• Korean Journal of Environment and Ecology
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• v.35 no.2
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• pp.193-203
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• 2021

As endangered species are gradually increasing due to land development by humans, it is essential to secure sufficient protected areas (PAs) proactively. Therefore, this study checked priority conservation areas to select candidate PAs when considering the impact of land development. We determined the conservation priorities by analyzing four scenarios based on existing conservation areas and reflecting the development impact using MARXAN, the decision-making support software for the conservation plan. The development impact was derived using the developed area ratio, population density, road network system, and traffic volume. The conservation areas of endangered species were derived using the data of the appearance points of birds, mammals, and herptiles from the 3rd National Ecosystem Survey. These two factors were used as input data to map conservation priority areas with the machine learning-based optimization methodology. The result identified many non-PAs areas that were expected to play an important role conserving endangered species. When considering the land development impact, it was found that the areas with priority for conservation were fragmented. Even when both the development impact and existing PAs were considered, the priority was higher in areas from the current PAs because many road developments had already been completed around the current PAs. Therefore, it is necessary to consider areas other than the current PAs to protect endangered species and seek alternative measures to fragmented conservation priority areas.

• Research in Plant Disease
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• v.26 no.4
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• pp.229-238
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• 2020

Field validation of PBcast, an infection risk model for Phytophthora blight of pepper, was conducted through a designed field experiment in 2012 and 2013. Conduciveness of weather conditions at 26 locations in Korea in 2014-2017 was also evaluated using PBcast. The PBcast estimated daily infection risk (IR) of Phytophthora capsici based on weather and soil texture data. In the designed filed experiment, four treatments including routine sprays at 7-day intervals (RTN7), forecast-based sprays when IR reached 200 (IR200) and 224 (IR224), and no spray (CTRL) were compared in terms of disease incidence and number of sprays recommended for disease control. In 2012, IR had reached over 200 twice, but never reached 224. In 2013, IR had reached over 200 three times and once higher than 224. The RTN7 plots were sprayed 17 and 18 times in 2012 and 2013, respectively. Weather conditions throughout the country were generally conducive for Phytophthora blight and 3-4 times of fungicide sprays would have been reduced if the PBcast forecast information was adopted in the decision-making for fungicide sprays. In conclusion, the PBcast forecast would be useful to reduce fungicide applications without losing the disease control efficacy to protect pepper crop from Phytophthora blight.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.15-17
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• 2022

In the aftermath of the avian influenza that occurred from the second half of 2020 to the beginning of 2021, 17.8 million laying hens were slaughtered. Although the government invested more than 100 billion won for egg imports as a measure to stabilize prices, the effort was not that easy. The sharp volatility of egg prices negatively affected both consumers and poultry farmers, so measures were needed to stabilize egg prices. To this end, the egg prices were successfully predicted in this study by using the analysis algorithm of a machine learning regression. For price prediction, a total of 8 independent variables, including monthly broiler chicken production statistics for 2012-2021 of the Korean Poultry Association and the slaughter performance of the national statistics portal (kosis), have been selected to be used. The Root Mean Square Error (RMSE), which indicates the difference between the predicted price and the actual price, is at the level of 103 (won), which can be interpreted as explaining the egg prices relatively well predicted. Accurate prediction of egg prices lead to flexible adjustment of egg production weeks for laying hens, which can help decision-making about stocking of laying hens. This result is expected to help secure egg price stability.

• Korean Journal of Agricultural and Forest Meteorology
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• v.6 no.2
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• pp.107-117
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• 2004

If doubtlessly contributes much to agriculture and rural development. The roles can be summarized as; 1. to activate rural areas and to provide more comfortable and safe rural life with equivalent services to those in urban areas, facilitating distance education, tole-medicine, remote public services, remote entertainment etc. 2. To initiate new agricultural and rural business such as e-commerce, real estate business for satellite officies, rural tourism and virtual corporation of small-scale farms. 3. To support policy-making and evaluation on optimal farm production, disaster management, effective agro-environmental resource management etc., providing tools such as GIS. 4. To improve farm management and farming technologies by efficient farm management, risk management, effective information or knowledge transfer etc., realizing competitive and sustainable farming with safe products. 5. To provide systems and tools to secure food traceability and reliability that has been an emerging issue concerning farm products since serious contamination such as BSE and chicken flu was detected. 6. To take an important and key role for industrialization of farming or lam business enterprise, combining the above roles.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.374-390
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• 2021

