This study divided the area capable of producing domestic forage into grazing pasture, hay production area, and silage crop area, calculated the required area according to the forage production volume, and examined whether self-sufficiency in forage leads to cost savings. When the self-sufficiency rate of forage for dairy cows and Hanwoo is 80%, the improvement in profitability per heaf ranges from 3% to 9%, typically around 5%, which is considered a significant benefit for both corporate and individual businesses. The average profit per ranch is expected to increase about KRW 50 million per year, and the country as a whole is expected to reduce forage costs by KRW 0.9 trillion per year. Recently, efforts are being made by the government and local authorities to cultivate summer forage at the rice fields for improving self-sufficiency in forage feed to stabilize rice supply and demand. Furthermore, it is also necessary to conduct research on reducing the cost of concentrated feed and TMR (Total mixed ration).

The current survey form of grassland has not played its role in managing the grassland effectively because of the ambiguous questionnaire items and the absence of method and time of the investigation. Therefore, this study was conducted to clarify and add the items for effective of grassland management. The survey form of grassland was regulated in Article 16 of the Enforcement Rules of the Grassland Act (Survey on Grasslands Management Status, etc.). The five contents that needed improvement were the grassland owner, the survey timing and method of established grassland, grassland used livestock, grassland usage condition, and grassland grade. In the improved survey form of grassland, the grassland owner was changed to the grassland manager. In addition, ‘Survey by each land’ was added to the improved survey form in which the managers can survey each land. The dimension of each grassland establishment method was deleted in the grassland establishment time and method. In the livestock using grassland, the number of livestock and the area where livestock are used were added, and the number of other livestock was added, too. The grassland grade was decided as the combined score by three evaluation categories; grassland usage, the ratio of forage production in grassland and kinds of forage. The high, middle, and low grades were over 8, 6~7, and under 5 points in the combined score, respectively. The results show that the changed survey form of grassland can make grassland management more efficient by materializing the subject of grassland management and the survey terminology and methods.

As a means to activate eco pastoral system in alpine grassland, the government can consider public pastures, which are currently unused, to scale them up into public ranches. Depending on ownership and operation, four management models proposed as follows: 1) Government-Owned and Operated with Balanced Profit and Loss 2) Government-Owned and Operated with Revenue Generation 3) Government-Owned but Privately Operated by Outsourced to Professional Manager 4) Full Privatization (Ownership and Operation by Private Individuals). The study outlined above proposes four management models for the activation of eco pastoral system in alpine grassland. It also suggests methods for the selection and performance evaluation of manager to establish a profitable structure. Additionally, the research provides management methods for the conservation of grazing ecology in pastoral ecosystems. Particularly, the adaptation of tools commonly used in South Korean business sector for the selection and performance evaluation of manager within the system of the proposed management models. This aspect is deemed valuable as it aligns these tools with the specific characteristics of eco pastoral system in alpine grassland, contributing not only to the effective implementation of the models but also to the enhancement of the revenue structure.

This study was conducted to calculate the damage of Italian ryegrass (IRG) by abnormal climate using machine learning and present the damage through the map. The IRG data collected 1,384. The climate data was collected from the Korea Meteorological Administration Meteorological data open portal.The machine learning model called xDeepFM was used to detect IRG damage. The damage was calculated using climate data from the Automated Synoptic Observing System (95 sites) by machine learning. The calculation of damage was the difference between the Dry matter yield (DMY)normal and DMYabnormal. The normal climate was set as the 40-year of climate data according to the year of IRG data (1986~2020). The level of abnormal climate was set as a multiple of the standard deviation applying the World Meteorological Organization (WMO) standard. The DMYnormal was ranged from 5,678 to 15,188 kg/ha. The damage of IRG differed according to region and level of abnormal climate with abnormal temperature, precipitation, and wind speed from -1,380 to 1,176, -3 to 2,465, and -830 to 962 kg/ha, respectively. The maximum damage was 1,176 kg/ha when the abnormal temperature was -2 level (+1.04℃), 2,465 kg/ha when the abnormal precipitation was all level and 962 kg/ha when the abnormal wind speed was -2 level (+1.60 ㎧). The damage calculated through the WMO method was presented as an map using QGIS. There was some blank area because there was no climate data. In order to calculate the damage of blank area, it would be possible to use the automatic weather system (AWS), which provides data from more sites than the automated synoptic observing system (ASOS).

