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17 pages, 2864 KiB  
Article
Organic Mulching Versus Soil Conventional Practices in Vineyards: A Comprehensive Study on Plant Physiology, Agronomic, and Grape Quality Effects
by Andreu Mairata, David Labarga, Miguel Puelles, Luis Rivacoba, Javier Portu and Alicia Pou
Agronomy 2024, 14(10), 2404; https://doi.org/10.3390/agronomy14102404 - 17 Oct 2024
Abstract
Research into alternative vineyard practices is essential to maintain long-term viticulture sustainability. Organic mulching on the vine row improves vine cultivation properties, such as increasing soil water retention and nutrient availability. This study overviewed the effects of three organic mulches (spent mushroom compost [...] Read more.
Research into alternative vineyard practices is essential to maintain long-term viticulture sustainability. Organic mulching on the vine row improves vine cultivation properties, such as increasing soil water retention and nutrient availability. This study overviewed the effects of three organic mulches (spent mushroom compost (SMC), straw (STR), and grapevine pruning debris (GPD)) and two conventional soil practices (herbicide application (HERB) and tillage (TILL)) on grapevine physiology, agronomy, and grape quality parameters over three years. SMC mulch enhanced soil moisture and nutrient concentration. However, its mineral composition increased soil electrical conductivity (0.78 dS m⁻1) and induced grapevine water stress due to osmotic effects without significantly affecting yield plant development. Only minor differences in leaf physiological parameters were observed during the growing season. However, straw (STR) mulch reduced water stress and increased photosynthetic capacity, resulting in higher pruning weights. Organic mulches, particularly SMC and STR, increased grape pH, potassium, malic acid, and tartaric acid levels, while reducing yeast assimilable nitrogen. The effect of organic mulching on grapevine development depends mainly on soil and mulch properties, soil water availability, and environmental conditions. This research highlights the importance of previous soil and organic mulch analysis to detect vineyard requirements and select the most appropriate soil management treatment. Full article
(This article belongs to the Section Horticultural and Floricultural Crops)
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<p>Summary of field climatic conditions. (<b>A</b>) Monthly accumulated precipitation (mm) in 2020, 2021, and 2022 and average monthly precipitation in 2005–2019. (<b>B</b>) Annual accumulated precipitation (P, mm), reference evapotranspiration (ET0, mm), and growing degree days (GDDs, °C day<sup>−1</sup>).</p>
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<p>Average soil volumetric water content (VWC, %) and standard deviations of the soil under SMC (<span class="html-fig-inline" id="agronomy-14-02404-i001"><img alt="Agronomy 14 02404 i001" src="/agronomy/agronomy-14-02404/article_deploy/html/images/agronomy-14-02404-i001.png"/></span>), STR (<span class="html-fig-inline" id="agronomy-14-02404-i002"><img alt="Agronomy 14 02404 i002" src="/agronomy/agronomy-14-02404/article_deploy/html/images/agronomy-14-02404-i002.png"/></span>), GPD (<span class="html-fig-inline" id="agronomy-14-02404-i003"><img alt="Agronomy 14 02404 i003" src="/agronomy/agronomy-14-02404/article_deploy/html/images/agronomy-14-02404-i003.png"/></span>), HERB (<span class="html-fig-inline" id="agronomy-14-02404-i004"><img alt="Agronomy 14 02404 i004" src="/agronomy/agronomy-14-02404/article_deploy/html/images/agronomy-14-02404-i004.png"/></span>), and TILL (<span class="html-fig-inline" id="agronomy-14-02404-i005"><img alt="Agronomy 14 02404 i005" src="/agronomy/agronomy-14-02404/article_deploy/html/images/agronomy-14-02404-i005.png"/></span>) through the day of year (DOY) of the vine vegetative cycle in 2021 (<b>A1</b>–<b>A3</b>) and 2022 (<b>B1</b>–<b>B3</b>) at three soil depths: 5 (1), 15 (2), and 25 (3) cm. Precipitation events (mm) are represented by blue columns (<span class="html-fig-inline" id="agronomy-14-02404-i006"><img alt="Agronomy 14 02404 i006" src="/agronomy/agronomy-14-02404/article_deploy/html/images/agronomy-14-02404-i006.png"/></span>). Black lines indicate the phenology stages of flowering (F), fruit set (S), veraison (V), and grape maturity (M) and the day when leaf gas exchange parameters and water potentials were recorded. The treatment soil water content was monitored with three field devices.</p>
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<p>The mean and standard deviation of grape carbon isotope discrimination (δ<sup>13</sup>C) of the soil management treatments studied. Statistical differences indicated by letters were accepted when <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Must and grape physical-chemical parameters of the five soil management treatments from 2020 to 2022: (<b>A</b>) total soluble solids (Brix), (<b>B</b>) pH, (<b>C</b>) total acidity (TA), (<b>D</b>) tartaric acid, (<b>E</b>) malic acid, (<b>F</b>) potassium, (<b>G</b>) yeast assimilable nitrogen (YAN), (<b>H</b>) berry weight, (<b>I</b>) anthocyanins, and (<b>J</b>) total polyphenol index at 280 nm (TPI). Significant differences between soil management treatments are represented by letters when <span class="html-italic">p</span> &lt; 0.05 (n.s. = non-significant differences).</p>
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11 pages, 8238 KiB  
Article
Enhancing the Tolerance of a Green Foxtail Biotype to Mesotrione via a Cytochrome P450-Mediated Herbicide Metabolism
by Yuning Lan, Yi Cao, Ying Sun, Ruolin Wang and Zhaofeng Huang
Agronomy 2024, 14(10), 2399; https://doi.org/10.3390/agronomy14102399 - 17 Oct 2024
Abstract
Green foxtail is a troublesome weed in crop fields across China. A nova target HPPD inhibitor is widely used to control weeds in agricultural production. Mesotrione, an HPPD inhibitor, cannot control green foxtail effectively under the recommended field dose, indicating that green foxtail [...] Read more.
Green foxtail is a troublesome weed in crop fields across China. A nova target HPPD inhibitor is widely used to control weeds in agricultural production. Mesotrione, an HPPD inhibitor, cannot control green foxtail effectively under the recommended field dose, indicating that green foxtail is tolerant to mesotrione. Interestingly, a green foxtail biotype that exhibits a greater tolerance to mesotrione (GR50 value 463.2 g ai ha−1) than that of the wild biotype (GR50 value 271.9 g ai ha−1) was found in Jilin Province, China. The HPPD genes isolated from the two biotypes genome were aligned, while no difference was found in the amino acid of the HPPD compared with that of the wild biotype. Through the qPCR experiment, the HPPD gene copy number variation and overexpression were also not found. Cytochrome P450 inhibitors (malathion and PBO), pretreatment, could effectively reverse the tolerance. Compared with the MT biotype, the in vivo activity of P450s was higher after the mesotrione treatment in the HT biotype. Therefore, P450s might be involved in the mechanism of tolerance. Full article
(This article belongs to the Section Pest and Disease Management)
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<p>The dose-response curves depicted plant growth of MT and HT biotypes under mesotrione treatment. Vertical bars represent standard errors (SEs), while the colored ribbons indicate the 95% confidence intervals (both R<sup>2</sup> &gt; 0.99).</p>
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<p>Sequence alignment of coding sequences of <span class="html-italic">HPPD</span> genes in MT biotype and HT biotype. The orange arrows in the figure indicated SNPs in the sequence. The <span class="html-italic">Setaria_viridis</span> means the reference <span class="html-italic">HPPD</span> gene sequence of <span class="html-italic">S. viridis</span> in NCBI.</p>
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<p>Target gene copy number and expression analysis. (<b>A</b>) Relative <span class="html-italic">HPPD</span> gene copy number. (<b>B</b>) Relative <span class="html-italic">HPPD</span> gene expression level (the T means treated with mesotrione, the CK means only treated with water, ns means not significant).</p>
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<p>(<b>A</b>) Whole-plant dose response of the tolerance to mesotrione with the malathion pretreatment in the MT and HT green foxtail biotypes at 21 DAT. (<b>B</b>) Whole-plant dose response of the tolerance to mesotrione with the PBO pretreatment in the MT and HT green foxtail biotypes at 21 DAT. (<b>C</b>) Only the malathion or PBO treatment to the MT and HT green foxtail biotypes at 21 DAT (the CK means only treated with water).</p>
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<p>Dose-response curves of the plant growth of the green foxtail biotypes under the mesotrione treatment with the P450 inhibitors pretreatment. (<b>A</b>) means both biotypes under the mesotrione treatment with the malathion pretreatment; (<b>B</b>) means both biotypes under the mesotrione treatment with the PBO pretreatment. Vertical bars indicate the SEs (all R<sup>2</sup> &gt; 0.99).</p>
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<p>Comparison of the activities of cytochrome P450 reductase between MT and HT biotypes at 0, 1, 2, 3, 4, and 5 days after mesotrione treatment.</p>
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18 pages, 644 KiB  
Article
Effect of Biostimulants and Glyphosate on Morphophysiological Parameters of Zea mays (L.) Seedlings under Controlled Conditions
by Tabisa Tandathu, Elmarie Kotzé, Elmarie Van Der Watt and Zenzile Peter Khetsha
Agronomy 2024, 14(10), 2396; https://doi.org/10.3390/agronomy14102396 - 16 Oct 2024
Viewed by 273
Abstract
Maize (Zea mays L.) is the major produced crop in South Africa, but numerous abiotic/biotic stressors threaten its production. Herbicides are mainly in the agricultural sector to minimise crop yield losses caused by weed competition. However, with most weeds becoming resistant to [...] Read more.
