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17 pages, 2768 KiB  
Article
The Antioxidant Profile of Some Species of Microgreens Cultivated on Hemp and Coconut Substrate Under the Action of a Biostimulator Based on Humic Acids
by Alina Elena Marta, Florina Stoica, Ștefănica Ostaci and Carmenica Doina Jităreanu
Horticulturae 2024, 10(12), 1238; https://doi.org/10.3390/horticulturae10121238 - 21 Nov 2024
Abstract
Microplants are vegetables, grains and aromatic herbs that are consumed in the stage of young plants, without roots, developed after the germination stage, in the stage of cotyledons and which have a high content of nutrients (antioxidants, vitamins, minerals, fatty acids, lutein, β-carotene, [...] Read more.
Microplants are vegetables, grains and aromatic herbs that are consumed in the stage of young plants, without roots, developed after the germination stage, in the stage of cotyledons and which have a high content of nutrients (antioxidants, vitamins, minerals, fatty acids, lutein, β-carotene, proteins and fibers, etc.), which makes them functional, concentrated foods capable of feeding the world’s ever-growing population. The significant amounts of antioxidants in microgreens have the role of neutralizing free radicals and reducing their harmful impact on human health. The microgreens studied were spinach (Spinacia oleracea) cultivar ‘Lorelay’, mustard (Sinapis alba) cultivar ‘White’ and radish (Raphanus sativus) cultivar ‘Red Rambo’, tested on hemp and coconut substrates and under the influence of the organic biostimulator Biohumussol, based on humic acids. The antioxidant content of the plants was determined by analyzing total carotenoids, lycopene, chlorophyll, β-carotene, polyphenols and flavonoids, as well as the antioxidant activity by ABTS and DPPH methods. The obtained results indicated that the reaction of the plant material depends on the composition of the substrate and the presence of the applied biostimulator. The highest contents of substances with an antioxidant role were obtained from the microgreens on the hemp substrate, especially mustard and radishes, and the biostimulator proved to be compatible with the spinach microgreens. Full article
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<p>Analysis of mustard, radish and spinach microgreens grown on hemp substrate and coconut substrate under the influence of Biohumussol biostimulator for (<b>a</b>) total carotenoid content (mg/100 g DW); (<b>b</b>) lycopene content (mg/100 g DW); (<b>c</b>) β-carotene content (mg/100 g DW) and (<b>d</b>) total content of chlorophyll pigments (mg/100 g DW). Error bars on the graphs represent the standard deviation, and letters on the graph were placed according to Tukey’s test with a significance level of 0.05. The raw data can be found in <a href="#app1-horticulturae-10-01238" class="html-app">Table S1</a> of the <a href="#app1-horticulturae-10-01238" class="html-app">Supplementary Information</a>. For each analysis, five replicates were performed (<span class="html-italic">n</span> = 5).</p>
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<p>Analysis of mustard, radish and spinach microgreens grown on hemp substrate and coconut substrate under the influence of Biohumussol biostimulator for (<b>a</b>) total polyphenol content (mg GAE/g DW) and (<b>b</b>) total flavonoid content (mg EC/g DW). Error bars on the graphs represent the standard deviation, and letters on the graph were placed according to Tukey’s test, with a significance level of 0.05. The raw data can be found in <a href="#app1-horticulturae-10-01238" class="html-app">Table S1 of the Supplementary Information</a>. For each analysis, five replicates were performed (<span class="html-italic">n</span> = 5).</p>
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<p>Analysis of mustard, radish and spinach microgreens grown on hemp substrate and coconut substrate under the influence of Biohumussol biostimulator for (<b>a</b>) antioxidant activity by the DPPH method (µmol TE/g DW) and (<b>b</b>) antioxidant activity by the ABTS method (µmol Trolox/g DW). Error bars on the graphs represent the standard deviation, and letters on the graph were placed according to Tukey’s test, with a significance level of 0.05. The raw data can be found in <a href="#app1-horticulturae-10-01238" class="html-app">Table S1 of the Supplementary Information</a>. For each analysis, five replicates were performed (<span class="html-italic">n</span> = 5).</p>
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<p>Graphic representation of Student’s <span class="html-italic">t</span>-tests performed for mustard (<b>a</b>), radish (<b>b</b>) and spinach (<b>c</b>). NS—<span class="html-italic">p</span> &gt; 0.05; *—<span class="html-italic">p</span> &lt; 0.05; **—<span class="html-italic">p</span> &lt; 0.01; ***—<span class="html-italic">p</span> &lt; 0.005; ****—<span class="html-italic">p</span> &lt; 0.001. The graphic representation was made with the help of a heat map. Specific notations explained in the text were made to create the heat map. The results of the performed <span class="html-italic">t</span>-tests can be found in <a href="#app1-horticulturae-10-01238" class="html-app">Table S2 of the Supplementary Information</a>.</p>
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<p>Analysis of mustard, radish and spinach microgreens grown on hemp substrate and coconut substrate under the influence of Biohumussol biostimulator for (<b>a</b>) height measured in cm and (<b>b</b>) weight measured in g. Error bars represent standard deviation, and letters are assigned using Tukey’s 0.05 significance level test. Averages of 10 analyses (<span class="html-italic">n</span> = 20) were performed, and values are in <a href="#app1-horticulturae-10-01238" class="html-app">Supplementary Information Tables S3 and S4</a>.</p>
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<p>Pearson correlation matrix between the total carotenoid content and the total chlorophyll content for the three species of microgreens tested grown on the two substrates (hemp and coconut) under the action of the Biohumussol biostimulator. The notations present on the matrix are formed as follows: Initial of the species: mustard (M), radish (R) and spinach (S); C—Hemp, CO—Coconut, to which the letters N—Untreated and B—Biohumussol were added, depending on whether or not the treatment was applied. The last letters were C—Total Carotenoids or Cl—Total Chlorophyll.</p>
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19 pages, 1458 KiB  
Article
Early Response of the Populus nigra L. × P. maximowiczii Hybrid to Soil Enrichment with Metals
by Monika Gąsecka, Kinga Drzewiecka, Zuzanna Magdziak, Włodzimierz Krzesiński, Jędrzej Proch and Przemysław Niedzielski
Int. J. Mol. Sci. 2024, 25(23), 12520; https://doi.org/10.3390/ijms252312520 - 21 Nov 2024
Abstract
This study aimed to investigate the response of Populus nigra L. × Populus maximowiczii to the addition of selected metals in soil. Rooted cuttings were planted in pots containing soil enriched with equimolar concentrations of Pb, Zn, Al, Ni, and Cu (500 mL [...] Read more.
This study aimed to investigate the response of Populus nigra L. × Populus maximowiczii to the addition of selected metals in soil. Rooted cuttings were planted in pots containing soil enriched with equimolar concentrations of Pb, Zn, Al, Ni, and Cu (500 mL of 4 mM solutions of single metal salts: (Pb(NO3)2; Zn(NO3)2 × 6H2O; Al(NO3)3 × 9H2O; Ni(NO3)2 × 6H2O; or Cu(NO3)2 × 3H2O). Growth parameters, metal accumulation, and physiological and biochemical parameters were assessed after four weeks of cultivation, simulating early response conditions. The results showed diverse metal accumulation in poplar organs, along with an increase in biomass and minor changes in gas exchange parameters or chlorophyll fluorescence. Among low-molecular-weight organic acids, citric and succinic acids were dominant in the rhizosphere, and roots with malonic acid were also present in the shoots. Only p-coumaric acid was found in the phenolic profile of the roots. The shoots contained both phenolic acids and flavonoids, and their profile was diversely modified by particular metals. Sucrose and fructose content increased in shoots that underwent metal treatments, with glucose increasing only in Cu and Al treatments. Principal component analysis (PCA) revealed variations induced by metal treatments across all parameters. Responses to Pb and Zn were partially similar, while Cu, Ni, or Al triggered distinct reactions. The results indicate the adaptation of P. nigra L. × P. maximowiczii to soil containing elevated levels of metals, along with potential for soil remediation and metal removal. However, further studies are needed to evaluate the effect of differences in early responses to particular metals on plant conditions from a long-term perspective. Full article
(This article belongs to the Section Molecular Plant Sciences)
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<p>Characterization of <span class="html-italic">Populus nigra</span> L. <span class="html-italic">× P. maximowiczii</span> hybrid biomass (<b>A</b>) and shoots length (<b>B</b>) after 4 weeks of growth in soil enriched with selected metal salts. n = 3; identical superscripts (a, b, c, …) denote non-significant differences between means according to the post hoc Newman–Keuls test.</p>
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<p>Separated groups based on PCA analysis for (<b>A</b>) all parameters, (<b>B</b>) minerals, (<b>C</b>) biomass, (<b>D</b>) chlorophyll fluorescence and gas exchange, (<b>E</b>) sugars, low−molecular−weight organic acids (LMWOAs), and phenolic compounds.</p>
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21 pages, 8344 KiB  
Article
Smart Automatic Irrigation Enhances Sap Flow, Growth, and Water Use Efficiency in Containerized Prunus × yedoensis Matsum. Seedling
by Eon-Ju Jin, Myung-Suk Choi, Hyeok Lee, Eun-Ji Bae, Do-Hyun Kim and Jun-Hyuck Yoon
Plants 2024, 13(23), 3270; https://doi.org/10.3390/plants13233270 - 21 Nov 2024
Abstract
This study conducted a comparative analysis on the effects of smart automatic and semi-automatic irrigation methods on the physiological characteristics and growth of Prunus × yedoensis Matsum. seedlings. The smart automatic irrigation system, which activates irrigation when the soil moisture drops below 15%, [...] Read more.
This study conducted a comparative analysis on the effects of smart automatic and semi-automatic irrigation methods on the physiological characteristics and growth of Prunus × yedoensis Matsum. seedlings. The smart automatic irrigation system, which activates irrigation when the soil moisture drops below 15%, demonstrated superior characteristics in sap-wood area and bark ratio, as well as excellent water management efficiency, compared to the semi-automatic irrigation method, which involves watering (2.0 L) for 10 min at 60 min intervals starting at 8 AM every day. The analysis of soil moisture content changes under varying weather conditions and irrigation methods showed that smart automatic irrigation effectively maintained optimal moisture levels. Moreover, sap flow in the smart automatic irrigation treatment was more efficiently regulated in response to seasonal variations, showing a strong correlation with climatic factors such as temperature and solar radiation. In contrast, the semi-automatic irrigation treatment led to excessive sap flow during the summer due to a fixed watering schedule, resulting in unnecessary water supply. Analysis of photosynthesis parameters and chlorophyll fluorescence also revealed that smart automatic irrigation achieved higher values in light compensation and saturation points, maximizing photosynthetic efficiency. These findings suggest that the smart automatic irrigation system can enhance plant growth and water use efficiency, contributing to sustainable water management strategies. This research provides critical foundational data for developing efficient agricultural and horticultural irrigation management strategies in response to future climate change. Full article
(This article belongs to the Section Plant Modeling)
20 pages, 5855 KiB  
Article
Improving Nitrogen Fertilizer Management for Yield and N Use Efficiency in Wetland Rice Cultivation in Bangladesh
by Md. Kamuruzzaman, Robert M. Rees, Md. Torikul Islam, Julia Drewer, Mark Sutton, Arti Bhatia, William J. Bealey and Md. Mahmodol Hasan
Agronomy 2024, 14(12), 2758; https://doi.org/10.3390/agronomy14122758 - 21 Nov 2024
Abstract
Achieving high-yielding crops while also improving nitrogen use efficiency is a significant challenge for agricultural production in Bangladesh. We investigated the impacts of applying nitrogen (N) using different management options in wetland rice on a calcareous dark gray soil over three seasons. These [...] Read more.
