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Search Results (994)

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15 pages, 2976 KiB  
Article
Foliar Biofortification of Maize (Zea mays L.) with Selenium: Effects of Compound Type, Application Rate, and Growth Stage
by Tomáš Mrština, Lukáš Praus, Jiřina Száková, Lukáš Kaplan and Pavel Tlustoš
Agriculture 2024, 14(12), 2105; https://doi.org/10.3390/agriculture14122105 - 21 Nov 2024
Abstract
Nowadays, attention is focused on the lack of selenium in the average diet, which is a highly valued element in the body’s antioxidant system. The major metabolites of selenium are selenoproteins, which have an irreplaceable function in the body. This study focused on [...] Read more.
Nowadays, attention is focused on the lack of selenium in the average diet, which is a highly valued element in the body’s antioxidant system. The major metabolites of selenium are selenoproteins, which have an irreplaceable function in the body. This study focused on optimizing conditions for the biofortification of maize (Zea mays L.) with selenium (Se). Three separate pot experiments were conducted to identify the key factors influencing the efficacy of foliar selenium application. The experiments were designed to investigate the effects of different forms of selenium (selenite, selenate, and selenium nanoparticles) on maize development, the influence of the phenological stage of maize at the time of foliar Se application, and the optimal application rate of Se (100, 150, 200, or 250 µg). The results indicated that sodium selenate without a wetting agent was the most effective form for enhancing total Se content in maize, with the greatest accumulation being in leaves (3.01 mg/kg dry matter). Phenological stages (BBCH) 51 and 60 were identified as the most suitable phenological stages for Se application in terms of total Se content about 1 mg/kg in leaves and about 0.4 mg/kg in grain and the presence of organic Se compounds (mostly selenate ion and selenomethionine). We concluded from the study that a foliar application of 200 µg of sodium selenate per pot during these stages resulted in maximum Se uptake without adversely affecting plant yield. Further research is recommended to validate these findings under field conditions, paving the way for improved agricultural practices in selenium biofortification. Full article
(This article belongs to the Section Crop Production)
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<p>Yield of maize plant parts after application of different Se forms. Different lowercase letters indicate a statistically significant differences among the treatments according to a one-way analysis of variance (<span class="html-italic">p</span> &lt; 0.05, <span class="html-italic">n</span> = 4).</p>
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<p>Total selenium content in parts of maize after application of different Se forms. Different lowercase letters indicate statistically significant differences among the treatments by a one-way analysis of variance (<span class="html-italic">p</span> &lt; 0.05, <span class="html-italic">n</span> = 4).</p>
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<p>Yields of maize parts after the application of Se in different phenological phases. Different lowercase letters indicate statistically significant differences among the treatments by a one-way analysis of variance (<span class="html-italic">p</span> &lt; 0.05, <span class="html-italic">n</span> = 4).</p>
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<p>Contents of total selenium in maize parts (<span class="html-italic">Zea mays</span> L.) after application of Se in different phenological phases. Different lowercase letters indicate statistically significant differences among treatments according to a one-way analysis of variance (<span class="html-italic">p</span> &lt; 0.05, <span class="html-italic">n</span> = 4).</p>
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<p>Yields of maize (<span class="html-italic">Zea mays</span> L.) parts after the application of different Se concentrations. Different lowercase letters indicate statistically significant differences among the treatments according to a one-way analysis of variance (<span class="html-italic">p</span> &lt; 0.05, <span class="html-italic">n</span> = 4).</p>
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<p>Content of total selenium in maize parts (<span class="html-italic">Zea mays</span> L.) after the application of different concentrations of Se. Different lowercase letters indicate a statistically significant differences among the treatments according to the one-way analysis of variance (<span class="html-italic">p</span> &lt; 0.05, <span class="html-italic">n</span> = 4).</p>
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<p>Regression curve slopes modelling the relationship between the applied Se dose and Se content in maize leaf.</p>
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<p>Regression curve slopes modelling the relationship between the applied Se dose and Se content in maize grain.</p>
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<p>Regression curve slopes modelling the relationship between the applied Se dose and Se content in maize stover.</p>
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17 pages, 4865 KiB  
Article
Morpho-Phenological, Chemical, and Genetic Characterization of Italian Maize Landraces from the Lazio Region
by Rita Redaelli, Laura Bassolino, Carlotta Balconi, Irma Terracciano, Alessio Torri, Federica Nicoletti, Gianluca Benedetti, Valentina Iacoponi, Roberto Rea and Paola Taviani
Plants 2024, 13(22), 3249; https://doi.org/10.3390/plants13223249 - 20 Nov 2024
Viewed by 326
Abstract
In the framework of a Collaboration Agreement between CREA and ARSIAL, a morpho-phenological, chemical, and genetic characterization of maize populations native to the Lazio region was carried out. During 2022 and 2023, a set of 50 accessions, belonging both to ARSIAL and CREA [...] Read more.
In the framework of a Collaboration Agreement between CREA and ARSIAL, a morpho-phenological, chemical, and genetic characterization of maize populations native to the Lazio region was carried out. During 2022 and 2023, a set of 50 accessions, belonging both to ARSIAL and CREA maize collections, were multiplied in Bergamo. Morpho-phenological descriptors were recorded in the field: plant height, ear height, and male and female flowering time. The grain chemical composition in terms of protein, lipid, starch, ash and fiber was evaluated by near-infrared spectroscopy (NIRS). A double-digest restriction-site-associated DNA sequencing (ddRADseq) strategy was used to genotype the landraces. The two collections were not significantly different in terms of grain chemical composition. On the other hand, the ARSIAL and CREA germplasm showed a different distribution in the three cluster-based population structure obtained by ddRADseq, which largely corresponded to the distribution map of their collection sites. The materials from the Lazio region maintained by ARSIAL and CREA were revealed to be different. The comparison between the two groups of landraces showed the importance of characterizing germplasm collections to promote the recovery and valorization of local biodiversity. Full article
(This article belongs to the Section Crop Physiology and Crop Production)
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<p>Box-plots of the field traits (DT, days to tasseling; ASI, anthesis–silking interval; PH, plant height; EH/PH, ear height/plant height ratio) registered during 2022 and 2023.</p>
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<p>PCA biplot of the grain chemical composition of the landraces analyzed.</p>
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<p>Heatmap showing the p-distance matrix for the 50 genotypes. The heatmap plot depicts the discrepancy of each sample by color intensity. The higher the value, the closer to red, and thus the larger is the discrepancy between two samples. The dendrograms on the top and on the left indicate the genetic relatedness between samples according to the p-distance matrix.</p>
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<p>Unveiling the genetic diversity of maize landraces from the Lazio region using ddRADseq. (<b>A</b>) PCA based on the Prevosti distance of the similarity between the 50 samples. The three clusters are represented by different colors, with cluster 1 (red), cluster 2 (green), and cluster 3 (blue) grouping 26, 15, and 5 individuals, respectively. The outlier sample VE-0439 was removed. (<b>B</b>) Population structure inferred by STRUCTURE. A Distruct plot representing the admixture of populations with the number of expected populations set to 3 (K = 3) is shown. Each landrace is represented on the <span class="html-italic">X</span>-axis and visualized into K colors according to its membership coefficient.</p>
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<p>Unveiling the genetic diversity of maize landraces from the Lazio region using ddRADseq. (<b>A</b>) PCA based on the Prevosti distance of the similarity between the 50 samples. The three clusters are represented by different colors, with cluster 1 (red), cluster 2 (green), and cluster 3 (blue) grouping 26, 15, and 5 individuals, respectively. The outlier sample VE-0439 was removed. (<b>B</b>) Population structure inferred by STRUCTURE. A Distruct plot representing the admixture of populations with the number of expected populations set to 3 (K = 3) is shown. Each landrace is represented on the <span class="html-italic">X</span>-axis and visualized into K colors according to its membership coefficient.</p>
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<p>(<b>A</b>) Sites of collection of maize landraces in Lazio (red dots, CREA collection; blue dots, ARSIAL collection); (<b>B</b>) geographical distribution according to the genetic clusters (cluster1, red; cluster 2, green; cluster 3, blue).</p>
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<p>Grains of the maize accessions from the CREA and ARSIAL collections (with codes VA and VE, respectively).</p>
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<p>Weather conditions during 2022 and 2023 in Bergamo: rainfall (mm) and temperatures (°C).</p>
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16 pages, 1989 KiB  
Article
Evaluation of Five Asian Lily Cultivars in Chongqing Province China and Effects of Exogenous Substances on the Heat Resistance
by Ningyu Bai, Yangjing Song, Yu Li, Lijun Tan, Jing Li, Lan Luo, Shunzhao Sui and Daofeng Liu
Horticulturae 2024, 10(11), 1216; https://doi.org/10.3390/horticulturae10111216 - 17 Nov 2024
Viewed by 393
Abstract
Lily is one of the world’s important ornamental flowers. Potted Asiatic lily is a further selected dwarf cultivar suitable for indoor or garden planting. However, there is a lack of relevant research on the cultivation adaptability of potted Asiatic lilies cultivars in the [...] Read more.
