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Search Results (10,783)

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24 pages, 9689 KiB  
Article
Genome-Wide Identification, Evolution, and Expression Analysis of the Dirigent Gene Family in Cassava (Manihot esculenta Crantz)
by Mingchao Li, Kai Luo, Wenke Zhang, Man Liu, Yunfei Zhang, Huling Huang, Yinhua Chen, Shugao Fan and Rui Zhang
Agronomy 2024, 14(8), 1758; https://doi.org/10.3390/agronomy14081758 (registering DOI) - 11 Aug 2024
Abstract
Dirigent (DIR) genes play a pivotal role in plant development and stress adaptation. Manihot esculenta Crantz, commonly known as cassava, is a drought-resistant plant thriving in tropical and subtropical areas. It is extensively utilized for starch production, bioethanol, and animal feed. [...] Read more.
Dirigent (DIR) genes play a pivotal role in plant development and stress adaptation. Manihot esculenta Crantz, commonly known as cassava, is a drought-resistant plant thriving in tropical and subtropical areas. It is extensively utilized for starch production, bioethanol, and animal feed. However, a comprehensive analysis of the DIR family genes remains unexplored in cassava, a crucial cash and forage crop in tropical and subtropical regions. In this study, we characterize a total of 26 cassava DIRs (MeDIRs) within the cassava genome, revealing their uneven distribution across 13 of the 18 chromosomes. Phylogenetic analysis classified these genes into four subfamilies: DIR-a, DIR-b/d, DIR-c, and DIR-e. Comparative synteny analysis with cassava and seven other plant species (Arabidopsis (Arabidopsis thaliana), poplar (Populus trichocarpa), soybean (Glycine max), tomato (Solanum lycopersicum), rice (Oryza sativa), maize (Zea mays), and wheat (Triticum aestivum)) provided insights into their likely evolution. We also predict protein interaction networks and identify cis-acting elements, elucidating the functional differences in MeDIR genes. Notably, MeDIR genes exhibited specific expression patterns across different tissues and in response to various abiotic and biotic stressors, such as pathogenic bacteria, cadmium chloride (CdCl2), and atrazine. Further validation through quantitative real-time PCR (qRT-PCR) confirmed the response of DIR genes to osmotic and salt stress. These findings offer a comprehensive resource for understanding the characteristics and biological functions of MeDIR genes in cassava, enhancing our knowledge of plant stress adaptation mechanisms. Full article
17 pages, 2672 KiB  
Article
Optimizing Ridge–Furrow Rainwater-Harvesting Strategies for Potato Cultivation in the Drylands of Northwestern China: A Regional Approach
by Lina Zhang, Siqi Ren, Feifei Pan, Jianshuo Zhou, Jingyan Jiang, Xuebiao Pan, Jing Wang, Baoru Sun and Qi Hu
Agronomy 2024, 14(8), 1759; https://doi.org/10.3390/agronomy14081759 (registering DOI) - 11 Aug 2024
Abstract
The arid and semi-arid region of Northwest China plays a significant role in potato production, yet yields are often hampered by drought due to limited precipitation and irrigation water. The ridge–furrow rainwater-harvesting technology is an efficient and widely used technique to relieve drought [...] Read more.
The arid and semi-arid region of Northwest China plays a significant role in potato production, yet yields are often hampered by drought due to limited precipitation and irrigation water. The ridge–furrow rainwater-harvesting technology is an efficient and widely used technique to relieve drought impact and improve crop yield by changing the micro-topography to harvest rainwater to meet the water demand of crops. An analysis of precipitation, water demand, and runoff data spanning 30 years guided the selection of suitable rainwater-harvesting methods tailored to meteorological conditions. The results showed that potato water demand exceeded precipitation in the region. The mulching approach performed best in the western arid region with the most significant increase in yield and water use efficiency (WUE) and was suitable for the western semi-arid region and the agro-pastoral ecotone. In the potato dryland farming areas, the water deficit increased from southeast to northwest. Specifically, northern Gansu, northern Ningxia, and midwestern Inner Mongolia experienced a water deficit of over 200 mm, and rainwater harvesting combined with irrigation was recommended. Conversely, regarding deficits below 200 mm in southern Gansu, Ningxia, and central Inner Mongolia, a 1:1 or 2:1 pattern of ridges could be applied, and mulching was needed only in the necessary areas. For the southern Qinghai, Shaanxi, and eastern Inner Mongolia regions, ridge–furrow rainwater harvesting could be replaced by flat potato cropping. In summary, rainwater harvesting addresses water deficits, aiding climate adaptation in Northwest China’s arid and semi-arid regions. The implementation of mulching and ridge–furrow technology must be location-specific. Full article
(This article belongs to the Section Water Use and Irrigation)
31 pages, 15968 KiB  
Article
Advanced Forecasting of Drought Zones in Canada Using Deep Learning and CMIP6 Projections
by Keyvan Soltani, Afshin Amiri, Isa Ebtehaj, Hanieh Cheshmehghasabani, Sina Fazeli, Silvio José Gumiere and Hossein Bonakdari
Climate 2024, 12(8), 119; https://doi.org/10.3390/cli12080119 (registering DOI) - 10 Aug 2024
Viewed by 266
Abstract
This study addresses the critical issue of drought zoning in Canada using advanced deep learning techniques. Drought, exacerbated by climate change, significantly affects ecosystems, agriculture, and water resources. Canadian Drought Monitor (CDM) data provided by the Canadian government and ERA5-Land daily data were [...] Read more.
This study addresses the critical issue of drought zoning in Canada using advanced deep learning techniques. Drought, exacerbated by climate change, significantly affects ecosystems, agriculture, and water resources. Canadian Drought Monitor (CDM) data provided by the Canadian government and ERA5-Land daily data were utilized to generate a comprehensive time series of mean monthly precipitation and air temperature for 199 sample locations in Canada from 1979 to 2023. These data were processed in the Google Earth Engine (GEE) environment and used to develop a Convolutional Neural Network (CNN) model to estimate CDM values, thereby filling gaps in historical drought data. The CanESM5 climate model, as assessed in the IPCC Sixth Assessment Report, was employed under four climate change scenarios to predict future drought conditions. Our CNN model forecasts CDM values up to 2100, enabling accurate drought zoning. The results reveal significant trends in temperature changes, indicating areas most vulnerable to future droughts, while precipitation shows a slow increasing trend. Our analysis indicates that under extreme climate scenarios, certain regions may experience a significant increase in the frequency and severity of droughts, necessitating proactive planning and mitigation strategies. These findings are critical for policymakers and stakeholders in designing effective drought management and adaptation programs. Full article
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Figure 1

Figure 1
<p>The relief map of the study area. The black dots show the distribution of sample points across Canada.</p>
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<p>The Schematic of the CNN’s structure.</p>
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<p>Research flowchart.</p>
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<p>(<b>a</b>) Classification Accuracy for 1022 ELM models. (<b>b</b>) Area Under the Curve (AUC) for 1022 ELM models.</p>
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<p>Zoning of Projected Average Annual Precipitation Anomalies in Canada (2024–2100) Compared to the Observed Period (1983–2023) using CanESM5 from CMIP6.</p>
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<p>Zoning of Projected Average Annual Precipitation Anomalies in Canada (2024–2100) Compared to the Observed Period (1983–2023) using CanESM5 from CMIP6.</p>
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<p>Zoning of Projected Average Annual Temperature Anomalies in Canada (2024–2100) Compared to the Observed Period (1983–2023) using CanESM5 from CMIP6.</p>
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<p>Zoning of Projected Average Annual Temperature Anomalies in Canada (2024–2100) Compared to the Observed Period (1983–2023) using CanESM5 from CMIP6.</p>
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<p>Canada’s Percentage of Drought-Affected Area, Pr, and T under the SSP126 Scenario: A Comparative Analysis from 2003–2023 to 2024–2100, Examining Historical Trends and Future Projections Using CanESM5 within the CMIP6 Framework.</p>
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<p>Canada’s Percentage of Drought-Affected Area, Pr, and T under the SSP245 Scenario: A Comparative Analysis from 2003–2023 to 2024–2100, Examining Historical Trends and Future Projections Using CanESM5 within the CMIP6 Framework.</p>
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<p>Canada’s Percentage of Drought-Affected Area, Pr, and T under the SSP370 Scenario: A Comparative Analysis from 2003–2023 to 2024–2100, Examining Historical Trends and Future Projections Using CanESM5 within the CMIP6 Framework.</p>
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<p>Canada’s Percentage of Drought-Affected Area, Pr, and T under the SSP585 Scenario: A Comparative Analysis from 2003–2023 to 2024–2100, Examining Historical Trends and Future Projections Using CanESM5 within the CMIP6 Framework.</p>
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<p>Policy Implications for Canada in Addressing Climate Change and Drought Conditions.</p>
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20 pages, 5427 KiB  
Article
Multivariate Analysis Techniques and Tolerance Indices for Detecting Bread Wheat Genotypes of Drought Tolerance
by Ibrahim Al-Ashkar
Diversity 2024, 16(8), 489; https://doi.org/10.3390/d16080489 (registering DOI) - 10 Aug 2024
Viewed by 214
Abstract
Drought stress is one of the biggest hardships in wheat cultivation because of the strong negative relationship between water deficit and crop yields owing to a lower grain weight, a shorter grain-filling period, a slower grain-filling rate, and reduced grain quality. Genotype–environment interaction [...] Read more.
