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.

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.

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