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17 pages, 3917 KiB  
Article
Efficiency of Desiccation, Biomass Production, and Nutrient Accumulation in Zuri and Quênia Guinea Grasses in Integrated Crop–Livestock Systems and Second-Crop Maize
by Bruno de Souza Marques, Kátia Aparecida de Pinho Costa, Hemython Luís Bandeira do Nascimento, Ubirajara Oliveira Bilego, Eduardo Hara, Rose Luiza Moraes Tavares, Juliana Silva Rodrigues Cabral, Luciana Maria da Silva, José Carlos Bento, Breno Furquim de Morais, Adriano Carvalho Costa and Tiago do Prado Paim
Plants 2024, 13(22), 3250; https://doi.org/10.3390/plants13223250 - 20 Nov 2024
Viewed by 197
Abstract
Modern agriculture faces the challenge of increasing production without expanding cultivated areas, promoting sustainable practices that ensure food security and environmental preservation. Integrated crop–livestock systems (ICLSs) stand out as an effective strategy, diversifying and intensifying agricultural production in a sustainable manner, ensuring adequate [...] Read more.
Modern agriculture faces the challenge of increasing production without expanding cultivated areas, promoting sustainable practices that ensure food security and environmental preservation. Integrated crop–livestock systems (ICLSs) stand out as an effective strategy, diversifying and intensifying agricultural production in a sustainable manner, ensuring adequate soil cover, and improving nutrient cycling efficiency. Thus, this study aimed to explore and compare integrated crop–livestock systems with Zuri guinea grass (Panicum maximum cv. BRS Zuri) and Quênia guinea grass (Panicum maximum cv. BRS Quênia) against the conventional soybean/maize succession method in a tropical region, and how these systems affect biomass decomposition, C:N ratio, nutrient cycling, and fertilizer equivalents. A field experiment was conducted in two phases: the first in the second-crop season and the second in the main season, using a randomized block design with four replicates. The treatments consisted of two ICLS systems, one with Zuri and Quênia guinea grasses established after soybean, and a succession system with maize established after soybean. The results indicated that Quênia guinea grass showed greater desiccation efficiency, with an injury rate of 86.5% at 21 days, 8.5% higher compared to Zuri guinea grass. In terms of biomass, Zuri and Quênia guinea grasses had average productions of 7021.1 kg ha−1, which was 43.25% higher compared to maize biomass. The biomass decomposition of the grasses was faster due to their lower C:N ratio, resulting in greater nutrient release into the soil. Both forage grasses (Zuri and Quênia guinea grasses) are suitable for integrated crop–livestock systems, as they showed similar biomass production and nutrient accumulation. Soybean yield was not influenced by the different cropping systems, showing similar results between the biomass of Zuri and Quênia guinea grasses and maize. However, grass biomass enriches the soil more through the return of fertilizer equivalents, which in future studies could be considered for the reduction of mineral fertilizers, ensuring greater sustainability of agricultural systems. Full article
(This article belongs to the Special Issue Ecophysiology and Quality of Crops)
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Figure 1
<p>Desiccation efficiency of Zuri and Quênia guinea grass.</p>
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<p>Remaining biomass (<b>a</b>) and C:N ratio (<b>b</b>) of maize and <span class="html-italic">Panicum maximum</span> cultivar cropping systems during soybean development (from 0 to 120 days).</p>
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<p>Accumulation of nitrogen (<b>a</b>), phosphorus (<b>b</b>), potassium (<b>c</b>), and sulfur (<b>d</b>) in the biomass of maize and <span class="html-italic">Panicum maximum</span> cultivar cropping systems during soybean development (from 0 to 120 days).</p>
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<p>Equivalent contents of K<sub>2</sub>O (<b>a</b>), N (<b>b</b>), and P<sub>2</sub>O<sub>5</sub> (<b>c</b>) in the biomass of maize, Zuri, and Quênia guinea grasses. Means followed by different letters differ significantly according to Tukey’s test at 5% probability.</p>
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<p>Pearson correlation (r) between parameters. Positive correlations are represented by blue backgrounds, and negative correlations are represented by red backgrounds. Parameters: phosphorous: phosphorus concentration, biomass: biomass accumulation, potassium: potassium concentration, equivalent K<sub>2</sub>O: equivalent concentration of K<sub>2</sub>O, nitrogen: nitrogen concentration, equivalent N: equivalent concentration of nitrogen, sulfur: sulfur concentration, CN: carbon/nitrogen ratio, productivity: crop yield, equivalent P<sub>2</sub>O<sub>5</sub>: equivalent concentration of P<sub>2</sub>O<sub>5</sub>.</p>
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<p>Two-dimensional scatter plot of principal component analysis and scores of the 10 variables, observations, and treatment means for initial biomass nutrient accumulation and soybean productivity. Maize: maize biomass; Quênia: Quênia guinea grass biomass; Zuri: Zuri guinea grass biomass; biomass; CN; nitrogen; phosphorus; potassium; sulfur; equivalent N; equivalent P<sub>2</sub>O<sub>5</sub>; equivalent K<sub>2</sub>O; productivity.</p>
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<p>Monthly precipitation and temperature recorded from February 2023 to March 2024, at the Centro Tecnológico COMIGO in Rio Verde–GO, Brazil.</p>
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<p>Aerial view of the experimental area (source: Google Earth). The orange lines delineate block 1, and the blue lines delineate block 2.</p>
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<p>Schematic representation of the cropping systems with <span class="html-italic">Panicum</span> genus forages in an integrated crop–livestock system (<b>a</b>) and the maize cropping system in succession to soybean (<b>b</b>).</p>
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24 pages, 1698 KiB  
Article
Integrating Mixed Livestock Systems to Optimize Forage Utilization and Modify Woody Species Composition in Semi-Arid Communal Rangelands
by Mhlangabezi Slayi and Ishmael Festus Jaja
Land 2024, 13(11), 1945; https://doi.org/10.3390/land13111945 - 18 Nov 2024
Viewed by 238
Abstract
Communally owned rangelands serve as critical grazing areas for mixed livestock species such as cattle and goats, particularly in the arid and semi-arid regions of Southern Africa. This study aimed to evaluate the nutritional composition and woody species composition of communal rangelands where [...] Read more.
Communally owned rangelands serve as critical grazing areas for mixed livestock species such as cattle and goats, particularly in the arid and semi-arid regions of Southern Africa. This study aimed to evaluate the nutritional composition and woody species composition of communal rangelands where cattle and goat flocks graze together and to investigate the influence of grazing intensity on vegetation dynamics. Vegetation surveys were conducted across varying grazing intensities to assess species richness, biomass, and dietary preferences, while soil properties were analyzed to determine their interaction with vegetation attributes. Stepwise regression and path analyses were used to explore the relationships between soil characteristics, vegetation structure, and livestock dietary choices. The results revealed that high grazing pressure significantly reduced grass biomass (p = 0.003) and woody species density (p = 0.007) while increasing shrub cover (p = 0.018). Nutritional analysis indicated that goats preferred woody shrubs, which contributed 42.1% of their diet compared to 27.8% for cattle (p = 0.008). Regression analysis further showed that soil organic carbon (p = 0.002) and tree height (p = 0.041) were strong predictors of shrub cover. Seasonal variation significantly affected forage availability and nutritional content, with higher crude protein levels recorded during the wet season (p = 0.007). These findings suggest that grazing management strategies should be tailored to the distinct forage needs of cattle and goats to maintain the productivity and ecological stability of communal rangelands. A holistic approach that considers livestock dietary preferences, vegetation composition, and soil health is essential for sustainable rangeland management in mixed-species grazing systems. Full article
(This article belongs to the Section Land–Climate Interactions)
16 pages, 3427 KiB  
Systematic Review
Slow-Release Fertilisers Control N Losses but Negatively Impact on Agronomic Performances of Pasture: Evidence from a Meta-Analysis
by Gunaratnam Abhiram
Nitrogen 2024, 5(4), 1058-1073; https://doi.org/10.3390/nitrogen5040068 (registering DOI) - 17 Nov 2024
Viewed by 303
Abstract
High nitrogen (N) losses and low nitrogen utilisation efficiency (NUE) of conventional-nitrogen fertilisers (CNFs) are due to a mismatch between N-delivery and plant demand; thus, slow-release N fertilisers (SRNFs) are designed to improve the match. A quantitative synthesis is lacking to provide the [...] Read more.