Drought stress is one of the biggest hardships in wheat cultivation because of the strong negative relationship between water deficit and crop yields owing to a lower grain weight, a shorter grain-filling period, a slower grain-filling rate, and reduced grain quality. Genotype–environment interaction (GEN:ENV) generates hardships in selecting wheat genotypes and ideotypes due to biased genetic estimates. Diverse strategies have been proposed to respond to the urgent need for concurrent improvements in yield performance and stability. This study’s purpose was to appraise genetic variation and GEN:ENV effects on yield and yield components to discover drought-stress-tolerant genotypes and ideotypes. This study evaluated 20 genotypes in three consecutive seasons under non-stressful and drought-stress conditions in a total of six ENVs. The broad-sense heritability ranged from 0.54 to 0.82 based on expected mean squares and ranged from 0.60 to 0.90 based on plot mean, but in the other three ways, it was usually greater than 0.90. The high values of (σgen:env2) revealed the effect that broad-sense heritability has on the expression of traits. G01, G03, G06, G07, G08, G10, G12, G13, G16, G17, and G18 were stable genotypes for grain yield (GY), according to additive main effects and a multiplicative interaction biplot for the six ENVs. Based on scores in the weighted average of absolute scores biplot (WAASB), G02, G04, G05, G08, G10, and G18 were selected as stable and high-performance for GY, and they were all selected as the best genotype groups using the WAASB-GY superiority index. From the results obtained from principal component analysis and hierarchical clustering and from the tolerance discrimination indices, G02, G04, G05, G18, and G19 are genotypes that produce a suitable yield under non-stressful and drought-stress conditions. In essence, combining approaches that take into consideration stability and high performance can contribute significantly to enhancing the reliability of recommendations for novel wheat genotypes. Full article
(This article belongs to the Special Issue Genetic Diversity and Plant Breeding)
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Figure 1

Figure 1
<p>Plotting the mean performance of absolute values and predicted by AMMI model of the four traits in six environments (E) for 20 wheat genotypes. The comparisons used t-test. Abbreviations as described in materials and methods.</p>
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<p>Plotting the mean performance of absolute values and predicted by AMMI model of the four traits as the mean across one season (S) for 20 wheat genotypes. The comparisons used t-test. Abbreviations as described in materials and methods.</p>
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<p>AMMI1 and AMMI2 Biplot for the NS, NKS, TKW, and GY traits of 20 wheat genotypes evaluated in six environments.</p>
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<p>WAASB analyses for 20 wheat genotypes were evaluated under six environments. (<b>A</b>) The WAAS biplot based on joint interpretation of storage root number (trait) and stability (WAASB), (<b>B</b>) Estimated values WAASB and mean performance (trait) (WAASB trait) for genotypes considering the weights for trait and stability, (<b>C</b>) Heatmap shows the ranks of genotypes concerning the number of IPCA used in the WAAS for the BLUPs of the genotype vs. environment interaction (WAASB) estimation, (<b>D</b>) Ranks of genotypes considering different weights for stability and yielding.</p>
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<p>PCA Biplot based on correlation matrix of 20 wheat genotypes for the GY trait and eighteen tolerance indices.</p>
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<p>The hierarchical clustering of 20 wheat genotypes based on the Euclidean distance for six tolerance indices.</p>
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17 pages, 8620 KiB  
Article
Physiological Phenotyping and Biochemical Characterization of Mung Bean (Vigna radiata L.) Genotypes for Salt and Drought Stress
by Mayur Patel, Divya Gupta, Amita Saini, Asha Kumari, Rishi Priya and Sanjib Kumar Panda
Agriculture 2024, 14(8), 1337; https://doi.org/10.3390/agriculture14081337 (registering DOI) - 10 Aug 2024
Viewed by 235
Abstract
Vigna radiata (L.) R. Wilczek, generally known as mung bean, is a crucial pulse crop in Southeast Asia that is renowned for its high nutritional value. However, its cultivation faces substantial challenges due to numerous abiotic stresses. Here, we investigate the influence [...] Read more.
Vigna radiata (L.) R. Wilczek, generally known as mung bean, is a crucial pulse crop in Southeast Asia that is renowned for its high nutritional value. However, its cultivation faces substantial challenges due to numerous abiotic stresses. Here, we investigate the influence of salt and drought stress on mung bean genotypes by evaluating its morpho-physiological traits and biochemical characteristics. This phenotypic analysis revealed that both salt and drought stress adversely affected mung bean, which led to reduced plant height, leaf senescence, loss of plant biomass, and premature plant death. Reactive oxygen species (ROS) production increased under these abiotic stresses. In response, to prevent damage by ROS, the plant activates defense mechanisms to scavenge ROS by producing antioxidants. This response was validated through morpho-physiological, histological, and biochemical assays that characterized KVK Puri-3 and KVK Jharsuguda-1 as salt and drought sensitive genotypes, respectively, and Pusa ratna was identified as a drought and salt tolerant genotype. Full article
(This article belongs to the Section Crop Production)
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Figure 1

Figure 1
<p>This figure shows the physical changes that occur in response to salt and drought stress. (<b>A</b>) Salt tolerant germplasm: Pusa Ratna; (<b>B</b>) salt sensitive germplasm: KVK Puri-3; (<b>C</b>) drought tolerant germplasm: Pusa Ratna; (<b>D</b>) drought sensitive germplasm: KVK Jharsuguda.</p>
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<p>Comparing sensitive and tolerant plant genotypes under salt and drought stress reveals distinct differences in their RWC, PHTI, and Fv/Fm values. (<b>A</b>) Graphs between the salt sensitive vs. salt tolerant; (<b>B</b>) graphs between drought sensitive and drought tolerant genotypes. Here, ***—<span class="html-italic">p</span>-value-0.001, ****—<span class="html-italic">p</span>-value-0.0002.</p>
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<p>Heat map data analysis of 61 mung bean genotypes for sensitive and tolerant genotypes. This heat map was made by measuring RWC, PHTI, and Fv/Fm parameters for (<b>A</b>) salt and (<b>B</b>) drought stress to identify the most sensitive and most tolerant genotype among all 61 genotypes.</p>
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<p>Principal component analysis of 61 mung bean genotypes. This PCA plot depicts the characterization of genotypes under salt stress. Here red dot corresponds to a named genotype.</p>
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<p>Principal component analysis of 61 mung bean genotypes. This PCA plot depicts the characterization of genotypes under drought stress. Here, red dot corresponds to a named genotype.</p>
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<p>Biochemical and antioxidative analysis for salt stress between sensitive and tolerant genotypes. The comparison graphs of MDA, H<sub>2</sub>O<sub>2,</sub> Proline, CAT, GPX, GR, APX, SOD, MDHAR, and DHAR, respectively. Here, *—<span class="html-italic">p</span>-value-0.01, **—<span class="html-italic">p</span>-value-0.001, ***—<span class="html-italic">p</span>-value-0.0002, ****—<span class="html-italic">p</span>-value &lt; 0.00001.</p>
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<p>Biochemical and antioxidative analysis for drought stress between sensitive and tolerant genotypes. The comparison graphs of MDA, H<sub>2</sub>O<sub>2,</sub> Proline, CAT, GPX, GR, APX, SOD, and MDHAR, respectively. Here, **—<span class="html-italic">p</span>-value-0.001, ***—<span class="html-italic">p</span>-value-0.0002, ****—<span class="html-italic">p</span>-value &lt; 0.00001.</p>
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<p>The loss of plasma membrane during stress was depicted by Evan’s blue staining.</p>
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<p>A scenario of a correlation study conducted in salt conditions using different biochemical assays. (<b>A</b>) In this instance, the circle’s size and color proportionately correspond to correlation coefficients; (<b>B</b>) we can analyze the correlation between each of the pairwise combinations of distinct variables in this image.</p>
Full article ">Figure 9 Cont.
