The effects of degradation of alpine wetland meadow on soil respiration (Rs) and the sensitivity of Rs to temperature (Q) were measured in the Napa Lake region of Shangri-La on the southeastern edge of the Qinghai-Tibet Plateau. Rs was measured for 24 h during each of three different stages of the growing season on four different degraded levels. The results showed: (1) peak Rs occurred at around 5:00 p.m., regardless of the degree of degradation and growing season stage, with the maximum Rs reaching 10.05 μmol·m·s in non-degraded meadows rather than other meadows; (2) the daily mean Rs value was 7.14-7.86 μmol·m·s during the mid growing season in non-degraded meadows, and declined by 48.4-62.6% when degradation increased to the severely degraded level; (3) Q ranged from 7.1-11.3 in non-degraded meadows during the mid growing season, 5.5-8.0 and 6.2-8.2 during the early and late growing seasons, respectively, and show a decline of about 50% from the non-degraded meadows to severely degraded meadows; (4) Rs was correlated significantly with soil temperature at a depth of 0-5 cm (p < 0.05) on the diurnal scale, but not at the seasonal scale; (5) significant correlations were found between Rs and soil organic carbon (SOC), between biomass and SOC, and between Q and Rs (p < 0.05), which indicates that biomass and SOC potentially impact Q. The results suggest that vegetation degradation impact both Rs and Q significantly. Also, we speculated that Q of alpine wetland meadow is probable greater at the boundary region than inner region of the Qinghai-Tibet Plateau, and shoule be a more sensitive indicator in the studying of climate change in this zone.
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http://dx.doi.org/10.1038/s41598-019-43904-1 | DOI Listing |
Sci Rep
December 2024
Grassland Technique Extension Station of Gansu Province, Lanzhou, 730000, Gansu, China.
Near-natural restoration is acknowledged as an effective strategy for enhancing soil organic carbon (SOC) sequestration in degraded grasslands. However, the alterations in SOC fractions, stability, and relative sequestration capacity after restoration of degraded alpine meadows remain uncertain. In this study, we utilized the degraded alpine meadows on the northeastern edge of the Tibetan Plateau as a research area, with grazing as the control (CK) and restoration of 20 years of banned grazing (BG) and growing season resting grazing (RG).
View Article and Find Full Text PDFTree Physiol
December 2024
Université du Québec à Chicoutimi, laboratoire écosystèmes terrestres boréaux (EcoTer) Chicoutimi, Québec, Canada.
In temperate and boreal ecosystems, trees undergo dormancy to avoid cold temperatures during the unfavorable season. This phase includes changes in frost hardiness, which is minimal during the growing season and reaches its maximum in winter. Quantifying frost hardiness is important to assess the frost risk and shifts of species distribution under a changing climate.
View Article and Find Full Text PDFMetabolites
December 2024
Hainan Key Laboratory of Crop Genetics and Breeding, Institute of Food Crops, Hainan Academy of Agricultural Sciences, Haikou 571100, China.
Green soybean ( (L.) Merrill) is a highly nutritious food that is a good source of protein and fiber. However, it is sensitive to low temperatures during the growing season, and enhancing cold tolerance has become a research hotspot for breeding improvement.
View Article and Find Full Text PDFBioTech (Basel)
December 2024
Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 32 Vavilova, 119991 Moscow, Russia.
The white poplar () is a dioecious woody plant with significant potential for the phytoremediation of soils. To realize this potential, it is necessary to utilize growth-promoting microorganisms. One potential source of such beneficial microorganisms is the rhizosphere community of wild-growing trees.
View Article and Find Full Text PDFFront Artif Intell
December 2024
School of Industrial Engineering and Management, Oklahoma State University, Stillwater, OK, United States.
The ability to accurately predict the yields of different crop genotypes in response to weather variability is crucial for developing climate resilient crop cultivars. Genotype-environment interactions introduce large variations in crop-climate responses, and are hard to factor in to breeding programs. Data-driven approaches, particularly those based on machine learning, can help guide breeding efforts by factoring in genotype-environment interactions when making yield predictions.
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