High nitrogen (N) losses and low nitrogen utilisation efficiency (NUE) of conventional-nitrogen fertilisers (CNFs) are due to a mismatch between N-delivery and plant demand; thus, slow-release N fertilisers (SRNFs) are designed to improve the match. A quantitative synthesis is lacking to provide the overall assessment of SRNFs on pasture. This meta-analysis analyses application rate and type of SRNFs on N losses and agronomic performances with 65 data points from 14 studies in seven countries. Standardized mean difference of SRNFs for nitrate leaching losses and N2O emission were −0.87 and −0.69, respectively, indicating their effectiveness in controlling losses. Undesirably, SRNFs had a more negative impact on dry matter (DM) yield and NUE than CNFs. Subgroup analysis showed that SRNF type and application rate had an impact on all tested parameters. The biodegradable coating-type of SRNF outperformed other types in controlling N losses and improving agronomic performances. High application rates (>100 kg N ha−1) of SRNFs are more effective in controlling N losses. In conclusion, SRNFs are more conducive to controlling N losses, but they showed a negative impact on yield and NUE in pasture. Further studies are recommended to assess the efficacy of SRNFs developed using advanced technologies to understand their impact on pastoral agriculture. Full article
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<p>Schematic diagram for inclusion criteria of articles for this systematic review and meta-analysis (PRISMA) [<a href="#B32-nitrogen-05-00068" class="html-bibr">32</a>].</p>
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<p>The summary of the reported parameters from each study included in this meta-analysis [<a href="#B10-nitrogen-05-00068" class="html-bibr">10</a>,<a href="#B24-nitrogen-05-00068" class="html-bibr">24</a>,<a href="#B25-nitrogen-05-00068" class="html-bibr">25</a>,<a href="#B27-nitrogen-05-00068" class="html-bibr">27</a>,<a href="#B35-nitrogen-05-00068" class="html-bibr">35</a>,<a href="#B36-nitrogen-05-00068" class="html-bibr">36</a>,<a href="#B37-nitrogen-05-00068" class="html-bibr">37</a>,<a href="#B38-nitrogen-05-00068" class="html-bibr">38</a>,<a href="#B39-nitrogen-05-00068" class="html-bibr">39</a>,<a href="#B40-nitrogen-05-00068" class="html-bibr">40</a>,<a href="#B41-nitrogen-05-00068" class="html-bibr">41</a>,<a href="#B42-nitrogen-05-00068" class="html-bibr">42</a>,<a href="#B43-nitrogen-05-00068" class="html-bibr">43</a>,<a href="#B44-nitrogen-05-00068" class="html-bibr">44</a>].</p>
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<p>The nitrate leaching losses of conventional nitrogen fertilisers (CNFs) and slow-release nitrogen fertilisers (SRNFs) based on (<b>a</b>) SRNF types (PC—polymer coating, BC—biodegradable coating, IC—inorganic coating, and PCH—polymer chain), (<b>b</b>) fertiliser application rates (kg N/ha) and (<b>c</b>) overall studies. SMD stands for standard mean difference. Numbers next to range graph indicate the number of studies included for analysis.</p>
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<p>The correlation between effect size (standardized mean difference: SMD) and application rate of SRNFs for (<b>a</b>) nitrate leaching losses, (<b>b</b>) ammonium leaching losses, (<b>c</b>) N<sub>2</sub>O emission, (<b>d</b>) dry matter yield, (<b>e</b>) nitrogen utilisation efficiency (NUE) and (<b>f</b>) herbage nitrogen. Dark shade and light shades indicate a 95% confidence interval and a 95% prediction level, respectively.</p>
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<p>The effect of SRNF on ammonium leaching losses. SRNF and CNF refer to slow-release nitrogen fertiliser and conventional nitrogen fertiliser, respectively [<a href="#B10-nitrogen-05-00068" class="html-bibr">10</a>,<a href="#B27-nitrogen-05-00068" class="html-bibr">27</a>,<a href="#B36-nitrogen-05-00068" class="html-bibr">36</a>].</p>
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<p>The effect of SRNF on N<sub>2</sub>O emission. SRNF and CNF refer to slow-release nitrogen fertiliser and conventional nitrogen fertiliser, respectively [<a href="#B10-nitrogen-05-00068" class="html-bibr">10</a>,<a href="#B27-nitrogen-05-00068" class="html-bibr">27</a>,<a href="#B42-nitrogen-05-00068" class="html-bibr">42</a>,<a href="#B44-nitrogen-05-00068" class="html-bibr">44</a>].</p>
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<p>Dry matter yield of conventional nitrogen fertilisers (CNFs) and slow-release nitrogen fertilisers (SRNFs) based on (<b>a</b>) SRNF types (PC—polymer coating, BC—biodegradable coating, IC—inorganic coating, and PCH—polymer chain), (<b>b</b>) fertiliser application rates (kg N/ha), and (<b>c</b>) overall studies. SMD stands for standard mean difference. Numbers next to range graph indicate the number of studies included for analysis.</p>
Full article ">Figure 8
<p>The plant nutrient demand (PND) and nutrient delivery by slow-release nitrogen fertiliser (NDSRNF) for (<b>a</b>) other crops and (<b>b</b>) pasture. The blue shaded area shows the PND of pasture during continuous grass grazing.</p>
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<p>Herbage nitrogen (HN) in a pasture of conventional nitrogen fertilisers (CNFs) and slow-release nitrogen fertilisers (SRNFs) based on (<b>a</b>) SRNF types (PC—polymer coating, BC—biodegradable coating, IC—inorganic coating, and PCH—polymer chain), (<b>b</b>) fertiliser application rates (kg N/ha), and (<b>c</b>) overall studies. SMD stands for standard mean difference. Numbers next to range graph indicate the number of studies included for analysis.</p>
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<p>Nitrogen utilisation efficiency (NUE) in a pasture of conventional nitrogen fertilisers (CNFs) and slow-release nitrogen fertilisers (SRNFs) based on (<b>a</b>) SRNF types (PC—polymer coating, BC—biodegradable coating, IC—inorganic coating, and PCH—polymer chain), (<b>b</b>) fertiliser application rates (kg N/ha), and (<b>c</b>) overall studies. SMD stands for standard mean difference. Numbers next to range graph indicate the number of studies included for analysis.</p>
Full article ">
21 pages, 9120 KiB  
Article
Differentiating Cheatgrass and Medusahead Phenological Characteristics in Western United States Rangelands
by Trenton D. Benedict, Stephen P. Boyte and Devendra Dahal
Remote Sens. 2024, 16(22), 4258; https://doi.org/10.3390/rs16224258 - 15 Nov 2024
Viewed by 350
Abstract
Expansions in the extent and infestation levels of exotic annual grass (EAG) within the rangelands of the western United States are well documented. Land managers are tasked with developing plans to limit EAG spread and prevent irreversible ecosystem deterioration. The most common EAG [...] Read more.
