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Search Results (483)

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17 pages, 6383 KiB  
Article
Potential of Cover Crop Use and Termination with a Roller-Crimper in a Strip-Till Silage Maize (Zea mays L.) Production System in the Central Valley of California
by Robert Willmott, Jennifer Valdez-Herrera, Jeffrey P. Mitchell and Anil Shrestha
Agronomy 2025, 15(1), 132; https://doi.org/10.3390/agronomy15010132 - 7 Jan 2025
Viewed by 224
Abstract
The potential of terminating cover crops with a roller-crimper is of increasing interest. A two-year (2020/21 and 2021/22) study was conducted in Fresno, CA, USA. Five cover crop treatments (rye (Secale cereale L.) alone, ultra-high diversity mix, multiplex cover crop mix, fava [...] Read more.
The potential of terminating cover crops with a roller-crimper is of increasing interest. A two-year (2020/21 and 2021/22) study was conducted in Fresno, CA, USA. Five cover crop treatments (rye (Secale cereale L.) alone, ultra-high diversity mix, multiplex cover crop mix, fava bean (Vicia faba L.) + phacelia (Phacelia tanacetifolia Benth.), and rye + field pea (Pisum sativum L.) + purple vetch (Vicia americana Muhl. Ex Willd.)) were planted in November, roller-crimped in April, and silage maize (Zea mays L.) was strip-till planted in the residue in May. Cover crop kill, soil cover by residue, weed cover, amount of organic residue, and silage maize yield were recorded. The roller-crimper resulted in 95 to 100% kill of the cover crops. Soil cover at maize canopy closure (mid-July) was approximately 90% in the rye plots while it was 30 to 70% in the other treatments. The fava bean + phacelia cover crop disintegrated the most rapidly. Weed cover was <5% in all the treatments until maize canopy closure. The cover crops added 6.7 to 14 MT ha−1 of residue. Maize silage yield was similar across the treatments. Therefore, in this study, cover crops were successfully terminated by the roller-crimper, allowing successful strip-till establishment and production of silage maize. Full article
(This article belongs to the Section Farming Sustainability)
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<p>Average monthly precipitation (in mm) during the experimental period in 2020/2021 and 2021/2022.</p>
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<p>Average monthly maximum and minimum temperatures (in degree Celsius) during the experimental period in 2020/2021 and 2021/2022.</p>
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<p>Termination of the cover crops in the treatment plots with a 15 ft wide rear-mounted roller-crimper.</p>
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<p>Maize planting in the strip-tilled rows with a GPS equipped John Deere 7300 Max Emerge 2 vacuum planter<sup>®</sup> (John Deere and Co., Moline, IL, USA).</p>
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<p>View of the treatment plots showing the very patchy bare spots and weed populations in the inter-row spaces.</p>
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<p>Treatment plots in mid-June in the preliminary study. ‘Trophy’ rape + ‘Tillage’ radish + phacelia (<b>top left</b>); bell bean + pea (<b>top right</b>); rye + purple vetch + bell bean (<b>bottom left</b>); multiplex mix (<b>bottom right</b>). (Photo: Lynn Sosnoskie).</p>
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<p>A picture of the plots taken on 4 April 2021 immediately after roller-crimping.</p>
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<p>Percent kill of the cover crops in the treatments after termination with the roller-crimper (average of 2020/21 and 2021/22) in different times of the season. Bars with the same letters at each evaluation time are not significantly different according to the Fisher’s Least Significant Difference (LSD) test at 0.05 level.</p>
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<p>Visuals of the kill of the cover crops after termination with the roller-crimper. Picture taken on 6 June 2021 (<b>left</b>) and on 29 June 2021 (<b>right</b>).</p>
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<p>Percent soil cover from the cover crop residues in the treatments after termination with the roller-crimper (average of 2020/21 and 2021/22) in different times of the season. Bars with the same letters for each evaluation time are not significantly different according to the Fisher’s Least Significant Difference (LSD) test at 0.05 level.</p>
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<p>Average (2020/21 and 2021/22) aboveground dry biomass (±SE) in the treatments after cover crop termination. Bars with the same letters for each evaluation time are not significantly different according to the Fisher’s Least Significant Difference (LSD) test at 0.05 level.</p>
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<p>Percent weed in the treatment plots after termination with the roller-crimper (average of 2020/21 and 2021/22) in different times of the season. Bars with the same letters for each evaluation time are not significantly different according to the Fisher’s Least Significant Difference (LSD) test at 0.05 level.</p>
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<p>Silage maize yield (average of 2020/21 and 2021/22 in metric tons per hectare) in the different treatments. Silage maize yield from four random areas of the adjacent standard conventional field is also presented for comparative purposes. There were no differences between the treatments at a 0.05 level of significance.</p>
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22 pages, 16143 KiB  
Article
Trends and Spatiotemporal Patterns of the Meteorological Drought in the Ili River Valley from 1961 to 2023: An SPEI-Based Study
by Su Hang, Alim Abbas, Bilal Imin, Nijat Kasim and Zinhar Zunun
Atmosphere 2025, 16(1), 43; https://doi.org/10.3390/atmos16010043 - 2 Jan 2025
Viewed by 233
Abstract
Drought presents significant challenges in arid regions, influencing local climate and environmental dynamics. While the large-scale climatic phenomena in Xinjiang, northwest China, are well-documented, the finer-scale climatic variability in subregions such as the Ili River Valley (IRV) remains insufficiently studied. This knowledge gap [...] Read more.
Drought presents significant challenges in arid regions, influencing local climate and environmental dynamics. While the large-scale climatic phenomena in Xinjiang, northwest China, are well-documented, the finer-scale climatic variability in subregions such as the Ili River Valley (IRV) remains insufficiently studied. This knowledge gap impedes effective regional planning and environmental management in this ecologically sensitive area. In this study, we analyze the spatiotemporal evolution of drought in the IRV from 1961 to 2023, using data from ten meteorological stations. The SPEI drought index, along with Sen’s trend analysis, the Mann–Kendall test, the cumulative departure method, and wavelet analysis, were employed to assess drought patterns. Results show a significant drying trend in the IRV, starting in 1995, with frequent drought events from 2018 onwards, and no notable transition year observed from wet to dry conditions. The overall drought rate was −0.09 per decade, indicating milder drought severity in the IRV compared to broader Xinjiang. Seasonally, the IRV experiences drier summers and wetter winters compared to regional averages, with negligible changes in autumn and milder drought conditions in spring. Abrupt changes in the drying seasons occurred later in the IRV than in Xinjiang, with delays of 21 years for summer, and over 17 and 35 years for spring and autumn, respectively, indicating a lagged response. Spatially, the western plains are more prone to aridification than the central and eastern mountainous regions. The study also reveals significant differences in drought cycles, which are longer than those in Xinjiang, with distinct wet–dry phases observed across multiple time scales and seasons, emphasizing the complexity of drought variability in the IRV. In conclusion, the valley exhibits unique drought characteristics, including milder intensity, pronounced seasonal variation, spatial heterogeneity, and notable resilience to climate change. These findings underscore the need for region-specific drought management strategies, as broader approaches may not be effective at the subregional scale. Full article
(This article belongs to the Section Meteorology)
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<p>Sketch map of the study area (the black line represents the country border, the green area indicates the Xinjiang Uyghur Autonomous Region of China, and the red area denotes the Ili River Valley).</p>
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<p>Fluctuation diagrams of SPEI-1 (<b>a</b>), SPEI-3 (<b>b</b>), and SPEI-12 (<b>c</b>) for the Ili River Valley region from 1961 to 2023 (The deeper the green, the more humid it is; the deeper the red, the more arid it becomes).</p>
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<p>Results of the Mann–Kendall (M-K) mutation test (<b>a</b>) and anomaly analysis (<b>b</b>).</p>
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<p>Temporal variations in SPEI in the Ili River Valley Region from 1961 to 2023.</p>
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<p>Changing characteristics of meteorological drought in the Ili River Valley from 1961 to 2023; spring (<b>a</b>), summer (<b>b</b>), autumn (<b>c</b>), and winter (<b>d</b>).</p>
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<p>Variation trends in seasonal SPEI interannual anomalies and cumulative anomalies in the Ili River Valley from 1961 to 2023; spring (<b>a</b>), summer (<b>b</b>), autumn (<b>c</b>), and winter (<b>d</b>).</p>
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<p>Spatial variation trends in seasonal SPEI in the Ili River Valley from 1961 to 2023; spring (<b>a</b>), summer (<b>b</b>), autumn (<b>c</b>), and winter (<b>d</b>).</p>
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<p>(<b>a</b>) Real contour map of the annual SPEI wavelet coefficients, (<b>b</b>) wavelet variance of the annual SPEI in the Ili River Valley from 1961 to 2023.</p>
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<p>(<b>a1</b>–<b>a4</b>) Real contour map of the seasonal SPEI wavelet coefficients, (<b>b1</b>–<b>b4</b>) wavelet variance of the seasonal SPEI in the Ili River Valley from 1961 to 2023; spring (<b>a1</b>,<b>b1</b>), summer (<b>a2</b>,<b>b2</b>), autumn (<b>a3</b>,<b>b3</b>), and winter (<b>a4</b>,<b>b4</b>).</p>
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<p>Three-dimensional scatter plot of the time scales and average periods of the SPEI on an annual scale at various stations in the Ili River Valley from 1961 to 2023.</p>
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<p>Three-dimensional scatter plot of the time scales and average periods of the SPEI on a seasonal scale at various stations in the Ili River Valley from 1961 to 2023; spring (<b>a</b>), summer (<b>b</b>), autumn (<b>c</b>), and winter (<b>d</b>).</p>
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19 pages, 19539 KiB  
Article
Seabed Acoustic Mapping Revealing an Uncharted Habitat of Circular Depressions Along the Southeast Brazilian Outer Shelf
by Ana Carolina Lavagnino, Marcos Daniel Leite, Tarcila Franco, Pedro Smith Menandro, Fernanda Vedoato Vieira, Geandré Carlos Boni and Alex Cardoso Bastos
Geosciences 2025, 15(1), 7; https://doi.org/10.3390/geosciences15010007 - 1 Jan 2025
Viewed by 502
Abstract
Initiatives such as the United Nations Decade of Ocean Science for Sustainable Development and Seabed 2030 promote seabed mapping worldwide. In Brazil, especially on the Espírito Santo Continental Shelf, high-resolution seabed mapping has revealed an unknown complex seascape. Circular depressions (CDs) were mapped [...] Read more.
