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15 pages, 6147 KiB  
Article
A Seed Endophytic Bacterium Cronobacter dublinensis BC-14 Enhances the Growth and Drought Tolerance of Echinochloa crus-galli
by Sheng Cheng, Qingling Wang, Dashan Yang, Quanlong He, Jianxin Deng, Yi Zhou, Lin Zhang and Jianwei Jiang
Microorganisms 2024, 12(12), 2544; https://doi.org/10.3390/microorganisms12122544 - 10 Dec 2024
Viewed by 413
Abstract
Successful seed germination and plant seedling growth often require association with endophytic bacteria. Barnyard grass (Echinochloa crus-galli (L.) P. Beauv.) is a main weed during rice cultivation and has frequently been found in drought-prone fields such as cornfields in recent years. To [...] Read more.
Successful seed germination and plant seedling growth often require association with endophytic bacteria. Barnyard grass (Echinochloa crus-galli (L.) P. Beauv.) is a main weed during rice cultivation and has frequently been found in drought-prone fields such as cornfields in recent years. To determine whether endophytic bacteria enhance the survival chances of barnyard grass in dryland conditions, endophytic bacteria were collected from barnyard grass seeds. An endophytic bacterial strain, BC-14, was selected and confirmed as Cronobacter dublinensis based on its morphology, physiology, biochemistry, and genomic information. Moreover, C. dublinensis BC-14 secreted IAA in the Luria–Bertani broth up to 28.44 mg/L after 5 days; it could colonize the roots of barnyard grass. After the inoculation with seeds or the well-mixed planting soil, the bacterium can significantly increase the root length and plant height of barnyard grass under drought conditions. When comparing with the control group on the 28th day, it can be seen that the bacterium can significantly increase the contents of chlorophyll b (up to 7.58 times) and proline (37.21%); improve the activities of superoxide dismutase, catalase, and peroxidase (36.90%, 51.51%, and 12.09%, respectively); and reduce the content of malondialdehyde around 25.92%, which are correlated to the drought tolerance. The bacterial genomic annotation revealed that it contains growth-promoting and drought-resistant functional genes. In a word, C. dublinensis BC-14 can help barnyard grass suppress drought stress, promote plant growth, and enhance biomass accumulation, which is helpful to interpret the mechanism of weed adaptability in dry environments. Full article
(This article belongs to the Section Plant Microbe Interactions)
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<p>The effects on barnyard grass ((<b>A</b>) root length; (<b>B</b>) leaf length; (<b>C</b>) fresh weight; (<b>D</b>) dry weight; and (<b>E</b>) seed germination rate) applied with four endophytic bacteria. There are significant differences between data groups represented by different lowercase letters (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Identification of <span class="html-italic">Cronobacter dublinensis</span> BC-14. (<b>A</b>) Cultural characteristics. (<b>B</b>) Gram staining. (<b>C</b>) Heatmap of ANI analysis based on the genome sequences of 10 <span class="html-italic">Cronobacter</span> strains. (<b>D</b>) The phylogenetic tree based on single-copy ortholog gene sequences of genomes using the maximum likelihood (ML) method. Bootstrap values from 1000 replicates are shown at nodes. <span class="html-italic">Cronobacter condimenti</span> LMG 26250 was selected as the outgroup. <sup>T</sup>: type strain. Scale bar: B = 25 μm.</p>
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<p>GFP-tagged <span class="html-italic">Cronobacter dublinensis</span> BC-14 localization in barnyard grass roots using confocal laser microscopic images ((<b>A</b>) white light; (<b>B</b>) fluorescence mode; and (<b>C</b>) overlay image). The white arrows represented bacteria colonizing the roots.</p>
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<p>The growth appearance (<b>A1</b>,<b>B1</b>), root length (<b>A2</b>,<b>B2</b>), and plant height (<b>A3</b>,<b>B3</b>) of barnyard grass treated with <span class="html-italic">Cronobacter dublinensis</span> BC-14 without (A: 0% PEG) and under (B: 20% PEG) drought stress conditions at 7, 14, 21, and 28 days. C−: The seeds were soaked in sterile water and then sown in aseptic soil; C+: The seeds were soaked in sterile water and then sown in bacterial (BC-14) mixed soil; S−: The seeds were soaked in the bacterial inoculum (BC-14, 10<sup>8</sup> CFU/mL) and then sown in aseptic soil. ***: significant difference compared with C− and C+, <span class="html-italic">p</span> &lt; 0.05; **: significant difference compared with C− and S−, <span class="html-italic">p</span> &lt; 0.05; *: significant difference compared with C, <span class="html-italic">p</span> &lt; 0.05. The same figure below.</p>
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<p>The variations of chlorophyll (<b>A1</b>,<b>A2</b>), chlorophyll a (<b>B1</b>,<b>B2</b>), chlorophyll b (<b>C1</b>,<b>C2</b>), proline (<b>D1</b>,<b>D2</b>), superoxide dismutase (<b>E1</b>,<b>E2</b>), malondialdehyde (<b>F1</b>,<b>F2</b>), catalase (<b>G1</b>,<b>G2</b>), and peroxidase (<b>H1</b>,<b>H2</b>) in barnyard grass at 7, 14, 21, and 28 days after the treatment with <span class="html-italic">Cronobacter dublinensis</span> BC-14 without ((<b>A1</b>–<b>H1</b>): 0% PEG) and under ((<b>A2</b>–<b>H2</b>): 20% PEG) drought stress conditions.</p>
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<p>The whole genome sequence of <span class="html-italic">Cronobacter dublinensis</span> BC-14.</p>
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22 pages, 12908 KiB  
Article
Elevation Determines Fungal Diversity, and Land Use Governs Community Composition: A Dual Perspective from Gaoligong Mountains
by Zhuanfei Zeng, Ruilong Huang and Wei Li
Microorganisms 2024, 12(11), 2378; https://doi.org/10.3390/microorganisms12112378 - 20 Nov 2024
Viewed by 602
Abstract
Soil fungi are closely tied to their surrounding environment. While numerous studies have reported the effects of land-use practices or elevations on soil fungi, our understanding of how their community structure and diversity vary with elevation across different land-use practices remains limited. In [...] Read more.
Soil fungi are closely tied to their surrounding environment. While numerous studies have reported the effects of land-use practices or elevations on soil fungi, our understanding of how their community structure and diversity vary with elevation across different land-use practices remains limited. In the present study, by collecting soil samples from four different land uses in the Gaoligong Mountain area, namely shrublands (SLs), coffee plantations (CPs), cornfields (CFs), and citrus orchards (COs), and combining them with the changes in altitude gradients (low: 900 m, medium: 1200 m, high: 1500 m), high-throughput sequencing technology was used to analyze the composition and diversity of soil fungal communities based on the collected soil samples. The results showed that the interaction between land-use types and elevation significantly influenced the structure and diversity of fungal communities, although their relative importance in shaping fungal diversity or community structure varied. Specifically, elevation posed a stronger effect on fungal community alpha-diversity and functional guilds, whereas land-use types had a greater influence over fungal community composition. Our study reveals the individual and combined effects of land-use practices and elevation on the structure and diversity of soil fungal communities in the Gaoligong Mountain region, enhancing our understanding of the distribution patterns and driving mechanisms of soil fungal communities in this biodiversity-rich region. Full article
(This article belongs to the Special Issue Soil Microbial Communities and Ecosystem Functions, 2nd Edition)
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<p>Study area and sampling site.</p>
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<p>Soil physicochemical properties with different land uses and altitudes in the Gaoligong Mountains. (Different letters indicate significant levels (<span class="html-italic">p</span> &lt; 0.05). At the same elevation, significant differences between different land uses are denoted by lowercase letters (e.g., a); at the same land use, significant differences between different elevations are denoted by uppercase letters (e.g., A). (<b>a</b>): pH. (<b>b</b>): TC, total carbon. (<b>c</b>): TN, total nitrogen. (<b>d</b>): TP, total phosphorus. (<b>e</b>): NH<sub>4</sub><sup>+</sup>-N, ammonium-nitrogen. (<b>f</b>): NO<sub>3</sub><sup>−</sup>-N, nitrate-nitrogen. (<b>g</b>): SOC, soil organic carbon. (<b>h</b>): DOC, dissolved organic carbon. (<b>i</b>): SWC, soil water content).</p>
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<p>Relative abundance of soil fungal phyla (<b>a</b>) and fungal genera (<b>b</b>). (SL1, SL2, and SL3 correspond to low, medium, and high elevation shrublands, respectively, and the same applies to other land uses. SL: shrubland, CP: coffee plantation, CF: cornfield, CO: citrus orchard).</p>
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<p>Alpha diversity of soil fungal community. ((<b>a</b>): Chao1 index; (<b>b</b>): ACE index; (<b>c</b>): Shannon index; (<b>d</b>): Simpson index). Lowercase letters indicate significant differences in alpha diversity of fungal communities between different land uses (<span class="html-italic">p</span> &lt; 0.05); uppercase letters indicate significant differences between different elevations (<span class="html-italic">p</span> &lt; 0.05). The values on the right side of each graph represent the F-value and significance results. ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>PCoA analyses of the soil fungal community. ((<b>a</b>–<b>c</b>) correspond to 900 m, 1200 m and 1500 m).</p>
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<p>Relative abundance of fungal trophic mode (<b>a</b>) and guild (<b>b</b>). SL1, SL2, and SL3 correspond to low, medium, and high-elevation shrublands, respectively, and the same applies to other land uses. SL: shrubland, CP: coffee plantation, CF: cornfield, CO: citrus orchard.</p>
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<p>RDA analysis applied to soil fungal community data and soil physicochemical properties. ((<b>a</b>–<b>c</b>) correspond to 900 m, 1200 m and 1500 m).</p>
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<p>Correlation between the 10 most abundant fungal phyla and the physicochemical properties. ((<b>a</b>–<b>c</b>) correspond to 900 m, 1200 m and 1500 m). * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Heat map of the correlation between functional guild of fungi and physicochemical properties. ((<b>a</b>–<b>c</b>) correspond to 900 m, 1200 m and 1500 m). * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01. U-S: Undefined Saprotroph, Ep: Epiphyte, E-LSU: Endophyte-Litter Saprotroph-Soil Saprotroph-Undefined Saprotroph, Ar-M: Arbuscular Mycorrhizal, W-S: Wood Saprotroph, An-FU: Animal Pathogen-Fungal Parasite-Undefined Saprotroph, An-U: Animal Pathogen-Undefined Saprotroph, P-W: Plant Pathogen-Wood Saprotroph, P-U: Plant Pathogen-Undefined Saprotroph, E-LPU: Endophyte-Lichen Parasite-Plant Pathogen-Undefined Saprotroph.</p>
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11 pages, 1806 KiB  
Article
Straw Mulching and Weather Conditions Affecting the Trade-Off Between Grain Yield and Agronomic Traits of Maize
by Kun Du, Zhao Li and Fadong Li
Agronomy 2024, 14(11), 2686; https://doi.org/10.3390/agronomy14112686 - 14 Nov 2024
Viewed by 316
Abstract
Straw mulching (SM) plays an important role in promoting the grain yield (GY) of maize under no-tillage conditions. However, there is still a lack of deep understanding on the interactive impact of SM and weather conditions on agronomic traits and the contributions to [...] Read more.