Expansions in the extent and infestation levels of exotic annual grass (EAG) within the rangelands of the western United States are well documented. Land managers are tasked with developing plans to limit EAG spread and prevent irreversible ecosystem deterioration. The most common EAG species and the subject of extensive study is Bromus tectorum (cheatgrass). Cheatgrass has spread rapidly in western rangelands since its initial invasion more than 100 years ago. Another concerning aggressive EAG, Taeniatherum caput-medusae (medusahead), is also commonly found in some of these areas. To control the spread of EAGs, researchers have investigated applying several control methods during different developmental stages of cheatgrass and medusahead. These control strategies require accurate maps of the timing and spatial patterns of the developmental stages to apply mitigation strategies in the correct areas at the right time. In this study, we developed annual phenological datasets for cheatgrass and medusahead with two objectives. The first objective was to determine if cheatgrass and medusahead can be differentiated at 30 m resolution using their phenological differences. The second objective was to establish an annual phenology metric regression tree model used to map the growing seasons of cheatgrass and medusahead. Harmonized Landsat and Sentinel-2 (HLS)-derived predicted weekly cloud-free 30 m normalized difference vegetation index (NDVI) images were used to develop these metric maps. The result of this effort was maps that identify the start and end of sustained growing season time for cheatgrass and medusahead at 30 m for the Snake River Plain and Northern Basin and Range ecoregions. These phenological datasets also identify the start and end-of-season NDVI values, along with maximum NDVI throughout the study period. These metrics may be utilized to characterize annual growth patterns for cheatgrass and medusahead. This approach can be utilized to plan time-sensitive control measures such as herbicide applications or cattle grazing. Full article
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<p>Study area boundaries of Snake River Plain and Northern Basin and Range ecoregions within the exotic annual grass (EAG) study area of western U.S. rangelands. The masked-out areas (white/hollow) within the ecoregion are elevations greater than 2350 m or areas not classified as shrub or grassland by the 2019 National Land Cover Database.</p>
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<p>Phenology metrics indicated on a 52-week normalized difference vegetation index (NDVI) time-series curve.</p>
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<p>Phenology method flowchart. This flowchart is the overview of capturing the phenology training data for cheatgrass (BRTE [<a href="#B44-remotesensing-16-04258" class="html-bibr">44</a>]) and medusahead (TACA8 [<a href="#B44-remotesensing-16-04258" class="html-bibr">44</a>]). (<b>a</b>) The methods for extracting high probability BRTE and TACA8 pixels, (<b>b</b>) the decision tree analysis development using exotic annual grass (EAG) training data from Benedict et al. [<a href="#B21-remotesensing-16-04258" class="html-bibr">21</a>], (<b>c</b>) developing the training data, and (<b>d</b>) developing the phenology model.</p>
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<p>Box and whisker plots comparing training data for start-of-season time (SOST) (<b>a</b>) and maximum time (MAXT) (<b>b</b>) metrics for cheatgrass (BRTE [<a href="#B44-remotesensing-16-04258" class="html-bibr">44</a>]) and medusahead (TACA8 [<a href="#B44-remotesensing-16-04258" class="html-bibr">44</a>]) based on pixels with at least 20% cover.</p>
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<p>Training data median values per year and combined years for cheatgrass (BRTE [<a href="#B44-remotesensing-16-04258" class="html-bibr">44</a>]) and medusahead (TACA8 [<a href="#B44-remotesensing-16-04258" class="html-bibr">44</a>]) start-of-season time (SOST), end-of-season time (EOST), and maximum time (MAXT) based on pixels with at least 20% cover.</p>
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<p>Phenology model cross-validation scatter plots for cheatgrass (<b>a</b>–<b>c</b>) and medusahead (<b>d</b>–<b>f</b>) based on five-fold cross-validation using median Pearson’s r (<span class="html-italic">r</span>) and mean absolute error (MAE). Model-estimated start-of-season time (SOST) (<b>a</b>,<b>d</b>), end-of-season time (EOST) (<b>b</b>,<b>e</b>), and maximum time (MAXT) (<b>c</b>,<b>f</b>). The black lines are the 1:1 lines, and the dark blue lines represent the linear regression between estimated and observed weeks. The number of samples for cheatgrass (<b>a</b>–<b>c</b>) was 31,281 samples, and medusahead (<b>d</b>–<b>f</b>) had 14,798 samples.</p>
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<p>Top five features within cheatgrass (<b>a</b>) and medusahead (<b>b</b>) phenology models for start-of-season time (SOST), maximum time (MAXT), and end-of-season time (EOST). Week of year is represented with “Wk” followed by the week number, and the percentage used within the model. The “Other” variable is the sum of the remaining variables that were used less than the top five labeled here.</p>
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<p>Cheatgrass start-of-season time (SOST) for 2017–2022 (<b>a</b>–<b>f</b>, respectively) and medusahead SOST for 2017–2022 (<b>g</b>–<b>l</b>, respectively). The grey masked pixels represent areas where medusahead or cheatgrass cover is estimated at less than 1% for the respective year or is outside of the study area.</p>
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<p>Comparison between the start-of-season time (SOST) (<b>a</b>) and maximum time (MAXT) (<b>b</b>) phenology maps for cheatgrass (BRTE [<a href="#B44-remotesensing-16-04258" class="html-bibr">44</a>]) and medusahead (TACA8 [<a href="#B44-remotesensing-16-04258" class="html-bibr">44</a>]) with at least 20% cover from stratified sampling except for 2022. In 2022, medusahead did not exceed 50% more cover than cheatgrass in the same pixels, so random samples were taken within pixels with at least 15% cover. Cheatgrass SOST and MAXT extracted from 104,963 points and medusahead SOST and MAXT extracted from 52,070 points total.</p>
Full article ">Figure 10
<p>Comparison between the start-of-season NDVI (SOSN) (<b>a</b>) and maximum NDVI (MAXN) (<b>b</b>) phenology maps for cheatgrass (BRTE [<a href="#B44-remotesensing-16-04258" class="html-bibr">44</a>]) and medusahead (TACA8 [<a href="#B44-remotesensing-16-04258" class="html-bibr">44</a>]) with at least 20% cover from stratified sampling except for 2022. In 2022, medusahead did not exceed 50% more cover than cheatgrass in the same pixels, so random samples were taken within pixels with at least 15% cover. Cheatgrass SOSN and MAXN extracted from 104,963 points and medusahead SOSN and MAXN extracted from 52,070 points total.</p>
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<p>Cheatgrass cover for 2017–2022 (<b>a</b>–<b>f</b>, respectively) and medusahead cover for 2017–2022 (<b>g</b>–<b>l</b>, respectively) [<a href="#B30-remotesensing-16-04258" class="html-bibr">30</a>].</p>
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16 pages, 4929 KiB  
Article
Investigating Genetic Diversity and Correlations Between Mineral Concentration and Neurotoxin (β-ODAP) Content in the Lathyrus Genus
by Fadoua Abdallah, Zakaria Kehel, Mohamed Amine El Kalchi, Ahmed Amri, Adil el Baouchi, Zine El Abidine Triqui, Moez Amri and Shiv Kumar
Plants 2024, 13(22), 3202; https://doi.org/10.3390/plants13223202 - 14 Nov 2024
Viewed by 480
Abstract
Grass pea (Lathyrus sativus L.) is a nutritious legume crop well-adapted to fragile agro-ecosystems that can survive under challenging climatic conditions. The cultivation of grass pea faces stigma primarily due to the presence of β-N-Oxalyl-L-α, [...] Read more.
Grass pea (Lathyrus sativus L.) is a nutritious legume crop well-adapted to fragile agro-ecosystems that can survive under challenging climatic conditions. The cultivation of grass pea faces stigma primarily due to the presence of β-N-Oxalyl-L-α, β-diaminopropionic acid (β-ODAP), which is associated with a risk of inducing neurolathyrism upon prolonged consumption of its grains as a staple diet. The grass pea improvement program of the International Center for Agricultural Research in the Dry Areas (ICARDA) aims to reduce β-ODAP content to a safe level along with improving yield potential and nutritional quality of grass pea. In this study, 183 germplasm accessions representing 13 different Lathyrus species and 11 L. sativus breeding lines were evaluated for β-ODAP content based on Rao protocol and mineral concentration using ICP-OES. Significant variability was observed among the accessions for the studied traits. The results showed low β-ODAP content and high mineral concentration in 25 accessions of crop wild relatives, which included L. cicera, L. ochrus, and L. cassius, with one accession IG65277 of L. cassius, in addition to two lines, IG117034 and ACC1335, of L. sativus having very low β-ODAP content. Furthermore, some accessions of L. pseudocicera, L. aphaca, L. cicera, L. marmoratus, L. gorgoni, and L. tingitanus also showed low β-ODAP content. The results showed significant positive correlations among different trait combinations, viz., K and P (r = 0.193 ***), K and Fe (r = 0.177 ***), Mn and Fe (r = 0.210 ***), Mn and Se (r = 0.137 ***), β-ODAP and Mg (r = 0.158 **), and β-ODAP and Ca (r = 0.140 **). L. cicera, L. ochrus, and L. cassius were identified as a great source for improving the mineral concentration and reducing β-ODAP content in the cultivated grass pea. Full article
(This article belongs to the Section Plant Physiology and Metabolism)
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Figure 1
<p>Frequency distribution of macro- and micronutrients and <span class="html-italic">β</span>-ODAP concentrations in grass pea species.</p>
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<p>(<b>A</b>) Biplot of the first two dimensions of PCA based on the macronutrient contents for all tested grass pea germplasms. (<b>B</b>) Dendrogram showing the level of macronutrients in the total examined accessions. (<b>C</b>) Box plots showing a comparison of different macronutrient concentrations among the whole analyzed species.</p>
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<p>(<b>A</b>) Biplot of the first two dimensions of PCA based on the micronutrient contents for grass pea accessions. (<b>B</b>) Dendrogram showing the level of micronutrients in the total examined accessions. (<b>C</b>) Box plots showing a comparison of different micronutrient concentrations among the whole analyzed species.</p>
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<p>(<b>A</b>) Biplot of the first two dimensions of PCA based on <span class="html-italic">β</span>-ODAP content and nutrient concentration for grass pea accessions. (<b>B</b>) Dendrogram revealing the neurotoxins and nutrient levels of different tested accessions. (<b>C</b>) Box plot showing a comparison of different <span class="html-italic">β</span>-ODAP% concentrations among the whole analyzed accessions.</p>
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<p>Correlogram with ggpairs among different parameters (macro- and micronutrient concentrations and percentage of <span class="html-italic">β</span>-ODAP) recorded for all grass pea accessions *** significant at (<span class="html-italic">p</span> &lt; 0.001); ** significant at (<span class="html-italic">p</span> &lt; 0.01); * significant at (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Heat map and hierarchical clustering of 25 selected grass pea accessions for the best performance parameters (high nutrient level and low <span class="html-italic">β</span>-ODAP content).</p>
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17 pages, 1322 KiB  
Article
Evaluation of Forage Grasses Yield and Nitrogen Use Efficiency in Response to Combined Application of Spent Mushroom Substrate and Mineral Nitrogen
by Beata Wiśniewska-Kadżajan and Elżbieta Malinowska
Agronomy 2024, 14(11), 2680; https://doi.org/10.3390/agronomy14112680 - 14 Nov 2024
Viewed by 240
Abstract
In the era of the intensive use of mineral fertilizers, which results in a negative impact on the natural environment, it is necessary to use agrotechnical practices that use the potential of biodegradable waste. The physicochemical properties of the substrate after mushroom cultivation [...] Read more.
