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Search Results (1,335)

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17 pages, 3872 KiB  
Article
Impact of Land Use Types on Soil Physico-Chemical Properties, Microbial Communities, and Their Fungistatic Effects
by Giuseppina Iacomino, Mohamed Idbella, Salvatore Gaglione, Ahmed M. Abd-ElGawad and Giuliano Bonanomi
Soil Syst. 2024, 8(4), 131; https://doi.org/10.3390/soilsystems8040131 - 16 Dec 2024
Viewed by 435
Abstract
Soilborne plant pathogens significantly impact agroecosystem productivity, emphasizing the need for effective control methods to ensure sustainable agriculture. Soil fungistasis, the soil’s ability to inhibit fungal spore germination under optimal conditions, is pivotal for biological control. This study explores soil fungistasis variability across [...] Read more.
Soilborne plant pathogens significantly impact agroecosystem productivity, emphasizing the need for effective control methods to ensure sustainable agriculture. Soil fungistasis, the soil’s ability to inhibit fungal spore germination under optimal conditions, is pivotal for biological control. This study explores soil fungistasis variability across land-use intensities, spanning deciduous and evergreen forests, grasslands, shrublands, and horticultural cultivations in both open fields and greenhouses. Soil characterization encompassed organic matter, pH, total nitrogen, C/N ratio, key cations (Ca2+, Mg2+, K+, Na+), enzymatic activities, microbial biomass, and soil microbiota analyzed through high-throughput sequencing of 16s rRNA genes. Fungistasis was evaluated against the pathogenic fungi Botrytis cinerea and the beneficial microbe Trichoderma harzianum. Fungistasis exhibited similar trends across the two fungi. Specifically, the application of glucose to soil temporarily annulled soil fungistasis for both B. cinerea and T. harzianum. In fact, a substantial fungal growth, i.e., fungistasis relief, was observed immediately (48 h) after the pulse application with glucose. In all cases, the fungistasis relief was proportional to the glucose application rate, i.e., fungal growth was higher when the concentration of glucose was higher. However, the intensity of fungistasis relief largely varied across soil types. Our principal component analysis (PCA) demonstrated that the growth of both Trichoderma and Botrytis fungi was positively and significantly correlated with organic carbon content, total nitrogen, iron, magnesium, calcium, and sodium while negatively correlated with fluorescein diacetate (FDA) hydrolysis. Additionally, bacterial diversity and composition across different ecosystems exhibited a positive correlation with FDA hydrolysis and a negative correlation with phosphoric anhydride and soil pH. Analysis of bacterial microbiomes revealed significant differences along the land use intensity gradient, with higher fungistasis in soils dominated by Pseudoarthrobacter. Soils under intensive horticultural cultivation exhibited a prevalence of Acidobacteria and Cyanobacteria, along with reduced fungistasis. This study sheds light on soil fungistasis variability in diverse ecosystems, underscoring the roles of soil texture rather than soil organic matter and microbial biomass to explain the variability of fungistasis across landscapes. Full article
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Figure 1
<p>Images of the selected ecosystems across a climatic and land use intensity gradient in terms of organic amendment input, synthetic fertilizers, and pesticide application in the Campania Region (Southern Italy). All pictures by Giuliano Bonanomi.</p>
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<p>Box plots illustrating the variation in species richness (<b>A</b>) and the Shannon diversity index (<b>B</b>) for bacterial communities across the ecosystem soils. The boxes represent the interquartile range (IQR), with the lower and upper bounds indicating the 25th and 75th percentiles, respectively. The horizontal line within each box marks the median, while the whiskers extend to the range of data within 1.5 times the IQR. Different letters indicate significant differences (<span class="html-italic">p</span> &lt; 0.05). (<b>C</b>) Non-metric multidimensional scaling (NMDS) plots depict bacterial community composition in the different soils. The MDS axis1 and MDS axis2 correspond to the two axes of the two-dimensional ordination space, with each point representing a replicate sample. The stress level, shown on each plot, indicates how well the distances between objects are preserved (values closer to 0 indicate a better representation of the data in the ordination space). The <span class="html-italic">p</span>- and F-values represent the results of the PERMANOVA test conducted with 999 permutations on the bacterial data. (<b>D</b>) Bar charts display the relative abundance of bacterial phyla in the different soils.</p>
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<p>Heatmap showing the relative abundance of the 100 most frequent Amplicon Sequence Variants in the bacterial community in the soil of each ecosystem. The grouping of variables is based on Whittaker’s association index.</p>
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<p>Fungal growth of <span class="html-italic">B. cinerea</span> conidia (expressed as a percentage compared to the control (0%)) on soil watery extracts from the selected ecosystems during a 168 h incubation period that followed a single application of glucose at four application rates (0.10%, 0.30%, 1%, and 3%). Values are averages ± standard deviation.</p>
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<p>Fungal growth of <span class="html-italic">T. harzianum</span> conidia (expressed as a percentage compared to the control (0%)) on soil watery extracts from the selected ecosystems during a 168 h incubation period that followed a single application of glucose at four application rates (0.10%, 0.30%, 1%, and 3%). Values are averages ± standard deviation.</p>
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<p>Principal component analysis (PCA) based on soil physico-chemical characteristics (<b>A</b>) and SIMPER resulting taxa (<b>B</b>) as variables. Microbial biomass, fungal growth, and bacterial diversity and composition were fitted as factors with significance &lt;0.05 onto the ordination.</p>
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18 pages, 4131 KiB  
Article
Influence of Floral Strip Width on Spider and Carabid Beetle Communities in Maize Fields
by Jia-Lu Li, Lan-Mei Huang, Zi-Yi Xiang, Jian-Ning Zhao, Dian-Lin Yang, Hui Wang and Yan-Jun Zhang
Insects 2024, 15(12), 993; https://doi.org/10.3390/insects15120993 - 15 Dec 2024
Viewed by 283
Abstract
The study explored the impact of floral strip width on the spider and carabid beetle communities in maize fields over two years. Three widths of floral strips (2 m, 4 m, and 6 m) were compared with maize-only control strips to evaluate species [...] Read more.
The study explored the impact of floral strip width on the spider and carabid beetle communities in maize fields over two years. Three widths of floral strips (2 m, 4 m, and 6 m) were compared with maize-only control strips to evaluate species diversity and distribution. The results showed significant differences in both spider and carabid populations between floral and control strips, with 4 m and 6 m widths consistently harboring higher biodiversity. The results also showed distinct community clustering within floral strips in 2021, which became more cohesive by 2022. Further analysis validated significant community dissimilarities between different strip widths and controls, highlighting the ecological advantages of wider floral strips for enhancing natural enemy biodiversity. Spider activity density was notably higher in floral strips than in adjacent farmland, peaking at the edges of 4 m-wide strips and decreasing in 6 m-wide strips, with the lowest density in 2 m-wide strips. Carabid beetle activity density varied considerably with strip width and proximity to the edge, typically peaking at the edges of wider strips. Spiders were more responsive to strip width than carabid beetles. Based on these findings, we suggest using 4 m- or 6 m-wide floral strips to enhance biodiversity and natural pest control in agricultural landscapes; the floral strips narrower than 4 m (such as 2 m) could not support optimal biodiversity, as spiders and carabid beetles do not disperse far into the maize field, with spiders having dispersal distances of less than 3 m and carabid beetles less than 10 m. Vegetation characteristics significantly influenced spider and carabid communities, impacting species richness, diversity indices, and community structures across two study years. These insights highlight the necessity of thoughtfully designing floral strips to enhance biodiversity and natural pest control in agricultural landscapes. Full article
(This article belongs to the Section Insect Ecology, Diversity and Conservation)
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Figure 1
<p>(<b>A</b>) schematic representation showing the spatial arrangement of floral/control strips and their arthropod sampling sites within each replicate, (<b>B</b>) a photograph of 2 m-wide floral strip as example, and (<b>C</b>) a photograph of 2 m-wide maize planted control strip as example.</p>
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<p>Species richness, active density, and Shannon Diversity Index of spiders in floral strips and control strips. (<b>A</b>–<b>C</b>) in 2021 and (<b>D</b>–<b>F</b>) in 2022. 2m-T: 2m-wide floral strip, 2m-C: 2 m-wide control strip. 4m-T: 4 m-wide floral strip, 4m-C: 4 m-wide control strip. 6m-T: 6 m-wide floral strip, 6m-C: 6 m-wide control strip. Boxplots display the interquartile range (25–75%; box) and the median (line in the box). Whiskers represent 1.5 times the lower or upper interquartile range. Different lowercase letters above bars indicate significant differences among treatments.</p>
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<p>Species richness, active density, and Shannon Diversity Index of carabids in floral strips and control strips. (<b>A</b>–<b>C</b>) in 2021 and (<b>D</b>–<b>F</b>) in 2022. 2m-T: 2 m-wide floral strip, 2m-C: 2 m-wide control strip. 4m-T: 4 m-wide floral strip, 4m-C: 4 m-wide control strip. 6m-T: 6 m-wide floral strip, 6m-C: 6 m-wide control strip. Boxplots display the interquartile range (25–75%; box) and the median (line in the box). Whiskers represent 1.5 times the lower or upper interquartile range. Different lowercase letters above bars indicate significant differences among treatments.</p>
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<p>Non-linear multi-dimensional scaling (NMDS) based on chord measure of spiders and carabids communities in floral strips and control strips. (<b>A</b>) Spiders in 2021, (<b>B</b>) carabids in 2021, (<b>C</b>) spiders in 2022, and (<b>D</b>) carabids in 2022.</p>
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<p>Active density of (<b>A</b>) Spiders and (<b>B</b>) carabids within floral strip (−1 m), at strip edge (0 m), and in adjacent farmland (1 m). 2m-T: 2 m-wide floral strip, 4m-T: 4 m-wide floral strip, 6m-T: 6 m-wide floral strip. Data presented as mean ± SE. Different lowercase and uppercase letters above bars indicated significant differences among distances for each width of floral strip and widths for each distance from edge, respectively.</p>
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<p>Active density of spiders and carabids at various distances from the strip edge into adjacent maize fields. (<b>A</b>) Spiders in 2021, (<b>B</b>) carabids in 2021, (<b>C</b>) spiders in 2022, and (<b>D</b>) carabids in 2022. 2m-T: 2 m-wide floral strip, 4m-T: 4 m-wide floral strip, 6m-T: 6 m-wide floral strip. Data presented as mean ± SE. Different lowercase in the tables denoted significant differences among distances for each width of floral strip.</p>
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<p>Redundance analysis (RDA) of spiders and carabids assemblage structures and vegetation characteristics of floral strips. (<b>A</b>) Spiders in 2021, (<b>B</b>) carabids in 2021, (<b>C</b>) spiders in 2022, and (<b>D</b>) carabids in 2022.</p>
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22 pages, 13335 KiB  
Article
An Integrated Drought Index (Vapor Pressure Deficit–Soil Moisture–Sun-Induced Chlorophyll Fluorescence Dryness Index, VMFDI) Based on Multisource Data and Its Applications in Agricultural Drought Management
by Caiyun Deng, Li Zhang, Tianhe Xu, Siqi Yang, Jian Guo, Lulu Si, Ran Kang and Hermann Josef Kaufmann
Remote Sens. 2024, 16(24), 4666; https://doi.org/10.3390/rs16244666 - 13 Dec 2024
Viewed by 315
Abstract
To more precisely monitor drought, a new remote sensing-based drought index, the Vapor Pressure Deficit–Soil Moisture–Sun-Induced Chlorophyll fluorescence Dryness Index (VMFDI), with a spatial resolution of 1 km based on vapor pressure deficit (VPD), soil moisture (SM), and sun-induced chlorophyll fluorescence (SIF) data [...] Read more.