Achieving high-yielding crops while also improving nitrogen use efficiency is a significant challenge for agricultural production in Bangladesh. We investigated the impacts of applying nitrogen (N) using different management options in wetland rice on a calcareous dark gray soil over three seasons. These included (1) the recommended dose of available N as prilled urea, (2) the recommended N dose plus 25% extra of available N as prilled urea, (3) 25% less than the recommended dose of available N as prilled urea, (4) the recommended dose of prilled urea in 2 t ha−1 cow dung, (5) the recommended dose as urea super granules (USGs) by deep placement, (6) 4 t ha−1 biochar with the recommended dose of prilled urea, and (7) Zero N. It was found that the growth, yield, and N use efficiency (NUE) were significantly different from the results obtained for prilled urea in all the alternative fertilizer options. The deep placement of USG consistently increased plant height, total number of tillers per plant, effective tillers per plant, chlorophyll content, panicle length, grains per panicle, and 1000-grain weight. The yield increases over recommended prilled urea were 5.22% for USG followed by biochar with the recommended dose. Similarly, using the deep placement of USG gave the highest yield and harvest index. In addition, compared to the recommended dose of prilled urea, the deep placement of USG increased NUE by 13%, agronomic N efficiency by 20%, and recovery N use efficiency by 19%. This suggests the rate of N application could be reduced by up to 8% without impacting yield by using deep placement of USG instead of prilled urea. The cost–benefit ratio was higher for the deep placement of USG than all other treatments. Biochar with the recommended dose of prilled urea also showed good results in terms of growth, yield, and NUE (41.8, 43.0, and 41.7, respectively, during three sequential years), but the extra cost of the biochar reduced the cost–benefit ratio. These findings suggest that the deep placement of USG is the best option for improving the yield of rice while also improving N use efficiency. Full article
(This article belongs to the Section Farming Sustainability)
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<p>Location map of the study area.</p>
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<p>Plant height (cm) of rice (BRRI dhan28) at harvest during three successive cropping years. The mean average of four replications and the capped line represent standard error. Significance difference compared to control at <span class="html-italic">p =</span> 0.05. *** = 0.1% level of significance. RDN = Recommended dose of nitrogen.</p>
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<p>Grain yield (t ha<sup>−1</sup>) of BRRI dhan28 influenced by the application of nitrogenous fertilizer management options. The mean average of four replications and the capped line represent standard error. Significance difference compared to control at <span class="html-italic">p =</span> 0.05; *** = 0.1% level of significance. RDN = Recommended dose of nitrogen.</p>
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<p>Straw yield (t ha<sup>−1</sup>) of BRRI dhan28 influenced by the application of nitrogenous fertilizer management options. The mean average of four replications and the capped line represent standard error. Significance difference compared to control at <span class="html-italic">p =</span> 0.05; *** = 0.1% level of significance. RDN = Recommended dose of nitrogen.</p>
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<p>Biological yield (t ha<sup>−1</sup>) of BRRI dhan28 influenced by the application of nitrogenous fertilizer management options. The mean average of four replications and the capped line represent standard error. Significance difference compared to control at <span class="html-italic">p =</span> 0.05; *** = 0.1% level of significance. RDN = Recommended dose of nitrogen.</p>
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<p>Harvest index (%) of BRRI dhan28 due to the application of nitrogenous fertilizer management options (2021, 2022, and 2023). The mean average of four replications and the capped line represent standard error. Significance difference compared to control at <span class="html-italic">p =</span> 0.05; *** = 0.1% level of significance. RDN = Recommended dose of nitrogen.</p>
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<p>Nitrogen use efficiency (NUE %) of BRRI dhan28 due to the application of nitrogenous fertilizer management options. The mean average of four replications and the capped line represent standard error. Significance difference compared to control at <span class="html-italic">p =</span> 0.05; *** = 0.1% level of significance. RDN = Recommended dose of nitrogen.</p>
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<p>Agronomic efficiency of N (AEN %) of BRRI dhan28 due to the application of nitrogenous fertilizers dozes (2021, 2022, and 2023). The mean average of four replications and the capped line represent standard error. Significance difference compared to control at <span class="html-italic">p =</span> 0.05; *** = 0.1% level of significance. RDN = Recommended dose of nitrogen.</p>
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<p>Recovery efficiency of N (REN %) of BRRI dhan28 due to the application of nitrogenous fertilizer management options (2021, 2022, and 2023). The mean average of four replications and the capped line represent standard error. Significance difference compared to control at <span class="html-italic">p =</span> 0.05; *** = 0.1% level of significance. RDN = Recommended dose of nitrogen.</p>
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<p>Effects of grain N uptake (Kg ha<sup>−1</sup>) of rice in response to applied N.</p>
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<p>Benefit–cost ratio of BRRI dhan28 due to effects of nitrogenous fertilizer management options (2021, 2022, and 2023). RDN = Recommended dose of nitrogen.</p>
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19 pages, 8590 KiB  
Article
Preharvest Application of Exogenous 2,4-Epibrassinolide and Melatonin Enhances the Maturity and Flue-Cured Quality of Tobacco Leaves
by Kesu Wei, Jiayi Tang, Lei Yang, Shaopeng Chen, Zhijun Cheng, Yijun Yang, Chen Xu, Shengjiang Wu, Yuhang Zhao, Hongmei Di, Ling Li, Dongyang Sun, Jianwei Li and Bo Sun
Plants 2024, 13(23), 3266; https://doi.org/10.3390/plants13233266 - 21 Nov 2024
Abstract
Tobacco (Nicotiana tabacum) is a globally cultivated crop, with its quality closely associated with the color and chemical composition of cured tobacco leaves. In this experiment, the effects of spraying exogenous 2, 4-epibrassinolide (EBR) and melatonin (MT) on the development of [...] Read more.
Tobacco (Nicotiana tabacum) is a globally cultivated crop, with its quality closely associated with the color and chemical composition of cured tobacco leaves. In this experiment, the effects of spraying exogenous 2, 4-epibrassinolide (EBR) and melatonin (MT) on the development of tobacco leaves at maturity stage and the quality after curing were investigated. Both EBR and MT treatments significantly enhanced the appearance quality of tobacco leaves at the stem-drying stage. Following preharvest applications, the sugar-to-alkali ratio and potassium content increased, while the contents of starch, total alkaloids, and proteins decreased. The levels of conventional chemical components were improved, enhancing the overall coordination of the tobacco. Transcriptome analysis revealed that EBR treatment down-regulated the chlorophyll biosynthetic genes hemA, MgPEC, and ChlD, while up-regulating the chlorophyll degradation genes CHL2, SGR, and PAOs. Similarly, MT treatment down-regulated the chlorophyll biosynthetic genes FC2 and MgPEC and up-regulated the degradation genes CHL2 and SGR, thus promoting chlorophyll degradation. Furthermore, in the downstream carotenoid biosynthetic pathway, both EBR and MT treatments regulated abscisic acid-related genes, with NCEDs being up-regulated and CYP707A1s down-regulated, thereby promoting the leaf ripening. Metabolomics analysis indicated that EBR treatment primarily regulated alkaloids, terpenoids, and flavonoids, while MT treatment mainly affected flavonoids. Both treatments also reduced the accumulation of the harmful substance aristolochic acid B. Comprehensive evaluations of appearance quality, physiological parameters, transcriptome, and metabolomics analyses demonstrated that exogenous spraying of EBR and MT treatments improved the maturity and quality of cured tobacco leaves, with EBR treatment exhibiting a greater effect than MT treatment. Full article
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<p>Tobacco during curing stage. (<b>A</b>) The appearance of tobacco during curing stage; (<b>B</b>) The appearance quality evaluation of tobacco leaves at stem-drying stage. EBR, tobacco leaves of EBR-treated; MT, tobacco leaves of MT-treated.</p>
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<p>Pigment content of tobacco leaves during curing stage. (<b>A</b>) Chlorophyll a; (<b>B</b>) Chlorophyll b; (<b>C</b>) Chlorophyll; (<b>D</b>) Carotenoids. EBR, tobacco leaves of EBR-treated; MT, tobacco leaves of MT-treated; F, fresh leaves; Y, yellowing stage; C, color fixing stage; D, stem-drying stage. “a, b, c” in the table mean significant difference among different treatments (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Physiological parameters of tobacco leaves during curing stage. (<b>A</b>) Moisture content; (<b>B</b>) Starch; (<b>C</b>) Total sugar; (<b>D</b>) Reducing sugar; (<b>E</b>) Total alkaloid; (<b>F</b>) Sugar-to-alkali ratio; (<b>G</b>) Protein; (<b>H</b>) Chlorinity; (<b>I</b>) Potassium; (<b>J</b>) Chlorogenic acid. EBR, tobacco leaves of EBR-treated; MT, tobacco leaves of MT-treated; F, fresh leaves; Y, yellowing stage; C, color fixing stage; D, stem-drying stage. “a, b” in the table mean significant difference among different treatments (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Analysis of gene expression level in transcriptome of fresh tobacco leaves. (<b>A</b>) Co-expression Venn diagram; (<b>B</b>) Number of differential genes; (<b>C</b>) Gene ontology (GO) enrichment analysis of differentially expressed genes (DEGs) in EF/WF; (<b>D</b>) GO enrichment analysis of DEGs in MF/WF; (<b>E</b>) Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis of DEGs in EF/WF; (<b>F</b>) KEGG enrichment analysis of DEGs in MF/WF. WF, fresh tobacco leaves of control; EF, fresh tobacco leaves of EBR-treated; MF, fresh tobacco leaves of MT-treated.</p>
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<p>The expression of differentially expressed genes (DEGs) in pigment pathway. (<b>A</b>) The expression of DEGs in Porphyrin and chlorophyll metabolism pathway; (<b>B</b>) The expression of DEGs in Carotenoid biosynthesis pathway. EBR, tobacco leaves of EBR-treated; MT, tobacco leaves of MT-treated. “a, b, c” in the table mean significant difference among different treatments (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Metabolomic analysis of tobacco leaves at stem-drying stage. (<b>A</b>) Metabolite classification; (<b>B</b>) Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis of metabolites; (<b>C</b>) Partial least squares discriminant analysis (PLS-DA) plot; (<b>D</b>) Clustering analysis of differentially abundant metabolites (DAMs) in ED/WD; (<b>E</b>) Clustering analysis of DAMs in MD/WD. WD, tobacco leaves of control at stem-drying stage; ED, tobacco leaves of EBR-treated at stem-drying stage; MD, tobacco leaves of MT-treated at stem-drying stage.</p>
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<p>Analysis of co-expressed differentially abundant metabolites (DAMs). (<b>A</b>) Co-expression Venn diagram; (<b>B</b>) Cluster analysis of co-expressed DAMs.</p>
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<p>Correlation analysis between co-expressed differentially abundant metabolites (DAMs) and pigment genes. The octagonal is DAMs, the square is the pigment gene.</p>
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<p>Physiological and molecular regulatory mechanisms by which exogenous 2,4-epibrassinolide and melatonin application enhances tobacco maturity and flue-cured quality. The red arrows indicate increases, while the blue arrows signify decreases in the levels of various substances and gene expression. The green box indicates the effective regulation of EBR treatment, the orange box indicates the effective regulation of MT treatment, and the green and orange mixed boxes indicate that both EBR and MT regulation are effective.</p>
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22 pages, 9495 KiB  
Article
De Novo Transcriptome Assembly of Anoectochilus roxburghii for Morphological Diversity Assessment and Potential Marker Development
by Wenting Zhang, Ke Chen, Yu Mei and Jihua Wang
Plants 2024, 13(23), 3262; https://doi.org/10.3390/plants13233262 - 21 Nov 2024
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Abstract
Anoectochilus roxburghii is a rare and precious medicinal and ornamental plant of Orchidaceae. Abundant morphological characteristics have been observed among cultivated accessions. Our understanding of the genetic basis of morphological diversity is limited due to a lack of sequence data and candidate genes. [...] Read more.