In the era of the intensive use of mineral fertilizers, which results in a negative impact on the natural environment, it is necessary to use agrotechnical practices that use the potential of biodegradable waste. The physicochemical properties of the substrate after mushroom cultivation (SMS—spent mushroom substrate) mean that this waste can be a safe and cheap source of ingredients for crops. The aim of this study was to investigate the effect of different doses of mineral fertilizers and SMS on the yield of two grass species, the nitrogen content in their biomass, and its uptake and use efficiency, as well as its accumulation in the soil. This research was based on a three-year (2017–2019) experiment that was conducted in bottomless pots in field conditions at the experimental facility of the University of Siedlce, Poland. The SMS was used together with mineral fertilization in various proportions. Two forage grass species were tested: Dactylis glomerata and Phleum pratense. In each year, three harvests of the cultivated grasses were collected. The introduction into the soil of the medium dose of the SMS nitrogen, supplemented annually with the medium dose of mineral nitrogen (SMS2 + N2PK), resulted in the greatest yield of the grasses (19.98 g·pot−1), as well as its greatest uptake (410.2 g·pot−1) and use efficiency (105%). The highest content of nitrogen (21.60 g·kg−1) was in the plants treated with the smallest dose of the SMS and supplemented with the greatest dose of the mineral nitrogen (SMS1 + N3PK). The greatest dose of the SMS nitrogen, applied with the smallest amount of the mineral nitrogen (SMS3 + N1PK), resulted in the most (1.70 g·kg−1) nitrogen accumulation in the soil. Full article
(This article belongs to the Section Soil and Plant Nutrition)
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<p>The cumulative monthly rainfall and average monthly air temperatures during the growing season.</p>
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<p>Biomass yield (g·pot<sup>−1</sup>) across fertilizer treatments and harvests. O—control plot (with no fertilizer treatment); N<sub>4</sub>PK (mineral fertilizers only)—3 g N, 1 g P, and 2.5 g K·pot<sup>−1</sup>; SMS<sub>1</sub> (SMS) + N<sub>3</sub>PK—1 g N·pot<sup>−1</sup> + 2 g N, 1 g P, and 2.5 g K·pot<sup>−1</sup>; SMS<sub>2</sub> + N<sub>2</sub>PK—1.5 g N·pot<sup>−1</sup> + 1.5 g N, 1 g P, and 2.5 g K·pot<sup>−1</sup>; SMS<sub>3</sub> + N<sub>1</sub>PK—2 g N·pot<sup>−1</sup> + 1 g N, 1 g P, and 2.5 g K·pot<sup>−1</sup>; a, b, c—different lower-case letters for the treatments indicate a significant difference in the mean values; A, B, C—different capital letters for the harvests indicate significant differences in the mean values.</p>
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<p>Biomass nitrogen content (g·kg<sup>−1</sup> DM), depending on fertilizer treatment and harvest. O—control plot (with no fertilizer treatment); N<sub>4</sub>PK (mineral fertilizers only)—3 g N, 1 g P, and 2.5 g K·pot<sup>−1</sup>; SMS<sub>1</sub> (SMS) + N<sub>3</sub>PK—1 g N·pot<sup>−1</sup> + 2 g N, 1 g P, and 2.5 g K·pot<sup>−1</sup>; SMS<sub>2</sub> + N<sub>2</sub>PK—1.5 g N·pot<sup>−1</sup> + 1.5 g N, 1 g P, and 2.5 g K·pot<sup>−1</sup>; SMS<sub>3</sub> + N<sub>1</sub>PK—2 g N·pot<sup>−1</sup> + 1 g N, 1 g P, and 2.5 g K·pot<sup>−1</sup>; a, b—different lower-case letters for the treatments indicate a significant difference in the mean values; A, B—different capital letters for the harvests indicate significant differences in the mean values; N.S.—not significant.</p>
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13 pages, 3956 KiB  
Article
Soil and Water Conservation Vegetation Restoration in Alpine Areas—Taking a Hydropower Station as an Example
by Yongxiang Cao, Sen Hou, Naichang Zhang, Zhen Bian and Haixing Wang
Water 2024, 16(22), 3270; https://doi.org/10.3390/w16223270 - 14 Nov 2024
Viewed by 377
Abstract
High-elevation and cold regions have harsh natural conditions with low temperatures and intense ultraviolet radiation, which impede plant growth and maintenance. Therefore, soil and water conservation vegetation restoration models are of great significance. In this study, a site condition analysis was performed based [...] Read more.
High-elevation and cold regions have harsh natural conditions with low temperatures and intense ultraviolet radiation, which impede plant growth and maintenance. Therefore, soil and water conservation vegetation restoration models are of great significance. In this study, a site condition analysis was performed based on three main limiting factors, including climatic and meteorological, soil, and topographic and geomorphological factors, providing a basis for vegetation restoration. The study area was divided into different site types. After investigating the situation of nurseries distributed in places such as Tibet, Qinghai, and Sichuan, trees, shrubs, and grasses with ecological characteristics similar to those of the local vegetation, including strong stress resistance, good soil and water conservation benefits, and well-established artificial cultivation practices, were selected as alternative vegetation for late-stage planting of indigenous tree species. Combining the results of site condition analysis and site type classification, the configuration of trees, shrubs, and grasses for different off-site condition types and the corresponding greening methods are discussed, providing a scientific reference for ecological restoration in high-elevation and low-temperature regions. Full article
(This article belongs to the Special Issue Soil Erosion and Soil and Water Conservation)
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<p>Study Area Location Map.</p>
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<p>Digital elevation model map of the study area. The legend in the figure, from bottom to top (red–dark green), represents the elevation ranges of 3200–3778.69 m, 2900–3200 m, 2700–2900 m, 2400–2700 m, and 2222.28–2400 m, respectively.</p>
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<p>Slope orientation map of the study area.</p>
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<p>Slope map of the study area. The legend in the figure, from bottom to top (red to dark green), represents the slope gradients as follows: 25.01–74.18°, 15.01–25°, 5.01–15°, and 0–5°.</p>
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<p>Satellite image of vegetation in the study area.</p>
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<p>Distribution of vegetation types in the study area. The legend in the figure, from bottom to top (red to dark green), represents the following types of vegetation: non-vegetated areas, river channels, natural grasslands, shrubs, trees, and cultivated land.</p>
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22 pages, 4584 KiB  
Article
Oviposition Preferences of the Fall Armyworm (Spodoptera frugiperda) (Lepidoptera: Noctuidae) in Response to Various Potential Repellent and Attractant Plants
by Kervin Can, Tsui-Ying Chang, Lekhnath Kafle and Wen-Hua Chen
Insects 2024, 15(11), 885; https://doi.org/10.3390/insects15110885 - 13 Nov 2024
Viewed by 575
Abstract
The fall armyworm (FAW), Spodoptera frugiperda, is a major polyphagous pest that mainly feeds on maize and other cash crops. Understanding S. frugiperda’s behavior on different host plants facilitates the development of effective integrated pest management (IPM) plans. Therefore, this study [...] Read more.