Straw mulching (SM) plays an important role in promoting the grain yield (GY) of maize under no-tillage conditions. However, there is still a lack of deep understanding on the interactive impact of SM and weather conditions on agronomic traits and the contributions to GY. This study selected a cornfield in the North China Plain as the research object and set up a straw management experiment, including SM and no straw mulching (NSM). The GY and agronomic traits of maize from 2018 to 2020 were monitored, and the relationship of agronomic traits with GY and the weather conditions were analyzed. The results show that SM promoted maize GY by 20.44%. Straw mulching increased the plant height, ear diameter, and ear height by 8.43%, 1.99%, and 12.65%, respectively. A correlation analysis showed that the ear length and ear height were the main factors affecting maize yield. Ear length was significantly correlated with kernel numbers per ear in SM. Growing degree days, hot dry wind, and air temperature significantly affected kernel numbers per ear and plant growth. This study highlights the contributions of agronomic factors to maize GY under SM and variable weather conditions and is helpful to improve cropland management. Full article
(This article belongs to the Section Agricultural Biosystem and Biological Engineering)
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<p>Mean, min, and max air temperature (°C) and precipitation (mm) during the growth stages of maize at Yucheng experiment station in the period 2018–2020. Maize growth stages: days 171–283.</p>
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<p>Wind speed (m/s) and air relative humidity (%) during the growth stages of maize at Yucheng experiment station in the period 2018–2020. Maize growth stages: days 171–283.</p>
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<p>Correlation plots were used to understand the relative degree between GY and agronomic traits in the period 2018−2020. PH: plant height; KNPE: kernel number per ear; EL: ear length; ED: ear diameter; SD: stem diameter; EH: ear height. A: NSM treatments; B: SM treatments. * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Correlation plots were used to understand the relative degree between GY and agronomic traits in the period 2018−2020. PH: plant height; KNPE: kernel number per ear; EL: ear length; ED: ear diameter; SD: stem diameter; EH: ear height. A: NSM treatments; B: SM treatments. * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01.</p>
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18 pages, 7562 KiB  
Article
Reliable and Resilient Wireless Communications in IoT-Based Smart Agriculture: A Case Study of Radio Wave Propagation in a Corn Field
by Blagovest Nikolaev Atanasov, Nikolay Todorov Atanasov and Gabriela Lachezarova Atanasova
Telecom 2024, 5(4), 1161-1178; https://doi.org/10.3390/telecom5040058 - 12 Nov 2024
Viewed by 874
Abstract
In the past few years, one of the largest industries in the world, the agriculture sector, has faced many challenges, such as climate change and the depletion of limited natural resources. Smart Agriculture, based on IoT, is considered a transformative force that will [...] Read more.
In the past few years, one of the largest industries in the world, the agriculture sector, has faced many challenges, such as climate change and the depletion of limited natural resources. Smart Agriculture, based on IoT, is considered a transformative force that will play a crucial role in the further advancement of the agri-food sector. Furthermore, in IoT-based Smart Agriculture systems, radio wave propagation faces unique challenges (such as attenuation in vegetation and soil and multiple reflections) because of sensor nodes deployed in agriculture fields at or slightly above the ground level. In our study, we present, for the first time, several models (Multi-slope, Weissberger, and COST-235) suitable for planning radio coverage in a cornfield for Smart Agriculture applications. We received signal level measurements as a function of distance in a corn field (R3 corn stage) at 0.9 GHz and 2.4 GHz using two transmitting and two receiving antenna heights, with both horizontal and vertical polarization. The results indicate that radio wave propagation in a corn field is influenced not only by the surrounding environment (i.e., corn), but also by the antenna polarization and the positions of the transmitting and receiving antennas relative to the ground. Full article
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<p>Measurements in a corn field in the agricultural area near Dabravata Village, Yablanitsa Municipality, Bulgaria: (<b>a</b>) Google Earth image; (<b>b</b>) photo from the corn field.</p>
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<p>Configuration of the measurement setup.</p>
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<p>Measurement site: (<b>a</b>) Google Earth image with Tx and Rx antenna locations in the corn field; (<b>b</b>) photo of Rx antenna placed below the corn height; (<b>c</b>) photo of Rx antenna placed above the corn height; (<b>d</b>) direction of measurement at the experimental corn field.</p>
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<p>Measured reflection coefficients for the two dipoles: (<b>a</b>) reference dipole used for measurements at 0.9 GHz; (<b>b</b>) reference dipole used for measurements at 2.4 GHz.</p>
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<p>Received signal level variation with distance at 0.9 GHz for co-polarized H-H and V-V antennas: (<b>a</b>) transmitting antenna is placed at a height of λ/3 m above ground (hTx = 0.11 m); (<b>b</b>) transmitting antenna is placed at a height of 0.5 m above ground (hTx = 0.5 m).</p>
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<p>Received signal level variation with distance at 2.4 GHz for co-polarized H-H and V-V antennas: (<b>a</b>) transmitting antenna is placed at a height of λ/3 m above ground (h<sub>Tx</sub> = 0.04 m); (<b>b</b>) transmitting antenna is placed at a height of 0.5 m above ground (h<sub>Tx</sub> = 0.5 m).</p>
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<p>Received signal level variation with distance at 0.9 GHz for co-polarized H-H and V-V antennas: (<b>a</b>) receiving antenna height below the corn height (h<sub>Rx</sub> = 2.0 m); (<b>b</b>) receiving antenna height above the corn height (h<sub>Rx</sub> = 3.4 m).</p>
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<p>Received signal level variation with distance at 2.4 GHz for co-polarized H-H and V-V antennas: (<b>a</b>) receiving antenna height below the corn height (h<sub>Rx</sub> = 2.0 m); (<b>b</b>) receiving antenna height above the corn height (h<sub>Rx</sub> = 3.4 m).</p>
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<p>Comparison between losses for co-polarized H-H antennas at transmitting antenna height λ/3 m with existing models: (<b>a</b>) 0.9 GHz, h<sub>Rx</sub> = 2.0 m; (<b>b</b>) 0.9 GHz, h<sub>Rx</sub> = 3.4 m; (<b>c</b>) 2.4 GHz, h<sub>Rx</sub> = 2.0 m; (<b>d</b>) 2.4 GHz, h<sub>Rx</sub> = 3.4 m.</p>
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<p>Comparison between losses for co-polarized V-V antennas at transmitting antenna height λ/3 m with existing models: (<b>a</b>) 0.9 GHz, h<sub>Rx</sub> = 2.0 m; (<b>b</b>) 0.9 GHz, h<sub>Rx</sub> = 3.4 m; (<b>c</b>) 2.4 GHz, h<sub>Rx</sub> = 2.0 m; (<b>d</b>) 2.4 GHz, h<sub>Rx</sub> = 3.4 m.</p>
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<p>Comparison between losses for co-polarized H-H antennas at transmitting antenna height 0.5 m with existing models: (<b>a</b>) 0.9 GHz, h<sub>Rx</sub> = 2.0 m; (<b>b</b>) 0.9 GHz, h<sub>Rx</sub> = 3.4 m; (<b>c</b>) 2.4 GHz, h<sub>Rx</sub> = 2.0 m; (<b>d</b>) 2.4 GHz, h<sub>Rx</sub> = 3.4 m.</p>
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<p>Comparison between losses for co-polarized V-V antennas at transmitting antenna height 0.5 m with existing models: (<b>a</b>) 0.9 GHz, h<sub>Rx</sub> = 2.0 m; (<b>b</b>) 0.9 GHz, h<sub>Rx</sub> = 3.4 m; (<b>c</b>) 2.4 GHz, h<sub>Rx</sub> = 2.0 m; (<b>d</b>) 2.4 GHz, h<sub>Rx</sub> = 3.4 m.</p>
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15 pages, 1579 KiB  
Article
Alfalfa Increases the Soil N Utilization Efficiency in Degraded Black Soil Farmland and Alleviates Nutrient Limitations in Soil Microbes
by Linlin Mei, Yulong Lin, Ang Li, Lingdi Xu, Yuqi Cao and Guowen Cui
Agronomy 2024, 14(10), 2185; https://doi.org/10.3390/agronomy14102185 - 24 Sep 2024
Cited by 1 | Viewed by 725
Abstract
Alfalfa (Medicago sativa L.) can fix N naturally within soils, which makes alfalfa cultivation useful for enhancing soil fertility while minimizing environmental impacts from pesticides, fertilizers, and soil pollution. To assess the influence of alfalfa cropping on degraded black soil, we determined [...] Read more.