Growth and yield of field vegetable crops would be affected by climate conditions, which cause a relatively large fluctuation in crop production and consumer price over years. The yield prediction system for these crops would support decision-making on policies to manage supply and demands. The objectives of this study were to compile literatures related to onion and garlic and to perform data-mining analysis, which would shed lights on the development of crop models for these major field vegetable crops in Korea. The literatures on crop growth and yield were collected from the databases operated by Research Information Sharing Service, National Science & Technology Information Service and SCOPUS. The keywords were chosen to retrieve research outcomes related to crop growth and yield of onion and garlic. These literatures were analyzed using text mining approaches including word cloud and semantic networks. It was found that the number of publications was considerably less for the field vegetable crops compared with rice. Still, specific patterns between previous research outcomes were identified using the text mining methods. For example, climate change and remote sensing were major topics of interest for growth and yield of onion and garlic. The impact of temperature and irrigation on crop growth was also assessed in the previous studies. It was also found that yield of onion and garlic would be affected by both environment and crop management conditions including sowing time, variety, seed treatment method, irrigation interval, fertilization amount and fertilizer composition. For meteorological conditions, temperature, precipitation, solar radiation and humidity were found to be the major factors in the literatures. These indicate that crop models need to take into account both environmental and crop management practices for reliable prediction of crop yield.

• Korean Journal of Agricultural and Forest Meteorology
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• v.26 no.1
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• pp.1-30
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• 2024

The international community adopts the SSP (Shared Socioeconomic Pathways) scenario as a new greenhouse gas emission pathway. As part of efforts to reflect these international trends and support for climate change adaptation measure in the agricultural sector, the National Institute of Agricultural Sciences (NAS) produced high-resolution (1 km) climate change scenarios for the Korean Peninsula based on SSP scenarios, certified as a "National Climate Change Standard Scenario" in 2022. This paper introduces SSP climate change scenario of the NAS and shows the results of the climate change projections. In order to produce future climate change scenarios, global climate data produced from 18 GCM models participating in CMIP6 were collected for the past (1985-2014) and future (2015-2100) periods, and were statistically downscaled for the Korean Peninsula using the digital climate maps with 1km resolution and the SQM method. In the end of the 21^st century (2071-2100), the average annual maximum/minimum temperature of the Korean Peninsula is projected to increase by 2.6~6.1℃/2.5~6.3℃ and annual precipitation by 21.5~38.7% depending on scenarios. The increases in temperature and precipitation under the low-carbon scenario were smaller than those under high-carbon scenario. It is projected that the average wind speed and solar radiation over the analysis region will not change significantly in the end of the 21st century compared to the present. This data is expected to contribute to understanding future uncertainties due to climate change and contributing to rational decision-making for climate change adaptation.

• Journal of Wetlands Research
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• v.25 no.2
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• pp.99-110
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• 2023

Non-point source (NP) pollutants in an agricultural landuse are discharged from a large area compared to those in other land uses, and thus effective source control measures are needed. To develop appropriate control measures, it is necessary to quantify discharge load of each source and evaluate the degree of water quality improvement by implementing different options of the control measures. This study used Hydrological Simulation Program-FORTRAN (HSPF) to quantify pollutant discharge loads from different sources and effects of different control measures on water quality improvements, thereby supporting decision making in developing appropirate pollutant control strategies. The study area is the Gyeseong river watershed in Changnyeong county, Gyeongsangnam-do, with agricultural areas occupying the largest proportion (26.13%) of the total area except for the forest area. The main pollutant sources include chemical and liquid fertilizers for agricultural activities, and manure produced from small scale livestock facilities and applied to agriculture lands or stacked near the facilities. Source loads of chemical fertilizers, liquid fertilizers and livestock manure of small scale livestock facilities, and point sources such as municipal wastewater treatment plants (WWTPs), community WWTPs, private sewage treament plants were considered in the HSPF model setup. Especially, NITR and PHOS modules were used to simulate detailed fate and transport processes including vegitation uptake, nutrient deposition, adsorption/desorption, and loss by deep percolation. The HSPF model was calibrated and validated based on the observed data from 2015 to 2020 at the outlet of the watershed. The calibrated model showed reasonably good performance in simulating the flow and water quality. Five Pollutants control scenarios were established from three sectors: agriculture pollution management (drainge outlet control, and replacement of controlled release fertilizers), livestock pollution management (liquid fertilizer reduction, and 'manure management of small scale livestock facilities) and private STP management. Each pollutant control measure was further divided into short-term, mid-term, and long-term scenarios based on the potential achievement period. The simulation results showed that the most effective control measure is the replacement of controlled release fertilizers followed by the drainge outlet control and the manure management of small scale livestock facilities. Furthermore, the simulation showed that application of all the control measures in the entire watershed can decrease the annual TN and TP loads at the outlet by 40.6% and 41.1%, respectively, and the annual average concentrations of TN and TP at the outlet by 35.1% and 29.2%, respectively. This study supports decision makers in priotizing different pollutant control measures based on their predicted performance on the water quality improvements in an agriculturally dominated watershed.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.23-45
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• 2020

Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.