To more precisely monitor drought, a new remote sensing-based drought index, the Vapor Pressure Deficit–Soil Moisture–Sun-Induced Chlorophyll fluorescence Dryness Index (VMFDI), with a spatial resolution of 1 km based on vapor pressure deficit (VPD), soil moisture (SM), and sun-induced chlorophyll fluorescence (SIF) data was constructed via a three-dimensional spatial distance model, and it was used to monitor dryness in the Yellow River Basin during 2003–2020. The spatiotemporal variations in and main factors of the VMFDI and agroecosystem responses were analyzed via the Theil–Sen median and Mann–Kendall tests and Liang–Kleeman information flow. The results revealed the following: (1) The VMFDI effectively monitors regional drought and is more sensitive than other indices like the standardized precipitation evapotranspiration index (SPEI) and GRACE drought severity index and single variables. (2) VMFDI values fluctuated seasonally in the Yellow River Basin, peaking in August and reaching their lowest in March. The basin becomes drier in winter but wetter in spring, summer, and autumn, with the middle and lower reaches, particularly Shaanxi and Gansu, being drought-prone. The VMFDI values in the agroecosystem were lower. (3) SM and VPD dominated drought at the watershed and agroecosystem scales, respectively. Key agroecosystem indicators, including greenness (NDVI), gross primary productivity (GPP), water use efficiency (WUE), and leaf area index (LAI), were negatively correlated with drought (p < 0.05). When VPD exceeded a threshold range of 7.11–7.17 ha, the relationships between these indicators and VPD shifted from positive to negative. The specific VPD thresholds in maize and wheat systems were 8.03–8.57 ha and 7.15 ha, respectively. Suggestions for drought risk management were also provided. This study provides a new method and high-resolution data for accurately monitoring drought, which can aid in mitigating agricultural drought risks and promoting high-quality agricultural development. Full article
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<p>Location and land use of study area.</p>
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<p>Technical flowchart.</p>
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<p>The concept of the VMFDI in a three-dimensional space model. A principle map of the VMFDI. The reference point D (1, 0, 0) is the driest point, where the value of the VMFDI is 0. Point W (0, 1, 1) is the wettest point, where the value of the VMFDI is <math display="inline"><semantics> <mrow> <msqrt> <mn>3</mn> </msqrt> </mrow> </semantics></math>.</p>
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<p>Significant temporal correlations between VMFDI and (<b>a</b>) SPEI01, (<b>b</b>) SPEI03, (<b>c</b>) SPEI12, (<b>d</b>) DSI, (<b>e</b>) PRE, (<b>f</b>) VPD, (<b>g</b>) SM, and (<b>h</b>) SIF (<span class="html-italic">p</span> &lt; 0.05). In (<b>i</b>), R &gt; 0 means that VMFDI results are consistent with those of SPEI01, SPEI03, SPEI12, GRACE_DSI, PRE, VPD, SM, and SIF.</p>
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<p>A comparison of the drought monitoring ability of different drought indices. In this Figure, the red, light gray, and purple dashed lines are the drought thresholds for the GRACE-DSI, SPEI, and VMFDI, respectively (classified by <a href="#remotesensing-16-04666-t002" class="html-table">Table 2</a>). The light pink columns represent the actual observed drought events in the Yellow River Basin recorded in the Bulletin of Flood and Drought Disasters in China.</p>
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<p>Correlation coefficients between the VMFDI and other indices in the Yellow River Basin (<b>a</b>) based on all monthly data and (<b>b1</b>–<b>b12</b>) for each month of data in the range of 2003~2020.</p>
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<p>Monthly spatiotemporal variations in the VMFDI values (1 km <math display="inline"><semantics> <mrow> <mo>×</mo> </mrow> </semantics></math> 1 km) of the Yellow River Basin from 2003 to 2020. (<b>a</b>) shows the distribution pattern of the multiyear mean value of the monthly VMFDI and the temporal series of the monthly VMFDI at the basin scale. In (<b>b</b>,<b>c</b>), the changes in VMFDI values and their significance from 2003 to 2020, respectively, are shown; an obvious increase or decrease represents a region of significant change (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>The distribution and movement tracks of the annual and monthly drought centers in the Yellow River Basin identified by VMFDI anomalies and the gravity model. (<b>a</b>) is an overview map showing the location of the drought centers. In (<b>b</b>,<b>c</b>), the color dots represent the center of gravity of drought in different months or years, where drought is most likely to occur. The lines are the trajectory of the drought center. The standard deviational ellipses represent the change direction of drought.</p>
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<p>A time series of monthly VMFDI, VPD, SM, SIF, and VMFDI anomalies in the agroecosystem of the Yellow River Basin from 2003 to 2020. In figure (<b>a</b>)., r represents the correlation between variables and * represents the level of significance (<span class="html-italic">p</span> &lt; 0.05). The box diagram represents the value distribution of each variable. In figure (<b>b</b>), <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">V</mi> <mi mathvariant="normal">M</mi> <mi mathvariant="normal">F</mi> <mi mathvariant="normal">D</mi> <mi mathvariant="normal">I</mi> <mo>_</mo> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">n</mi> <mi mathvariant="normal">m</mi> <mi mathvariant="normal">o</mi> <mi mathvariant="normal">m</mi> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">l</mi> <mi mathvariant="normal">i</mi> <mi mathvariant="normal">e</mi> <mi mathvariant="normal">s</mi> </mrow> <mrow> <mi mathvariant="normal">i</mi> <mo>,</mo> <mi mathvariant="normal">j</mi> </mrow> </msub> </mrow> </semantics></math> is the difference between the VMFDI value in month <math display="inline"><semantics> <mrow> <mi mathvariant="normal">i</mi> </mrow> </semantics></math> of year <math display="inline"><semantics> <mrow> <mi mathvariant="normal">j</mi> </mrow> </semantics></math> and the multiyear mean value in month <math display="inline"><semantics> <mrow> <mi mathvariant="normal">i</mi> </mrow> </semantics></math>. The red bars represent the values below zero.</p>
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<p>Correlations between the monthly VMFDI and crop growth status indicators in the agroecosystem of the Yellow River Basin from 2003 to 2020. The corresponding data for the agroecosystem (<b>a</b>), maize (<b>b</b>), and wheat (<b>c</b>) included data from January to December, April to September (the maize growth cycle), and March to June (wheat regreening to maturity) from 2003 to 2020, respectively. r is the correlation efficiency, and * indicates that there is a significant correlation with a <span class="html-italic">p</span> value less than 0.05.</p>
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<p>Causalities between the monthly VMFDI and other corresponding variables. <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">T</mi> </mrow> <mrow> <mi mathvariant="normal">i</mi> <mo>→</mo> <mi mathvariant="normal">j</mi> </mrow> </msub> </mrow> </semantics></math> is the rate of the information flow from <math display="inline"><semantics> <mrow> <mi mathvariant="normal">i</mi> </mrow> </semantics></math> to <math display="inline"><semantics> <mrow> <mi mathvariant="normal">j</mi> </mrow> </semantics></math>. * represents a 95% significance level.</p>
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<p>Thresholds in the relationships between VPD and the NDVI, GPP, or LAI in various agroecosystems. The temporal ranges of the corresponding data in (<b>a</b>–<b>c</b>) were 12 months (January to December), 6 months (April to September, which is the maize growing season), and 4 months (March to June, in which wheat regreens to maturity) from 2003 to 2020, respectively.</p>
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18 pages, 4507 KiB  
Article
Different Impacts of Long-Term Tillage and Manure on Yield and N Use Efficiency, Soil Fertility, and Fungal Community in Rainfed Wheat in Loess Plateau
by Mengni Chen, Hailiang Yang, Qingshan Yang, Yongshan Li, Hui Wang, Juanling Wang, Qiaolan Fan, Na Yang, Ke Wang, Jiancheng Zhang, Jiawei Yuan, Peng Dong and Lu Wang
Plants 2024, 13(24), 3477; https://doi.org/10.3390/plants13243477 - 12 Dec 2024
Viewed by 378
Abstract
Conservation tillage and fertilization are widely adopted in agricultural systems to enhance soil fertility and influence fungal communities, thereby improving agroecosystems. However, the effects of no-tillage combined with manure on grain yield, nitrogen use efficiency (NUE), soil fertility, and rhizosphere fungal communities remain [...] Read more.