Initiatives such as the United Nations Decade of Ocean Science for Sustainable Development and Seabed 2030 promote seabed mapping worldwide. In Brazil, especially on the Espírito Santo Continental Shelf, high-resolution seabed mapping has revealed an unknown complex seascape. Circular depressions (CDs) were mapped for the first time in the Costa das Algas Marine Protection Area. Herein, we aim to present the CD metrics characteristics and discuss their relationship with morphology and relevance as a habitat based on multibeam bathymetry and ground truthing. A total of 3660 depressions were mapped between 46 and 85 m in depth, reaching an area of 460 m2 and 5 m relief. The continental shelf morphology was subdivided into three sectors based on morphology: inter-valleys, valley edges, and valley flanks, and eleven sites were selected for direct sampling/imaging at the CDs along the sectors. The direct sampling was carried out by scuba-diving with video images and sediment samples collected inside and outside the depressions. The deeper central parts of the circular depressions appear to function as a sink, presenting aggregations of rhodoliths or other carbonate fragments. In most inter-valley depressions, mounds were observed along the edges of the depression. We did not have any indication of gas seeps and no clear sedimentological or morphological control on their occurrence. We first hypothesize that their origin results from combined diachronous processes. The circular depressions mapped at high resolution could be related to sea level processes acting during the last glacial period and shelf exposure, i.e., relict features. The CDs are responsible for biomass aggregation and fish bioturbation, forming holes and rubble mounds, representing a modern process occurring on a centimetric scale. The data collected so far indicate that this fine-scale feature is an important habitat for different fish species. The modern maintenance of these structures could be due to low sedimentation regime areas shaped by biotic excavation. Full article
(This article belongs to the Special Issue Progress in Seafloor Mapping)
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<p>The acoustic mapping and direct sampling location in the study area in southeast Brazil, in the Espírito Santo continental outer shelf. ADCP mooring location is also shown.</p>
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<p>(<b>A</b>) Bathymetry map overlapped by circular depressions in black; (<b>B</b>) zoomed in is the black rectangle that depicts an area with the depressions, note that on the edges of the depression are higher reliefs; (<b>C</b>) a profile through 3 depressions showing the edge of the features being a positive relief; (<b>D</b>) a 3D view of one of the circular depressions showing aggregation of material on the sides; (<b>E</b>) harmonized backscatter map 20 m resolution; (<b>F</b>,<b>H</b>) a 2 m resolution backscatter insert not interpreted and interpreted as circular depressions, respectively. Note that scale inserts only account for the data within the rectangle.</p>
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<p>Workflow of the data processing methodology following [<a href="#B33-geosciences-15-00007" class="html-bibr">33</a>,<a href="#B34-geosciences-15-00007" class="html-bibr">34</a>], identification of the circular depression, and ground truthing.</p>
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<p>Density kernel of all mapped features (CD/km<sup>2</sup>).</p>
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<p>A 3D visualization of the bathymetry overlapped by the circular depressions in black; (<b>A</b>) inter-valley area where 4 depressions were investigated (13, 14, 15, and 16); (<b>B</b>) inter-valley where 2 depressions were investigated (08 and 10); (<b>b</b>′) valley flank on the west side of the image showing depressions 19 and 20 and valley edge depressions on the east side of the image 01 and 02; (<b>b″</b>) valley edge depression 06; (<b>C</b>) region where none of the depressions were directly investigated.</p>
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<p>Stacked histogram showing the distribution of the circular depressions (n = 3660) depth range according to (<b>A</b>) area of the CD and (<b>B</b>) relief of the CD. Note that diamond symbol represents outliers’ measurements.</p>
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<p>Grain size, carbonate content, and backscatter value for each sampled CD. “I” represents samples taken inside the depressions, and “O” represents samples outside.</p>
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<p>Schematic model of the circular depressions mapped in the area depicting a summary of the 3 categories: valley edge, inter-valley, and valley flanks. The depths shown on the model correspond to the mean water depth of the mapped depressions. Biota illustrated were spotted in the designated depressions. Gravel and sand correspond to carbonate content, while mud has mixed content (carbonate and terrigenous), according to [<a href="#B35-geosciences-15-00007" class="html-bibr">35</a>].</p>
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<p>Potential benthic habitat map [<a href="#B23-geosciences-15-00007" class="html-bibr">23</a>] overlapping hillshade bathymetry and circular depressions.</p>
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24 pages, 26318 KiB  
Article
Ecological Security Patterns Based on Ecosystem Services and Local Dominant Species in the Kunlun Mountains
by Jianglong Yuan, Ran Wang, Xiaohuang Liu, Jiufen Liu, Liyuan Xing, Xinping Luo, Ping Zhu, Junnan Li, Chao Wang and Honghui Zhao
Diversity 2024, 16(12), 779; https://doi.org/10.3390/d16120779 - 23 Dec 2024
Viewed by 581
Abstract
Constructing an ecological security pattern in ecologically fragile areas is crucial for maintaining regional ecological stability. This study focuses on the Kunlun Mountain region, identifying ecological sources based on habitat suitability assessments and ecosystem services. An ecological resistance evaluation index system is constructed, [...] Read more.
Constructing an ecological security pattern in ecologically fragile areas is crucial for maintaining regional ecological stability. This study focuses on the Kunlun Mountain region, identifying ecological sources based on habitat suitability assessments and ecosystem services. An ecological resistance evaluation index system is constructed, considering topography, land use, and habitat quality. The minimum cumulative resistance model is then applied to identify ecological corridors, with areas exhibiting higher ecological currents designated as ecological nodes. By integrating the spatial characteristics of ecosystem services, an ecological security pattern is established. The results are as follows: (1) The ecological source area covers approximately 11.30% of the study area. (2) The cumulative length of ecological corridors is 21,111 km, mainly distributed along valleys, gentle slopes, and oasis areas. (3) The areas of ecological nodes and ecological barriers are 126.75 km2 and 46.75 km2, respectively. Ecological nodes are mainly distributed on both sides of the Kunlun Mountains, while ecological barriers are primarily located in the central mountainous area of the Kunlun Mountains. (4) The findings recommend establishing an ecological security pattern consisting of “2 horizontal and 4 vertical corridors and 5 zones” to ensure the ecological security of the Kunlun Mountains. The integration of ecological corridors and ecosystem services in constructing a regional ecological security pattern provides valuable decision-making tools for protecting ecosystems and species in fragile areas. Full article
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<p>Location of the study area.</p>
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<p>Research framework.</p>
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<p>Species location points.</p>
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<p>Four levels of ecosystem services.</p>
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<p>Suitability grading for all species.</p>
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<p>Habitability zones for life.</p>
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<p>Suitable habitat range for major species.</p>
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<p>Land use types in 2020.</p>
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<p>Biological resistance surface related content (<b>a</b>) slope resistance, (<b>b</b>) land use resistance, (<b>c</b>) Elevation resistance and (<b>d</b>) Biological resistance surface.</p>
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<p>Ecological resistance surface. (<b>a</b>) Habitat quality; (<b>b</b>) Ecological resistance.</p>
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<p>Biological pathways and electric currents.</p>
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<p>Overall status of ecological security pattern in the Kunlun Mountains.</p>
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<p>Ecological security pattern of “2 horizontal and 4 vertical 5 zones” in the Kunlun Mountain area.</p>
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13 pages, 10017 KiB  
Article
Estimation of Nitrous Oxide Emissions from Agricultural Sources and Characterization of Spatial and Temporal Changes in Anhui Province (China)
by Zhou Ye, Yujuan Sun, Xianglin Zhang and Youzhi Yao
Atmosphere 2024, 15(12), 1538; https://doi.org/10.3390/atmos15121538 - 22 Dec 2024
Viewed by 333
Abstract
To evaluate the estimation and spatiotemporal variation characteristics of nitrous oxide emissions from agricultural sources in Anhui Province, the nitrous oxide emissions generated during crop cultivation and manure management were assessed based on the recommended methods in the “Guidelines for Provincial Greenhouse Gas [...] Read more.
To evaluate the estimation and spatiotemporal variation characteristics of nitrous oxide emissions from agricultural sources in Anhui Province, the nitrous oxide emissions generated during crop cultivation and manure management were assessed based on the recommended methods in the “Guidelines for Provincial Greenhouse Gas Inventories” and official statistical data. The results showed that the overall emission of nitrous oxide from agricultural land showed a downward trend, reaching a valley value in 2019 with an emission of 2.83 × 104 tons. The annual average emissions of nitrous oxide from agricultural land and manure management account for 80.98% and 19.02% of the total annual average emissions of nitrous oxide from agricultural activities in Anhui Province, respectively. Both agricultural land emissions and livestock manure management show a trend of nitrous oxide emissions decreasing from the northern region of Anhui > central region of Anhui > southern region of Anhui. In this paper, we explored and discussed the intrinsic driving factors behind the spatiotemporal changes in nitrous oxide emissions, and analyzed the potential for future emission reductions. It is suggested that the emissions of nitrous oxide from agricultural sources can be reduced through measures such as reasonable nitrogen application, adjustment of aquaculture structures, and the improvement of manure treatment methods, providing a theoretical reference for the estimation of greenhouse gas emissions from agricultural sources. Full article
(This article belongs to the Section Air Quality)
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<p>Changes in the number of farmed animals: (<b>a</b>) for non-dairy cows (×10<sup>3</sup>), (<b>b</b>) for poultry (×10<sup>5</sup>), (<b>c</b>) for sheep (×10<sup>3</sup>), and (<b>d</b>) for pigs (×10<sup>5</sup>).</p>
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<p>Mechanism diagram of nitrous oxide-emissions from agricultural sources [<a href="#B21-atmosphere-15-01538" class="html-bibr">21</a>].</p>
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<p>Historical changes in nitrous oxide emissions from agricultural activities in Anhui Province.</p>
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<p>Annual emissions of nitrous oxide from agricultural land in Anhui Province.</p>
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<p>Annual emissions of nitrous oxide from animal fecal management.</p>
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<p>Statistical chart of nitrous oxide emissions from agricultural land at some prefecture-level cities in Anhui Province (<b>a</b>) and nitrous oxide emissions from livestock and poultry manure management (<b>b</b>).</p>
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<p>Contribution of animal manure management (<b>a</b>) and agricultural land (<b>b</b>) to nitrous oxide emissions in some prefecture-level cities of Anhui Province in 2014 and 2022.</p>
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14 pages, 954 KiB  
Article
Serological Evidence of Cryptic Rift Valley Fever Virus Transmission Among Humans and Livestock in Central Highlands of Kenya
by Silvia Situma, Evans Omondi, Luke Nyakarahuka, Raymond Odinoh, Marshal Mweu, Marianne W. Mureithi, Martin M. Mulinge, Erin Clancey, Jeanette Dawa, Isaac Ngere, Eric Osoro, Bronwyn Gunn, Limbaso Konongoi, Samoel A. Khamadi, Johan Michiels, Kevin K. Ariën, Barnabas Bakamutumaho, Robert F. Breiman and Kariuki Njenga
Viruses 2024, 16(12), 1927; https://doi.org/10.3390/v16121927 - 17 Dec 2024
Viewed by 684
Abstract
Although the highlands of East Africa lack the geo-ecological landmarks of Rift Valley fever (RVF) disease hotspots to participate in cyclic RVF epidemics, they have recently reported growing numbers of small RVF clusters. Here, we investigated whether RVF cycling occurred among livestock and [...] Read more.
Although the highlands of East Africa lack the geo-ecological landmarks of Rift Valley fever (RVF) disease hotspots to participate in cyclic RVF epidemics, they have recently reported growing numbers of small RVF clusters. Here, we investigated whether RVF cycling occurred among livestock and humans in the central highlands of Kenya during inter-epidemic periods. A 2-year prospective hospital-based study among febrile patients (March 2022–February 2024) in Murang’a County of Kenya was followed by a cross-sectional human–animal survey. A total of 1468 febrile patients were enrolled at two clinics and sera tested for RVF virus RNA and antiviral antibodies. In the cross-sectional study, humans (n = 282) and livestock (n = 706) from randomly selected households were tested and questionnaire data were used to investigate sociodemographic and environmental risk factors by multivariate logistic regression. No human (n = 1750) or livestock (n = 706) sera tested positive for RVFV RNA. However, 4.4% livestock and 2.0% humans tested positive for anti-RVFV IgG, including 0.27% febrile patients who showed four-fold IgG increase and 2.4% young livestock (<12 months old), indicating recent virus exposure. Among humans, the odds of RVF exposure increased significantly (p < 0.05, 95% CI) in males (aOR: 4.77, 2.08–12.4), those consuming raw milk (aOR: 5.24, 1.13–17.9), milkers (aOR: 2.69, 1.23–6.36), and participants residing near quarries (aOR: 2.4, 1.08–5.72). In livestock, sheep and goats were less likely to be seropositive (aOR: 0.27, 0.12–0.60) than cattle. The increase in RVF disease activities in the highlands represents a widening geographic dispersal of the virus, and a greater risk of more widespread RVF epidemics in the future. Full article
(This article belongs to the Special Issue Emerging Highlights in the Study of Rift Valley Fever Virus)
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<p>Map of Murang’a county, Kenya, showing locations where study participants were recruited.</p>
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<p>Flow chart showing RVFV serological and molecular diagnostics algorithm for the samples.</p>
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27 pages, 10559 KiB  
Article
A Comparative Study of Deep Learning Frameworks Applied to Coffee Plant Detection from Close-Range UAS-RGB Imagery in Costa Rica
by Sergio Arriola-Valverde, Renato Rimolo-Donadio, Karolina Villagra-Mendoza, Alfonso Chacón-Rodriguez, Ronny García-Ramirez and Eduardo Somarriba-Chavez
Remote Sens. 2024, 16(24), 4617; https://doi.org/10.3390/rs16244617 - 10 Dec 2024
Viewed by 569
Abstract
Introducing artificial intelligence techniques in agriculture offers new opportunities for improving crop management, such as in coffee plantations, which constitute a complex agroforestry environment. This paper presents a comparative study of three deep learning frameworks: Deep Forest, RT-DETR, and Yolov9, customized for coffee [...] Read more.
Introducing artificial intelligence techniques in agriculture offers new opportunities for improving crop management, such as in coffee plantations, which constitute a complex agroforestry environment. This paper presents a comparative study of three deep learning frameworks: Deep Forest, RT-DETR, and Yolov9, customized for coffee plant detection and trained from images with a high spatial resolution (cm/pix). Each frame had dimensions of 640 × 640 pixels acquired from passive RGB sensors onboard a UAS (Unmanned Aerial Systems) system. The image set was structured and consolidated from UAS-RGB imagery acquisition in six locations along the Central Valley, Costa Rica, through automated photogrammetric missions. It was evidenced that the RT-DETR and Yolov9 frameworks allowed adequate generalization and detection with mAP50 values higher than 90% and mAP5095 higher than 54%, in scenarios of application with data augmentation techniques. Deep Forest also achieved good metrics, but noticeably lower when compared to the other frameworks. RT-DETR and Yolov9 were able to generalize and detect coffee plants in unseen scenarios that include complex forest structures within tropical agroforestry Systems (AFS). Full article
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<p>High-level description of the automatic detection system for coffee plants based on deep learning (DL). The annotation block allows adapting the label formats according to the network architecture. The deep learning block allows the Deep Forest, Yolov9, or RT-DETR framework to perform training, validation, and test stages.</p>
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<p>Location map of image acquisition sites in productive coffee farms on the Central Valley area, Costa Rica. The sections highlighted in yellow were the areas used for this study.</p>
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<p>UAS-digital photogrammetry processing approach: (<b>a</b>) description of the UAS-photogrammetrical flight. San Pedro, Poás coffee plantation section illustrates the flight trajectory, height, frontal-side overlap, and land/take-off point; (<b>b</b>) digital photogrammetric processing description with Agisoft Metashape.</p>
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<p>Example of image annotations using the online tool called Roboflow.</p>
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<p>Data augmentation process from 1356 raw images with 28,415 annotations for the class called “coffee” using the Raw Dataset.</p>
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<p>Case study of an AFS with coffee plantations: (<b>a</b>) inference with pre-trained model with (<b>a</b>) Deep Forest, (<b>b</b>) RT-DETR, and (<b>c</b>) Yolov9. Model trained with Raw Dataset for (<b>d</b>) Deep Forest, (<b>e</b>) RT-DETR, and (<b>f</b>) Yolov9. For Deep Forest, annotations are in orange and detections in white, for RT-DETR and Yolov9 detections are represented with in red.</p>
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<p>Training and validation performance metrics obtained using a Raw Dataset and Data Augmentation Set: (<b>a</b>) precision, (<b>b</b>), recall, (<b>c</b>) F1 Score, and (<b>d</b>) mAP50.</p>
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<p>Results obtained for the loss functions during the training and validation of the models: (<b>a</b>) Deep Forest, (<b>b</b>) RT-DETR, and (<b>c</b>) Yolov9.</p>
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<p>Results obtained for the loss functions during the training and validation of the models: (<b>a</b>) Deep Forest, (<b>b</b>) RT-DETR, and (<b>c</b>) Yolov9.</p>
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<p>Results obtained for the RT-DETR and Yolov9 frameworks: (<b>a</b>) curve AUC-PR and (<b>b</b>) mAP5095.</p>
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<p>Analysis case evaluated with a model trained with a Data Augmentation Set. Deep Forest evaluated in a scenario with (<b>a</b>) low complexity, (<b>b</b>) medium complexity, and (<b>c</b>) high complexity. RT-DETR evaluated in a scenario with (<b>d</b>) low complexity, (<b>e</b>) medium complexity, and (<b>f</b>) high complexity. Yolov9 evaluated in a scenario with (<b>g</b>) low complexity, (<b>h</b>) medium complexity, and (<b>i</b>), high complexity.</p>
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<p>Analysis case evaluated with a model trained with a Data Augmentation Set. Deep Forest evaluated in a scenario with (<b>a</b>) low complexity, (<b>b</b>) medium complexity, and (<b>c</b>) high complexity. RT-DETR evaluated in a scenario with (<b>d</b>) low complexity, (<b>e</b>) medium complexity, and (<b>f</b>) high complexity. Yolov9 evaluated in a scenario with (<b>g</b>) low complexity, (<b>h</b>) medium complexity, and (<b>i</b>), high complexity.</p>
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<p>Results obtained for the loss functions during the training and validation of the models using a new data augmentation scenario: (<b>a</b>) RT-DETR and (<b>b</b>) Yolov9.</p>
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<p>Training and validation performance metrics obtained using a new data augmentation approach: (<b>a</b>) precision, (<b>b</b>) recall, (<b>c</b>) F1 Score, and (<b>d</b>) mAP50.</p>
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<p>Results obtained for the RT-DETR and Yolov9 using new data augmentation approach: (<b>a</b>) curve AUC-PR and (<b>b</b>) mAP5095.</p>
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<p>Coffee plant detection trough inference process from Test Images Set: (<b>a</b>) RT-DETR@200 epochs, (<b>b</b>) RT-DETR@36 epochs, (<b>c</b>) Yolov9@200 epochs, and (<b>d</b>) Yolov9@36 epochs. For instance, these images were in the Poás location.</p>
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<p>Coffee plant detection trough inference process from Test Images Set: (<b>a</b>) RT-DETR@200 epochs, (<b>b</b>) RT-DETR@36 epochs, (<b>c</b>) Yolov9@200 epochs, and (<b>d</b>) Yolov9@36 epochs. For instance, these images were in the Poás location.</p>
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13 pages, 432 KiB  
Article
A Comprehensive Evaluation of Water-Saving Society Construction in Xinxiang, Henan Province, China
by Mingliang Jiang and Chengcai Zhang
Sustainability 2024, 16(23), 10737; https://doi.org/10.3390/su162310737 - 6 Dec 2024
Viewed by 684
Abstract
Water is a crucial and fundamental resource. It is well known that agricultural cultivation, industrial production, and human daily life are not possible without water. Efficiently utilizing water resources is of great significance for achieving global Sustainable Development Goals (SDGs). In order to [...] Read more.