Anoectochilus roxburghii is a rare and precious medicinal and ornamental plant of Orchidaceae. Abundant morphological characteristics have been observed among cultivated accessions. Our understanding of the genetic basis of morphological diversity is limited due to a lack of sequence data and candidate genes. In this study, a high-quality de novo transcriptome assembly of A.roxburghii was generated. A total of 138,385 unigenes were obtained, and a BUSCO (Benchmarking Universal Single-Copy Orthologs) analysis showed an assembly completeness of 98.8%. Multiple databases were used to obtain a comprehensive annotation, and the unigenes were functionally categorized using the GO (Gene Ontology), KOG (Eukaryotic Orthologous Groups), KEGG (Kyoto Encyclopedia of Genes and Genomes), and Nr databases. After comparing the phenotypic characteristics of five representative cultivars, a set of cultivar-specific, highly expressed unigenes was identified based on a comparative transcriptome analysis. Then, a WGCNA (Weighted Gene Co-expression Network Analysis) was performed to generate gene regulatory modules related to chlorophyll content (red) and sucrose synthase activity (black). In addition, the expression of six and four GO enrichment genes in the red and black modules, respectively, was analyzed using qRT-PCR to determine their putative functional roles in the leaves of the five cultivars. Finally, in silico SSR (Simple Sequence Repeat) mining of the assembled transcriptome identified 44,045 SSRs. Mononucleotide was the most dominant class of SSRs, followed by complex SSRs. In summary, this study reports on the phenomic and genomic resources of A. roxburghii, combining SSR marker development and validation. This report aids in morphological diversity assessments of Anoectochilus roxburghii. Full article
(This article belongs to the Section Plant Genetics, Genomics and Biotechnology)
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<p>Transcriptome sequencing and sugar composition analysis of <span class="html-italic">A. roxburghii</span> (JXL28). (<b>A</b>) The leaf, stem, and root of JXL28, aged six (up) and twelve (down) months, respectively, are utilized for the assessment of total polysaccharide levels. (<b>B</b>) The total polysaccharide levels in the leaf, stem, and root of <span class="html-italic">A. roxburghii</span> after six and twelve months of growth. (<b>C</b>) A PCA analysis of various tissues of JXL28 transcriptome. (<b>D</b>) Length distribution of assembled transcripts and unigenes of the JXL28 transcriptome. (<b>E</b>) A Venn diagram showing differentially expressed unigenes unique to or shared among differential groups. (<b>F</b>) Heatmap of the levels of ten sugars in various tissues of JXL28. Ara: <span class="html-italic">D</span>-Arabinose; Fru: <span class="html-italic">D</span>-Fructose; Fuc: <span class="html-italic">L</span>-Fucose; Glu: Glucose; Mal: Maltose; Rha: <span class="html-italic">L</span>-Rhamnose; Suc: Sucrose; Tre: Trehalose.</p>
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<p>Functional annotation of unigenes. (<b>A</b>) GO ontology annotation of the <span class="html-italic">A. roxburghii</span> (JXL28) transcriptome showing the major GO terms in the molecular function, biological process, and cellular component categories. (<b>B</b>) Histogram representation of the cluster of orthologous group (COG) classification for assembled unigenes. (<b>C</b>) A KEGG analysis of the JXL28 transcriptome showing the top 20 highly represented KEGG pathways. The <span class="html-italic">X</span>-axis indicates the KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways, and the <span class="html-italic">Y</span>-axis indicates the number of transcripts in each pathway. (<b>D</b>) Species-based distribution of blastx matches for each clustered unitranscript of the JXL28 transcriptome. The species with a match &lt; 1% are grouped in the “Other” category.</p>
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<p>Morphological diversity of <span class="html-italic">A. roxburghii.</span> (<b>A</b>) Morphology of leaf adaxial, leaf abaxial, and seedling of five representative “Jinxianlian” cultivars. (<b>B</b>) An analysis of chlorophyll content (<b>up</b>) and sucrose synthase activity (<b>down</b>); analysis of five representative “Jinxianlian” cultivars. (<b>C</b>) Investigation of ten agronomic characteristics, including weight, height, leaf number, diameter, aerial root number, stem node number, leaf length, and leaf width. The hollow circles represent discrete values. (<b>D</b>) Comparison of leaf surface morphology among five representative “Jinxianlian” cultivars using scanning electron microscopy (SEM). ad: adaxial; ab: abaxial.</p>
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<p>Transcriptome analysis of five representative <span class="html-italic">A. roxburghii</span> cultivars. (<b>A</b>) Results of 3D-PCA of five “Jinxianlian” cultivars based on the expression level of all unigenes, with each dot representing an independent experimental repeat. (<b>B</b>) Numbers of up- and down-regulated DEGs in each comparison (others vs. JXL28). (<b>C</b>) Venn diagram showing the number of DEGs in each combination. (<b>D</b>) Results of five main clusters from <span class="html-italic">K</span>-means clustering analysis. (<b>E</b>) RNA-seq results for several transcription factor candidate from five main clusters.</p>
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<p>Identification of important modules and biomarkers based on a WGCNA. (<b>A</b>) A cluster dendrogram and the color display of co-expression network modules for all unigenes. (<b>B</b>) A correlation matrix of the module eigengene values obtained from the WGCNA. Nine modules were identified, and each module eigengene was tested for correlation with traits. In each cell, the upper values are the correlation coefficients between the module eigengenes and the traits; the lower values are the corresponding <span class="html-italic">p</span>-values; the co-expression modules significantly associated with the content of Chl a, Chl b, and total chlorophyll content and sucrose synthase activity are highlighted in red boxes. (<b>C</b>,<b>D</b>) A scatterplot describing the relationship between MM and GS in the red (<b>C</b>) and black (<b>D</b>) modules; key genes are screened out in the upper-right area, where GS &gt; 0.8 and MM &gt; 0.8. (<b>E</b>,<b>F</b>) A heatmap of the genes in the red (<b>E</b>) and black (<b>F</b>) modules; (<b>G</b>,<b>H</b>) A dotplot of the GO enrichment analysis of the genes in the red (<b>G</b>) and black (<b>H</b>) modules.</p>
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<p>Verification of RNA-seq results via qRT-PCR of candidate unigenes. (<b>A</b>) Six unigenes selected from the hub gene of the red module. (<b>B</b>) Two unigenes selected from the hub gene of the black module. (<b>C</b>) One unigene selected from the DEGs. Error bars indicate SD (n = 3).</p>
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<p>Characterization of potential simple sequence repeat (SSR) markers using MISA software. (<b>A</b>) The distribution of the different nucleotide repeat types (complex; Mono—mononucleotide; Di—dinucleotide; Tr—trinucleotide; Tetra—tetranucleotide; Penta—pentanucleotide; Hexa—hexanucleotide). (<b>B</b>) A stacked bar chart representing the abundance of trinucleotide repeats. (<b>C</b>) PCR amplification of genic-SSR markers in 20 <span class="html-italic">A. roxburghii</span> genotypes.</p>
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16 pages, 4595 KiB  
Article
Effects of Two Trichoderma Strains on Apple Replant Disease Suppression and Plant Growth Stimulation
by Wen Du, Pengbo Dai, Mingyi Zhang, Guangzhu Yang, Wenjing Huang, Kuijing Liang, Bo Li, Keqiang Cao, Tongle Hu, Yanan Wang, Xianglong Meng and Shutong Wang
J. Fungi 2024, 10(11), 804; https://doi.org/10.3390/jof10110804 - 20 Nov 2024
Viewed by 268
Abstract
Fusarium oxysporum, the pathogen responsible for apple replant disease (ARD), is seriously threatening the apple industry globally. We investigated the antagonistic properties of Trichoderma strains against F. oxysporum HS2, aiming to find a biological control solution to minimize the dependence on chemical [...] Read more.
Fusarium oxysporum, the pathogen responsible for apple replant disease (ARD), is seriously threatening the apple industry globally. We investigated the antagonistic properties of Trichoderma strains against F. oxysporum HS2, aiming to find a biological control solution to minimize the dependence on chemical pesticides. Two of the thirty-one Trichoderma strains assessed through plate confrontation assays, L7 (Trichoderma atroviride) and M19 (T. longibrachiatum), markedly inhibited = F. oxysporum, with inhibition rates of 86.02% and 86.72%, respectively. Applying 1 × 106 spores/mL suspensions of these strains notably increased the disease resistance in embryonic mung bean roots. Strains L7 and M19 substantially protected Malus robusta Rehd apple rootstock from ARD; the plant height, stem diameter, leaf number, chlorophyll content, and defense enzyme activity were higher in the treated plants than in the controls in both greenhouse and field trials. The results of fluorescent labeling confirmed the effective colonization of these strains of the root soil, with the number of spores stabilizing over time. At 56 days after inoculation, the M19 and L7 spore counts in various soils confirmed their persistence. These results underscore the biocontrol potential of L7 and M19 against HS2, offering valuable insights into developing sustainable ARD management practices. Full article
(This article belongs to the Section Fungal Pathogenesis and Disease Control)
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Figure 1

Figure 1
<p>Antagonistic observation of biocontrol <span class="html-italic">Trichoderma</span> strains L7 and M19 against <span class="html-italic">F. oxysporum</span> HS2. The morphology of L7 and M19 Petri dishes is observed on the (<b>left</b>), showing a distinct light yellow antagonistic zone forming at the mycelial intersection, with <span class="html-italic">Trichoderma</span> gradually covering <span class="html-italic">F. oxysporum</span> HS2. The red boxes indicate the confrontation observation zones. In the (<b>middle</b>), microscopic observation reveals that the test strains cause twisting, collapsing, and rupturing of HS2 mycelia during the parasitism process. On the (<b>right</b>), scanning electron microscope images show <span class="html-italic">Trichoderma</span> strains coiling around the mycelia of HS2.</p>
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<p>Identification of the tested <span class="html-italic">Trichoderma</span> isolates. (<b>A</b>) Colony morphology and microscopic observation of the tested <span class="html-italic">Trichoderma</span> isolates. L7 has circular and velvety colonies with light green conidia and slender mycelia. The phialides are slender, and the conidia are nearly spherical or ovoid, measuring 3.0–4.5 μm and 2.5–4.0 μm. M19 exhibits light green conidia with colonies radiating outward from the center, showing high sporulation rates centrally. The mycelia are tree-like, and the oval-shaped conidia measure 2.0–3.0 μm and 2.0–6.0 μm. (<b>B</b>) Phylogenetic trees of two <span class="html-italic">Trichoderma</span> strains constructed based on ITS sequences. Phylogenetic tree constructed by the neighbor-joining method based on ITS sequences. The percentage of replicate trees in which the associated taxa clustered together in the bootstrap test (1000 replicates) is shown next to the branches. The tree is drawn to scale, with branch lengths in the same units as those of the evolutionary distances used to infer the phylogenetic tree. The evolutionary distances were computed using the Poisson correction method and are in the units of the number of amino acid substitutions per site. Based on the tree, strain M19 clustered within the <span class="html-italic">T. longibrachiatum</span> branch, while L7 clustered within the <span class="html-italic">T. atroviride</span> branch. All positions containing gaps and missing data were eliminated. Evolutionary analyses were conducted in MEGA6.</p>