Conservation tillage and fertilization are widely adopted in agricultural systems to enhance soil fertility and influence fungal communities, thereby improving agroecosystems. However, the effects of no-tillage combined with manure on grain yield, nitrogen use efficiency (NUE), soil fertility, and rhizosphere fungal communities remain poorly understood, particularly in rainfed wheat fields on the Loess Plateau. A 15-year field experiment was conducted at the Niujiawa Experimental Farm of the Cotton Research Institute, Shanxi Agricultural University. Five treatments were assessed: conventional tillage without fertilizer (C), no-tillage with chemical fertilizer (NT), no-tillage with chemical fertilizer and manure (NTM), conventional tillage with chemical fertilizer (T), and conventional tillage with chemical fertilizer and manure (TM). The results demonstrated that the NTM treatment significantly increased grain yield by 124.95%, NT by 65.88%, TM by 68.97%, and T by 41.75%, compared to the C treatment (p < 0.05). NUE in the NTM treatment was improved by 58.73%–200.59%. Compared with the C treatment, NTM significantly enhanced soil nutrients, including organic matter (OM) by 70.68%, total nitrogen (TN) by 8.81%, total phosphorus (TP) by 211.53%, available nitrogen (AN) by 90.00%, available phosphorus (AP) by 769.12%, and available potassium (AK) by 89.01%. Additionally, the NTM treatment altered the rhizosphere fungal community of winter wheat, with Ascomycota (81.36%–90.24%) being the dominant phylum, followed by Mucoromycota (5.40%–12.83%) and Basidiomycota (1.50%–8.53%). At the genus level, NTM significantly increased the abundance of Mortierella and Dendrostilbella. An α-diversity analysis revealed that the richness and diversity of soil fungi were highest under NTM. The unweighted pair-group method with arithmetic mean (UPGMA) and principal coordinates analysis (PCoA) based on Bray-Curtis distances indicated that NTM formed a distinct fungal community with the highest phylogenetic diversity, which differed significantly from other treatments. Redundancy analysis (RDA) demonstrated that soil chemical properties variably influenced fungal community dynamics, with higher abundances of Ascomycota and Zoopagomycota positively correlated with OM, AN, AP, TP, and AK. Correlation analysis showed that wheat yield and NUE were positively correlated with Mortierella and Dendrostilbella, and negatively correlated with Fusarium, Chaetomium, and Alternaria. In conclusion, no-tillage with manure not only enhanced soil fertility but also enhanced soil fungal community structure, leading to greater wheat yield and NUE. These findings provide guidance for agricultural practices in rainfed wheat fields of the Loess Plateau. Full article
(This article belongs to the Special Issue Water and Nitrogen Management in the Soil–Crop System (3rd Edition))
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<p>(<b>A</b>) The number of OTUs of fungal communities under different treatments. Different lowercase letters indicate significant differences at <span class="html-italic">p</span> &lt; 0.05. The error bar represents SD. (<b>B</b>) Venn chart based on the operation classification unit (OTU). Each petal corresponds to a sample group. The shared overlapping area represents the OTUs of all samples. The number on a single petal represents the number of OTUs unique to a given sample group.</p>
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<p>Relative abundances of fungal community under different treatments at phylum level.</p>
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<p>The relative abundance of the main 3 fungi phyla under different treatments. Different lowercase letters indicate significant differences at <span class="html-italic">p</span> &lt; 0.05, while the same letters indicate nonsignificant differences at <span class="html-italic">p</span> &gt; 0.05. The error bar represents SD.</p>
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<p>Relative abundances of the fungal community under different treatments at the genus level.</p>
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<p>The relative abundance of the 6 main fungi genera under different treatments. Different lowercase letters indicate significant differences at <span class="html-italic">p</span> &lt; 0.05, while the same letters indicate nonsignificant differences at <span class="html-italic">p</span> &gt; 0.05. The error bar represents SD.</p>
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<p>Cladogram based on LEfSe analysis of soil fungal community under different treatments. Color nodes indicate the taxa under different treatments. The diameter of each node shows the relative abundance of each taxon.</p>
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<p>(<b>A</b>) Hierarchical clustering tree of fungal community at the out level (97% sequence similarity) based on Bray-Curtis distances. (<b>B</b>) principal coordinates analysis-PCOA of the fungal community. The PCoA plot was based on Bray-Curtis distances at toutOTU level (97% sequence similarity) for the fungal community.</p>
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<p>Redundancy analysis (RDA) of the relationship between the distribution of fungal community at phylum level and soil properties.</p>
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<p>Correlation of fungal community with yield and N use efficiency at genus level.</p>
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28 pages, 2618 KiB  
Review
The Importance of Mycorrhizal Fungi and Their Associated Bacteria in Promoting Crops’ Performance: An Applicative Perspective
by Miriana Bortolot, Beatrice Buffoni, Sonia Mazzarino, Gregory Hoff, Elena Martino, Valentina Fiorilli and Alessandra Salvioli Di Fossalunga
Horticulturae 2024, 10(12), 1326; https://doi.org/10.3390/horticulturae10121326 - 11 Dec 2024
Viewed by 380
Abstract
Agricultural systems are particularly impacted by global climate change (CC), responsible for the introduction of multiple environmental stressors negatively affecting plant growth. Soil microbial communities are crucial in agricultural practices, influencing crop performance and soil health. Human activities and CC threaten soil microbial [...] Read more.