This study was conducted to estimate the damage of Whole Crop Corn (WCC; Zea Mays L.) according to abnormal climate using machine learning as the Representative Concentration Pathway (RCP) 4.5 and present the damage through mapping. The collected WCC data was 3,232. The climate data was collected from the Korea Meteorological Administration's meteorological data open portal. The machine learning model used DeepCrossing. The damage was calculated using climate data from the automated synoptic observing system (ASOS, 95 sites) by machine learning. The calculation of damage was the difference between the dry matter yield (DMY)normal and DMYabnormal. The normal climate was set as the 40-year of climate data according to the year of WCC data (1978-2017). The level of abnormal climate by temperature and precipitation was set as RCP 4.5 standard. The DMYnormal ranged from 13,845-19,347 kg/ha. The damage of WCC which was differed depending on the region and level of abnormal climate where abnormal temperature and precipitation occurred. The damage of abnormal temperature in 2050 and 2100 ranged from -263 to 360 and -1,023 to 92 kg/ha, respectively. The damage of abnormal precipitation in 2050 and 2100 was ranged from -17 to 2 and -12 to 2 kg/ha, respectively. The maximum damage was 360 kg/ha that the abnormal temperature in 2050. As the average monthly temperature increases, the DMY of WCC tends to increase. The damage calculated through the RCP 4.5 standard was presented as a mapping using QGIS. Although this study applied the scenario in which greenhouse gas reduction was carried out, additional research needs to be conducted applying an RCP scenario in which greenhouse gas reduction is not performed.

홀스타인 착유우에 수입건초 대신 WCRS로 조사료 일부를 대 체 급여하였을 때 산유성적 및 수익성에 미치는 영향을 검토하였 다. 대조구(C)는 농가 관행 급여방법으로 자가 혼합건초(13kg)와 농후사료(2.6~9.6kg), 오차드그라스 및 버뮤다그라스 건초(1.8kg) 를 급여 하였고, 처리구(T)는 자가 혼합건초(9.6kg)와 농후사료 (2.6~9.6kg) 및 WCRS (2.2kg)를 급여하였다. 건물수량(DMI)을 기준으로 C에 대한 T의 조사료 대체비율은 20% 였다. CP함량은 오차드그라스 및 버뮤다그라스 건초가 각각 11.3 및 8.4%였고, WCRS는 4.6%로, WCRS에서 낮았다. 이는 벼를 수확적기보다 약 30일정도 늦게 수확한 것에 기인하고 있다. DMI는 비유초기, 중기 및 후기에서 각각 T가 C보다 유의적으로 낮게 나타났다 (p<0.05). 유량은 비유초기, 중기 및 후기 모두 처리간 유의적 차 이는 없었다(p>0.05). 실험 기간 중 평균유량도 C 및 T가 각각 26.9 및 26.3kg으로 처리간 유의적인 차이는 없었다(p>0.05). 유 지율, 유단백 및 총고형물에서 각 비유기 공히 처리 간에 유의적 인 차이는 없었다(p>0.05). 두당 조수입은 C가 21,141원, T가 21,915원으로 T에서 다소 높게 나타났다. 유사비는 T가 22.9%로 C의 27.8%보다 낮았다. 이상에서 수입산 건초 대신 WCRS로 20% 대체 급여하여도 산유량 및 유성분에 유의적인 차이가 없으 며 수익은 높아 경제성이 있는 것으로 사료된다.

This study was conducted to estimate the damage of Whole Crop Maize (WCM) according to abnormal climate using machine learning and present the damage through mapping. The collected WCM data was 3,232. The climate data was collected from the Korea Meteorological Administration's meteorological data open portal. Deep Crossing is used for the machine learning model. The damage was calculated using climate data from the Automated Synoptic Observing System (95 sites) by machine learning. The damage was calculated by difference between the Dry matter yield (DMY)normal and DMYabnormal. The normal climate was set as the 40-year of climate data according to the year of WCM data (1978~2017). The level of abnormal climate was set as a multiple of the standard deviation applying the World Meteorological Organization(WMO) standard. The DMYnormal was ranged from 13,845~19,347 kg/ha. The damage of WCM was differed according to region and level of abnormal climate and ranged from -305 to 310, -54 to 89, and -610 to 813 kg/ha bnormal temperature, precipitation, and wind speed, respectively. The maximum damage was 310 kg/ha when the abnormal temperature was +2 level (+1.42 ℃), 89 kg/ha when the abnormal precipitation was -2 level (-0.12 mm) and 813 kg/ha when the abnormal wind speed was -2 level (-1.60 ㎧). The damage calculated through the WMO method was presented as an mapping using QGIS. When calculating the damage of WCM due to abnormal climate, there was some blank area because there was no data. In order to calculate the damage of blank area, it would be possible to use the automatic weather system (AWS), which provides data from more sites than the automated synoptic observing system (ASOS).