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<p>Effect of <span class="html-italic">Trichoderma</span> on the growth of <span class="html-italic">M. robusta</span> Rehd seedlings. (<b>A</b>) Determination of growth indexes of <span class="html-italic">Trichoderma</span> on <span class="html-italic">M. robusta</span> Rehd. Labels (<b>a</b>–<b>f</b>) represent seedling height, root length, fresh weight, root fresh weight, leaf number, and chlorophyll content, respectively. Values with superscript letters a and b are significanty diferent across columns (<span class="html-italic">p</span> &lt; 0.05). Results showed significant improvements in <span class="html-italic">M. robusta</span> Rehd seedling parameters after treatment with strains M19 and L7 compared to the control (CK). (<b>B</b>) The effect of <span class="html-italic">Trichoderma</span> on the growth of <span class="html-italic">M. robusta</span> Rehd. CK represents <span class="html-italic">M. robusta</span> Rehd seedlings treated with only water, L7 represents seedlings treated with L7 spore suspension, and M19 represents seedlings treated with M19 spore suspension.</p>
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<p>The effect of <span class="html-italic">Trichoderma</span> on the activity of defense enzymes in the roots of <span class="html-italic">M. robusta</span> seedlings. (<b>a</b>) SOD activity, (<b>b</b>) CAT activity, (<b>c</b>) PAL activity, and (<b>d</b>) root activity. CAT activity, SOD activity, PAL activity, and root vitality were all higher in <span class="html-italic">M. robusta</span> Rehd seedlings treated with the two <span class="html-italic">Trichoderma</span> strains compared to CK. Values with superscript letters a and b are significanty diferent across columns (<span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 5
<p>Effect of <span class="html-italic">Trichoderma</span> on <span class="html-italic">M. robusta</span> Rehd seedlings in normal cropping soil (60 days). (<b>A</b>) Growth of <span class="html-italic">M. robusta</span> Rehd seedlings in normal cropping soil for 60 days. CK represents <span class="html-italic">M. robusta</span> Rehd seedlings treated with only water, L7 represents seedlings treated with L7 spore suspension, and M19 represents seedlings treated with M19 spore suspension. The treatment of <span class="html-italic">Trichoderma</span> spore suspension in normal cropping soil significantly increased seedling height and demonstrated a strong growth-promoting effect. (<b>B</b>) Determination of physiological indexes of <span class="html-italic">M. robusta</span> Rehd seedlings growing in normal cropping soil for 60 days. Labels (<b>a</b>–<b>d</b>) represent seedling height, stem diameter, chlorophyll content, and leaf number, respectively. Values with superscript letters a and b are significanty diferent across columns (<span class="html-italic">p</span> &lt; 0.05). Significant enhancements in seedling height, leaf number, chlorophyll content, and root health were noted, indicating a strong growth-promoting effect.</p>
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<p>Effect of <span class="html-italic">Trichoderma</span> on <span class="html-italic">M. robusta</span> Rehd seedlings in continuous cropping soil (60 days). (<b>A</b>) Growth of <span class="html-italic">M. robusta</span> Rehd plants in continuous cropping soil for 60 days. CK represents <span class="html-italic">M. robusta</span> Rehd seedlings treated with only water, L7 represents seedlings treated with L7 spore suspension, and M19 represents seedlings treated with M19 spore suspension. The treatment of <span class="html-italic">Trichoderma</span> spore suspension in continuous cropping soil significantly increased seedling height and demonstrated a strong growth-promoting effect. (<b>B</b>) Determination of physiological indexes of <span class="html-italic">M. robusta</span> Rehd seedlings growing in continuous cropping soil for 60 days. Labels (<b>a</b>–<b>d</b>) represent seedling height, stem diameter, chlorophyll content, and leaf number, respectively. Values with superscript letters a, b and c are significanty diferent across columns (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Fluorescence observation and colonization status of two transformants in soil suspension and <span class="html-italic">M. robusta</span> Rehd root soil. (<b>A</b>) Fluorescence observed by two transformants in soil suspension. (<b>a</b>) Represents normal cropping soil; (<b>b</b>) represents continuous cropping soil; L7, M19, and MOCK are fluorescence of L7 transformant in soil, fluorescence of M19 transformant in soil, and CK of soil. Samples were taken after root drenching treatment, diluted 100 times, and fluorescence was observed under a fluorescence microscope. (<b>B</b>) Colonization status of two transformants in the soil of <span class="html-italic">M. robusta</span> Rehd root. Over time, the spore counts of the marked strains fluctuated before stabilizing. Notably, the colonization spore count of strain M19 was higher than that of strain L7 in both soil types.</p>
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23 pages, 5158 KiB  
Article
Development of Analytical Model to Describe Reflectance Spectra in Leaves with Palisade and Spongy Mesophyll
by Ekaterina Sukhova, Yuriy Zolin, Kseniya Grebneva, Ekaterina Berezina, Oleg Bondarev, Anastasiia Kior, Alyona Popova, Daria Ratnitsyna, Lyubov Yudina and Vladimir Sukhov
Plants 2024, 13(22), 3258; https://doi.org/10.3390/plants13223258 - 20 Nov 2024
Viewed by 273
Abstract
Remote sensing plays an important role in plant cultivation and ecological monitoring. This sensing is often based on measuring spectra of leaf reflectance, which are dependent on morphological, biochemical, and physiological characteristics of plants. However, interpretation of the reflectance spectra requires the development [...] Read more.
Remote sensing plays an important role in plant cultivation and ecological monitoring. This sensing is often based on measuring spectra of leaf reflectance, which are dependent on morphological, biochemical, and physiological characteristics of plants. However, interpretation of the reflectance spectra requires the development of new tools to analyze relations between plant characteristics and leaf reflectance. The current study was devoted to the development, parameterization, and verification of the analytical model to describe reflectance spectra of the dicot plant leaf with palisade and spongy mesophyll layers (on the example of pea leaves). Four variables (intensities of forward and backward collimated light and intensities of forward and backward scattered light) were considered. Light reflectance and transmittance on borders of lamina (Snell’s and Fresnel’s laws), light transmittance in the palisade mesophyll (Beer–Bouguer–Lambert law), and light transmittance and scattering in the spongy mesophyll (Kubelka–Munk theory) were described. The developed model was parameterized based on experimental results (reflectance spectra, contents of chlorophylls and carotenoid, and thicknesses of palisade and spongy mesophyll in pea leaves) and the literature data (final R2 was 0.989 for experimental and model-based reflectance spectra). Further model-based and experimental investigations showed that decreasing palisade and spongy mesophyll thicknesses in pea leaves (from 35.5 to 25.2 µm and from 58.6 to 47.8 µm, respectively) increased reflectance of green light and decreased reflectance of near-infrared light. Similarity between model-based and experimental results verified the developed model. Thus, the model can be used to analyze leaf reflectance spectra and, thereby, to increase efficiency of the plant remote and proximal sensing. Full article
(This article belongs to the Special Issue Integration of Spectroscopic and Photosynthetic Analyses in Plants)
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Figure 1

Figure 1
<p>The scheme of light flows in the model of reflectance and transmission in the leaf of a dicot plant. Black continuous lines show forward light flows. Black dotted lines show backward light flows. Orange lines show transformation between light flows. <span class="html-italic">I<sub>C</sub></span> is the forward collimated light, <span class="html-italic">I<sub>S</sub></span> is the forward scattered light, <span class="html-italic">J<sub>C</sub></span> is the backward collimated light, and <span class="html-italic">J<sub>S</sub></span> is the backward scattered light. <span class="html-italic">I<sub>0</sub></span> and <span class="html-italic">J<sub>0</sub></span> are intensities of the forward and backward collimated light directed to adaxial and abaxial leaf surfaces, respectively (the incident light). <span class="html-italic">J<sub>C</sub><sup>RO</sup></span> and <span class="html-italic">I<sub>C</sub><sup>RO</sup></span> are intensities of the collimated light reflecting from adaxial and abaxial leaf surfaces in air. <span class="html-italic">J<sub>S</sub><sup>RO</sup></span> and <span class="html-italic">I<sub>S</sub><sup>RO</sup></span> are intensities of the scattered light reflecting from adaxial and abaxial leaf surfaces in air. <span class="html-italic">J<sub>C</sub><sup>T</sup></span> and <span class="html-italic">I<sub>C</sub><sup>T</sup></span> are intensities of the collimated light transferring from leaf to air across adaxial and abaxial surfaces. <span class="html-italic">J<sub>S</sub><sup>T</sup></span> and <span class="html-italic">I<sub>S</sub><sup>T</sup></span> are intensities of the scattered light transferring from leaf to air across adaxial and abaxial surfaces.</p>
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<p>Image of pea leaf cross-section (<b>a</b>) and average thickness of palisade (<span class="html-italic">h</span>) and spongy (<span class="html-italic">l</span>) mesophyll (<span class="html-italic">n</span> = 6) (<b>b</b>). The second mature leaf was used. Pea plants were cultivated for 16 days under the moderate light intensity.</p>
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<p>Spectra of specific light absorption coefficients for chlorophyll a, chlorophyll b, and carotenoids (on an example of β-carotene) (<b>a</b>) and average concentrations of these pigments in pea leaves (<span class="html-italic">n</span> = 6) (<b>b</b>). The spectra of light absorption were constructed on the basis of [<a href="#B58-plants-13-03258" class="html-bibr">58</a>]. The pigment concentrations were experimentally measured. The second mature leaf was used. Pea plants were cultivated for 16 days under moderate light intensity.</p>
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<p>Spectra of leaf reflectance calculated on basis of the developed model with different quantities of the iterations (<span class="html-italic">N</span>). The leaf reflectance was calculated as a ratio of the sum of all components of backward collimated and backward scattered light transferring through the adaxial leaf surface (<span class="html-italic">J<sub>out</sub></span>) to the intensity of incident light (<span class="html-italic">I</span><sub>0</sub>). The influence of <span class="html-italic">N</span> on <span class="html-italic">J<sub>out</sub></span> was calculated in accordance with Equation (58). The following parameters were used (see <a href="#sec2dot1-plants-13-03258" class="html-sec">Section 2.1</a> for details): <span class="html-italic">β</span><sub>O1</sub> = 35° (in accordance with angle of the leaf illumination by PolyPen RP 410), <span class="html-italic">I<sub>0</sub> </span>= 1000 μmol m<sup>−2</sup>s<sup>−1</sup> and <span class="html-italic">J</span><sub>0</sub> = 0 μmol m<sup>−2</sup>s<sup>−1</sup> (assumed), <span class="html-italic">F<sub>S</sub> </span>= 0 (assumed), <span class="html-italic">n<sub>I</sub> </span>= 1.415 [<a href="#B50-plants-13-03258" class="html-bibr">50</a>], <span class="html-italic">f</span> = 0.5 (assumed), <span class="html-italic">h</span> = 35.5 μm and <span class="html-italic">l</span> = 58.6 μm (<a href="#plants-13-03258-f002" class="html-fig">Figure 2</a>b), <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>s</mi> </mrow> <mrow> <mi>P</mi> </mrow> </msub> <mo>=</mo> </mrow> </semantics></math> 5 cm<sup>−1</sup> and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>s</mi> </mrow> <mrow> <mi>S</mi> <mi>p</mi> </mrow> </msub> </mrow> </semantics></math> = 1000 cm<sup>−1</sup> [<a href="#B49-plants-13-03258" class="html-bibr">49</a>], C<sub>ChA</sub> = 2.77 mg cm<sup>−3</sup>, C<sub>ChB</sub> = 1.69 mg cm<sup>−3</sup>, and C<sub>Car</sub> = 0.94 mg cm<sup>−3</sup> corresponded to average experimental concentrations of these pigments (1.39, 0.85, and 0.47 mg cm<sup>−3</sup>, respectively, <a href="#plants-13-03258-f003" class="html-fig">Figure 3</a>b) at <span class="html-italic">N<sub>Sp/P</sub> </span>= 0.2 [<a href="#B49-plants-13-03258" class="html-bibr">49</a>]); <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>a</mi> </mrow> <mrow> <mi>C</mi> <mi>h</mi> <mi>A</mi> </mrow> </msub> <mfenced separators="|"> <mrow> <mi>λ</mi> </mrow> </mfenced> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>a</mi> </mrow> <mrow> <mi>C</mi> <mi>h</mi> <mi>B</mi> </mrow> </msub> <mfenced separators="|"> <mrow> <mi>λ</mi> </mrow> </mfenced> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>a</mi> </mrow> <mrow> <mi>C</mi> <mi>a</mi> <mi>r</mi> </mrow> </msub> <mfenced separators="|"> <mrow> <mi>λ</mi> </mrow> </mfenced> </mrow> </semantics></math> are shown in <a href="#plants-13-03258-f003" class="html-fig">Figure 3</a>a.</p>
