c</b>). Values are means ± SE (showed as vertical bars) of three replicates for each plot. <span class="html-italic">Denitr</span> rate was detected in plots 1–2, while <span class="html-italic">Nitr</span> rate was only detected in plot 3. Values not followed by the same small letter are significantly different among plots within the same soil depth, according to ANOVA (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Abundance of bacteria, archaea, and fungi expressed as gene copy numbers (g<sup>−1</sup> of soil dry weight) as detected along the investigated soil depth profile (0–100 cm) in each plot (bacteria as blue line and dots, archaea as red line and dots, and fungi as green line and dots). The abundance of each microbial community represents the average value of duplicate quantifications using 16S rDNA q-PCR analysis. Gene copy numbers were expressed in scientific notation. 0E+00 and 6E+13 refer to numbers ranging from 5.83 × 10<sup>8</sup> to 5.40 × 10<sup>13</sup>.</p>
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<p>Vertical changes in numbers of bacteria, archaea, and fungi phylotypes detected along the investigated soil depth in each plot (bacteria as blue dots, archaea as red squares, and fungi as green triangles). The number of phylotypes corresponds to the number of bands on the T-RFLP profiles.</p>
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<p>Dendrograms show similarity of T-RFLP profiles using Bray–Curtis hierarchical cluster analysis along the investigated soil depth in each plot.</p>
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<p>Boxplots of diversity index (Shannon index; <span class="html-italic">H′</span>). Three different soil layers (i.e., SL, superficial layer; IL, intermediate layer; and DL, deeper layer) were discriminated according to an arbitrary analysis of soil profile. Diversity was calculated from the number and the relative peak area of bands on the T-RFLP profiles.</p>
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<p>Principal component analysis (PCA) biplot was based on soil chemical and physical parameters (pH; SOC: soil organic carbon concentration; TN: total nitrogen concentration; C/N ratio; SWC: soil water content; Soil Temp: soil temperature), microbial processes <span class="html-italic">(Rs</span>: soil respiration; <span class="html-italic">Denitr</span>: denitrification rate; <span class="html-italic">Nitr</span>: gross nitrification rate), soil CO<sub>2</sub> emissions (measured using survey soil respiration chamber), microbial abundance (<span class="html-italic">Arch</span>: Archaea abundance; <span class="html-italic">Bact</span>: bacteria abundance; <span class="html-italic">Fungi</span>: fungi abundance) and Shannon index (Arch <span class="html-italic">H′</span>: Archaea Shannon index; Bact <span class="html-italic">H′</span>: Bacteria Shannon index; Fungi <span class="html-italic">H′</span>: Fungi Shannon index). All such parameters were used as variables, while replicate plots were used as observations in the 0–20 cm soil depth. Soil chemical and physical variables are shown by black continuous arrows, microbial processes and soil CO<sub>2</sub> emissions are shown by grey heavy dotted arrows, microbial <span class="html-italic">H’</span> is shown by black heavy dotted arrows, and microbial abundance is shown by light dotted arrows. Observations are represented by black stars (plot 1), white triangles (plot 2), and white squares (plot 3). PC 1 and PC 2 axes together accounted for 77.78% of the variability and were significant (<span class="html-italic">p</span> &lt; 0.05) (<b>a</b>). PCA biplot was based on soil chemical parameters, microbial abundance, and <span class="html-italic">H’</span>, which were used as variables, while soil depths were considered as observations. Observations (10: 0–10 cm, 20:10–20 cm, 40: 20–40 cm, 60: 40–60 cm, 80: 60–80 cm, 100: 80–100 cm) are represented by black circles. In plot 1, the PC 1 and PC 2 axes together accounted for 76.04% of the variability and were significant (<span class="html-italic">p</span> &lt; 0.05) (<b>b</b>); in plot 2, the PC 1 and PC 2 axes together accounted for 81.22% of the variability and were significant (<span class="html-italic">p</span> &lt; 0.05) (<b>c</b>); in plot 3, the PC 1 and PC 2 axes together accounted for 82.84% of the variability and were significant (<span class="html-italic">p</span> &lt; 0.05) (<b>d</b>).</p>