Lily is one of the world’s important ornamental flowers. Potted Asiatic lily is a further selected dwarf cultivar suitable for indoor or garden planting. However, there is a lack of relevant research on the cultivation adaptability of potted Asiatic lilies cultivars in the Chongqing region which in the southwest of China. This study selected five potted Asiatic lily cultivars, and the phenological period, stem and leaf characteristics, and flowering traits were assessed through statistical observation. The Asiatic lily ‘Tiny Ghost’ and ‘Tiny Double You’ are well-suited for both spring and autumn planting in Chongqing, while ‘Sugar Love’ and ‘Curitiba’ are best planted in the spring. The ‘Tiny Diamond’ is more appropriate for autumn planting due to its low tolerance to high temperature. The application of exogenous substances, including calcium chloride (CaCl2), potassium fulvic acid (PFA) and melatonin (MT), can mitigate the detrimental effects of high-temperature stress on ‘Tiny Diamond’ by regulating photosynthesis, antioxidant systems, and osmotic substance content. A comprehensive evaluation using the membership function showed that the effect of exogenous CaCl2 treatment is the best, followed by exogenous PFA treatment. CaCl2 acts as a positive regulator of heat stress tolerance in Asian lilies, with potential applications in Asian lily cultivation. This study provides reference for cultivation and application of Asian lily varieties in Chongqing region, and also laid the foundation for further research on the mechanism of exogenous substances alleviating heat stress in lilies. Full article
(This article belongs to the Special Issue Emerging Insights into Horticultural Crop Ecophysiology)
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<p>Asian lily cultivars. (<b>A</b>). ‘Tiny Double You’; (<b>B</b>). ‘Curitiba’; (<b>C</b>). ‘Tiny Diamond’; (<b>D</b>). ‘Sugar Love’; (<b>E</b>). ‘Tiny Ghost’.</p>
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<p>Oxidative stress indexes of ‘Tiny Diamond’ after exogenous application of different substances under high temperature stress. (<b>A</b>). The relative water content of lily. (<b>B</b>). The MDA content of lily. (<b>C</b>). The REL rate of lily. Note: CK: H<sub>2</sub>O; M1: 100 μmol/L MT; M2: 200 μmol/L MT; P1: 0.5 g/L PFA; P2: 1.0 g/L PFA; C1: 20 mmol/L CaCl<sub>2</sub>; C2: 40 mmol/L CaCl<sub>2</sub>. Different lowercase letters indicate significant differences between treatments (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Chlorophyll content of ‘Tiny Diamond’ after application of exogenous substances. Different lowercase letters indicate significant differences between treatments (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>SOD content of ‘Tiny Diamond’ after application of exogenous substances. Different lowercase letters indicate significant differences between treatments (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Content of osmoregulatory substances in ‘Tiny Diamond’ after application of exogenous substances. (<b>A</b>). Proline content. (<b>B</b>). Soluble protein content. (<b>C</b>). Total soluble sugar content. Different lowercase letters indicate significant differences between treatments (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Correlation analysis of ten indicators under treatment with three exogenous substances. Note: * means correlation is extremely significant at the 0.05 level, ** means correlation is extremely significant at the 0.01 level.</p>
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18 pages, 1768 KiB  
Article
Off-Crop and Off-Season Monitoring, Key Elements to Be Integrated into an Effective Strategy for the Control of Drosophila suzukii (Diptera: Drosophilidae)
by Ana A. R. M. Aguiar, Joana Neto, Pedro A. S. Sousa, Vanessa Roque and Leonor Chichorro
Agronomy 2024, 14(11), 2714; https://doi.org/10.3390/agronomy14112714 - 17 Nov 2024
Viewed by 424
Abstract
Drosophila suzukii is a pest affecting a wide range of host plants, causing severe damage to small fruits, berries, and grapes. This study analyzed environmental factors influencing its population dynamics in regions where temperature is not a limiting factor. Data were collected in [...] Read more.
