This study was conducted to determine the possibility of estimating the daily mean temperature for a specific location based on the climatic data collected from the nearby Automated Synoptic Observing System (ASOS) and Automated Weather System(AWS) to improve the accuracy of the climate data in forage yield prediction model. To perform this study, the annual mean temperature and monthly mean temperature were checked for normality, correlation with location information (Longitude, Latitude, and Altitude) and multiple regression analysis, respectively. The altitude was found to have a continuous effect on the annual mean temperature and the monthly mean temperature, while the latitude was found to have an effect on the monthly mean temperature excluding June. Longitude affected monthly mean temperature in June, July, August, September, October, and November. Based on the above results and years of experience with climate-related research, the daily mean temperature estimation was determined to be possible using longitude, latitude, and altitude. In this study, it is possible to estimate the daily mean temperature using climate data from all over the country, but in order to improve the accuracy of daily mean temperature, climatic data needs to applied to each city and province.

The objective of this study was to access the effect of climate and soil factors on alfalfa dry matter yield (DMY) by the contribution through constructing the yield prediction model in a general linear model considering climate and soil physical variables. The processes of constructing the yield prediction model for alfalfa was performed in sequence of data collection of alfalfa yield, meteorological and soil, preparation, statistical analysis, and model construction. The alfalfa yield prediction model used a multiple regression analysis to select the climate variables which are quantitative data and a general linear model considering the selected climate variables and soil physical variables which are qualitative data. As a result, the growth degree days(GDD) and growing days(GD), and the clay content(CC) were selected as the climate and soil physical variables that affect alfalfa DMY, respectively. The contributions of climate and soil factors affecting alfalfa DMY were 32% (GDD, 21%, GD 11%) and 63%, respectively. Therefore, this study indicates that the soil factor more contributes to alfalfa DMY than climate factor. However, for examming the correct contribution, the factors such as other climate and soil factors, and the cultivation technology factors which were not treated in this study should be considered as a factor in the model for future study.

Rye, whole-crop barley and Italian Ryegrass are major winter forage species in Korea, and yield monitoring of winter forage species is important to improve forage productivity by precision management of forage. Forage monitoring using Unmanned Aerial Vehicle (UAV) has offered cost effective and real-time applications for site-specific data collection. To monitor forage crop by multispectral camera with UAV, we tested four types of vegetation index (Normalized Difference Vegetation Index; NDVI, Green Normalized Difference Vegetation Index; GNDVI, Normalized Green Red Difference Index; NGRDI and Normalized Difference Red Edge Index; NDREI). Field measurements were conducted on paddy field at Naju City, Jeollanam-do, Korea between February to April 2019. Aerial photos were obtained by an UAV system and NDVI, GNDVI, NGRDI and NDREI were calculated from aerial photos. About rye, whole-crop barley and Italian Ryegrass, regression analysis showed that the correlation coefficients between dry matter and NDVI were 0.91∼0.92, GNDVI were 0.92∼0.94, NGRDI were 0.71∼0.85 and NDREI were 0.84∼0.91. Therefore, GNDVI were the best effective vegetation index to predict dry matter of rye, wholecrop barley and Italian Ryegrass by UAV system.

In this study, we performed algorithms to predict algae of Chlorophyll-a (Chl-a). Water quality and quantity data of the middle Nakdong River area were used. At first, the correlation analysis between Chl-a and water quality and quantity data was studied. We extracted ten factors of high importance for water quality and quantity data about the two weirs. Algorithms predicted how ten factors affected Chl-a occurrence. We performed algorithms about decision tree, random forest, elastic net, gradient boosting with Python. The root mean square error (RMSE) value was used to evaluate excellent algorithms. The gradient boosting showed 10.55 of RMSE value for the Gangjeonggoryeong (GG) site and 11.43 of RMSE value for the Dalsung (DS) site. The gradient boosting algorithm showed excellent results for GG and DS sites. Prediction value for the four algorithms was also evaluated through the Receiver operating characteristic (ROC) curve and Area under curve (AUC). As a result of the evaluation, the AUC value was 0.877 at GG site and the AUC value was 0.951 at DS site. So the algorithm‘s ability to interpret seemed to be excellent.

본 연구는 고추의 생육특성인 초장, 엽면적, 생체중, 건물중을 조사하였고, 기상요인에 따른 수량 예측 모델 개발을 위하여 수행되었다. 생육도일온도에 따른 고추의 생체중, 건물중, 초장 및 엽면적에 대한 생장 모델(시그모이드 곡선)을 개발하였다. 고추는 정식 후 50일 전후로 초장, 엽면적, 생체중 및 건물중이 지수 함수적으로 증가하였으며, 140일 이후에는 생장요인들이 평행을 이루었다. 그리고 생육도일온도에 따른 고추의 생장을 분석 한 결과 지수 함수적으로 생장이 늘어나는 시점의 GDD는 1,000였다. 고추의 건물중에 대한 상대생장 속도를 계산하는 식은 RGR (dry weight) = 0.0562 + 0.0004 × DAT − 0.00000557 × DAT2 였다. 수확한 적과의 생체중과 건물중으로 고추의 단수를 구하였을 때, 정식 후 112일에 1,3871kg/10a였고, 건고추의 단수는 정식 후 112일에 291kg/10a이였다. 고추 작황예측 프로그램 개발을 위해서는 고추의 생산성에 관여하는 주요 요인을 분석하고, 실시간으로 계측한 생육 및 기상자료를 기반으로 하여 생육모델을 보정 및 검증해야 할 것이다.

파프리카 수확량 예측을 위한 목적으로 온실 환경과 작물의 생육 특성 및 수확량 패턴을 조사 분석하였다. 경남 거창 지역(해발고도 667m)의 유리온실에서 적색계 파프리카 ‘Cupra’와 황색계 파프리카 ‘Fiesta’를 2016년 7월 5일 파종하고, 35일 후인 8월 10일 정식하여 2017년 7월 15일까지 재배하였다. 재식밀도는 두 품종 동일하게 3.66plants/m2로 2줄기로 유인하였다. 정식 후 재배기간 동안 시설의 외부 평균 광량은 14.36MJ/m2/day였고, 온실 내부의 관리에서 24시간 평균온도 20~22oC, CO2 400~700ppm, 24시간 평균 습도 60~75% 수준으로 유지하고자 하였다. 정식 42주 후까지 신장속도는 ‘Cupra’가 7.3cm/week, ‘Fiesta’가 6.9cm/week로 ‘Cupra’가 빨랐다. 첫 착과는 ‘Cupra’가 1.0마디, ‘Fiesta’는 2.7마디에서 나타났으며, 첫 수확은 정식 후 ‘Cupra’가 14주, ‘Fiesta’가 11주로 ‘Fiesta’가 빨랐다. 재배 종료 시까지의 10a당 수확량을 비교해 보면, ‘Fiesta’가 18,848kg, ‘Cupra’가 19,307kg로 ‘Fiesta’가 2.4% 높게 나타났으며, L 사이즈인 200g 이상의 과중 비율은 ‘Cupra’가 27.7%로 ‘Fiesta’보다 7.7%로 높았다. 6월까지의 수확량에서, 착과에서 수확까지의 평균 소요일수는 ‘Cupra’가 72.6일, ‘Fiesta’가 63.8일로 ‘Cupra’가 8.8일이 더 소요되었다. 수확소요일수와 그 기간 누적된 광량과의 관계를 보면, 광량이 증가하는 2월 이후 두 품종 모두 누적광이 많을수록 수확소요일수는 짧아지는 부의 관계를 나타냈다. 1월에 가장 긴 소요일수가 요구되었는데, 이는 낮은 광량으로 생육과 착색이 지연되어 소요일수가 늘어난 것으로 판단된다. 수확량과의 관계에서는 ‘Cupra’는 광량이 증가됨에 따라 수확량이 증가되는 반면, ‘Fiesta’는 불규칙적인 패턴을 보여 품종간의 차이를 보였다.

This study was aimed to find yield prediction model of Italian ryegrass using climate big data and geographic information. After that, mapping the predicted yield results using Geographic Information System (GIS) as follows; First, forage data were collected; second, the climate information, which was matched with forage data according to year and location, was gathered from the Korean Metrology Administration (KMA) as big data; third, the climate layers used for GIS were constructed; fourth, the yield prediction equation was estimated for the climate layers. Finally, the prediction model was evaluated in aspect of fitness and accuracy. As a result, the fitness of the model (R2) was between 27% to 95% in relation to cultivated locations. In Suwon (n=321), the model was; DMY = 158.63AGD –8.82AAT +169.09SGD - 8.03SAT +184.59SRD -13,352.24 (DMY: Dry Matter Yield, AGD: Autumnal Growing Days, SGD: Spring Growing Days, SAT: Spring Accumulated Temperature, SRD: Spring Rainfall Days). Furthermore, DMY was predicted as 9,790±120 (kg/ha) for the mean DMY(9,790 kg/ha). During mapping, the yield of inland areas were relatively greater than that of coastal areas except of Jeju Island, furthermore, northeastern areas, which was mountainous, had lain no cultivations due to weak cold tolerance. In this study, even though the yield prediction modeling and mapping were only performed in several particular locations limited to the data situation as a startup research in the Republic of Korea.

The objective of this study was to select a model showing high-levels of interpretability which is high in R-squared value in terms of predicting the yield in the mixed pasture using the factors of fertilization, seeding rate and years after pasture establishment in steps, as well as the climate as a basic factor. The processes of constructing the yield prediction model for the mixed pasture were performed in the sequence of data collection (forage and climatic data), preparation, analysis, and model construction. Through this process, six models were constructed after considering climatic variables, fertilization management, seeding rates, and periods after pasture establishment years in steps, thereafter the optimum model was selected through considering the coincidence of the models to the forage production theories. As a result, Model VI (R squared = 53.8%) including climatic variables, fertilization amount, seeding rates, and periods after pasture establishment was considered as the optimum yield prediction model for mixed pastures in South Korea. The interpretability of independent variables in the model were decreased in the sequence of climatic variables(24.5%), fertilization amount(17.8%), seeding rates(10.7%), and periods after pasture establishment(0.8%). However, it is necessary to investigate the reasons of positive correlation between dry matter yield and days of summer depression (DSD) by considering cultivated locations and using other cumulative temperature related variables instead of DSD. Meanwhile the another research about the optimum levels of fertilization amounts and seeding rates is required using the quadratic term due to the certain value-centered distribution of these two variables

충청북도 제천시의 상수도 취수를 목적으로 건설하는 취수장 예정지를 중심으로 평창강의 담수어류상을 1994년 9월 25일과 1994년 10월 7일부터 1994년 10월 9일까지 두 차례 조사하고 하천 유지수량의 변동이 어류의 생태에 미치는 영향을 조사하였다. 6개 조사 지역에서 총 6과 21속 28종의 담수어류가 관찰되었으며 천연기념물 및 한국 특산종이 많이 관찰되었다. 평창강은 사행천으로 다양한 담수어류가 서식하기에 적합한 환경과 유지수량을 갖는다. 취수를 위한 급격한 유지 수량의 감소는 평창강의 담수어류가 대부분 멸종할 우려가 매우 높다.