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<p>Principal component analysis (PCA) biplot was based on soil chemical and physical parameters (pH; SOC: soil organic carbon concentration; TN: total nitrogen concentration; C/N ratio; SWC: soil water content; Soil Temp: soil temperature), microbial processes <span class="html-italic">(Rs</span>: soil respiration; <span class="html-italic">Denitr</span>: denitrification rate; <span class="html-italic">Nitr</span>: gross nitrification rate), soil CO<sub>2</sub> emissions (measured using survey soil respiration chamber), microbial abundance (<span class="html-italic">Arch</span>: Archaea abundance; <span class="html-italic">Bact</span>: bacteria abundance; <span class="html-italic">Fungi</span>: fungi abundance) and Shannon index (Arch <span class="html-italic">H′</span>: Archaea Shannon index; Bact <span class="html-italic">H′</span>: Bacteria Shannon index; Fungi <span class="html-italic">H′</span>: Fungi Shannon index). All such parameters were used as variables, while replicate plots were used as observations in the 0–20 cm soil depth. Soil chemical and physical variables are shown by black continuous arrows, microbial processes and soil CO<sub>2</sub> emissions are shown by grey heavy dotted arrows, microbial <span class="html-italic">H’</span> is shown by black heavy dotted arrows, and microbial abundance is shown by light dotted arrows. Observations are represented by black stars (plot 1), white triangles (plot 2), and white squares (plot 3). PC 1 and PC 2 axes together accounted for 77.78% of the variability and were significant (<span class="html-italic">p</span> &lt; 0.05) (<b>a</b>). PCA biplot was based on soil chemical parameters, microbial abundance, and <span class="html-italic">H’</span>, which were used as variables, while soil depths were considered as observations. Observations (10: 0–10 cm, 20:10–20 cm, 40: 20–40 cm, 60: 40–60 cm, 80: 60–80 cm, 100: 80–100 cm) are represented by black circles. In plot 1, the PC 1 and PC 2 axes together accounted for 76.04% of the variability and were significant (<span class="html-italic">p</span> &lt; 0.05) (<b>b</b>); in plot 2, the PC 1 and PC 2 axes together accounted for 81.22% of the variability and were significant (<span class="html-italic">p</span> &lt; 0.05) (<b>c</b>); in plot 3, the PC 1 and PC 2 axes together accounted for 82.84% of the variability and were significant (<span class="html-italic">p</span> &lt; 0.05) (<b>d</b>).</p>
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20 pages, 1836 KiB  
Article
Effects of Long-Term Nitrogen Fertilization and Application Methods on Fruit Yield, Plant Nutrition, and Soil Chemical Properties in Highbush Blueberries
by Charitha P. A. Jayasinghege, Carine Bineng and Aimé J. Messiga
Horticulturae 2024, 10(11), 1205; https://doi.org/10.3390/horticulturae10111205 - 15 Nov 2024
Viewed by 344
Abstract
Nitrogen (N) fertilizer is routinely applied in highbush blueberry (Vaccinium corymbosum L.) production. The recommended N fertilizer rate increases as the plants mature, and is usually determined based on regional growing conditions. However, the effects of N fertilizer rates and application methods [...] Read more.
Nitrogen (N) fertilizer is routinely applied in highbush blueberry (Vaccinium corymbosum L.) production. The recommended N fertilizer rate increases as the plants mature, and is usually determined based on regional growing conditions. However, the effects of N fertilizer rates and application methods over the long term remain poorly understood. In this study, ammonium sulfate was applied as an N source at the recommended rate (100%), which corresponds to a maximum of 155 kg N ha−1 for plants older than eight years, along with higher rates at 150% and 200% of the recommended level, as well as a control treatment of no N. Treatments were applied to the blueberry cultivar ‘Duke’ as either broadcast (BROAD) or fertigation (FERT), and impacts were analyzed after 12 and 13 years of treatment. In the 14th year, the 100% N rate was uniformly applied as BROAD across all plants to separate the