5 research outputs found

    Distinct morphological, physiological, and biochemical responses to light quality in barley leaves and roots

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    Light quality modulates plant growth, development, physiology, and metabolism through a series of photoreceptors perceiving light signal and related signaling pathways. Although the partial mechanisms of the responses to light quality are well understood, how plants orchestrate these impacts on the levels of above- and below-ground tissues and molecular, physiological, and morphological processes remains unclear. However, the re-allocation of plant resources can substantially adjust plant tolerance to stress conditions such as reduced water availability. In this study, we investigated in two spring barley genotypes the effect of ultraviolet-A (UV-A), blue, red, and far-red light on morphological, physiological, and metabolic responses in leaves and roots. The plants were grown in growth units where the root system develops on black filter paper, placed in growth chambers. While the growth of above-ground biomass and photosynthetic performance were enhanced mainly by the combined action of red, blue, far-red, and UV-A light, the root growth was stimulated particularly by supplementary far-red light to red light. Exposure of plants to the full light spectrum also stimulates the accumulation of numerous compounds related to stress tolerance such as proline, secondary metabolites with antioxidative functions or jasmonic acid. On the other hand, full light spectrum reduces the accumulation of abscisic acid, which is closely associated with stress responses. Addition of blue light induced accumulation of Îģ-aminobutyric acid (GABA), sorgolactone, or several secondary metabolites. Because these compounds play important roles as osmolytes, antioxidants, UV screening compounds, or growth regulators, the importance of light quality in stress tolerance is unequivocal

    āļāļēāļĢāļ›āļĢāļ°āđ€āļĄāļīāļ™āļžāļ·āđ‰āļ™āļ—āļĩāđˆāđ€āļŠāļĩāđˆāļĒāļ‡āļ™āđ‰āļģāļ—āđˆāļ§āļĄāļ āļēāļĒāđƒāļ•āđ‰āļāļēāļĢāđ€āļ›āļĨāļĩāđˆāļĒāļ™āđāļ›āļĨāļ‡āļŠāļ āļēāļžāļ āļđāļĄāļīāļ­āļēāļāļēāļĻ āļāļĢāļ“āļĩāļĻāļķāļāļĐāļē: āļžāļ·āđ‰āļ™āļ—āļĩāđˆāļĢāļēāļšāļ™āđ‰āļģāļ—āđˆāļ§āļĄ āļ„āļ‡āđ€āļ‹āđ‚āļ”āļ™ āļˆāļąāļ‡āļŦāļ§āļąāļ”āļŠāļēāļĨāļ°āļ§āļąāļ™ āļŠāļ›āļ›. āļĨāļēāļ§ Flood Risk Assessment under Climate Change: Study Case Khongsedon Floodplain, Salavan Province, Lao PDR