Agricultural systems are particularly impacted by global climate change (CC), responsible for the introduction of multiple environmental stressors negatively affecting plant growth. Soil microbial communities are crucial in agricultural practices, influencing crop performance and soil health. Human activities and CC threaten soil microbial biodiversity, leading to soil quality degradation and decreasing plant health and productivity. Among plant-beneficial microorganisms, mycorrhizal fungi are widespread in terrestrial ecosystems, including agroecosystems, and they play a key role by enhancing plants’ fitness and resilience to both abiotic and biotic stresses. Therefore, exploring the role of mycorrhizal symbiosis in sustainable agriculture has become increasingly critical. Moreover, the application of mycorrhizal bioinoculants could reduce dependence on inorganic fertilizers, enhance crop yield, and support plants in overcoming environmental stresses. This review, after briefly introducing taxonomy, morphology and mechanisms supporting the symbiosis establishment, reports the roles of mycorrhizal fungi and their associated bacteria in improving plant nutrition and mitigating CC-induced abiotic stresses such as drought and salinity, also giving specific examples. The focus is on arbuscular mycorrhizal fungi (AMF), but ericoid mycorrhizal (ErM) fungi are also considered as promising microorganisms for a sustainable agricultural model. New emerging concepts are illustrated, such as the role of AMF hyphosphere in acting as a preferential niche to host plant growth-promoting bacteria and the potential of ErM fungi to improve plant performance on Ericaceae plants but also on non-host plants, behaving as endophytes. Finally, the potential and limitations of mycorrhizal-based bioinoculants are discussed as possible alternatives to chemical-based products. To this aim, possible ways to overcome problems and limitations to their use are discussed such as proper formulations, the systematic check of AMF propagule viability and the application of suitable agronomical practices in the field. Full article
(This article belongs to the Special Issue Microbial Interaction with Horticulture Plant Growth and Development)
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<p>Schematic presentation of the AM colonization process and the rhizosphere and hyphosphere microbiome. AM fungal spores (S) germinate after a molecular dialog between partners (i.e., SLs and COs). Fungal hyphae reach the root epidermis (EP) and develop a swollen structure called hyphopodia (HP). After this contact, the fungal hyphae penetrate the root and progress until the cortex cells, where AM fungi form arbuscules (ARB); in these structures, the nutrient exchange occurs bidirectionally. The arbuscule is surrounded by a plant-derived membrane (PAM, peri-arbuscular membrane in gray) and between the PAM and fungal cell wall there is an apoplastic space called the peri-arbuscular space (in yellow). Outside the root, bacteria live in association with the plant in a narrow space called rhizosphere (in non-mycorrhizal root plants, area in blue), mycorrhizosphere (in mycorrhizal root plants, area in green), and in association with the hyphae (hyphosphere area in orange). Bacteria that live inside the spore are called endobacteria [<a href="#B67-horticulturae-10-01326" class="html-bibr">67</a>]. ARB = arbuscule (Y, young, or M, mature), ERM = extraradical mycelium, HB = hyphal branching, IRM = intraradical mycelium, N = nucleus, EN = endodermis.</p>
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<p>Publications per year related to: (<b>a</b>) bacterial–fungal interaction in crop plants and soil (keywords used in PUBMED: interaction, crop, plant, soil, fungi, fungal, mycorrhizal, bacteria), and (<b>b</b>) mycorrhiza helper bacteria in crop plants and soil (keywords used in PUBMED: crop, plant, soil, mycorrhiza helper bacteria). In figure (<b>c</b>), we have a focus on mycorrhiza helper bacteria publications in the last 20 years.</p>
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<p>(<b>a</b>–<b>e</b>) ErM symbiosis morphology. (<b>a</b>) <span class="html-italic">In vitro V. myrtillus</span> non-inoculated seedlings. (<b>b</b>) <span class="html-italic">In vitro V. myrtillus</span> seedlings inoculated with <span class="html-italic">O. maius</span>. (<b>c</b>) Transverse section of a <span class="html-italic">V. oxycoccos</span> hair root characterized by very large epidermal cells (Ep) colonized by an ericoid fungus (arrow), a single layer of cortical cells (C), the endodermis (E), the vascular cylinder (VC). (<b>d</b>) Light microscope observation of a <span class="html-italic">V. myrtillus</span> hair root colonized by <span class="html-italic">O. maius</span>; the typical coils formed by the fungus inside the root epidermal cells can be seen (cotton blue staining). (<b>e</b>) Morphology of a densely intertwined intracellular fungal coil; the outer tangential wall of the epidermal cells is very thick. The intracellular hyphae are separated from the cytoplasm of the epidermal root cell by the plant cell membrane that surrounds the fungal coil (perisymbiotic membrane) and by an interface matrix (modified with permission from [<a href="#B138-horticulturae-10-01326" class="html-bibr">138</a>] (<b>a</b>,<b>b</b>,<b>d</b>); [<a href="#B174-horticulturae-10-01326" class="html-bibr">174</a>] (<b>c</b>); [<a href="#B175-horticulturae-10-01326" class="html-bibr">175</a>] (<b>e</b>)). (<b>f</b>) Microcuttings (the insertion on the upper left corner) derived from <span class="html-italic">in vitro</span> cultured <span class="html-italic">R. fortunei</span> grown on a sterilized peat-based substrate non-inoculated (CK) and inoculated with an <span class="html-italic">O. maius</span> strain. (<b>g</b>) Ex vitro rooting of <span class="html-italic">R. fortunei</span> microcuttings non-inoculated (CK) and inoculated with an <span class="html-italic">O. maius</span> strain three months after being transplanted to 10-centimeter diameter containers. (<b>h</b>) Plant hair roots after washing away the substrate ((<b>f</b>–<b>h</b>): modified with permission from [<a href="#B167-horticulturae-10-01326" class="html-bibr">167</a>]).</p>
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23 pages, 6297 KiB  
Article
Building Towards One Health: A Transdisciplinary Autoethnographic Approach to Understanding Perceptions of Sustainable Aquatic Foods in Vietnam
by Saihong Li, Soon Yong Ang, Angus M. Hunter, Seda Erdem, John Bostock, Chau Thi Da, Ngoc Tuan Nguyen, Amina Moss, William Hope, Charles Howie, Richard Newton, Mercedes Arguello Casteleiro and Dave Little
Sustainability 2024, 16(24), 10865; https://doi.org/10.3390/su162410865 - 11 Dec 2024
Viewed by 561
Abstract
As Vietnam navigates challenges to its animal, human, and environmental health (One Health) during rapid economic transitions, understanding local perceptions of sustainable food systems, particularly aquatic foods, is vital. This study employs a transdisciplinary, autoethnographic approach to exploring the cultural significance of aquatic [...] Read more.
As Vietnam navigates challenges to its animal, human, and environmental health (One Health) during rapid economic transitions, understanding local perceptions of sustainable food systems, particularly aquatic foods, is vital. This study employs a transdisciplinary, autoethnographic approach to exploring the cultural significance of aquatic food perceptions within Vietnamese communities. Data were primarily sourced through an autoethnographic triangulation method, involving detailed field diaries, vignettes, and interactive workshop data collected from local stakeholders. Our distinctive approach, involving researchers from environmental science, computer science, linguistics, political ecology, aquaculture, nutrition, human physiology, marketing, and accounting and accountability, as both participants and observers, illuminates the lived experiences that shape food perceptions within Vietnam’s specific food agro-ecosystems. By embedding aquatic food perceptions within the One Health framework, we identify key intersections between human, animal, and environmental health. Through cross-disciplinary narrative analysis, our study uncovers the social, political, economic, cultural, and linguistic dimensions surrounding aquatic food perceptions at local, regional, and national levels in Vietnam. Our study highlights the unique contribution of qualitative methods to addressing questions that hard data cannot answer in understanding perceptions of aquatic foods. The study emphasizes the need for an integrated, culturally informed, and transdisciplinary approach to addressing the complex factors influencing One Health outcomes in Vietnam. This research contributes to the broader discourse on sustainable food practices and One Health initiatives, proposing culturally informed interventions aimed at enhancing ecological resilience and public health. Full article
(This article belongs to the Section Sustainable Food)
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<p>Vendors’ electronic displays.</p>
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<p>The open market space is frequently teeming with marginalized fish vendors.</p>
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<p>Crab legs are collected by old women, either for their own sustenance or for potential sale.</p>
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<p>Water quality in a river and rice field.</p>
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<p>Fish trap in the Mekong Delta.</p>
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<p>Vinh Hoang company’s vision, mission, and business philosophy.</p>
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<p>Workshop themes discussed.</p>
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23 pages, 1892 KiB  
Review
Are Agroecosystem Services Under Threat? Examining the Influence of Climate Externalities on Ecosystem Stability
by Temidayo Olowoyeye, Gideon Abegunrin and Mariusz Sojka
Atmosphere 2024, 15(12), 1480; https://doi.org/10.3390/atmos15121480 - 11 Dec 2024
Viewed by 319
Abstract
This study examines the impacts of climate-induced externalities on the stability of agroecosystems and the ecosystem services they provide. Using the PRISMA approach, we review literature published from 2015 to 2024. The study identifies how extreme weather events such as droughts, floods, heatwaves, [...] Read more.
This study examines the impacts of climate-induced externalities on the stability of agroecosystems and the ecosystem services they provide. Using the PRISMA approach, we review literature published from 2015 to 2024. The study identifies how extreme weather events such as droughts, floods, heatwaves, and altered precipitation patterns disrupt the provisioning, regulating, and supporting services critical to food security, soil fertility, water purification, and biodiversity. Our findings show a continued increase in climate extremes, raising concerns about food security, environmental resilience, and socio-economic stability. It also reveals that regions dependent on rain-fed agriculture, such as parts of Africa, Asia, and the Mediterranean, are particularly vulnerable to these stressors. Adaptation strategies, including conservation agriculture, crop diversification, agroforestry, and improved water management, are identified as crucial for mitigating these impacts. This study emphasises the importance of proactive, policy-driven approaches to foster climate resilience, support agroecosystem productivity, and secure ecosystem services critical to human well-being and environmental health. Full article
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<p>Conceptual framework for the review synthesis.</p>
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<p>PRISMA flow diagram showing the process and shape of the systematic literature review.</p>
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<p>Text analysis of selected articles.</p>
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<p>Frequency of primary keywords.</p>
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<p>Map showing the distribution of climate externalities research, highlighting regions with reported extreme climate threats to environmental stability based on papers included in the review.</p>
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16 pages, 4923 KiB  
Article
Impacts of Micro/Nanoplastics Combined with Graphene Oxide on Lactuca sativa Seeds: Insights into Seedling Growth, Oxidative Stress, and Antioxidant Gene Expression
by Xuancheng Yuan, Fan Zhang and Zhuang Wang
Plants 2024, 13(24), 3466; https://doi.org/10.3390/plants13243466 - 11 Dec 2024
Viewed by 292
Abstract
Global pollution caused by micro/nanoplastics (M/NPs) is threatening agro-ecosystems, compromising food security and human health. Also, the increasing use of graphene-family nanomaterials (GFNs) in agricultural products has led to their widespread presence in agricultural systems. However, there is a large gap in the [...] Read more.