The fall armyworm (FAW), Spodoptera frugiperda, is a major polyphagous pest that mainly feeds on maize and other cash crops. Understanding S. frugiperda’s behavior on different host plants facilitates the development of effective integrated pest management (IPM) plans. Therefore, this study investigated the oviposition preferences of S. frugiperda females among different host plants using no-choice, two-choice, and multiple-choice bioassays. In no-choice bioassays, para grass, Urochloa mutica (Forssk.) (Poales: Poaceae); maize, Zea mays (L.) (Poales: Poaceae); and napier grass, Pennisetum purpureum (Schumach) (Poales: Poaceae) were identified as highly attractive, while sweet sorghum, Sorghum dochna (Forssk.) (Poales: Poaceae); sunhemp, Crotalaria juncea (L.) (Fabales:Fabacea); Egyptian clover, Trifolium alexandrinum (L.) (Fabales:Fabacea); desmodium, Desmodium uncinatum (Jacq.) (Fabales:Fabacea); natal grass, melinis repens (Zizka) (Poales: Poaceae); molasses grass, Melinis minutiflora (P.Beauv.) (Poales: Poaceae); and mung bean, Vigna radiata (R. wilczek) (Fabales: Fabaceae) exhibited reduced oviposition effects. Two-choice bioassays revealed different levels of attractiveness and repellency among different plant combinations. In multiple-choice bioassays, mimicking an intercropping scenario, differences in the number of eggs and egg mass were observed for M:S:D:W (maize, sunhemp, desmodium, and cage wall), S:D:M:W (sunhemp, desmodium, maize, and cage wall), and D:M:S:W (desmodium, maize, sunhemp, and cage wall). This study provides insights into the egg-laying preferences of S. frugiperda females among different host plants, valuable for the management of S. frugiperda. This encourages further research and further identification of novel repellent and attractant host plants, which will ultimately contribute to the development of sustainable and environmentally friendly crop production practices and techniques. Full article
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Graphical abstract

Graphical abstract
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<p>Schematic illustrations of the oviposition bioassays conducted under laboratory conditions. No-choice (<b>A</b>), two-choice (<b>B</b>), and multiple-choice (<b>C</b>) (note for multiple-choice: sunhemp (S), desmodium (D), maize (M), and cage walls (W); three plant arrangements were tested—S:D:M:W, D:M:S:W, and M:S:D:W).</p>
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<p>Number of egg masses in no-choice bioassays, under laboratory conditions, for molasses grass (<b>A</b>), desmodium (<b>B</b>), Egyptian clover (<b>C</b>), sunhemp (<b>D</b>), faba beans (<b>E</b>), napier grass (<b>F</b>), maize (<b>G</b>), natal grass (<b>H</b>), sweet sorghum (<b>I</b>), para grass (<b>J</b>), nill grass (<b>K</b>), and mung bean (<b>L</b>). Treatments that are significantly different by unpaired <span class="html-italic">t</span>-test are indicated by; ns, <span class="html-italic">p</span> &gt; 0.05; *: <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 ****: <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p><span class="html-italic">Spodoptera frugiperda</span> egg masses oviposited in the no-choice bioassays. Twelve different host plants (faba beans, para grass, molasses grass, maize, desmodium, Egyptian clover, sweet sorghum, sunhemp, nill grass, natal grass, napier grass, and mung bean) were tested in no-choice comparisons for oviposition by mated adult <span class="html-italic">S. frugiperda</span> moths. Data are presented as mean ± SE. Means with the same letter are not significantly different according to Fisher’s LSD, <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Number of eggs in no-choice bioassays, under laboratory conditions, for molasses grass (<b>A</b>), desmodium (<b>B</b>), Egyptian clover (<b>C</b>), sunhemp (<b>D</b>), faba beans (<b>E</b>), napier grass (<b>F</b>), maize (<b>G</b>), natal grass (<b>H</b>), sweet sorghum (<b>I</b>), para grass (<b>J</b>), nill grass (<b>K</b>), and mung bean (<b>L</b>). Treatments that are significantly different by unpaired <span class="html-italic">t</span>-test are indicated by; ns, <span class="html-italic">p</span> &gt; 0.05; *: <span class="html-italic">p</span> &lt; 0.05; ***: <span class="html-italic">p</span> &lt; 0.001; and ****: <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p><span class="html-italic">Spodoptera frugiperda</span> eggs oviposited in the no-choice bioassays. Twelve different host plants (faba beans, para grass, molasses grass, maize, desmodium, Egyptian clover, sweet sorghum, sunhemp, nill grass, natal grass, napier grass, and mung bean) were tested in no-choice comparisons for oviposition by mated adult <span class="html-italic">S. frugiperda</span> moths. Data are presented as mean ± SE. Means with the same letter are not significantly different according to Fisher’s LSD, <span class="html-italic">p</span> &lt; 0.05.</p>
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<p><span class="html-italic">Spodoptera frugiperda</span> percentage egg masses oviposited in no-choice bioassays. Twelve different host plants (faba beans, para grass, molasses grass, maize desmodium, Egyptian clover, sweet sorghum, sunhemp, nill grass, natal grass, napier grass, and mung bean) were tested in no-choice comparisons for oviposition by mated adult <span class="html-italic">S. frugiperda</span> moths. Treatments that are significantly different by unpaired <span class="html-italic">t</span>-test are indicated by; ns, <span class="html-italic">p</span> &gt; 0.05; ***: <span class="html-italic">p</span> &lt; 0.001; and ****: <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p><span class="html-italic">Spodoptera frugiperda</span> percentage eggs oviposited in no-choice bioassays. Twelve different host plants (faba beans, para grass, molasses grass, maize desmodium, Egyptian clover, sweet sorghum, sunhemp, nill grass, natal grass, napier grass, and mung bean) were tested in no-choice comparisons for oviposition by mated adult <span class="html-italic">S. frugiperda</span> moths. Treatments that are significantly different by unpaired <span class="html-italic">t</span>-test are indicated by; ns, <span class="html-italic">p</span> &gt; 0.05; ***: <span class="html-italic">p</span> &lt; 0.001; and ****: <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>Number of egg masses in two-choice bioassays, under laboratory conditions, for napier grass and nill grass (<b>A</b>), maize and sunhemp (<b>B</b>), para grass and napier grass (<b>C</b>), maize and para grass (<b>D</b>), natal grass and sunhemp (<b>E</b>), sunhemp and desmodium (<b>F</b>), napier grass and desmodium (<b>G</b>), napier grass and Egyptian clover (<b>H</b>), napier napier grass and sunhemp (<b>I</b>), natal grass and nill grass (<b>J</b>), maize and desmodium (<b>K</b>), sweet sorghum and nill grass (<b>L</b>), sweet sorghum and desmodium (<b>M</b>), nill grass and sunhemp (<b>N</b>), natal grass and desmodium (<b>O</b>), sweet sorghum and sunhemp (<b>P</b>), napier napier grass and sweet sorghum (<b>Q</b>). Treatments that are significantly different by Tukey’s post hoc test are indicated by; ns, <span class="html-italic">p</span> &gt; 0.05; *: <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 ****: <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>Number of eggs in two-choice bioassays, under laboratory conditions, for napier grass and nill grass (<b>A</b>), maize and sunhemp (<b>B</b>), para grass and napier grass (<b>C</b>), maize and para grass (<b>D</b>), natal grass and sunhemp (<b>E</b>), sunhemp and desmodium (<b>F</b>), napier grass and desmodium (<b>G</b>), napier grass and Egyptian clover (<b>H</b>), napier grass and sunhemp (<b>I</b>), natal grass and nill grass (<b>J</b>), maize and desmodium (<b>K</b>), sweet sorghum and nill grass (<b>L</b>), sweet sorghum and desmodium (<b>M</b>), nill grass and sunhemp (<b>N</b>), natal grass and desmodium (<b>O</b>), sweet sorghum and sunhemp (<b>P</b>), and napier grass and sweet sorghum (<b>Q</b>). Treatments that are significantly different by Tukey’s post hoc test are indicated by; ns, <span class="html-italic">p</span> &gt; 0.05; *: <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 ****: <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p><span class="html-italic">Spodoptera frugiperda</span> percentage egg masses oviposited in two-choice bioassays. Twelve different combinations were tested in two-choice comparisons for oviposition by mated adult <span class="html-italic">S. frugiperda</span> moths. Combinations that are significantly different by unpaired <span class="html-italic">t</span>-test are indicated by; ns, <span class="html-italic">p</span> &gt; 0.05; *: <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 ****: <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p><span class="html-italic">Spodoptera frugiperda</span> percentage eggs oviposited in two-choice bioassays. Twelve different combinations were tested in two-choice comparisons for oviposition by mated adult <span class="html-italic">S. frugiperda</span> moths. Treatments that are significantly different by unpaired <span class="html-italic">t</span>-test are indicated by; ns, <span class="html-italic">p</span> &gt; 0.05; *: <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 ****: <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>Number of egg masses in multiple-choice bioassays, under laboratory conditions, for maize, sunhemp, desmodium, and cage wall (M:S:D:W) (<b>A</b>); desmodium, maize, sunhemp, and cage wall (D:M:S:W) (<b>B</b>); and sunhemp, desmodium, maize, and cage wall (S:D:M:W) (<b>C</b>). Data are presented as mean ± SE. Treatments that are significantly different by Tukey’s post hoc test are indicated by; ns, <span class="html-italic">p</span> &gt; 0.05; *: <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 ****: <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>Number of eggs in multiple-choice bioassays, under laboratory conditions, for maize, sunhemp, desmodium, and cage wall (M:S:D:W) (<b>A</b>); desmodium, maize, sunhemp, and cage wall (D:M:S:W) (<b>B</b>); and sunhemp, desmodium, maize, and cage wall (S:D:M:W) (<b>C</b>). Data are presented as mean ± SE. Combinations that are significantly different by Tukey’s post hoc test are indicated by; ns, <span class="html-italic">p</span> &gt; 0.05; *: <span class="html-italic">p</span> &lt; 0.05 and ****: <span class="html-italic">p</span> &lt; 0.0001.</p>
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25 pages, 2165 KiB  
Review
Avian Responses to Different Grazing Management Practices in Neotropical Temperate Grasslands: A Meta-Analysis
by Facundo Niklison, David Bilenca and Mariano Codesido
Birds 2024, 5(4), 712-736; https://doi.org/10.3390/birds5040049 - 12 Nov 2024
Viewed by 421
Abstract
Bird populations inhabiting the Rio de la Plata Grasslands in southern Brazil, Argentina, and Uruguay are known to be affected by livestock grazing practices. Cattle grazing can lead to changes in bird assemblages by affecting the heterogeneity of vegetation structures. We conducted a [...] Read more.