effects of different N rates from those caused by long-term soil condition changes. The BROAD treatment at the 100% N rate achieved the highest yield, and the FERT treatment at 200% resulted in the lowest yield in the 12th year, suggesting that excessive N rates can reduce fruit yield. However, no significant yield differences were observed in the 13th year. Higher N rates were associated with reduced titratable acidity in fruits and fewer flower buds. The soil pH declined across all N treatments, with the FERT at 200% showing the most significant reduction. All N treatments generally increased soil electrical conductivity (EC). High N rates also decreased plant accumulation of magnesium, calcium, and copper, with the latter reaching deficiency levels. These findings emphasize the importance of adhering to recommended N application rates and adjusting soil pH and EC to mitigate the adverse effects of prolonged N treatments. Full article
(This article belongs to the Special Issue Irrigation and Fertilization Management in Horticultural Production)
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<p>Effects of long-term nitrogen (N) treatments on blueberry fruit yield and properties. (<b>A</b>–<b>C</b>) Average fruit yield, (<b>D</b>–<b>F</b>) total soluble solids (TSS) content, and (<b>G</b>–<b>I</b>) titratable acidity (TA) of fruits in 2020 (<b>A</b>,<b>D</b>,<b>G</b>), 2021 (<b>B</b>,<b>E</b>,<b>H</b>), and 2022 (<b>C</b>,<b>F</b>,<b>I</b>). Plants were treated with N at 100%, 150%, or 200% of the rates recommended by the British Columbia Blueberry Production Guide, applied either as fertigation (FERT) or broadcast (BROAD) until 2021. In 2022, all plants received N at 100% as BROAD treatments, but data are presented according to historical N treatments. Data are means ± SD (n = 4 for 2020; n = 6 for 2021 and 2022). Different letters indicate significant differences (one-way ANOVA followed by Holm–Sidak post hoc test; <span class="html-italic">p</span> ≤ 0.05).</p>
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<p>Effects of nitrogen (N) fertilization on reproductive development in blueberry plants. (<b>A</b>) Number of flower and leaf buds and the percentage of flower buds relative to the total bud count, measured in 25 cm long canes in 2021. Plants received N at 100%, 150%, or 200% of the rates recommended by the British Columbia Blueberry Production Guide, applied as fertigation (FERT) or broadcast (BROAD). (<b>B</b>) Number of flowers per inflorescence in the second and third inflorescence positions. (<b>C</b>,<b>D</b>) Percentage of fruit set, determined by visually estimating the proportion of developing fruits (DF) compared to non-developing fruits that remained in the cluster (NDF) or dropped. Data are means ± SD. For bud counts, n = 4–5, with each sample composed of 16 canes. For assessment of flowers per inflorescence and fruit set percentage, n = 4, with each sample composed of eight canes. Different letters denote significant differences. No significant differences were detected in graphs without letters (one-way ANOVA followed by Holm–Sidak post hoc test; <span class="html-italic">p</span> ≤ 0.05).</p>
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<p>Soil NH₄⁺ and NO<sub>3</sub><sup>−</sup> nitrogen (N) levels in response to N rate and application method in 2020. NH₄⁺ levels (<b>A</b>–<b>C</b>) and NO<sub>3</sub><sup>−</sup> levels (<b>D</b>–<b>F</b>) were measured in the sawdust mulch (<b>A</b>,<b>D</b>) and at 0–15 cm (<b>B</b>,<b>E</b>), and 15–30 cm (<b>C</b>,<b>F</b>) soil depths in September 2020. Blueberry plants were treated with N at 100%, 150%, or 200% of the rates recommended by the British Columbia Blueberry Production Guide, applied as fertigation (FERT) or broadcast (BROAD). Data are presented as means (dry-weight basis) ± SD (n = 4). Different letters indicate significant differences; graphs without letters found no significant differences (one-way ANOVA followed by Holm–Sidak post hoc test; <span class="html-italic">p</span> ≤ 0.05).</p>