Water is a crucial and fundamental resource. It is well known that agricultural cultivation, industrial production, and human daily life are not possible without water. Efficiently utilizing water resources is of great significance for achieving global Sustainable Development Goals (SDGs). In order to improve water use efficiency in various industries and promote water-saving development, China has been implementing water-saving society construction since 2002. Henan Province is the main grain-producing area in China, with wheat production accounting for a quarter of the country’s total. As the core area of “Central Plains Agricultural Valley” in Henan Province, Xinxiang City plays an important role in agricultural technology innovation and agricultural production. However, Xinxiang City is facing problems of water scarcity and pollution, which constrain the sustainability of agricultural production. Therefore, building a water-saving society can solve the current water problems faced by Xinxiang City and ensure the sustainable development of the economy and society. This study built an evaluation index system for water-saving society construction in Xinxiang, Henan Province, China. The proposed evaluation index system includes 20 evaluation indices from six aspects—integrated, agricultural water, industrial water, domestic water, water ecology and environment, and water-saving management—and then divides its development level into several stages. The Analytical Hierarchy Process (AHP) was adopted to calculate the index weight. Then, a comprehensive evaluation model for water-saving society construction in Xinxiang City was established by combining it with grey relative analysis (GRA). The results showed that the overall level of water-saving society construction in Xinxiang City is in the excellent stage, whereas water consumption per CNY 10,000 of GDP, the effective utilization coefficient of irrigation water, the reuse rate of industrial water, and the leakage rate of urban water supply network are all in the good stage. However, the urban recycled water utilization rate is still in the poor stage. These research results can effectively and reasonably reflect the development level of water-saving society construction in Xinxiang City and guide the continued implementation of water-saving society construction. At the same time, the comprehensive evaluation of water-saving society construction helps to formulate and adjust water resource management policies and measures; it also holds significant value for sustainable water management and combating water scarcity. Full article
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<p>Methodology adopted in this study.</p>
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35 pages, 99630 KiB  
Article
Tornadic Storm over the Foothills of Central Nepal Himalaya
by Toshihiro Kitada, Sajan Shrestha, Sangeeta Maharjan, Suresh Bhattarai and Ram Prasad Regmi
Meteorology 2024, 3(4), 412-446; https://doi.org/10.3390/meteorology3040020 - 1 Dec 2024
Viewed by 740
Abstract
On the evening of 31 March 2019, Parsa and Bara Districts in central Nepal were severely hit by a wind storm which was the first documented tornadic incidence in Nepal.In this paper, we investigate the background of the tornado formation via numerical simulations [...] Read more.
On the evening of 31 March 2019, Parsa and Bara Districts in central Nepal were severely hit by a wind storm which was the first documented tornadic incidence in Nepal.In this paper, we investigate the background of the tornado formation via numerical simulations with the WRF-ARW model. The results show that: (1) a flow situation favorable to the generation of mesocyclones was formed by a combination of local plain-to-mountain winds consisting of warm and humid southwesterly wind in the lower atmosphere and synoptic northwesterly wind aloft over the southern foothills of the Himalayan Mountain range, leading to significant vertical wind shear and strong buoyancy; (2) the generated mesocyclone continuously shed rain-cooled outflow with 600∼800 m depth above the ground into the Chitwan valley while moving southeastward along the Mahabharat Range at the northeastern rim of the Chitwan valley; (3) the cold outflow propagated in the valley, forming a front; and (4) the tornado was generated when this cold outflow passed over the Siwalik Hills bordering the southern rim of the Chitwan valley. At this point, descending flow around a high mountain generated positive vertical vorticity near the ground; blocking by this high mountain and channeling through a mountain pass enhanced updrafts at the front by forming a hydraulic jump. These updrafts amplified the positive vertical vorticity via stretching, and this interaction of the cold outflow with the Siwalik Hills contributed to tornadogenesis. The simulated location and time of the disaster showed generally good agreement with the reported location and time. Full article
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<p>Pictures showing damage caused by the storm on 31 March 2019 over the Parsa and Bara Districts of Nepal. Pictures adapted from various online sources.</p>
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<p>Footprint of damage due to the 31 March 2019 storm over the Parsa and Bara Districts of the Central Nepal Himalaya foothills. Images from the European Space Agency (ESA)’s Sentinel satellite (<b>a</b>) before the disaster (27 March 2019) and (<b>b</b>) after the disaster (1 April 2019). The appearance of a narrow strip of soil color running northwest–southeast in the middle of (<b>b</b>) and not seen in (<b>a</b>) resembles the tornado tracks seen in other parts of the world.</p>
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<p>Three-dimensional terrain structure of the area in and around the storm-hit area enclosed by the fine domain (D2). The inset shows the same area enclosed by the finest domain (D3). Important places are indicated in the figure. The left and right polygons in the central area of the inset indicate Parsa and Bara Districts, which were severely hit by the 31 March 2019 storm (see pictures in <a href="#meteorology-03-00020-f001" class="html-fig">Figure 1</a>).</p>
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<p>The triply-nested WRF simulation domain configuration centered at <math display="inline"><semantics> <msup> <mn>27.24</mn> <mo>∘</mo> </msup> </semantics></math> N, <math display="inline"><semantics> <msup> <mn>84.78</mn> <mo>∘</mo> </msup> </semantics></math> E (see star in the figure). Important places are indicated in the figure.</p>
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<p>Comparison of three-day WRF simulated and observed (<b>a</b>–<b>m</b>) diurnal variation of temperature, relative humidity, wind speed, and wind direction and (<b>a’</b>–<b>m’</b>) the corresponding scatter plots for different ground stations. Temperature and relative humidity comparisons are presented only for Kathmandu station. The name of the stations are noted in the figure.</p>
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<p>Comparison of vertical temperature sounding at 0000 UTC (0545 LST) 31 March 2019 at different radiosonde stations in India and China. In the figure, the left and right wind barbs represent the observed and simulated wind speeds. The locations of the sounding stations are mentioned above the corresponding observed sounding data; see <a href="#meteorology-03-00020-f002" class="html-fig">Figure 2</a> for the locations.</p>
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<p>Satellite images of clouds over Nepal taken by the Himawari-8 satellite (Band 13): 0940 UTC (1525 LST) 31 March 2019–1500 UTC (2045 LST) 31 March 2019. Source: <a href="https://www.data.jma.go.jp/mscweb/data/himawari/sat_img.php" target="_blank">https://www.data.jma.go.jp/mscweb/data/himawari/sat_img.php</a> (accessed on 1 April 2019). The national border was overlaid over the images by the authors. The red circles in (<b>d</b>) through (<b>l</b>) denote the Parsa–Bara area where the tornado passed from 1345 UTC (1930 LST) to 1445 UTC (2030 LST). The blue circles in (<b>a</b>,<b>b</b>,<b>d</b>,<b>h</b>,<b>i</b>) indicate a strong convective cloud cluster identified with the bright spots in the images.</p>
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<p>Horizontal wind vectors with (<b>a</b>) equivalent-potential temperature (K) at 850 hPa and (<b>b</b>) horizontal wind velocity (m s<sup>−1</sup>) at 700 hPa in D2 on 1015 UTC (1600 LST). The location of the red ellipse in (<b>a</b>,<b>b</b>) roughly corresponds to that of the blue circle in <a href="#meteorology-03-00020-f007" class="html-fig">Figure 7</a>b.</p>
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<p>Horizontal wind vectors and wind velocity at 1230 UTC (1815LST) in D3: (<b>a</b>) surface, (<b>b</b>) 850, (<b>c</b>) 700, and (<b>d</b>) 500 hPa; (<b>e</b>) rain fall (mm h<sup>−1</sup>), and (<b>f</b>) vertical cross-section of wind and vertical wind velocity along SN line through x = 60 km from [y] = 0 to 130 km (i.e., y = 10 to 140 km in D3). In (<b>a</b>–<b>e</b>), the color dots represent specific locations: green for the center of D3, red for Kathmandu, and pink, white, and black for specific tornado-damaged places.</p>
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<p>Wind vectors and equivalent potential temperature at 800 hPa in D2 at 1230 UTC (1815 LST). Domain D3 is shown at the center. The red-circled area corresponds to the same areas of D3 in <a href="#meteorology-03-00020-f009" class="html-fig">Figure 9</a>. The thin solid contours indicate terrain heights, with an interval of 400 m.</p>
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<p>The area damaged by the storm at around 1345 UTC (1930 LST) 31 March 2019–1445 UTC (2030 LST) 31 March 2019. The blue circle with small white dot shows places that reported damage plotted using the information in [<a href="#B2-meteorology-03-00020" class="html-bibr">2</a>]. The red box depicts the D3 domain, while the black circle with white cross indicates the center of D3. The blue circle with the small white circle is the city of Bharatpur; similarly, the red circle with the white circle is the city of Pokhara. The teardrops indicate meteorological observatories: blue for Devachuli, red for Parwanipur (only surface observation), and black for Gorakhpur. The diagonal line across D3 approximately coincides with the tornado’s path.</p>
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<p>Simulated horizontal wind vectors in D3 (<b>b</b>–<b>f</b>) or D’3 (<b>a</b>) at 1400 UTC (1945 LST) at (<b>a</b>) surface level with potential temperature (color shaded) and topographical contours (thin solid lines in intervals of 200 m), (<b>b</b>) 950 hPa with topography, (<b>c</b>) 850 hPa with horizontal wind velocity (color shaded), (<b>d</b>) 800 hPa with topography, (<b>e</b>) 700 hPa with horizontal wind velocity (color shaded), and (<b>f</b>) 500 hPa with horizontal wind velocity (color shaded). Note that D’3 with X = 0 to 100 km and Y = 50 to 150 km is a part of D3. The green dot marks the center of domain D3, while the red dot represents Kathmandu. The pink, white, and black dots show some of the tornado-damaged places (see <a href="#meteorology-03-00020-f011" class="html-fig">Figure 11</a> for the whole damaged area). The tornado moved roughly parallel to the diagonal line of the domain, indicated by the thin black line (see <a href="#meteorology-03-00020-f011" class="html-fig">Figure 11</a> for the path of the tornado). The black circle in <a href="#meteorology-03-00020-f012" class="html-fig">Figure 12</a>a is the suggested location of tornadogenesis, as discussed in <a href="#sec4dot3-meteorology-03-00020" class="html-sec">Section 4.3</a>. See the text for the definitions of other lines and circled areas.</p>
