• Journal of Climate Change Research
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• v.4 no.2
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• pp.77-93
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• 2013

Global warming accelerates both carbon (C) input through increased forest productivity and heterotrophic C emission in forest soils, and a future trend in soil C dynamics is uncertain. In this study, the Korean forest soil carbon model (KFSC model) was applied to 1,467,458 ha of Pinus densiflora forests in Korea to predict future C dynamics under RCP 8.5 climate change scenario (RCP scenario). Korea was divided into 16 administrative regions, and P. densiflora forests in each region were classified into six classes by their stand ages : 1 to 10 (I), 11 to 20 (II), 21 to 30 (III), 31 to 40 (IV), 41 to 50 (V), and 51 to 80-year-old (VI+). The forest of each stand age class in a region was treated as a simulation unit, then future net primary production (NPP), soil respiration (SR) and forest soil C stock of each simulation unit were predicted from the 2012 to 2100 under RCP scenario and constant temperature scenario (CT scenario). As a result, NPP decreased in the initial stage of simulation then increased while SR increased in the initial stage of simulation then decreased in both scenarios. The mean NPP and SR under RCP scenario was 20.2% and 20.0% higher than that under CT scenario, respectively. When the initial age class was I, IV, V or VI+, predicted soil C stock under CT scenario was higher than that under RCP scenario, however, the countertrend was observed when the initial age class was II or III. Also, forests having a lower site index showed a lower soil C stock. It suggested that the impact of temperature on NPP was higher when the forests grow faster. Soil C stock under RCP scenario decreased at the end of simulation, and it might be derived from exponentially increased SR under the higher temperature condition. Thus, the difference in soil C stock under two scenarios will be much larger in the further future.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.163-169
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• 2024

India is a developing nation and has come with comprehensive way in modernizing its reducing poverty, economy and rising living standards for an outsized fragment of its residents. The STEM (Science, Technology, Engineering, and Mathematics) education plays an important role in it. STEM is an educational curriculum that emphasis on the subjects of "science, technology, engineering, and mathematics". In traditional education scenario, these subjects are taught independently, but according to the educational philosophy of STEM that teaches these subjects together in project-based lessons. STEM helps the students in his holistic development. Youth unemployment is the biggest concern due to lack of adequate skills. There is a huge skill gap behind jobless engineers and the question arises how we can prepare engineers for a better tomorrow? Now a day's Industry 4.0 is a new fourth industrial revolution which is an intelligent networking of machines and processes for industry through ICT. It is based upon the usage of cyber-physical systems and Internet of Things (IoT). Industrial revolution does not influence only production but also educational system as well. IoT in academics is a new revolution to the Internet technology, which introduced "Smartness" in the entire IT infrastructure. To improve socio-economic status of the India students must equipped with 21st century digital skills and Universities, colleges must provide individual learning kits to their students which can help them in enhancing their productivity and learning outcomes. The major goal of this paper is to present a low cost, effective learning mechanism for STEM implementation using Raspberry Pi 3+ model (Single board computer) and Node Red open source visual programming tool which is developed by IBM for wiring hardware devices together. These tools are broadly used to provide hands on experience on IoT fundamentals during teaching and learning. This paper elaborates the appropriateness and the practicality of these concepts via an example by implementing a user interface (UI) and Dashboard in Node-RED where dashboard palette is used for demonstration with switch, slider, gauge and Raspberry pi palette is used to connect with GPIO pins present on Raspberry pi board. An LED light is connected with a GPIO pin as an output pin. In this experiment, it is shown that the Node-Red dashboard is accessing on Raspberry pi and via Smartphone as well. In the final step results are shown in an elaborate manner. Conversely, inadequate Programming skills in students are the biggest challenge because without good programming skills there would be no pioneers in engineering, robotics and other areas. Coding plays an important role to increase the level of knowledge on a wide scale and to encourage the interest of students in coding. Today Python language which is Open source and most demanding languages in the industry in order to know data science and algorithms, understanding computer science would not be possible without science, technology, engineering and math. In this paper a small experiment is also done with an LED light via writing source code in python. These tiny experiments are really helpful to encourage the students and give play way to learn these advance technologies. The cost estimation is presented in tabular form for per learning kit provided to the students for Hands on experiments. Some Popular In addition, some Open source tools for experimenting with IoT Technology are described. Students can enrich their knowledge by doing lots of experiments with these freely available software's and this low cost hardware in labs or learning kits provided to them.