Full article ">Figure 5
<p>Experimental and model-based spectra of leaf reflectance. The average experimental reflectance spectrum (<span class="html-italic">n</span> = 6) measured in the second mature leaf of pea plants (PolyPen RP 410) is shown. Standard errors are not shown because they are small. Pea plants were cultivated for 16 days under the moderate light intensity. Parameters of the model are shown in <a href="#plants-13-03258-f004" class="html-fig">Figure 4</a>; <span class="html-italic">N</span> = 6 was used for the analysis. R<sup>2</sup> and RMSE are the determination coefficient and root mean square error between the experimental and model-based spectra.</p>
Full article ">Figure 6
<p>Model-based spectra of leaf reflectance at <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>s</mi> </mrow> <mrow> <mi>S</mi> <mi>p</mi> </mrow> </msub> <mo>=</mo> </mrow> </semantics></math> 1000 cm<sup>−1</sup> (<b>a</b>), <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>s</mi> </mrow> <mrow> <mi>S</mi> <mi>p</mi> </mrow> </msub> <mo>=</mo> </mrow> </semantics></math> 800 cm<sup>−1</sup> (<b>b</b>), <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>s</mi> </mrow> <mrow> <mi>S</mi> <mi>p</mi> </mrow> </msub> <mo>=</mo> </mrow> </semantics></math> 600 cm<sup>−1</sup> (<b>c</b>), and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>s</mi> </mrow> <mrow> <mi>S</mi> <mi>p</mi> </mrow> </msub> <mo>=</mo> </mrow> </semantics></math> 400 cm<sup>−1</sup> (<b>d</b>) and the experimental spectrum (from <a href="#plants-13-03258-f005" class="html-fig">Figure 5</a>). Other parameters of the model were the same as the parameters that were used for the simulation of the spectrum in <a href="#plants-13-03258-f005" class="html-fig">Figure 5</a>. R<sup>2</sup> and RMSE are, respectively, the determination coefficient and root mean square error between the experimental and model-based spectra.</p>
Full article ">Figure 7
<p>(<b>a</b>) Model-based spectrum of leaf reflectance at C<sub>ChA</sub> = 3.19 mg cm<sup>−3</sup> and C<sub>ChB</sub> = 2.09 mg cm<sup>−3</sup> and the experimental spectrum (from <a href="#plants-13-03258-f005" class="html-fig">Figure 5</a>). Other parameters of the model were the same as the parameters that were used for the simulation of the spectrum in <a href="#plants-13-03258-f006" class="html-fig">Figure 6</a>c. R<sup>2</sup> and RMSE are, respectively, the determination coefficient and root mean square error between the experimental and model-based spectra. (<b>b</b>) Average concentrations of chlorophyll a and b, which corresponded to C<sub>ChA</sub> = 3.19 mg cm<sup>−3</sup> and C<sub>ChB</sub> = 2.09 mg cm<sup>−3</sup>, and their experimental concentrations in pea leaves (form <a href="#plants-13-03258-f003" class="html-fig">Figure 3</a>).</p>
Full article ">Figure 8
<p>Model-based spectra of leaf reflectance at <span class="html-italic">F<sub>S</sub> </span>= 0 (<b>a</b>), <span class="html-italic">F<sub>S</sub> </span>= 0.075 (<b>b</b>), <span class="html-italic">F<sub>S</sub> </span>= 0.15 (<b>c</b>), and <span class="html-italic">F<sub>S</sub> </span>= 0.225 (<b>d</b>) and the experimental spectrum (from <a href="#plants-13-03258-f005" class="html-fig">Figure 5</a>). Other parameters of the model were the same as the parameters that were used for the simulation of the spectrum in <a href="#plants-13-03258-f007" class="html-fig">Figure 7</a>. R<sup>2</sup> and RMSE are, respectively, the determination coefficient and root mean square error between the experimental and model-based spectra.</p>
Full article ">Figure 9
<p>Experimental concentrations of chlorophyll a, chlorophyll b, and carotenoids (<b>a</b>) and thicknesses of palisade and spongy mesophyll (<b>b</b>) in second mature leaves of pea plants, which were cultivated for 16 days under low and moderate light intensity (<span class="html-italic">n</span> = 6). *, the value is significantly different from this value in plants cultivated under moderate light intensity.</p>
Full article ">Figure 10
<p>Experimental (<b>a</b>) and model-based (<b>b</b>) spectra of leaf reflectance in pea plants, which were cultivated for 16 days under low and moderate light intensity (<span class="html-italic">n</span> = 6 for experiments). The average experimental reflectance spectrum (<span class="html-italic">n</span> = 6) measured in the second mature leaf of pea plants (PolyPen RP 410) is shown. Standard errors are not shown because they are small. In variant “Moderate light intensity”, parameters of the model were the same as the parameters that were used for the simulation of the spectrum in <a href="#plants-13-03258-f007" class="html-fig">Figure 7</a>. In variant “Low light intensity”, <span class="html-italic">h</span> = 25.2 μm and <span class="html-italic">l</span> = 47.8 μm (see <a href="#plants-13-03258-f009" class="html-fig">Figure 9</a>b); other parameters were not changed. R<sup>2</sup> between experimental and model-based dependences were 0.989 (variant “moderate light intensity”) and 0.982 (variant “low light intensity”).</p>
Full article ">Figure 11
<p>Spectra of leaf reflectance calculated at <span class="html-italic">h</span> = 45 μm, <span class="html-italic">h</span> = 35 μm, and <span class="html-italic">h</span> = 25 μm (<b>a</b>) and at <span class="html-italic">l</span> = 80 μm, <span class="html-italic">l</span> = 60 μm, and <span class="html-italic">l</span> = 40 μm (<b>b</b>). Other parameters of the model were the same as the parameters that were used for the simulation of the spectrum in <a href="#plants-13-03258-f008" class="html-fig">Figure 8</a>c.</p>
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16 pages, 3825 KiB  
Article
Effects of Humic Acids, Seaweed Extract and Equisetum arvense L. Extracts on Morphological, Histological and Physiological Parameters of the Ornamental Plant Ocimum basilicum Rokokó
by Szilvia Kisvarga, Katalin Horotán, Dóra Hamar-Farkas and László Orlóci
Horticulturae 2024, 10(11), 1231; https://doi.org/10.3390/horticulturae10111231 - 20 Nov 2024
Viewed by 242
Abstract
Ocimum basilicum L. is a multipurpose plant species used in the horticultural sector as a medicinal, herbaceous and ornamental plant. In our experiment, the Hungarian cultivar O. basilicum Rokokó was treated with algae (Ecklonia maxima (Osbeck) Papenf.), horsetail (Equisetum arvense L.) [...] Read more.
Ocimum basilicum L. is a multipurpose plant species used in the horticultural sector as a medicinal, herbaceous and ornamental plant. In our experiment, the Hungarian cultivar O. basilicum Rokokó was treated with algae (Ecklonia maxima (Osbeck) Papenf.), horsetail (Equisetum arvense L.) extracts and humic extracts. The effect of the biostimulants on the groups was assessed by morphological (leaf number, leaf area, fresh green mass, fresh root mass), histological (number of volatile oil glands) and physiological (chlorophyll content, peroxidase enzyme activity, proline levels) measurements. Obtained results were evaluated and it was concluded that the plants treated with algae and E. arvense extracts showed remarkable results for all the parameters measured. It was concluded that these extracts can be used as biostimulants in the cultivation of basil seedlings as ornamental plants, as they have a beneficial effect on the development of the plant. The humic extracts were less effective during the time period studied, probably due to their high molecular weight, which would have resulted in a longer absorption time. For the humic extracts, foliar application was less effective than irrigation, probably due to rapid damping-off, which reduced the penetration of humic extracts into the leaves. Though morphological characteristics are especially important for basil used as an ornamental plant, the plant’s essential oil content can also be important in attracting attention in urban plantings. It was found that humic extracts applied (22.8 pcs/sampling area) with irrigation had a strong effect on essential oil glands, in contrast when used as a spray (13.1 pcs/sampling area). The lowest stress levels were obtained in the group treated with irrigated humus extracts (274.96 µg/mg), which may be related to the continuous supply of nutrients, and in the group treated with E. arvense extract, silicon (219.05 µg/mg) may be the result of hermetic effects. In conclusion, E. arvense and algae extracts can be effective biostimulants in the horticultural sector for the seedling production of ornamental basil, and after a longer growing period, humic extracts can be used effectively by irrigation after planting. The use of natural extracts can also give a green light to this segment for sustainable and environmentally friendly cultivation, which can also better resist the effects of climate change and urbanisation. Full article
(This article belongs to the Special Issue Emerging Insights into Horticultural Crop Ecophysiology)
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<p><span class="html-italic">O. basilicum</span> Rokokó plants at the beginning of the final evaluation: (<b>a</b>) algae; (<b>b</b>) <span class="html-italic">E. arvense</span>; (<b>c</b>) humus soil; (<b>d</b>) humus leaf; (<b>e</b>) control.</p>
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<p><span class="html-italic">O. basilicum</span> Rokokó typical ruffled leaf form: (<b>a</b>) control; (<b>b</b>) humus; (<b>c</b>) <span class="html-italic">E. arvense</span>. Samples collected during final evaluation.</p>
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<p>Different maturity stages of glands observed on the leaf surface of <span class="html-italic">O. basilicum</span> Rokokó: (<b>a</b>) intact, young glandular cell; (<b>b</b>) intact, mature glandular cell; (<b>c</b>) mature glandular cell; (<b>d</b>) blown/destroyed glandular cell appears as a spot on the leaf.</p>
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<p>Changes in response to plant extracts and biostimulants in <span class="html-italic">O. basilicum</span> Rokokó: (<b>a</b>) leaf number; (<b>b</b>) leaf area; (<b>c</b>) fresh stem; (<b>d</b>) fresh root weight. The abbreviations shown in the pictures mean the following: EQ—<span class="html-italic">E. arvense;</span> HS—humus soil treated; HL—humus leaf treated. Different letters indicate different statistical groups of means according to the results of Tukey’s test (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Microscopic images of leaf surfaces in <span class="html-italic">O. basilicum</span> Rokokó as a result of treatment: (<b>a</b>) algal extract; (<b>b</b>) <span class="html-italic">E. arvense</span> extract; (<b>c</b>) humus soil treatment; (<b>d</b>) humus leaf treatment; (<b>e</b>) control. The arrows indicate the glands.</p>
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<p>(<b>a</b>) Number of pelpate glands in the sampling area in <span class="html-italic">O. basilicum</span> Rokokó; (<b>b</b>) pelpate glands number in comparison to leaf area in <span class="html-italic">O. basilicum</span> Rokokó. The abbreviations shown in the pictures mean the following: EQ—<span class="html-italic">E. arvense</span>; HS—humus soil treated; HL—humus leaf treated. Different letters indicate different statistical groups of means according to the results of Tukey’s test (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>(<b>a</b>) SPAD in <span class="html-italic">O. basilicum</span> Rokokó; (<b>b</b>) peroxidase enzyme activity in <span class="html-italic">O. basilicum</span> Rokokó; (<b>c</b>) proline level in <span class="html-italic">O. basilicum</span> Rokokó. The abbreviations shown in the pictures mean the following: EQ—<span class="html-italic">E. arvense</span>; HS—humus soil treated; HL—humus leaf treated. Different letters indicate different statistical groups of means according to the results of Tukey’s test (<span class="html-italic">p</span> &lt; 0.05).</p>
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15 pages, 2768 KiB  
Article
A Combined Impact of Low-Voltage Electrostatic Field and Essential Oil on the Postharvest Properties of Chili Pepper: Insights into Related Molecular Mechanisms
by Xiaoqian Guo, Weihua Liu, Liyong Zhang, Xianghong Wang and Si Mi
Foods 2024, 13(22), 3686; https://doi.org/10.3390/foods13223686 - 19 Nov 2024
Viewed by 263
Abstract
This research is intended to ascertain the impact of low-voltage electrostatic field (LVEF) together with chili pepper leaf essential oil (CLEO) on the storage quality of chili pepper. Four groups of samples were investigated, namely, control (CK), CLEO, LVEF, and CLEO + LVEF. [...] Read more.