<p>A scenario of a correlation study conducted in salt conditions using different biochemical assays. (<b>A</b>) In this instance, the circle’s size and color proportionately correspond to correlation coefficients; (<b>B</b>) we can analyze the correlation between each of the pairwise combinations of distinct variables in this image.</p>
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<p>A scenario of a correlation study conducted in drought conditions using different biochemical assays. (<b>A</b>) Drought; (<b>B</b>) salt. In this instance, the circle’s size and color proportionately correspond to correlation coefficients, and we can analyze the correlation between each of the pairwise combinations of distinct variables in this image.</p>
Full article ">Figure 10 Cont.
<p>A scenario of a correlation study conducted in drought conditions using different biochemical assays. (<b>A</b>) Drought; (<b>B</b>) salt. In this instance, the circle’s size and color proportionately correspond to correlation coefficients, and we can analyze the correlation between each of the pairwise combinations of distinct variables in this image.</p>
Full article ">
16 pages, 4237 KiB  
Article
Comparison of Festuca glauca ‘Uchte’ and Festuca amethystina ‘Walberla’ Varieties in a Simulated Extensive Roof Garden Environment
by Dóra Hamar-Farkas, Szilvia Kisvarga, Máté Ördögh, László Orlóci, Péter Honfi and Ildikó Kohut
Plants 2024, 13(16), 2216; https://doi.org/10.3390/plants13162216 (registering DOI) - 9 Aug 2024
Viewed by 143
Abstract
One of the most effective means of increasing urban green areas is the establishment of roof gardens. They have many positive properties and ecological functions, such as filling empty spaces with plants, protecting buildings, dust retention and air cleaning. In the case of [...] Read more.
One of the most effective means of increasing urban green areas is the establishment of roof gardens. They have many positive properties and ecological functions, such as filling empty spaces with plants, protecting buildings, dust retention and air cleaning. In the case of extensive constructions, mostly Sedum species are used, planted as carpet-like “grass” sods or by installing modular units as plugs; however, with the use of other plant genera, the efficiency of ecological services could be increased by expanding the diversity. Festuca taxa have good drought resistance, and these plants tolerate temperature alterations well. Their application would increase the biodiversity, quality and decorative value of roof gardens. Experiments were carried out on nursery benches imitating a roof garden, with the use of modular elements intended for Sedum species, which facilitate the establishment of green roofs. In our trial, varieties of two European native species, Festuca glauca Vill. ‘Uchte’ and F. amethystina L. ‘Walberla’, were investigated. In order to find and determine the differences between the cultivars and the effects of the media (leaf mold and rhyolite tuff), we drew inferences after morphological (height, circumference, root weight, fresh and dry weight) and physiological tests (peroxidase and proline enzyme activity). We concluded that F. glauca ‘Uchte’ is recommended for roof garden conditions, planted in modular elements. Although the specimens were smaller in the medium containing fewer organic components than in the version with larger amounts, they were less exposed to the effects of drought stress. This can be a key factor for survival in extreme roof gardens or even urban conditions for all plants. Full article
(This article belongs to the Section Horticultural Science and Ornamental Plants)
21 pages, 7061 KiB  
Article
Screening New Mungbean Varieties for Terminal Drought Tolerance
by Sobia Ikram, Surya Bhattarai and Kerry B. Walsh
Agriculture 2024, 14(8), 1328; https://doi.org/10.3390/agriculture14081328 (registering DOI) - 9 Aug 2024
Viewed by 179
Abstract
Rainfed mungbean crops in Queensland Australia frequently experience terminal drought (drought stress in the final stages of reproductive development), highlighting the importance of drought-tolerant varieties for sustainable mungbean production. Given there is limited information on the relative drought tolerance of current mungbean varieties [...] Read more.
Rainfed mungbean crops in Queensland Australia frequently experience terminal drought (drought stress in the final stages of reproductive development), highlighting the importance of drought-tolerant varieties for sustainable mungbean production. Given there is limited information on the relative drought tolerance of current mungbean varieties in Australia, the study of genetic variations and mechanisms of drought tolerance in summer mungbean can provide a basis for developing drought-tolerant mungbean varieties. This study evaluated the physiological, biochemical, and phenological traits underpinning yield attributes associated with drought tolerance in selected mungbean varieties. Four new mungbean varieties (AVTMB#1 to 4) and the Australian commercial line (Jade-AU) were grown in tall (75 cm) polyvinyl chloride (PVC) lysimeters where drought stress was imposed at the early flowering stage (R1) and maintained until maturity. Drought stress significantly impacted all the varieties. Averaged across all the varieties, drought stress was associated with a reduction in stomatal conductance (gs) and photosynthetic rate (Asat) by 78% and 86%, respectively, compared to well-watered plants. Internal carbon dioxide concentration (Ci), the effective quantum yield of photosystem II (ΦPSII) and maximum light-use efficiency of light-acclimated photosystem II (PSII) centres (Fv’/Fm’) were also decreased, while excitation pressure (1-qP) increased with drought treatment. A positive correlation (r = 0.60) existed between seed yield and ΦPSII assessed at R1, while a weak correlation with Fv’/Fm’ (r = 0.24) was observed. Excitation pressure (1-qP) at the R1 stage was negatively correlated with seed yield (r = −0.66). Therefore, leaf fluorescence measures, viz., 1-qP and ΦPSII, were recommended for use in screening mungbean varieties for drought tolerance. The varieties, AVTMB#1 and AVTMB#4, respectively achieved 39 and 38% greater seed yields relative to the commercial variety, Jade-AU, under terminal drought conditions. Full article
(This article belongs to the Special Issue Feature Papers in Genotype Evaluation and Breeding)
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Figure 1

Figure 1
<p>Trial set up of five mungbean varieties in lysimeter pots under two water treatments (well-watered and drought stress) in a glasshouse.</p>
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<p>Time course of water used by five mungbean varieties in two water treatments: Well-watered (WW) = 100% water holding capacity (WHC) and drought stress (DS) = 40% WHC. Data are presented as means ± standard errors (SE) (<span class="html-italic">n</span> = 4 plants). The water in DS treatment was withheld from 10 DAS, with 40% water holding capacity achieved at 37 DAS.</p>
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<p>Water-use efficiency (WUE; gL<sup>−1</sup>) of five mungbean varieties in two water treatments, Well-watered (WW) = 100% water holding capacity (WHC), drought stress (DS) = 40% WHC. The water in DS treatment was withheld from 10 DAS and 40% WHC was achieved at 37 DAS. Each vertical bar represents mean values (<span class="html-italic">n</span> = 6) and error bars indicate the standard errors. Letters above vertical bars indicate significant differences among varieties in drought stress and well-watered conditions.</p>
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<p>Light-saturated photosynthetic rate (A<sub>sat;</sub> µmol CO<sub>2</sub> m<sup>−2</sup> s<sup>−1</sup>)—(<b>A</b>) and stomatal conductance (g<sub>s</sub>; mol H<sub>2</sub>O m<sup>−2</sup> s<sup>−1</sup>)—(<b>B</b>) of five mungbean varieties under well-watered (WW) and drought stress (DS), well-watered (WW = 100% water holding capacity; WHC) and drought stress (DS = 40% WHC). The water in DS treatment was withheld from 10 DAS, and 40% water holding capacity was achieved at 37 DAS. Values are mean (±SE) of <span class="html-italic">n</span> = 4 plants and error bars represent standard error. Statistics are reported in <a href="#app1-agriculture-14-01328" class="html-app">Table S2, Supplementary Data</a>.</p>
Full article ">Figure 4 Cont.