Bird populations inhabiting the Rio de la Plata Grasslands in southern Brazil, Argentina, and Uruguay are known to be affected by livestock grazing practices. Cattle grazing can lead to changes in bird assemblages by affecting the heterogeneity of vegetation structures. We conducted a meta-analysis using studies that reported bird richness and abundance under different grazing management practices. We compared ranches under continuous grazing management (control, CGM) to (1) ranches under technological inputs management (TIM, herbicides and exotic pastures) and (2) ranches under ecological process-based management (EPM), which include ranches that utilise controlled and rotational grazing. We used random effects multilevel linear models to evaluate grazing regimen impacts. Our results indicate a negative impact of TIM on both bird abundance and richness (mean ± SE: −0.25 ± 0.07 and −0.92 ± 0.10, respectively) since the use of inputs simplifies vegetation structure and results in the loss of ecological niches. Compared to CGM, the influence of EPM on total bird abundance appears to be more dependent on grassland height, as evidenced by a decline in short grasses and increase in tall grasses. Our meta-analysis suggests that EPM practices may be beneficial for the conservation of endangered tall-grass birds. Full article
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<p>Río de la Plata Grasslands [<a href="#B3-birds-05-00049" class="html-bibr">3</a>] with the location of the case studies included in the meta-analysis (black diamonds for tall grasslands and white circles for short/medium grasslands) and the ecological units (red lines). Ecological units: (1) Rolling Pampas; (2) Flat Inland Pampas; (3) West Inland Pampas; (4) Flooding Pampas; (5) Southern Pampas; (6) Mesopotamic Pampas; (7) Southern Campos, and (8) Northern Campos.</p>
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<p>Flow chart of the literature search and the article selection for the meta-analysis (PRISMA 2020) [<a href="#B35-birds-05-00049" class="html-bibr">35</a>].</p>
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<p>Mean effect size (square) of technological input management (TIM) on (<b>A</b>) total bird abundance and Southeastern South America (SESA) grasslands bird abundance and (<b>B</b>) bird richness. All variables include 95% confidence intervals (lines). Sample size indicates the number of study cases included in the meta-analysis, and the number of independent studies is given in parentheses. Significant difference among categories: NS—not significant, *** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Mean effect size (square) of ecological process-based management (EPM) on (<b>A</b>) total bird abundance (overall effect and different grassland heights) and (<b>B</b>) bird richness. All variables include 95% confidence intervals (lines). Sample size indicates the number of study cases included in the meta-analysis, and the number of independent studies is given in parentheses. Significant difference among categories: NS—not significant, * <span class="html-italic">p</span> &lt; 0.1.</p>
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<p>Funnel plots with the relation between standardized mean difference and standard error for (<b>A</b>) total bird abundance (technological input management–continuous grazing management); (<b>B</b>) Southeastern South America (SESA) grasslands bird abundance (technological input management–continuous grazing management); (<b>C</b>) bird richness (technological input management–continuous grazing management); and (<b>D</b>) total bird abundance (ecological process-based management–continuous grazing management).</p>
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17 pages, 4481 KiB  
Article
Exploring the Complex Association Between Urban Built Environment, Sociodemographic Characteristics and Crime: Evidence from Washington, D.C.
by Kaixin Liu, Longhao Zhang, Shangen Tsou, Lei Wang, Yike Hu and Ke Yang
Land 2024, 13(11), 1886; https://doi.org/10.3390/land13111886 - 11 Nov 2024
Viewed by 436
Abstract
The urban built environment and sociodemographic characteristics have complex relationships with urban crime. However, previous studies have had limitations such as generalizing urban green space types, urban functionality, and sociodemographic characteristics. Given these, this study aimed to explore the relationship between them using [...] Read more.
The urban built environment and sociodemographic characteristics have complex relationships with urban crime. However, previous studies have had limitations such as generalizing urban green space types, urban functionality, and sociodemographic characteristics. Given these, this study aimed to explore the relationship between them using more detailed indicators. The study utilized Google Street View and points of interest to depict the built environment. Building on previous work that segmented natural and artificial elements in streetscape images, this study further distinguished trees, bush, and grass. Additionally, it incorporated data from the Data Analysis and Visualization Unit of the DC Office of Planning to reflect a broader range of individual characteristics. Weighted least squares regression and Pearson correlation analysis were used to test the relationship between the built environment, sociodemographic, and crime, respectively. Some of the key findings are as follows. (1) Trees, bushes, and grass all reduce crime. (2) Urban functionality is hard to curb crime by enhancing informal public surveillance. (3) Among the sociodemographic variables, the walking commute rate is the variable most strongly positively correlated with crime. (4) Family relationships play an important role in suppressing crime. This study examined a more comprehensive range of indicators affecting urban crime in favor of safer cities. Full article
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<p>Location of Washington, D.C.</p>
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<p>Counts of crimes.</p>
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<p>The operational process of the DeepLabV3+ neural network and results.</p>
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<p>Streetscape physical environment variables. (<b>a</b>) Proportions of wall. (<b>b</b>) Proportions of building. (<b>c</b>) Proportions of road. (<b>d</b>) Proportions of sidewalk. (<b>e</b>) Proportions of tree. (<b>f</b>) Proportions of bush. (<b>g</b>) Proportions of grass.</p>
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<p>Urban functionality variables. (<b>a</b>) Counts of commerce. (<b>b</b>) Counts of medical. (<b>c</b>) Counts of education. (<b>d</b>) Counts of public. (<b>e</b>) Counts of recreation.</p>
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<p>Crime attractor variables. (<b>a</b>) Counts of banks and ATMs. (<b>b</b>) Counts of intensive entertainment venues. (<b>c</b>) Counts of religious sites. (<b>d</b>) Counts of transportation station. (<b>e</b>) Counts of sale of valuable items.</p>
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<p>The relationship between urban crime and some of the sociodemographic variables.</p>
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24 pages, 4644 KiB  
Article
Study of the Degradation and Utilization of Cellulose from Auricularia heimuer and the Gene Expression Level of Its Decomposition Enzyme
by Xianqi Shan, Fangjie Yao, Lixin Lu, Ming Fang, Jia Lu and Xu Sun
Agriculture 2024, 14(11), 2027; https://doi.org/10.3390/agriculture14112027 - 11 Nov 2024
Viewed by 352
Abstract
Auricularia heimuer is a wood-rotting edible mushroom, and with the continuous development of the industry, the research on its grass-rotting cultivation is becoming more and more important. In this study, A. heimuer was cultivated using herbaceous substrate (reed) completely replacing the traditional woody [...] Read more.