This research is intended to ascertain the impact of low-voltage electrostatic field (LVEF) together with chili pepper leaf essential oil (CLEO) on the storage quality of chili pepper. Four groups of samples were investigated, namely, control (CK), CLEO, LVEF, and CLEO + LVEF. Chili pepper from the CLEO + LVEF group reduced the weight loss and malondialdehyde content but improved the ascorbic acid contents, antioxidant potential, firmness, and color attributes. CLEO and LVEF could maintain the integral structure and stability of the cell wall by suppressing the activities of hydrolases of pectin, cellulose, and hemicellulose. The positive role of CLEO + LVEF on the color quality was explained by the significantly higher chlorophyll content and lower activities of chlorophyllase, pheophytinase, and Mg-dechelatase compared to the CK group. Taken together, all data provide supporting evidence for a synergistic effect of CLEO and LVEF on the enhancement of postharvest traits of chili peppers. Full article
(This article belongs to the Section Food Packaging and Preservation)
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<p>Effect of different treatments on the (<b>A</b>) weight loss, (<b>B</b>) soluble solid content, (<b>C</b>) ascorbic acid content, (<b>D</b>) firmness, (<b>E</b>) L* value, (<b>F</b>) a* value, and (<b>G</b>) b* value of chili pepper over a 0–21 d storage period. CK, control group; CLEO, chili pepper leaf essential oil; LVEF, low-voltage electrostatic field; CLEO + LVEF, chili pepper leaf essential oil together with low-voltage electrostatic field. * <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, **** <span class="html-italic">p</span> &lt; 0.0001, CLEO compared to control (CK); # <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, #### <span class="html-italic">p</span> &lt; 0.0001, LVEF compared to control (CK); ▽ <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, ▽▽▽▽ <span class="html-italic">p</span> &lt; 0.0001, CLEO + LVEF compared to control (CK).</p>
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<p>Effect of different treatments on the (<b>A</b>) malondialdehyde content, (<b>B</b>) peroxidase activity, and (<b>C</b>) catalase activity of chili pepper over a 0–21 d storage period. * <span class="html-italic">p</span> &lt; 0.05, CLEO compared to control (CK); ## <span class="html-italic">p</span> &lt; 0.01, ### <span class="html-italic">p</span> &lt; 0.001, LVEF compared to control (CK); ▽ <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; 0.0001, CLEO + LVEF compared to control (CK).</p>
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<p>Effect of different treatments on the (<b>A</b>) protopectin content, (<b>B</b>) water-soluble pectin content, (<b>C</b>) chelate soluble pectin content, (<b>D</b>) sodium carbonate soluble pectin content, (<b>E</b>) cellulose content, (<b>F</b>) hemicellulose content, (<b>G</b>) polygalacturonase activity, (<b>H</b>) pectin methylesterase activity, (<b>I</b>) β-glucosidase activity, and (<b>J</b>) cellulase activity of chili pepper over a 0–21 d storage period. * <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, **** <span class="html-italic">p</span> &lt; 0.0001, CLEO compared to control (CK); # <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, #### <span class="html-italic">p</span> &lt; 0.0001, LVEF compared to control (CK); ▽ <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, ▽▽▽▽ <span class="html-italic">p</span> &lt; 0.0001, CLEO + LVEF compared to control (CK).</p>
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<p>Effect of different treatments on the (<b>A</b>) chlorophyll a content, (<b>B</b>) chlorophyll b content, (<b>C</b>) total chlorophyll content, (<b>D</b>) chlorophyllase activity, (<b>E</b>) pheophytinase activity, and (<b>F</b>) Mg-dechelatase activity of chili pepper over a 0–21 d storage period. * <span class="html-italic">p</span> &lt; 0.05, *** <span class="html-italic">p</span> &lt; 0.001, CLEO compared to control (CK); # <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, #### <span class="html-italic">p</span> &lt; 0.0001, LVEF compared to control (CK); ▽ <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, ▽▽▽▽ <span class="html-italic">p</span> &lt; 0.0001, CLEO + LVEF compared to control (CK).</p>
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18 pages, 2453 KiB  
Article
The Application of Conventional and Organic Fertilizers During Wild Edible Species Cultivation: A Case Study of Purslane and Common Sowthistle
by Efraimia Hajisolomou, Giannis Neofytou, Spyridon A. Petropoulos and Nikolaos Tzortzakis
Horticulturae 2024, 10(11), 1222; https://doi.org/10.3390/horticulturae10111222 - 19 Nov 2024
Viewed by 285
Abstract
The introduction of alternative crops, including wild edible and medicinal plants, in organic cultivation systems presents an attractive approach to producing healthy and high-quality products due to their content in beneficial compounds and increased nutritional value. The current study evaluated the impact of [...] Read more.
The introduction of alternative crops, including wild edible and medicinal plants, in organic cultivation systems presents an attractive approach to producing healthy and high-quality products due to their content in beneficial compounds and increased nutritional value. The current study evaluated the impact of organic and conventional fertilization on the growth, quality, nutrient status and stress response of the two wild edible species, e.g., purslane (Portulaca oleracea L.) and common sowthistle (Sonchus oleraceus L.), under field conditions. The fertilization treatments included the following: a control (NoFert) treatment with no fertilizers added, base dressing with conventional fertilization (CoFert), base dressing with organic fertilization (OrFert), base dressing and side dressing with conventional fertilization (OrFert + SCoFert) and base dressing and side dressing with organic fertilization (CoFert + SCoFert). Organic fertilization was carried out using a commercial vinasse-based organic fertilizer. In both purslane and common sowthistle, the application of organic fertilizers provided comparable or even enhanced plant growth traits, macronutrient content (i.e., P and K for purslane, and N for sowthistle) and quality (i.e., total soluble solids) compared to the application of conventional fertilizers. On the other hand, conventional fertilization with supplementary fertilization positively influenced the plant growth of purslane (i.e., plant height and stems biomass), as well as its physiological parameters (i.e., chlorophylls content), total phenolics content and antioxidant capacity (i.e., DPPH and FRAP). Similarly, conventional fertilization led to increased total phenolics and antioxidants in common sowthistle, while variable effects were observed regarding plant physiology, stress response and antioxidant capacity indices. In conclusion, the use of organic fertilization in both purslane and common sowthistle exhibited a performance similar to that of conventional fertilization, although further optimization of fertilization regimes is needed to improve the quality of the edible products. Full article
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<p>The effect of different fertilization regimes on purslane’s (<b>A</b>) total phenols content (mg g<sup>−1</sup> FW) and antioxidant capacity according to (<b>B</b>) DPPH, (<b>C</b>) FRAP, and (<b>D</b>) ABTS•+ (mg Trolox g<sup>−1</sup> FW); (<b>E</b>) flavonoids content (mg Rutin g<sup>−1</sup> FW) and (<b>F</b>) ascorbic acid content (mg 100 g<sup>−1</sup> FW); no fertilization (NoFert), conventional fertilization–base dressing (CoFert), organic fertilization–base dressing (OrFert), conventional fertilization with side dressing of CoFert (CoFert + S<sub>CoFert</sub>) and organic fertilization with side dressing of OrFert (OrFert + S<sub>OrFert</sub>); significant differences (<span class="html-italic">p</span> &lt; 0.05) among the treatments are indicated by different letters above the vertical bars. Values are means (±SE) of six replicates for each treatment.</p>
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<p>The effect of different fertilization regimes on purslane’s (<b>A</b>) total phenols content (mg g<sup>−1</sup> FW) and antioxidant capacity according to (<b>B</b>) DPPH, (<b>C</b>) FRAP, and (<b>D</b>) ABTS•+ (mg Trolox g<sup>−1</sup> FW); (<b>E</b>) flavonoids content (mg Rutin g<sup>−1</sup> FW) and (<b>F</b>) ascorbic acid content (mg 100 g<sup>−1</sup> FW); no fertilization (NoFert), conventional fertilization–base dressing (CoFert), organic fertilization–base dressing (OrFert), conventional fertilization with side dressing of CoFert (CoFert + S<sub>CoFert</sub>) and organic fertilization with side dressing of OrFert (OrFert + S<sub>OrFert</sub>); significant differences (<span class="html-italic">p</span> &lt; 0.05) among the treatments are indicated by different letters above the vertical bars. Values are means (±SE) of six replicates for each treatment.</p>
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<p>The effect of different fertilization regimes on common sowthistle’s (<b>A</b>) total phenols (mg GA g<sup>−1</sup> FW) and antioxidant capacity according to (<b>B</b>) DPPH, (<b>C</b>) FRAP, (<b>D</b>) ABTS•+ (mg Trolox g<sup>−1</sup> FW); (<b>E</b>) flavonoids (mg Rutin g<sup>−1</sup> FW) and (<b>F</b>) ascorbic acid content (mg 100 g<sup>−1</sup> FW); no fertilization (NoFert), conventional fertilization–base dressing (CoFert), organic fertilization-base dressing (OrFert), conventional fertilization with side dressing of CoFert (CoFert + S<sub>CoFert</sub>) and organic fertilization with side dressing of OrFert (OrFert + S<sub>OrFert</sub>); significant differences (<span class="html-italic">p</span> &lt; 0.05) among applications are indicated by different letters above the vertical bars. Values are means (±SE) of six replicates for each treatment.</p>
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<p>The effect of different fertilization regimes on common sowthistle’s (<b>A</b>) total phenols (mg GA g<sup>−1</sup> FW) and antioxidant capacity according to (<b>B</b>) DPPH, (<b>C</b>) FRAP, (<b>D</b>) ABTS•+ (mg Trolox g<sup>−1</sup> FW); (<b>E</b>) flavonoids (mg Rutin g<sup>−1</sup> FW) and (<b>F</b>) ascorbic acid content (mg 100 g<sup>−1</sup> FW); no fertilization (NoFert), conventional fertilization–base dressing (CoFert), organic fertilization-base dressing (OrFert), conventional fertilization with side dressing of CoFert (CoFert + S<sub>CoFert</sub>) and organic fertilization with side dressing of OrFert (OrFert + S<sub>OrFert</sub>); significant differences (<span class="html-italic">p</span> &lt; 0.05) among applications are indicated by different letters above the vertical bars. Values are means (±SE) of six replicates for each treatment.</p>
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<p>The effect of different fertilization regimes on (<b>A</b>) hydrogen peroxide–H<sub>2</sub>O<sub>2</sub> (μmol g<sup>−1</sup>) and (<b>B</b>) lipid peroxidation–MDA (nmol g<sup>−1</sup>) of purslane plants. No fertilization (NoFert), conventional fertilization–base dressing (CoFert), organic fertilization–base dressing (OrFert), conventional fertilization with side dressing of CoFert (CoFert + S<sub>CoFert</sub>) and organic fertilization with side dressing of OrFert (OrFert + S<sub>OrFert</sub>); significant differences (<span class="html-italic">p</span> &lt; 0.05) among applications are indicated by different letters above the vertical bars. Values are means (±SE) of six replicates for each treatment.</p>
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<p>The effect of different fertilization regimes on (<b>A</b>) hydrogen peroxide–H<sub>2</sub>O<sub>2</sub> (μmol g<sup>−1</sup>) and (<b>B</b>) lipid peroxidation–MDA (nmol g<sup>−1</sup>) of common sowthistle plants. No fertilization (NoFert), conventional fertilization–base dressing (CoFert), organic fertilization–base dressing (OrFert), conventional fertilization with side dressing of CoFert (CoFert + S<sub>CoFert</sub>) and organic fertilization with side dressing of OrFert (OrFert + S<sub>OrFert</sub>); significant differences (<span class="html-italic">p</span> &lt; 0.05) among applications are indicated by different letters above the vertical bars. Values are means (±SE) of six replicates for each treatment.</p>
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23 pages, 13244 KiB  
Article
Model for Inverting the Leaf Area Index of Green Plums by Integrating IoT Environmental Monitoring Data and Leaf Relative Content of Chlorophyll Values
by Caili Yu, Haiyang Tong, Daoyi Huang, Jianqiang Lu, Jiewei Huang, Dejing Zhou and Jiaqi Zheng
Agriculture 2024, 14(11), 2076; https://doi.org/10.3390/agriculture14112076 - 18 Nov 2024
Viewed by 370
Abstract
The quantitative inversion of the leaf area index (LAI) of green plum trees is crucial for orchard field management and yield prediction. The data on the relative content of chlorophyll (SPAD) in leaves and environmental data from orchards show a significant correlation with [...] Read more.