Alfalfa (Medicago sativa L.) can fix N naturally within soils, which makes alfalfa cultivation useful for enhancing soil fertility while minimizing environmental impacts from pesticides, fertilizers, and soil pollution. To assess the influence of alfalfa cropping on degraded black soil, we determined the nutrient stoichiometry of the soil and soil microbial biomass under four corn cultivation systems at the Harbin Corn Demonstration Base (Heilongjiang, China), which is located in Wujia (126°23′ E, 45°31′ N), Shuangcheng district, Harbin, Heilongjiang Province. The cultivation systems included continuous corn cultivation for more than 30 years (CK), 2 years of alfalfa–corn rotation (AC), three years of alfalfa cropping (TA), and four years of alfalfa cropping (FA). Overall, AC, TA, and FA treatment increased the soil pH, reduced the soil salinity, and increased the organic matter content of the 0–15 cm soil layer. TA and FA presented soil nutrient levels comparable to those of degraded cornfields that were fertilized annually. The TA and FA treatments increased the soil available N:P, soil N:P, and soil C:P ratios. Moreover, TA significantly increased the soil microbial biomass P (SMBP) in the 0–15 cm (surface) soil layer and reduced the soil microbial biomass C (SMBC):SMBP ratio. AC, TA, and FA increased the storage and mineralization rates of soil N and alleviated the microbial P limitations in degraded black soil farmland. Compared with FA, TA resulted in greater improvements in the quality of degraded black soil farmland. The ability of alfalfa to enhance soil fertility makes an important component of sustainable agricultural practices aimed at rehabilitating degraded soils. Full article
(This article belongs to the Section Soil and Plant Nutrition)
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<p>Effects of different planting practices on the NH<sub>4</sub><sup>+</sup>-N (<b>A</b>) and NO<sub>3</sub><sup>−</sup>-N (<b>B</b>) contents. The different lowercase and capital letters above the bars indicate significant differences (<span class="html-italic">p</span> &lt; 0.05) among the different planting practices in the 0–15 cm soil layer and the 15–30 cm soil layer. The asterisks indicate significant differences (<span class="html-italic">p</span> &lt; 0.05) between the 0–15 cm and 15–30 cm soil layers. The data are presented as the means ± standard errors (four repetitions).</p>
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<p>Effects of different planting practices on the soil AN (<b>A</b>) and AP (<b>B</b>) contents and the AN and P ratios (<b>C</b>). The different lowercase and capital letters above the bars indicate significant differences (<span class="html-italic">p</span> &lt; 0.05) among the different planting practices in the 0–15 cm soil layer and the 15–30 cm soil layer. The asterisks indicate significant differences (<span class="html-italic">p</span> &lt; 0.05) between the 0–15 cm and 15–30 cm soil layers. The data are presented as the means ± standard errors (four repetitions).</p>
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<p>Soil organic C, total N, and total P contents (<b>A</b>–<b>C</b>) and stoichiometric ratios (<b>D</b>–<b>F</b>) under different planting practices. The different lowercase and capital letters above the bars indicate significant differences (<span class="html-italic">p</span> &lt; 0.05) among the different planting practices in the 0–15 cm soil layer and the 15–30 cm soil layer. The asterisks indicate significant differences (<span class="html-italic">p</span> &lt; 0.05) between the 0–15 cm and 15–30 cm soil layers. The data are presented as the means ± standard errors (four repetitions).</p>
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<p>Effects of different planting practices on the soil microbial biomass C, N, and P contents (<b>A</b>–<b>C</b>) and their stoichiometric ratios (<b>D</b>–<b>F</b>). The different lowercase and capital letters above the bars indicate significant differences (<span class="html-italic">p</span> &lt; 0.05) among the different planting practices in the 0–15 cm soil layer and the 15–30 cm soil layer. The asterisks indicate significant differences (<span class="html-italic">p</span> &lt; 0.05) between the 0–15 cm and 15–30 cm layers. The data are presented as the means ± standard errors (four repetitions).</p>
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<p>Effects of different planting practices on soil C:N:P stoichiometry (<b>A</b>) and soil microbial biomass stoichiometry (<b>B</b>).</p>
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22 pages, 20392 KiB  
Article
AI-Driven Computer Vision Detection of Cotton in Corn Fields Using UAS Remote Sensing Data and Spot-Spray Application
by Pappu Kumar Yadav, J. Alex Thomasson, Robert Hardin, Stephen W. Searcy, Ulisses Braga-Neto, Sorin C. Popescu, Roberto Rodriguez, Daniel E. Martin and Juan Enciso
Remote Sens. 2024, 16(15), 2754; https://doi.org/10.3390/rs16152754 - 27 Jul 2024
Viewed by 1054
Abstract
To effectively combat the re-infestation of boll weevils (Anthonomus grandis L.) in cotton fields, it is necessary to address the detection of volunteer cotton (VC) plants (Gossypium hirsutum L.) in rotation crops such as corn (Zea mays L.) and sorghum ( [...] Read more.