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    āļāļēāļĢāļ§āļīāļˆāļąāļĒāļ™āļĩāđ‰āļĄāļĩāļ§āļąāļ•āļ–āļļāļ›āļĢāļ°āļŠāļ‡āļ„āđŒāđ€āļžāļ·āđˆāļ­āļ›āļĢāļ°āđ€āļĄāļīāļ™āļžāļ·āđ‰āļ™āļ—āļĩāđˆāđ€āļŠāļĩāđˆāļĒāļ‡āļ™āđ‰āļģāļ—āđˆāļ§āļĄāļ­āļąāļ™āđ€āļ™āļ·āđˆāļ­āļ‡āļˆāļēāļāļāļēāļĢāđ€āļ›āļĨāļĩāđˆāļĒāļ™āđāļ›āļĨāļ‡āļŠāļ āļēāļžāļ āļđāļĄāļīāļ­āļēāļāļēāļĻ āđƒāļ™āļžāļ·āđ‰āļ™āļ—āļĩāđˆāļĢāļēāļšāļ™āđ‰āļģāļ—āđˆāļ§āļĄāļ–āļķāļ‡āļ„āļ‡āđ€āļ‹āđ‚āļ”āļ™ āļˆāļąāļ‡āļŦāļ§āļąāļ”āļŠāļēāļĨāļ°āļ§āļąāļ™ āļŠāļ›āļ›. āļĨāļēāļ§ āđ‚āļ”āļĒāđāļšāđˆāļ‡āļ§āļīāļ˜āļĩāļāļēāļĢāļĻāļķāļāļĐāļēāļ­āļ­āļāđ€āļ›āđ‡āļ™ 5 āļŠāđˆāļ§āļ™āļĒāđˆāļ­āļĒ āđ„āļ”āđ‰āđāļāđˆ 1) āļāļēāļĢāļŠāļāļąāļ”āđāļĨāļ°āļāļēāļĢāļ›āļĢāļąāļšāđāļāđ‰āļ‚āđ‰āļ­āļĄāļđāļĨāļ āļđāļĄāļīāļ­āļēāļāļēāļĻāđƒāļ™āļŠāđˆāļ§āļ‡ āļ„.āļĻ. 2020–2100 āļˆāļēāļāđāļšāļšāļˆāļģāļĨāļ­āļ‡ Mixed Resolution version of Max Planck Institute Earth System Model (MPI-ESM-MR) āļ āļēāļĒāđƒāļ•āđ‰āļāļēāļĢāļ›āļĨāđˆāļ­āļĒāļāđŠāļēāļ‹āđ€āļĢāļ·āļ­āļ™āļāļĢāļ°āļˆāļāđƒāļ™āļĢāļ°āļ”āļąāļšāļ›āļēāļ™āļāļĨāļēāļ‡ (RCP4.5) āđāļĨāļ°āļĢāļ°āļ”āļąāļšāļŠāļđāļ‡āļĄāļēāļ (RCP8.5) āļ‚āđ‰āļ­āļĄāļđāļĨāļ āļđāļĄāļīāļ­āļēāļāļēāļĻāđ„āļ”āđ‰āļ–āļđāļāļŠāļāļąāļ”āđāļĨāļ°āļ›āļĢāļąāļšāđāļāđ‰āļ„āļ§āļēāļĄāļ–āļđāļāļ•āđ‰āļ­āļ‡āļ”āđ‰āļ§āļĒāđāļšāļšāļˆāļģāļĨāļ­āļ‡ CMhyd 2) āļāļēāļĢāļ›āļĢāļ°āđ€āļĄāļīāļ™āļ›āļĢāļīāļĄāļēāļ“āļ™āđ‰āļģāļ—āđˆāļēāđƒāļ™āļ­āļ™āļēāļ„āļ•āļ āļēāļĒāđƒāļ•āđ‰āđ€āļ‡āļ·āđˆāļ­āļ™āđ„āļ‚ RCP4.5 āđāļĨāļ° RCP8.5 āđ‚āļ”āļĒāđƒāļŠāđ‰āđāļšāļšāļˆāļģāļĨāļ­āļ‡ SWAT 3) āļāļēāļĢāļ§āļīāđ€āļ„āļĢāļēāļ°āļŦāđŒāļ‚āļ™āļēāļ”āđāļĨāļ°āļ„āļ§āļēāļĄāļ–āļĩāđˆāļ‚āļ­āļ‡āļ™āđ‰āļģāļ—āđˆāļ§āļĄāđ‚āļ”āļĒāđƒāļŠāđ‰āļāļēāļĢāđāļˆāļāđāļˆāļ‡ Log Pearson Type III āļˆāļēāļāļ›āļĢāļīāļĄāļēāļ“āļ™āđ‰āļģāļ—āđˆāļēāļŠāļđāļ‡āļŠāļļāļ”āļĢāļēāļĒāļ›āļĩāļˆāļēāļāđāļšāļšāļˆāļģāļĨāļ­āļ‡ SWAT āđāļĨāļ°āļˆāļēāļāļāļēāļĢāļ•āļĢāļ§āļˆāļ§āļąāļ”āđƒāļ™āļ­āļ”āļĩāļ•āđƒāļ™āļŠāđˆāļ§āļ‡ āļ„.āļĻ. 1993–2019 āļ‹āļķāđˆāļ‡āļ›āļĢāļīāļĄāļēāļ“āļ™āđ‰āļģāļ—āđˆāļ§āļĄāļŠāļđāļ‡āļŠāļļāļ”āļ—āļĩāđˆāļ„āļēāļ”āļ§āđˆāļēāļˆāļ°āđ€āļāļīāļ”āļ‚āļķāđ‰āļ™āđƒāļ™āļ­āļ™āļēāļ„āļ•āđ„āļ”āđ‰āļāļģāļŦāļ™āļ”āļĢāļ­āļšāļ›āļĩāļāļēāļĢāđ€āļāļīāļ”āļ‹āđ‰āļģāļ—āļĩāđˆāđāļ•āļāļ•āđˆāļēāļ‡āļāļąāļ™ āđ„āļ”āđ‰āđāļāđˆ āļĢāļ­āļš 25, 50, 100 āđāļĨāļ° 200 āļ›āļĩ 4) āļāļēāļĢāļˆāļģāļĨāļ­āļ‡āļĨāļąāļāļĐāļ“āļ°āļ—āļēāļ‡āļŠāļĨāļĻāļēāļŠāļ•āļĢāđŒ āđ€āļžāļ·āđˆāļ­āļŦāļēāļ„āļ§āļēāļĄāļĨāļķāļāļ‚āļ­āļ‡āļ™āđ‰āļģāļ—āđˆāļ§āļĄāđāļĨāļ°āļ„āļ§āļēāļĄāđ€āļĢāđ‡āļ§āļ™āđ‰āļģāļ—āđˆāļ§āļĄ āļ‚āļ­āļ‡āđāļ•āđˆāļĨāļ°āļĢāļ­āļšāļ›āļĩāļāļēāļĢāđ€āļāļīāļ”āļ‹āđ‰āļģāđ‚āļ”āļĒāđƒāļŠāđ‰ āđāļšāļšāļˆāļģāļĨāļ­āļ‡ HEC-RAS āđāļĨāļ° 5) āļāļēāļĢāļ›āļĢāļ°āđ€āļĄāļīāļ™āļĢāļ°āļ”āļąāļšāļžāļ·āđ‰āļ™āļ—āļĩāđˆāđ€āļŠāļĩāđˆāļĒāļ‡āļ™āđ‰āļģāļ—āđˆāļ§āļĄ āđ‚āļ”āļĒāđƒāļŠāđ‰āļŦāļĨāļąāļāļāļēāļĢ Flood Hazard Rating (FHR) āļ”āđ‰āļ§āļĒāđ‚āļ›āļĢāđāļāļĢāļĄ ArcGIS āļœāļĨāļāļēāļĢāļĻāļķāļāļĐāļēāļŠāļĩāđ‰āđƒāļŦāđ‰āđ€āļŦāđ‡āļ™āļ§āđˆāļēāļžāļ·āđ‰āļ™āļ—āļĩāđˆāđ€āļŠāļĩāđˆāļĒāļ‡āļ™āđ‰āļģāļ—āđˆāļ§āļĄāđ€āļ™āļ·āđˆāļ­āļ‡āļˆāļēāļāļāļēāļĢāđ€āļ›āļĨāļĩāđˆāļĒāļ™āđāļ›āļĨāļ‡āļŠāļ āļēāļžāļ āļđāļĄāļīāļ­āļēāļāļēāļĻāļ āļēāļĒāđƒāļ•āđ‰āđ€āļ‡āļ·āđˆāļ­āļ™āđ„āļ‚ RCP4.5 āđāļĨāļ° RCP8.5 āļĄāļĩāđāļ™āļ§āđ‚āļ™āđ‰āļĄāļ—āļĩāđˆāļˆāļ°āđ€āļžāļīāđˆāļĄāļ‚āļķāđ‰āļ™ āđāļĨāļ°āļžāļ·āđ‰āļ™āļ—āļĩāđˆāđ€āļŠāļĩāđˆāļĒāļ‡āļ™āđ‰āļģāļ—āđˆāļ§āļĄāļ āļēāļĒāđƒāļ•āđ‰āđ€āļ‡āļ·āđˆāļ­āļ™āđ„āļ‚ RCP8.5 āļˆāļ°āļŠāļđāļ‡āļāļ§āđˆāļē RCP4.5 āđ‚āļ”āļĒāđ€āļ‰āļĨāļĩāđˆāļĒāļ›āļĢāļ°āļĄāļēāļ“ 7.44% āļ„āđˆāļēāļ„āļ§āļēāļĄāđāļ•āļāļ•āđˆāļēāļ‡āļ™āļĩāđ‰āļŠāļĩāđ‰āđƒāļŦāđ‰āđ€āļŦāđ‡āļ™āļ§āđˆāļēāļāļēāļĢāļˆāļąāļ”āļāļēāļĢāļ„āļ§āļēāļĄāđ€āļŠāļĩāđˆāļĒāļ‡āļˆāļēāļāļ™āđ‰āļģāļ—āđˆāļ§āļĄāļ āļēāļĒāđƒāļ•āđ‰āđ€āļ‡āļ·āđˆāļ­āļ™āđ„āļ‚ RCP8.5 āļ„āļ§āļĢāđ€āļžāļīāđˆāļĄāļĢāļ°āļ”āļąāļšāļāļēāļĢāļˆāļąāļ”āļāļēāļĢāļĄāļēāļāļāļ§āđˆāļē RCP4.5 āļžāļ·āđ‰āļ™āļ—āļĩāđˆāđ€āļŠāļĩāđˆāļĒāļ‡āļ™āđ‰āļģāļ—āđˆāļ§āļĄāļĢāļ°āļ”āļąāļšāļĄāļēāļ āļŠāđˆāļ§āļ™āđƒāļŦāļāđˆāļ­āļĒāļđāđˆāļšāļĢāļīāđ€āļ§āļ“āļ”āđ‰āļēāļ™āļ•āđ‰āļ™āļ™āđ‰āļģāđāļĨāļ°āļ—āđ‰āļēāļĒāļ™āđ‰āļģ āđāļĨāļ°āļžāļ·āđ‰āļ™āļ—āļĩāđˆāđ€āļŠāļĩāđˆāļĒāļ‡āļ›āļēāļ™āļāļĨāļēāļ‡āļŠāđˆāļ§āļ™āđƒāļŦāļāđˆāļ­āļĒāļđāđˆāļšāļĢāļīāđ€āļ§āļ“āļ•āļ­āļ™āļāļĨāļēāļ‡āļ—āļąāđ‰āļ‡āđƒāļ™āđ€āļ‡āļ·āđˆāļ­āļ™āđ„āļ‚ RCP4.5 āđāļĨāļ° RCP8.5 āļŠāđˆāļ§āļ™āļ›āļĢāļīāļĄāļēāļ“āļāļ™āđƒāļ™āļŠāđˆāļ§āļ‡āļĪāļ”āļđāđāļĨāđ‰āļ‡āđ€āļžāļīāđˆāļĄāļ‚āļķāđ‰āļ™ 9.27% (RCP4.5) āđāļĨāļ° 1.27% (RCP8.5) āđāļĨāļ°āļŠāđˆāļ§āļ‡āļĪāļ”āļđāļāļ™āđ€āļžāļīāđˆāļĄāļ‚āļķāđ‰āļ™ 17.42% (RCP4.5) āđāļĨāļ° 21.98% (RCP8.5) āļŠāļģāļŦāļĢāļąāļšāļ›āļĢāļīāļĄāļēāļ“āļ™āđ‰āļģāļ—āđˆāļēāđƒāļ™āļŠāđˆāļ§āļ‡āļĪāļ”āļđāđāļĨāđ‰āļ‡āđ€āļžāļīāđˆāļĄāļ‚āļķāđ‰āļ™ 8.16% (RCP4.5) āđāļĨāļ° 4.07% (RCP8.5) āļŠāđˆāļ§āļ™āđƒāļ™āļĪāļ”āļđāļāļ™āđ€āļžāļīāđˆāļĄāļ‚āļķāđ‰āļ™ 13.43% (RCP4.5) āđāļĨāļ° 18.11% (RCP8.5) āļœāļĨāļ—āļĩāđˆāđ„āļ”āđ‰āļĢāļąāļšāļˆāļēāļāļāļēāļĢāļ§āļīāļˆāļąāļĒāļ„āļĢāļąāđ‰āļ‡āļ™āļĩāđ‰āļĄāļĩāļ›āļĢāļ°āđ‚āļĒāļŠāļ™āđŒāļ­āļĒāđˆāļēāļ‡āļĒāļīāđˆāļ‡āđƒāļ™āļāļēāļĢāđƒāļŦāđ‰āļ‚āđ‰āļ­āļĄāļđāļĨāđ€āļšāļ·āđ‰āļ­āļ‡āļ•āđ‰āļ™āđāļāđˆāļŦāļ™āđˆāļ§āļĒāļ‡āļēāļ™āļ—āļĩāđˆāđ€āļāļĩāđˆāļĒāļ§āļ‚āđ‰āļ­āļ‡āđāļĨāļ°āļ„āļ™āđƒāļ™āļŠāļļāļĄāļŠāļ™āđ€āļžāļ·āđˆāļ­āđ€āļ•āļĢāļĩāļĒāļĄāļĢāļąāļšāļĄāļ·āļ­āļāļąāļšāđ€āļŦāļ•āļļāļāļēāļĢāļ“āđŒāļ­āļļāļ—āļāļ āļąāļĒāđ‚āļ”āļĒāđ€āļ‰āļžāļēāļ°āļāļēāļĢāļˆāļąāļ”āļāļēāļĢāļāļēāļĢāđƒāļŠāđ‰āļ—āļĩāđˆāļ”āļīāļ™This study aims to assess flood risk areas due to climate change in the Khongsedon floodplain, Salavan province, Lao PDR. The methodology is divided into five subsections: 1) extracting and bias correcting climatic data in the period of A.D. 2020–2100 from MPI-ESM-MR model under moderate greenhouse gas emissions (RCP4.5) and very high greenhouse gas emissions (RCP8.5). The climatic data are extracted and bias corrected by CMhyd model; 2) estimating the future streamflow under RCP4.5 and RCP8.5 using SWAT model; 3) analysis of flood magnitude and frequency using Log Pearson type III distribution of annual maximum streamflow obtained from SWAT model and historical annual maximum streamflow during the period between A.D. 1993–2019. The peak flows were estimated for different return periods such as 25, 50, 100, and 200 years; 4) simulating the hydraulic characteristic s (i.e. flood depth and flood velocity) of each return period using HEC-RAS; and 5) assessment levels of flood prone areas using Flood Hazard Rating (FHR) by ArcGIS. The results indicate that the flood risk areas due to climate change under RCP4.5 and RCP8.5 are more likely to increase, and the flood risk areas under RCP8.5 will be higher than RCP4.5 on average approximately 7.44%. This difference value suggests that under RCP8.5 condition, the degree of management should be greater than the one of RCP4.5. The upper and downstream areas are susceptible to high flood risks while the central area is more likely to experience moderate risks under the RCP4.5 and RCP8.5 conditions. The rainfall in dry season increased by 9.27% (RCP4.5) and 1.27% (RCP8.5). In rainy season, it was found to increase by 17.42% (RCP4.5) and 21.98% (RCP8.5). The dry season streamflow under RCP4.5 and RCP8.5 increased by 8.16% and 4.07%, respectively. In rainy season, runoff increased by 13.43% (RCP4.5) and 18.11% (RCP8.5). The results obtained from this study are particularly useful in providing preliminary information to relevant agencies and people in the community for preparing and dealing with floods events, especially land use management

    The Dynamics of Immature Rubber Photosynthetic Capacities Under Macronutrients Deficiencies

