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.

This study aimed to confirm the importance ratio of climate and management variables on production of orchardgrass in Korea (1982―2014). For the climate, the mean temperature in January (MTJ, ℃), lowest temperature in January (LTJ, ℃), growing days 0 to 5 (GD 1, day), growing days 5 to 25 (GD 2, day), Summer depression days (SSD, day), rainfall days (RD, day), accumulated rainfall (AR, mm), and sunshine duration (SD, hr) were considered. For the management, the establishment period (EP, 0―6 years) and number of cutting (NC, 2nd―5th) were measured. The importance ratio on production of orchardgrass was estimated using the neural network model with the perceptron method. It was performed by SPSS 26.0 (IBM Corp., Chicago). As a result, EP was the most important variable (100%), followed by RD (82.0%), AR (79.1%), NC (69.2%), LTJ (66.2%), GD 2 (63.3%), GD 1 (61.6%), SD (58.1%), SSD (50.8%) and MTJ (41.8%). It implies that EP, RD, AR, and NC were more important than others. Since the annual rainfall in Korea is exceed the required amount for the growth and development of orchardgrass, the damage caused by heavy rainfall exceeding the appropriate level could be reduced through drainage management. It means that, when cultivating orchardgrass, factors that can be controlled were relatively important. Although it is difficult to interpret the specific effect of climates on production due to neural networking modeling, in the future, this study is expected to be useful in production prediction and damage estimation by climate change by selecting major factors.

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).

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.

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 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.

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.

This study was conducted to suggest the new grassland grade system on evaluating the grassland status. The grassland status has been evaluated based on the forage yield (good, fair and poor) by municipal authorities. The grassland grades by current system were 19 good, 11 fair and 11 poor among the 41 grassland farms from 6 provinces. This evaluation result differed greatly from the result of actual measurement of forage yields which showed all poor. The big difference was resulted from failing the reflection of the various characteristics, such as different seasonal growth and harvest frequency. Furthermore, the lack of consistent examining date and method added the inaccuracy of current grassland grade system. The new grassland grade system based on the grassland vegetation ratio (grass, weed and bare soil) was initially designed into 6-grade system (1st; 100~80%, 2nd; 79~60%, 3rd; 59~40%, 4th; 39~20%; 5th; 19~1% and 6th; 0% on the basis of grasses proportion), but later was changed into 4-grade system (1st, 2nd, 3rd, and 4th grades are 70% or more, 50% or more, 50% or less, and 0% of forage proportion, respectively) after reflecting the opinion of grassland farms and municipal authorities. Re-evaluation on the grassland status using the 4-grade system resulted in the total 80% consisted of 2nd, 3rd and 4th grade which means most grasslands needs the partial reseeding or the rehabilitation of entire grassland. Pictures and schematic diagrams depicting the 4-grade system were presented to improve the objectivity of evaluation. The optimal time for assessing grassland status is fall when plant height 20~30 cm. Conclusively, the 4-grade system is an efficient method for all non-professionals including grassland farms or municipal authorities in assessing the grassland status. To apply this system to the field, the institutional arrangements such as amendment of grassland act should take place in advance.

This study was conducted to investigate 106 grassland farms in six provinces including Chungcheongbuk-do, Gangwon-do, Gyeonggi-do, Gyeongsangbuk-do, Gyeongsangnam-do, Jeju-do, and Jeollanam-do to present their problems and causes by surveying the actual state of grassland farms during three years(2014~2016). The grassland survey was divided into three categories; used as the perennial grasses and annual forage crops (Complied with Grassland Act), not used as the perennial grasses and annual forage crops (Not complied with Grassland Act) and failed to meet the farmer and some items are missing or inaccurate (Insufficient contents). Among the surveyed grassland farm, 68 farms (64.2 %) were complied with Grassland Act but 30 farms (28.3 %) were not complied with Grassland Act. Especially, the 8 farms (26.7 %) not complied with Grassland Act used the grassland as other purposes such as tree growing, golf club and swine farm etc.. Therefore, strict on-site investigation by local governments is required to prevent the grassland from being used by illegal purposes. And there’s a strong likelihood that 5 farms (62.5 %) avoided the survey violate the positive law. Grassland grades used by the local administrative agencies were not influenced by the factors affecting the yield (existence and non-existence of overseeding and fertilization by grassland grade, soil pH and organic matter content). This results suggest that there is a fundamental problem on the current grassland grade system based on the yield and the irregular time of investigation and lack of on-site investigation are another causes for inaccurate grassland grade. Therefore, the new method evaluating grassland grades which is not based on yield and the thorough on-site investigation by local administrative agencies are necessary when the grassland grade is evaluated.