To effectively combat the re-infestation of boll weevils (Anthonomus grandis L.) in cotton fields, it is necessary to address the detection of volunteer cotton (VC) plants (Gossypium hirsutum L.) in rotation crops such as corn (Zea mays L.) and sorghum (Sorghum bicolor L.). The current practice involves manual field scouting at the field edges, which often leads to the oversight of VC plants growing in the middle of fields alongside corn and sorghum. As these VC plants reach the pinhead squaring stage (5–6 leaves), they can become hosts for boll weevil pests. Consequently, it becomes crucial to detect, locate, and accurately spot-spray these plants with appropriate chemicals. This paper focuses on the application of YOLOv5m to detect and locate VC plants during the tasseling (VT) growth stage of cornfields. Our results demonstrate that VC plants can be detected with a mean average precision (mAP) of 79% at an Intersection over Union (IoU) of 50% and a classification accuracy of 78% on images sized 1207 × 923 pixels. The average detection inference speed is 47 frames per second (FPS) on the NVIDIA Tesla P100 GPU-16 GB and 0.4 FPS on the NVIDIA Jetson TX2 GPU, which underscores the relevance and impact of detection speed on the feasibility of real-time applications. Additionally, we show the application of a customized unmanned aircraft system (UAS) for spot-spray applications through simulation based on the developed computer vision (CV) algorithm. This UAS-based approach enables the near-real-time detection and mitigation of VC plants in corn fields, with near-real-time defined as approximately 0.02 s per frame on the NVIDIA Tesla P100 GPU and 2.5 s per frame on the NVIDIA Jetson TX2 GPU, thereby offering an efficient management solution for controlling boll weevil pests. Full article
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<p>Experiment field located at Texas A&amp;M University farm near College Station, TX in Burleson County (96°25′45.9″W, 30°32′07.4″N) where cotton plants were planted in the middle of corn field to mimic the presence of volunteer cotton plants.</p>
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<p>A customized sprayer UAS (broadcast sprayer converted to spot sprayer) with RedEdge-MX multispectral camera for capturing aerial imagery and NVIDIA Jetson TX2 computing platform [<a href="#B7-remotesensing-16-02754" class="html-bibr">7</a>].</p>
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<p>(<b>A</b>) The customized spot-sprayer UAS flying over an experimental corn field (containing some cotton plants planted to mimic as volunteer cotton (VC) plants) capturing five band multispectral images; (<b>B</b>) RGB (Red, Green Blue) composite image showing a section of experimental plot where corn at vegetative tassel state (VT) and some cotton plants mimicking as VC plants can be seen.</p>
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<p>Reflectance panel of type RP 04 image with blue band sensor of RedEdge-MX camera taken on the day of flight.</p>
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<p>General overview of YOLOv5 network architecture.</p>
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<p>A flowchart that shows complete workflow representing each step used in this study.</p>
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<p>Different types of losses that were obtained during the training process of YOLOv5m on training and validation datasets.</p>
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<p>Different types of performance metrices that were obtained during the training process of YOLOv5m.</p>
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<p>(<b>A</b>) Precision-recall plot, (<b>B</b>) F1-score vs confidence score plot, and (<b>C</b>) confusion matrix obtained after training YOLOv5m.</p>
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<p>VC plants detected in the middle of a corn field within the red bounding boxes (BBs) by trained YOLOv5m model. The values associated with each BB show model’s certainty that the bounding box contains an object of interest, i.e., VC plant.</p>
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<p>YOLOv5m detection of VC plants in a corn field by being deployed on NVIDIA Jetson TX2 mounted on a custom spot-spray-capable UAS.</p>
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<p>Optimal flight path generated by ACO algorithms and output shown by <span class="html-italic">Streamlit</span> Python package on a webpage.</p>
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<p>Spot-spray UAS simulation on MAVProxy (<b>A</b>,<b>B</b>) and Mission Planner (<b>C</b>) GCS. Image <span class="html-italic">A</span> shows the simulated UAS flying from node 1 to 2 while image <span class="html-italic">B</span> shows it flying from node 4 to 5. Image <span class="html-italic">C</span> shows the simulated UAS flying from node 8 to 9.</p>
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<p>Spot-spray nodes generated by Agrosol software (2.87.5) after uploading the CSV file containing nodes generated by ACO algorithm.</p>
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14 pages, 3949 KiB  
Article
Does the Biennial Straw Return Have an Identical Characteristic of Soil Organic Carbon Sequestration as the Annual? A Case Study of Cornfield in Northeast China
by Jinhua Liu, Xingmin Zhao, Zhongqing Zhang, Chenyu Zhao, Ning Huang and Hongbin Wang
Agronomy 2024, 14(6), 1174; https://doi.org/10.3390/agronomy14061174 - 30 May 2024
Viewed by 598
Abstract
Straw return is a common cultivation to improve soil fertility and realize sustainable agricultural development. However, the effect of returning interval on the corn straw humification process in northeast China is little known. In this study, a four-year field trial was conducted to [...] Read more.
Straw return is a common cultivation to improve soil fertility and realize sustainable agricultural development. However, the effect of returning interval on the corn straw humification process in northeast China is little known. In this study, a four-year field trial was conducted to investigate the characteristics of soil carbon sequestration under the annual deep straw return (T1), the biennial deep straw return (T2), and the non-straw return (T3) in Jilin Province, China. In order to precisely evaluate the soil organic carbon density (SOCD), each soil horizon was divided differently according to the actual situation, rather than a fixed thickness. The results show that both the annual and the biennial deep straw return had a significantly positive influence on the content of soil organic carbon (SOC), humic acid, fulvic acid, and humin in the plough pan (straw-applied horizon), compared to the no-straw return. SOC of the cambic horizon and the C horizon in annual straw return was 28.78%, 47.44% higher than the biennial straw return, but it was 27.58% lower in the plough pan. The SOCD in the plough pan in the biennial straw return was higher than the annual straw return, but their difference in the entire soil profile was not significant. However, the conversion rate of straw carbon to SOC was 18.42% in the annual straw return and 21.05% in the biennial straw return. The straw return amount was not a key factor affecting the SOC sequestration in the cold area; it was restricted by the comprehensive effects of the cold weather, the intensity of soil disturbance, C/V and the initial SOC content. In conclusion, the biennial deep straw return was a better management tool, as it generally had an identical quality and quantity of soil organic carbon and a higher straw conversion rate relative to the annual deep straw return. Full article
(This article belongs to the Section Soil and Plant Nutrition)
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<p>The geographical location of the field experiment.</p>
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<p>Soil stratification and corresponding depths in 2023 after 4 years of spring corn cultivation (2020, 2021, 2022, and 2023). A is the cultivated horizon, P is the plough pan, B is the cambic horizon and C is the C horizon. T1 is the annual straw return (2019, 2020, 2021, and 2022), T2 is the biennial straw return (2019 and 2021), and T3 is the no-straw return. Differing lowercase letters indicate a statistically significant difference between treatments in the same soil horizon (LSD, <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>SOC concentration in the cultivated horizon (A), the plough pan (P), the cambic horizon (B), and the C horizon (C) in 2023, after 4 years of spring corn cultivation under annual deep straw return (T1), biennial deep straw return (T2), and no-straw return (T3) in Northeast China (g/kg). Different lowercase letters indicate a statistically significant difference between treatments in the same soil horizon (LSD, <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>SOCD in the cultivated horizon (A), the plough pan (P), the cambic horizon (B), and the C horizon (C) in 2023, after 4 years of spring corn cultivation under annual straw return (T1), biennial straw return (T2), and non-straw return (T3) in Northeast China (g/kg). Different lowercase letters indicate a statistically significant difference between treatments in the same soil horizon (LSD, <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>The content of humic acid (HA-C) and fulvic acid (FA-C) in the cultivated horizon (A), the plough pan (P), the cambic horizon (B), and the C horizon (C) in 2023, after 4 years of spring corn cultivation under annual straw return (T1), biennial straw return (T2), and non-straw return (T3) in Northeast China (g/kg). Different lowercase letters indicate a statistically significant difference for the same item between treatments in the same soil horizon (LSD, <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>HA/FA in the cultivated horizon (A), the plough pan (P), the cambic horizon (B), and the C-horizon (C) in 2023, after 4 years of spring corn cultivation under annual straw return (T1), biennial straw return (T2), and non-straw return (T3) in Northeast China. Different lowercase letters indicate a statistically significant difference between treatments in the same soil horizon (LSD, <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>The content of HM-C in the cultivated horizon (A), the plough pan (P), the cambic horizon (B), and the C horizon (C) in 2023, after 4 years of spring corn cultivation under annual straw return (T1), biennial straw return (T2), and non-straw return (T3) in Northeast China (g/kg). Different lowercase letters indicate a statistically significant difference between treatments in the same soil horizon (LSD, <span class="html-italic">p</span> &lt; 0.05).</p>
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14 pages, 1655 KiB  
Review
Dieta de la Milpa: A Culturally-Concordant Plant-Based Dietary Pattern for Hispanic/Latine People with Chronic Kidney Disease
by Annabel Biruete, Gabriela Leal-Escobar, Ángeles Espinosa-Cuevas, Luis Mojica and Brandon M. Kistler
Nutrients 2024, 16(5), 574; https://doi.org/10.3390/nu16050574 - 20 Feb 2024
Cited by 1 | Viewed by 5773
Abstract
Chronic kidney disease (CKD) disproportionately affects minorities in the United States, including the Hispanic/Latine population, and is a public health concern in Latin American countries. An emphasis on healthy dietary patterns, including the Mediterranean and the Dietary Approaches to Stop Hypertension (DASH) diets, [...] Read more.
Chronic kidney disease (CKD) disproportionately affects minorities in the United States, including the Hispanic/Latine population, and is a public health concern in Latin American countries. An emphasis on healthy dietary patterns, including the Mediterranean and the Dietary Approaches to Stop Hypertension (DASH) diets, has been suggested as they are associated with a lower incidence of CKD, slower CKD progression, and lower mortality in kidney failure. However, their applicability may be limited in people from Latin America. The Dieta de la Milpa (Diet of the Cornfield) was recently described as the dietary pattern of choice for people from Mesoamerica (Central Mexico and Central America). This dietary pattern highlights the intake of four plant-based staple foods from this geographical region, corn/maize, common beans, pumpkins/squashes, and chilies, complemented with seasonal and local intake of plant-based foods and a lower intake of animal-based foods, collectively classified into ten food groups. Limited preclinical and clinical studies suggest several health benefits, including cardiometabolic health, but there is currently no data concerning CKD. In this narrative review, we describe and highlight the potential benefits of the Dieta de la Milpa in CKD, including acid-base balance, protein source, potassium and phosphorus management, impact on the gut microbiota, inflammation, and cultural appropriateness. Despite these potential benefits, this dietary pattern has not been tested in people with CKD. Therefore, we suggest key research questions targeting measurement of adherence, feasibility, and effectiveness of the Dieta de la Milpa in people with CKD. Full article
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<p><span class="html-italic">Dieta de la Milpa</span> description by food groups. Adapted with permission from [<a href="#B20-nutrients-16-00574" class="html-bibr">20</a>].</p>
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<p>Proposed benefits of the <span class="html-italic">Dieta de la Milpa</span> in CKD.</p>
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36 pages, 4384 KiB  
Article
Radio Telemetry and Harmonic Radar Tracking of the Spotted Lanternfly, Lycorma delicatula (White) (Hemiptera: Fulgoridae)
by Matthew S. Siderhurst, Kelly M. Murman, Kyle T. Kaye, Matthew S. Wallace and Miriam F. Cooperband
Insects 2024, 15(1), 17; https://doi.org/10.3390/insects15010017 - 30 Dec 2023
Cited by 3 | Viewed by 1786
Abstract
Lycorma delicatula (White) (Hemiptera: Fulgoridae), spotted lanternfly (SLF), is an invasive pest that feeds and oviposits on numerous woody and herbaceous plants important to agricultural, forest, ornamental, and nursery industries. Describing and understanding SLF movements is key to implementing surveillance and control strategies [...] Read more.