This study was carried out to evaluate the seeding date and performance of early maturing rye cultivars for the Dry matter yield (DMY) and nutritive value during 2016 and 2017 in Yeongseo region of Gangwondo, South Korea. The experimental field was designed as a split-plot arrangement. The treatments were two planting dates on September 25 and October 02 as the main plots, and two cultivars of forage rye including Gogu and Koolgrazer as sub-plots. The cultivars were harvested on April 26 at the heading stage of both years. In this experiment, the sowing dates and cultivars of the forage rye did not effect on DMY. The DMY had no significant differences among the cultivars of forage rye and seeding date of both years. Similarly, no significant difference was observed in the DMY of Gogu and Koolgrazer in both seeding date and years. The CP, NDF, ADF, and RFV had no significant differences among the cultivars of forage rye and seeding date of both years. Considering the DMY and nutritive value of the current experiment, seeding of forage rye cultivars Gogu and Koolgrazer on September 25 and October 2 could be used as an recommended seeding date at northern area. In addition, based on the climate characteristics of the region, both cultivars had relatively similar forage yield and quality that makes them to be recommended for cultivation in the region. This study is meaningful in that DMY was first presented in Yeongseo region where there is no cultivation data for forage rye.

In this study, the appropriate unit cost in grassland establishment was redeveloped by the grassland establishment method and work criteria. The grassland establishment method was divided into tillage establishment (all logging) and no-tillage establishment (all logging and partial logging). The price for the work criteria by the establishment method was presented for each permission/authorization and establishment work. In permission/authorization for grassland establishment, the cost of each work criteria was of environmental impact (small scale environmental impact) assessment, disaster impact assessment, cadastral serving fee, forest survey, and connection fee for control of mountainous districts. In establishment was of logging, cleaning/gruffing, plowing/soil preparation, seeding, fertilization, livestock manure compost, seed, herbicide, labor cost (fertilizer, seed and herbicide), soil consolidation, cattle trail, and fence. The unit cost of grassland establishment was KRW 115,894,212 for the tillage establishment, and KRW 110,281,572 and KRW 106,680,122 for the all and partial logging of the no-tillage establishment, respectively. The current study redeveloped the establishment method, work criteria, and estimation of the unit cost of grassland establishment. It can be usefully used to carry out government projects to support related to establishment and maintenance of grassland.

This study was carried out to compare the DMY (dry matter yield) of IRG (Italian ryegrass) in the southern coastal regions of Korea due to seasonal climate scenarios such as the Kaul-Changma (late monsoon) in autumn, extreme winter cold, and drought in the next spring. The IRG data (n = 203) were collected from various Reports for Collaborative Research Program to Develop New Cultivars of Summer Crops in Jeju, 203 Namwon, and Yeungam from the Rural Development Administration (1993 – 2013). In order to define the seasonal climate scenarios, climate variables including temperature, humidity, wind, sunshine were used by collected from the Korean Meteorological Administration. The discriminant analysis based on 5% significance level was performed to distinguish normal and abnormal climate scenarios. Furthermore, the DMY comparison was simulated based on the information of sample distribution of IRG. As a result, in the southern coastal regions, only the impact of next spring drought on DMY of IRG was critical. Although the severe winter cold was clearly classified from the normal, there was no difference in DMY. Thus, the DMY comparison was simulated only for the next spring drought. Under the yield comparison simulation, DMY (kg/ha) in the normal and drought was 14,743.83 and 12,707.97 respectively. It implies that the expected damage caused by the spring drought was about 2,000 kg/ha. Furthermore, the predicted DMY of spring drought was wider and slower than that of normal, indicating on high variability. This study is meaningful in confirming the predictive DMY damage and its possibility by spring drought for IRG via statistical simulation considering seasonal climate scenarios.

This study aimed to analyze causality of climatic factors that affecting the yield of whole crop barley (WCB) by constructing a network within the natural ecosystem via the structural equation model. The WCB dataset (n=316) consisted of data on the forage information and climatic information. The forage information was collected from numerous experimental reports from New Cultivars of Winter Crops (1993-2012) and included details of fresh and dry matter yield, and the year and location of cultivation. The climatic information included details of the daily mean temperature, precipitation, and sunshine duration from the weather information system of the Korea Meteorological Administration. The variables were growing days, accumulated temperature, precipitation, and sunshine duration in the season for the period of seeding to harvesting. The data was collected over 3 consecutive seasons—autumn, winter, and the following spring. We created a causality network depicting the effect of climatic factors on production by structural equation modeling. The results highlight: (i) the differences in the longitudinal effects between autumn and next spring, (ii) the factors that directly affect WCB production, and (iii) the indirect effects by certain factors, via two or more paths. For instance, the indirect effect of precipitation on WCB production in the following spring season via its effect on temperature was remarkable. Based on absolute values, the importance of WCB production in decreasing order was: the following spring temperature (0.45), autumn temperature (0.35), wintering (-0.16), and following spring precipitation (0.04). Therefore, we conclude that other climatic factors indirectly affect production through the final pathway, temperature and growing days in the next spring, in the climate-production network for WCB including temperature, growing days, precipitation and sunshine duration.