Drosophila suzukii is a pest affecting a wide range of host plants, causing severe damage to small fruits, berries, and grapes. This study analyzed environmental factors influencing its population dynamics in regions where temperature is not a limiting factor. Data were collected in the spring–summer seasons of 2018 and 2019 across three vineyards in northwestern Portugal, examining the relationship between captured D. suzukii females, climatic variables, vine phenological stages, and ecological infrastructures. A stepwise linear model and Pearson correlation matrix were used. In 2020, a winter study was conducted in nine vineyards, focusing on landscape composition and its effect on D. suzukii populations. An ecological infrastructure index was created and correlated with captures data. Results show that vine phenological stages and nearby ecological infrastructures significantly affect population dynamics in spring and summer. Vineyards surrounded by complex landscapes, especially with wild hosts, supported higher D. suzukii populations during winter. These findings highlight the importance of ecological infrastructures in managing D. suzukii populations year-round and suggest their consideration in pest control strategies. Full article
(This article belongs to the Section Pest and Disease Management)
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<p>Classification of ecological infrastructures in the landscape, defining the indicators to be evaluated and grouping them into criteria.</p>
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<p>Monthly DSF captured in the EDM region, temperatures and relative humidity registered during 2018 and 2019 spring and summer, and harvest period in vineyards.</p>
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<p>Log10 of <span class="html-italic">D. suzukii</span> captured collected during winter, on each vineyard. Each boxplot associated with the same letter did not show significant differences in Dunn’s post hoc test (α = 0.05). Each number corresponds to one of nine vineyards analyzed: 1—Cermouros 2—Veiga da Correlhã, 3—Quinta Sra. da Piedade, 4—Requião, 5—Gestaçô, 6—Quinta da Massôrra, 7—Santa Marinha do Zêzere, 8—Fermil, and 9—Fundões.</p>
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<p>Maps of each vineyard with representation of the area for photointerpretation (<b>A</b>) and with classification of ecological infrastructures according to indicators (<b>B</b>). Each number corresponds to one of the nine vineyards analyzed: 1—Cermouros 2—Veiga da Correlhã, 3—Quinta Sra. da Piedade, 4—Requião, 5—Gestaçô, 6—Quinta da Massôrra, 7—Santa Marinha do Zêzere, 8—Fermil, and 9—Fundões.</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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23 pages, 3871 KiB  
Article
Automating the Derivation of Sugarcane Growth Stages from Earth Observation Time Series
by Neha Joshi, Daniel M. Simms and Paul J. Burgess
Remote Sens. 2024, 16(22), 4244; https://doi.org/10.3390/rs16224244 - 14 Nov 2024
Viewed by 727
Abstract
Sugarcane is a high-impact crop used in the majority of global sugar production, with India being the second largest global producer. Understanding the timing and length of sugarcane growth stages is critical to improving the sustainability of sugarcane management. Earth observation (EO) data [...] Read more.
Sugarcane is a high-impact crop used in the majority of global sugar production, with India being the second largest global producer. Understanding the timing and length of sugarcane growth stages is critical to improving the sustainability of sugarcane management. Earth observation (EO) data have been shown to be sensitive to the variation in sugarcane growth, but questions remain as to how to reliably extract sugarcane phenology over wide areas so that this information can be used for effective management. This study develops an automated approach to derive sugarcane growth stages using EO data from Landsat-8 and Sentinel-2 satellite data in the Indian state of Andhra Pradesh. The developed method is then evaluated in the State of Telangana. Normalised difference vegetation index (NDVI) EO data from Landsat-8 and Sentinel-2 were pre-processed to filter out clouds and to harmonise sensor response. Pixel-based cloud filtering was selected over filtering by scene in order to increase the temporal frequency of observations. Harmonising data from two different sensors further increased temporal resolution to 3–6 days (70% of sampled fields). To automate seasonal decomposition, harmonised signals were resampled at 14 days, and low-frequency components, related to seasonal growth, were extracted using a fast Fourier transform. The start and end of each season were extracted from the time series using difference of Gaussian and were compared to assessments based on visual observation for both Unit 1 (R2 = 0.72–0.84) and Unit 2 (R2 = 0.78–0.82). A trapezoidal growth model was then used to derive crop growth stages from satellite-measured phenology for better crop management information. Automated assessments of the start and the end of mid-season growth stages were compared to visual observations in Unit 1 (R2 = 0.56–0.72) and Unit 2 (R2 = 0.36–0.79). Outliers were found to result from cloud cover that was not removed by the initial screening as well as multiple crops or harvesting dates within a single field. These results demonstrate that EO time series can be used to automatically determine the growth stages of sugarcane in India over large areas, without the need for prior knowledge of planting and harvest dates, as a tool for improving sustainable production. Full article
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Figure 1
<p>Sugarcane fields associated with processing Units 1 and 2 located in southern India. Sugarcane area Unit 1 is situated in (<b>a</b>) Andhra Pradesh, and (<b>b</b>) Unit 2 is in Telangana.</p>
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<p>A schematic diagram depicting the four stages of sugarcane growth in India based on the FAO model for sugarcane evapotranspiration [<a href="#B31-remotesensing-16-04244" class="html-bibr">31</a>].</p>
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<p>The methodology was established using data from Unit 1 and then evaluated using data from Unit 2.</p>
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<p>Before cloud filtering, cloud cover caused many low values of the Normalised Difference Vegetation Index (NDVI) (grey lines) from the Sentinel-2 (left in (<b>a</b>)) and Landsat-8 (right in (<b>b</b>)) data for sugarcane fields from September 2017 to October 2019. Using Method 1 to filter the clouds removed many of the low NDVI values (red lines), but more low values were removed using Method 2 (blue lines).</p>
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<p>A frequency diagram presenting the average number of cloud-free observations received by each field, from three datasets, in one year (1 January 2018–1 January 2019). The number of observations received by the Landsat-8 (L8 TOA) dataset is presented by the green line, and the number of observations received by the Sentinel-2 dataset (S2 TOA) is presented by the orange line. The purple line represents the harmonised (L8/S2 TOA) dataset, which is the Landsat-8 and Sentinel-2 dataset combined.</p>
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<p>Correlation between the Landsat-8 (L8 TOA) NDVI observations and the Sentinel-2 (S2 TOA) observations for Unit 1. The solid black line shows ordinary least-squares (OLS) regression of the Landsat-8 data against the Sentinel-2 data. Only NDVI values in the range of 0.0 to 1.0 are illustrated. The hashed red line shows a reference 1:1 line, and the colour bar represents the density of observations.</p>
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<p>An illustration of how field reference data of sugarcane growth stages were manually interpreted using an example sugarcane field in Unit 1 for the 2018 season. The cloud-filtered NDVI time series data for the example field are displayed in grey, with Sentinel-2 observations marked in red and Landsat-8 observations marked in blue. An idealized trapezoid profile for the field marked with hashed black lines superimposed on top of the NDVI time series profile helped with the manual interpretations of growth stages. The start of the season was manually interpreted as the first point of the sugarcane growing season detected from EO imagery, and the end of the season was interpreted as the last low observation after the end of the mid-season before a rapid increase in NDVI (marking the start of a new growth cycle).</p>
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<p>The effect of changing the best index slope extraction (BISE) sliding period on the number of additional erroneous troughs removed per field per growth cycle (left-hand scale) and the root mean square error (RMSE) (right-hand scale) in the calculation of the start and end of the sugarcane growth cycle.</p>
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<p>Time series NDVI decomposed into individual seasons using the low-frequency components of a fast Fourier transform for a sugarcane field with four ratoons.</p>
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<p>A schematic diagram illustrating how the start of the mid-season (SMS) and the end of the mid-season (EMS) were derived.</p>
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<p>Unit 1 sugarcane fields used for algorithm development showing the relationship between automated and manually derived (<b>left</b>) start of the season and (<b>right</b>) end of season, using 14-day resampled time series, where day is the number of days after 1 September 2017. Equations show the linear relationship before the removal of outliers.</p>
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<p>Unit 2 sugarcane fields used for validation showing the relationship between the automated and manually derived (<b>left</b>) start of the season and (<b>right</b>) end of season using 14-day resampled time series, where day is the number of days after 1 September 2017. Results are not affected by field size. Equations show the linear relationship before the removal of outliers.</p>
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<p>Sugarcane fields from Unit 1 used for calibration, showing the relationship between automated and manually derived (<b>left</b>) start of mid-season and (<b>right</b>) end of mid-season, using 14-day resampled time series, where day is the number of days after 1 September 2017. The equations show the linear relationship after the removal of outliers.</p>
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<p>Sugarcane fields from Unit 2 used for validation, showing the relationship between automated and manually derived (<b>left</b>) start of mid-season and (<b>right</b>) end of mid-season, using 14-day resampled time series, where day is the number of days after 1 September 2018. The equations show the linear relationship after the removal of outliers.</p>
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13 pages, 3913 KiB  
Article
Configuration of Low-Cost Miniature Heat Pulse Probes to Monitor Heat Velocity for Sap Flow Assessments in Wheat (Triticum durum L.)