Maize (Zea mays L.) is the major produced crop in South Africa, but numerous abiotic/biotic stressors threaten its production. Herbicides are mainly in the agricultural sector to minimise crop yield losses caused by weed competition. However, with most weeds becoming resistant to glyphosate, South African farmers have used higher herbicide concentrations than typically recommended. This study was conducted to determine the effect of two biostimulants (brassinosteroids and KELPAK) and glyphosate on the morphophysiological parameters of maize seedlings. Experiments were carried out in the glasshouses of the Department of Soil, Crop, and Climate Sciences at the University of the Free State in Bloemfontein for eight weeks over two seasons, 2017/2018 and 2018/2019. The treatments did not significantly affect all maize morphological parameters except the plant dry mass. Compared to the control, plant dry mass was significantly (p < 0.05) increased by 15.72 g when glyphosate was applied in combination with brassinosteroids during the 2019 growing season. The application of glyphosate, brassinosteroids, and KELPAK differed significantly (p < 0.05) between weeks across the physiological parameters in the two seasons: an irrefutable significant increase was recorded in the rates of transpiration between the weeks. Although significant differences were recorded in the chlorophyll fluorescence, chlorophyll, and carotenoid content, these parameters were similar to the control, especially in the last week of data collection. During the early developmental stage of maize, farmers can administer biostimulants—brassinosteroid (5 g ai ha−1) and KELPAK (5% ai ha−1)—alone and in combination in glyphosate-resistant maize cultivars treated with glyphosate to aid maize seedlings. Full article
(This article belongs to the Section Weed Science and Weed Management)
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<p>Effects of biostimulants and glyphosate on GR maize seedlings shoot fresh (<b>A</b>) and dry (<b>B</b>) mass in the 2019 growing season; different letters within the same column indicate significant differences between means after Tukey’s protected LSD test at <span class="html-italic">p</span> = 0.05, * = <span class="html-italic">p</span> &lt; 0.05.</p>
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22 pages, 9166 KiB  
Article
Real-Time Detection and Localization of Weeds in Dictamnus dasycarpus Fields for Laser-Based Weeding Control
by Yanlei Xu, Zehao Liu, Jian Li, Dongyan Huang, Yibing Chen and Yang Zhou
Agronomy 2024, 14(10), 2363; https://doi.org/10.3390/agronomy14102363 - 13 Oct 2024
Viewed by 421
Abstract
Traditional Chinese medicinal herbs have strict environmental requirements and are highly susceptible to weed damage, while conventional herbicides can adversely affect their quality. Laser weeding has emerged as an effective method for managing weeds in precious medicinal herbs. This technique allows for precise [...] Read more.
Traditional Chinese medicinal herbs have strict environmental requirements and are highly susceptible to weed damage, while conventional herbicides can adversely affect their quality. Laser weeding has emerged as an effective method for managing weeds in precious medicinal herbs. This technique allows for precise weed removal without chemical residue and protects the surrounding ecosystem. To maximize the effectiveness of this technology, accurate detection and localization of weeds in the medicinal herb fields are crucial. This paper studied seven species of weeds in the field of Dictamnus dasycarpus, a traditional Chinese medicinal herb. We propose a lightweight YOLO-Riny weed-detection algorithm and develop a YOLO-Riny-ByteTrack Multiple Object Tracking method by combining it with the ByteTrack algorithm. This approach enables accurate detection and localization of weeds in medicinal fields. The YOLO-Riny weed-detection algorithm is based on the YOLOv7-tiny network, which utilizes the FasterNet lightweight structure as the backbone, incorporates a lightweight upsampling operator, and adds structure reparameterization to the detection network for precise and rapid weed detection. The YOLO-Riny-ByteTrack Multiple Object Tracking method provides quick and accurate feedback on weed identification and location, reducing redundant weeding and saving on laser weeding costs. The experimental results indicate that (1) YOLO-Riny improves detection accuracy for Digitaria sanguinalis and Acalypha australis, ultimately amounting to 5.4% and 10%, respectively, compared to the original network. It also diminishes the model size by 2 MB and inference time by 10 ms, making it more suitable for resource-constrained edge devices. (2) YOLO-Riny-ByteTrack enhances Multiple Object Tracking accuracy by 3%, reduces ID switching by 14 times, and improves overall tracking accuracy by 3.4%. The proposed weed-detection and localization method for Dictamnus dasycarpus offers fast detection speed, high localization accuracy, and stable tracking, supporting the implementation of laser weeding during the seedling stage of Dictamnus dasycarpus. Full article
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<p><span class="html-italic">Dictamnus dasycarpus</span> cultivation base in Shizijie Town, Gaizhou City, Liaoning Province. (<b>a</b>) Location map of Liaoning Province; (<b>b</b>) Location map of Shizi Street, Gaizhou City; (<b>c</b>) <span class="html-italic">Dictamnus dasycarpus</span> cultivation base.</p>
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<p>Examples of the seven selected weed species and crop <span class="html-italic">Dictamnus dasycarpus</span> from the dataset images.</p>
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<p>Examples of data-enhancement techniques applied to weed images. (<b>a</b>) Data enhanced visualization; (<b>b</b>) Mosic enhanced visualization. Online enhanced labels 0: Chenopodium album; 1: Acalypha australis; 2: Poa annua; 4: Acalypha australis; 5: Bidens Pilosa; 6: Capsella bursa-pastoris.</p>
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<p>Comparison of sample sizes before and after dataset pre-processing.</p>
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<p>Architecture of YOLO-Riny network model. (<b>a</b>) General model structure; (<b>b</b>) Module internal structure.</p>
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<p>PConv structure.</p>
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<p>The structure of CARAFE.</p>
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<p>Structure of the RepBlock. (<b>a</b>) Training module structure; (<b>b</b>) <span class="html-italic">BN</span> Layer fusion; (<b>c</b>) Reasoning module structure.</p>
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<p>Flowchart of ByteTrack algorithm.</p>
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<p>Visualization of YOLO-Riny detection results. In this figure, 1: Acalypha australis; 3: Capsella bursa-pastoris; 4: Poa annua; 5: Commelina communis; 6: Digitaria sanguinalis; 7: Chenopodium album.</p>
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<p>YOLO-Riny confusion matrix.</p>
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<p>Multiple thermogram visualization tests. Columns 1 and 4 show weed images collected on a sunny day, while columns 2 and 3 display weed images collected on a cloudy day. Row (<b>A</b>) presents the original images; row (<b>B</b>) shows the EigenCAM visualization results; row (<b>C</b>) shows the GradCAM visualization results; and row (<b>D</b>) shows the LayerCAM visualization results. The more the model focuses on the target the closer its color is to a warm color.</p>
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<p>Visualization of the impact of simulated motion noise tests. (<b>A</b>) Original image; (<b>B</b>) Image with 20% added blur; (<b>C</b>) Image with 40% added blur; (<b>D</b>) Image with 60% added blur.</p>
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<p>Visualization of the effects of real motion noise tests. (<b>A</b>) No blurring; (<b>B</b>) Approximate 20% blur; (<b>C</b>) Approximate 40% blur; (<b>D</b>) Approximate 60% or more blur.</p>
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<p>Visualization of YOLO-Riny-ByteTrack tracking performance. (<b>a</b>–<b>c</b>) represent three times segments of video clips, with each set consisting of five images extracted from a 40-frame video.</p>
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<p>Visualization of YOLO-Riny-ByteTrack tracking performance. (<b>a</b>–<b>c</b>) represent three times segments of video clips, with each set consisting of five images extracted from a 40-frame video.</p>
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<p>Model deployment experiments. (<b>a</b>) Jetson Orin Nano device; (<b>b</b>) Experimentation of models on embedded devices.</p>
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11 pages, 2580 KiB  
Article
Introgression of Herbicide-Resistant Gene from Genetically Modified Brassica napus L. to Brassica rapa through Backcrossing
by Subramani Pandian, Young-Sun Ban, Eun-Kyoung Shin, Senthil Kumar Thamilarasan, Muthusamy Muthusamy, Young-Ju Oh, Ho-Keun An and Soo-In Sohn
Plants 2024, 13(20), 2863; https://doi.org/10.3390/plants13202863 - 13 Oct 2024
Viewed by 342
Abstract
Interspecific hybridization between two different Brassicaceae species, namely Brassica rapa ssp. pekinensis (♀) (AA, 2n = 2x = 20) and genetically modified Brassica napus (♂) (AACC, 2n = 4x = 38), was performed to study the transmission of a herbicide resistance gene from [...] Read more.
Interspecific hybridization between two different Brassicaceae species, namely Brassica rapa ssp. pekinensis (♀) (AA, 2n = 2x = 20) and genetically modified Brassica napus (♂) (AACC, 2n = 4x = 38), was performed to study the transmission of a herbicide resistance gene from a tetraploid to a diploid Brassica species. Initially, four different GM B. napus lines were used for hybridization with B. rapa via hand pollination. Among the F1 hybrids, the cross involving the B. rapa (♀) × GM B. napus (♂) TG#39 line exhibited the highest recorded crossability index of 14.7 ± 5.7. However, subsequent backcross progenies (BC1, BC2, and BC3) displayed notably lower crossability indices. The F1 plants displayed morphological characteristics more aligned with the male parent B. napus, with significant segregation observed in the BC1 generation upon backcrossing with the recurrent parent B. rapa. By the BC2 and BC3 generations, the progeny stabilized, manifesting traits from both parents to varying degrees. Cytogenetic analysis revealed a substantial reduction in chromosome numbers, particularly in backcrossing progenies. BC1 plants typically exhibited 21–25 chromosomes, while BC2 progenies showed 21–22 chromosomes, and by the BC3 generation, stability was achieved with an average of 20 chromosomes. SSR marker analysis confirmed the progressive reduction of C-genome regions, retaining minimal C-genome-specific bands throughout successive backcrossing. Despite the extensive elimination of C-genome-specific genomic regions, the glyphosate resistance gene from the male parent B. napus was introgressed into BC3 progenies, suggesting that the glyphosate resistance gene located and introgressed in A-chromosome/genome regions of the Brassica plants. Full article
(This article belongs to the Special Issue Advances in Molecular Genetics and Breeding of Brassica napus L.)