Global pollution caused by micro/nanoplastics (M/NPs) is threatening agro-ecosystems, compromising food security and human health. Also, the increasing use of graphene-family nanomaterials (GFNs) in agricultural products has led to their widespread presence in agricultural systems. However, there is a large gap in the literature on the combined effects of MNPs and GFNs on agricultural plants. This study was conducted to explore the individual and combined impacts of polystyrene microplastics (PSMPs, 1 μm) or nanoplastics (PSNPs, 50–100 nm), along with agriculturally relevant graphene oxide (GO), on the seed germination and seedling growth of lettuce (Lactuca sativa). The results showed that the combined effects of mixtures of PSMPs/PSNPs and GO exhibited both synergism and antagonism, depending on different toxicity indicators. The cellular mechanism underlying the combined effects on the roots and shoots of seedlings involved oxidative stress. Three SOD family genes, namely, Cu/Zn-SOD, Fe-SOD, and Mn-SOD, played an important role in regulating the antioxidant defense system of seedlings. The extent of their contribution to this regulation was associated with both the distinct plastic particle sizes and the specific tissue locations within the seedlings. Full article
(This article belongs to the Section Plant Response to Abiotic Stress and Climate Change)
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<p>Morphological characterization: (<b>A</b>) PSMPs; (<b>B</b>) PSNPs; (<b>C</b>) GO; (<b>D</b>) PSMPs + GO; (<b>E</b>) PSNPs + GO via TEM.</p>
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<p>Single and combined effects of PSMPs/PSNPs and GO on seed germination and seedling growth of <span class="html-italic">Lactuca sativa</span>: (<b>A</b>) germination potential (3 d); (<b>B</b>) germination rate (7 d); (<b>C</b>) root elongation (7 d); (<b>D</b>) shoot length (7 d). Different letters represent statistically significant differences between the exposure treatments within the same concentration (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>TP content, SOD activity, MDA content, and GSH content in the roots (<b>A</b>,<b>C</b>,<b>E</b>,<b>G</b>) and shoots (<b>B</b>,<b>D</b>,<b>F</b>,<b>H</b>) of lettuce seedlings (7 d) exposed to single and combined PSMPs/PSNPs and GO. All values are expressed as mean ± standard deviation (<span class="html-italic">n</span> = 3). Different letters represent statistically significant differences between the exposure treatments within the same concentration (<span class="html-italic">p</span> &lt; 0.05). SOD = superoxide dismutase; MDA = malondialdehyde; GSH = glutathione.</p>
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<p>Expression of antioxidant pathway-related genes (Cu/Zn-SOD, Fe-SOD, and Mn-SOD) in the roots (<b>A</b>,<b>C</b>,<b>E</b>) and shoots (<b>B</b>,<b>D</b>,<b>F</b>) of lettuce seedlings (7 d) exposed to single and combined PSMPs/PSNPs and GO at a mixed concentration of 100 mg/L. All values are expressed as mean ± standard deviation (<span class="html-italic">n</span> = 3). Different letters represent statistically significant differences between the exposure treatments (<span class="html-italic">p</span> &lt; 0.05). SOD = superoxide dismutase.</p>
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<p>Interaction types between either PSMPs and GO (<b>A</b>) or PSNPs and GO (<b>B</b>) on lettuce seedling growth parameters, antioxidant activity, and the corresponding gene expression. GP = germination potential; GR = germination rate; FW = fresh weight; RE = root elongation; SL = shoot length; TP = total protein; SOD = superoxide dismutase; MDA = malondialdehyde; GSH = glutathione.</p>
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<p>Heat map of correlation coefficients between growth parameters and antioxidant activity in the lettuce seedlings exposed to the PSMPs + GO (<b>A</b>) and PSNPs + GO (<b>B</b>) combinations. GP = germination potential; GR = germination rate; FW = fresh weight; RE = root elongation; SL = shoot length; TP = total protein; SOD = superoxide dismutase; MDA = malondialdehyde; GSH = glutathione.</p>
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<p>Heat map of correlation coefficients between total superoxide dismutase (SOD) activity and the corresponding SOD family gene expression in the lettuce seedlings exposed to the PSMPs + GO (<b>A</b>) and PSNPs + GO (<b>B</b>) combinations.</p>
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15 pages, 1608 KiB  
Article
Toxicity Assessment of Molybdenum Nanooxide in Relation to Various Components of the Agroecosystem in a Model Experiment
by Lyudmila Galaktionova, Irina Vershinina and Svyatoslav Lebedev
Soil Syst. 2024, 8(4), 130; https://doi.org/10.3390/soilsystems8040130 - 10 Dec 2024
Viewed by 435
Abstract
(1) Background: The rapid growth in the number of nanoparticles today raises questions about studying their impact on the environment, including the soil, as the main absorber of nanoparticles. The purpose of our research was to study the effect of MoO3 nanoparticles [...] Read more.
(1) Background: The rapid growth in the number of nanoparticles today raises questions about studying their impact on the environment, including the soil, as the main absorber of nanoparticles. The purpose of our research was to study the effect of MoO3 nanoparticles (NPs; 50, 100, 250, 500, and 1000 mg/kg of soil) on the physiological and biochemical parameters of Eisenia fetida, the number of certain ecologo-trophic groups of soil microorganisms, and enzymatic soil activity. (2) Methods: We used 92 ± 0.3 nm nanoparticles of MoO3 at concentrations of 50, 100, 250, 500, and 1000 mg/kg dry soil. Texture-carbonate chernozem was used in the study. Eisenia fetida worms were used as test objects. (3) Results: The introduction of MoO3 nanoparticles showed a weak toxic effect towards the animal and microbiological components of the soil at a concentration of 50–250 mg/kg, a medium toxic effect at 500 mg/kg, and a strong or unacceptable toxic effect at 1000 mg/kg. The oxidative stress response of E. fetida depended on the concentration of the NPs. MoO3 NPs at a concentration of up to 100 mg/kg reduced the number of amylolytic bacteria, oligotrophs, and Azotobacter. In soil, urease and catalase showed mild activity, whereas the activity of invertase decreased by 34%. (4) Conclusions: The entry into the environment and the further deposition of nanoparticles of Mo and its oxides in the soil will lead to the suppression of the vital activity of beneficiary soil animals and the activity of soil enzymes. This phenomenon presents special kinds of ecological risks for the ecosystem. Full article
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<p>MoO<sub>3</sub> nanoparticles (scale corresponds to 100 nm).</p>
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<p>Mass change in <span class="html-italic">E. fetida</span> at different concentrations of MoO<sub>3</sub> NPs in soil substrate (% of initial mass).</p>
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<p>The difference in the protein content in the body of <span class="html-italic">E. fetida</span> with different concentrations of MoO<sub>3</sub> NPs (% of control, 0 mg/kg). Bars reflect standard deviations of the mean from three replications.</p>
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<p>The difference in the activity of antioxidant enzymes in <span class="html-italic">E. fetida</span>, with different concentrations of MoO<sub>3</sub> NPs (50; 100; 250; 500; 1000 mg/kg) in the soil (% of control).</p>
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<p>Concentration MoO<sub>3</sub> in soil, mg/kg.</p>
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<p>Activity of soil exoenzymes.</p>
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<p>Activity of soil exoenzymes.</p>
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17 pages, 2016 KiB  
Article
Different Species and Cultivars of Broad Beans, Lupins, and Clovers Demonstrated Varying Environmental Adaptability and Nitrogen Fixation Potential When Cultivated as Green Manures in Northeastern Portugal
by Peltier Aguiar, Margarida Arrobas, Ezar Alfredo Nharreluga and Manuel Ângelo Rodrigues
Sustainability 2024, 16(23), 10725; https://doi.org/10.3390/su162310725 - 6 Dec 2024
Viewed by 521
Abstract
The success of growing legumes as green manure depends on their spatial and temporal integration within agroecosystems, which minimizes competition with cash crops, and on their nitrogen (N) fixation potential. This study evaluated seven legume species for biomass production, N fixation, and suitability [...] Read more.