by Oscar Parra-Camara, Luis A. Méndez-Barroso, R. Suzuky Pinto, Jaime Garatuza-Payán and Enrico A. Yépez
Grasses 2024, 3(4), 320-332; https://doi.org/10.3390/grasses3040024 - 14 Nov 2024
Viewed by 325
Abstract
Heat velocity (Vh) is a key metric to estimate sap flow which is linked to transpiration rate and is commonly measured using thermocouples implanted in plant stems or tree trunks. However, measuring transpiration rates in the Gramineae family, characterized by thin [...] Read more.
Heat velocity (Vh) is a key metric to estimate sap flow which is linked to transpiration rate and is commonly measured using thermocouples implanted in plant stems or tree trunks. However, measuring transpiration rates in the Gramineae family, characterized by thin and hollow stems, is challenging. Commercially available sensors based on the measurement of heat velocity can be unaffordable, especially in developing countries. In this work, a real-time heat pulse flux monitoring system based on the heat ratio approach was configured to estimate heat velocity in wheat (Triticum durum L.). The heat velocity sensors were designed to achieve optimal performance for a stem diameter smaller than 5 mm. Sensor parameterization included the determination of casing thermal properties, stabilization time, and time to achieve maximum heat velocity which occurred 30 s after applying a heat pulse. Heat velocity sensors were able to track plant water transport dynamics during phenological stages with high crop water demand (milk development, dough development, and end of grain filling) reporting maximum Vh values in the order of 0.004 cm s−1 which scale to sap flow rates in the order of 3.0 g h−1 comparing to reports from other methods to assess sap flow in wheat. Full article
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<p>Location of the facilities where the experiments were performed. (<b>A</b>) Location of the Nainari campus of the Sonora Institute of Technology (ITSON) in Ciudad Obregon, Mexico (yellow polygon). (<b>B</b>) Location of Ciudad Obregon within the Northwestern Mexican State of Sonora. (<b>C</b>) Location of the greenhouse and laboratory facilities within ITSON’s campus (star).</p>
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<p>The general appearance of the sensor for estimating heat velocity in wheat stems (<b>A</b>), detailed dimensions of the rectangular-shaped casing showing the heater with upper and lower thermocouples (<b>B</b>). Components of the heat velocity monitoring system depicting the heat velocity gauge and a heat pulse control panel (<b>C</b>), which are then connected to a datalogger aided by a multiplexer.</p>
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<p>Insulation capsule used to minimize the effects of environmental factors on temperature readings from the sensor thermocouples and ensure good contact between the sensor and the wheat stem. (<b>A</b>) The heat velocity sensor installed in a main wheat stem. The capsule wrapped around the sensor using a couple of layers of the following materials: (<b>B</b>) Parafilm<sup>®</sup>, (<b>C</b>) polyurethane sheets, (<b>D</b>) bubble wrap, and (<b>E</b>) aluminum foil. (<b>F</b>) Appearance of the insulation capsule installed on the wheat stem.</p>
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<p>Aspect of the experiment carried out within a greenhouse at the beginning of the grain filling stage. As observed, a heat velocity sensor wrapped with an insulation capsule was installed on each wheat plant and connected to a multiplexer and datalogger (both were inside the enclosure, located to the right of the image). In addition, temperature and relative humidity near the plants were continuously measured with a HMP45C (Vaisala) sensor (left of the image).</p>
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<p>Temperature variation in the downstream (<span class="html-italic">δT<sub>d</sub></span>; blue solid line) and upstream (<span class="html-italic">δT<sub>u</sub></span>; red solid line) thermocouples, including their standard error of the sample mean (<span class="html-italic">n</span> = 14) (orange and blue-colored polygons), in response to a 6-s thermal pulse (shown as a red dashed line between 0 and 6 s). The period of temperature stabilization used for averaging the upstream and downstream temperatures ranged between 20 and 40 s after the heat pulse with a midpoint of <span class="html-italic">t</span><sub>m</sub> = 30 s. The gray zone represents the range of temperature variations during 18 days of observations integrating roughly 2500 pulses.</p>
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<p>Time series of heat velocity (V<span class="html-italic"><sub>h</sub></span>) with its standard deviation of the mean (shaded red in the upper panel), photosynthetically active radiation (PAR, middle panel), and vapor pressure deficit (VPD, lower panel) with a 10-min timestep. The measurements were taken in three periods of six days each during milk development (Z7.0), dough development, and (Z8.0) end of grain filling or beginning of ripening (Z9.0) in the Zadoks’decimal scale [<a href="#B37-grasses-03-00024" class="html-bibr">37</a>]. The data presented corresponds to the mean of fourteen heat pulse probes performed on an equal number of wheat plants.</p>
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14 pages, 716 KiB  
Article
Supplemental Low-Irradiance Mono/Polychromatic LED Lighting Significantly Enhances Floral Biology of the Long-Day F1 Hybrid Strawberry ‘Soraya’ (Fragaria x ananassa Duch.)
by Edward Durner
Int. J. Plant Biol. 2024, 15(4), 1187-1200; https://doi.org/10.3390/ijpb15040082 - 13 Nov 2024
Viewed by 353
Abstract
Floral and vegetative responses of the strawberry (Fragaria x ananassa Duch.) to specific light wavelengths are not well documented. LED lights make it feasible for precise exposure to specific wavelengths during a 24 h cycle to alter growth responses regulated by phytochromes [...] Read more.