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<p>Morphological characteristics of parental, F1 hybrid, and backcross progenies in different growth stages. Representatives of <span class="html-italic">B. napus</span> (Youngsan), GM <span class="html-italic">B. napus</span> (TG39), <span class="html-italic">B. rapa</span> ssp. <span class="html-italic">pekinensis</span> (Jankang), <span class="html-italic">B. rapa</span> ssp. <span class="html-italic">pekinensis</span> F1 hybrid (JKF1), and <span class="html-italic">B. rapa</span> ssp. <span class="html-italic">pekinensis</span> backcross progenies (BC1, BC2, and BC3).</p>
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<p>Principal coordinate analysis of parental, F1, and backcross progenies based on morphological characteristics. Representation of <span class="html-italic">B. napus</span> (YS), GM <span class="html-italic">B. napus</span> (TG), <span class="html-italic">B. rapa</span> ssp. <span class="html-italic">pekinensis</span>(JK), F1 hybrid (JKF1), and backcross progenies until BC3 (BC1, BC2, and BC3), and morphological characteristics as variables, namely plant height (PH), plant length (PL), number of branches 1 (NOB 1), number of branches 2 (NOB 2), flower length (FL), flower width (FW), flower diagonal (FL), filament length—short (FIS), filament length—long (FIL), and style length (STL).</p>
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<p>Chromosome enumeration of non-GM <span class="html-italic">B. napus</span> (YS), GM <span class="html-italic">B. napus</span> (TG), <span class="html-italic">B. rapa</span> (JK), and a cross combination of <span class="html-italic">B. rapa</span> ssp. <span class="html-italic">pekinensis</span> and GM <span class="html-italic">B. napus</span> F1 hybrid (F1) and their backcross progenies (BC1, BC2, and BC3).</p>
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<p>Genetic distance between parental and backcross (BC1, BC2, and BC3) progenies. The dendrogram was developed using an UPGMA clustering method based on Jaccard’s Similarity Coefficient.</p>
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25 pages, 1070 KiB  
Review
Effects of Various Herbicide Types and Doses, Tillage Systems, and Nitrogen Rates on CO2 Emissions from Agricultural Land: A Literature Review
by Zainulabdeen Khalaf Hashim, Agampodi Gihan Shyamal Dharmendra De Silva, Ali Adnan Hassouni, Viktória Margit Vona, László Bede, Dávid Stencinger, Bálint Horváth, Sándor Zsebő and István Mihály Kulmány
Agriculture 2024, 14(10), 1800; https://doi.org/10.3390/agriculture14101800 - 13 Oct 2024
Viewed by 852
Abstract
Although herbicides are essential for global agriculture and controlling weeds, they impact soil microbial communities and CO2 emissions. However, the effects of herbicides, tillage systems, and nitrogen fertilisation on CO2 emissions under different environmental conditions are poorly understood. This review explores [...] Read more.
Although herbicides are essential for global agriculture and controlling weeds, they impact soil microbial communities and CO2 emissions. However, the effects of herbicides, tillage systems, and nitrogen fertilisation on CO2 emissions under different environmental conditions are poorly understood. This review explores how various agricultural practices and inputs affect CO2 emissions and addresses the impact of pest-management strategies, tillage systems, and nitrogen fertiliser usage on CO2 emissions using multiple databases. Key findings indicate that both increased and decreased tendencies in greenhouse gas (GHG) emissions were observed, depending on the herbicide type, dose, soil properties, and application methods. Several studies reported a positive correlation between CO2 emissions and increased agricultural production. Combining herbicides with other methods effectively controls emissions with minimal chemical inputs. Conservation practices like no-tillage were more effective than conventional tillage in mitigating carbon emissions. Integrated pest management, conservation tillage, and nitrogen fertiliser rate optimisation were shown to reduce herbicide use and soil greenhouse gas emissions. Fertilisers are similarly important; depending on the dosage, they may support yield or harm the soil. Fertiliser benefits are contingent on appropriate management practices for specific soil and field conditions. This review highlights the significance of adaptable management strategies that consider local environmental conditions and can guide future studies and inform policies to promote sustainable agriculture practices worldwide. Full article
(This article belongs to the Section Agricultural Soils)
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<p>Major sources of greenhouse gas (GHG) emissions. Abbreviations: perfluorocarbons (PFCs), sulfur hexafluoride (SF<sub>6</sub>), carbon dioxide (CO<sub>2</sub>), methane (CH<sub>4</sub>), hydrofluorocarbons (HFCs), nitrous oxide (N<sub>2</sub>O), ozone-depleting substance (ODS), global warming potential (GWP) [<a href="#B21-agriculture-14-01800" class="html-bibr">21</a>].</p>
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<p>Contribution of different activities to the total emissions related to agricultural land use and land-use change in 2018 (3.9 Gt CO<sub>2</sub>-eq) [<a href="#B30-agriculture-14-01800" class="html-bibr">30</a>].</p>
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13 pages, 932 KiB  
Article
Effect of Bio-Herbicide Application on Durum Wheat Quality: From Grain to Bread Passing through Wholemeal Flour
by Umberto Anastasi, Alfio Spina, Paolo Guarnaccia, Michele Canale, Rosalia Sanfilippo, Silvia Zingale, Giorgio Spina, Andrea Comparato and Alessandra Carrubba
Plants 2024, 13(20), 2859; https://doi.org/10.3390/plants13202859 - 12 Oct 2024
Viewed by 490
Abstract
Using plant extracts to replace traditional chemical herbicides plays an essential role in sustainable agriculture. The present work evaluated the quality of durum wheat cv Valbelice in two years (2014 and 2016) using plant aqueous extracts of sumac (Rhus coriaria L.) and [...] Read more.
Using plant extracts to replace traditional chemical herbicides plays an essential role in sustainable agriculture. The present work evaluated the quality of durum wheat cv Valbelice in two years (2014 and 2016) using plant aqueous extracts of sumac (Rhus coriaria L.) and mugwort (Artemisia arborescens L.) as bio-herbicides on the main quality characteristics of durum wheat. The untreated, water-treated, and chemically treated durum wheat products were also analyzed as controls. Following the official methodologies, grain commercial analyses and defects of the kernels were determined. The main chemical and technological features were determined on the wholemeal flour: proteins, dry matter, dry gluten, gluten index, colorimetric parameters, mixograph, falling number, and sedimentation test in SDS. An experimental bread-making test was performed, and the main parameters were detected on the breads: bread volume, weight, moisture, porosity, hardness, and colorimetric parameters on crumb and crust. Within the two years, grain commercial analyses of the total five treatments showed no statistically significant differences concerning test weight (range 75.47–84.33 kg/hL) and thousand kernel weight (range 26.58–35.36 kg/hL). Differently, significant differences were observed in terms of kernel defects, particularly starchy kernels, black pointed kernels, and shrunken kernels, mainly due to the year factor. Analyses on the whole-grain flours showed significant differences. This affected dry gluten content (7.35% to 16.40%) and gluten quality (gluten index from 6.44 to 45.81). Mixograph results for mixing time ranged from 1.90 min to 3.15 min, whilst a peak dough ranged from 6.83 mm to 9.85 mm, showing, in both cases, statistically significant differences between treatments. The falling number showed lower values during the first year (on average 305 s) and then increased in the second year (on average 407 s). The sedimentation test showed no statistically significant differences, ranging from 27.75 mm to 34.00 mm. Regarding the bread produced, statistically significant year-related differences were observed for the parameters loaf volume during the first year (on average 298.75 cm3) and then increased in the second year (on average 417.33 cm3). Weight range 136.85 g to 145.18 g and moisture range 32.50 g/100 g to 39.51 g/100 g. Hardness range 8.65 N to 12.75 N and porosity (range 5.00 to 8.00) were closely related to the type of treatment. Finally, the color of flour and bread appeared to be not statistically significantly affected by treatment type. From a perspective of environmental and economic sustainability, the use of plant extracts with a bio-herbicidal function could replace traditional chemical herbicides. Full article
(This article belongs to the Special Issue Advanced in Cereal Science and Cereal Quality, Volume 2)
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<p>Experimental groups of bread loaves baked using flour from grains of wheat submitted to the different treatments: water-treated, untreated, chemically treated, treated with plant aqueous extracts of mugwort (<span class="html-italic">Artemisia arborescens</span> L.) and of sumac (<span class="html-italic">Rhus coriaria</span> L.) (year of research 2016).</p>
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<p>Principal component analysis (PCA) biplot, defined by the first two principal components. Vectors represent the loadings of the physical, chemical, and technological quality characteristics of grain, flours, doughs, and breads obtained in 2014 and 2016 from five treatments, including plant extracts from <span class="html-italic">Rhus coriaria</span> L. and <span class="html-italic">Artemisia arborescens</span> L., no treatment, chemical treatment, and treatment with only water.</p>
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16 pages, 2674 KiB  
Article
Ginger Phytotoxicity: Potential Efficacy of Extracts, Metabolites and Derivatives for Weed Control
by Jesús G. Zorrilla, Carlos Rial, Miriam I. Martínez-González, José M. G. Molinillo, Francisco A. Macías and Rosa M. Varela
Agronomy 2024, 14(10), 2353; https://doi.org/10.3390/agronomy14102353 - 12 Oct 2024
Viewed by 286
Abstract
The negative implications for weeds encourage the finding of novel sources of phytotoxic agents for sustainable management. While traditional herbicides are effective, especially at large scales, the environmental impact and proliferation of resistant biotypes present major challenges that natural sources could mitigate. In [...] Read more.
