• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.427-435
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• 2023

Changes in flowering time due to weather fluctuations impact plant growth and ecosystem dynamics. Accurate prediction of flowering timing is crucial for effective forest ecosystem management. This study uses a process-based model to predict flowering timing in 2023 for five major tree species in Korean forests. Models are developed based on nine years (2009-2017) of flowering data for Abeliophyllum distichum, Robinia pseudoacacia, Rhododendron schlippenbachii, Rhododendron yedoense f. poukhanense, and Sorbus commixta, distributed across 28 regions in the country, including mountains. Weather data from the Automatic Mountain Meteorology Observation System (AMOS) and the Korea Meteorological Administration (KMA) are utilized as inputs for the models. The Single Triangle Degree Days (STDD) and Growing Degree Days (GDD) models, known for their superior performance, are employed to predict flowering dates. Daily temperature readings at a 1 km spatial resolution are obtained by merging AMOS and KMA data. To improve prediction accuracy nationwide, random forest machine learning is used to generate region-specific correction coefficients. Applying these coefficients results in minimal prediction errors, particularly for Abeliophyllum distichum, Robinia pseudoacacia, and Rhododendron schlippenbachii, with root mean square errors (RMSEs) of 1.2, 0.6, and 1.2 days, respectively. Model performance is evaluated using ten random sampling tests per species, selecting the model with the highest R². The models with applied correction coefficients achieve R² values ranging from 0.07 to 0.7, except for Sorbus commixta, and exhibit a final explanatory power of 0.75-0.9. This study provides valuable insights into seasonal changes in plant phenology, aiding in identifying honey harvesting seasons affected by abnormal weather conditions, such as those of Robinia pseudoacacia. Detailed information on flowering timing for various plant species and regions enhances understanding of the climate-plant phenology relationship.

• Journal of the Korean Applied Science and Technology
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• v.41 no.2
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• pp.219-232
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• 2024

The aim of this study was to explore the impact of body mass index (BMI) and drumming exercise on autonomic nervous system function in adult women. Ten adult women (aged 30-50) were divided into two groups based on their BMI: a normal BMI group (Low BMI, LBMI < 23 kg/m²) and an overweight BMI group (High BMI, HBMI > 23 kg/m²). Both groups participated in a drumming exercise program, consisting of 50-minute sessions, three times a week, for a duration of 8 weeks. Body composition and heart rate variability were assessed before and after the 8-week exercise period. Heart rate variability was evaluated using linear analysis in the time domain and frequency domain. Additionally, non-linear analysis was conducted using a Poincaré plot. The autonomic nervous system index was determined by measuring parasympathetic nervous system index and sympathetic nervous system index. Following the 8-week intervention, the HBMI group exhibited a significant decrease in weight (p=0.034), BMI (p=0.044), body fat mass (p=0.032), and waist circumference (p=0.013) compared to the LBMI group. Furthermore, the HBMI group demonstrated significant increases in RMSSD (p=0.018) and TP (p=0.033) in linear analysis, as well as SD1 (p=0.018) in non-linear analysis and PNS Index (p=0.040) compared to the LBMI group. RMSSD, SD1, and PNS Index serve as indicators of parasympathetic nervous system activity in linear and non-linear analyses, respectively. These findings indicate that drumming exercise significantly enhances autonomic nervous system function in overweight women.