Floral and vegetative responses of the strawberry (Fragaria x ananassa Duch.) to specific light wavelengths are not well documented. LED lights make it feasible for precise exposure to specific wavelengths during a 24 h cycle to alter growth responses regulated by phytochromes and cryptochromes and thereby potentially enhance fruit productivity in both a controlled environment and field systems or to enhance stolon production for controlled environment propagation. This research developed a systematic method to assess the effects of supplemental, low-irradiance LED lighting on strawberry flowering and vegetative biology. Growth of the long-day F1 seed-propagated cultivar ‘Soraya’ was evaluated during and following 6 or 12 weeks of exposure to supplemental red (660 nm), far-red (730 nm), blue (454 nm), or incandescent lighting at various times during the dark period of a 24 h cycle under a 10 h non-inductive photoperiod at non-inductive temperatures (>27/18 °C, day/night). Treatment effects were monitored via flower mapping and phenology during treatment, field and greenhouse production after treatment, and floral scores derived by ranking treatment effects within the evaluation method and then combining them into a single, simple score. The most promising treatment for enhancing the floral nature of plug plants was exposure to far-red + red light as a 5 h night interruption. This treatment increased inflorescence production in the greenhouse by 285% and resulted in multi-branched, floral plants with the potential for enhancing yield in either greenhouse or field production. Greenhouse runner production increased by 483% following exposure to incandescent lighting at the beginning of the dark period; thus, this treatment or one using a spectral distribution similar to incandescent may be suitable for enhancing vegetative propagation in controlled environments. Full article
(This article belongs to the Section Plant Physiology)
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<p>Relative spectral distributions for (<b>a</b>) full-spectrum, (<b>b</b>) red, (<b>c</b>) far-red, (<b>d</b>) far-red + red, (<b>e</b>) incandescent, and (<b>f</b>) blue light fixtures described in <a href="#sec2-ijpb-15-00082" class="html-sec">Section 2</a> of the text.</p>
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19 pages, 2250 KiB  
Review
Climate Change as an Existential Threat to Tropical Fruit Crop Production—A Review
by Chinnu Raju, Sellaperumal Pazhanivelan, Irene Vethamoni Perianadar, Ragunath Kaliaperumal, N. K. Sathyamoorthy and Vaithiyanathan Sendhilvel
Agriculture 2024, 14(11), 2018; https://doi.org/10.3390/agriculture14112018 - 8 Nov 2024
Viewed by 458
Abstract
Climate change is an emerging threat to global food and nutritional security. The tropical fruits such as mango, bananas, passionfruit, custard apples, and papaya are highly sensitive to weather changes especially; changes of monsoon onset and elevated temperature are influencing crop growth and [...] Read more.
Climate change is an emerging threat to global food and nutritional security. The tropical fruits such as mango, bananas, passionfruit, custard apples, and papaya are highly sensitive to weather changes especially; changes of monsoon onset and elevated temperature are influencing crop growth and production. There is a need for more specific studies concerning individual crops and regional variations. Long-term effects and interactions of weather parameters and increased concentration of greenhouse gases, especially carbon dioxide, with phenological stages of the plant, pests, and diseases remain understudied, while adaptation strategies require further exploration for comprehensive understanding and effective mitigation. Few researchers have addressed the issues on the effect of climate change on tropical fruits. This paper focuses on the impact of abiotic (temperature, rainfall, humidity, wind speed, evaporation, carbon dioxide concentration) and biotic (pest and pathogens dynamics) factors affecting the fruit crop ecosystem. These factors influence flowering, pollination, fruit set, fruit yield and quality. This review paper will help develop adaptive strategies, policy interventions and technological innovations aimed at mitigating the adverse effects of climate change on tropical fruit production and safeguarding global food and nutritional security. Full article
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<p>PRISMA flow diagram.</p>
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<p>Effect of high temperature stress on different phenological stages of fruit crops.</p>
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<p>Effects of high-temperature stress on major tropical fruits.</p>
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<p>Effects of low-temperature stress on major tropical fruits.</p>
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<p>Responses of fruit crops to elevated CO<sub>2</sub> levels in the atmosphere.</p>
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<p>Responses of insects under climate change scenario.</p>
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19 pages, 4916 KiB  
Article
Sylleptic over Proleptic Type of Free Growth in Young Norway Spruce Plantations: Stem Quality, Tree Height and Phenology Considerations
by Darius Danusevičius, Simonas Šilingas and Gerda Šilingienė
Forests 2024, 15(11), 1965; https://doi.org/10.3390/f15111965 - 7 Nov 2024
Viewed by 712
Abstract
It is crucial for northerly Norway spruce to understand how seasonal warming and site conditions influence the intensity of free growth and what the effects of free growth on stem quality and adaptedness are. We studied the intensity of sylleptic and proleptic free [...] Read more.
It is crucial for northerly Norway spruce to understand how seasonal warming and site conditions influence the intensity of free growth and what the effects of free growth on stem quality and adaptedness are. We studied the intensity of sylleptic and proleptic free growth in 660 6-to-9-year-old Norway spruce trees planted on normally irrigated and temporary overmoistured sites of variable fertility. We focused on the ability of individual trees to retain a type of free growth over three seasons and examined the associations between free growth, stem quality, and phenology traits. The results show that 23% to 50% of trees exhibited free growth, depending on the season. Mild and warm conditions in August and September tended to promote free growth. Among trees aged 6 to 9 years, 82% to 84% of those without free growth maintained this status over the following two seasons. While sylleptic growth decreased with age, proleptic growth increased. Over the seasons, individual trees were more consistent in maintaining proleptic growth than sylleptic growth. Trees on moist site types exhibited significantly more free growth than those in normally irrigated sites across all seasons. Trees with both sylleptic and proleptic free growth were significantly taller than those without free growth; however, sylleptic trees showed a markedly lower frequency of stem defects compared with those with proleptic growth. Free growth intensity was weakly associated with spring phenology and appeared to disrupt the well-established associations between phenology traits within the annual cycle. We conclude that selecting trees for overall height, particularly those with sylleptic free growth, may well exploit the benefits of free growth without significantly increasing the risk of autumn or winter frost damage. Full article
(This article belongs to the Section Forest Ecology and Management)
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<p>(<b>A</b>) Types of free growth of the leader shoot in fall of three years, 2020, 2021, 2022, in the Norway spruce plantations established in 2016: 0—no free growth. 1—sylleptic type of free growth, where no visible bud scale burst is observed on the apical surrounding terminal buds at the top of the leader shoot (the boxed top). In fall, all buds counted on the leader, including the terminals (11 buds in the figure with type 1), along with the buds that broke the scales and elongated for at least a few millimeters (4 such buds in the figure with type 1); length of the longest lateral shoot that formed after the second flush was measured (in type 1, marked by an arrow, the lower right); 2—proleptic type of free growth where budburst of the terminal buds on the leader shoot was observed. The buds were counted in the same way as for sylleptic growth (11 buds in total of these 7 with budbust). Length of the longest elongating terminal shot was measured (marked by arrow, upper right in type 2). (<b>B</b>) Spring budburst scores from 0 to 6.</p>
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<p>The main characteristics of 20% superior by height trees at age 8 (that was the oldest age of the assessments) compared with the overall mean and standard error for the same year 2022 (age 8). Budburst is the budburst in spring (growth onset). Columns of the bar plots are abbreviated at the bottom of the figure to indicate what features the corresponding variable represents. Numbers in the brackets below the X-axis labels show the sample sizes. At the bar top, the mean trait values are shown along with the results for Tukey LSD test from the ANOVA, where the same letter at the top of each bar indicates no significant difference at the 0.05 significance level. The error bars are the standard errors. “% of fall bud burst” is the % of the burst buds on the leader shoot in fall (the main indicator of free growth intensity).</p>