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<p>Vertical cross-sections oriented south–north through a west–east distance x = 70 km in D3 (see <a href="#meteorology-03-00020-f012" class="html-fig">Figure 12</a>) of the simulated meteorological variables at 1400 UTC (1945 LST): (<b>a</b>) wind vectors (with y- and z-velocity components), on which vertical velocity “w” (color shaded) and potential temperature <math display="inline"><semantics> <mi>θ</mi> </semantics></math> (green contours with 0.5 K interval) are overlaid; (<b>b</b>) CAPE (color shaded); and (<b>c</b>) CIN (contoured every 10 J kg<sup>−1</sup>). The south–north axis [y] in this figure corresponds to the south–north distance y in D3 minus 10 km, e.g., [y] = 75 km in this figure is equal to y = 85 km in D3 in <a href="#meteorology-03-00020-f012" class="html-fig">Figure 12</a>. In (<b>a</b>), the thick dashed line shows an isotherm at <math display="inline"><semantics> <mi>θ</mi> </semantics></math> = 308 K and indicates that a layer of colder air lies below the line and above the ground (see discussion in the text). The thick solid arrow illustrates a sample trajectory of the colder air flowing down the mountain pass (see discussion in <a href="#sec4dot3dot2-meteorology-03-00020" class="html-sec">Section 4.3.2</a>). The vertical dashed line at [y] = 75 km in (<b>a</b>–<b>c</b>) shows the front of the cold outflow.</p>
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<p>Horizontal distributions of geopotential heights (GPH in m; contoured every 1 m) in D3 at 1400 UTC (1945 LST) 31 March 2019. (<b>a</b>) 500 hPa; pink, red, green, and blue lines are used for 5782, 5784, 5786, and 5788 m, respectively; the location of the lowest GPH of 5767 m (i.e., the lowest pressure, indicating a mesocyclone) is shown with a yellow solid circle at (x, y) = (91, 97) in km for both x (west–east) and y (south–north) directions, while the red circle shows the location of the same mesocyclone at 1315 UTC (1900 LST). (<b>b</b>) 700 hPa; pink, red, green, and blue lines are used for 3122, 3124, 3126, and 3128 m, respectively; there are three low pressure eyes with GPH = 3121∼3122 m at (x, y) = (79, 95.5), (44, 105), and (18, 104) in km, the same as in (<b>a</b>). (<b>c</b>) 850 hPa; pink, red, blue, green, and gold lines are used for 1480, 1482, 1484, 1486, and 1488 m, respectively; two low pressure eyes with GPH = 1480 m are at (x, y) = (61, 86), and (8, 98) in km, the same as in (<b>a</b>). (<b>d</b>) 900 hPa; pink, red, blue, green, and gold lines are used for 980, 982, 984, 986, and 988 m, respectively; two low pressure eyes with 980 m are at (x, y) = (60, 86), and (8, 88) in km, the same as in (<b>a</b>). The thick solid black lines indicate the higher mountains comprising the Siwalik and Someshwar Ranges. The cross-point of two dashed lines parallel to the x- or y-axis (the black ellipse in (<b>c</b>,<b>d</b>) is where we think the tornado was generated (see text for discussion)). The thin black diagonal line in each figure indicates the tornado’s path.</p>
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<p>Vertical cross-section of the simulated meteorological variables along a south–north line (parallel to the y-axis) through the west–east distance x = 90 km in D3 at 1400 UTC (1945 LST). The vertical cross-section intercepts the center of the mesocyclone. A black vertical dashed line at the south-north axis [y] = 86 km indicates the location of the mesocyclone. (The solid yellow circle in <a href="#meteorology-03-00020-f014" class="html-fig">Figure 14</a> and so on depicts the mesocyclone in 1400 UTC (1945 LST)). The figure plots wind-vectors (with y- and z-velocity components), and overlays vertical wind velocity “w” (color shaded) and potential temperature <span class="html-italic">θ</span> (green-contours with 0.5 K interval). The south-north axis [y] in this figure corresponds to the south-north distance y in D3 minus 10 km; for example, [y] = 86 km in this figure is equal to y = 96 km in D3 of <a href="#meteorology-03-00020-f012" class="html-fig">Figure 12</a>, etc.</p>
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<p>Horizontal distribution of simulated one-hour rainfall at the surface for 1400 UTC (1945 LST) 31 March 2019. The thick black solid lines depict high mountains in the Siwalik and Someshwar Ranges (see <a href="#meteorology-03-00020-f011" class="html-fig">Figure 11</a>) with heights exceeding 800 m ASL. The dashed lines are parallel to each relevant axis, and the cross-point of two dashed lines is the supposed location of tornadogenesis (see the text for detail). The colored dots represent specific locations: green for the center of domain D3, red for Kathmandu, and pink, white, and black for specific tornado-damaged sites.</p>
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<p>Horizontal distributions of the simulation-derived winds and vertical vorticity at 1400 UTC (1945 LST) 31 March 2019: (<b>a</b>) vertical wind velocity (w) in m s<sup>−1</sup> (color shaded, with warm and cold colors for updraft and downdraft, respectively) and horizontal wind vectors at 500 m AGL; (<b>b</b>) vertical vorticity (<math display="inline"><semantics> <mi>ζ</mi> </semantics></math>) in s<sup>−1</sup> (color shaded, with warm and cold colors for cyclonic and anti-cyclonic, respectively) at 30 m AGL; and (<b>c</b>) the same as (<b>b</b>) but for 500 m AGL. The heavy black lines depict high mountain ranges with heights above 800 m ASL. The small yellow disk with black boarder indicates the center of the mesocyclone. The horizontal (vertical) dashed line is parallel to x- (y-) axis, and the intersection of the two dashed lines is the assumed location of tornadogenesis. The difference in potential temperature between both ends of the short red line at around (x, y) = (72, 95) (km) in (<b>b</b>) is discussed in relation to baroclinic production of streamwise vorticity along the trajectory of descending air parcels; see <a href="#sec4dot3dot2-meteorology-03-00020" class="html-sec">Section 4.3.2</a>.</p>
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<p>Schematic of tornado formation. The “T” on the rain-cooled outflow boundary represents the place where the tornado was born. The area circled by the dashed line on the lee side of the mountain shows where the flow situation favored the production of positive vertical vorticity near the ground. The cold airflow descending around the mountain tends to have streamwise vorticity, which can be converted to positive vertical vorticity by tilting (see the area circled by the red line in <a href="#meteorology-03-00020-f017" class="html-fig">Figure 17</a>b). The difference in potential temperature between two ends of the red double-arrow line shows the production of streamwise vorticity due to baroclinity (see text). The updraft at the cold outflow boundary subsequently stretched the vortex, leading to tornado formation; the updrafts are shown along the vertical line at around [y] = 75 km in <a href="#meteorology-03-00020-f013" class="html-fig">Figure 13</a>a, also indicated in horizontal distribution of vertical wind at 500 m AGL in <a href="#meteorology-03-00020-f017" class="html-fig">Figure 17</a>a. The updraft is enhanced by hydraulic jump of the cold outflow on the lee of the mountain and by convection of the moist air with high CAPE value in the layer of 1∼2 km AMSL, shown around [y] = 75 km in <a href="#meteorology-03-00020-f013" class="html-fig">Figure 13</a>b.</p>
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<p>Simulated trajectories passing over the intersection of the lines oriented south–north through x = 70 km and west–east through y = 85 km, i.e., the two dashed lines in <a href="#meteorology-03-00020-f017" class="html-fig">Figure 17</a> at 1400 UTC (1945 LST). The intersection is supposed as the place of the tornadogenesis, which is schematically shown by the “T” in <a href="#meteorology-03-00020-f018" class="html-fig">Figure 18</a>. The trajectory calculation was performed from 1350 UTC (1935 LST) to 1410 UTC (1955 LST). The details of the calculation are described in the text. The starting heights of the trajectory calculations at 1400 UTC (1945 LST) were set every 50 m from 100 m AGL (350 AMSL) to 300 m AGL (550 m AMSL). (<b>a</b>) Horizontal projection of the two trajectories at the lowest (100 m AGL) and highest (300 m) starting heights; (<b>b</b>) vertical projection of the same trajectories as in (<b>a</b>); and (<b>c</b>) time records of the heights of the simulated trajectories. The red dashed line shows the starting time of 1400 UTC (1945 LST) for the backward and forward calculations, demonstrating that each trajectory is near its lowest height at around 1400 UTC (1945 LST).</p>
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<p>Development of horizontal vorticity magnitude due to both streamwise and crosswise vorticity and vertical vorticity along the simulated trajectories for the period from 1350 UTC (1935 LST) to 1410 UTC (1955 LST). The calculation was started at 1400 UTC (1945 LST) in backward or forward manner with starting heights at (<b>a</b>) 100 m AGL (i.e., 350 m AMSL) and (<b>b</b>) 300 m AGL (550 m AMSL). The vorticities were evaluated using WRF-simulated wind fields, as described in the text.</p>
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<p>(<b>a</b>) Same in <a href="#meteorology-03-00020-f013" class="html-fig">Figure 13</a>a but for vertical vorticity (color shaded) at 1400 UTC (1945 LST); the wind vectors (u and w components) and potential temperature (green contours) are also plotted. Note that the vertical axis is shown up to 2.5 km AMSL, and is different from <a href="#meteorology-03-00020-f013" class="html-fig">Figure 13</a>a. The red horizontal line emphasizes the height of 1.1 km AMSL at which the upward wind speed is maximum on a vertical line through [y] = 75 km (see (<b>b</b>) and <a href="#meteorology-03-00020-f013" class="html-fig">Figure 13</a>a). The black vertical line shows the front of the cold outflow. (<b>b</b>) Vertical profiles of the vertical wind speed (m s<sup>−1</sup>) and vertical vorticity (s<sup>−1</sup>) at [y] = 75 km at the front of the cold outflow.</p>
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<p>Isochrones of the leading edge (front) of the cold outflow on 31 March 2019, plotted every 30 min with different colors. The leading edge was identified as where the vertical wind velocity at the 900 hPa level exceeds 1.2 m s<sup>−1</sup>. The black and red circles in D3 respectively indicate the location of the front at 1245 UTC (1830 LST) and 1445 UTC (2030 LST). The dashed arrow represents the main propagation direction of the cold outflow associated with the mesocyclone. The figure shows domain D2, in which domain D3 is located at the center. The gray color with black contours represents the terrain elevation. The minimum value of the terrain contour is 800 m AMSL, and the contour interval is 800 m.</p>
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<p>(<b>a</b>) Same as <a href="#meteorology-03-00020-f017" class="html-fig">Figure 17</a>a, but for the wind relative to the advancing front of cold outflow (front-relative wind). (<b>b</b>) Same as <a href="#meteorology-03-00020-f013" class="html-fig">Figure 13</a>a, but for the front-relative wind. The thick white arrow indicates the advancing direction of the front, while the dashed arrows show possible trajectories of air parcels reaching the point of the tornadogenesis. Plan view (<b>a</b>) and vertical view (<b>b</b>). The advancing velocity vector of the front applied to calculate the front-relative wind is estimated at (u<sub><span class="html-italic">f</span></sub>, v<sub><span class="html-italic">f</span></sub>) = (8.1, −8.1) in m s<sup>−1</sup> from <a href="#meteorology-03-00020-f022" class="html-fig">Figure 22</a>, while the advancing speed of the front is about 11.5 m s<sup>−1</sup> (see text for details).</p>
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22 pages, 4551 KiB  
Article
Optimizing Sorghum for California: A Multi-Location Evaluation of Biomass Yield, Feed Quality, and Biofuel Feedstock Potential
by Jackie Atim, Tadeo Kaweesi, Robert B. Hutmacher, Daniel H. Putnam, Julie Pedraza, Christopher M. de Ben, Tarilee Schramm, Jorge Angeles, Nicholas E. Clark and Jeffery A. Dahlberg
Agronomy 2024, 14(12), 2866; https://doi.org/10.3390/agronomy14122866 - 1 Dec 2024
Viewed by 578
Abstract
Sorghum cultivars, particularly those used for forage and biomass, present significant potential as drought-resistant crops suitable for animal feed and biofuel production. This study evaluated 59 sorghum hybrids over five years (2019–2023) across three University of California research farm locations in the Central [...] Read more.