The negative implications for weeds encourage the finding of novel sources of phytotoxic agents for sustainable management. While traditional herbicides are effective, especially at large scales, the environmental impact and proliferation of resistant biotypes present major challenges that natural sources could mitigate. In this study, the potential of ginger metabolites as phytotoxic agents has been investigated for the first time. Root extracts, prepared via various extraction techniques, showed phytotoxicity in wheat (Triticum aestivum L. cv. Burgos) coleoptile bioassays at 800–100 ppm, and the most active extract (prepared by sonication with ethyl acetate) was purified by chromatographic methods, yielding seven compounds: five phenolic metabolites with gingerol and shogaol structures, β-sitosterol, and linoleic acid. Some of the major phenolic metabolites, especially [6]-shogaol and [6]-gingerol, exerted phytotoxicity on wheat coleoptiles, Plantago lanceolata and Portulaca oleracea (broadleaf dicotyledon weeds). This promoted the study of a collection of derivatives, revealing that the 5-methoxy, oxime, and acetylated derivatives of [6]-shogaol and [6]-gingerol had interesting phytotoxicities, providing clues for improving the stability of the isolated structures. Ginger roots have been demonstrated to be a promising source of bioactive metabolites for weed control, offering novel materials with potential for the development of agrochemicals based on natural products. Full article
(This article belongs to the Section Weed Science and Weed Management)
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<p>Chemical structures of compounds <b>1</b>–<b>7</b> isolated from ginger roots through bio-guided purification.</p>
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<p>Chemical structures of zingerone (<b>8</b>) and the synthetic derivatives <b>9</b>–<b>13</b> prepared from [6]-shogaol or [6]-gingerol.</p>
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<p>Phytotoxicity profiles on etiolated wheat (<span class="html-italic">Triticum aestivum</span> L. cv. Burgos) coleoptile bioassay of the ginger extracts obtained by maceration in ethyl acetate (M-E), maceration in methanol (M-M), sonication in ethyl acetate (S-E), sonication in methanol (S-M), ethyl acetate extract in liquid–liquid extraction (E-E), and methanol extract in liquid–liquid extraction (E-M), and the herbicide Logran<sup>®</sup> (Syngenta, Madrid, Spain) used as a positive control. Negative values indicate inhibition vs. the negative control. Error bars represent the standard error of the mean (<span class="html-italic">n</span> = 3).</p>
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<p>Phytotoxicity profiles of the fractions (A–M) from the ethyl acetate extract obtained by sonication, in the etiolated wheat (<span class="html-italic">Triticum aestivum</span> L. cv. Burgos) coleoptile bioassay. Positive values indicate stimulation vs. the negative control, and negative values indicate inhibition. The error bars represent the standard error of the mean (<span class="html-italic">n</span> = 3).</p>
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<p>Phytotoxicity profiles obtained for the isolated compounds [6]-shogaol (<b>1</b>), [10]-shogaol (<b>2</b>), [6]-gingerol (<b>3</b>), [8]-gingerol (<b>4</b>), β-sitosterol (<b>6</b>) and linoleic acid (<b>7</b>), and the herbicide Logran<sup>®</sup> (Syngenta, Madrid, Spain) used as a positive control, in the etiolated wheat (<span class="html-italic">Triticum aestivum</span> L. cv. Burgos) coleoptile bioassay. Positive values indicate stimulation vs. the negative control, and negative values indicate inhibition. The error bars represent the standard error of the mean (<span class="html-italic">n</span> = 3).</p>
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<p>Phytotoxicity against germination and root and shoot growth of <span class="html-italic">Portulaca oleracea</span> and <span class="html-italic">Plantago lanceolata</span> weeds obtained for the isolated compounds [6]-shogaol (<b>1</b>), [10]-shogaol (<b>2</b>), [6]-gingerol (<b>3</b>), [8]-gingerol (<b>4</b>), β-sitosterol (<b>6</b>) and linoleic acid (<b>7</b>), and the active principle of the herbicide Stomp<sup>®</sup> Aqua (Tokyo Chemical Industry, Tokyo, Japan) used as a positive control in the bioassay (Her). Positive values indicate stimulation vs. the negative control, and negative values indicate inhibition. The error bars represent the standard error of the mean (<span class="html-italic">n</span> = 3). Significance levels: <span class="html-italic">p</span> &lt; 0.01 (a), or 0.01 &lt; <span class="html-italic">p</span> &lt; 0.05 (b).</p>
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<p>Phytotoxicity profiles obtained for the synthetized zingerone (<b>8</b>), the derivatives <b>9</b>–<b>13</b> prepared from [6]-shogaol (<b>1</b>) and [6]-gingerol (<b>3</b>), and the herbicide Logran<sup>®</sup> (Syngenta, Madrid, Spain) used as a positive control, in the etiolated wheat (<span class="html-italic">Triticum aestivum</span> L. cv. Burgos) coleoptile bioassay. Positive values indicate stimulation vs. the negative control, and negative values indicate inhibition. The error bars represent the standard error of the mean (<span class="html-italic">n</span> = 3).</p>
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<p>Phytotoxicity against the germination and root and shoot growth of <span class="html-italic">Portulaca oleracea</span> and <span class="html-italic">Plantago lanceolata</span> weeds obtained for the synthetized zingerone (<b>8</b>) and the derivatives <b>9</b> and <b>11</b>–<b>13</b> prepared from [6]-shogaol (<b>1</b>) and [6]-gingerol (<b>3</b>), and active principle of the herbicide Stomp<sup>®</sup> Aqua (Tokyo Chemical Industry, Tokyo, Japan) used as a positive control in the bioassay (Her). Positive values indicate stimulation vs. the negative control, and negative values indicate inhibition. The error bars represent the standard error of the mean (<span class="html-italic">n</span> = 3). Significance levels: <span class="html-italic">p</span> &lt; 0.01 (a), or 0.01 &lt; <span class="html-italic">p</span> &lt; 0.05 (b).</p>
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23 pages, 4465 KiB  
Article
How Climate Change Will Shape Pesticide Application in Quebec’s Golf Courses: Insights with Deep Learning Based on Assessing CMIP5 and CMIP6
by Isa Ebtehaj, Josée Fortin, Hossein Bonakdari and Guillaume Grégoire
Appl. Sci. 2024, 14(20), 9209; https://doi.org/10.3390/app14209209 - 10 Oct 2024
Viewed by 423
Abstract
The accelerating impact of climate change on golf course conditions has led to a significant increase in pesticide dependency, underscoring the importance of innovative management strategies. The shift from Coupled Model Intercomparison Project Phase 5 (CMIP5) to the latest CMIP6 phase has drawn [...] Read more.