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<p>Effect of autumn air temperature (T) and air humidity (rh) on intensity of free growth during the three seasons in 2020, 2021, 2022 (age 6 to 9 years). Simplified diagram of mean daily values of T and rh is given (see actual climatic data in <a href="#app1-forests-15-01965" class="html-app">Figure S4</a>). The blue shadings show rh by decade to be accounted for in the rh scores to the left (60%–90%). The red line drawn over the humidity bars shows temperature to be accounted for by the scale on the right side of the box. The three bar plots show free growth intensity: % of budburst of all buds on the leader in fall (<b>upper left</b>), proportion of trees with more than one budburst on the leader in fall (<b>upper right</b>), and length of the longest lateral (sylleptic) shoot on the leader (<b>lower left</b>). Number of trees by type of free growth (<b>lower right</b>). Moist and warm August together with warm September, such as in 2020, is likely to promote free growth (shown by arrows).</p>
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<p>The two upper plots: associations between intensity of free growth between 2020 and the two following seasons by taking the % of the autumn budburst on the leader as the indicator variable. Free growth category shows the shares of trees that fall into the following three autumn budbust intensity classes: 0 = no budbust (no free growth), 1 = up to 30% of the buds on the leader busted in autumn and 23 over 30% autumn budbust ton the leader (classes 2 and 3 pooled). The lower two plots show ability to retain specific type of free growth during the three seasons.</p>
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<p>Site effect on intensity free growth (based on % autumn budburst on the leader (<b>upper left</b>); the error bars are standard errors and tree frequency with autumn budburst on the leader (<b>upper right</b>)). The lower line of bar plots shows the effects of site type on shifting between the free growth types over the three years (data values at the bars are the tree numbers). Over the years, the proleptic type increased mainly in moist fertile sites (LC).</p>
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<p>Associations between the type of free growth and growth, sprig phenology, stem quality, and free growth intensity variables. The numbers at the X-axis labels are the sample sizes for trees with no free growth (0), sylleptic, and proleptic types of free growth. The letters at the data labels show the Tukey LSD test results, where the same letter indicates no significant difference at 0.05 <span class="html-italic">p</span> level. The error bars are standard errors.</p>
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<p>Leader height for the trees with no free growth (0): sylleptic and proleptic types are given by site type and year. LC—temporarily overmoistured rich, NC—normally irrigated rich. The letters at the data labels show the Tukey LSD test results, where the same letter indicates no significant difference at 0.05 <span class="html-italic">p</span> level. The error bars are standard errors.</p>
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21 pages, 5011 KiB  
Article
The Phenology of Coffea arabica var. Esperanza L4A5 Under Different Agroforestry Associations and Fertilization Conditions in the Caribbean Region of Costa Rica
by Victor Hugo Morales Peña, Argenis Mora Garcés, Elias de Melo Virginio Filho, Mario Villatoro Sánchez, Willy William Pazmiño Pachay and Esteban Chanto Ares
Agriculture 2024, 14(11), 1988; https://doi.org/10.3390/agriculture14111988 - 6 Nov 2024
Viewed by 510
Abstract
This study focused on the phenology of Coffea arabica var. Esperanza L4A5, an F1 interspecific hybrid obtained by crossing commercial varieties with wild genotypes from Ethiopia and Sudan. Most phenological studies on C. arabica have been conducted in traditional high-altitude regions, leaving a [...] Read more.
This study focused on the phenology of Coffea arabica var. Esperanza L4A5, an F1 interspecific hybrid obtained by crossing commercial varieties with wild genotypes from Ethiopia and Sudan. Most phenological studies on C. arabica have been conducted in traditional high-altitude regions, leaving a gap in the understanding of its behavior in non-traditional areas such as the Caribbean region of Costa Rica. To establish a baseline on the phenological behavior of the Esperanza L4A5 hybrid in this region, we conducted a four-year study examining the effects of different agroforestry associations: (1) Albizia saman; (2) Hymenaea courbaril and Erythrina poeppigiana; (3) Anacardium excelsum and Erythrina poeppigiana; and coffee plots under full sun. Additionally, the phenology of the coffee plants was evaluated under differentiated fertilizations (physical, chemical, and without fertilization), considering meteorological factors such as temperature, humidity, and rainfall. The observed variables included the development of floral nodes, pre-anthesis, anthesis, and fruiting stages. To analyze the relationships between environmental factors, tree cover, fertilization, and the phenological stages, we employed multiple linear regression (MLR), which revealed that both tree cover and physical and chemical fertilizations had significant effects on the presence of developed floral nodes and, consequently, on fruit production. Furthermore, the random forest (RF) model was applied to capture complex interactions between variables and to rank the importance of meteorological factors, tree cover, and fertilization practices. These analyses demonstrated that the Esperanza L4A5 hybrid exhibited viable phenological development under the atypical conditions of the Caribbean region of Costa Rica, suggesting its potential to adapt and thrive in non-traditional coffee-growing areas. Full article
(This article belongs to the Special Issue Agroforestry Systems: Strategies for Mitigating Climate Change)
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<p>Location of agroforestry research area based on hybrids of <span class="html-italic">Coffea arabica</span> var. Esperanza L4A5, established in September 2019. The map base was used: <a href="https://paintmaps.com/blank-maps/52/samples" target="_blank">https://paintmaps.com/blank-maps/52/samples</a>.</p>
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<p>Spatial arrangement of the agroforestry trial: (<b>a</b>) The location of the agroforestry associations and full-sun sectors and (<b>b</b>) the spatial location of the plots under differentiated fertilization and with amendment only.</p>
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<p>Characterization of the aerial vegetative organs: (<b>a</b>,<b>b</b>) floral nodes development, (<b>c</b>) pre-anthesis, and (<b>d</b>) floral anthesis.</p>
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<p>The phenological characterization of the first year: (<b>a</b>) the adaptability stage of the plant material in the nursery; (<b>b</b>) the establishment of the coffee plants in the trial area; (<b>c</b>) the maintenance of the trial area, focused on weed control; (<b>d</b>) the first fertilization of the coffee plants, considering differentiated fertilizations and experimental control areas with only liming; (<b>e</b>) the full development of floral buds; (<b>f</b>) flowering; (<b>g</b>) the start of the fruit-filling process; (<b>h</b>) the general development of the fruits; (<b>i</b>) the full development of the fruit; and (<b>j</b>) fruit maturity, initiating the harvesting process.</p>
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<p>Monthly variations of meteorological variables (2020–2023): (<b>a</b>) average temperature (°C); (<b>b</b>) relative humidity (%); and (<b>c</b>) accumulated precipitation (mm).</p>
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<p>Phenological phases of Esperanza L4A5, established in the Caribbean region of Costa Rica.</p>
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16 pages, 4276 KiB  
Article
Preliminary Mapping of the Spatial Variability in the Microclimate in Tropical Greenhouses: A Pepper Crop Perspective
by Angel Triana, Alfonso Llanderal, Pedro García-Caparrós, Manuel Donoso, Rafael Jiménez-Lao, John Eloy Franco Rodríguez and María Teresa Lao
Agriculture 2024, 14(11), 1972; https://doi.org/10.3390/agriculture14111972 - 3 Nov 2024
Viewed by 774
Abstract
The objectives of this experiment were to (1) discern the spatial variability in climatic parameters within a greenhouse throughout different phenological stages of pepper cultivation and (2) develop an empirical model aimed at establishing predictive equations for temperature, relative humidity, vapor pressure deficit, [...] Read more.