<p>Light-saturated photosynthetic rate (A<sub>sat;</sub> µmol CO<sub>2</sub> m<sup>−2</sup> s<sup>−1</sup>)—(<b>A</b>) and stomatal conductance (g<sub>s</sub>; mol H<sub>2</sub>O m<sup>−2</sup> s<sup>−1</sup>)—(<b>B</b>) of five mungbean varieties under well-watered (WW) and drought stress (DS), well-watered (WW = 100% water holding capacity; WHC) and drought stress (DS = 40% WHC). The water in DS treatment was withheld from 10 DAS, and 40% water holding capacity was achieved at 37 DAS. Values are mean (±SE) of <span class="html-italic">n</span> = 4 plants and error bars represent standard error. Statistics are reported in <a href="#app1-agriculture-14-01328" class="html-app">Table S2, Supplementary Data</a>.</p>
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<p>Ci (µmol mol<sup>−1</sup>)—(<b>A</b>) and iWUE—(<b>B</b>) of five mungbean varieties under well-watered (WW) and drought stress (DS). Leaf internal carbon concentration (Ci, µmol mol<sup>−1</sup>)—(<b>A</b>) and Intrinsic water use of light-adapted leaves—(<b>B</b>) of five mungbean varieties in two water treatments: well-watered (WW = 100% water holding capacity; WHC) and drought stress (DS = 40% WHC). The water in DS treatment was withheld from 10 DAS, and 40% water holding capacity was achieved at 37 DAS. Values are mean (±SE) of <span class="html-italic">n</span> = 4 plants and error bars represent standard error. Statistics are reported in; <a href="#app1-agriculture-14-01328" class="html-app">Supplementary Data: Table S2</a>.</p>
Full article ">Figure 5 Cont.
<p>Ci (µmol mol<sup>−1</sup>)—(<b>A</b>) and iWUE—(<b>B</b>) of five mungbean varieties under well-watered (WW) and drought stress (DS). Leaf internal carbon concentration (Ci, µmol mol<sup>−1</sup>)—(<b>A</b>) and Intrinsic water use of light-adapted leaves—(<b>B</b>) of five mungbean varieties in two water treatments: well-watered (WW = 100% water holding capacity; WHC) and drought stress (DS = 40% WHC). The water in DS treatment was withheld from 10 DAS, and 40% water holding capacity was achieved at 37 DAS. Values are mean (±SE) of <span class="html-italic">n</span> = 4 plants and error bars represent standard error. Statistics are reported in; <a href="#app1-agriculture-14-01328" class="html-app">Supplementary Data: Table S2</a>.</p>
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<p>PhiPS2 (ΦPSII)—(<b>A</b>) and Fv’/Fm’—(<b>B</b>) of five mungbean varieties under well-watered (WW) and drought stress (DS). Light accounted for photochemistry PSII (ΦPSII)—(<b>A</b>) and photochemical efficiency of open PSII centres in light-adapted leaves Fv’/Fm’—(<b>B</b>) of five mungbean varieties in two water treatments: well-watered (WW = 100% water holding capacity; WHC) and drought stress (DS = 40% WHC). The water in DS treatment was withheld from 10 DAS, and 40% water holding capacity was achieved at 37 DAS. Values are mean of <span class="html-italic">n</span> = 4 plants with associated error bars representing standard error. Statistics are reported in <a href="#app1-agriculture-14-01328" class="html-app">Table S2, Supplementary Data</a>.</p>
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<p>PhiPS2 (ΦPSII)—(<b>A</b>) and Fv’/Fm’—(<b>B</b>) of five mungbean varieties under well-watered (WW) and drought stress (DS). Light accounted for photochemistry PSII (ΦPSII)—(<b>A</b>) and photochemical efficiency of open PSII centres in light-adapted leaves Fv’/Fm’—(<b>B</b>) of five mungbean varieties in two water treatments: well-watered (WW = 100% water holding capacity; WHC) and drought stress (DS = 40% WHC). The water in DS treatment was withheld from 10 DAS, and 40% water holding capacity was achieved at 37 DAS. Values are mean of <span class="html-italic">n</span> = 4 plants with associated error bars representing standard error. Statistics are reported in <a href="#app1-agriculture-14-01328" class="html-app">Table S2, Supplementary Data</a>.</p>
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<p>Fv/Fm—(<b>A</b>) and 1-qP)—(<b>B</b>) of five mungbean varieties under well-watered (WW) and drought stress (DS). Quantum yield efficiency of dark-adapted leaves (Fv/Fm)—(<b>A</b>) and excitation pressure (1-qP)—(<b>B</b>) of five mungbean varieties in two water treatments: well-watered (WW = 100% WHC) and drought stress (DS = 40% WHC). The water in DS treatment was withheld from 10 DAS, and 40% water holding capacity achieved at 37 DAS. Values are mean (±SE) of <span class="html-italic">n</span> = 4 plants and error bars represent standard error. Statistics are reported in <a href="#app1-agriculture-14-01328" class="html-app">Table S2, Supplementary Data</a>.</p>
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<p>Leaf chlorophyll content (SPAD units) of five mungbean varieties under well-watered (WW = 100% WHC) and drought stress (DS = 40% WHC). The water in DS treatment was withheld from 10 DAS and 40% water holding capacity was achieved at 37 DAS. Values are mean (±SE) of <span class="html-italic">n</span> = 4 plants and error bars represent standard error. Statistics are reported in <a href="#app1-agriculture-14-01328" class="html-app">Table S2, Supplementary Data</a>.</p>
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<p>Seed yield (g plant<sup>−1</sup>) of five mungbean varieties in two water treatments, well-watered (WW) = 100% water holding capacity (WHC), drought stress (DS) = 40% WHC. The water in DS treatment was withheld from 10 DAS and 40% WHC was achieved at 37 DAS. Each vertical bar represents mean values (<span class="html-italic">n</span> = 4) and error bars indicate the standard errors.</p>
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<p>Correlations among studied parameters under drought stress. Correlations amongst A<sub>sat</sub>, g<sub>s</sub>, Ci, iWUE, ΦPSII (PhiPS2), Fv’/Fm’, Fv/Fm, 1-qP, leaf chlorophyll contents (SPAD units), leaf count (LC; #/plant), plant height (PH; cm), leaf dry weight (LDW; g DW/plant), stem dry weight (SDW; g DW/plant), pod dry weight (PDW; g DW/plant), above-ground biomass (AGB; g DW/plant), Root biomass (RB; g DW/plant), root:shoot ratio (R:S), seed yield (YLD; g/plant), 100-seed weight (100SW; g), and harvest index (HI) at flowering stage 37 DAS in drought stress (40% WHC) treatment.</p>
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15 pages, 613 KiB  
Article
A Technological Perspective of Bringing Climate Change Adaptation, Disaster Risk Reduction, and Food Security Together in South Africa
by Annegrace Zembe, Livhuwani David Nemakonde, Paul Chipangura, Christo Coetzee and Fortune Mangara
Sustainability 2024, 16(16), 6844; https://doi.org/10.3390/su16166844 - 9 Aug 2024
Viewed by 420
Abstract
As disasters and climate change risks, particularly droughts and floods, continue to affect food security globally, most governments, including South Africa, have resorted to the use of technology to incorporate climate change adaptation and disaster risk reduction to address FS issues. This is [...] Read more.
As disasters and climate change risks, particularly droughts and floods, continue to affect food security globally, most governments, including South Africa, have resorted to the use of technology to incorporate climate change adaptation and disaster risk reduction to address FS issues. This is because most institutions and policies that address climate change adaptation, disaster risk reduction, and food security operate in parallel, which usually leads to the polarisation of interventions and conflicting objectives, thus leaving the issue of FS unresolved. The study aimed to investigate how food security projects are incorporating climate change adaptation and disaster risk reduction using technology. A qualitative research design was applied, whereby in-depth interviews were conducted with ten project participants from two projects, while 24 key informants were purposively selected from government and research institutions. The study’s main findings revealed that both projects incorporate climate change adaptation and disaster risk reduction measures in most of their food value chains. Although the projects are different, they still face similar challenges, such as a lack of expertise, resources, and funding, and an inadequate regulatory environment to improve their farming practices. The study brings in the practical side of addressing the coherence between food security, climate change adaptation, and disaster risk reduction through technology. Full article
(This article belongs to the Section Sustainable Agriculture)
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<p>Rooftop farm situated on Chamber of Mines building in Johannesburg [<a href="#B64-sustainability-16-06844" class="html-bibr">64</a>].</p>
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19 pages, 2771 KiB  
Article
The Influence of Chitosan Derivatives in Combination with Bacillus subtilis Bacteria on the Development of Systemic Resistance in Potato Plants with Viral Infection and Drought
by Liubov Yarullina, Joanna Kalatskaja, Vyacheslav Tsvetkov, Guzel Burkhanova, Ninel Yalouskaya, Katerina Rybinskaya, Evgenia Zaikina, Ekaterina Cherepanova, Kseniya Hileuskaya and Viktoryia Nikalaichuk
Plants 2024, 13(16), 2210; https://doi.org/10.3390/plants13162210 - 9 Aug 2024
Viewed by 186
Abstract
Viral diseases of potatoes are among the main problems causing deterioration in the quality of tubers and loss of yield. The growth and development of potato plants largely depend on soil moisture. Prevention strategies require comprehensive protection against pathogens and abiotic stresses, including [...] Read more.