본 연구는 기계학습을 통한 수량예측모델을 이용하여 이상기상에 따른 WCM의 DMY 피해량을 산출하기 위한 목적으로 수행하였다. 수량예측모델은 WCM 데이터 및 기상 데이터를 수집 후 가공하여 8가지 기계학습을 통해 제작하였으며 실험지역은 경기도로 선정하였다. 수량예측모델은 기계학습 기법 중 정확성이 가장 높은 DeepCrossing (R2=0.5442, RMSE=0.1769) 기법을 통해 제작하였다. 피해량은 정상기상 및 이상기상의 DMY 예측값 간 차이로 산출하였다. 정상기상에서 WCM의 DMY 예측값은 지역에 따라 차이가 있으나 15,003~17,517 kg/ha 범위로 나타났다. 이상기온, 이상강수량 및 이상풍속에서 WCM의 DMY 예측 값은 지역 및 각 이상기상 수준에 따라 차이가 있었으며 각각 14,947~17,571 kg/ha, 14,986~17,525 kg/ha 및 14,920~17,557 kg/ha 범위로 나타났다. 이상기온, 이상강수량 및 이상풍속에서 WCM의 피해량은 각각 –68~89 kg/ha, -17~17 kg/ha 및 – 112~121 kg/ha 범위로 피해로 판단할 수 없는 수준이었다. WCM의 정확한 피해량을 산출하기 위해서는 수량예측모델에 이용하는 이상기상 데이터 수의 증가가 필요하다.

본 연구는 객토를 한 간척지에서 석고시용 수준이 알팔파의 수량과 사료성분에 미치는 영향을 알아보고자 수행하였다. 실험장소는 간척한지 17~33년 경과된 석문간척지로서 약 70 cm 정도 객토한 토양이었다. 객토에 사용한 흙은 섬토양의 제염을 하지 않은 것 이었다. 처리는 석고를 시용하지 않은 0 ton/ha 구(G0), 석 고를 2 ton/ha(G2) 및 4 ton/ha(G4) 시용한 구로 하였다. 수확은 알팔파가 개화초기(개화 10%)에 도달할 때 1차 수확하였으며 이 후 수확은 약 35일 간격으로 수확을 하였다. 알팔파의 건물수량은 1차 년도는 G2가 G0와 G4보다 유의적으로 높았으며 2차 년도는 처리간 유의적인 차이는 없었으나 G2가 G0와 G4보다 높은 경향을 보였다. G2에서 알팔파의 건물수량이 높은 이유는 토양의 pH 및 EC가 각각 재배가능 및 재배적합 수준이었고 피복도 및 알팔파 식생비율도 높은 것에 기인하였다. 1차 및 2차 년도 모두 석고 처리 간 CP, NDF 및 ADF 함량 및 RFV는 차이가 없었다. 한편 1차 및 2차 년도의 연구결과를 통해서 알팔파 건물수량에 부정적인 영향을 주는 요인은 봄의 가뭄과 여름의 집중된 강수로 나타났다. 이상으로부터 객토 간척지에서 석고 처리는 알팔파의 건물수량을 높이는데 효과적인 것으로 판단되며 2 ton/ha이 적정 수준인 것으로 사료된다.

This study aimed to determine the trend in dry matter yield (DMY) of a new sorghum-sudangrass hybrid (SSH) in the central inland regions of Korea. The metadata (n=388) were collected from various reports of the experiments examining the adaptability of this new variety conducted by the Rural Development Administration (1988–2013). To determine the trend, the parameters of autoregressive (AR) and moving average (MA) were estimated from correlogram of Autocorrelation function (ACF) and partial ACF (PACF) using time series modeling. The results showed that the trend increased slightly year by year. Furthermore, ARIMA (1, 1, 0) was found to be the optimal model to describe the historical trend. This means that the trend in the DMY of the SSH was associated with changes over the past two years but not with changes from three years ago. Although climatic variables, such as temperature, precipitation, and sunshine were also considered as environmental factors for the annual trends, no clear association was observed between DMY and climates. Therefore, more precise processing and detailed definition of climate considering specific growth stages are required to validate this association. In particular, research on the impact of heavy rainfall and typhoons, which are expected to cause damage in the short term, on DMY trends is ongoing, and the model confirmed in this study is expected to play an important role in studying this aspect. Furthermore, we plan to add the environmental factors such as soil and cultivation management as well as climate to our future studies.