Sorghum cultivars, particularly those used for forage and biomass, present significant potential as drought-resistant crops suitable for animal feed and biofuel production. This study evaluated 59 sorghum hybrids over five years (2019–2023) across three University of California research farm locations in the Central Valley: Kearney REC (KARE), West Side REC (WSREC), and Davis. The primary aim was to identify genotypes that exhibit high yield and stability across diverse environments in California, which is crucial for meeting the state’s significant feed needs associated with dairy operations and animal production. The evaluation focused on biomass yields, forage quality traits such as Relative Feed Quality (RFQ) and milk yield per ton (milk/ton), and biofuel-relevant chemical compositions like Neutral Detergent Fiber (NDF) and starch. A multi-trait stability index was employed to pinpoint superior genotypes that combine high yield with desirable quality traits. Results indicated significant genotypic, environmental, and genotype-by-environment (GxE) interaction effects for all traits except fat and water-soluble sugars. Eight hybrids were notable for maintaining high and stable biomass yields across different locations. Additionally, high fat and starch content were found to correlate with improved milk/ton potential, while lower fiber content (ADF, NDF) was associated with enhanced RFQ. Specifically, nine hybrids were identified as optimal for dairy forage due to their combination of high yield, RFQ, and milk/ton. Furthermore, distinct hybrids were identified for first-generation (starch-based) and second-generation (NDF-based) biofuel strategies. Three hybrids stood out as having desirable traits for both feed and biofuel applications, underscoring their versatility. This study highlights the utility of a multi-trait stability index in selecting superior sorghum genotypes for specific trait combinations. The identified candidates for forage and biofuel use, especially the multipurpose varieties, offer valuable insights that can aid growers and industry stakeholders in developing more sustainable and versatile sorghum production systems in California. Findings from this study contribute significantly to the development of more resilient sorghum production systems. By identifying hybrids that excel in both yield and quality across various environments, this research supports future cropping decisions aimed at enhancing water use efficiency and drought resilience in sorghum cultivation. These advancements are crucial for maintaining competitive dairy operations and advancing biofuel production in the face of climate change-induced challenges. Full article
(This article belongs to the Section Innovative Cropping Systems)
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<p>Correlation between different traits, quality (RFQ), and production indexes (milk/ton) among the 59 sorghum genotypes.</p>
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<p>Characterization of sorghum hybrids based on a superior index (WAASBY) [<a href="#B26-agronomy-14-02866" class="html-bibr">26</a>] that combines the mean performance of each hybrid and its weighted average of absolute score (WAASB) stability value for the biomass yield. Sorghum hybrids were ranked based on the mean WAASBY value (blue for those that had values above the mean WAASBY value, while the red had values below the mean WAASBY).</p>
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<p>Performance of different sorghum hybrids in terms of biomass yield in the three locations (KARE, WREC and DAVIS) for the five years of evaluation. The general biomass yield at KARE was comparatively lower than that of WREC and DAVIS. The dotted line represents the mean biomass yield for the three locations and the five years of evaluation.</p>
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<p>Categorizing sorghum hybrids based on the weighted average of absolute score (WAASB) index from the singular-value decomposition of the matrix of BLUPs for the GEI effects generated by a linear mixed model. Environment E1 (KARE), E2 (WREC) and E3 (DAVIS). Group I had sorghum hybrids that were both not productive and not stable, group II had hybrids that were highly productive but not stable, group III had hybrids that were highly stable but not productive, and Group IV comprised sorghum hybrids that were both highly productive and stable.</p>
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<p>Radar plot showing the best sorghum hybrids selected for forage based on Yield, Milk, and RFQ using multi-trait stability index.</p>
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<p>Radar plot showing the best sorghum hybrids selected for first-generation biofuel based on Yield, and starch content using multi-trait stability index.</p>
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<p>Radar plot showing the best sorghum hybrids selected for second-generation biofuel based on Yield, and NDF using multi-trait stability index.</p>
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25 pages, 7020 KiB  
Article
Floristic Diversity and Distribution Pattern along an Altitudinal Gradient in the Central Andes: A Case Study of Cajatambo, Peru
by Yakov Quinteros-Gómez, Jehoshua Macedo-Bedoya, Victor Santos-Linares, Franco Angeles-Alvarez, Doris Gómez-Ticerán, José Campos-De la Cruz, Julio Solis Sarmiento, Abel Salinas-Inga and Zinnia Valencia-Saavedra
Plants 2024, 13(23), 3328; https://doi.org/10.3390/plants13233328 - 27 Nov 2024
Cited by 1 | Viewed by 866
Abstract
Introduction: This study focuses on the central mountain region of the Peruvian Andes, particularly its western slopes, high-altitude areas, and inter-Andean valleys situated above 3000 m.a.s.l. Despite its ecological importance, the region remains understudied, resulting in significant information gaps. Objective: To identify flora [...] Read more.
Introduction: This study focuses on the central mountain region of the Peruvian Andes, particularly its western slopes, high-altitude areas, and inter-Andean valleys situated above 3000 m.a.s.l. Despite its ecological importance, the region remains understudied, resulting in significant information gaps. Objective: To identify flora species along an altitudinal gradient in the Cajatambo district. Methods: Sampling was carried out at five distinct altitudinal levels using a combination of sampling techniques. Taxonomic identification was performed, and statistical analyses including ANOVA, the Mantel test, and NMDS were applied. Results: 424 plant species were identified, revealing the dominance of Asteraceae. The approach used allowed for the identification of floristic and structural patterns in various habitats, ranging from arid montane scrub to puna grassland. Surprisingly, Asteraceae richness had a significant impact on species diversity, while altitude did not. Additionally, floristic similarity between nearby altitudinal levels was not related to geographical distance. The analysis of ecosystems has shown that certain families are adaptable. Additionally, floristic diversity has been affected by human activity near the district capital. The distribution of medicinal species has been limited due to selective extraction. Conclusions: The shrubland and thorny scrub was the most diverse ecosystem and had the widest distribution across the altitudinal gradient. Full article
(This article belongs to the Section Plant Ecology)
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<p>Species observed in the altitudinal gradient of Cajatambo. The following abbreviations are used for life forms (plant’s growth habit): H: herbaceous; B: blush; Suc: succulent; Li: liana; T: tree; Epi: epiphytes; StH: stoloniferous herbaceous.</p>
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<p>Vegetation profile along an altitudinal gradient in Cajatambo, Peru.</p>
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<p><span class="html-italic">Aa paleacea</span> (<b>A</b>), <span class="html-italic">Clematis peruviana</span> (<b>B</b>), <span class="html-italic">Altensteinia fimbriata</span> (<b>C</b>), <span class="html-italic">Cantua buxifolia</span> (<b>D</b>), <span class="html-italic">Alstroemeria lineatiflora</span> (<b>E</b>), <span class="html-italic">Austrocylindropuntia subulata</span> (<b>F</b>), <span class="html-italic">Mutisia acuminata</span> (<b>G</b>), <span class="html-italic">Alonsoa</span> sp. (<b>H</b>), <span class="html-italic">Cerastium</span> sp. (<b>I</b>), <span class="html-italic">Paranephelius ovatus</span> (<b>J</b>).</p>
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<p><span class="html-italic">Puya alpestris</span> (<b>A</b>), <span class="html-italic">Salvia sagittata</span> (<b>B</b>), <span class="html-italic">Verbena litoralis</span> (<b>C</b>), <span class="html-italic">Passiflora peduncularis</span> (<b>D</b>), <span class="html-italic">Dicliptera hookeriana</span> (<b>E</b>), <span class="html-italic">Siphocampylus tupaeformis</span> (<b>F</b>), <span class="html-italic">Presiliophytum incanum</span> (<b>G</b>), <span class="html-italic">Passiflora mixta</span> (<b>H</b>), <span class="html-italic">Arenaria</span> sp. (<b>I</b>), <span class="html-italic">Silybum marianum</span> (<b>J</b>).</p>
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<p>Heat map showing the results of a family composition analysis within arid montane scrub (<b>a</b>), Cactaceae floor (<b>b</b>), shrubland and thorny scrub (<b>c</b>), the transition territory from humid scrubland to grassland (<b>d</b>), puna grassland (<b>e</b>), and altitudinal gradient (<b>f</b>). A greater degree of darkness in the color tones indicates a higher number of species per family.</p>
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<p>Species accumulation curves that define the efficiency of sampling in an altitudinal gradient in Cajatambo, Lima.</p>
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<p>Multidimensional non-metric analysis (NMDS) for the abundance and richness of families in the five study areas (altitudinal gradient) in the Cajatambo district. The numbers represent the transect evaluated at each elevation level.</p>
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<p>Threatened flora according to the UICN Red List and the Decreto Supremo N.° 043–2006–AG.</p>
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<p>Location of the transects in the altitudinal gradient in the Cajatambo district.</p>
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25 pages, 1568 KiB  
Article
Reflexive-Reciprocal Syncretism in Eastern Bantu Languages of Tanzania: Distribution and Origins
by Aron Zahran and Sebastian Dom
Languages 2024, 9(11), 347; https://doi.org/10.3390/languages9110347 - 8 Nov 2024
Viewed by 1370
Abstract
This paper presents an overview of the distribution of reflexive-reciprocal syncretism in Eastern Bantu languages spoken in Tanzania. Most Bantu languages encode reflexive and reciprocal constructions by means of two distinct verbal affixes. However, the Tanzanian Eastern Bantu languages under study have developed [...] Read more.