Lycorma delicatula (White) (Hemiptera: Fulgoridae), spotted lanternfly (SLF), is an invasive pest that feeds and oviposits on numerous woody and herbaceous plants important to agricultural, forest, ornamental, and nursery industries. Describing and understanding SLF movements is key to implementing surveillance and control strategies for this pest and projecting population spread. We used radio telemetry (RT) and harmonic radar (HR) to track the movements of individual SLF at field sites in eastern Pennsylvania and northwestern New Jersey. SLF equipped with HR or RT tags were tracked in 2019 and 2020 from adult emergence until oviposition time, and their movements are described. Although the bulkier RT tags disproportionately affected the distance traveled by males, which are smaller than females, both males and females were more likely to be lost due to signal attenuation when affixed with the lighter-weight HR tags. Females were tracked moving longer distances than males, with maximum distances of 434 m by a single female and 57 m by a single male. A significant positive relationship was found between their height in trees and the distance of subsequent movement. Adult SLF were found in trees predominantly at heights between 6–9 m high. For the fraction of SLF found at eye level, males, but not females, significantly moved above eye level in the weeks prior to mating, likely resulting in the observed sex ratio shift that defines the Early-2 stage. During mating time, tracked SLF were significantly higher than 8 m and oriented to trees where tight aggregations of SLF were present. This orientation towards tight aggregations started when mating began and peaked in the following 2.5 weeks for males in Late-1 and the beginning of Late-2 (after oviposition began), whereas females started this orientation behavior a half-week after males, and this activity peaked for two weeks. Male and female SLF adults exhibited slight differences in host preference, and strong preferences for wild grape, black walnut, sweet birch, and tree-of-heaven were observed. The HR-tagged nymphs moved up to 27.6 m over a five-day period in a cornfield. Nitinol wire HR tags performed better than Wollaston process or tungsten wire tags. SLF movement parameters in the field are described. Full article
(This article belongs to the Section Insect Behavior and Pathology)
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Graphical abstract

Graphical abstract
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<p>Satellite views of Trexler Nature Preserve used in 2019 (<b>A</b>) and Beaver Brook Wildlife Management Area used in 2020 (<b>B</b>), where Experiments 1 and 2 were conducted (parklands outlined in orange). Adult spotted lanternflies <span class="html-italic">Lycorma delicatula</span> (SLF) were tagged and released at designated release points (R). The two primary release trees at each site were approximately 1 km apart. The white box in Trexler Nature Preserve (<b>A</b>) is enlarged in (<b>C</b>) to depict the sample movements of four different SLF originating from release tree R<sub>2</sub> (shown as blue, green, yellow, and pink travel vectors). The longest two vectors (shown in blue and green) represent two SLF that crossed over Jordan Creek, suggesting flight occurred with the RT tags (satellite images by Google Earth).</p>
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<p>Spotted lanternfly, <span class="html-italic">Lycorma delicatula</span>, example tag attachments and step frequency for each experiment: Experiment 1 (<b>A</b>) (Photo Credit: Kelly Murman), Experiment 2 (<b>B</b>) (Photo Credit: Kyle Kaye), and Experiment 3 (<b>C</b>) (Photo Credit: Matthew Siderhurst). The HR-tagged adult (<b>B</b>) and nymph (<b>C</b>) are each shown with a nitinol wire tag. In (<b>A</b>–<b>C</b>), the points representing the frequency of step distances of zero m are highlighted with a yellow circle while all other distances are shown in black. The total distance moved for Experiments 1 (<b>D</b>), 2 ((<b>E</b>), data from both years combined), and 3 (<b>F</b>) are shown in the second row of graphs. Differently colored circles in (<b>D</b>,<b>E</b>) represent different individual tracked SLF. Turning angle and flight direction are shown for Experiments 1 (<b>G</b>,<b>H</b>), 2, (<b>I</b>,<b>J</b>), and 3 (<b>K</b>,<b>L</b>), respectively, with N representing the number of insects used in each calculation.</p>
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<p>The period of time (days) between successive observations, or step duration, of individual adult spotted lanternflies (SLF), <span class="html-italic">Lycorma delicatula,</span> tracked with radio telemetry (Experiment 1), ranged from 1 to 20 d and varied over time, but SLF were located most often after a period of 1–3 d (<b>A</b>). Thus, standard-sized tracking periods of 1–3 d were used to calculate movement parameters. The frequency of movement (<b>B</b>) (when SLF moved from their previously known location as opposed to staying in the same place), over these 1–3 d tracking periods, changed significantly over five adult stages for both females (white) and males (gray) (asterisks indicate when observations with movement were significantly outnumbered by those without movement, Chi-square test, <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Parameters for distance moved by female (white) and male (gray) adult spotted lanternflies (SLF), <span class="html-italic">Lycorma delicatula,</span> tracked with RT (Experiment 1). Total distance moved (sum of all step-distances per SLF) by sex and release date are shown as means (±SE) (<b>A</b>) and individual SLF (<b>B</b>). The total time (days) that individual SLF were tracked is plotted against the total distance (m) they moved (with linear regression lines) in (<b>C</b>). The average non-zero distances travelled during steps that were 1–3 d in duration and 4–20 d in duration are shown for females (<b>D</b>) and males (<b>E</b>) over five adult stages, with the number of steps represented by each bar indicated by (N). The frequency of total distances that tracked individual SLF males and females traveled is shown in (<b>F</b>). In (<b>A</b>,<b>D</b>,<b>E</b>), bars in the same comparison with no letters in common are significantly different (ANOVA using ranked data for A, and log-transformed data for (<b>D</b>,<b>E</b>), followed by Tukey HSD means separations, <span class="html-italic">p</span> &lt; 0.05, back-transformed data are shown).</p>
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<p>Rate of movement over time of adult female (white) and male (gray) spotted lanternflies (SLF), <span class="html-italic">Lycorma delicatula</span>, tracked with radio telemetry (Experiment 1). The movement rates (m/d) of all non-zero individual steps by date are shown in (<b>A</b>), plotted on a logarithmic scale. The mean rate of movement (m/d) (±SE) of SLF tracked over shorter tracking periods (1–3 d), and longer tracking periods (4–20 d) are shown for females (<b>B</b>) and males (<b>C</b>) over five stages. The number of steps represented in each bar is indicated by (N). Bars in the same comparison that do not share the same letter are significantly different (ANOVA on log-transformed data followed by Tukey HSD means separations, <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>The height (m) above ground of male (gray diamonds) and female (open circles) (with linear lines for each) spotted lanternflies (SLF) <span class="html-italic">L. delicatula</span> tracked with radio telemetry (RT) is shown in scatter plots of raw step data by observation date in 2020 (<b>A</b>) and 2019 (<b>B</b>). Average SLF height in trees ± SE (m) at Trexler and Beaver Brook sites in 2019 and 2020, respectively (<b>C</b>) (Wilcoxon, <span class="html-italic">p</span> &lt; 0.05, with different letters indicating significant difference). The number and frequency of SLF steps at different height ranges tracked with RT in 2020 for males and females combined over the entire season are shown in (<b>D</b>). The frequency of those steps that occurred above or below 8 m is shown in (<b>E</b>) for males (gray) and females (white) at each stage, with an asterisk indicating when the frequency was significantly greater in one height range than the other (Chi-square test, <span class="html-italic">p</span> &lt; 0.05). Frequencies of male and female heights during each stage compared to the average height of 8 m are shown in (<b>F</b>).</p>
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<p>The step frequencies of female (<b>A</b>) and male (<b>B</b>) spotted lanternflies (SLF), <span class="html-italic">L. delicatula,</span> (including both RT- and HR-tagged SLF from both years) above and below eye level (&lt;2 m) were compared against the expected frequency at eye level (15%), using a chi-square test for each stage. The total number of steps for each test is shown as N. Asterisks indicate that the frequency of steps above and below eye level deviated significantly from expected (<span class="html-italic">p</span> &lt; 0.05). The frequencies that females and males were found at eye level for each stage were tabulated and used to calculate and plot the sex ratio of steps at eye level (solid line with squares) and above eye level (dashed line with triangles) (<b>C</b>).</p>