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    International audienceParÃĄ rubber produces natural latex which is essential for the industries. Rubber plant in immature phase is prone to macronutrient deficiencies due to improper management practices in the field and the nature of immature plants that have sensitive physiological responses under stress conditions. The study aimed to assess the effect of macronutrient limitation on immature rubber trees’ photosynthetic capacity and growth. The immature rubber was pot-grown inside the greenhouse with a completely randomized design experiment and nutrient limitations used as the treatments. The treatments consisted of 5 levels, namely, NPK; NP (-K); NK (-P); PK (-N); Control (-NPK). Photosynthetic capacity parameters (Vc max: maximum rate RuBisCO carboxylation, Jmax: RuBP regeneration rate, and TPU: Triose Phosphate Utilization), tree growth (plant height, flush number, leaf number, stem diameter), and leaf macronutrient (N, P, and K) concentrations were periodically measured. Welsch’s test (Îą = 0.05) continued with Games-Howell pairwise comparison, followed by Pearson’s correlation test and polynomial regressions were performed to describe the nutrient limitation and photosynthetic capacity relationships. Results showed that the leaf nutrient concentration corresponds with the given treatments, even though it was above the critical level for immature rubber. The limitation of N fertilization slightly reduced plant development and growth such as height, leaf number, flush number, relative growth rate, and photosynthetic capacities. However, the P and K limitation effect could not be observed clearly in the observation periods on growth and photosynthetic capacity parameters. Furthermore, the mobility rate of nutrients from the soil to the plants and its translocation inside plant organs played more essential role in plant growth and photosynthetic capacities. Prolonged observation periods on various rubber clones have to be performed to deeply understand the effects of nutrient deficiencies on immature rubber tree morphophysiological activities. HIGHLIGHTS Rubber plant in immature phase have sensitive physiological responses under stress conditions, and it is prone to macronutrient deficiencies due to improper management practices in the field Assessment