This study was conducted to perform the suitability analysis of whole-crop rye (Secale cereale L.) based on the climatic information in the Republic of Korea to present useful information for producers and policy makers to determine the site-selection for the cultivation of the whole-crop rye. The criteria to analyze the climatic suitability of whole-crop rye was developed firstly. Then, the climatic suitability map for spatial analysis was developed through weighted overlaying the raster layers of climatic items in the evaluation criteria. Meanwhile, 16 geographically representative weather stations were selected to show examples of the calculation process of the climatic suitability score of a specific cultivation area. The results of the climatic suitability mapping indicated that the climatic conditions in most arable lands of the Republic of Korea such as the coastal, southern, western areas in the southern region of the Korean Peninsula and central areas in Jeju Island are suitable for the cultivation of whole-crop rye. The climatic suitability scores of the 16 weather stations were all in line with the results of the climatic suitability map.

Yield prediction model for mixed pasture was developed with a shortage that the relationship between dry matter yield (DMY) and days of summer depression (DSD) was not properly reflected in the model in the previous research. Therefore, this study was designed to eliminate the data of the regions with distinctly different climatic conditions and then investigate their relationships DMY and DSD using the data in each region separately of regions with distinct climatic characteristics and classify the data based on regions for further analysis based on the previous mixed pasture prediction model. The data set used in the research kept 582 data points from 11 regions and 41 mixed pasture types. The relationship between DMY and DSD in each region were analyzed through scatter plot, correlation analysis and multiple regression analysis in each region separately. In the statistical analysis, DMY was taken as the response variable and 5 climatic variables including DSD were taken as explanatory variables. The results of scatter plot showed that negative correlations between DMY and DSD were observed in 7 out of 9 regions. Therefore, it was confirmed that analyzing the relationship between DMY and DSD based on each region is necessary and 5 regions were selected (Hwaseong, Suwon, Daejeon, Siheung and Gwangju) since the data size in these regions is large enough to perform the further statistical analysis based on large sample approximation theory. Correlation analysis showed that negative correlations were found between DMY and DSD in 3 (Hwaseong, Suwon and Siheung) out of the 5 regions, meanwhile the negative relationship in Hwaseong was confirmed through multiple regression analysis. Therefore, it was concluded that the interpretability of the yield prediction model for mixed pasture could be improved based on constructing the models using the data from each region separately instead of using the pooled data from different regions.

The objective of this study was to evaluate the accuracy of equation being used to estimate the total digestible nutrients (TDN) of whole crop rice silage (WCRS) in sheep. To compare the observed and estimated TDN contents [estimated TDN content=87.57-(0.737×ADF)], two varieties of WCRS from Nokyang (NS) and Samgwang (SS) as forage and food source, respectively, were used as a treatment. Nine female Corriedale sheep (average body weight: 49.2±6.3 kg) were used as the experimental animals. The ewes were fed according to their nutrient requirements at the maintenance level. To check the difference between the observed and estimated TDN contents, one sample non-parametric t-test was applied. The CP, NFE and CF contents of the NS were 43.6, 74.2 and 64.2%, respectively, and that of the SS were 46.2, 58.1 and 44.9%, respectively. The observed and estimated TDN contents of the NS were 63.5 and 61.5%, where there was no significant difference. The observed and estimated TDN contents of the SS were 48.9 and 59.0%, where there was significant difference (p<0.05) This research confirmed the validity of TDN estimation equation being used for estimation of TDN of WCRS as forage source, but further research is recommended on the equation for estimating TDN contents of WCRS as food source.