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<p>Survey results (<b>A</b>) showing the frequency of woody plants by genus in a 15 m radius around the release trees at the three main release sites for 2019 (R<sub>2</sub>) and 2020 (R<sub>3</sub>, and R<sub>4</sub>). The only <span class="html-italic">Ailanthus</span> present in either survey was the release tree in 2019 (‡), and SLF that never left their release trees were excluded from this analysis. The plant-weighted visit frequency (omitting release trees) is shown by genus in (<b>B</b>), where <span class="html-italic">p</span> is the plant frequency and <span class="html-italic">v</span> is the frequency of visits by spotted lanternflies (SLF), <span class="html-italic">L. delicatula</span>. A result between 0 and 1 is given, where 0.5 indicates plants were visited at the same frequency as their presence, greater than 0.5 suggests that species was favored, and less than 0.5 suggests that species was avoided. The number of encounters (N steps) with host plants recorded for adult male (<b>C</b>) and female (<b>D</b>) SLF, tracked with radio telemetry in 2019 (top) and 2020 (bottom) is shown by genus, with stages indicated by different colors. Asterisks indicate when one sex was found on a species at a relative frequency significantly greater than the other sex (chi-square test, <span class="html-italic">p &lt;</span> 0.05).</p>
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<p>The frequencies at which adult spotted lanternfly (SLF), <span class="html-italic">L. delicatula,</span> males (gray) and females (white), tracked with both technologies in 2019 and 2020, and excluding those that never left their release trees, were found on the same surface as naturally occurring SLF populations at lower or higher densities (as estimated at eye level), at different stages. Asterisks indicate when SLF were found near one density significantly more than the other (<span class="html-italic">p</span> &lt; 0.05, chi-square test). Numbers inside bars represent numbers of SLF found.</p>
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<p>Comparison between (<b>A</b>) radio telemetry (RT) and harmonic radar (HR) showing the average distances (±SE) flown during flight tests by males (gray) and females (white) affixed with either HR or RT tags (Wilcoxon test comparing tag type within each sex); (<b>B</b>) non-zero 1–3 d step distances of males and females for each tag technology (ANOVA on log-transformed data); and (<b>C</b>) the amount of time over which tracking took place for individual <span class="html-italic">L. delicatula</span>, spotted lanternflies (SLF), until the last time each SLF was located. In statistical comparisons, bars with different letters are significantly different.</p>
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<p>Fourth-instar nymphs of spotted lanternfly, <span class="html-italic">L. delicatula</span>, observed feeding on corn in 2019 in Lehigh County, PA, USA (<b>A</b>). The tangling of an HR tag made with a Wollaston process (platinum) wire antenna effectively reduced antenna length and severely attenuated the detection range of tags (<b>B</b>).</p>
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49 pages, 2248 KiB  
Review
Movement Ecology of Adult Western Corn Rootworm: Implications for Management
by Thomas W. Sappington and Joseph L. Spencer
Insects 2023, 14(12), 922; https://doi.org/10.3390/insects14120922 - 3 Dec 2023
Cited by 3 | Viewed by 2499
Abstract
Movement of adult western corn rootworm, Diabrotica virgifera virgifera LeConte, is of fundamental importance to this species’ population dynamics, ecology, evolution, and interactions with its environment, including cultivated cornfields. Realistic parameterization of dispersal components of models is needed to predict rates of range [...] Read more.
Movement of adult western corn rootworm, Diabrotica virgifera virgifera LeConte, is of fundamental importance to this species’ population dynamics, ecology, evolution, and interactions with its environment, including cultivated cornfields. Realistic parameterization of dispersal components of models is needed to predict rates of range expansion, development, and spread of resistance to control measures and improve pest and resistance management strategies. However, a coherent understanding of western corn rootworm movement ecology has remained elusive because of conflicting evidence for both short- and long-distance lifetime dispersal, a type of dilemma observed in many species called Reid’s paradox. Attempts to resolve this paradox using population genetic strategies to estimate rates of gene flow over space likewise imply greater dispersal distances than direct observations of short-range movement suggest, a dilemma called Slatkin’s paradox. Based on the wide-array of available evidence, we present a conceptual model of adult western corn rootworm movement ecology under the premise it is a partially migratory species. We propose that rootworm populations consist of two behavioral phenotypes, resident and migrant. Both engage in local, appetitive flights, but only the migrant phenotype also makes non-appetitive migratory flights, resulting in observed patterns of bimodal dispersal distances and resolution of Reid’s and Slatkin’s paradoxes. Full article
(This article belongs to the Special Issue Corn Rootworm: Biology, Ecology, Behavior and Integrated Management)
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<p>Schematic of types of flight behavior by <span class="html-italic">resident</span> and <span class="html-italic">migrant</span> phenotypes of western corn rootworm, and resulting spatial displacement across local and regional landscapes relative to natal and colonized cornfields. The schematic crop fields in the figure represent typical dimensions of rectangular fields in the U.S. Corn Belt, where the smallest squares shown are 0.25 mi (0.40 km) per side, or 40 acres (16.2 ha). In this depiction, western corn rootworm adults eclose in two natal fields (<b>N1</b> and <b>N2</b>). A <span class="html-italic">resident</span> may ultimately engage only in appetitive station-keeping behaviors throughout its lifetime, resulting in net displacement of only short distances within the natal field or after emigration into habitat in the natal field’s immediate vicinity. This area of station-keeping activity constitutes the natal home range (purple circle) and is observed as diffusive spread of the field’s population. Some residents engage in an appetitive ranging flight (meandering brown arrow) in search of a needed resource or better habitat, emigrating from the natal field and beyond the natal home range. Ranging flight is facultative and triggered by proximate conditions such as lack of a needed resource or deteriorating habitat. A ranging resident stops when it encounters the sought-after resource, which may be a relatively short distance away (e.g., <b>N1</b> → <b>A</b>, <b>N2</b> → <b>C</b>), or a longer distance across the local landscape (e.g., <b>N1</b> → <b>B</b>). In either case, after immigration into a suitable habitat, station-keeping behaviors resume in the colonized field, and a new home range (brown circle) emerges. A <span class="html-italic">migrant</span> adult emigrates from the natal field via a non-appetitive migratory flight (straight blue arrow) over relatively long distances, not only beyond the natal home range, but often beyond the local landscape (e.g., <b>N1</b> → <b>C</b>, <b>N1</b> → <b>D</b>, <b>N2</b> → <b>A</b>). Migration is not initiated in direct response to proximate conditions. Thus, migrants may emigrate from a highly suitable natal field in which a large number of residents remain to reproduce. Migration is innate to the migrant phenotype and is initiated in females during a narrow developmental window after mating but before egg maturation. Migratory flight is straight-line (non-meandering), and in western corn rootworm is not directed toward a geographic goal or in a preferred direction (e.g., beetles on opposite trajectories, such as <b>N1</b> → <b>D</b> and <b>N2</b> → <b>A</b>, can issue from the same field). The migrating insect does not respond to resource cues or cease flight when encountering suitable habitat (e.g., blue arrow passing over field <b>B</b>). Instead, migration is terminated in response to global environmental cues like sunset, or intrinsic cues such as an internal clock or physiological status, which have not yet been elucidated for western corn rootworm. If the insect fortuitously terminates its migratory flight in suitable habitat (e.g., fields <b>A</b>, <b>C</b>, and <b>D</b>), it then resumes station-keeping behavior, and a new home range emerges (blue circle). If migration terminates in unsuitable habitat (e.g., <b>N1</b> → <b>E</b>), the migrant initiates appetitive ranging behavior through the local landscape in search of appropriate habitat (e.g., <b>E</b> → <b>D</b>). Once suitable habitat is encountered, ranging ends, station-keeping behaviors resume, and a new home range emerges.</p>
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<p>Approximate scales of net displacement during a bout of flight activity by western corn rootworm and assignment of behaviors to both <span class="html-italic">resident</span> and <span class="html-italic">migrant</span> phenotypes, or exclusively to the <span class="html-italic">migrant</span> phenotype. Movement behaviors [<a href="#B64-insects-14-00922" class="html-bibr">64</a>] differ fundamentally as either appetitive or non-appetitive. Appetitive behaviors (station-keeping and ranging behaviors) are motivated by search for a needed resource. Non-appetitive behavior is migratory, motivated by the goal of spatial displacement itself. Migrant western corn rootworms begin migratory flight by purposely ascending high into the atmosphere where fast winds increase speed and distance of displacement.</p>
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18 pages, 10237 KiB  
Article
Available Forage and the Conditions for Avoiding Predation of the Siberian Roe Deer (Capreolus pygargus) in the Lesser Xing’an Mountains
by Yueyuan Li, Yuehui Li, Yuanman Hu, Yue Li, Jia Guo, Xuefeng Shao and Huifang Gao
Forests 2023, 14(10), 2072; https://doi.org/10.3390/f14102072 - 17 Oct 2023
Viewed by 1100
Abstract
Our study focused on quantifying available forage and the conditions for avoiding predation provided within the home ranges of Siberian roe deer (Capreolus pygargus). We conducted transect surveys in both winter and summer–autumn home ranges of the Siberian roe deer in [...] Read more.