The accelerating impact of climate change on golf course conditions has led to a significant increase in pesticide dependency, underscoring the importance of innovative management strategies. The shift from Coupled Model Intercomparison Project Phase 5 (CMIP5) to the latest CMIP6 phase has drawn the attention of professionals, including engineers, decision makers, and golf course managers. This study evaluates how climate projections from CMIP6, using Canadian Earth System Models (CanESM2 and CanESM5), impact pesticide application trends on Quebec’s golf courses. Through the comparison of temperature and precipitation projections, it was found that a more substantial decline in precipitation is exhibited by CanESM2 compared to CanESM5, while the latter projects higher temperature increases. A comparison between historical and projected pesticide use revealed that, in most scenarios and projected periods, the projected pesticide use was substantially higher, surpassing past usage levels. Additionally, in comparing the two climate change models, CanESM2 consistently projected higher pesticide use across various scenarios and projected periods, except for RCP2.6, which was 27% lower than SSP1-2.6 in the second projected period (PP2). For all commonly used pesticides, the projected usage levels in every projected period, according to climate change models, surpass historical levels. When comparing the two climate models, CanESM5 consistently forecasted greater pesticide use for fungicides, with a difference ranging from 65% to 222%, and for herbicides, with a difference ranging from 114% to 247%, across all projected periods. In contrast, insecticides, growth regulators, and rodenticides displayed higher AAIR values in CanESM2 during PP1 and PP3, showing a difference of 28% to 35.6%. However, CanESM5 again projected higher values in PP2, with a difference of 1.5% to 14%. Full article
(This article belongs to the Section Computing and Artificial Intelligence)
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<p>Spatial comparison of historical and climate change modeling results for total precipitation (mm) and average temperature (°C) during the golf season in the province of Quebec (from May to November). In all maps, the star indicates the location of Quebec City. The color gradients represent the range of values, with darker shades of green and yellow representing lower values and orange to red indicating higher values. The scale bars represent the distance in kilometers.</p>
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<p>Distribution of the total precipitation (<b>A</b>) and average temperature (<b>B</b>) during the golf season in the province of Quebec (May to November) across historical data (yellow) and projected climate change models (CanESM2 (light orange) and CanESM5 (cyan)).</p>
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<p>Comparison of pesticide use (in kilograms) calculated based on CanESM2 and CanESM5 climate models across three projected periods (PP1, PP2, and PP3, represented by blue bars) and total pesticide use (represented by red bars). The scenarios compared include RCP2.6, RCP4.5, and RCP8.5 for CanESM2 and SSP1-2.6, SSP2-4.5, and SSP5-8.5 for CanESM5. Historical pesticide use (green bar) is included as a reference.</p>
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<p>The relative difference in pesticide use between historical data and projected use for 2023–2048 is based on the CanESM2 and CanESM5 across three scenarios. The figures show the spatial distribution of relative difference (RD, in %) across golf courses in the province of Quebec. The color gradient indicates the relative difference in pesticide use: green (0–50%), yellow (200–500%), and red (2000–5000%). The star denotes Quebec City, and the black dots indicate the locations of golf courses across the province.</p>
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<p>The relative difference in pesticide use between historical data and projected use for 2049–2074 is based on the CanESM2 and CanESM5 across three scenarios. The figures show the spatial distribution of relative difference (RD, in %) across golf courses in the province of Quebec. The color gradient indicates the relative difference in pesticide use: green (0–50%), yellow (200–500%), and red (2000–5000%). The star denotes Quebec City, and the black dots indicate the locations of golf courses across the province.</p>
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<p>The relative difference in pesticide use between historical data and projected use for 2075–2100 is based on the CanESM2 and CanESM5 across three scenarios. The figures show the spatial distribution of relative difference (RD, in %) across golf courses in the province of Quebec. The color gradient indicates the relative difference in pesticide use: green (0–50%), yellow (200–500%), and red (2000–5000%). The star denotes Quebec City, and the black dots indicate the locations of golf courses across the province.</p>
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<p>Comparison of the observed (historical) and projected average pesticide use (in kilograms) for different pesticide types across various projected periods (PP1, PP2, and PP3) based on CanESM2 and CanESM5 climate models (F: fungicides, H: herbicides, I: insecticides, RC: growth regulators, Ro: rodenticides, and A: others).</p>
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15 pages, 2550 KiB  
Communication
Altered Expression of Thyroid- and Calcium Ion Channels-Related Genes in Rat Testes by Short-Term Exposure to Commercial Herbicides Paraquat or 2,4-D
by Enoch Luis, Vanessa Conde-Maldonado, Edelmira García-Nieto, Libertad Juárez-Santacruz, Mayvi Alvarado and Arely Anaya-Hernández
J. Xenobiot. 2024, 14(4), 1450-1464; https://doi.org/10.3390/jox14040081 - 9 Oct 2024
Viewed by 399
Abstract
Exposure to pesticides such as paraquat and 2,4-dichlorophenoxyacetic acid (2,4-D) has been linked to harmful health effects, including alterations in male reproduction. Both herbicides are widely used in developing countries and have been associated with reproductive alterations, such as disruption of spermatogenesis and [...] Read more.
Exposure to pesticides such as paraquat and 2,4-dichlorophenoxyacetic acid (2,4-D) has been linked to harmful health effects, including alterations in male reproduction. Both herbicides are widely used in developing countries and have been associated with reproductive alterations, such as disruption of spermatogenesis and steroidogenesis. The thyroid axis and Ca2+-permeable ion channels play a key role in these processes, and their disruption can lead to reproductive issues and even infertility. This study evaluated the short-term effects of exposure to commercial herbicides based on paraquat and 2,4-D on gene expression in rat testes. At the molecular level, exposure to paraquat increased the expression of the thyroid hormone transporters monocarboxylate transporter 8 (Mct8) and organic anion-transporting polypeptide 1C1 (Oatp1c1) and the thyroid receptor alpha (TRα), suggesting a possible endocrine disruption. However, it did not alter the expression of the sperm-associated cation channels (CatSper1-2) or vanilloid receptor-related osmotically activated channel (Trpv4) related to sperm motility. In contrast, exposure to 2,4-D reduced the expression of the Mct10 transporter, Dio2 deiodinase, and CatSper1, which could affect both the availability of T3 in testicular cells and sperm quality, consistent with previous studies. However, 2,4-D did not affect the expression of CatSper2 or Trpv4. Deregulation of gene expression could explain the alterations in male reproductive processes reported by exposure to paraquat and 2,4-D. These thyroid hormone-related genes can serve as molecular biomarkers to assess endocrine disruption due to exposure to these herbicides, aiding in evaluating the health risks of pesticides. Full article
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<p>Experimental design of short-term exposure to commercial herbicides paraquat (PQT) or 2,4-D, showing herbicide administration (+) or no herbicide administration (−).</p>
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<p>Effect of short-term exposure to paraquat or 2,4-D on the expression of thyroid hormone transporters. (<b>A</b>) Representative image of 2.5% agarose gel electrophoresis stained with ethidium bromide. Amplified bands of thyroid hormone transporters in rat testes of control (CNT, n = 6), paraquat (PQT, n = 6), and 2,4-D (n = 6) groups. (<b>B</b>) Comparison between groups of transporters’ expression relative to <span class="html-italic">Ppia</span> gene expression. Means ± SEM are shown. Statistical analysis was performed using the normality test (S-W) and <span class="html-italic">t</span>-test (* <span class="html-italic">p</span> ≤ 0.05).</p>
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<p>Effect of short-term exposure to paraquat or 2,4-D on the expression of deiodinases. (<b>A</b>) Representative image of 2.5% agarose gel electrophoresis stained with ethidium bromide. Amplified bands of <span class="html-italic">Dio2</span> and <span class="html-italic">Dio3</span> in rat testes of control (CNT, n = 6), paraquat (PQT, n = 6), and 2,4-D (n = 6) groups. (<b>B</b>) Comparison between groups of <span class="html-italic">Dio2</span> and <span class="html-italic">Dio3</span> expression relative to <span class="html-italic">Ppia</span> gene expression. Means ± SEM are shown. Statistical analysis was performed using a normality test (S-W) and <span class="html-italic">t</span>-test (* <span class="html-italic">p</span> ≤ 0.05).</p>
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<p>Effect of short-term exposure to paraquat or 2,4-D on the relative expression of thyroid hormone receptor alpha (<span class="html-italic">TRα</span>). (<b>A</b>) Representative image of 2.5% agarose gel electrophoresis stained with ethidium bromide. Amplified bands of <span class="html-italic">TRα</span> in rat testes of control (CNT, n = 6), paraquat (PQT, n = 6), and 2,4-D (n = 6) groups. (<b>B</b>) Comparison between groups of <span class="html-italic">TRα</span> expression relative to <span class="html-italic">Ppia</span> gene expression. Means ± SEM are shown. Statistical analysis was performed using a normality test (S-W) and <span class="html-italic">t</span>-test (* <span class="html-italic">p</span> ≤ 0.05).</p>
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<p>Effect of short-term exposure to paraquat or 2,4-D on the expression of ion channels. (<b>A</b>) Representative image of 2.5% agarose gel electrophoresis stained with ethidium bromide. Amplified bands of <span class="html-italic">CatSper1</span>, <span class="html-italic">CatSper2</span>, and <span class="html-italic">Trpv4</span> in rat testes of control (CNT, n = 6), paraquat (PQT, n = 6), and 2,4-D (n = 6) groups. (<b>B</b>) Comparison between groups of ion channel expression relative to <span class="html-italic">Ppia</span> gene expression. Means ± SEM are shown. Statistical analysis was performed using a normality test (S-W) and <span class="html-italic">t</span>-test (* <span class="html-italic">p</span> ≤ 0.05).</p>
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13 pages, 817 KiB  
Article
Resistance to Acetyl Coenzyme A Carboxylase (ACCase) Inhibitor in Lolium multiflorum: Effect of Multiple Target-Site Mutations
by Gulab Rangani, Ana Claudia Langaro, Shilpi Agrawal, Reiofeli A. Salas-Perez, Juan Camilo Velásquez, Christopher E. Nelson and Nilda Roma-Burgos
Agronomy 2024, 14(10), 2316; https://doi.org/10.3390/agronomy14102316 - 9 Oct 2024
Viewed by 478
Abstract
Italian ryegrass (Lolium multiflorum Lam.) is a persistent weed species that poses significant management challenges in key agricultural crops such as wheat, corn, cotton, and soybean. This study investigated the prevalence of resistance to ACCase inhibitor herbicides, specifically diclofop and pinoxaden, among [...] Read more.