The success of growing legumes as green manure depends on their spatial and temporal integration within agroecosystems, which minimizes competition with cash crops, and on their nitrogen (N) fixation potential. This study evaluated seven legume species for biomass production, N fixation, and suitability for use in cropping systems in northern Portugal. Oats (Avena sativa L.) were grown to estimate the N fixation using the difference method, as a non-legume reference crop is required for this purpose, and oats are widely grown in the region. The study was conducted over four cropping cycles (2021–2024) in two climate zones across four land plots. The results indicated that the biomass production and N fixation varied by the species/cultivar and cropping cycle, which was significantly influenced by spring precipitation. Broad beans (Vicia faba L.) failed to develop in one cycle on highly acidic soil (pH 4.9), showing negative N fixation values when calculated by the difference method. Conversely, the lupins maintained a relatively high level of N fixation across all the conditions, demonstrating strong environmental adaptability. Thus, the N fixation values across the four cycles ranged from −5.4 to 419.4 kg ha−1 for broad bean (cv. Favel), while yellow lupin (Lupinus luteus L.) exhibited average values between 204.0 and 274.0 kg ha−1. The percentage of N derived from the atmosphere (%Ndfa) ranged from −13.3 to 91.6, −39.4 to 85.8, 83.8 to 94.7, 74.9 to 94.3, 72.8 to 92.2, 23.1 to 75.8, and 11.7 to 21.7 for these species/cultivars. Due to their environmental adaptability, biomass production, and N fixation capacity, these legumes could be used as green manure in inter-rows of woody crops or in summer annual crops like tomatoes and maize, grown in winter as an alternative to fallow land. The lupins showed strong promise due to their environmental resilience. Full article
(This article belongs to the Section Sustainable Agriculture)
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<p>Climatological normal (N) and annual values (Y) of monthly temperature (T) and precipitation (P) in Mirandela (<b>left</b>) and Bragança (<b>right</b>) for the respective study periods.</p>
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<p>Dry matter yield (DMY) of eight species/cultivars grown under four location/year combinations [Bragança 2022 (L1/22), Mirandela 2023 (L2/23), Bragança 2023 (L3/23), and Bragança 2024 (L4/24)]. The <span class="html-italic">X</span>-axis represents Julian dates (starting from 1 January) to express the dates as a continuous variable. Error bars represent standard deviations (n = 3).</p>
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<p>Dry matter yield (DMY) of eight species/cultivars grown under four location/year combinations [Bragança 2022 (L1/22), Mirandela 2023 (L2/23), Bragança 2023 (L3/23), and Bragança 2024 (L4/24)]. The <span class="html-italic">X</span>-axis represents Julian dates (starting from 1 January) to express the dates as a continuous variable. Error bars represent standard deviations (n = 3).</p>
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<p>Tissue nitrogen (N) concentrations in eight species/cultivars grown under four location/year combinations [Bragança 2022 (L1/22), Mirandela 2023 (L2/23), Bragança 2023 (L3/23), and Bragança 2024 (L4/24)]. The <span class="html-italic">X</span>-axis represents Julian dates (starting from 1 January) to express the dates as a continuous variable. Error bars represent standard deviations (n = 3).</p>
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<p>Nitrogen (N) recovery in aboveground biomass in eight species/cultivars grown under four location/year combinations [Bragança 2022 (L1/22), Mirandela 2023 (L2/23), Bragança 2023 (L3/23), and Bragança 2024 (L4/24)]. The <span class="html-italic">X</span>-axis represents Julian dates (starting from January 1) to express the dates as a continuous variable. Error bars represent standard deviations (n = 3).</p>
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<p>Nitrogen (N) recovery in aboveground biomass in eight species/cultivars grown under four location/year combinations [Bragança 2022 (L1/22), Mirandela 2023 (L2/23), Bragança 2023 (L3/23), and Bragança 2024 (L4/24)]. The <span class="html-italic">X</span>-axis represents Julian dates (starting from January 1) to express the dates as a continuous variable. Error bars represent standard deviations (n = 3).</p>
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21 pages, 2258 KiB  
Article
Identification of Soil Quality Factors and Indicators in Mediterranean Agro-Ecosystems
by Eleftherios Evangelou and Christina Giourga
Sustainability 2024, 16(23), 10717; https://doi.org/10.3390/su162310717 - 6 Dec 2024
Viewed by 802
Abstract
Soil quality offers a holistic approach for understanding the relationships between soil’s biological, chemical, and physical properties, which is crucial for sustainable land use and the management of non-renewable soil resources. This study evaluates the impact of land use on a set of [...] Read more.
Soil quality offers a holistic approach for understanding the relationships between soil’s biological, chemical, and physical properties, which is crucial for sustainable land use and the management of non-renewable soil resources. This study evaluates the impact of land use on a set of 23 soil quality indicators (SQIs) across 5 land uses of the Mediterranean agro-ecosystems: forest, olive groves, wheat fields, a corn/wheat crop rotation system, and pasture. Seasonal soil sampling was carried out over two consecutive years in three conventionally managed fields representing each land use type. For each sampling, physicals SQIs (soil moisture, porosity-Vp-, bulck density-BD-, water holding capacity-WHC-, clay, silt, sand), chemical SQIs (organic carbon-Corg-, total Nitrogen-TN-, C/N, PH, electrical conductivity-EC-, ammonium-NH4-N-, nitrate-NO3-N- and available nitrogen-Nmin-), and biological SQIs (soil microbial biomass C-Cmic- and N-Nmic-, Cmic/Nmic, Cmic/Corg, Nmic/TN, active carbon—Cact-, Cact/Corg) were evaluated. Through multivariate analysis, five key soil quality factors—organic matter, microbial biomass, nutrients, C/N ratio, and compaction—were identified as indicators of soil quality changes due to land use, explaining 82.9% of the total variability in the data. Discriminant analysis identified organic matter and the C/N factors as particularly sensitive indicators of soil quality changes, reflecting the quantity and quality of soil organic matter, incorporating 87.8% of the SQIs information resulting from the 23 indicators. ΤΝ, accounting for 84% of the information on the organic matter factor, emerges as a key indicator for predicting significant changes in soil quality due to land use or management practices. The TN and C/N proposed indicators offer a simplified yet effective means of assessing soil resource sustainability in the Mediterranean agroecosystems, providing practical tools for monitoring and managing soil quality. Full article
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<p>The study area and sampling sites in the watershed of Kaloni Gulf (Lesvos Island, Greece).</p>
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<p>Mean monthly air temperature (line) and rainfall (bars), starting from March (M) 1st year to February (F) 2nd year.</p>
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<p>Seasonal variation of soil moisture Cmic (<b>a</b>), Nmic (<b>b</b>), Cmic (<b>c</b>), and the Cmic/Nim (<b>d</b>) ratio. The values represent the means of two samplings conducted during the same season across two consecutive years of the study.</p>
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<p>Scatter plot of land uses regarding the discriminant scores of the first two functions.</p>
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17 pages, 1709 KiB  
Article
Non-Destructive Biomarkers in Non-Target Species Earthworm Lumbricus terrestris for Assessment of Different Agrochemicals
by Antonio Calisi, Tiziana Cappello, Mario Angelelli, Maria Maisano, Davide Rotondo, Davide Gualandris, Teodoro Semeraro and Francesco Dondero
Environments 2024, 11(12), 276; https://doi.org/10.3390/environments11120276 - 3 Dec 2024
Viewed by 456
Abstract
In many agroecosystems, agrochemicals are widely used to control crop pests, but often affect many non-target species of ecological and agronomic interest, such as earthworms. Earthworms are considered useful indicators of soil contamination. Exposure of these organisms to contaminants occurs mainly through the [...] Read more.
In many agroecosystems, agrochemicals are widely used to control crop pests, but often affect many non-target species of ecological and agronomic interest, such as earthworms. Earthworms are considered useful indicators of soil contamination. Exposure of these organisms to contaminants occurs mainly through the large amount of soil ingested, which passes through the digestive tract, which is closely associated with the coelom and its fluids. In this work, we used the coelomic fluids of earthworms exposed to copper sulfate and chlorpyrifos to standardize a set of non-destructive biomarkers useful for assessing the contamination in agroecosystems. Metallothionein concentrations, acetylcholinesterase inhibition, lysosomal membrane stability, micronucleus frequency, morphometric alterations, and granulocyte cytoskeleton polymerization were analyzed. The results showed that all the biomarkers used were detectable in the coelomic fluid. Furthermore, the data obtained showed highly significant variations for all biomarkers studied, thus demonstrating that the use of coelomic fluid for biomarker assessment in non-target species offers numerous advantages for field applications. Full article
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<p>Area of two-dimensional digitized granulocyte images in control and treated animals during the dose–response of exposure experiments (see <a href="#sec1-environments-11-00276" class="html-sec">Section 2</a> Materials and Methods). (<b>a</b>) Representative granulocyte images from control (upper) earthworms and treated (lower) earthworms (100×, Bar 20 µm); (<b>b</b>) copper sulfate exposure; (<b>c</b>) chlorpyrifos exposure. Data are reported as mean ± SEM (n = 80). The statistical significance of data was determined by one-way ANOVA. The homogeneity of variance was tested by Cochran’s test. Dunnett’s multiple comparison tests were used for comparison of different concentrations of a single contaminant. * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Cytoskeleton polymerization. (<b>a</b>) Representative images of granulocytes from control (top) earthworms and treated (bottom) earthworms stained with rhodamine phalloidin. Images show the increase in actin polymerization in treated granulocytes with round morphology, short and blunt filopodia. Arrows indicate polymerization mainly localized in the cell cortex (100×, bar 20 µm), N = nucleus. (<b>b</b>,<b>c</b>) Fluorescence intensity (pixels) per cell emitted by rhodamine phalloidin-labeled granulocytes from organisms exposed to copper (<b>b</b>), and chlorpyrifos (<b>c</b>). The measured fluorescence intensity is specific to the presence of F-actin in the cell. Data are expressed as mean ± SEM (n = 80). Statistical significance of data was determined by one-way ANOVA. Homogeneity of variance was tested by Cochran’s test. Dunnett’s multiple comparison tests was used to compare different concentration of a single contaminant. ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>(<b>a</b>) Lysosomal membrane stability measured on granulocytes by the NRRA method in animals exposed to agrochemicals. Data are expressed as mean ± SEM. Statistical significance of data was determined by one-way ANOVA. Homogeneity of variance was tested by Cochran’s test. ** <span class="html-italic">p</span> &lt; 0.01, * <span class="html-italic">p</span> &lt; 0.05. (<b>b</b>) Frequency of micronuclei wasobserved in earthworm granulocytes. Data are expressed as frequency ‰. At least 1000 cells were identified for each condition examined. Statistical significance of the data (values greater than 5‰) was determined by one-way ANOVA followed by Cochran’s test. ** <span class="html-italic">p</span> &lt; 0.01, * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>(<b>a</b>,<b>b</b>) Metallothionein concentration measured in organisms exposed to copper (<b>a</b>), and chlorpyrifos (<b>b</b>). (<b>c</b>,<b>d</b>) Acetylcholinesterase activity measured in organisms exposed to copper (<b>c</b>), and chlorpyrifos (<b>d</b>). Data are expressed as mean ± SEM (n = 10). Statistical significance of data was determined by one-way ANOVA. Homogeneity of variance was tested by Cochran’s test. Dunnett’s multiple comparison test was used to compare different concentrations of single contaminants. * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01.</p>
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28 pages, 7193 KiB  
Article
Country-Scale Crop-Specific Phenology from Disaggregated PROBA-V
by Henry Rivas, Nicolas Delbart, Fabienne Maignan, Emmanuelle Vaudour and Catherine Ottlé
Remote Sens. 2024, 16(23), 4521; https://doi.org/10.3390/rs16234521 - 2 Dec 2024
Viewed by 367
Abstract
Large-scale crop phenology monitoring is essential for agro-ecosystem policy. Remote sensing helps track crop development but requires high-temporal and spatial resolutions. While datasets with both attributes are now available, their large-scale applications require significant resources. Medium-resolution data offer daily observations but lack detail [...] Read more.