Our study focused on quantifying available forage and the conditions for avoiding predation provided within the home ranges of Siberian roe deer (Capreolus pygargus). We conducted transect surveys in both winter and summer–autumn home ranges of the Siberian roe deer in the Tieli Forestry Bureau of the Lesser Xing’an Mountains. Our results revealed significant differences: (1) In terms of the quantity and quality of available forage, the summer–autumn home range had substantially more available forage than the winter home range, with meadows and cornfields showing the highest edible biomass in each, respectively. In terms of forage quality, there were differences in hemicellulose, cellulose, and lignin content between the two ranges. (2) In terms of the conditions for avoiding predation, the winter home range had lower vegetation coverage and greater visibility, making escape strategies more viable. In contrast, the summer–autumn home range had denser vegetation and limited visibility, making hiding strategies more viable. Our study offers comprehensive insights into the available forage and the conditions for avoiding predation, which is crucial for wildlife conservation strategies and habitat management in the region, as it directly informs strategies that address the seasonal forage requirements and predation avoidance of these deer, ultimately enhancing their prospects for survival in the area. Full article
(This article belongs to the Section Forest Ecology and Management)
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<p>Sampling transect design within the winter and summer–autumn home ranges of the Siberian roe deer (<span class="html-italic">Capreolus pygargus</span>) in the Lesser Xing’an Mountains.</p>
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<p>Vegetation coverage at four height layers in different landscape types within the winter and summer–autumn home ranges of the Siberian roe deer in the Lesser Xing’an Mountains. (<b>a</b>) The samples in different landscape types within the winter home range were from forests (<span class="html-italic">n</span> = 79), meadows (<span class="html-italic">n</span> = 18), soybean fields (<span class="html-italic">n</span> = 36), and cornfields (<span class="html-italic">n</span> = 18). (<b>b</b>) The samples in different landscape types within the summer–autumn home range were from broadleaf forests (<span class="html-italic">n</span> = 70), coniferous forests (<span class="html-italic">n</span> = 56), mixed forests (<span class="html-italic">n</span> = 7), meadows (<span class="html-italic">n</span> = 71), soybean fields (<span class="html-italic">n</span> = 5), cornfields (<span class="html-italic">n</span> = 4), and rice fields (<span class="html-italic">n</span> = 3). Statistical differences are denoted by lowercase letters, with a significance level of 0.05. Different lowercase letters in the same height layer denote significant differences in vegetation coverage among landscape types (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Visibility in different landscape types within the winter and summer–autumn home ranges of the Siberian roe deer in the Lesser Xing’an Mountains. (<b>a</b>) The samples in different landscape types within the winter home range were from forests (<span class="html-italic">n</span> = 79), meadows (<span class="html-italic">n</span> = 18), soybean fields (<span class="html-italic">n</span> = 36), and cornfields (<span class="html-italic">n</span> = 18). (<b>b</b>) The samples in different landscape types within the summer–autumn home range were from broadleaf forests (<span class="html-italic">n</span> = 70), coniferous forests (<span class="html-italic">n</span> = 56), mixed forests (<span class="html-italic">n</span> = 7), meadows (<span class="html-italic">n</span> = 71), soybean fields (<span class="html-italic">n</span> = 5), cornfields (<span class="html-italic">n</span> = 4), and rice fields (<span class="html-italic">n</span> = 3). Statistical differences are denoted by lowercase letters, with a significance level of 0.05. Different lowercase letters denote significant differences in visibility among landscape types (<span class="html-italic">p</span> &lt; 0.05).</p>
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14 pages, 11032 KiB  
Article
Visible and Near-Infrared Hyperspectral Diurnal Variation Calibration for Corn Phenotyping Using Remote Sensing
by Jinnuo Zhang, Dongdong Ma, Xing Wei and Jian Jin
Remote Sens. 2023, 15(12), 3057; https://doi.org/10.3390/rs15123057 - 11 Jun 2023
Cited by 1 | Viewed by 2000
Abstract
Remote sensing coupled with hyperspectral technology has become increasingly popular to investigate plant traits, showcasing its advantages in studying plant growth, health, and productivity. The quality of the collected hyperspectral images is crucial for subsequent data analysis and plant phenotyping studies. However, diurnal [...] Read more.
Remote sensing coupled with hyperspectral technology has become increasingly popular to investigate plant traits, showcasing its advantages in studying plant growth, health, and productivity. The quality of the collected hyperspectral images is crucial for subsequent data analysis and plant phenotyping studies. However, diurnal variations in spectral characteristics introduce more data variance in canopy reflectance spectra, raising the cost of subsequent analyses and compromising the performance of trait estimation models. In this study, a fixed gantry platform in a cornfield was used to capture visible and near-infrared (VNIR) hyperspectral images of corn canopies at consecutive time intervals. By applying reference board calibration and locally weighted scatterplot smoothing to minimize the effects of ambient light and daily growth, diurnal spectral changes across all involved VNIR wavelengths were investigated. Several distinct diurnal patterns were observed to have close connections with the plants’ physiological effects. Diurnal calibration models were established at every wavelength by employing the least squares polynomial algorithm, with the highest coefficient of determination reaching 0.84. Moreover, by employing diurnal calibration in canopy spectra processing, the reduction in spectral variance brought about by varying imaging time was evidently exhibited. This study not only reveals the diurnal spectral variation pattern at VNIR bands but also offers a reliable, straightforward, and low-cost approach to improve the quality of remote sensing data and reduce the inherent variance brought about via the different imaging times ensuring that comparable spectral analysis can be performed under relatively fair conditions. Full article
(This article belongs to the Special Issue Within-Season Agricultural Monitoring from Remotely Sensed Data)
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<p>The hyperspectral image data collection procedures and the following diurnal calibration model establishment flowchart (FN: full nitrogen treatment; LN: low nitrogen treatment; blue boxes refer to B73 × Mo17 and red boxes refer to P1105AM).</p>
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<p>The comparison curves result from the original NDVI value at different DAP and the NDVI value after trend and seasonal decomposition based on LOESS. ((<b>A</b>): the original NDVI value had relative severe fluctuation with the changing of time; (<b>B</b>): the NDVI value after LOESS kept a relative stable fluctuation at different DAP).</p>
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<p>Several diurnal calibration curves derived from polynomial models at various characteristic wavelengths ((<b>A</b>–<b>I</b>): 400 nm, 420 nm, 440 nm, 470 nm, 620 nm, 670 nm, 710 nm, 910 nm, 1001 nm).</p>
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<p>The normalized reflectance heatmaps of diurnal changing pattern under varying times of day and wavelengths ((<b>A</b>): the view of 2D heatmap; (<b>B</b>): the view of 3D surface maps at both 45° and 325° view angles).</p>
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<p>The regression performance of the established diurnal calibration model in the VNIR spectral range.</p>
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<p>The averaged spectra under full nitrogen and low nitrogen treatment from two corn genotypes’ testing sets. ((<b>A</b>,<b>C</b>): the original spectra; (<b>B</b>,<b>D</b>): the spectra after diurnal calibration to solar noon). The shaded areas indicate the standard deviation range.</p>
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<p>Variance reduction ratio at different wavelengths after diurnal calibration ((<b>A</b>): variance reduction ratio at all available wavelengths; (<b>B</b>): statistical comparison between the before and after diurnal calibration based on reflectance at representative wavelengths).</p>
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<p>Variation in spectral signatures at 670 nm (<b>A</b>) and 1001 nm (<b>B</b>) during diurnal cycles across diverse growth phases.</p>
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<p>Statistical plot of the reflectance distribution at 900 nm, 936 nm, and 1002 nm.</p>
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14 pages, 1840 KiB  
Article
Mating Competitiveness of Male Spodoptera frugiperda (Smith) Irradiated by X-rays
by Shan Jiang, Xiao-Ting Sun, Shi-Shuai Ge, Xian-Ming Yang and Kong-Ming Wu
Insects 2023, 14(2), 137; https://doi.org/10.3390/insects14020137 - 29 Jan 2023
Cited by 5 | Viewed by 2163
Abstract
Spodoptera frugiperda, an invasive pest, has a huge impact on food production in Asia and Africa. The potential and advantages of sterile insect techniques for the permanent control of S. frugiperda have been demonstrated, but the methods for their field application are [...] Read more.