Viral diseases of potatoes are among the main problems causing deterioration in the quality of tubers and loss of yield. The growth and development of potato plants largely depend on soil moisture. Prevention strategies require comprehensive protection against pathogens and abiotic stresses, including modeling the beneficial microbiome of agroecosystems combining microorganisms and immunostimulants. Chitosan and its derivatives have great potential for use in agricultural engineering due to their ability to induce plant immune responses. The effect of chitosan conjugate with caffeic acid (ChCA) in combination with Bacillus subtilis 47 on the transcriptional activity of PR protein genes and changes in the proteome of potato plants during potato virus Y (PVY) infection and drought was studied. The mechanisms of increasing the resistance of potato plants to PVY and lack of moisture are associated with the activation of transcription of genes encoding PR proteins: the main protective protein (PR-1), chitinase (PR-3), thaumatin-like protein (PR-5), protease inhibitor (PR-6), peroxidase (PR-9), and ribonuclease (PR-10), as well as qualitative and quantitative changes in the plant proteome. The revealed activation of the expression of marker genes of systemic acquired resistance and induced systemic resistance under the influence of combined treatment with B. subtilis and chitosan conjugate indicate that, in potato plants, the formation of resistance to viral infection in drought conditions proceeds synergistically. By two-dimensional electrophoresis of S. tuberosum leaf proteins followed by MALDI-TOF analysis, 10 proteins were identified, the content and composition of which differed depending on the experiment variant. In infected plants treated with ChCA, the synthesis of proteinaceous RNase P 1 and oxygen-evolving enhancer protein 2 was enhanced in conditions of normal humidity, and 20 kDa chaperonin and TMV resistance protein N-like was enhanced in conditions of lack of moisture. The virus coat proteins were detected, which intensively accumulated in the leaves of plants infected with potato Y-virus. ChCA treatment reduced the content of these proteins in the leaves, and in plants treated with ChCA in combination with Bacillus subtilis, viral proteins were not detected at all, both in conditions of normal humidity and lack of moisture, which suggests the promising use of chitosan derivatives in combination with B. subtilis bacteria in the regulation of plant resistance. Full article
(This article belongs to the Special Issue The Role of Signaling Molecules in Plant Stress Tolerance)
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<p>The appearance of a leaf of a healthy (1) and PVY-infected (2) potato plant.</p>
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<p>Detection of PVY by ELISA in the sap of potato leaves after treatment with ChCA and <span class="html-italic">B. subtilis</span> 47 in normal conditions (1) and water deficiency (2). Different letters denote significantly different values.</p>
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<p>The influence of ChCA and <span class="html-italic">B. subtilis</span> 47 on the proline content (<b>a</b>) and pyrroline-5-carboxylate synthase transcription level (<b>b</b>) in healthy (1) and PVY-infected (2) potato plants on the 10th day after PVY inoculation. Different letters denote significantly different values.</p>
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<p>The effect of the ChCA and <span class="html-italic">B. subtilis</span> 47 on the relative number of transcripts of the PR-1 ((<b>a</b>), main protective protein) and PR-3 ((<b>b</b>), chitinase) genes in healthy (1) and PVY-infected (2) plants under normal conditions and under conditions of water deficiency. Different letters denote significantly different values.</p>
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<p>The effect of the ChCA and <span class="html-italic">B. subtilis</span> 47 on the relative number of transcripts of the PR-5 ((<b>a</b>), thaumatin-like protein) and PR-6 ((<b>b</b>), protease inhibitor) genes in healthy (1) and PVY-infected (2) plants under normal conditions and under conditions of water deficiency. Different letters denote significantly different values.</p>
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<p>The effect of the ChCA and <span class="html-italic">B. subtilis</span> 47 on the relative number of transcripts of the PR-9 ((<b>a</b>), peroxidase) and PR-10 ((<b>b</b>), ribonuclease) genes in healthy (1) and PVY-infected (2) plants under normal conditions and under conditions of water deficiency. Different letters denote significantly different values.</p>
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<p>The effect of the ChCA and <span class="html-italic">B. subtilis</span> 47 on the relative number of transcripts of the StMT (methyltransferase) gene in healthy (1) and PVY-infected (2) plants under normal conditions and under conditions of water deficiency. Different letters denote significantly different values.</p>
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27 pages, 5829 KiB  
Article
Monitoring Changes in the Enhanced Vegetation Index to Inform the Management of Forests
by Peter S. Rodriguez, Amanda M. Schwantes, Andrew Gonzalez and Marie-Josée Fortin
Remote Sens. 2024, 16(16), 2919; https://doi.org/10.3390/rs16162919 - 9 Aug 2024
Viewed by 180
Abstract
In the absence of forest ecosystem time series data, monitoring proxies such as the enhanced vegetation index (EVI) can inform the capacity of forests to provide ecosystem services. We used MODIS-derived EVI at 250 m and 16-day resolution and Breaks for Additive and [...] Read more.
In the absence of forest ecosystem time series data, monitoring proxies such as the enhanced vegetation index (EVI) can inform the capacity of forests to provide ecosystem services. We used MODIS-derived EVI at 250 m and 16-day resolution and Breaks for Additive and Seasonal Trend (BFAST) algorithms to monitor forest EVI changes (breaks and trends) in and around the Algonquin Provincial Park (Ontario, Canada) from 2003 to 2022. We found that relatively little change occurred in forest EVI pixels and that most of the change occurred in non-protected forest areas. Only 5.3% (12,348) of forest pixels experienced one or more EVI breaks and 27.8% showed detectable EVI trends. Most breaks were negative (11,969, 75.3%; positive breaks: 3935, 24.7%) with a median magnitude of change of −755.5 (median positive magnitude: 722.6). A peak of negative breaks (2487, 21%) occurred in the year 2013 while no clear peak was seen among positive breaks. Most breaks (negative and positive) and trends occurred in the eastern region of the study area. Boosted regression trees revealed that the most important predictors of the magnitude of change were forest age, summer droughts, and warm winters. These were among the most important variables that explained the magnitude of negative (R2 = 0.639) and positive breaks (R2 = 0.352). Forest composition and protection status were only marginally important. Future work should focus on assessing spatial clusters of EVI breaks and trends to understand local drivers of forest vegetation health and their potential relation to forest ecosystem services. Full article
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<p>The study area is the Algonquin Provincial Park and the surrounding area, which we refer to as the Algonquin Greater Park Ecosystem (AGPE). Elevation (in meters above sea level, masl) is shown in (<b>a</b>). The forest’s mean age in 2019 is shown in (<b>b</b>). Protected areas within the AGPE are shown in (<b>c</b>). About 16% of forest pixels belong to protected areas. The geographic distribution of three disturbance agents in the period 2002–2020 is shown in (<b>d</b>). The gray color represents forested pixels and white non-forest pixels. The dashed black line shows the study area footprint (≈15,000 km<sup>2</sup> or 1.5 M ha). The solid black line shows Algonquin Provincial Park’s footprint. The two perpendicular dashed lines in (<b>a</b>) divide the study area into quadrants (northwest, NW; northeast, NE; southwest, SW; and southeast, SE) to ease the description of the results. Map projection is NAD83 Statistics Canada Lambert, EPSG: 3347.</p>
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<p>Main methodological steps used in this study. After downloading MODIS EVI data, pixels were filtered to only keep pixels with good quality data, within forested areas. EVI time series were then created and processed with three BFAST algorithms: bfast, bfast01, and bfastclassify. The bfast algorithm decomposes a time series into a seasonal component, trend, and noise. Using piece-wise linear regression on the trend component, it detects one or more breaks (if any). Here, we use three main outputs provided by bfast: type of break, magnitude of break, and time of break with 95% confidence intervals (CIs) The bfast01 algorithm runs a seasonally adjusted regression model on the ts and only detects the major break (if any). The bfastclassify algorithm then uses bfast01’s output to classify trends into one of eight possible trend types (<a href="#remotesensing-16-02919-f0A1" class="html-fig">Figure A1</a>). Only the magnitude of break values estimated by bfast was used in boosted regression trees (XGBoost models) to explore their relationship with predictor variables (dashed box at bottom, Equation (<a href="#FD1-remotesensing-16-02919" class="html-disp-formula">1</a>) in main text). Satellite icon from <a href="http://flaticon.com" target="_blank">flaticon.com</a> (accessed on 29 April 2022).</p>