The objectives of this experiment were to (1) discern the spatial variability in climatic parameters within a greenhouse throughout different phenological stages of pepper cultivation and (2) develop an empirical model aimed at establishing predictive equations for temperature, relative humidity, vapor pressure deficit, and crop evapotranspiration (ETc) within the greenhouse considering the climatic parameters recorded on the outside. The experiment was conducted in the coastal area of Ecuador within a bamboo-constructed greenhouse facility. Pepper plants were cultivated in plastic bags using a specific cultivation medium common in Ecuador and a fertigation system. Climatic parameters were monitored within the greenhouse using data loggers, and the external conditions were recorded using an external meteorological station throughout the duration of the pepper cultivation. Statistical analyses revealed correlations between internal climatic parameters and plant growth stages, as well as external climatic conditions. The spatial distribution analysis of climatic parameters within the greenhouse revealed that the lowest values for temperature (27 °C) and vapor pressure deficit (VPD) (1.25 kPa) and the highest values for relative humidity (RH) (68%) were observed on the northwest corner of the greenhouse. This observed pattern was linked to the prevailing wind direction (south–east (SE)) outside the greenhouse. Stepwise regression analyses identified significant outdoor climate variables (RH, temperature, VPD, and instantaneous wind speed (WS) Inst) in the climatic conditions recorded within the greenhouse. Full article
(This article belongs to the Section Agricultural Technology)
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<p>The structural layout of the greenhouse and the spatial distribution of climatic sensors within the greenhouse. (<b>A</b>) Frontal perspective, (<b>B</b>) lateral perspectives, and (<b>C</b>) data logger (blue marks) positioning within the greenhouse.</p>
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<p>Radar plot analysis for (<b>a</b>) wind direction frequency and (<b>b</b>) instantaneous wind speed average and maximum over the experimental period in the outdoor conditions.</p>
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<p>Relationship between data logger positioning and environmental parameters in the greenhouse (x = length, y = width, and z = parameter): (<b>a</b>) temperature (°C), (<b>b</b>) relative humidity (%), and (<b>c</b>) vapor pressure deficit (VPD) expressed in absolute value (kPa) during the vegetative stage.</p>
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<p>Relationship between data logger positioning and environmental parameters in the greenhouse (x = length, y = width, and z = parameter): (<b>a</b>) temperature (°C), (<b>b</b>) relative humidity (%), and (<b>c</b>) vapor pressure deficit expressed in absolute value (kPa) during the flowering stage.</p>
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<p>Relationship between data logger positioning and environmental parameters in the greenhouse (x = length, y = width, and z = parameter): (<b>a</b>) temperature (°C), (<b>b</b>) relative humidity (%), and (<b>c</b>) vapor pressure deficit expressed in absolute value (kPa) during the fruit development stage.</p>
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<p>Relationship between data logger positioning and environmental parameters in the greenhouse (x = length, y = width, and z = parameter): (<b>a</b>) temperature (°C), (<b>b</b>) relative humidity (%), and (<b>c</b>) vapor pressure deficit expressed in absolute value (kPa) during the harvesting stage.</p>
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<p>Relationship between data logger positioning and environmental parameters in the greenhouse (x = length, y = width, and z = parameter): ETc (mm day<sup>−1</sup>).</p>
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14 pages, 2640 KiB  
Article
Image-Based Quantitative Analysis of Epidermal Morphology in Wild Potato Leaves
by Ulyana S. Zubairova, Ivan N. Fomin, Kristina A. Koloshina, Alisa I. Barchuk, Tatyana V. Erst, Nadezhda A. Chalaya, Sophia V. Gerasimova and Alexey V. Doroshkov
Plants 2024, 13(21), 3084; https://doi.org/10.3390/plants13213084 - 1 Nov 2024
Viewed by 649
Abstract
The epidermal leaf patterns of plants exhibit remarkable diversity in cell shapes, sizes, and arrangements, driven by environmental interactions that lead to significant adaptive changes even among closely related species. The Solanaceae family, known for its high diversity of adaptive epidermal structures, has [...] Read more.
The epidermal leaf patterns of plants exhibit remarkable diversity in cell shapes, sizes, and arrangements, driven by environmental interactions that lead to significant adaptive changes even among closely related species. The Solanaceae family, known for its high diversity of adaptive epidermal structures, has traditionally been studied using qualitative phenotypic descriptions. To advance this, we developed a workflow combining multi-scale computer vision, image processing, and data analysis to extract digital descriptors for leaf epidermal cell morphology. Applied to nine wild potato species, this workflow quantified key morphological parameters, identifying descriptors for trichomes, stomata, and pavement cells, and revealing interdependencies among these traits. Principal component analysis (PCA) highlighted two main axes, accounting for 45% and 21% of variance, corresponding to features such as guard cell shape, trichome length, stomatal density, and trichome density. These axes aligned well with the historical and geographical origins of the species, separating southern from Central American species, and forming distinct clusters for monophyletic groups. This workflow thus establishes a quantitative foundation for investigating leaf epidermal cell morphology within phylogenetic and geographic contexts. Full article
(This article belongs to the Special Issue Microscopy Techniques in Plant Studies—2nd Edition)
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<p>A workflow of microscopy and computer vision methods for quantifying morphological parameters of leaf epidermal patterns. The stages of the experimental protocol, along with the intermediate steps of data acquisition, analysis, and integration, are shown on colored backings.</p>
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<p>Morphological features of the epidermal leaf pattern in nine wild potato species. (<b>A</b>) Phylogenetic relationships among the species and the shape of their leaves. (<b>B</b>) The morphology of pavement cells and stomata in the leaf epidermis. (<b>C</b>) Trichome types specific to each species. “ab” and “ad” refer to the abaxial and adaxial sides of the leaf, respectively. Types II, III, VI-c, VI-a, and VII are referred to according to classification from [<a href="#B37-plants-13-03084" class="html-bibr">37</a>].</p>
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<p>Dimensional and dimensionless parameters of leaf epidermal cell morphology in wild potatoes. Samples of 3D images of adaxial (<b>A</b>) and abaxial (<b>B</b>) leaf epidermis of <span class="html-italic">S. pinnatisectum</span>, showing different epidermal cell types (pavement cells, stomata, trichomes). (<b>C</b>) Evaluation of dimensional parameters and (<b>D</b>) dimensionless parameters of epidermal cell shape. (<b>E</b>) Correlation matrix (Kendall tau) for the estimated epidermal cell shape parameters in the wild potato species studied. Parameters are grouped according to cell types and are highlighted with gray lines for further analysis. Abbreviations for parameters: for morphometric parameters, L is length, D is density, A is area, CA is convex hull area, P is perimeter, CP is convex hull perimeter, W is width, C is circularity, R is rectangularity, CC is convex hull coverage, E is elongation; for cell types, tr is trichome, gtr is glandular trichome, st is stomata, and there is no designation for pavement cells; ab is the abaxial and ad is the adaxial side of the leaf.</p>
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<p>Comparison between geographical origins of wild potato species and morphological characteristics of leaf epidermal cells: (<b>A</b>) Proposed geographical origins of wild potato species. (<b>B</b>) Principal component analysis (PCA) and (<b>C</b>) cluster analysis of nine wild potato species, based on pairwise distances between the medians of morphological characteristics of the leaf epidermis.</p>
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9 pages, 892 KiB  
Article
Thermal Stress as a Critical Factor in the Viability and Duration of Spittlebug Eggs
by Milena Duarte, Luís Augusto Calsavara and Alexander Machado Auad
Stresses 2024, 4(4), 676-684; https://doi.org/10.3390/stresses4040043 - 21 Oct 2024
Viewed by 375
Abstract
The spittlebug Mahanarva spectabilis (Distant, 1909) (Hemiptera: Cercopidae) is an important pest that causes significant losses in the production of forage crops for cattle feed. Information on the thermal requirements of this insect during the egg stage is crucial in assessing the interaction [...] Read more.