Large-scale crop phenology monitoring is essential for agro-ecosystem policy. Remote sensing helps track crop development but requires high-temporal and spatial resolutions. While datasets with both attributes are now available, their large-scale applications require significant resources. Medium-resolution data offer daily observations but lack detail for smaller plots. This study generated crop-specific phenomaps for mainland France (2016–2020) using PROBA-V data. A spatial disaggregation method reconstructed NDVI time series for individual crops within mixed pixels. Then, phenometrics were extracted from disaggregated PROBA-V and Sentinel-2 separately and compared to observed phenological stages. Results showed that PROBA-V-based phenomaps closely matched observations at regional level, with moderate accuracy at municipal level. PROBA-V demonstrated a higher detection rate than Sentinel-2, especially in cloudy periods, and successfully generated phenomaps before Sentinel-2B’s launch. The study highlights PROBA-V’s potential for operational crop monitoring, i.e., wheat heading and oilseed rape flowering, with performance comparable to Sentinel-2. PROBA-V outputs complement Sentinel-2: phenometrics cannot be generated at plot level but are efficiently produced at regional or national scales to study phenological gradients more easily than with Sentinel-2 and with similar accuracy. This approach could be extended to MODIS or SPOT-VGT, to generate historical phenological data, providing that a crop map is available. Full article
(This article belongs to the Special Issue Remote Sensing of Land Surface Phenology II)
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<p>General flowchart of this study. Crop-specific phenological mapping procedure using (<b>A</b>) PROBA-V (300 m) and (<b>B</b>) Sentinel-2 (10 m) data. PROBA-V-based phenometrics were extracted at pixel level, while those from Sentinel-2 were extracted at plot level. Both are inter-compared and compared with TEMPO data at the municipal level. PROBA-V-based phenometrics are then compared with Céré’Obs data at the regional level. For phenometrics extraction, thresholds were calibrated in Block A using disaggregated PROBA-V NDVI time series and TEMPO data, and were then applied identically to both disaggregated PROBA-V and Sentinel-2 NDVI time series.</p>
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<p>Study area location. The validation of the phenomaps was carried out at regional and municipal scales. (<b>A</b>) The regional comparison between the phenometrics (from PROBA-V) and the observed (from Céré’Obs) median phenological dates was made across administrative regions, highlighted in both yellow and orange (n = 14), while the comparison between phenometrics and observed phenological progression (i.e., the percentage of area reaching a given phenological stage, as a function of date) focused on regions highlighted in orange only (n = 3). Regional ground data were not available for the three regions highlighted in gray. (<b>B</b>,<b>C</b>) The comparison at municipal level between the phenometrics (from PROBA-V and Sentinel-2) and the observed (from TEMPO) median phenological stage dates was made across points highlighted in blue (winter wheat) and red (oilseed rape) within inter-comparison sites. Inter-comparison sites were delimited by seven Sentinel-2 tiles: 30UWU, 31UCQ, 31UDP, 31TCN, 31UGQ, 31TFN, and 31TCJ. Additionally, PROBA-V-based phenomaps were also compared to TEMPO data available outside these inter-comparison sites. Finally, the green mask shows the winter wheat and oilseed rape areas declared in 2019 according to LPIS.</p>
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<p>NDVI time profile for (<b>A</b>) winter wheat and (<b>B</b>) oilseed rape. Values represent the national average derived from disaggregated PROBA-V (300 m) data in 2019, which were fitted using the Whittaker smoother model. Vertical lines indicate observed median dates across France in 2019, of all phenological stages of interest. Vertical line labels represent winter wheat tillering (BBCH29), stem elongation (BBCH31), heading (BBCH51), development of fruits (BBCH75), and senescence (BBCH99); and oilseed rape stem elongation (BBCH31), flowering (BBCH65), and development of fruits (BBCH73). These dates were obtained from the TEMPO dataset, except for those of winter wheat stem elongation (BBCH31) and senescence (BBCH99), which were obtained from the Céré’Obs dataset. Panel (A) details the amplitude definition for each side of the curve and the calibrated threshold value for each phenometric associated with each phenological stage of interest. Curve sides are relative to the maximum of the growing season. In panel (B), calibrated threshold values are shown for phenometrics associated with stem elongation (BBCH31) and development of fruits (BBCH73), which were obtained using the same amplitude definition process detailed in panel (A). For flowering (BBCH65), the associated phenometric (NDVI<sub>local_min</sub>) is shown within the corresponding temporal window (gray band). A local minimum occurs when the first derivative (dashed line) is zero at time <span class="html-italic">t</span>, negative at <span class="html-italic">t</span> − 1, and positive at <span class="html-italic">t</span> + 1.</p>
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<p>PROBA-V-based phenomaps of winter wheat. Each column represents a phenometric associated with a phenological stage available in the Céré’Obs database, i.e., SOS<sub>54–60</sub> with stem elongation (BBCH31), SOS<sub>95–98</sub> with heading (BBCH51) and EOS<sub>10–15</sub> with senescence (BBCH99), respectively. Each row represents a year in our study period. The color palette represents the day of the year (DoY) on which the phenometric was detected.</p>
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<p>Regional comparison of phenometrics (from PROBA-V) and phenological stages (from Céré’Obs) for winter wheat. (<b>A</b>) SOS<sub>54–60</sub> vs. stem elongation, (<b>B</b>) SOS<sub>95–98</sub> vs. Heading, and (<b>C</b>) EOS<sub>10–15</sub> vs. senescence. Each point represents a region median date and its color the year of interest. Median dates are expressed in the day of the year (DoY).</p>
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<p>Intra-regional comparison of phenometrics and associated phenological stages in terms of phenological progression in 2019. Phenometrics progression (in % of area) was obtained from the PROBA-V-based phenomaps (dashed lines), while phenological stage progression was obtained from Céré’Obs (solid lines). Each column represents a specific winter wheat phenometric and its associated phenological stage: (<b>A</b>–<b>C</b>) SOS<sub>54–60</sub> vs. stem elongation and (<b>D</b>–<b>F</b>) SOS<sub>95–98</sub> vs. heading, while each row represents a region of interest. The curves represent the values fitted with a logistic function.</p>
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<p>Municipal comparison of phenometrics and associated phenological stages in terms of median dates. Phenometrics dates were obtained from both PROBA-V- and Sentinel-2-based phenomaps, while phenological stages were obtained from the TEMPO dataset. Each column represents a specific winter wheat phenometric compared to associated phenological stage: (<b>A</b>–<b>D</b>) SOS<sub>42–53</sub> vs. tillering, (<b>E</b>–<b>H</b>) SOS<sub>95–98</sub> vs. Heading, and (<b>I</b>–<b>L</b>) EOS<sub>60–89</sub> vs. development of fruits. First row shows the locations where ground observations of each stage were made. Second row shows scatter-plots from all these observed municipalities, while third and fourth rows show scatter-plots from only the observed municipalities within the inter-comparison sites (dashed boxes), where we were able to extract phenometrics from Sentinel-2: third row represents estimated dates from PROBA-V, while fourth row represents those from Sentinel-2. In these last two rows, assessed municipalities were identical for both sensors. Scatter-plots and maps share the same color code representing the year of interest. Median dates are expressed in the day of the year (DoY).</p>
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<p>Municipal comparison of phenometrics and associated phenological stages in terms of median dates. Phenometrics dates were obtained from both PROBA-V and Sentinel-2-based phenomaps, while phenological stages came from the TEMPO dataset. Each column represents a specific oilseed rape phenometric compared to associated phenological stage: (<b>A</b>–<b>D</b>) SOS<sub>30–45</sub> vs. stem elongation, (<b>E</b>–<b>H</b>) NDVI<sub>local_min</sub> vs. Flowering, and (<b>I</b>–<b>L</b>) EOS<sub>97–99</sub> vs. development of fruits. First row shows the locations where ground observations of each stage were made. Second row shows scatter-plots from all these observed municipalities, while third and fourth rows show scatter-plots from only the observed municipalities within the inter-comparison sites (dashed boxes), where we were able to extract phenometrics from Sentinel-2: third row represents estimated dates from PROBA-V, while fourth row represents those from Sentinel-2. In these last two rows, assessed municipalities were identical for both sensors. Scatter-plots and maps share the same color code representing the year of interest. Median dates are expressed in the day of the year (DoY).</p>
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<p>Phenological detection rate from both PROBA-V and Sentinel-2 data. Each row represents a specific crop, and each column a specific phenometric: (<b>A</b>–<b>C</b>) winter wheat SOS<sub>42–53</sub>, SOS<sub>95–98</sub> and EOS<sub>60–89</sub>, (<b>D</b>–<b>F</b>) oilseed rape SOS<sub>30–45</sub>, NDVI<sub>local_min</sub>, and EOS<sub>97–99</sub>. The assessment was conducted based on all municipalities in the intercomparison sites, not only on those in which ground observations were carried out. On average between 2016 and 2020, 1943 municipalities were assessed for winter wheat and 1293 for oilseed rape.</p>
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15 pages, 2808 KiB  
Article
Influence of Land-Use Practices on Soil Organic Carbon and Microbial Biomass in Coffee and Orange Agroecosystems
by Barsha Parajuli, Nabin Lamichhane, Nikolaos Monokrousos, Chandra Prasad Pokhrel and Ram Kailash Prasad Yadav
Land 2024, 13(12), 2076; https://doi.org/10.3390/land13122076 - 2 Dec 2024
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Abstract
The agroforestry and intercropping systems are recognized as important options for greenhouse gas mitigation. The primary objective of this study was to assess the impacts of land use change from Orange (O) monoculture to Coffee-Orange (CO) intercropping and Coffee-Forest (CF) agroforest systems, implemented [...] Read more.