• Korean Journal of Agricultural Science
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• v.35 no.1
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• pp.85-100
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• 2008

The United States Department of Agriculture (USDA) funded collaborative project on The Economic Feasibility Analysis of Crop Cultivation Practice Project in Pirganj and Kurigram Districts in Bangladesh will started during 2008-2012, for 4 years with total project cost of US$ 571,270. The project will be implemented in 6 villages; has 1,097 hectares areas which is divided into 948 hectares of agricultural land, 52 hectares of forest land and 345 hectares of other land, covered 1,059 households equal to 5,305 persons in Pirganj and Kurigram districts The project has proposed to be implemented in joint collaboration by Bangladesh Agricultural Research Council (BARC), Bangladesh Agricultural Research Institute (BARI) and Rangpur Dinajpur Rural Service (RDRS) Bangladesh with full participation of the farmers' groups of respective project site. The specific objectives of the project are: (1) to estimate the productivity of paddy, wheat, maize, tobacco and sugarcane (2) to determine the cost of production and returns to the above mentioned crops (3) to study the interrelationship between inputs and output of the above mentioned crops and (4) to examine the resource utilization patterns at farm level. In this project analysis, the net incremental profit is US$33,028. The expected incremental project benefit and incremented production cost are estimated as US$ 219,959 and US$ 186,931 respectively. The financial decision making criteria would be followed in this crop cultivation practice project. After the project implementation, the expected project benefits are assumed to be continued for 15 years. The benefit cost ratio (B/C) of the project is estimated at 1.077 (table 11) when using discount rate of 10% as an opportunity cost of capital in Bangladesh. FIRR of project is estimated at 26.15% which is bigger than the opportunity cost by more than double. So this project is financially feasible and acceptable. Therefore, this project should be extended to other areas to increase the farm income and economic growth of marginal poor farmers in Bangladesh.

• Journal of Intelligence and Information Systems
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• v.27 no.4
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• pp.73-95
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• 2021

This study uses Node2vec graph embedding method and Light GBM link prediction to explore undeveloped export candidate countries in Korea's food and beverage industry. Node2vec is the method that improves the limit of the structural equivalence representation of the network, which is known to be relatively weak compared to the existing link prediction method based on the number of common neighbors of the network. Therefore, the method is known to show excellent performance in both community detection and structural equivalence of the network. The vector value obtained by embedding the network in this way operates under the condition of a constant length from an arbitrarily designated starting point node. Therefore, it has the advantage that it is easy to apply the sequence of nodes as an input value to the model for downstream tasks such as Logistic Regression, Support Vector Machine, and Random Forest. Based on these features of the Node2vec graph embedding method, this study applied the above method to the international trade information of the Korean food and beverage industry. Through this, we intend to contribute to creating the effect of extensive margin diversification in Korea in the global value chain relationship of the industry. The optimal predictive model derived from the results of this study recorded a precision of 0.95 and a recall of 0.79, and an F1 score of 0.86, showing excellent performance. This performance was shown to be superior to that of the binary classifier based on Logistic Regression set as the baseline model. In the baseline model, a precision of 0.95 and a recall of 0.73 were recorded, and an F1 score of 0.83 was recorded. In addition, the light GBM-based optimal prediction model derived from this study showed superior performance than the link prediction model of previous studies, which is set as a benchmarking model in this study. The predictive model of the previous study recorded only a recall rate of 0.75, but the proposed model of this study showed better performance which recall rate is 0.79. The difference in the performance of the prediction results between benchmarking model and this study model is due to the model learning strategy. In this study, groups were classified by the trade value scale, and prediction models were trained differently for these groups. Specific methods are (1) a method of randomly masking and learning a model for all trades without setting specific conditions for trade value, (2) arbitrarily masking a part of the trades with an average trade value or higher and using the model method, and (3) a method of arbitrarily masking some of the trades with the top 25% or higher trade value and learning the model. As a result of the experiment, it was confirmed that the performance of the model trained by randomly masking some of the trades with the above-average trade value in this method was the best and appeared stably. It was found that most of the results of potential export candidates for Korea derived through the above model appeared appropriate through additional investigation. Combining the above, this study could suggest the practical utility of the link prediction method applying Node2vec and Light GBM. In addition, useful implications could be derived for weight update strategies that can perform better link prediction while training the model. On the other hand, this study also has policy utility because it is applied to trade transactions that have not been performed much in the research related to link prediction based on graph embedding. The results of this study support a rapid response to changes in the global value chain such as the recent US-China trade conflict or Japan's export regulations, and I think that it has sufficient usefulness as a tool for policy decision-making.