Italian ryegrass (Lolium multiflorum Lam.) is a persistent weed species that poses significant management challenges in key agricultural crops such as wheat, corn, cotton, and soybean. This study investigated the prevalence of resistance to ACCase inhibitor herbicides, specifically diclofop and pinoxaden, among field-collected Italian ryegrass populations. The survey revealed widespread resistance to diclofop and emerging cross-resistance to pinoxaden. To elucidate the physiological mechanism of ACCase herbicide resistance, we investigated mutations in the carboxyl-transferase (CT) domain of the ACCase enzyme, a critical region for herbicide sensitivity. Using dCAPS assays and CT domain sequencing, several known resistance-conferring mutations were detected in diclofop survivors, including I1781L, W2027C, I2041N, D2078G, and C2088R. Additionally, other mutations such as L1701M, E1874A, N1878H, G1946E/Q, V1992D, and E2039D were identified. To understand the functional role of these mutations in herbicide resistance, homology modeling was performed using AutoDock Vina for selected mutation combinations. The computational analysis revealed that all mutations and their combinations resulted in reduced binding affinity with diclofop and pinoxaden compared to the wild-type ACCase CT domain. Computational binding energy predictions indicated that the G1946E mutation and the L1701M + I1781L + E1874A + N1878H combination exhibited the lowest affinities for diclofop and pinoxaden, respectively. This study provides valuable insights into the molecular basis of ACCase inhibitor resistance in Italian ryegrass. However, further research is needed to validate the functional significance of each new substitution and its combinations in conferring herbicide resistance. Full article
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<p>Visible injury (%) among Italian ryegrass populations in response to ACCase inhibitors. Populations are arranged primarily in increasing levels of susceptibility to diclofop (blue line). Response to pinoxaden is represented by the orange line. Clones of all populations were treated with field recommended rate (1×) of diclofop (1120 g ai ha<sup>−1</sup>) and pinoxaden (60 g ai ha<sup>−1</sup>). Each data point is the average of four replications.</p>
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<p>Frequency of known mutations among the diclofop survivors of <span class="html-italic">L. multiflorum</span> populations. Mutation frequency in a population was calculated as % from the number of each mutation found within the five plants per population. The populations are listed in the order of increasing visible injury (shown on the right <span class="html-italic">y</span>-axis) upon diclofop treatment, which is depicted as a line graph (red line).</p>
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15 pages, 3188 KiB  
Article
The Relationship between the Density of Winter Canola Stand and Weed Vegetation
by Lucie Vykydalová, Tomáš Jiří Kubík, Petra Martínez Barroso, Igor Děkanovský and Jan Winkler
Agriculture 2024, 14(10), 1767; https://doi.org/10.3390/agriculture14101767 - 7 Oct 2024
Viewed by 491
Abstract
Canola (Brassica napus L.) is an important oilseed crop that provides essential vegetable oil but faces significant competition from weeds that are influenced by various agronomic practices and environmental conditions. This study examines the complex interactions between canola stand density and weed [...] Read more.
Canola (Brassica napus L.) is an important oilseed crop that provides essential vegetable oil but faces significant competition from weeds that are influenced by various agronomic practices and environmental conditions. This study examines the complex interactions between canola stand density and weed intensity over three growing seasons, identifying a total of 27 weed species. It is important to establish a connection between the density of winter canola stands, the intensity of weeding and the response of individual weed species in real conditions. The case study was executed on plots located in the Přerov district (Olomouc region, Czech Republic). The assessment was carried out during two periods—autumn in October and spring in April. Canola plants (plant density) were counted in each evaluated area, weed species were identified, and the number of plants for each weed species was determined. Half of the plots were covered with foil before herbicide application to prevent these areas from being treated with herbicides. We used redundancy analysis (RDA) to evaluate the relationships between canola density and weed dynamics, both with and without herbicide treatment. The results show the ability of canola to compete with weeds; however, that is factored by the density of the canola stand. In dense stands (over 60 plants/m²), canola is able to suppress Galium aparine L., Geranium pusillum L., Lamium purpureum L., Papaver rhoeas L. and Chamomilla suaveolens (Pursh) Rydb. Nevertheless, there are weed species that grow well even in dense canola stands (Echinochloa crus-galli (L.) P. Beauv., Phragmites australis (Cav.) Steud., Tripleurospermum inodorum (L.) Sch. Bip. and Triticum aestivum L.). These findings highlight the potential for using canola stand density as a strategic component of integrated weed management to reduce herbicide reliance and address the growing challenge of herbicide-resistant weed populations. This research contributes significantly to our understanding of the dynamics of weed competition in canola systems and informs sustainable agricultural practices for improved crop yield and environmental stewardship. Full article
(This article belongs to the Section Crop Protection, Diseases, Pests and Weeds)
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<p>The relationship between canola density, weed intensity (<b>a</b>) and occurrence of weed species (<b>b</b>) found in the first evaluation term without herbicide treatment (RDA result; figure: total explained variability = 6.3%, F-ratio = 3.9, <span class="html-italic">p</span>-value = 0.001). Explanatory notes of abbreviations weeds: <span class="html-italic">Cirsium arvense</span> (<span class="html-italic">CirArve</span>), <span class="html-italic">Echinochloa crus-galli</span> (<span class="html-italic">EchCrus</span>), <span class="html-italic">Euphorbia helioscopia</span> (<span class="html-italic">EupHeli</span>), <span class="html-italic">Fagopyrum convolvulus</span> (<span class="html-italic">FagConv</span>), <span class="html-italic">Fumaria officinalis</span> (<span class="html-italic">FumOffi</span>), <span class="html-italic">Galium aparine</span> (<span class="html-italic">GalApar</span>), <span class="html-italic">Geranium pusillum</span> (<span class="html-italic">GerPusi</span>), <span class="html-italic">Chamomilla suaveolens</span> (<span class="html-italic">ChaSuav</span>), <span class="html-italic">Chenopodium album</span> (<span class="html-italic">CheAlbu</span>), <span class="html-italic">Lamium purpureum</span> (<span class="html-italic">LamPurp</span>), <span class="html-italic">Papaver rhoeas</span> (<span class="html-italic">PapRhoe</span>), <span class="html-italic">Phragmites australis</span> (<span class="html-italic">PhrAust</span>), <span class="html-italic">Poa annua</span> (<span class="html-italic">PoaAnnu</span>), <span class="html-italic">Polygonum aviculare</span> (<span class="html-italic">PolAvic</span>), <span class="html-italic">Polygonum lapathifolia</span> (<span class="html-italic">PolLapa</span>), <span class="html-italic">Raphanus raphanistrum</span> (<span class="html-italic">RapRaph</span>), <span class="html-italic">Sinapis alba</span> (<span class="html-italic">SinAlba</span>), <span class="html-italic">Stellaria media</span> (<span class="html-italic">SteMedi</span>), <span class="html-italic">Thlaspi arvense</span> (<span class="html-italic">ThlArve</span>), <span class="html-italic">Tripleurospermum inodorum</span> (<span class="html-italic">TriInod</span>), <span class="html-italic">Veronica persica</span> (<span class="html-italic">VerPers</span>), <span class="html-italic">Veronica polita</span> (<span class="html-italic">VerPoli</span>), <span class="html-italic">Viola arvensis</span> (<span class="html-italic">VioArve</span>). Explanation of unwanted crop abbreviations: <span class="html-italic">Brassica rapa</span> (<span class="html-italic">BraRapa</span>), <span class="html-italic">Hordeum vulgare</span> (<span class="html-italic">HorVulg</span>), <span class="html-italic">Phacelia tanacetifolia</span> (<span class="html-italic">PhaTana</span>), <span class="html-italic">Triticum aestivum</span> (<span class="html-italic">TriAest</span>).</p>
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<p>The relationship between canola density, weed intensity (<b>a</b>) and occurrence of weed species (<b>b</b>) found in the second term of evaluation without herbicide treatment (RDA result; figure: total explained variability = 7.2%, F-ratio = 4.5, <span class="html-italic">p</span>-value = 0.001). Explanatory notes of abbreviations: <span class="html-italic">Cirsium arvense (CirArve)</span>, <span class="html-italic">Echinochloa crus-galli (EchCrus)</span>, <span class="html-italic">Euphorbia helioscopia (EupHeli)</span>, <span class="html-italic">Fagopyrum convolvulus (FagConv)</span>, <span class="html-italic">Fumaria officinalis (FumOffi)</span>, <span class="html-italic">Galium aparine (GalApar)</span>, <span class="html-italic">Geranium pusillum (GerPusi)</span>, <span class="html-italic">Chamomilla suaveolens (ChaSuav)</span>, <span class="html-italic">Chenopodium album (CheAlbu)</span>, <span class="html-italic">Lamium purpureum (LamPurp)</span>, <span class="html-italic">Papaver rhoeas (PapRhoe)</span>, <span class="html-italic">Phragmites australis (PhrAust)</span>, <span class="html-italic">Poa annua (PoaAnnu)</span>, <span class="html-italic">Polygonum aviculare (PolAvic)</span>, <span class="html-italic">Polygonum lapathifolia (PolLapa)</span>, <span class="html-italic">Sinapis alba (SinAlba)</span>, <span class="html-italic">Stellaria media (SteMedi)</span>, <span class="html-italic">Thlaspi arvense (ThlArve)</span>, <span class="html-italic">Tripleurospermum inodorum (TriInod)</span>, <span class="html-italic">Veronica persica (VerPers)</span>, <span class="html-italic">Veronica polita (VerPoli)</span>, <span class="html-italic">Viola arvensis (VioArve)</span>. Explanation of unwanted crop abbreviations: <span class="html-italic">Hordeum vulgare (HorVulg)</span>, <span class="html-italic">Phacelia tanacetifolia (PhaTana)</span>, <span class="html-italic">Triticum aestivum (TriAest)</span>.</p>