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<p>Spatial distribution of EVI negative and positive breaks in the AGPE from 2003 to 2022. Most of the breaks were found in the eastern half of the AGPE and more particularly in the northeast quadrant. There were 11,871 pixels with negative breaks (red pixels) and 3893 pixels with positive breaks (cyan pixels). These breaks were estimated with the bfast algorithm. The dashed black line shows the study area footprint. The solid black line shows Algonquin Provincial Park’s footprint. The two perpendicular dashed lines divide the study area into four quadrants (northwest, NW; northeast, NE; southwest, SW; and southeast, SE) to ease the interpretation of results. Map projection is NAD83 Statistics Canada Lambert, EPSG: 3347.</p>
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<p>Spatial distribution of EVI trend types in the AGPE from 2003 to 2022. These trends were produced with the bfastclassify algorithm. The trend types are those proposed by de Jong et al. [<a href="#B57-remotesensing-16-02919" class="html-bibr">57</a>]. Abbreviations—MIG: monotonic increasing, greening trend (<span class="html-italic">n</span> = 33,683); MDB: monotonic decreasing, browning trend (<span class="html-italic">n</span> = 4981); IInb: interruption, increasing trend with a negative break (<span class="html-italic">n</span> = 11,637); RBG: reversal, browning to greening trend (<span class="html-italic">n</span> = 11,654). (Trends MIGpb, MDBnb, IDpb, and RGB are not shown given their low percentages). The dashed black line shows the study area footprint. The solid black line shows Algonquin Provincial Park’s footprint. The two perpendicular dashed lines divide the study area into four quadrants (northwest, NW; northeast, NE; southwest, SW; and southeast, SE) to ease the interpretation of results. Map projection across all figures is NAD83 Statistics Canada Lambert, EPSG: 3347.</p>
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<p>Predictors of the magnitude of EVI breaks (negative breaks in red and positive breaks in cyan tone boxes) from 2003 to 2022. The ranking reflects feature importance using the gain metric as estimated by XGBoost models. The same five predictors are in the top five but with slightly different rankings (connecting lines with slopes) except for the summer climate moisture index with a 3-year lag, which ranks fourth in both (connecting line with no slope). Forest protection status is low-ranking for both types of breaks. The XGBoost models were run with subsets of the detected breaks (negative breaks, <span class="html-italic">n</span> = 116 records; positive breaks, <span class="html-italic">n</span> = 3263 records).</p>
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<p>Schematic of break and trend types employed in this study. A time series (ts) can be characterized by the presence or absence of breaks and trends. Breaks represent abrupt changes in a ts whereas trends represent gradual changes. Here, we refer to three major groups of trends: monotonic (<b>a</b>,<b>b</b>), interruption (<b>c</b>), and reversal trends (<b>d</b>). Similarly, we refer to two break types: negative (red downward arrow) and positive (green upward arrow) breaks. Trends can be monotonic, either increasing (green slopes) or decreasing (orange slopes). The former are referred to as greening trends and the latter as browning trends. Monotonic trends can show no breaks (<b>a</b>) or show one or more breaks (negative or positive) but in concordance with the slope of the trend segments (<b>b</b>). Conversely, interruption (<b>c</b>) and reversal (<b>d</b>) trends are characterized by having a break type in discordance with the slope of the trend segments. Interruption trends can have two positive trend segments divided by a negative break and vice versa (two negative trend segments divided by a positive break). Reversal trends have opposite trend segments divided by a negative or positive break. Lastly, some ts may not change or show changes that are too small to be detected with the methods employed (horizontal gray dashed line in (<b>a</b>)). Here, we use the trend classification proposed by de Jong et al. [<a href="#B57-remotesensing-16-02919" class="html-bibr">57</a>]—MIG: monotonic increasing, greening trend (without breaks) (bottom line in (<b>a</b>)); MDB: monotonic decreasing, browning trend (without breaks) (top line in (<b>a</b>)); MIGpb: monotonic increasing, greening trend with a positive break (top set of lines in (<b>b</b>)); MDBnb: monotonic decreasing, browning trend with a negative break (bottom set of lines in (<b>b</b>)); IInb: interruption, the increasing trend with a negative break (top set of lines in (<b>c</b>)); IDpb: interruption, decreasing trend with a positive break (bottom set of lines in (<b>c</b>)); RGB: reversal, greening to browning trend (top two sets of lines in (<b>d</b>)); RBG: reversal, browning to greening trend (bottom two sets of lines in (<b>d</b>)).</p>
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<p>Density plots of forest EVI magnitude of breaks in the AGPE from 2003 to 2022. The number of breaks and their magnitudes broken down by year are shown. Extreme magnitude values have been omitted to aid visualization. Vertical lines show medians—solid: yearly; dashed: entire time series. The total number of breaks was 15,904 (11,969 negative and 3935 positive). The time of break was rounded up prior to plotting which caused 2003 breaks (8 negative and 18 positive) to be part of 2004. No breaks were detected in 2022.</p>
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<p>Ranking of all predictors (features) used in the XGBoost models. Panel (<b>a</b>) shows predictors of negative break magnitudes and panel (<b>b</b>) shows those of positive break magnitudes. These models were run with subsets of the detected breaks (negative breaks, <span class="html-italic">n</span> = 116 records; positive, <span class="html-italic">n</span> = 3263 records). Variable abbreviations—for_age: forest age; dd5_wt: winter degree days above 5 °C; cmi_sm: summer climate moisture index; for_con: percentage of conifers; for_pro_0: non-protected forest; lag#: 1-, 2- or 3-year lags. The protected forest variable is not present in (<b>a</b>) given its lack of importance in explaining negative breaks.</p>
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<p>Partial dependence plots of magnitude of negative break predictors (features). The relationships between predictors and response (magnitude of EVI breaks) variables were mostly non-linear. Plots were created from the output of XGBoost. The model was run with a subset of all detected negative breaks (<span class="html-italic">n</span> = 116). The values on the y-axes are absolute values of negative magnitudes. Variable abbreviations—for_age: forest age; dd5_wt: winter degree days above 5 °C; cmi_sm: summer climate moisture index; for_con: percentage of conifers; for_pro_0: non-protected forest equals 1, protected equals 0; lag#: 1-, 2- or 3-year lags.</p>
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<p>Partial dependence plots of magnitude of positive break predictors (features). The relationships between predictors and response (magnitude of EVI breaks) variables were mostly non-linear. Plots were created from the output of XGBoost. The model was run with a subset of all the detected positive breaks (<span class="html-italic">n</span> = 3263). Variable abbreviations—for_age: forest age; dd5_wt: winter degree days above 5 °C; cmi_sm: summer climate moisture index; for_con: percentage of conifers; for_pro_0: non-protected forest equals 1, protected equals 0; lag#: 1-, 2- or 3-year lags.</p>
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<p>Geographic distribution of trends in Algonquin Park and the surrounding area, which we refer to as the Algonquin Greater Park Ecosystem (AGPE). All maps show trends that were derived from the output of bfastclassify. Compared to greening trends (MIG) which occurred throughout the AGPE (<b>a</b>), browning trends (MDB) mostly occurred in the NE quadrant (<b>b</b>). Most increasing trends with negative breaks (interruptions, IInb) occurred in the NW quadrant (<b>c</b>) while most of the relatively few decreasing trends with positive breaks (interruptions, IDpb) occurred in the NE quadrant (<b>c</b>). Notably, browning to greening reverse trends (RBG) co-occurred with browning trends in the NE quadrant (<b>d</b>). The dashed black line shows the study area footprint (1.5 M ha). The solid black line shows Algonquin Provincial Park’s footprint. The perpendicular dashed lines divide the study area into four quadrants (northwest, NW; northeast, NE; southwest, SW; and southeast, SE) to ease the description of the results. Map projection across all figures is NAD83 Statistics Canada Lambert, EPSG: 3347.</p>
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13 pages, 4945 KiB  
Article
Adaptation of the Invasive Plant Sphagneticola trilobata (L.) Pruski to Drought Stress
by Qilei Zhang, Ye Wang, Zhilong Weng, Guangxin Chen and Changlian Peng
Plants 2024, 13(16), 2207; https://doi.org/10.3390/plants13162207 - 9 Aug 2024
Viewed by 260
Abstract
Invasive species and their hybrids with native species threaten biodiversity. However, there are few reports on the drought stress adaptability of invasive species Sphagneticola trilobata (L.) Pruski and its hybrid with native species S. calendulacea. In this study, relative water content (RWC), [...] Read more.