Auricularia heimuer is a wood-rotting edible mushroom, and with the continuous development of the industry, the research on its grass-rotting cultivation is becoming more and more important. In this study, A. heimuer was cultivated using herbaceous substrate (reed) completely replacing the traditional woody substrate (oak), and the correlation between the relative expression of cellulase gene, cellulase activity, cellulose degradation and yield of different strains of A. heimuer were studied by combining qRT-PCR technology at different growth stages. The results showed that the cellulose degradation were positively correlated with the yield of reed and sawdust substrate at two growth stages, and were positively correlated with three cellulase activities. The relative expression of four cellulase genes were positively correlated with enzyme activity. There were inter-strain differences in the expression of the enzyme genes, which were basically consistent with the trend of the enzyme activity of the strains; g5372 and g7270 were more actively expressed in the mycelium period, while g9664 and g10234 were more actively expressed in the fruiting period. The results of SEM showed that the mycelium of A15 and A125 were different in their ability to degrade and utilize lignocellulose in reed substrate. The parental hybridization test further verified that qRT-PCR could be used as a rapid method to evaluate the cellulose degradation ability of A. heimuer strains. Seven strains (A12, A15, A184, A224, Z6, Z12, and Z18) with high cellulose degradation ability were screened. This study provides a reference for further understanding the role of A. heimuer cellulase genes in the degradation and metabolism of cellulose and for breeding new varieties more suitable for herbaceous substrate cultivation. Full article
(This article belongs to the Special Issue Genetics and Breeding of Edible Mushroom)
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<p>The amplicon length and specificity of candidate reference genes. (<b>A</b>): Amplified fragments of candidate reference genes shown by agarose gel electrophoresis with ethidium bromide staining; (<b>B</b>): melting curves generated by qRT-PCR.</p>
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<p>The amplicon length and specificity of candidate reference genes. (<b>A</b>): Amplified fragments of candidate reference genes shown by agarose gel electrophoresis with ethidium bromide staining; (<b>B</b>): melting curves generated by qRT-PCR.</p>
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<p>Degradation amount of cellulose in two growth stages of different strains. RS: Reed substrate. SS: Sawdust substrate.</p>
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<p>Surface structure and morphology of reed used for <span class="html-italic">A. heimuer</span> mycelium degradation. (<b>A1</b>,<b>A2</b>) indicate the surface structural morphology of the reed substrate before mycelium utilization under 100 and 500 times microscope, respectively. (<b>B1</b>,<b>B2</b>) indicate the surface structural morphology of reed substrate during the fruiting period of strains A15 under 100 and 500 times microscope, respectively. (<b>C1</b>,<b>C2</b>) indicate the surface structural morphology of reed substrate during the fruiting period of strains A125 under 100 and 500 times microscope, respectively.</p>
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<p>Activity of carboxymethyl cellulase at the period of full-bag of mycelium (<b>left</b>) and fruiting-body maturation (<b>right</b>). * indicates the significance of difference at <span class="html-italic">p</span> &lt; 0.05 level, ** indicates the significance of difference at <span class="html-italic">p</span> &lt; 0.01 level. The significant difference was expressed by Duncan’s multiple range test.</p>
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<p>Filter-paper cellulase activity at the period of full-bag of mycelium (<b>left</b>) and fruiting-body maturation (<b>right</b>). * indicates the significance of difference at <span class="html-italic">p</span> &lt; 0.05 level, ** indicates the significance of difference at <span class="html-italic">p</span> &lt; 0.01 level.</p>
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<p>β-glucosidase activity at the period of full-bag of mycelium (<b>left</b>) and fruiting-body maturation (<b>right</b>). * indicates the significance of difference at <span class="html-italic">p</span> &lt; 0.05 level, ** indicates the significance of difference at <span class="html-italic">p</span> &lt; 0.01 level.</p>
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<p>Correlation analysis between cellulase activity and relative expression of enzyme genes in different strains. <span class="html-italic">g5372</span>-1 indicates the relative gene expression level at the mycelium period of <span class="html-italic">g5372</span>, and <span class="html-italic">g5372</span>-2 indicates the relative gene expression level at the fruiting period of <span class="html-italic">g5372</span>. The same applies to <span class="html-italic">g7270</span>, <span class="html-italic">g9664</span>, and <span class="html-italic">g10234</span>.</p>
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<p>Expression of cellulase gene in mycelium period of different strains. The (<b>A</b>–<b>D</b>) in the graphs indicate the relative expression of genes <span class="html-italic">g5372</span>, <span class="html-italic">g7270</span>, <span class="html-italic">g9664</span>, and <span class="html-italic">g10234</span> in the test strains, in that order. Different lowercase letters in the figure indicate the significant difference between different strains (<span class="html-italic">p</span> &lt; 0.05). The significant difference was expressed by Duncan’s multiple range test.</p>
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<p>Expression of cellulase gene in fruiting period of different strains. The (<b>A</b>–<b>D</b>) in the graphs indicate the relative expression of genes <span class="html-italic">g5372</span>, <span class="html-italic">g7270</span>, <span class="html-italic">g9664</span>, and <span class="html-italic">g10234</span> in the test strains, in that order. Different lowercase letters in the figure indicate the significant difference between different strains (<span class="html-italic">p</span> &lt; 0.05). The significant difference was expressed by Duncan’s multiple range test.</p>
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<p>Degradation of cellulose in different growth stages of hybrids (Z1–Z26).</p>
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14 pages, 1875 KiB  
Article
Effects of Heat Stress on the Muscle Meat Quality of Rainbow Trout
by Yalan Li, Changqing Zhou, Yong Zhang and Xingxu Zhao
Fishes 2024, 9(11), 459; https://doi.org/10.3390/fishes9110459 - 11 Nov 2024
Viewed by 551
Abstract
The effects of heat stress on aquatic animals are increasingly being discerned, but little is known about the effects of heat stress on muscle meat quality or the flavor of muscle. This study aimed to evaluate the effects of heat stress on the [...] Read more.
The effects of heat stress on aquatic animals are increasingly being discerned, but little is known about the effects of heat stress on muscle meat quality or the flavor of muscle. This study aimed to evaluate the effects of heat stress on the muscle antioxidant properties, structural and physical properties (e.g., pH, muscle color, shear force, and expressible moisture), chemical composition (e.g., nucleotides, organic acids, amino acids, and minerals), and volatile substances of rainbow trout. We observed that the antioxidant capacity of muscle decreased after stress experiments at 22.5 °C for 24 h. The content of inflammatory factors notably increased (p < 0.05), the pH value and red value of muscle decreased (p < 0.05), the interfiber space increased, and several muscle fibers were broken. Heat stress changed the contents of nucleotides, organic acids, minerals, and amino acids in muscle. The contents of IMP and AMP, which play an important role in the flavor of muscle, decreased (p < 0.05). The contents of two amino acids that provide a sweet taste decreased; those of five amino acids that provide a bitter taste increased (p < 0.05). Heat stress also affected the amount and type of volatile substances in muscle, which affected muscle odor. These results suggest that heat stress may exert adverse effects on the oxidative stability, structure, meat quality, and flavor of muscle, requiring attention and prevention. Full article
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<p>Effects of heat stress on muscle histology (<b>A</b>,<b>B</b>) muscle transection; (<b>C</b>,<b>D</b>) longitudinal sectioning of muscle; (<b>A</b>,<b>C</b>) control group; (<b>B</b>,<b>D</b>) heat stress group; ▲: rupture of muscle fibers).</p>
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<p>Effects of heat stress on volatile substance content in muscle of rainbow trout. (<b>A</b>) Relative volatile substance content in the control group (%); (<b>B</b>) relative volatile substance content in the heat stress group (%).</p>
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19 pages, 3615 KiB  
Article
Analysis of Football Pitch Performances Based on Different Cutting Systems: From Visual Evaluation to YOLOv8
by Sofia Matilde Luglio, Christian Frasconi, Lorenzo Gagliardi, Michele Raffaelli, Andrea Peruzzi, Marco Volterrani, Simone Magni and Marco Fontanelli
Agronomy 2024, 14(11), 2645; https://doi.org/10.3390/agronomy14112645 - 10 Nov 2024
Viewed by 463
Abstract
The quality of sports facilities, especially football pitches, has gained significant attention due to the growing importance of sports globally. This study examines the effect of two different cutting systems, a traditional ride-on mower and an autonomous mower, on the quality and functional [...] Read more.