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<p>The relationship between canola density, weed intensity (<b>a</b>) and occurrence of weed species (<b>b</b>) found in the first term of evaluation with the application of herbicides (RDA result; figure: total explained variability = 2.9%, F-ratio = 1.7, <span class="html-italic">p</span>-value = 0.0114). Explanatory notes of abbreviations: <span class="html-italic">Alsinula media (AlsMedi)</span>, <span class="html-italic">Cirsium arvense (CirArve)</span>, <span class="html-italic">Echinochloa crus-galli (EchCrus)</span>, <span class="html-italic">Euphorbia helioscopia (EupHeli)</span>, <span class="html-italic">Fagopyrum convolvulus (FagConv)</span>, <span class="html-italic">Fumaria officinalis (FumOffi)</span>, <span class="html-italic">Galium aparine (GalApar)</span>, <span class="html-italic">Geranium pusillum (GerPusi)</span>, <span class="html-italic">Chamomilla suaveolens (ChaSuav)</span>, <span class="html-italic">Chenopodium album (CheAlbu)</span>, <span class="html-italic">Lamium purpureum (LamPurp)</span>, <span class="html-italic">Papaver rhoeas (PapRhoe)</span>, <span class="html-italic">Phragmites australis (PhrAust)</span>, <span class="html-italic">Poa annua (PoaAnnu)</span>, <span class="html-italic">Polygonum aviculare (PolAvic)</span>, <span class="html-italic">Polygonum lapathifolia (PolLapa)</span>, <span class="html-italic">Raphanus raphanistrum (RapRaph)</span>, <span class="html-italic">Sinapis alba (SinAlba)</span>, <span class="html-italic">Stellaria media (SteMedi)</span>, <span class="html-italic">Thlaspi arvense (ThlArve)</span>, <span class="html-italic">Tripleurospermum inodorum (TriInod)</span>, <span class="html-italic">Veronica polita (VerPoli)</span>, <span class="html-italic">Viola arvensis (VioArve)</span>. Explanation of unwanted crop abbreviations: <span class="html-italic">Brassica rapa (BraRapa)</span>, <span class="html-italic">Hordeum vulgare (HorVulg)</span>, <span class="html-italic">Phacelia tanacetifolia (PhaTana)</span>, <span class="html-italic">Triticum aestivum (TriAest)</span>.</p>
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<p>The relationship between canola density, weed intensity (<b>a</b>) and occurrence of weed species (<b>b</b>) found in the second term of evaluation with the application of herbicides (RDA result; figure: total explained variability = 4.0%, F-ratio = 2.3, <span class="html-italic">p</span>-value = 0.008). Explanatory notes of abbreviations: <span class="html-italic">Cirsium arvense (CirArve)</span>, <span class="html-italic">Euphorbia helioscopia (EupHeli)</span>, <span class="html-italic">Fagopyrum convolvulus (FagConv)</span>, <span class="html-italic">Fumaria officinalis (FumOffi)</span>, <span class="html-italic">Galium aparine (GalApar)</span>, <span class="html-italic">Geranium pusillum (GerPusi)</span>, <span class="html-italic">Chamomilla suaveolens (ChaSuav)</span>, <span class="html-italic">Chenopodium album (CheAlbu)</span>, <span class="html-italic">Lamium purpureum (LamPurp)</span>, <span class="html-italic">Papaver rhoeas (PapRhoe)</span>, <span class="html-italic">Phragmites australis (PhrAust)</span>, <span class="html-italic">Poa annua (PoaAnnu)</span>, <span class="html-italic">Polygonum aviculare (PolAvic)</span>, <span class="html-italic">Polygonum lapathifolia (PolLapa)</span>, <span class="html-italic">Stellaria media (SteMedi)</span>, <span class="html-italic">Thlaspi arvense (ThlArve)</span>, <span class="html-italic">Tripleurospermum inodorum (TriInod)</span>, <span class="html-italic">Veronica polita (VerPoli)</span>, <span class="html-italic">Viola arvensis (VioArve)</span>. Explanation of unwanted crop abbreviations: <span class="html-italic">Hordeum vulgare (HorVulg)</span>, <span class="html-italic">Phacelia tanacetifolia (PhaTana)</span>, <span class="html-italic">Triticum aestivum (TriAest)</span>.</p>
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16 pages, 4271 KiB  
Article
Dicamba: Dynamics in Straw (Maize) and Weed Control Effectiveness
by Tamara Thais Mundt, Giovanna Larissa Gimenes Cotrick Gomes, Gilmar José Picoli Junior, Ramiro Fernando Lopez Ovejero, Edivaldo Domingues Velini and Caio Antonio Carbonari
Agronomy 2024, 14(10), 2294; https://doi.org/10.3390/agronomy14102294 - 6 Oct 2024
Viewed by 310
Abstract
Dicamba is a post-herbicide, showing some activity in soil, and its dynamics can be influenced by several factors, including the presence of straw. Brazil has more than 50% of its production area in a no-till system; thus, a good amount of the herbicide [...] Read more.
Dicamba is a post-herbicide, showing some activity in soil, and its dynamics can be influenced by several factors, including the presence of straw. Brazil has more than 50% of its production area in a no-till system; thus, a good amount of the herbicide is intercepted by the straw. This study aimed to evaluate dicamba dynamics in straw and weed control efficacy when sprayed as a PRE herbicide. For this, five different studies were conducted: we utilized different straw amounts (1) and different drought periods (2) for straw sprayed with dicamba and dicamba + glyphosate to evaluate its release from straw, different straw amounts (3), different drought periods (4), and wet and dry straw (5) to evaluate pre-emergence weed control (Bidens pilosa and Ipomoea grandifolia) and dicamba availability in medium-texture soil. Around 80% of dicamba was released from the straw after 100 mm of rainfall. One day after dicamba application, 65–70% of dicamba was released from the straw with 20 mm of rainfall, while for 7 and 14 DAA, 60% was released. Dicamba was efficient in controlling the pre-emergence of both species studied, and the amount of straw did not interfere in weed control; however, dicamba was less available in the soil after rainfall when sprayed in the straw than when sprayed directly in the soil. Up to 80% of dicamba can be released from the straw after 100 mm of rainfall and weed control was efficient for the species studied; however, the carryover effect in sensitive crops might become an issue. Full article
(This article belongs to the Section Weed Science and Weed Management)
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Figure 1
<p>The effect of different maize straw amounts and different accumulated rainfall simulations in the release of dicamba from the straw.</p>
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<p>The effect of different drought periods for (<b>a</b>) dicamba sprayed standalone and (<b>b</b>) dicamba sprayed in a mixture with glyphosate in terms of dicamba release from straw.</p>
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<p>Weed control (%) for <span class="html-italic">I. grandifolia</span> and <span class="html-italic">B. pilosa</span> under different amounts of maize straw (study 3) at 7, 14, 21, and 28 days after emergence (DAE).</p>
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<p>Weed control (%) for <span class="html-italic">I. grandifolia</span> and <span class="html-italic">B. pilosa</span> with and without the presence of straw and different periods of rainfall at 7, 14, 28, and 28 DAE.</p>
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<p>Dicamba availability in the soil with and without maize straw and different periods without rainfall sampled 14 days after the rainfall simulation.</p>
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<p>Weed control for <span class="html-italic">I. grandifolia</span> and <span class="html-italic">B. pilosa</span> with dicamba sprayed in dry and wet maize straw at 7, 14, and 21 days after emergence.</p>
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<p>Dicamba availability in the soil when sprayed in dry and wet maize straw sampled 1 and 14 days after the application.</p>
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15 pages, 1577 KiB  
Article
Control of the Field Herbicide Dissipation by Cover Crop Mulch in Conservation Agriculture
by Marwa Douibi, María José Carpio, M. Sonia Rodríguez-Cruz, María J. Sánchez-Martín and Jesús M. Marín-Benito
Agronomy 2024, 14(10), 2284; https://doi.org/10.3390/agronomy14102284 - 4 Oct 2024
Viewed by 376
Abstract
The effects of mulch on the dissipation of S-metolachlor-SMOC, foramsulfuron-FORAM, and thiencarbazone-methyl-TCM and the formation of their main degradation metabolites were studied here. The herbicides were jointly applied in preemergence of maize on two separate occasions to two agricultural soils under conventional tillage [...] Read more.
The effects of mulch on the dissipation of S-metolachlor-SMOC, foramsulfuron-FORAM, and thiencarbazone-methyl-TCM and the formation of their main degradation metabolites were studied here. The herbicides were jointly applied in preemergence of maize on two separate occasions to two agricultural soils under conventional tillage (CT) and non-tillage (NT) over two wheat-maize cycles. Herbicide concentrations were determined in topsoil samples at different times after both applications, and they were fitted to kinetic models. The half-life (DT50) values for SMOC were higher under CT management than under NT (mean values: 25.6 and 7.38 days, respectively) in both soils over the two years. The faster herbicide dissipation with mulch could be because it is partially intercepted and strongly adsorbed/retained through different potential pathways, especially biodegradation, which was supported by the detection of SMOC-ESA and SMOC-OA metabolites. The mean DT50 values for FORAM (6.15 and 6.07 days, respectively) were very close for both soils under NT and CT management over the two-year experiment. The mulch had a lesser impact than for SMOC due to the former’s higher water solubility and lower adsorption, with dissipation being controlled mainly by biodegradation and likely also by leaching. TCM recorded intermediate DT50 values (mean value 20.8 days) in both soils+CT in the two-year experiment compared to SMOC and FORAM. The mulch effect on TCM dissipation was observed only after the second application because the DT50 values were higher in soils+NT after the first application (mean value: 26.9 days) than after the second one (mean value: 5.9 days). The amount of soil surface covered by the mulch controlled the herbicide dissipation, and soil and herbicide properties determine their adsorption behaviour by both mulch and soils. Full article
(This article belongs to the Section Innovative Cropping Systems)
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<p>Observed and fitted (single first-order model, SFO; or first-order multi-compartment model, FOMC) dissipation kinetics of S-metolachlor (SMOC) in soils under conventional tillage (S1+CT, S2+CT) and non-tillage (S1+NT, S2+NT) after the first (<b>a</b>,<b>b</b>) and second (<b>c</b>,<b>d</b>) application of herbicides to field plots. Bars indicate the standard deviation of the mean value (n = 4).</p>
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<p>Observed and fitted (single first-order model, SFO; or first-order multi-compartment model, FOMC) dissipation kinetics of foramsulfuron (FORAM) in soils under conventional tillage (S1+CT, S2+CT) and non-tillage (S1+NT, S2+NT) after the first (<b>a</b>,<b>b</b>) and second (<b>c</b>,<b>d</b>) application of herbicides to field plots. Bars indicate the standard deviation of the mean value (n = 4).</p>
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<p>Observed and fitted (single first-order model, SFO; or first order multi-compartment model, FOMC) dissipation kinetics of thiencarbazone-methyl (TCM) in soils under conventional tillage (S1+CT, S2+CT) and non-tillage (S1+NT, S2+NT) after the first (<b>a</b>,<b>b</b>) and second (<b>c</b>,<b>d</b>) application of herbicides to field plots. Bars indicate the standard deviation of the mean value (n = 4).</p>
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19 pages, 21418 KiB  
Article
Genetic Transformation of Triticum dicoccum and Triticum aestivum with Genes of Jasmonate Biosynthesis Pathway Affects Growth and Productivity Characteristics
by Dmitry N. Miroshnichenko, Alexey V. Pigolev, Alexander S. Pushin, Valeria V. Alekseeva, Vlada I. Degtyaryova, Evgeny A. Degtyaryov, Irina V. Pronina, Andrej Frolov, Sergey V. Dolgov and Tatyana V. Savchenko
Plants 2024, 13(19), 2781; https://doi.org/10.3390/plants13192781 - 4 Oct 2024
Viewed by 455
Abstract
The transformation protocol based on the dual selection approach (fluorescent protein and herbicide resistance) has been applied here to produce transgenic plants of two cereal species, emmer wheat and bread wheat, with the goal of activating the synthesis of the stress hormone jasmonates [...] Read more.