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<p>Soil NH₄⁺ and NO<sub>3</sub><sup>−</sup> nitrogen (N) levels in response to N rate and application method in 2021. NH₄⁺ levels (<b>A</b>–<b>C</b>) and NO<sub>3</sub><sup>−</sup> levels (<b>D</b>–<b>F</b>) were measured in the sawdust mulch (<b>A</b>,<b>D</b>) and at the 0–15 cm (<b>B</b>,<b>E</b>), and 15–30 cm (<b>C</b>,<b>F</b>) soil depths in November 2021. Blueberry plants were treated with N at 100%, 150%, or 200% of the rates recommended by the British Columbia Blueberry Production Guide, applied as fertigation (FERT) or broadcast (BROAD). Data are presented as means (dry-weight basis) ± SD (n = 6). Different letters indicate significant differences; graphs without letters found no significant differences (one-way ANOVA followed by Holm–Sidak post hoc test; <span class="html-italic">p</span> ≤ 0.05).</p>
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<p>Effects of nitrogen (N) fertilization on soil pH and electrical conductivity (EC). Soil pH (<b>A</b>,<b>C</b>,<b>E</b>) and EC (<b>B</b>,<b>D</b>,<b>F</b>) were assessed at three different soil depths in 2020 (<b>A</b>,<b>B</b>), 2021 (<b>C</b>,<b>D</b>) and 2022 (<b>E</b>,<b>F</b>). Plants were treated with N at 100%, 150%, or 200% of the rates recommended by the British Columbia Blueberry Production Guide, applied either as fertigation (FERT) or broadcast (BROAD) until 2021. In 2022, all plants received N at 100% as BROAD treatments; however, data are presented according to historical N treatments. Data are means ± SD (n = 4, 6, and 5 in 2020, 2021, and 2022, respectively). Different letters indicate significant differences (one-way ANOVA followed by Holm–Sidak post hoc test; <span class="html-italic">p</span> ≤ 0.05).</p>
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<p>Soil NH₄⁺ and NO<sub>3</sub><sup>−</sup> nitrogen (N) levels in response to historical N application rates and methods. NH₄⁺ levels (<b>A</b>–<b>C</b>) and NO<sub>3</sub><sup>−</sup> levels (<b>D</b>–<b>F</b>) were measured in the sawdust mulch (<b>A</b>,<b>D</b>) and at the 0–15 cm (<b>B</b>,<b>E</b>) and 15–30 cm (<b>C</b>,<b>F</b>) soil depths. Treatments included N fertilizer applied at 100%, 150%, or 200% of the rates recommended by the British Columbia Blueberry Production Guide, using either fertigation (FERT) or broadcast (BROAD) methods until 2021. In 2022, all plants received N at 100% using BROAD applications, but data are presented based on historical N treatments. Values are expressed as means (dry-weight basis) ± SD (n = 5). Different letters indicate significant differences; graphs without letters found no significant differences (one-way ANOVA followed by Holm–Sidak post hoc test; <span class="html-italic">p</span> ≤ 0.05).</p>
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<p>Impact of long-term nitrogen (N) fertilization and application methods. N fertilizer applied as broadcast (BROAD) treatments initially remains in the sawdust after application, gradually becoming available in the root zone as it dissolves with irrigation water. Fertilizer not directly under drip irrigation tends to dissolve more slowly, with some loss due to volatilization. In contrast, fertigation (FERT)-applied N passes through the mulch layer more readily and becomes immediately available to plants, though a higher portion may be lost through leaching compared to BROAD treatments. All N treatments increase electrical conductivity (EC) and reduce pH, but the pH decline occurs more rapidly with FERT treatments (illustrated by arrows, with arrow heights indicating the extent of change). These soil changes also reduce the availability of calcium (Ca), magnesium (Mg), and copper (Cu) to plants, while N availability appears slightly higher in BROAD treatments due to differences in the rate and pattern of fertilizer distribution.</p>
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29 pages, 9120 KiB  
Article
Collapsible Gypseous Soil Stabilization by Calcium Carbide Residue and Sulfonic Acid
by Rasha F. Abaas, Mohammed Y. Fattah, Maha H. Naif and Mohamed Hafez
Sustainability 2024, 16(22), 9974; https://doi.org/10.3390/su16229974 - 15 Nov 2024
Viewed by 457
Abstract
Gypseous soil is a collapsing soil that has not yet been approved as a construction material since its behavior under water, temperature, and pressure is unreliable and unpredictable. Researchers and scientists are always searching for new and creative ways to optimize the benefits [...] Read more.