The agroforestry and intercropping systems are recognized as important options for greenhouse gas mitigation. The primary objective of this study was to assess the impacts of land use change from Orange (O) monoculture to Coffee-Orange (CO) intercropping and Coffee-Forest (CF) agroforest systems, implemented 20 years ago, on soil properties at three different soil depth layers (0–10 cm, 10–20 cm, 20–30 cm), with a particular focus on microbial biomass carbon (MBC) and soil organic carbon (SOC) levels. Although there were no changes in most of the soil’s physical properties, the soil’s chemical properties varied significantly across different land use types. SOC was higher in CF and CO systems compared to the O system; however, only in the CO system was the SOC incorporated evenly across all depths. Regression analysis showed that, in the CO system, microbial biomass carbon increased significantly, suggesting that these systems are more promising for carbon sequestration. The low pH and phosphorus values in the agroforest system were identified as limiting factors for microbial biomass enhancement. Importantly, the integration of coffee into orange cultivation not only enhances economic benefits but also contributes to long-term carbon sequestration by increasing SOC in deeper soil layers. Full article
(This article belongs to the Special Issue Soils and Land Management under Climate Change)
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<p>Map of the study area. (<b>A</b>) Location of Syangja District in Nepal. (<b>B</b>) Syangja District highlighting Putali Bazaar Municipality and the designated sampling area. (<b>C</b>) Putalibazer municipality with sampling points: O represents orange agroecosystems, CF denotes coffee-forest agroforestry systems, and CO indicates coffee-orange agroforestry systems.</p>
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<p>Mean monthly temperature and rainfall of the study area (2011–2021). Data Source: Department of Hydrology and Meteorology (DHM), Government of Nepal.</p>
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<p>Soil Carbon and Nitrogen Ratio across Land Use Types. Bars with different letters indicate significant differences between land use types at a 0.05 significance level. CO: Coffee orange system, CF: Coffee Forest system and O: Orange system. Results from the two-way ANOVA are presented as follows: L = effect of land use type, D = effect of soil depth, L × D = interaction effect. Significance levels are denoted as ** <span class="html-italic">p</span> &lt; 0.01 and ns <span class="html-italic">p</span> &gt; 0.05.</p>
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<p>The effect of land use type (L), soil depth (D), and their interaction (L × D) on soil Microbial biomass carbon (MBC) across Land Use Types, as revealed by two-way ANOVA. Bars with different lower-case letters indicate significant differences between depth, and uppercase letters indicate the difference between the land use types at a 0.05 significance level. CO: Coffee orange system, CF: Coffee Forest system and O: Orange system. Significance levels are denoted as, *** <span class="html-italic">p</span> &lt; 0.001, and ns <span class="html-italic">p</span> &gt; 0.05.</p>
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<p>Regression analysis between soil microbial carbon (mg kg<sup>−1</sup>) and soil organic carbon across different land use systems. (<b>A</b>) Coffee-Orange system (CO), (<b>B</b>) Coffee-Forest system (CF), and (<b>C</b>) Orange land use system (O). MBC: soil microbial biomass carbon, SOC: Soil Organic Carbon.</p>
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<p>The effect of land use type (L), soil depth (D), and their interaction (L × D) on the ratio of soil Microbial biomass carbon (MBC) and soil organic carbon (SOC) across Land Use Types, as revealed by two-way ANOVA. Bars with different lower-case letters indicate significant differences between depth, and uppercase letters indicate the difference between the land use types at a 0.05 significance level. CO: Coffee orange system, CF: Coffee Forest system and O: Orange system. Significance levels are denoted as ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, and ns <span class="html-italic">p</span> &gt; 0.05.</p>
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<p>Correlation between the measured parameters. SOC: soil organic carbon, TN: total nitrogen, BD: bulk density, pH, P: available phosphorous, MBC: microbial biomass carbon. Significance levels are denoted as * <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, and ns <span class="html-italic">p</span> &gt; 0.05.</p>
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<p>Principal Component Analysis (PCA) biplot of soil properties. The first two principal components (Dim 1 and Dim 2) explain 50% and 21.5% of the total variance, respectively. Arrows represent soil properties, with length reflecting influence strength and direction indicating their contribution to principal components. (<b>A</b>) Depths and (<b>B</b>) Land use are displayed as distinct clusters, showing variations in soil properties among systems. Variables BD: bulk density, pH, phosphorus, EC: electric conductivity, TN: total nitrogen, MBC: soil microbial biomass carbon, SOC: soil organic carbon.</p>
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10 pages, 1823 KiB  
Article
Natural Increases in Parasitoid and Predator Abundances and a Shift in Species Dominance Point to Improved Suppression of the Sorghum Aphid Since Its Invasion into North America
by Pius A. Bradicich, Ashleigh M. Faris, John W. Gordy and Michael J. Brewer
Insects 2024, 15(12), 958; https://doi.org/10.3390/insects15120958 - 2 Dec 2024
Viewed by 648
Abstract
Melanaphis sorghi (Theobald) (Hemiptera: Aphididae), commonly called the sorghum aphid, is an invasive pest of sorghum (Sorghum bicolor) (L.) in North America. It was first observed in 2013 along the Gulf Coastal Plains ecoregion of Texas, Louisiana (USA), and Mexico, where [...] Read more.
Melanaphis sorghi (Theobald) (Hemiptera: Aphididae), commonly called the sorghum aphid, is an invasive pest of sorghum (Sorghum bicolor) (L.) in North America. It was first observed in 2013 along the Gulf Coastal Plains ecoregion of Texas, Louisiana (USA), and Mexico, where it quickly established itself as an economically important pest within a few years. This ecoregion contains an established complex of aphid natural enemies, including both predators and parasitoids. In the decade since its invasion, indicators of increased suppression observed across six years and five locations from south to north Texas were as follows: (1) aphid abundances trending downwards across the years, (2) overall natural enemy abundances trending upwards during the same time period, and (3) a key parasitoid and coccinellid species increasing in dominance. Two key taxa, Aphelinus nigritus (Howard) (Hymenoptera: Aphelinidae) and six species of coccinellids (Coleoptera: Coccinellidae), were likely responsible for the majority of the pest’s suppression. In light of these findings, the importance of monitoring and stewarding natural enemies of invasive insect pests is discussed as part of a comprehensive strategy to measure and reduce the impact of a pest invasion in large-scale agroecosystems. Full article
(This article belongs to the Section Insect Pest and Vector Management)
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<p>Pie graphs depicting the change in natural enemy frequencies for each location and year combination. The recovered number of taxa (# at bottom) and specific natural enemy taxa (boxed numbers) are given for each chart. Each pie slice depicts the frequency of each natural enemy (NE) group from the whole. For all individual 6 (group) by 1 (location year) contingency tables, χ<sup>2</sup> &gt; 100, and <span class="html-italic">p</span>-values &lt; 0.001. A total of 20 location years were analyzed. Empty spaces indicate location years that were not sampled. The inset map of Texas depicts the approximate coordinates of the five sampling locations used in the study.</p>
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<p>Line graphs showing <span class="html-italic">M. sorghi</span> and dominant natural enemy population changes across the years for each of the five locations: Solid lines show insects (<span class="html-italic">M. sorghi</span>, <span class="html-italic">A. nigritus,</span> and coccinellids) per leaf, and the dashed lines show the ratio of dominant natural enemies (<span class="html-italic">A. nigritus</span> and coccinellids) to <span class="html-italic">M. sorghi</span>. For interpretability, natural enemy data have been inflated by a factor of ten.</p>
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