The transformation protocol based on the dual selection approach (fluorescent protein and herbicide resistance) has been applied here to produce transgenic plants of two cereal species, emmer wheat and bread wheat, with the goal of activating the synthesis of the stress hormone jasmonates by overexpressing ALLENE OXIDE SYNTHASE from Arabidopsis thaliana (AtAOS) and bread wheat (TaAOS) and OXOPHYTODIENOATE REDUCTASE 3 from A. thaliana (AtOPR3) under the strong constitutive promoter (ZmUbi1), either individually or both genes simultaneously. The delivery of the expression cassette encoding AOS was found to affect morphogenesis in both wheat species negatively. The effect of transgene expression on the accumulation of individual jasmonates in hexaploid and tetraploid wheat was observed. Among the introduced genes, overexpression of TaAOS was the most successful in increasing stress-inducible phytohormone levels in transgenic plants, resulting in higher accumulations of JA and JA-Ile in emmer wheat and 12-OPDA in bread wheat. In general, overexpression of AOS, alone or together with AtOPR3, negatively affected leaf lamina length and grain numbers per spike in both wheat species. Double (AtAOS + AtOPR3) transgenic wheat plants were characterized by significantly reduced plant height and seed numbers, especially in emmer wheat, where several primary plants failed to produce seeds. Full article
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Figure 1
<p>Emmer wheat plants transformed with <span class="html-italic">AtAOS</span> and <span class="html-italic">AtOPR3</span> genes around the flowering stage as grown in the greenhouse; note developmental differences between the primary T0 plants, RAB4, which is silenced for expression of introduced genes and the RAB2a, RAB5a, and RAB5b plants with a high level of constitutive expression of both the <span class="html-italic">AtAOS</span> and <span class="html-italic">AtOPR3</span> genes.</p>
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<p>Relative expression levels of <span class="html-italic">AtAOS</span> and <span class="html-italic">AtOPR3</span> in leaves of transgenic emmer (<b>a</b>) and bread (<b>b</b>) wheat lines; T4 homozygous plants, with the exceptions of RAB2 and RAR5, where leaf extracts of T0 plants are analyzed; data are means of at least three biological replicates ± SE; (<b>a</b>,<b>b</b>) expression levels in plants of ‘double’ transgenic lines carrying <span class="html-italic">AtAOS</span> and <span class="html-italic">AtOPR3</span> genes; (<b>c</b>) expression levels of <span class="html-italic">AtAOS</span> gene in transgenic lines of bread wheat Sar-60, for normalization, the relative expression level detected in SAB1 plants (panel (<b>b</b>)) is used.</p>
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<p>Production of transgenic wheat plants constitutively overexpressing <span class="html-italic">TaAOS</span> gene. (<b>a</b>) Transient <span class="html-italic">RFP</span> gene expression; morphogenic explant 24 h after the delivery of pANIC-<span class="html-italic">TaAOS</span> plasmid to Runo cells; (<b>b</b>) aging and necrosis of Runo wheat tissue with <span class="html-italic">RFP</span> expression; 45 days of in vitro culture; (<b>c</b>) early stage of transgenic somatic embryo formation of emmer wheat Runo, 60 days after bombardment with decreased concentration of herbicide; (<b>d</b>) formation of the RFP-positive single embryo-like structure of Sar-60 surrounded by leafy structures with RFP fluorescence on the medium with decreased herbicide concentration, 80 days after bombardment; (<b>e</b>) segregation of introduced expression cassette in T1 embryos germinated in vitro; 5 days of culture; transgenic line SD3 (<b>f</b>) RFP fluorescence in T2 kernels of homozygous sub-line RD1 in comparison with non-transgenic kernels of emmer wheat Runo. Bright field images are shown on the left side and fluorescent images are shown on the right side.</p>
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<p>Expression levels of <span class="html-italic">TaAOS</span> gene in leaves of transgenic wheat lines; (<b>a</b>) emmer wheat (cv. Runo) transgenic lines; (<b>b</b>) bread wheat (Sar-60) transgenic lines; data are means of at least five biological replicates ± SE; stars above the graphs indicate statistically significant differences with non-transgenic wheat (* <span class="html-italic">p</span> ≤ 0.05, ** <span class="html-italic">p</span> ≤ 0.01, **** <span class="html-italic">p</span> ≤ 0.001).</p>
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<p>Analysis of leaf length of non-transgenic emmer wheat (cv. Runo) and transgenic plants with overexpression of <span class="html-italic">AtAOS</span> and <span class="html-italic">AtOPR3</span> (RAB1) or <span class="html-italic">TaAOS</span> (RD1 and RD4). Values represent the lengths of 1st, 2nd, 3rd, and 4th leaves measured in 22–25 plants (transgenic lines) or 38 plants (non-transgenic (Runo)) (average ± sd). Stars indicate statistically significant differences calculated according Dunnett’s multiple comparison test: (“****”, <span class="html-italic">p</span> &lt; 0.001), (NS, non-significant).</p>
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<p>Analysis of leaf length of non-transgenic bread emmer wheat Sar-60 and transgenic lines with overexpression of <span class="html-italic">AtAOS</span> (SA7), <span class="html-italic">TaAOS</span> (SD2, SD3), or <span class="html-italic">AtAOS</span> and <span class="html-italic">AtOPR3</span> simultaneously (SAB1, SAB3). Values represent the lengths of 1st, 2nd, 3rd, and 4th leaves measured in 22–25 plants (average ± sd). Stars indicate statistically significant differences calculated according to Dunnett’s multiple comparisons test (“*”, <span class="html-italic">p</span> &lt; 0.05), (“**”, <span class="html-italic">p</span> &lt; 0.01), (“***”, <span class="html-italic">p</span> &lt; 0.005), (“****”, <span class="html-italic">p</span> &lt; 0.001), (NS, non-significant).</p>
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<p>The morphology of transgenic bread wheat lines of plants transformed with <span class="html-italic">AtAOS</span> (SA7), <span class="html-italic">TaAOS</span> (SD2, SD3) and with both <span class="html-italic">AtAOS</span> and <span class="html-italic">AtOPR3</span> (SAB1, SAB3) genes. (<b>a</b>,<b>b</b>) plants are in boot developmental stage; (<b>c</b>,<b>d</b>) plants are in early ripening developmental stage.</p>
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<p>Average plant height and the productivity of transgenic wheat plants transformed with <span class="html-italic">AtAOS</span> (SA7), <span class="html-italic">TaAOS</span> (RD1, RD4, SD2, SD3), and <span class="html-italic">AtAOS</span> and <span class="html-italic">AtOPR3</span> simultaneously (RAB1, SAB1, SAB3). (<b>a</b>,<b>b</b>), average plant height; (<b>c</b>,<b>d</b>), mean number of seeds per spike; stars indicate statistically significant differences with corresponding non-transgenic wheat cultivar calculated according to Dunnett’s multiple comparisons test (“*”, <span class="html-italic">p</span> &lt; 0.05), (“**”, <span class="html-italic">p</span> &lt; 0.01), (“****”, <span class="html-italic">p</span> &lt; 0.001), (ns, not significant).</p>
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<p>Schematic representation of the pANIC-<span class="html-italic">TaAOS</span> expression cassette used for emmer wheat and bread wheat transformation. <span class="html-italic">OsAct1</span>, rice <span class="html-italic">Actin 1</span> promoter; <span class="html-italic">BAR</span>, BASTA resistance gene (phosphinothricin acetyl transferase); 35ST, CaMV 35S terminator; PvUbi1, <span class="html-italic">Ubiquitin 1</span> promoter from <span class="html-italic">Panicum virgatum</span>; pporRFP, Red Fluorescent Protein gene from <span class="html-italic">Porites porites</span>; <span class="html-italic">NosT</span>, <span class="html-italic">Nopaline Synthase</span> terminator; <span class="html-italic">ZmUbi1</span>, maize <span class="html-italic">Ubiquitin 1</span> promoter; OCS T, octopine synthase terminator sequence; attB1 and attB2—site-specific recombination sequences; <span class="html-italic">Amp<sup>R</sup></span>, ampicillin resistance gene; <span class="html-italic">Kan<sup>R</sup></span>, kanamycin resistance gene. Arrows indicate promoters; regions controlling the expression of <span class="html-italic">TaAOS</span> gene are highlighted in green color.</p>
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