Invasive species and their hybrids with native species threaten biodiversity. However, there are few reports on the drought stress adaptability of invasive species Sphagneticola trilobata (L.) Pruski and its hybrid with native species S. calendulacea. In this study, relative water content (RWC), abscisic acid (ABA), reactive oxygen species, antioxidant capacity, and photosynthetic capacity were measured in the hybrid and its parents under drought stress (13% PEG-6000). Under drought stress, the ABA content and RWC in S. trilobata were the highest. RWC decreased by 28% in S. trilobata, 41% in S. calendulacea, and 33% in the hybrid. Activities of the antioxidant enzymes in S. trilobata were the highest, and the accumulation of malondialdehyde (MDA) was the lowest (4.3 μg g−1), while it was the highest in S. calendulacea (6.9 μg g−1). The maximum photochemical efficiency (Fv/Fm) of S. calendulacea was the lowest (0.71), and it was the highest in S. trilobata (7.5) at 8 h under drought stress. The results suggest that the drought resistance of the hybrid was weaker than that of S. trilobata but stronger than that of S. calendulacea. Therefore, the survival of S. calendulacea may be threatened by both the invasive species S. trilobata and the hybrid. Full article
(This article belongs to the Special Issue Plant Ecophysiological Adaptation to Environmental Stress II)
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<p>Leaf phenotypic changes in <span class="html-italic">S. calendulacea</span>, <span class="html-italic">S. trilobata,</span> and their hybrid at 0 h (0 h, <b>A</b>), 2 h (2 h, <b>B</b>), 4 h (4 h, <b>C</b>), 6 h (6 h, <b>D</b>), and 8 h (8 h, <b>E</b>) under PEG-6000-simulated drought stress.</p>
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<p>Under PEG-6000-simulated drought stress, the changes in relative water content (<b>A</b>), abscisic acid (ABA, <b>B</b>) content, relative expression of the zeaxanthin-epoxidase gene (<span class="html-italic">ABA1</span>, <b>C</b>), and 9-cis-epoxycarotenoid dioxygenase gene (<span class="html-italic">NCED</span>, <b>D</b>) in the leaves of <span class="html-italic">S. calendulacea</span>, <span class="html-italic">S. trilobata,</span> and their hybrid. FW, fresh weight. Five biological replicates. Above bars, different lowercase letters indicate statistical significance (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Under PEG-6000-simulated drought stress, the changes in leaf stomatal size (<b>A</b>–<b>F</b>), proline (<b>G</b>), and soluble sugar (<b>H</b>) content in leaves of the hybrid and its parents <span class="html-italic">S. calendulacea</span> and <span class="html-italic">S. trilobata</span>. FW, fresh weight; DW, dry weight. Five biological replicates.</p>
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<p>Under PEG-6000-simulated drought stress, the accumulation of hydrogen peroxide (DAB staining, <b>A</b>), superoxide anion (NBT staining, <b>B</b>), the changes in content of malondialdehyde (MDA, <b>C</b>), and activities of superoxide dismutase (SOD, <b>D</b>), catalase (CAT, <b>E</b>), and peroxidase (POD, <b>F</b>) in leaves of the hybrid and its parents <span class="html-italic">S. calendulacea</span> and <span class="html-italic">S. trilobata</span>. DAB: 3,3′-Diaminobenzidine; NBT: nitroblue tetrazolium. FW, fresh weight. Five biological replicates.</p>
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<p>Under PEG-6000-simulated drought stress, the changes in maximum photochemical efficiency (F<sub>v</sub>/F<sub>m</sub>, <b>A</b>), actual photochemical efficiency (yield, <b>B</b>), electron transport rate (ETR, <b>C</b>), non-photochemical quenching (NPQ, <b>D</b>), net photosynthetic rate (P<sub>n</sub>, <b>E</b>), transpiration rate (T<sub>r</sub>, <b>F</b>), stomatal conductance (G<sub>s</sub>, <b>G</b>), and intercellular CO<sub>2</sub> content (C<sub>i</sub>, <b>H</b>) in leaves of the hybrid and its parents <span class="html-italic">S. calendulacea</span> and <span class="html-italic">S. trilobata</span>. Five biological replicates.</p>
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15 pages, 1021 KiB  
Article
Physiological Responses of Hollyhock (Alcea rosea L.) to Drought Stress
by Arezoo Sadeghi, Hassan Karimmojeni, Jamshid Razmjoo and Timothy C. Baldwin
Horticulturae 2024, 10(8), 841; https://doi.org/10.3390/horticulturae10080841 - 8 Aug 2024
Viewed by 218
Abstract
Hollyhock (Alcea rosea L.) is an aromatic, ornamental/medicinal plant species for which the selection of drought-tolerant varieties based on physio-chemical traits is desirable. The data presented resulted from a field experiment. This experiment was designed as a split-plot, based on a randomized complete [...] Read more.
Hollyhock (Alcea rosea L.) is an aromatic, ornamental/medicinal plant species for which the selection of drought-tolerant varieties based on physio-chemical traits is desirable. The data presented resulted from a field experiment. This experiment was designed as a split-plot, based on a randomized complete block design, in which the main plots consisted of the three irrigation regimes (30, 60 and 80% permissible discharge moisture available in the soil), and the subplots consisted of nine hollyhock varieties. Photosynthetic pigments, Fv/Fm, proline content and selected antioxidant enzymes were measured throughout the period of induced drought stress. The data obtained illustrate the nature of the physiological response of hollyhock to drought stress. Based on the measured traits the varieties Isfahan 1, Shiraz 1 and Tabriz were shown to display the highest degree of resistance to drought stress. These data suggest that the effect of drought stress is dependent upon the drought level, variety and the trait in question. In this regard, future plant breeders for this species may find it useful to utilize ascorbate peroxidase (APX), catalase (CAT) and guayacol peroxidase (POX) activities as biochemical markers to select for drought-tolerant genotypes. As such, hollyhock can be considered a promising ornamental/medicinal species for cultivation in semi-arid environments. Full article
(This article belongs to the Section Biotic and Abiotic Stress)
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<p>Floral phenotypes observed, during the flowering phase of growth of a selection of the hollyhock varieties used in the current study. From the top left to the bottom right, the varieties shown are Khomeini Shahr 1, a mixture of varieties in the experimental plot, Tabriz, Isfahan 2, a mixture of varieties in the experimental plot, Mahallat, Shiraz 2 and Isfahan 1.</p>
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<p>Monthly average maximum and minimum temperature (°C) and total monthly precipitation (mm) in growing season in 2017.</p>
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21 pages, 2482 KiB  
Review
The Function of Macronutrients in Helping Soybeans to Overcome the Negative Effects of Drought Stress
by Mariola Staniak, Ewa Szpunar-Krok, Edward Wilczewski, Anna Kocira and Janusz Podleśny
Agronomy 2024, 14(8), 1744; https://doi.org/10.3390/agronomy14081744 - 8 Aug 2024
Viewed by 382
Abstract
Nutrient deficiencies are a major cause of yield loss under abiotic stress conditions, so proper nutrient management can reduce the negative effects of stress to some extent. Nutrients can alleviate stress by activating resistance genes, enhancing antioxidant enzyme activity, creating osmoprotectants in cells, [...] Read more.