This paper presents an overview of the distribution of reflexive-reciprocal syncretism in Eastern Bantu languages spoken in Tanzania. Most Bantu languages encode reflexive and reciprocal constructions by means of two distinct verbal affixes. However, the Tanzanian Eastern Bantu languages under study have developed reflexive-reciprocal syncretism, in which the originally reflexive prefix has developed into a polyfunctional morpheme coding both reflexive and reciprocal constructions, to the detriment of the original reciprocal suffix. In a sample of 79 languages, reflexive-reciprocal syncretism is attested in 27 neighboring languages, thus constituting a clear areal feature. We propose that reflexive-reciprocal syncretism is not a language-internal innovation but was rather adopted from neighboring non-Bantu languages and subsequently spread out to its current distribution. We locate the heart of this contact-induced spread in the Tanzanian Rift Valley, a convergence zone in north-central Tanzania where languages from multiple African language families are spoken and have been in contact for an extensive period. Full article
(This article belongs to the Special Issue Recent Developments on the Diachrony and Typology of Bantu Languages)
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<p>Geographical distribution of Tanzanian Bantu languages with reflexive-reciprocal prefix (map adapted from <a href="http://www.muturzikin.com" target="_blank">http://www.muturzikin.com</a> accessed on 5 January 2022).</p>
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<p>Reflexive-reciprocal polysemy in the Tanzanian Rift Valley and surrounding languages<a href="#fn008-languages-09-00347" class="html-fn">8</a>. (Map adapted from <a href="#B60-languages-09-00347" class="html-bibr">Kießling et al.</a> (<a href="#B60-languages-09-00347" class="html-bibr">2008</a>)).</p>
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21 pages, 1114 KiB  
Review
Innovation Reefs (I-Reef): Innovation Ecosystems Focused on Regional Sustainable Development
by Angelica Duarte Lima, André Luiz Przybysz, David Nunes Resende and Regina Negri Pagani
Sustainability 2024, 16(22), 9679; https://doi.org/10.3390/su16229679 - 6 Nov 2024
Viewed by 921
Abstract
The creation of successful innovation ecosystems, like Silicon Valley, is challenging due to significant cultural, infrastructural, and resource differences between regions. In this context, the Innovation Reef (I-Reef) model emerges as a promising alternative, offering an approach for regions with limited resources to [...] Read more.
The creation of successful innovation ecosystems, like Silicon Valley, is challenging due to significant cultural, infrastructural, and resource differences between regions. In this context, the Innovation Reef (I-Reef) model emerges as a promising alternative, offering an approach for regions with limited resources to develop successful innovation ecosystems based on cooperation and mutual benefit among participants. This model has great potential to promote regional development, especially due to its focus on retaining and sharing the value generated. However, the role of I-Reef in sustainable regional development still needs to be further explored. Thus, the objective of this study is to deepen the theoretical understanding of the I-Reef model by analyzing its contribution to sustainable development. To achieve this, a comparison was made between I-Reef and established models such as business, innovation, knowledge, and entrepreneurial ecosystems. A systematic literature review conducted on Scopus found 704 articles published in the last three decades. The purpose was to identify the similarities and differences between the models of innovation business ecosystem models. The results show that there is alignment between I-Reef and the different ecosystems on several points. A central aspect of I-Reef is that it relies on a strong network of mutually beneficial relationships, much more oriented to sustainable development than the other models, which is a key factor in generating competitive advantage and development for the region. This characteristic is either not addressed or not placed at the core of the ecosystems discussed in the literature. For future research, empirical studies and validation of the I-Reef model with experts are suggested, as this theoretical study lays the foundation for more in-depth analyses. Full article
(This article belongs to the Special Issue AI-Driven Entrepreneurship and Sustainable Business Innovation)
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<p>Database search results.</p>
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<p>The nine steps of Methodi Ordinatio. Source [<a href="#B18-sustainability-16-09679" class="html-bibr">18</a>,<a href="#B54-sustainability-16-09679" class="html-bibr">54</a>,<a href="#B55-sustainability-16-09679" class="html-bibr">55</a>].</p>
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<p>Diagnostic model of I-Reef contribution.</p>
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15 pages, 7289 KiB  
Article
The Different Effects of Two Types of El Niño on Eastern China’s Spring Precipitation During the Decaying Stages
by Dezhi Zhang, Chujie Gao, Zhichao Yang, Zhi Yuan, Xuanke Wang, Bei Xu and Haozhong Qian
Atmosphere 2024, 15(11), 1331; https://doi.org/10.3390/atmos15111331 - 5 Nov 2024
Cited by 1 | Viewed by 733
Abstract
El Niño is one of the most significant global climatic phenomena affecting the East Asian atmospheric circulation and climate. This study uses multi-source datasets, including observations and analyses, and statistical methods to investigate the variations and potential causes of boreal spring precipitation anomalies [...] Read more.
El Niño is one of the most significant global climatic phenomena affecting the East Asian atmospheric circulation and climate. This study uses multi-source datasets, including observations and analyses, and statistical methods to investigate the variations and potential causes of boreal spring precipitation anomalies in eastern China under different El Niño sea surface temperature conditions, namely, the Eastern Pacific and Central Pacific (EP and CP) El Niño cases. The findings reveal that, particularly along the Yangtze–Huaihe valley, spring precipitation markedly increases in most regions of eastern China during the EP El Niño decaying stages. Conversely, during the CP El Niño decaying stages, precipitation anomalies are weak, with occurrences of weak negative anomalies in the same regions. Further analyses reveal that during the decaying spring of different El Niño cases, differences in the location and strength of the Northwest Pacific (NWP) abnormal anticyclone, which is associated with the central–eastern Pacific warm sea surface temperature anomaly (SSTA), result in distinct anomalous precipitation responses in eastern China. The SSTA center of the EP El Niño is more easterly and stronger. In the meantime, NWP abnormal anticyclones are more easterly and have a broader range, facilitating water vapor transport over eastern China. By contrast, the CP El Niño SSTA center is westward and relatively weaker, leading to a relatively weak, westward, and narrower anomalous NWP anticyclone that causes less significant water vapor transport anomalies in eastern China. This paper highlights the diverse impacts of El Niño diversity on regional atmospheric circulation and precipitation, providing valuable scientific references for studying regional climate change in East Asia. Full article
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<p>Schematic diagram of spring precipitation anomalies in the (<b>a</b>) EP El Niño and (<b>b</b>) CP El Niño cases. The colored area indicates a significant precipitation anomaly and the +/− indicates a positive/negative precipitation anomaly. The diagram is drawn based on the results of Yuan and Yang [<a href="#B1-atmosphere-15-01331" class="html-bibr">1</a>].</p>
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<p>NCT (DJF) and NWP (DJF) indices (unit: °C) from 1960 to 2020.</p>
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<p>Regression of seasonal SSTA (unit: °C) on (left) NCT and (right) NWP for (<b>a</b>,<b>b</b>) previous summer, (<b>c</b>,<b>d</b>) previous autumn, (<b>e</b>,<b>f</b>) winter, (<b>g</b>,<b>h</b>) subsequent spring, and (<b>i</b>,<b>j</b>) subsequent summer. Colored areas indicate regression coefficients above the 95% confidence level.</p>
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<p>Correlations of MAM anomalous precipitation of the following year with (<b>a</b>) NCT and (<b>b</b>) NWP indices. The dotted areas indicate correlation coefficients above the 95% confidence level.</p>
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<p>Composites of MAM anomalous cumulated precipitation (unit: mm) over East Asia in the (<b>a</b>) EP El Niño and (<b>b</b>) CP El Niño cases. The dotted areas indicate the composite anomalies above the 95% confidence level.</p>
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<p>Composite of monthly SSTAs (unit: °C) averaged over the Niño 3 (5° S–5° N, 150° W–90° W) and Niño 4 (5° S−5° N, 160° E−150° W) for the (<b>a</b>) EP El Niño and (<b>b</b>) CP El Niño cases. (<b>c</b>) Composite of monthly SSTAs (unit: °C) averaged over the Niño 3.4 (5° S−5° N, 170° W−120° W) for the EP El Niño and CP El Niño cases.</p>
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<p>Composites of DJF SSTA (unit: °C) and 850 hPa wind anomalies (unit: m/s) in the (<b>a</b>) EP El Niño and (<b>b</b>) CP El Niño cases. Composites of MAM SSTA and 850 hPa wind anomalies in the (<b>c</b>) EP El Niño and (<b>d</b>) CP El Niño cases. The dotted areas and black arrowheads indicate the composite anomalies above the 95% confidence level.</p>
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<p>Composites of MAM 850 hPa stream function (unit: m<sup>2</sup>/s) anomalies and 850 hPa rotating wind anomalies (unit: m/s) in the (<b>a</b>) EP El Niño and (<b>b</b>) CP El Niño cases. The dotted area and black arrowhead indicate the composite anomalies above the 95% confidence level.</p>
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<p>Composites of MAM water vapor flux divergence anomalies (unit: 10<sup>−5</sup> kg/(m<sup>2</sup>·s)) and water vapor flux anomalies (unit: 10<sup>2</sup> kg/(m·s)) in the (<b>a</b>) EP El Niño and (<b>b</b>) CP El Niño cases. The dotted area and black arrowhead indicate the composite anomalies above the 95% confidence level.</p>
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<p>Scatterplots (<b>a</b>) between the normalized SPEC [spring precipitation averaged over eastern China (20° S–40° N, 110° E–125° E)] and winter NCT indices, and (<b>b</b>) the normalized SPEC and winter NWP indices during 1961–2020. The term <span class="html-italic">r</span> denotes the corresponding correlation coefficient for each panel. All data are linearly detrended and standardized for the correlation analysis.</p>
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<p>Bubble chart representing the relationship between the central longitude of significant vorticity anomalies and the normalized SPEC index (<a href="#atmosphere-15-01331-f010" class="html-fig">Figure 10</a>) for 12 El Niño events (the first six EP El Niño cases are selected according to the spring Nino 3.4 index, the same as the CP El Niño) during the decaying spring within the region 5° N–40° N and 105° E–150° E. The size of each bubble corresponds to the number of grid points that indicate anomalies above the 95% confidence level, with larger bubbles indicating more significant grid points. The <span class="html-italic">x</span>-axis denotes the average central longitude of significant vorticity anomalies (exceeding the 95% confidence level), while the <span class="html-italic">y</span>-axis indicates the normalized SPEC index.</p>
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19 pages, 2093 KiB  
Review
Histology Agnostic Drug Development: An Updated Review
by Kevin Nguyen, Karina Fama, Guadalupe Mercado, Yin Myat and Kyaw Thein
Cancers 2024, 16(21), 3642; https://doi.org/10.3390/cancers16213642 - 29 Oct 2024
Viewed by 1384
Abstract
Recent advancements in oncology have led to the development of histology-agnostic therapies, which target genetic alterations irrespective of the tumor’s tissue of origin. This review aimed to provide a comprehensive update on the current state of histology-agnostic drug development, focusing on key therapies, [...] Read more.