The spittlebug Mahanarva spectabilis (Distant, 1909) (Hemiptera: Cercopidae) is an important pest that causes significant losses in the production of forage crops for cattle feed. Information on the thermal requirements of this insect during the egg stage is crucial in assessing the interaction between insects and forage. The aim of this research was to evaluate the effects of constant and oscillating (diurnal/nocturnal) temperatures on the viability of M. spectabilis eggs and the duration of the egg stage. Temperatures of 20 °C to 30 °C were ideal for the development of this insect pest, resulting in greater viability and faster development of the embryos. In addition, it should be noted that a variation of up to 8 days is feasible for synchronizing the phenological stages of the forage plants and the eggs to be laid on these plants when subjected to 30 °C (16.6 days) or 20 °C (25.7 days) without significantly altering the viability of the eggs. Notably, a temperature oscillation of 25 °C during the day and 15 °C at night increased the viability of the eggs after exiting diapause. These results are essential for the rearing of M. spectabilis in the laboratory, allowing for the supply of eggs for experiments and contributing to advances in studies aimed at developing effective integrated management strategies for this pest. Full article
(This article belongs to the Collection Feature Papers in Human and Animal Stresses)
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<p>Duration (in days) of embryonic stages S1 (<b>A</b>), S2 (<b>B</b>), S3 (<b>C</b>), and S4 (<b>D</b>) and total egg stage (<b>E</b>) of <span class="html-italic">M. spectabilis</span> specimens subjected to constant temperatures (10, 15, 20, 25, and 30 ± 1 °C), an RH of 70% ± 10%, and a 12 h photophase, and durations (in days) of total egg stage of insects after exposure to different periods of temperature oscillation (diurnal/nocturnal) (<b>F</b>).</p>
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<p>Viability (%) of embryonic stages S1 (<b>A</b>), S2 (<b>B</b>), S3 (<b>C</b>), and S4 (<b>D</b>) and total egg stage (<b>E</b>) of <span class="html-italic">M. spectabilis</span> specimens subjected to constant temperatures (10, 15, 20, 25, and 30 ± 1 °C), 70% ± 10% RH, and 12 h photophase, and viability (%) of total-egg-stage insects after exposure to different periods of temperature oscillation (diurnal/nocturnal) (<b>F</b>).</p>
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16 pages, 4157 KiB  
Article
Chilling and Forcing Requirements of Wintersweet (Chimonanthus praecox L.) Flowering in China
by Yulong Hao, Junhu Dai, Mengyao Zhu, Lijuan Cao and Khurram Shahzad
Forests 2024, 15(10), 1832; https://doi.org/10.3390/f15101832 - 20 Oct 2024
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Abstract
Numerous studies have reported phenological changes and their driving mechanisms in spring flowering plants. However, there is little research on the shifts of winter flowering phenology and its response to forcing and chilling requirements. Based on the China Phenological Observation Network (CPON) ground [...] Read more.
Numerous studies have reported phenological changes and their driving mechanisms in spring flowering plants. However, there is little research on the shifts of winter flowering phenology and its response to forcing and chilling requirements. Based on the China Phenological Observation Network (CPON) ground observation data from nine sites over the past 20 years, we explored the spatial and temporal variation patterns of flowering plants and their response to chilling and forcing in wintersweet (Chimonanthus praecox L.), a common winter flowering plant species in temperate and subtropical zones of China. We used three chilling models (chilling hour, Utah, and dynamic models) and the growing degree hours (GDHs) model to calculate each site’s daily chilling and forcing. Using the partial least squares (PLSs) regression approach, we established the relationship between the first flowering date (FFD) and pre-season chilling and forcing in wintersweet, based on which we identified chilling and forcing periods and calculated chilling and forcing requirements. This study found that the FFD of wintersweet in China showed an overall advancement trend during the last 20 years. Still, there were temporal and spatial differences in the FFD of wintersweet among different sites. The PLS results showed that wintersweet also had periods of chilling and forcing, both of which co-regulated wintersweet flowering. We found the forcing and chilling requirements of wintersweet varied significantly from site to site. The higher the latitude is, the more chilling requirements are needed. The chilling requirements for wintersweet were about 6.9–34.9 Chill Portions (CPs) and 1.4–21.6 CP in the temperate and subtropical zones, respectively, with corresponding forcing requirements of 3.2–1922.9 GDH and 965.3–8482.6 GDH, respectively. In addition, we found that the temperature requirements of wintersweet were correlated by a negative exponential relationship, suggesting that chilling and forcing requirements have an antagonistic effect on initiating flowering phenology. Our results could help us understand how flowering dates of winter flowering plants respond to climate change. Full article
(This article belongs to the Special Issue Woody Plant Phenology in a Changing Climate)
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<p>Overview of the study sites. (<b>a</b>) Geographical locations of phenological observation stations in China. (<b>b</b>) Mean daily temperature at nine stations in China during 2000–2020.</p>
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<p>Trends of first flowering date (FFD) of wintersweet at nine sites in China during the past two decades.</p>
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<p>Chilling and forcing periods of Chinese wintersweet in the last two decades were identified using the dynamic model. The blue and red dots in the figure indicate the start and end of the chilling period and the start and end of the forcing period, respectively. The dotted line in the graph indicates the last day of the year, corresponding to a DOY of 365.</p>
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<p>Model coefficients for partial least squares regression between wintersweet flowering date and chilling and forcing. Using the dynamic model and GDH model for chilling and forcing calculation. The left panel (<b>a</b>) indicates chilling accumulation and the right panel (<b>b</b>) indicates forcing accumulation. Negative and significant model coefficients are marked by red bars and positive and significant model coefficients by green bars. Blue shading and pink shading indicate chilling and forcing periods, respectively. The range of flowering dates is indicated by gray shading, and the dashed line indicates the mean flowering date.</p>
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<p>Model coefficients for partial least squares regression between wintersweet flowering date and chilling and forcing. Chilling and forcing were calculated by using the Utah model and GDH model, respectively. (<b>a</b>) indicates chilling accumulation; (<b>b</b>) indicates forcing accumulation. See the caption of <a href="#forests-15-01832-f004" class="html-fig">Figure 4</a> for details.</p>
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<p>Model coefficients for partial least squares regression between wintersweet flowering date and chilling and forcing. Chilling and forcing were calculated by using the chilling hour model and GDH model, respectively. (<b>a</b>) indicates chilling accumulation; (<b>b</b>) indicates forcing accumulation. See the caption of <a href="#forests-15-01832-f004" class="html-fig">Figure 4</a> for details.</p>
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<p>The relationship between chilling requirements and forcing requirements by using three chilling models, dynamic model (<b>a</b>); chilling hour model (<b>b</b>); Utah model (<b>c</b>). The black dots represent the chilling and forcing requirements for each site. The red line is the fitted curve.</p>
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