Nutrient deficiencies are a major cause of yield loss under abiotic stress conditions, so proper nutrient management can reduce the negative effects of stress to some extent. Nutrients can alleviate stress by activating resistance genes, enhancing antioxidant enzyme activity, creating osmoprotectants in cells, reducing reactive oxygen species (ROS) activity, increasing cell membrane stability, synthesizing proteins associated with stress tolerance, and increasing chlorophyll content in leaves. The current review highlights changes in soybean metabolic activity caused by drought stress and changes in vital functions caused by the deficiency of primary (N, K, P) and secondary macronutrients (Ca, Mg, S). The role of macronutrients in reducing the adverse effects of water deficit stress is highlighted. Under stressed conditions, appropriate nutrient management options can be implemented to minimize the effects of drought and ensure good yields. Balanced nutrient fertilization helps activate various plant mechanisms to mitigate the effects of abiotic stresses and improve soybean drought resistance/tolerance. Nutrient management is therefore a viable technique for reducing environmental stress and increasing crop productivity. Full article
(This article belongs to the Special Issue Advances in Soil Fertility, Plant Nutrition and Nutrient Management)
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<p>Examples of activation of selected plant mechanisms by application of N, P, and K to alleviate plant stress (modified from Kumari et al. [<a href="#B36-agronomy-14-01744" class="html-bibr">36</a>]).</p>
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<p>Examples of activation of selected plant mechanisms by application of secondary nutrients (Ca, Mg, and S) to alleviate plant stress (modified from Kumari et al. [<a href="#B36-agronomy-14-01744" class="html-bibr">36</a>]).</p>
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<p>Physiological mechanisms of legume–rhizobia responses to P, K and S deficiencies. (Divito and Sadras [<a href="#B145-agronomy-14-01744" class="html-bibr">145</a>]). Pathway [<a href="#B1-agronomy-14-01744" class="html-bibr">1</a>] involves reduction in shoot growth in response to nutrient deficit. Pathway [<a href="#B2-agronomy-14-01744" class="html-bibr">2</a>] involves a relative accumulation of N in shoot mass. Pathway [<a href="#B3-agronomy-14-01744" class="html-bibr">3</a>] involves the N-feedback mechanism that down regulates biological N fixation (BNF). Asparagine is mentioned as a main regulator. Pathway [<a href="#B4-agronomy-14-01744" class="html-bibr">4</a>] involves reduction in nodule mass and number and pathway [<a href="#B5-agronomy-14-01744" class="html-bibr">5</a>] reduction in nodule productivity. Pathway [<a href="#B6-agronomy-14-01744" class="html-bibr">6</a>] involves direct effects in nodule growth and functioning. Pathway [<a href="#B7-agronomy-14-01744" class="html-bibr">7</a>] involves the effect of carbon limitation in nodule functioning. Pathway [<a href="#B8-agronomy-14-01744" class="html-bibr">8</a>] involves maintenance of high nutrient concentration in nodules.</p>
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<p>Accumulation of nitrogen (<b>A</b>), phosphorus (<b>B</b>), potassium (<b>C</b>), calcium (<b>D</b>), magnesium (<b>E</b>) and sulfur (<b>F</b>) under water deficiency and nitrogen fertilization depending on the soybean development stage (Setubal et al. [<a href="#B168-agronomy-14-01744" class="html-bibr">168</a>]).</p>
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15 pages, 2520 KiB  
Article
Cataloging the Genetic Response: Unveiling Drought-Responsive Gene Expression in Oil Tea Camellia (Camellia oleifera Abel.) through Transcriptomics
by Zhen Zhang, Yanming Xu, Caixia Liu, Longsheng Chen, Ying Zhang, Zhilong He, Rui Wang, Chengfeng Xun, Yushen Ma, Xiaokang Yuan, Xiangnan Wang, Yongzhong Chen and Xiaohu Yang
Life 2024, 14(8), 989; https://doi.org/10.3390/life14080989 - 8 Aug 2024
Viewed by 217
Abstract
Drought stress is a critical environmental factor that significantly impacts plant growth and productivity. However, the transcriptome analysis of differentially expressed genes in response to drought stress in Camellia oleifera Abel. is still unclear. This study analyzed the transcriptome sequencing data of C. [...] Read more.
Drought stress is a critical environmental factor that significantly impacts plant growth and productivity. However, the transcriptome analysis of differentially expressed genes in response to drought stress in Camellia oleifera Abel. is still unclear. This study analyzed the transcriptome sequencing data of C. oleifera under drought treatments. A total of 20,674 differentially expressed genes (DEGs) were identified under drought stress, with the number of DEGs increasing with the duration of drought. Specifically, 11,793 and 18,046 DEGs were detected after 8 and 15 days of drought treatment, respectively, including numerous upregulated and downregulated genes. Gene Ontology (GO) enrichment analysis showed that the DEGs were primarily involved in various biological processes. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis revealed that carbon metabolism, glyoxylate and dicarboxylate metabolism, proteasome, glycine, serine, and threonine metabolism were the main affected pathways. Among the DEGs, 376 protein kinases, 42 proteases, 168 transcription factor (TF) genes, and 152 other potential functional genes were identified, which may play significant roles in the drought response of C. oleifera. The expression of relevant functional genes was further validated using quantitative real-time PCR (qRT-PCR). These findings contribute to the comprehension of drought tolerance mechanisms in C. oleifera and bolster the identification of drought-resistant genes for molecular breeding purposes. Full article
(This article belongs to the Special Issue Plant Functional Genomics and Breeding)
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<p>Study site.</p>
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<p>Phenotypic changes of <span class="html-italic">C. oleifera</span> under drought stress. (<b>a</b>) 0 d; (<b>b</b>) 8 d; and (<b>c</b>) 15 d.</p>
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<p>Distribution of reads in different regions of <span class="html-italic">C. oleifera</span> genome.</p>
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<p>Venn diagram analysis of DEGs at different time points. (<b>a</b>) Upregulated DEGs and (<b>b</b>) downregulated DEGs.</p>
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<p>Top 20 KEGG enrichment pathways under drought treatment. Rich factor represents the ratio of the number of DEGs in the pathway.(<b>a</b>) Top 20 KEGG enichment between 0 d vs 8 d and (<b>b</b>) Top 20 KEGG enichment between 0 d vs 15 d.</p>
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12 pages, 1759 KiB  
Article
Zooplankton Assemblages of an Argentinean Saline Lake during Three Contrasting Hydroperiods and a Comparison with Hatching Experiments
by Santiago Andrés Echaniz, Alicia María Vignatti and Gabriela Cecilia Cabrera
Limnol. Rev. 2024, 24(3), 301-312; https://doi.org/10.3390/limnolrev24030018 - 8 Aug 2024
Viewed by 187
Abstract
Many saline lakes are temporary, with large variations in salinity, and their biota is adapted to withstand unfavorable periods. Utracan Lake, in a protected area in central Argentina, was studied on three occasions under different environmental conditions. In 2007, depth and salinity were [...] Read more.
Many saline lakes are temporary, with large variations in salinity, and their biota is adapted to withstand unfavorable periods. Utracan Lake, in a protected area in central Argentina, was studied on three occasions under different environmental conditions. In 2007, depth and salinity were 2 m and 33 g/L, and six species were recorded in the zooplankton. In 2009–2010, its maximum depth was 0.3 m, its salinity exceeded 230 g/L, and only Artemia persimilis was recorded. Field studies to compare the active zooplankton of a third period were combined with laboratory tests to ascertain the composition of the egg bank (flotation with sucrose) and zooplankton succession (hatching from sediments). In 2017–2018 (third period), the depth and salinity were 1.75 ± 0.17 m and 47.19 ± 11.40 g/L, respectively. Five species were recorded, and A. persimilis was found coexisting with cladocerans, copepods, and rotifers. Brachionus plicatilis, Hexarthra fennica, Boeckella poopoensis, A. persimilis, and a single specimen of Moina eugeniae were recorded in hatching experiments; however, the latter species was not recorded again. No cladoceran ephippia were recorded in the flotation tests. Salt accumulation on the sediments during the Utracan drought (2010–2016) would have deteriorated the ephippia. The register of M. eugeniae in 2017–2018 could be largely because of recolonization by waterfowl. The conservation of Utracan Lake is therefore advisable, and the same goes for other nearby saline lakes, which can act as sources of propagules that cross terrestrial areas through transport by wind or zoochory. Full article
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<p>Geographic location of Utracan Lake.</p>
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<p>Average annual rainfall between 1921 and 2023 in the region where Utracan Lake is located, determined in General Acha City. Solid line: annual average. Dashed line: time trend.</p>
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<p>Variation in dissolved oxygen concentration and pH (<b>A</b>) and salinity (<b>B</b>) throughout the hatching bioassays from the sediment of Utracan Lake.</p>
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<p>Species that hatched from the egg bank of the sediment of Utracan Lake and the periods during which they were recorded in the hatching bioassays.</p>
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<p>Variation in the average density of the three typical species of the mesosaline periods registered throughout the hatching bioassays from the sediment of Utracan Lake. <span class="html-italic">Boeckella poopoensis</span> includes copepodites and adults.</p>
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