A feeding trial was carried out to determine the effects of chromium methionine (Cr-Met) chelate and forage level over two years, 1st fattening and 2nd fattening period on growth parameters, carcass characteristics and blood metabolites of 46 Korean native (Hanwoo, Bos Taurus, BW = 183 ± 44 kg) steers. Treatments were: 1) Steers in the low forage (LF) group were fed diets that consisted of 60% concentrate and 40% forage; 2) Steers in the high forage (HF) group were fed diets that consisted of 40% concentrate and 60% forage. Following the 1st fattening period, steers (BW = 480 ± 37.6 kg) were randomly assigned to four treatment groups: LF (40 F plus no Cr-Met supplementation in the 2nd fattening period), LFCM (40LF plus added 400 ppb of Cr-Met during the 2nd fattening period), HF (60 F plus no added Cr-Met during the 2nd fattening period) and HFCM (60 F plus added 400 ppb of Cr-Met in the 2nd fattening period). Dry matter intake of the treatment diets did not differ during the raising and 1th fattening period (P > 0.05). The ADG in the raising period showed no difference between the 40 F and 60 F groups (P > 0.05). Carcass characteristics including rib-eye area and meat yield index were higher in HF than the other treatment groups (P < 0.05). The HF group tended to show a higher (P = 0.08) marbling score than the LF group whereas the HFCM group showed a higher marbling score than the LFCM group (P < 0.05). HDL was higher and LDL lower in groups fed with Cr-Met than in other groups whereas glucose showed the lowest value in HF group (P < 0.05). Triglyceride (TG), Cholesterol, PUN and total protein (TP) were the same among all treatment groups (P > 0.05). The Insulin concentration in the blood was significantly higher for the HFCM group than for the LF, LFCM and HF groups (P < 0.05). It is concluded that supplementation of chromium-methionine chelate could improve meat quality in beef steers.

A yield prediction model for Italian ryegrass (IRG) was constructed based on climatic data by locations in South Korea using a general linear model. The sample size of the final dataset was 312 during 25 years. The forage crop and climatic data were collected from the reports of two national research projects on forage crops and Korean meteorological administration, respectively. Five optimal climatic variables were selected through the stepwise multiple regression analysis with dry matter yield (DMY) as the response variable. Subsequently, three climatic variables were selected after considering the interpretability of the five variables. The three selected climatic variables were spring accumulated temperature, mean temperature in January and spring rainfall days. Then, the yield prediction model was constructed based on these three climatic variables using general linear model with the cultivated locations as dummy variables. The model constructed in this research could explain 73.6% of variation in DMY of IRG. The goodness‐of‐fit of the model was tested through residual diagnostics and 10‐fold cross‐validation. For climatic variables, the high partial eta squared value of spring accumulated temperature and spring rainfall may reflect the growth characteristics that spring is the main growing period for IRG and IRG has strong waterlogging tolerance and weak drought tolerance. The results may also support the possibility to sow IRG in the subsequent spring if autumnal seeding was missed in South Korea.

The purpose of this research is to identify the significance of climate factors related to the significance of change of dry matter yield (DMY) of whole crop maize (WCM) by year through the exploratory data analysis. The data (124 varieties; n=993 in 7 provinces) was prepared after deletion and modification of the insufficient and repetitive data from the results (124 varieties; n=1027 in 7 provinces) of import adaptation experiment done by National Agricultural Cooperation Federation. WCM was classified into early-maturity (25 varieties, n=200), mid-maturity (40 varieties, n=409), late-maturity (27 varieties, n=234) and others (32 varieties, n=150) based on relative maturity and days to silking. For determining climate factors, 6 weather variables were generated using weather data. For detecting DMY and climate factors, SPSS21.0 was used for operating descriptive statistics and Shapiro-Wilk test. Mean DMY by year was classified into upper and lower groups, and a statistically significant difference in DMY was found between two groups (p<0.05). To find the reasons of significant difference between two groups, after statistics analysis of the climate variables, it was found that Seeding-Harvesting Accumulated Growing Degree Days (SHAGDD), Seeding-Harvesting Precipitation (SHP) and Seeding-Harvesting Hour of sunshine (SHH) were significantly different between two groups (p<0.05), whereas Seeding-Harvesting number of Days with Precipitation (SHDP) had no significant effects on DMY (p>0.05). These results indicate that the SHAGDD, SHP and SHH are related to DMY of WCM, but the comparison of R 2 among three variables (SHAGDD, SHP and SHH) couldn't be obtained which is needed to be done by regression analysis as well as the prediction model of DMY in the future study.

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This study was aimed to detect the dry matter yield (DMY) trend of whole crop maize (WCM) considering the climatic factors responsible for growth and development of WCM using time series analysis in the Republic of Korea. The dataset consisted of DMY and climatic factors responsible for WCM yield from 1982 to 2011. The stationarity of the DMY was detected using augmented Dickey–Fuller (ADF) test, whereas the parameters of Autoregressive (AR) and Moving average (MA) were estimated from correlogram of Autocorrelation function (ACF) and partial ACF (PACF). The stationary DMY data was fitted to AR Integrated MA (ARIMA), and based on model selection criterion, ARIMA (2, 0, 1) was detected as the optimal model to describe the DMY trend of WCM. The DMY trend followed the mean of the preceding 2 years and residual of preceding 1 year. ARIMA with exogenous variables (ARIMAX) detected Seeding‐Harvesting Growing Degree Days (SHGDD, °C), Seeding‐Harvesting Rainfall Amount (SHRFA, mm), and Seeding‐Harvesting Rainfall Days (SHRFD, days) as major climatic factors responsible for the DMY trend of WCM. Furthermore, the amount and timing of rainfall found to be an important factor for the observed DMY trend. The fluctuation in the DMY trend implies the need to come up with a holistic approach that include new varieties development and improved agronomic management system to overcome the expected challenge from climate variability.