The quantitative inversion of the leaf area index (LAI) of green plum trees is crucial for orchard field management and yield prediction. The data on the relative content of chlorophyll (SPAD) in leaves and environmental data from orchards show a significant correlation with LAI. Effectively integrating these two data types for LAI inversion is important to explore. This study proposes a multi−source decision fusion LAI inversion model for green plums based on their adjusted determination coefficient (MDF−ADRS). First, three statistical methods—Pearson, Spearman rank, and Kendall rank correlation analyses—were used to measure the linear relationships between variables, and the six environmental factors most highly correlated with LAI were selected from the orchard’s environmental data. Then, using multivariate statistical analysis methods, LAI inversion models based on environmental feature factors (EFs−PM) and SPAD (SPAD−PM) were established. Finally, a weight optimization allocation strategy was employed to achieve a multi−source decision fusion LAI inversion model for green plums. This strategy adaptively allocates weights based on the predictive performance of each data source. Unlike traditional models that rely on fixed weights or a single data source, this approach allows the model to increase the influence of a key data source when its predictive strength is high and reduce noise interference when it is weaker. This dynamic adjustment not only enhances the model’s robustness under varying environmental conditions but also effectively mitigates potential biases when a particular data source becomes temporarily unreliable. Our experimental results show that the MDF−ADRS model achieves an R2 of 0.88 and an RMSE of 0.39 in the validation set, outperforming other fusion methods. Compared to the EFs−PM and SPAD−PM models, the R2 increased by 0.19 and 0.26, respectively, and the RMSE decreased by 0.16 and 0.22. This model effectively integrates multiple sources of data from green plum orchards, enabling rapid inversion and improving the accuracy of green plum LAI estimation, providing a technical reference for monitoring the growth and managing the production of green plums. Full article
(This article belongs to the Section Digital Agriculture)
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<p>(<b>a</b>) shows the geographical location of the experimental site, and (<b>b</b>) displays the plum trees studied.</p>
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<p>Diagram of the layout of soil temperature and humidity sensors. Sensors should be placed based on differences in soil conditions. If multiple trees share consistent soil conditions, 1 to 2 sensors are sufficient. If conditions vary, sensors should be installed separately.</p>
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<p>A drone aerial image of the experimental area, displaying the locations of the sample green plum trees, meteorological stations, and soil temperature and humidity sensors. Circles numbered 1 to 14 represent the sample green plum trees, the rectangles mark the meteorological stations, and the black dots indicate the placement of the soil temperature and humidity sensors.</p>
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<p>This figure presents the experimental flowchart of the study, which consists of three steps: data acquisition, decision fusion, and Inversion. Data acquisition: the SPAD−502 chlorophyll analyzer is used to collect SPAD data, while environmental characteristic factor data on green plums are gathered through meteorological facilities in the orchard. Decision fusion: This section introduces the MDF−ADRS decision fusion model developed in this study. First, the SPAD−PM and EFs−PM inversion models are constructed using SPAD data and environmental feature factor data, respectively (with the environmental feature factors needing to be screened). The results from the SPAD−PM and EFs−PM models are then combined using a weight reassignment strategy for decision fusion. Inversion: the fused results are used to inverse the LAI of the green plums.</p>
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<p>The leaf area index (LAI) measuring instrument, with its fish−eye lens facing upwards, is placed horizontally above the crown of the tree during measurement.</p>
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<p>Leaves and the SPAD−502 chlorophyll analyzer: the SPAD−502 instrument is used to clamp the leaves and obtain chlorophyll values.</p>
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<p>The orchard meteorological station collects environmental data within the orchard and promptly uploads the data to a cloud platform for storage.</p>
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<p>We selected 12 leaves from the upper, middle, and lower levels of the sample, as well as from the east, south, west, and north directions, to measure SPAD values, and used label plates to mark these leaves.</p>
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<p>When using leaf area index instruments to measure the LAI from different directions, the projections in the instrument—labeled (<b>a</b>–<b>d</b>)—represent the east, west, south, and north directions, respectively.</p>
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<p>This figure illustrates the process of integrating the output results of two different inversion models (SPAD−PM and EFs−PM) to improve the final inversion accuracy. Both the SPAD−PM and EFs−PM models provide evaluation metrics (<math display="inline"><semantics> <msup> <mi>R</mi> <mn>2</mn> </msup> </semantics></math>) and inversion results (<math display="inline"><semantics> <mover accent="true"> <mi>y</mi> <mo>^</mo> </mover> </semantics></math>). In the fusion process, their adjusted <math display="inline"><semantics> <msup> <mi>R</mi> <mn>2</mn> </msup> </semantics></math> value is first calculated, taking into account the number of virtual samples (<span class="html-italic">n</span>) and the variables (<span class="html-italic">p</span>) that affect model evaluation. Next, the score (<math display="inline"><semantics> <msub> <mi>x</mi> <mi>m</mi> </msub> </semantics></math>) for each model is computed, and their weight coefficients (<math display="inline"><semantics> <msub> <mi>W</mi> <mi>m</mi> </msub> </semantics></math>) are standardized. Using a weighted average formula, a weighted sum is applied to obtain the fused inversion result. The weights are adjusted using an exponential function based on model scores to ensure a reasonable weight distribution. Finally, the weighted average method is used to combine multiple inversion results (<math display="inline"><semantics> <mrow> <msub> <mi>y</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>y</mi> <mn>2</mn> </msub> <mo>,</mo> <mo>…</mo> <mo>,</mo> <msub> <mi>y</mi> <mi>n</mi> </msub> </mrow> </semantics></math>), outputting the fused inversion results (<math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>R</mi> <mn>2</mn> </msub> <mo>,</mo> <mo>…</mo> <mo>,</mo> <msub> <mi>R</mi> <mi>n</mi> </msub> </mrow> </semantics></math>) and thereby improving the overall performance of the model’s inversion.</p>
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<p>The correlation analysis between the LAI and environmental characteristic factors indicates that a coefficient with a larger absolute value signifies a stronger correlation.</p>
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<p>This figure presents the comparative results of our model and the simple average method (AVE), voting method (VOT), and linear weighting method based on Log <math display="inline"><semantics> <msup> <mi>R</mi> <mn>2</mn> </msup> </semantics></math> (LW−Log <math display="inline"><semantics> <msup> <mi>R</mi> <mn>2</mn> </msup> </semantics></math>).</p>
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<p>Results of comparison between model inversion values and measured values.</p>
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<p>Results of comparison between model inversion values and measured values.</p>
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18 pages, 3797 KiB  
Article
Bacillus cereus: An Ally Against Drought in Popcorn Cultivation
by Uéliton Alves de Oliveira, Antônio Teixeira do Amaral Junior, Samuel Henrique Kamphorst, Valter Jário de Lima, Fábio Lopes Olivares, Shahid Khan, Monique de Souza Santos, Jardel da Silva Figueiredo, Samuel Pereira da Silva, Flávia Nicácio Viana, Talles de Oliveira Santos, Gabriella Rodrigues Gonçalves, Eliemar Campostrini, Alexandre Pio Viana and Freddy Mora-Poblete
Microorganisms 2024, 12(11), 2351; https://doi.org/10.3390/microorganisms12112351 - 18 Nov 2024
Viewed by 340
Abstract
Despite the development of adapted popcorn cultivars such as UENF WS01, strategies such as bacterial inoculation are being explored to enhance plant resilience to abiotic stress. This study investigates the impact of drought stress on popcorn cultivation. Specifically, the aim was to identify [...] Read more.
Despite the development of adapted popcorn cultivars such as UENF WS01, strategies such as bacterial inoculation are being explored to enhance plant resilience to abiotic stress. This study investigates the impact of drought stress on popcorn cultivation. Specifically, the aim was to identify the benefits of Bacillus cereus interaction with the drought-tolerant hybrid UENF WS01 for its morphophysiology and growth by comparing inoculated and non-inoculated plants under water-stressed (WS) and well-watered (WW) conditions. This evaluation was conducted using a randomized complete block design in a factorial arrangement. For WS with inoculation samples, there were significant increases in relative chlorophyll content, maximum fluorescence intensity, and agronomic water use efficiency. Chlorophyll content increased by an average of 50.39% for WS samples, compared to a modest increase of 2.40% for WW samples. Both leaf and stem biomass also significantly increased for WS relative to WW conditions. Overall, B. cereus inoculation mitigated the impact of water stress, significantly enhancing the expression of physiological and morphological traits, even when paired with a drought-tolerant hybrid. Full article
(This article belongs to the Section Plant Microbe Interactions)
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<p>Comparison of the percentage reduction (%) in physiological traits between treatments under water stress (WSI and WSC) and well-watered (WWI and WWC) conditions. Relative chlorophyll content (Chl), leaf anthocyanin content (Anth), flavonoids (Flv), nitrogen balance index (NBI), maximum fluorescence intensity (Fm), variable fluorescence (Fv), basal quantum production of non-photochemical processes in PSII (Fm/Fo), the potential quantum efficiency of PSII (Fv/Fo), potential quantum yield (Fv/Fm), net photosynthetic rate (A), stomatal conductance (Gs), intercellular CO<sub>2</sub> concentration (Ci), transpiration rate (E), non-photochemical quenching (NPQT), the quantum yield of PSII (PHI2), relative leaf water content (RLWC), intrinsic water use efficiency (WUEint), instantaneous water use efficiency (WUEinst), and agronomic water use efficiency (WUEagro).</p>
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<p>Comparison of means of physiological traits evaluated in hybrid UENF WS01 inoculated with <span class="html-italic">Bacillus cereus</span> under two water conditions. Uppercase letters indicate significantly different treatments between water conditions (WSI*WWI and WSC*WWC), and lowercase letters represent significantly different treatments within the water condition (WSI*WSC and WWI*WWC) at the 5% level by Tukey’s test. Error bars show the standard deviation. Relative chlorophyll content (Chl), leaf anthocyanin content (Anth), flavonoids (Flv), nitrogen balance index (NBI), maximum fluorescence intensity (Fm), variable fluorescence (Fv), basal quantum production of non-photochemical processes in PSII (Fm/Fo), the potential quantum efficiency of PSII (Fv/Fo), potential quantum yield (Fv/Fm), net photosynthetic rate (A), stomatal conductance (Gs), intercellular CO<sub>2</sub> concentration (Ci), transpiration rate (E), non-photochemical quenching (NPQT), the quantum yield of PSII (PHI2), relative leaf water content (RLWC), intrinsic water use efficiency (WUEint), instantaneous water use efficiency (WUEinst), and agronomic water use efficiency (WUEagro).</p>
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<p>Comparison of the percentage reduction (%) in morphological and root traits between water stress (WSI and WSC) and well-watered (WWI and WWC) conditions. Plant height (PH), stem diameter (SD), leaf length (LL), leaf width (LW), leaf biomass (LB), stem biomass (SB), specific leaf area (SLA), abaxial stomata density (SDAB), abaxial epidermal cell density (ECDAB), adaxial stomata density (SDAD), adaxial epidermal cell density (ECDAD), abaxial stomatal index (SIAB), adaxial stomatal index (SIAD), mean root number (MNR, sections a, b, c, and d), specific root length (SRLe), and root weight density (RWDc).</p>
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<p>Comparison of means of morphological traits evaluated in hybrid UENF WS01 inoculated with <span class="html-italic">Bacillus cereus</span> under two water conditions. Uppercase letters indicate significantly different treatments between water conditions (WSI*WWI and WSC*WWC), and lowercase letters represent significantly different treatments within water conditions (WSI*WSC and WWI*WWC) at a 5% probability level by Tukey’s test. Error bars show the standard deviation. Plant height (PH), stem diameter (SD), leaf length (LL), leaf width (LW), leaf biomass (LB), stem biomass (SB), specific leaf area (SLA), abaxial stomata density (SDAB), abaxial epidermal cell density (ECDAB), adaxial stomata density (SDAD), adaxial epidermal cell density (ECDAD), abaxial stomatal index (SIAB), adaxial stomatal index (SIAD), mean number of roots (MNR, sections a, b, c, and d), specific root length (SRLe), and root weight density (RWDc).</p>
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14 pages, 3803 KiB  
Article
Deciphering Whether Illite, a Natural Clay Mineral, Alleviates Cadmium Stress in Glycine max Plants via Modulation of Phytohormones and Endogenous Antioxidant Defense System
by Sang-Mo Kang, Shifa Shaffique, Md. Injamum-Ul-Hoque, Ho-Jun Gam, Ji-In Woo, Jin Ryeol Jeon, Da-Sol Lee, In-Jung Lee and Bong-Gyu Mun
Sustainability 2024, 16(22), 10039; https://doi.org/10.3390/su162210039 - 18 Nov 2024
Viewed by 501
Abstract
Globally, cadmium (Cd) stress dramatically reduces agricultural yield. Illite, a natural clay mineral, is a low-cost, environmentally acceptable, new promising method of reducing the heavy metal (HM) stress of cereal crops. In research statistics, there is little research on stress tolerance behavior of [...] Read more.