• Korean Journal of Soil Science and Fertilizer
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• v.15 no.1
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• pp.2-15
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• 1982

In this report, a total domestic demand for major commercial fertilizer for crop production in Korea up to 1996 was estimated. The agricultural products and area for demand for both 1982 and 1986 was quoted from the estimate of the 5th Five-year Economic plan. And the demands estimated for 1991 and 1996 reflected possible changes of diet from cereal to meat and their indirect effects on the increase of cereal consumption. As the advanced countries followed, consequently, the demands for soybean, corn and other feed grains were expected to be increased as well as the land for growing those crops. 1. Total annual demands for nitrogen, phosphorus and potassium fertilizers were estimated 1,050,000M/T, 1,110,000M/T, 1,280,000M/T and 1,010,000M/T for the year 1982, 1986, 1991, and 1996 respectively. 2. It was assumed that there would be difficulties in self-sufficiency of grains at the cost of the maximum utilization of land and fertilizers in 1996. 3. It was clear that the increase of the productivity per unit area is possible by improving the conditions of arable land which could resulted a self-sufficiency of food in Korea. As a consequence, the demand for fertilizers at that time would exceed the level of estimates. 4. The recent decrease in demand for commercial fertilizers (currently estimated 850,000M/T) was due to an inadequate application of fertilizers for respective crop reqirement. This inadequacy should be checked and encouraged the consumptions of fertilizers to be increased by supporting the price of grain.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.337-345
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• 2023

Estimating the rice heading date is one of the most crucial agricultural tasks related to productivity. However, due to abnormal climates around the world, it is becoming increasingly challenging to estimate the rice heading date. Therefore, a more objective classification method for estimating the rice heading date is needed than the existing methods. This study, we aimed to classify the rice heading stage from various images using a CNN classification model. We collected top-view images taken from a drone and a phenotyping tower, as well as slanted-view images captured with a RGB camera. The collected images underwent preprocessing to prepare them as input data for the CNN model. The CNN architectures employed were ResNet50, InceptionV3, and VGG19, which are commonly used in image classification models. The accuracy of the models all showed an accuracy of 0.98 or higher regardless of each architecture and type of image. We also used Grad-CAM to visually check which features of the image the model looked at and classified. Then verified our model accurately measure the rice heading date in paddy fields. The rice heading date was estimated to be approximately one day apart on average in the four paddy fields. This method suggests that the water head can be estimated automatically and quantitatively when estimating the rice heading date from various paddy field monitoring images.

• Journal of Food Hygiene and Safety
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• v.36 no.1
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• pp.68-76
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• 2021

Foodborne outbreaks frequently occur worldwide and result in huge economic losses. It is the therefore important to estimate the costs associated with foodborne diseases to minimize the economic damage. At the same time, it is difficult to accurately estimate the economic loss from foodborne disease due to a wide variety of cost components. In Korea, there are a limited number of analytical studies attempting to estimate such costs. In this study we investigated the components of economic cost used in foreign countries to better estimate the cost of foodborne disease in Korea. Seven recent studies investigated the cost components used to estimate the cost of foodborne disease in humans. This study categorized the economic loss into four types of cost: direct costs, indirect costs, food business costs, and government administration costs. The healthcare costs most often included were medical (outpatient) and hospital costs (inpatient). However, these cost components should be selected according to the systems and budgets of medical services by country. For non-healthcare costs, several other studies considered transportation costs to the hospital as an exception to the cost of inpatient care. So, further discussion is needed on whether to consider inpatient care costs. Among the indirect costs, premature mortality, lost productivity, lost leisure time, and lost quality of life/pain, grief and suffering costs were considered, but the opportunity costs for hospital visits were not considered in any of the above studies. As with healthcare costs, government administration costs should also be considered appropriate cost components due to the difference in government budget systems, for example. Our findings will provide fundamental information for economic analysis associated with foodborne diseases to improve food safety policy in Korea.