of macronutrient limitation effect on immature rubber trees’ photosynthetic capacity and growth is essential to understand how the plants strive under the nutrient scarcity and providing a perspective which nutrient is more essential The N fertilization played more essential role compared to P and K, for immature rubber growth and photosynthetic activity GRAPHICAL ABSTRAC

    Distinct morphological, physiological, and biochemical responses to light quality in barley leaves and roots

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    Light quality modulates plant growth, development, physiology, and metabolism through a series of photoreceptors perceiving light signal and related signaling pathways. Although the partial mechanisms of the responses to light quality are well understood, how plants orchestrate these impacts on the levels of above- and below-ground tissues and molecular, physiological, and morphological processes remains unclear. However, the re-allocation of plant resources can substantially adjust plant tolerance to stress conditions such as reduced water availability. In this study, we investigated in two spring barley genotypes the effect of ultraviolet-A (UV-A), blue, red, and far-red light on morphological, physiological, and metabolic responses in leaves and roots. The plants were grown in growth units where the root system develops on black filter paper, placed in growth chambers. While the growth of above-ground biomass and photosynthetic performance were enhanced mainly by the combined action of red, blue, far-red, and UV-A light, the root growth was stimulated particularly by supplementary far-red light to red light. Exposure of plants to the full light spectrum also stimulates the accumulation of numerous compounds related to stress tolerance such as proline, secondary metabolites with antioxidative functions or jasmonic acid. On the other hand, full light spectrum reduces the accumulation of abscisic acid, which is closely associated with stress responses. Addition of blue light induced accumulation of Îģ-aminobutyric acid (GABA), sorgolactone, or several secondary metabolites. Because these compounds play important roles as osmolytes, antioxidants, UV screening compounds, or growth regulators, the importance of light quality in stress tolerance is unequivocal
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