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.

Alpha-lipoic acid (ALA) is a naturally occurring antioxidant and has been previously used to treat diabetes and cardiovascular disease. However, the autophagy effects of ALA against oxidative stress-induced dopaminergic neuronal cell injury remain unclear. The aim of this study was to investigate the role of ALA in autophagy and apoptosis against oxidative stress in the SH-SY5Y human dopaminergic neuronal cell line. We examined SH-SY5Y phenotypes using the 3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyltetrazolium bromide assay (cell viability/proliferation), 4′,6-diamidino-2-phenylindole dihydrochloride nuclear staining, Live/Dead cell assay, cellular reactive oxygen species (ROS) assay, immunoblotting, and immunocytochemistry. Our data showed ALA attenuated hydrogen peroxide (H2O2)-induced ROS generation and cell death. ALA effectively suppressed Bax up-regulation and Bcl-2 and BclxL down-regulation. Furthermore, ALA increased the expression of the antioxidant enzyme, heme oxygenase-1. Moreover, the expression of Beclin-1 and LC-3 autophagy biomarkers was decreased by ALA in our cell model. Combined, these data suggest ALA protects human dopaminergic neuronal cells against H2O2-induced cell injury by inhibiting autophagy and apoptosis.

This experiment was carried out to study the effects of different environmental conditions and cultivation techniques on productivity of grasslands in central and southern area of Korea on 2017 and 2018. Average dry matter yield of grasslands at 10 actual production sites was 7,496 kg/ha. that was ranged from 4,652 to 13,292 kg/ha with least significant difference(LSD) of 1,577kg/ha between grasslands (p<0.05) on 2017. Average dry matter yield of grasslands at 10 actual production sites was 7,914 kg/ha. that was ranged from 3,927 to 12,372 kg/ha with LSD of 1,577kg/ha between grasslands(p<0.05) on 2018. Dry matter(DM) yield of grasslands have positive correlation with soil fertility (p<0.01) but not correlated with rainfall and air temperature among cultivation environments. And also DM yield of grasslands have positive correlation with grassland management techniques(p<0.01). These results suggest that practices of grassland management techniques and improvement of soil fertility are more important than cultivation environments by climate change for increasing the DM yield of grassland in central and southern area of Korea.

This study aimed to discuss the optimal seeding and harvesting dates with growing degree days(GDD) via meta-data of whole crop maize(WCM). The raw data (n=3,152) contains cultivation year, cultivars, location, seeding and harvesting dates collected from various reports such as thesis, science journals and research reports (1982-2012). The processing was: recording, screening and modification of errors; Then, the final dataset (n=121) consists of seeding cases (n=29), and harvesting cases (n=92) which were used to detect the optimum. In addition, the optimal periods considering tolerance range and GDD also were estimated. As a result, the optimum seeding and harvesting periods were 14th April ~ 3rd May and 15th August ~ 4th September, respectively; where, their GDDs were 23.7~99.6℃ and 1,328.7~ 1,602.1℃, respectively. These GDDs could be used as a judge standard for selecting the seeding and harvesting dates.

This study was conducted to investigate the possibility of replacing imported Timothy hay (TH) with domestic Italian ryegrass silage (IRGS) as a horse feed considering feed quality, nutrient digestibility and feed price. Two experimental diets (TH and IRGS) were fed to six-headed Thoroughbred (body weight, 475.7±33.3kg) of the Korea Racing Authority of Wondang Stud Farm. The 3 head animals were assigned to Control group (TH) and Treatment group (IRGS), respectively. The nutrient digestibility was determined by the total collection method. IRGS is enough for using as a horse feed because its Relative feed value(RFV) was higher than TH and its fermentation quality is suitable for horses. Although no difference was observed in nutrient digestibility, Total digestible nutrients(TDN), and Digestible energy(DE) between Control and Treatment group (p>0.05), the fact that price of IRGS was much lower (53.7~62.4%) than that of TH indicates IRGS has competitive advantage over TH as a horse forage feed. The present study indicates that IRGS can be fully replaced with TH due to its superior economic value even though the similarity of its nutrient digestibility, TDN, and DE to TH.