• Korean Journal of Poultry Science
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• v.16 no.4
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• pp.239-252
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• 1989

The present study was carried out to investigate the improvement of major production traits with the published data of twenty-two years'random sample tests held in Korea from 1966 to 1988. Eight traits-roaring viability, laying viability, age of sexual maturity, hen day egg production, hen-housed egg production, egg weight, feed requirement, 500 days body weight-were dealt with in this study. The results obtained in this study are summerized as follows； 1. Total mean value for rearing viability was 99.4% in 1980s. Mean value for rearing viability of colored hen was 99.5% and white hen was 99.06% in 1980s. Mean value for laying viability was increased by 0.98%, 0.86% and 0.86% per year in pool data, white hen and colored hen, respectively. 2, Age of sexual maturity was decreased from 171.1 day to 160.8 day in pool data during 1960s- 1910s but increased to 162.4 day in 1980s； and decreased from 160.5 day to 1595 nay in white hen but increased from 163.7 day to 166.1 day in colored hen during 1970s-1980s. 3. Mean values for hen-day egg production were increased by 0.96%, 1.09% and 0.63% per year in pool data, colored hen and white hen, respectively. 4. Mean values for hen housed egg production were increased by 45, 5.37 and 4.23 per year in pool data, colored hen and white hen, respectively. 5. Egg weight were improved by 0.22g and 0.25g per year in pool data and colored hen but decreased by 0.03g in white hen. 6. feed requirement were improved by 0.04, 0.05 and 0.1 per year in pool data, white hen and colored hen, respectively. 7. 500 days body weights were increased by 0.38g per year in pool data but decreased by 14.95g and 10.37g in colored hen and white hen, respectively. 8. Estimate of correlation coefficient between age of sexual maturity and other factors such as hen day egg production. hen housed egg production, egg weight and 500 days body weight were -0.4512, -0.2876, -0.4376 and 0.2045 in pool data； -0.358, -0.1530 0.3475 and 0.1208 in white hen； 0.0989, 0.1181, 0.2885 and 0.2248 in colored hen, respectively. Estimates of correlation coefficient between hen day egg production and egg weight were 0.6233, -0.2259 and 0.2973 in Pool data, white hen and colored hen； between hen day egg production and 500 days body weight, 0.2417, 0.0774, -0.4787 ： between hen-housed egg production and egg weight, 0.6171, -0.2706, 0.4579： between hen housed egg production and 500 days body weight, 0.3082, -0.0792, -0.3368： between egg weight and 500 days body weight, 0.2742, 0.2205, 0.1354, respectively.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.329-339
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• 2021

Soybeans (Glycine max), one of major upland crops, require precise management of environmental conditions, such as temperature, water, and soil, during cultivation since they are sensitive to environmental changes. Application of spectral technologies that measure the physiological state of crops remotely has great potential for improving quality and productivity of the soybean by estimating yields, physiological stresses, and diseases. In this study, we developed and validated a soybean growth prediction model using multispectral imagery. We conducted a linear regression analysis between vegetation indices and soybean growth data (fresh weight and LAI) obtained at Miryang fields. The linear regression model was validated at Goesan fields. It was found that the model based on green ratio vegetation index (GRVI) had the greatest performance in prediction of fresh weight at the calibration stage (R²=0.74, RMSE=246 g/m², RE=34.2%). In the validation stage, RMSE and RE of the model were 392 g/m² and 32%, respectively. The errors of the model differed by cropping system, For example, RMSE and RE of model in single crop fields were 315 g/m² and 26%, respectively. On the other hand, the model had greater values of RMSE (381 g/m²) and RE (31%) in double crop fields. As a result of developing models for predicting a fresh weight into two years (2018+2020) with similar accumulated temperature (AT) in three years and a single year (2019) that was different from that AT, the prediction performance of a single year model was better than a two years model. Consequently, compared with those models divided by AT and a three years model, RMSE of a single crop fields were improved by about 29.1%. However, those of double crop fields decreased by about 19.6%. When environmental factors are used along with, spectral data, the reliability of soybean growth prediction can be achieved various environmental conditions.