The quality of sports facilities, especially football pitches, has gained significant attention due to the growing importance of sports globally. This study examines the effect of two different cutting systems, a traditional ride-on mower and an autonomous mower, on the quality and functional parameters of a municipal football field. The analysis includes visual assessments, measurements of grass height, and evaluations of surface hardness, comparing the performance of the two cutting systems. Additionally, studies of turfgrass composition and machine learning techniques, particularly with YOLOv8s and YOLOv8n, are conducted to test the capability of assessing weed and turfgrass species distribution. The results indicate significant differences in grass color based on the position (5.36 in the corners and 3.69 in the central area) and surface hardness between areas managed with a traditional ride-on mower (15.25 Gmax) and an autonomous mower (10.15 Gmax) in the central region. Higher height values are recorded in the area managed with the ride-on mower (2.94 cm) than with the autonomous mower (2.61 cm). Weed presence varies significantly between the two cutting systems, with the autonomous mower demonstrating higher weed coverage in the corners (17.5%). Higher overall performance metrics were obtained through YOLOv8s. This study underscores the importance of innovative management practices and monitoring techniques in optimizing the quality and playability of a football field while minimizing environmental impact and management efforts. Full article
(This article belongs to the Special Issue Robotics and Automation in Farming)
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<p>Weather conditions during the trial period.</p>
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<p>The two different cutting systems in the selected soccer pitch: (<b>a</b>) the autonomous mower with systematic trajectories and (<b>b</b>) the ride-on mower.</p>
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<p>Preliminary photo acquisition system. In (<b>a</b>), there are the Jetson dwarf (A), the converter (B), the screen (C), and the GPS (D); in (<b>b</b>), there is the RGBD camera (E) on the front of the trolley at 47 cm high.</p>
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<p>Effect of the interaction between management (autonomous mower (AM) and ride-on mower (RM)) and position on the surface hardness. Means denoted by different letters indicate statistically significant differences at <span class="html-italic">p</span> &lt; 0.05 (LSD test). LCL (Lower Confidence Limit) and UCL (Upper Confidence Limit) are reported.</p>
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<p>Effect of the interaction between management (autonomous mower (AM) and ride-on mower (RM)) and position on the weed percentage. Means denoted by different letters indicate statistically significant differences at <span class="html-italic">p</span> &lt; 0.05 (LSD test). LCL (Lower Confidence Limit) and UCL (Upper Confidence Limit) are reported.</p>
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<p>Mean value of turfgrass parameter evaluation in function of the type of management. LCL (Lower Confidence Limit) and UCL (Upper Confidence Limit) are reported. Parameters indagated: athlete–surface interaction (athlete surface), ball surface (ball–surface interaction), color, density, hardness (Hard.), slipperiness (Slip.), traction, and uniformity.</p>
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13 pages, 1356 KiB  
Article
Determination of Particle Size for Optimum Biogas Production from Ouagadougou Municipal Organic Solid Waste
by Mahamadi Nikiema, Narcis Barsan, Amidou S. Ouili, Emilian Mosnegutu, K. Marius Somda, Ynoussa Maiga, Compaoré Cheik Omar Tidiane, Cheik A. T. Ouattara, Valentin Nedeff and Aboubakar S. Ouattara
Sustainability 2024, 16(22), 9792; https://doi.org/10.3390/su16229792 - 10 Nov 2024
Viewed by 564
Abstract
Anaerobic digestion’s contribution to sustainable development is well established. It is a sustainable production process that enables energy to be saved and produced and efficient pollution control processes to be implemented, thereby contributing to the sustainable development of our societies. Optimizing biogas yields [...] Read more.
Anaerobic digestion’s contribution to sustainable development is well established. It is a sustainable production process that enables energy to be saved and produced and efficient pollution control processes to be implemented, thereby contributing to the sustainable development of our societies. Optimizing biogas yields from the anaerobic digestion of municipal organic waste is crucial for maximum energy recovery and has become an important topic of interest. Substrate particle size is a key process parameter in biogas production and precedes other pretreatment methods for most organic materials. This study aims to evaluate the impact of particle size and incubation period on biomethane production from municipal solid waste. Sampling of municipal solid waste was carried out in waste pre-collection in the city of Ouagadougou, Burkina Faso. Waste characterization showed lignocellulolytic green waste (grass, dead leaves), waste composed of fruit and leafy vegetables and leftover food waste. TableCurve 3D v4.0 software was used to develop an optimal mathematical model to correlate particle size and biomethane productivity to describe optimal production parameters. Particle sizes ranging from 2000 to 63 µm high biogas production values, specifically 385.33 and 201.25 L·kg−1 of MSV. PCA analysis clearly showed a high correlation between particle size and biogas production, with optimum production recorded for size 250 µm with a biomethane production value of 187.53 L·kg−1 of MSV. The average relative errors and RMSE for CH4 content were improved by 24.31% and 44.97%, respectively. The data calculated with the developed mathematical model and the existing experimental data were compared and permutated to validate the model. This work enabled the identification of a mathematical model that describes the correlations between the input parameters of an experiment and the monitored parameters, as well as the definition of the particle size that allows for the optimal production of biomethane. Full article
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<p>Different sizes of waste used in anaerobic digestion tests.</p>
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<p>Principal component analysis plot of variables biogas production, CO<sub>2</sub> and CH<sub>4</sub> proportions and distribution of combinations on 1 × 2 axis of principal components.</p>
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<p>Three-dimensional-view response surface plot corresponding to the chosen equation: (<b>a</b>) effect of particle size and incubation time on CH<sub>4</sub> content and (<b>b</b>) effect of particle size and incubation time on CO<sub>2</sub> content.</p>
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14 pages, 3081 KiB  
Article
Regulatory Threshold of Soil and Water Conservation Measures on Runoff and Sediment Processes in the Sanchuan River Basin
by Xinhui Ding, Xiaoying Liu and Guangquan Liu
Water 2024, 16(22), 3223; https://doi.org/10.3390/w16223223 - 9 Nov 2024
Viewed by 327
Abstract
Research on the runoff and sediment reduction effects of soil and water conservation measures has always been a topic of interest, which is of great significance for carrying out sustainable strategies for soil and water conservation in the Yellow River Basin. This study [...] Read more.
Research on the runoff and sediment reduction effects of soil and water conservation measures has always been a topic of interest, which is of great significance for carrying out sustainable strategies for soil and water conservation in the Yellow River Basin. This study aims to find the threshold years of soil and water conservation measures for reductions in runoff and sediment. Through the analysis of various soil and water conservation measures, runoff, sediment, and rainfall data in the Sanchuan River Basin from 1960 to 2019, we determined the threshold years of soil and water conservation measures on runoff and sediment processes using the Hydrology and Lagrange Multiplier method. The results are as follows: The trend in flood season rainfall and annual rainfall in the Sanchuan River Basin is consistent. The 1990s was a turning period in the annual rainfall and flood season rainfall of the Sanchuan River Basin. The 2000s was a turning period of the runoff in the Sanchuan River Basin, while the sediment entered a stable period after 2000. The best periods for reducing runoff and sediment were the initial treatment period (1967–1979) and the centralized treatment period (1980–1996). The runoff and sediment reduction effects of each soil and water conservation measure during the initial treatment period (1967–1979) were terrace (32.8%) > dam (30.1%) > grass (18.6%) > forest (18.5%), while their effects during the centralized treatment period (1980–1996) were grass (53.7%) > terrace (20.7%) > dam (14.6%) > forest (11.0%). The runoff and sediment reduction effects of various soil and water conservation measures during different treatment periods indicate that the runoff reduction effect reached its peak in 2003–2005, while the sediment reduction benefit reached its peak in 2013–2015. Based on the comprehensive benefits of runoff and sediment regulation, 2013–2015 are considered to be the threshold years for various soil and water conservation measures, with the measures covering respective average areas of 4.85 × 104, 17.80 × 104, 1.15 × 104, and 0.82 × 104 hm2. These research results will have a certain significance for the reasonable allocation of soil and water conservation measures and sustainable development in the Yellow River Basin. Full article
(This article belongs to the Special Issue Research on Soil and Water Conservation and Vegetation Restoration)
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<p>The locations of rainfall and hydrological stations in the Sanchuan River Basin in Shanxi Province.</p>
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<p>Polynomial fitting of runoff and sediment data in the Sanchuan River Basin using different orders. Note: (<b>a</b>) and (<b>b</b>) respectively show the fit goodness for runoff and sediment using different order polynomials during the study period.</p>
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<p>Flood season rainfall and annual rainfall variation in the Sanchuan River Basin.</p>
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<p>Changes in the runoff and sediment in the Sanchuan River Basin.</p>
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<p>The relationship between runoff, sediment, and rainfall in the Sanchuan River Basin.</p>
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<p>Changes in the soil and water conservation measures and runoff. Note: (<b>a</b>), (<b>b</b>), (<b>c</b>) and (<b>d</b>) respectively depicted the changes in runoff with the implementation of terrace, forest, grass, and dam measures during the study period.</p>
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<p>Changes in the soil and water conservation measures and sediment. Note: (<b>a</b>), (<b>b</b>), (<b>c</b>) and (<b>d</b>) respectively depicted the changes in sediment with the implementation of terrace, forest, grass, and dam measures during the study period.</p>
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