Recent advancements in oncology have led to the development of histology-agnostic therapies, which target genetic alterations irrespective of the tumor’s tissue of origin. This review aimed to provide a comprehensive update on the current state of histology-agnostic drug development, focusing on key therapies, including pembrolizumab, larotrectinib, entrectinib, dostarlimab, dabrafenib plus trametinib, selpercatinib, trastuzumab deruxtecan, and reprotrectinib. We performed a detailed analysis of each therapy’s mechanism of action, clinical trial outcomes, and associated biomarkers. The review further explores challenges in drug resistance, such as adaptive signaling pathways and neoantigen variability, as well as diagnostic limitations in identifying optimal patient populations. While these therapies have demonstrated efficacy in various malignancies, significant hurdles remain, including intratumoral heterogeneity and resistance mechanisms that diminish treatment effectiveness. We propose considerations for refining trial designs and emerging biomarkers, such as tumor neoantigen burden, to enhance patient selection. These findings illustrate the transformative potential of histology-agnostic therapies in precision oncology but highlight the need for continued research to optimize their use and overcome existing barriers. Full article
(This article belongs to the Special Issue Feature Paper in Section “Cancer Therapy” in 2024)
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<p><b>Overview of the FDA-approved tumor-agnostic treatments.</b> This figure summarizes the tumor-agnostic treatments approved by the U.S. Food and Drug Administration (FDA). Immune checkpoint inhibitors include pembrolizumab, approved for both <span class="html-italic">dMMR/MSI-H</span> and <span class="html-italic">TMB-H</span> solid tumors, and dostarlimab, approved for <span class="html-italic">TMB-H</span> cancers. Targeted therapies include one combinational regimen consisting of dabrafenib plus trametinib, approved for <span class="html-italic">BRAF<sup>V600E</sup></span> non-CRC tumors. Larotrectinib, entrectinib, and repotrectinib were both approved for <span class="html-italic">NTRK</span> fusions. Selpercatinib was approved for <span class="html-italic">RET</span> fusion-positive cancers. Trastuzumab deruxtecan represents the first antibody conjugate drug approved for <span class="html-italic">HER2</span>-positive cancers. These targeted therapies represent the emerging era of personalized cancer care driven by mutation type across multiple tumor types. Created in BioRender. Thein, K. (2024) <a href="http://BioRender.com" target="_blank">BioRender.com</a>/d44b340 (accessed on 12 March 2024).</p>
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<p><b>Timeline of tumor-agnostic FDA approvals.</b> The number of FDA-approved tumor-agnostic therapies continues to grow, reflecting advances in targeted cancer treatments. This figure summarizes the year of FDA approval, the name of the approved regimen, and the targeted biomarker, and lists the patient population (n) and objective response rate (ORR) of the clinical trials that formed the basis of approval. 1. <b>Pembrolizumab</b> was approved in 2017 for <span class="html-italic">dMMR/MSI-H</span> solid tumors, based on 149 patients, with an ORR of 39.6%. 2. <b>Larotrectinib</b> was approved in 2018 for <span class="html-italic">NTRK</span> fusion-positive solid tumors, based on 55 patients, with an ORR of 75%. 3. <b>Entrectinib</b> was approved in 2019 for <span class="html-italic">NTRK</span> fusion-positive solid tumors, based on 55 patients, with an ORR of 75%. 4. <b>Pembrolizumab</b> was approved in 2020 for <span class="html-italic">TMB-H</span> solid tumors. The cutoff value used for <span class="html-italic">TMB-H</span> status was at least 10 mutations per megabase, based on 102 patients, with an ORR of 29%. 5. <b>Dostarlimab</b> was approved in 2021 for <span class="html-italic">dMMR</span> solid tumors, based on 209 patients, with an ORR of 41.6%. 6. <b>Dabrafenib plus trametinib</b> was approved in 2022 for <span class="html-italic">BRAF V600E</span> solid tumors (excluding colorectal cancers), based on 131 adult patients with an ORR of 41% and 36 pediatric patients with an ORR of 25%. 7. <b>Selpercatinib</b> was approved in 2022 for <span class="html-italic">RET</span> fusion-positive solid tumors, based on 41 patients, with an ORR of 44%. 8. <b>Trastuzumab deruxtecan</b> was approved in 2024 for <span class="html-italic">HER2</span>-positive (immunohistochemistry 3+ score) solid tumors, based on a pooled patient population of 192 across three key clinical trials: Destiny-PanTumor02 (ORR = 51.4%), Destiny-Lung01 (ORR = 52.9%), and Destiny-CRC02 (ORR = 46.9%). 9. <b>Repotrectinib</b> was approved in 2024 for <span class="html-italic">NTRK</span> fusion-positive solid tumors. Patients were divided into TRK tyrosine kinase inhibitor (TKI)-pretreated and TKI-naïve cohorts. The TKI-pretreated group, with 48 patients, had an ORR of 50%, while the TKI-nave cohort, with 40 patients, had an ORR of 58%.</p>
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<p><b>Mechanism of action of FDA-approved tumor-agnostic therapy.</b> This figure summarizes the basic mechanism of action regarding the FDA-approved tumor-agnostic therapies. <b>Immune checkpoint inhibitor—PD-1 inhibitors:</b> PD-1 is a receptor expressed on T cells, and its ligand, PD-L1, is expressed on cancer cells. When PD-1 binds to PD-L1, it suppresses the activity of cytotoxic T cells, allowing tumor cells to evade immune detection and proliferate unchecked. PD-1 inhibitors, such as pembrolizumab and dostarlimab, block this interaction, restoring T-cell activity and enhancing the immune system’s ability to detect and destroy cancer cells. <b>Antibody-drug conjugate (ADC):</b> Trastuzumab deruxtecan consists of a monoclonal antibody, a cytotoxic payload, and a linker. Upon binding to the HER2 receptor on the cancer cell surface, the ADC is internalized through endocytosis. Inside the cell, the endosome fuses with a lysosome, where the complex is degraded, releasing the cytotoxic drug. This payload damages the tumor’s DNA, effectively inhibiting cell proliferation and inducing cancer cell death. <b>BRAF/MEK inhibitor combination:</b> Dabrafenib (a BRAF inhibitor) combined with trametinib (a MEK inhibitor) targets the MAPK signaling pathway, a critical cascade of phosphorylation events involving RAS, BRAF, MEK, and ERK proteins. This pathway regulates cell proliferation, survival, and differentiation. In cancers with BRAF V600E mutations, abnormal activation of this pathway leads to uncontrolled tumor cell growth. By blocking the mutant BRAF protein and inhibiting MEK downstream, this combination therapy effectively disrupts hyperproliferation and prevents paradoxical activation of the MAPK pathway. <b>NTRK fusion inhibitors and RET fusion inhibitors:</b> Gene fusions involving the NTRK and RET genes lead to the production of constitutively active receptor tyrosine kinases, which continuously signal even in the absence of normal regulatory inputs. These aberrantly activated receptors stimulate key proliferative and survival pathways, including the MAPK and PI3K/AKT pathways, driving oncogenic signaling that promotes tumor growth, survival, and metastasis. Targeted therapies, such as NTRK inhibitors (e.g., larotrectinib and entrectinib) and RET inhibitors (e.g., selpercatinib and pralsetinib), block these fusion-driven tyrosine kinases, effectively halting tumor progression by disrupting these essential growth signals. Created by BioRender. Thein, K. (2024) <a href="http://BioRender.com" target="_blank">BioRender.com</a>/i35k906 (accessed on 12 March 2024).</p>
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