• Journal of The Korean Society of Grassland and Forage Science
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• v.41 no.4
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• pp.223-233
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• 2021

The objective of this study was to investigate the effect of gypsum on the dry matter yield and the chemical composition of alfalfa in reclaimed tideland with soil dressing. The experimental site was Sukmoon reclaimed tideland. The tideland was reclaimed approximately 17 to 33 years ago and the 70 cm of soil was top-dressed. The soil that covers the reclaimed tideland brought from the island did not treat di-salinized. Treatments were consisted of three groups; control group where no gypsum (G0) was applied and two experimental groups where 2 ton/ha (G2) and 4 ton/ha (G4) of gypsum were applied, respectively. The first harvest was conducted when the alfalfa reached early flowering (open the flower 10%), and after that subsequent harvest was conducted at approximately 35 days intervals. The dry matter yield of the alfalfa showed that G2 was significantly higher in the first year than G0 and G4, and G2 tended to be higher in the second year than G0 and G4, although there were no significant differences between treatments. The reason for the high dry matter yield in G2 was that the soil pH and EC of the soil were at marginal and ideal levels and the coverage and alfalfa botanical composition were also high. In both years, there were no differences in the crude protein, neutral detergent fiber and acid detergent fiber contents and relative feed value between gypsum treatments. Meanwhile, the results in the first and second years showed that the alfalfa dry matter yield were negatively affected by droughts stress in spring and concentrated precipitation in summer. Therefore, this study suggests gypsum treatment in reclaimed tidal land could increase the dry matter yield of alfalfa, and 2 ton/ha of gypsum was the optimum rate.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.627-646
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• 2022

Recently, the seriousness of climate change-related problems caused by global warming is growing, and the average temperature is also rising. As a result, it is affecting the environment in which various temperature-sensitive creatures and creatures live, and changes in the ecosystem are also being detected. Seasons are one of the important factors influencing the types, distribution, and growth characteristics of creatures living in the area. Among the most popular and easily recognized plant seasonal phenomena among the indicators of the climate change impact evaluation, the blooming day of flower and the peak day of autumn leaves were modeled. The types of plants used in the modeling were forsythia and cherry trees, which can be seen as representative plants of spring, and maple and ginkgo, which can be seen as representative plants of autumn. Weather data used to perform modeling were temperature, precipitation, and solar radiation observed through the ASOS Observatory of the Korea Meteorological Administration. As satellite data, MODIS NDVI was used for modeling, and it has a correlation coefficient of about -0.2 for the flowering date and 0.3 for the autumn leaves peak date. As the model used, the model was established using multiple regression models, which are linear models, and Random Forest, which are nonlinear models. In addition, the predicted values estimated by each model were expressed as isopleth maps using spatial interpolation techniques to express the trend of plant seasonal changes from 2003 to 2020. It is believed that using NDVI with high spatio-temporal resolution in the future will increase the accuracy of plant phenology modeling.