Gypseous soil is a collapsing soil that has not yet been approved as a construction material since its behavior under water, temperature, and pressure is unreliable and unpredictable. Researchers and scientists are always searching for new and creative ways to optimize the benefits of calcium carbide residue (CCR) recycling, which is a byproduct of the acetylene industry and includes a substantial quantity of Ca(OH)2. Therefore, it is a suitable choice for utilization as a chemical stabilizer to improve the engineering features of problematic soils. However, this study explores the potential for enhancing the engineering characteristics of gypseous soil by utilizing (CCR) combined with linear alkyl benzene sulfonic acid (LABSA) to form a geopolymer. The soils utilized in this work are gypseous collapsible soils. Standard tests were conducted on these soils to identify the physical and mechanical characteristics. The geopolymer preparation was accomplished by merging a dilution of LABSA with a geopolymer (solid to liquid), blending the proportions. Three different types of disturbed natural granular-gypseous collapsible soils with different properties and various gypsum contents with percentages of 20%, 35%, and 50% were used. Mixtures of soils containing (2.5%, 5%, and 7.5%) of the geopolymer mix content were made. The single oedometer test (SOT) and the double oedometer test (DOT) were carried out to ascertain the lowest collapse potential value correlated with the ideal geopolymer mixing ratio. The adequate geopolymer percentage was found to be 5% since it resulted in the maximum reduction in collapse potential compared to the natural soil. The direct shear test is employed to ascertain the soil samples’ cohesiveness and friction angle. The results show a slight reduction in the angle of internal friction and increased cohesion (c). For stabilizing gypseous soil in engineering projects, a combination of LABSA and CCR can be utilized as a workable, sustainable, and environmentally friendly substitute. Full article
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<p>The grain size distribution curves of the soils.</p>
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<p>CCR powder.</p>
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<p>EDX spectrometry for the CCR.</p>
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<p>EDX spectrometry of LABSA.</p>
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<p>Single oedometer test result for the natural (untreated) gypseous soil 20.</p>
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<p>Double oedometer test result for natural (untreated) gypseous soil 20.</p>
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<p>Single oedometer test result for the natural (untreated) gypseous soil 35.</p>
Full article ">Figure 8
<p>Double oedometer test result for the natural (untreated) gypseous soil 35.</p>
Full article ">Figure 9
<p>Single oedometer test result for the natural (untreated) gypseous soil 50.</p>
Full article ">Figure 10
<p>Double oedometer test result for the natural (untreated) gypseous soil 50.</p>
Full article ">Figure 11
<p>Single oedometer test result for the geopolymer-treated gypseous soil 20.</p>
Full article ">Figure 12
<p>Double oedometer test result for the 2.5% geopolymer-treated gypseous soil 20.</p>
Full article ">Figure 13
<p>Double oedometer test result for the 5% geopolymer-treated gypseous soil 20.</p>
Full article ">Figure 14
<p>Double oedometer test result for the 7.5% geopolymer-treated gypseous soil 20.</p>
Full article ">Figure 15
<p>Effect of geopolymer concentration on single and double oedometer test collapse potential for soil 20.</p>
Full article ">Figure 16
<p>Effect of geopolymer concentration on single and double oedometer test collapse potential for soil 35.</p>
Full article ">Figure 17
<p>Effect of geopolymer concentration on single and double oedometer test collapse potential for soil 50.</p>
Full article ">Figure 18
<p>Direct shear test result for natural (untreated) gypseous soil 20.</p>
Full article ">Figure 19
<p>Direct shear test result for natural (untreated) gypseous soil 35.</p>
Full article ">Figure 20
<p>Direct shear test result for natural (untreated) gypseous soil 50.</p>
Full article ">Figure 21
<p>Direct shear test result for 2.5% geopolymer-treated gypseous soil 20.</p>
Full article ">Figure 22
<p>Direct shear test result for the 5% geopolymer-treated gypseous soil 20.</p>
Full article ">Figure 23
<p>Direct shear test result for the 7.5% geopolymer-treated gypseous soil 20.</p>
Full article ">Figure 24
<p>Direct shear test result for the 2.5% geopolymer-treated gypseous soil 35.</p>
Full article ">Figure 25
<p>Direct shear test result for the 5% geopolymer-treated gypseous soil 35.</p>
Full article ">Figure 26
<p>Direct shear test result for the 7.5% geopolymer-treated gypseous soil 35.</p>
Full article ">Figure 27
<p>Direct shear test result for the 2.5% geopolymer-treated gypseous soil 50.</p>
Full article ">Figure 28
<p>Direct shear test result for the 5% geopolymer-treated gypseous soil 50.</p>
Full article ">Figure 29
<p>Direct shear test result for the 7.5% geopolymer-treated gypseous soil 50.</p>
Full article ">Figure A1
<p>Single oedometer test result for the geopolymer-treated gypseous soil 35.</p>
Full article ">Figure A2
<p>Double oedometer test result for the 2.5% geopolymer-treated gypseous soil 35.</p>
Full article ">Figure A3
<p>Double oedometer test result for the 5% geopolymer-treated gypseous soil 35.</p>
Full article ">Figure A4
<p>Double oedometer test result for the 7.5% geopolymer-treated gypseous soil 35.</p>
Full article ">Figure A5
<p>Single oedometer test result for the geopolymer-treated gypseous soil 50.</p>
Full article ">Figure A6
<p>Double oedometer test result for the 2.5% geopolymer-treated gypseous soil 50.</p>
Full article ">Figure A7
<p>Double oedometer test result for the 5% geopolymer-treated gypseous soil 50.</p>
Full article ">Figure A8
<p>Double oedometer test result for the 7.5% geopolymer-treated gypseous soil 50.</p>
Full article ">
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