Globally, cadmium (Cd) stress dramatically reduces agricultural yield. Illite, a natural clay mineral, is a low-cost, environmentally acceptable, new promising method of reducing the heavy metal (HM) stress of cereal crops. In research statistics, there is little research on stress tolerance behavior of Illite (IL) on an experimental soybean plant. In the present study, we took IL and examined it for tolerance to Cd, as well as for other plant-growth-promoting (PGP) characteristics in Glycine max (soybean). The results showed that applying clay minerals in different concentrations enhanced the level of SA (defense hormone) and reduced the level of ABA (stress hormone). Cd 1 mM significantly reduces plant growth by altering their morphological characteristics. However, the application of IL significantly enhanced the seedling characteristics, such as root length (RL), 29.6%, shoot length (SL), 14.5%, shoot fresh biomass (SFW), 10.8%, and root fresh biomass (RFB), 6.4%, in comparison with the negative control group. Interestingly, IL 1% also enhanced the chlorophyll content (C.C), 15.5%, and relative water content (RWC), 12.5%, in all treated plants. Moreover, it resulted in an increase in the amount of superoxide dismutase (SOD), phenolics, and flavonoids in soybean plants, while lowering the levels of peroxidase (POD) and H2O2. Furthermore, compared to control plants, soybean plants treated with the Illite exhibited increased Si absorption and lower Cd levels, according to inductively coupled plasma mass spectrometry (ICP-MS). Thus, the IL can operate as an environmentally beneficial biofertilizer and sustainable approach under Cd stress by promoting plant development by activating signaling events. Full article
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<p>Impact of IL on the soybean plant growth under Cd-induced stress.</p>
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<p>(<b>A</b>) Measurement of chlorophyll content (SPAD) and (<b>B</b>) leaf relative water contents (RWC) in different treatment groups. Different lowercase letters on top of the bars show significant differences between the treatments at <span class="html-italic">p</span>  ≤  0.05. The error bar represents the mean  ±  standard error of the mean (SEM) among the replicates.</p>
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<p>(<b>A</b>) Quantification of the abscisic acid (ABA) and (<b>B</b>) salicylic acid (SA) in all treatment groups. Different lowercase letters on top of the bars indicate significant differences between the treatments at <span class="html-italic">p</span>  ≤  0.05. The error bar represents the mean  ±  standard error of the mean (SEM) among the replicates.</p>
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<p>Application of Illite under cadmium-induced stress modulated the antioxidant defense system, such as (<b>A</b>) superoxide dismutase (SOD), (<b>B</b>) catalase (CAT), (<b>C</b>) peroxidase, (<b>D</b>) hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>), (<b>E</b>) polyphenolic activity and (<b>F</b>) flavonoids content. Different lowercase letters on top of the bars indicate significant differences between the treatments at <span class="html-italic">p</span>  ≤  0.05. The error bar represents the mean  ±  standard error of the mean (SEM) among the replicates.</p>
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<p>Modulation in technical fluorescence and specific energy flux parameters.</p>
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<p>Analysis of all the essential amino acids of all the treatment groups under Cd-induced stress.</p>
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<p>ICP analysis of the ions, such as, (<b>A</b>) silicon (Si), (<b>B</b>) cadmium (Cd), and (<b>C</b>) aluminium (Al) content in all Cd-induced stress treatment groups. Different lowercase letters on top of the bars indicate significant differences between the treatments at <span class="html-italic">p</span>  ≤  0.05. The error bar represents the mean  ±  standard error of the mean (SEM) among the replicates.</p>
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18 pages, 3462 KiB  
Article
Evaluating Physiological and Yield Indices of Egyptian Barley Cultivars Under Drought Stress Conditions
by Wessam A. Abdelrady, Elsayed E. Elshawy, Hassan A. Abdelrahman, Syed Muhammad Hassan Askri, Zakir Ibrahim, Mohamed Mansour, Ibrahim S. El-Degwy, Taha Ghazy, Aziza A. Aboulila and Imran Haider Shamsi
Agronomy 2024, 14(11), 2711; https://doi.org/10.3390/agronomy14112711 - 17 Nov 2024
Viewed by 304
Abstract
Climate change significantly threatens crops, mainly through drought stress, affecting barley, which is essential for food and feed globally. Ten barley cultivars were evaluated under normal and drought stress conditions during the 2019/20 and 2020/21 seasons, focusing on traits such as days to [...] Read more.
Climate change significantly threatens crops, mainly through drought stress, affecting barley, which is essential for food and feed globally. Ten barley cultivars were evaluated under normal and drought stress conditions during the 2019/20 and 2020/21 seasons, focusing on traits such as days to heading and maturity, plant height, number of spikes m−2, spike length, 1000-kernel weight, and biological and grain yield. Drought stress significantly reduced most of these traits. The genotypes showed significant differences in their responses to irrigation treatments, with the interaction between seasons and cultivars also being significant for most traits. The grain yield and 1000-kernel weight were among the least affected traits under drought stress, respectively. Notably, Giza138 and Giza126 showed strong drought tolerance, suitable for drought-resilient breeding. In season one, Giza126, Giza134, and Giza138 yielded 13%, 9%, and 11%, respectively, while Giza135 and Giza129 showed higher reductions at 31% and 39%. In season two, Giza126, Giza134, and Giza138 had reductions of 14%, 10%, and 13%, respectively, while Giza135 and Giza129 again exhibited higher reductions at 31% and 38%. These cultivars also showed strong performance across various stress tolerance indices, including the MP, YSI, STI, GMP, and YI. Giza 134 demonstrated the lowest values for the SDI and TOL, indicating superior drought stress tolerance. On the other hand, Giza 129 and Giza 135 were the most sensitive to drought stress, experiencing significant reductions across critical traits, including 6.1% in days to heading, 18.37% in plant height, 28.21% in number of kernel spikes−1, 38.45% in grain yield, and 34.91% in biological yield. In contrast, Giza 138 and Giza 2000 showed better resilience, with lower reductions in the 1000-kernel weight (6.41%) and grain yield (10.61%), making them more suitable for drought-prone conditions. Giza 126 and Giza 132 also exhibited lower sensitivity, with minimal reductions in days to heading (2%) and maturity (2.4%), suggesting potential adaptability to water-limited environments. Giza 126 maintained the highest root lengths and had the highest stomatal conductance. Giza 138 consistently had the highest chlorophyll content, with SPAD values decreasing to 79% under drought. Despite leading in shoot length, Giza 135 decreased to 42.59% under drought stress. In conclusion, Giza 126 and Giza 138 showed adaptability to water-limited conditions with minimal impact on phenological traits. Giza 126 had the longest roots and highest stomatal conductance, while Giza 138 consistently maintained a high chlorophyll content. Together, they and Giza 134 are valuable for breeding programs to improve barley drought tolerance. Full article
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Graphical abstract

Graphical abstract
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<p>Comparative analysis of ten cultivars under varying drought conditions. The figure illustrates the impact of normal conditions, moderate drought stress, and severe drought stress on the growth and root development of different Giza cultivars, highlighting the varying levels of drought tolerance across cultivars.</p>
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<p>Performance and drought tolerance of ten cultivars for shoot length (cm) (<b>a</b>), root length (cm) (<b>b</b>), fresh shoot weight (<b>c</b>), and shoot dry weight (<b>d</b>). Different letters indicated significant variations among the cultivars using LSD 0.05.</p>
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<p>Photosynthetic efficiency, Photosystem II quantum efficiency, stomatal conductance, and SPAD values of ten Giza cultivars under normal, moderate, and severe drought conditions. (<b>a</b>,<b>b</b>) show the photosynthetic efficiency and Photosystem II quantum efficiency percentages; (<b>c</b>) the stomatal conductance under drought stress (gsw) values; (<b>d</b>) SPAD values under different drought stress. Different letters indicated significant variations among the cultivars using LSD 0.05.</p>
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<p>Mean performance of days to heading (<b>a</b>) and number of days to maturity (<b>b</b>), plant height (cm) (<b>c</b>), spike length (cm) (<b>d</b>), and number of kernel spikes<sup>−1</sup> (<b>e</b>) for studied cultivars under normal and drought stress conditions across irrigation treatments and two seasons. Different letters indicated significant variations among the cultivars using LSD 0.05.</p>
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<p>Performance of the number of kernel spikes<sup>−1</sup> (<b>a</b>), 1000-kernel weight (<b>b</b>), biological yield ha<sup>−1</sup> (<b>c</b>), and grain yield ha<sup>−1</sup> (<b>d</b>) for the studied cultivars under normal and drought stress conditions in the two seasons across the irrigation treatments and the two seasons. Different letters indicated significant variations among the cultivars using LSD 0.05.</p>
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<p>Grain yield performance and stability of ten cultivars under normal and drought stress across two seasons. A GGE biplot was used to rank 10 cultivars (G1–G10: Giza 123, Giza 126, Giza 132, Giza 134, Giza 130, Giza 136, Giza 138, Giza 2000, Giza 135, Giza 129) for grain yield across four environments: E1 (normal, 2019/20), E2 (drought stress, 2019/20), E3 (normal, 2020/21), and E4 (drought stress, 2020/21). The Average Environment Axis (AEA) indicated higher mean performance, while its perpendicular axis indicated greater variability or instability. The analysis highlighted yield performance and stability differences under normal and drought conditions.</p>
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