Spodoptera frugiperda, an invasive pest, has a huge impact on food production in Asia and Africa. The potential and advantages of sterile insect techniques for the permanent control of S. frugiperda have been demonstrated, but the methods for their field application are still unavailable. For the purposes of this study, male pupae of S. frugiperda were irradiated with an X-ray dose of 250 Gy to examine the effects of both the release ratio and the age of the irradiated males on the sterility of their offspring. The control effect of the irradiated male release ratio on S. frugiperda was evaluated using field-cage experiments in a cornfield. The results showed that when the ratio of irradiated males to non-irradiated males reached 12:1, the egg-hatching rate of the offspring of S. frugiperda decreased to less than 26%, and there was also no significant difference in mating competitiveness among the different ages. Field-cage testing showed that when irradiated males were released at ratios of 12:1–20:1 to normal males, the leaf protection effect for the corn reached 48–69% and the reduction in the insect population reached 58–83%. In this study, an appropriate release ratio is suggested, and the mating competitiveness of irradiated and non-irradiated males of S. frugiperda is investigated, thus providing a theoretical basis for the use of sterile insect techniques to control S. frugiperda. Full article
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<p>Average number of eggs laid per female (<b>A</b>), infertility rate (probability of non-hatching eggs) (mean ± standard error) and the fitting of the release ratio to the infertility rate (probability of non-hatching eggs) (<b>B</b>) in <span class="html-italic">Spodoptera frugiperda</span> at different release ratios; different lowercase letters indicate significant differences (one-way ANOVA, Tukey’s HSD; <span class="html-italic">p</span> &lt; 0.05). T = treated with irradiation; N = not irradiated.</p>
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<p>Effect of the ages of irradiated <span class="html-italic">Spodoptera frugiperda</span> males on egg production and the infertility rate (probability of non-hatching eggs). Data in the figure are the mean ± standard error; different lowercase letters indicate significant differences (one-way ANOVA, Tukey’s HSD; <span class="html-italic">p</span> &lt; 0.05). In this experiment, the irradiated male: non-irradiated male: non-irradiated female ratio = 12:1:1, where both the non-irradiated male and the female were 1 day old.</p>
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<p>Leaf damage index in the treated plots on the 3rd (<b>A</b>), 5th (<b>B</b>), 7th (<b>C</b>), 9th (<b>D</b>), 14th (<b>E</b>), and 21st (<b>F</b>) days after <span class="html-italic">Spodoptera frugiperda</span> release. Data in the figure are the mean ± standard error; different lowercase letters indicate significant differences (one-way ANOVA, Tukey’s HSD; <span class="html-italic">p</span> &lt; 0.05); T = treated with irradiation; N = not irradiated; insecticide was sprayed on the third day after insect release.</p>
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<p>Corrected non-hatching rate of eggs (<b>A</b>), corrected population decline rate (<b>B</b>), and corrected leaf protection rate (<b>C</b>) after 21 days of field release of <span class="html-italic">Spodoptera frugiperda</span> in different proportions. Data in the figure are the mean ± standard error; different lowercase letters indicate significant differences (one-way ANOVA, Tukey’s HSD; <span class="html-italic">p</span> &lt; 0.05); T = treated with irradiation; N = not irradiated; insecticide were sprayed on the third day after insect release.</p>
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14 pages, 2711 KiB  
Article
Occurrence and Risk Assessment of Atrazine and Diuron in Well and Surface Water of a Cornfield Rural Region
by Brenda Lagunas-Basave, Alhelí Brito-Hernández, Hugo Albeiro Saldarriaga-Noreña, Mariana Romero-Aguilar, Josefina Vergara-Sánchez, Gabriela Eleonora Moeller-Chávez, José de Jesús Díaz-Torres, Mauricio Rosales-Rivera and Mario Alfonso Murillo-Tovar
Water 2022, 14(22), 3790; https://doi.org/10.3390/w14223790 - 21 Nov 2022
Cited by 11 | Viewed by 3482
Abstract
Herbicides have contributed to increased agricultural production. However, their residual amount can cause negative effects on environmental and public health. Therefore, this work aimed to determine the occurrence of both atrazine and diuron in surface and well water and investigate their link with [...] Read more.
Herbicides have contributed to increased agricultural production. However, their residual amount can cause negative effects on environmental and public health. Therefore, this work aimed to determine the occurrence of both atrazine and diuron in surface and well water and investigate their link with drinking use. The samples were collected during dry and rainy seasons in three wells and surface water from a river and a pond located in the low plains of the Ixcatepec catchment, at the Amacuáhuitl community of the municipality of Arcelia, Guerrero State, in the center south of México, which is a rural community where farming is the main activity. The compounds were obtained by solid phase extraction and determined by HPLC-MS quadrupole with positive electrospray ionization mode. A geomorphic analysis was conducted inside the Ixcatepec catchment using the digital elevation model of the Shuttle Radar Topography Mission, SRTM-v4. The human risk for drinking water was calculated according to the Hazard Quotient. The concentrations of atrazine and diuron were between 5.77 and 402 ng L−1. Atrazine was the most abundant and frequent pesticide found with an average concentration of 105.18 ng L−1, while that of diuron was 86.56 ng L−1. The highest levels were found in pond Ushe, likely being the result of the lowest flow and stagnation of water, and during the cold-dry season a consequence of mobilization by irrigation runoff. The morphological analysis indicated that the compounds mainly reached body water located in the lower surfaces from cultivated areas. Therefore, the occurrence is mainly linked to agriculture activity within the rural community. However, chemical properties of compounds, crop irrigation, and environmental conditions could be contributing to the dispersion of residual amounts of herbicides within the hydrological system. The estimation of risk showed that atrazine can mainly generate health problems for children using the Azul well as a source of drinking water. Full article
(This article belongs to the Section Water Quality and Contamination)
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<p>Topographic map of the study area based on the digital elevation model (Data sources: <a href="https://srtm.csi.cgiar.org/" target="_blank">https://srtm.csi.cgiar.org/</a> (accessed on 15 November 2021) [<a href="#B31-water-14-03790" class="html-bibr">31</a>].</p>
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<p>Classification of sample sites and herbicide concentrations.</p>
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<p>Land use inside the Ixcatepec catchment. Sample sites: Wells (Drinking water, 0; Brocal, 3; Azul, 4; Tello, 6); Stream water (Ixcatepec river and Arroyo, 1 and 2); pond (El Ushe, 5).</p>
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<p>Seasonal distribution of herbicides atrazine and diuron.</p>
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<p>Map of the slope less than 15° into the Ixcatepec catchment (47.4% of the total catchment). Sample sites: Wells (Drinking water, 0; Brocal, 3; El Azul, 4; Tello, 6); Stream water (Ixcatepec river and Arroyo, 1 and 2); pond (El Ushe, 5).</p>
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19 pages, 3004 KiB  
Article
The Diurnal Variation Characteristics of Latent Heat Flux under Different Underlying Surfaces and Analysis of Its Drivers in The Middle Reaches of the Heihe River
by Ji He, Qing-Min Li, Wen-Chuan Wang, Dong-Mei Xu and Yu-Rong Wan
Water 2022, 14(21), 3514; https://doi.org/10.3390/w14213514 - 2 Nov 2022
Cited by 3 | Viewed by 1964
Abstract
The Latent Heat Flux (LE) is an important component of surface water heat transfer and hydrological cycle, and monitoring it is of great value for water resource management and crop water demand estimation. The Heihe River Basin has complex topography, which ensures better [...] Read more.
The Latent Heat Flux (LE) is an important component of surface water heat transfer and hydrological cycle, and monitoring it is of great value for water resource management and crop water demand estimation. The Heihe River Basin has complex topography, which ensures better variable control in LE analysis. In this paper, the time series analysis and statistics of LE under different underlying surface conditions in summer were carried out by using the eddy correlation observation data in the Heihe River Basin, and the regression factors were analyzed. The results show that when the underlying surface types are greatly different, there are obvious differences in the daily distribution of LE, the daily variation trend of LE and the influencing factors. The range of diurnal distribution of LE in dune, Gobi and desert from −50 W/m2 to 100 W/m2. The diurnal LE distribution of vegetable fields, cornfields and wetlands were about 55% concentrated between −50 W/m2 and 100 W/m2. Temperature and carbon dioxide concentration (CO2) are the dominant factors affecting latent heat flux. Further analysis of temperature and CO2 is carried out by stepwise regression analysis, and multiple regression models are established. In terms of correlation and confidence, the results are better than the single factor fitting, which can better reflect the synergistic effect of temperature and CO2 on LE. Full article
(This article belongs to the Section Hydrogeology)
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<p>Geographical location and site distribution map of the research area.</p>
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<p>Land cover map of the Heihe River Basin.</p>
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<p>Stepwise regression calculation flow chart.</p>
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<p>Frequency distribution within LE days under different underlying surface types.</p>
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<p>Histogram of LE frequency distribution for 9 days under different underlying surface types.</p>
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<p>LE intraday variation trend of nine days under different underlying surface types.</p>
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<p>LE intraday variation trend of nine days under different underlying surface types.</p>
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<p>LE intraday variation trend under different pad types on June 29, July 13 and August 27.</p>
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<p>Regression analysis curves of LE and influencing factors under different underlying surface types.</p>
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<p>Regression analysis curves of LE and influencing factors under different underlying surface types.</p>
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