This study was conducted to find out an alternative way of rapid and accurate analysis of chemical composition of permanent pastures in hilly grazing area. Near reflectance infrared spectroscopy (NIRS) was used to evaluate the potential for predicting proximate analysis of permanent pastures in a vegetative stage. 386 pasture samples obtained from hilly grazing area in 2015 and 2016 were scanned for their visible–NIR spectra from 400~2,400nm. 163 samples with different spectral characteristics were selected and analysed for moisture, crude protein (CP), crude ash (CA), acid detergent fiber (ADF) and neutral detergent fiber (NDF). Multiple linear regression was used with wet analysis data and spectra for developing the calibration and validation mode1. Wavelength of 400 to 2500nm and near infrared range with different critical T outlier value 2.5 and 1.5 were used for developing the most suitable equation. The important index in this experiment was SEC and SEP. The R2 value for moisture, CP, CA, CF, Ash, ADF, NDF in calibration set was 0.86, 0.94, 0.91, 0.88, 0.48 and 0.93, respectively. The value in validation set was 0.66, 0.86, 0.83, 0.71, 0.35 and 0.88, respectively. The results of this experiment indicate that NIRS is a reliable analytical method to assess forage quality for CP, CF, NDF except ADF and moisture in permanent pastures when proper samples incorporated into the equation development.

This study was conducted to determine the effect of mathematical transformation on near infrared spectroscopy (NIRS) calibrations for the prediction of chemical composition and fermentation parameters in corn silage. Corn silage samples (n=407) were collected from cattle farms and feed companies in Korea between 2014 and 2015. Samples of silage were scanned at 1 nm intervals over the wavelength range of 680~2,500 nm. The optical data were recorded as log 1/Reflectance (log 1/R) and scanned in intact fresh condition. The spectral data were regressed against a range of chemical parameters using partial least squares (PLS) multivariate analysis in conjunction with several spectral math treatments to reduce the effect of extraneous noise. The optimum calibrations were selected based on the highest coefficients of determination in cross validation (R2 cv) and the lowest standard error of cross validation (SECV). Results of this study revealed that the NIRS method could be used to predict chemical constituents accurately (correlation coefficient of cross validation, R2 cv, ranging from 0.77 to 0.91). The best mathematical treatment for moisture and crude protein (CP) was first-order derivatives (1, 16, 16, and 1, 4, 4), whereas the best mathematical treatment for neutral detergent fiber (NDF) and acid detergent fiber (ADF) was 2, 16, 16. The calibration models for fermentation parameters had lower predictive accuracy than chemical constituents. However, pH and lactic acids were predicted with considerable accuracy (R2 cv 0.74 to 0.77). The best mathematical treatment for them was 1, 8, 8 and 2, 16, 16, respectively. Results of this experiment demonstrate that it is possible to use NIRS method to predict the chemical composition and fermentation quality of fresh corn silages as a routine analysis method for feeding value evaluation to give advice to farmers.

The growth of Italian ryegrass (IRG) after wintering was very low in 2015 when IRG was broadcasted under growing rice in fall of 2014. To determine growth inhibitory factors of IRG, we examined the growth conditions of IRG in Nonsan region and meteorological conditions in Daejeon nearby Nonsan. Minimum temperature and maximum instantaneous wind speed on Feb. 8th and 9th of 2015 after wintering of IRG were 8.8℃, 10.7 m/s and 12.4℃, 9.6m/s, respectively. Air temperature was suddenly dropped due to strong wind with snow showers, which had unfavorable effect on root growth of IRG exposed at the soil surface. The minimum temperature and maximum instantaneous wind speed on Feb. 12th, 13th, and 14th of 2015 were 4.1℃, 11.6 m/s, － 5.6℃, 10.3 m/s, and －4.7℃, 7.5 m/s, respectively. The growth circumstance of IRG was not good because soil was dried due to drought continued from January. The minimum temperature and maximum instantaneous wind speed on Feb. 26th, 27th, and 28th of 2015 were 1.8℃, 13.7 m/s, －3.5℃, 10.6 m/s, and 4.1℃, 6.8 m/s, respectively. The number of wilting of IRG was more than 59% until Mar. 3rd of 2015. IRG faced irreparable environment (low minimum temperatures and extreme instantaneous wind speeds) for 9 days from Mar. 4th to Mar. 12th of 2015. The main reason for the decrease of IRG productivity was collection delay of rice straw after rice harvest because there was continuous rain between Oct. and Nov. of 2014. For this reason, weakly grown IRG under rice straw was withered after wintering. IRG was withered by frost heaving, drought, and instantaneous wind speed in the spring. Furthermore, the root of IRG was damaged while growing in excess moisture in the surface of paddy soil during the winter season due to rain.

본 연구는 2014 / 2015년 동계 사료작물의 월동 후 생육 조사를 통하여 전국적인 작황을 파악함으로서 사료작물 생 육에 문제점이 있을시 근본 원인을 구명할 수 있는 기초자 료로 이용하거나 조사료의 연중 안정 생산, 공급에 도움을 주고자 수행하였다. 2015년도 전국 동계 사료작물의 월동 후 생육 상황은 전반적으로 저조하였는데, 월동률 분포에 따른 지역별 분포 상황을 살펴보면 월동률 80% 이상인 지 역이 66%, 월동률 79~50% 분포지역이 24.9%, 50%미만 지 역이 9.1%의 지역별 분포를 나타내어 전체적으로는 79%의 월동률을 나타냈다. 월동 후 월동률 및 피복률은 배수로가 설치된 논에서는 각각 83%와 80%로서 양호했으나 배수로 가 설치 안 된 습한 논에서는 각각 67%와 66%로 낮은 경 향을 나타냈다. 전국 동계작물 조사료 생산량은 강원, 충 북, 충남, 경남, 전남지역은 10~15%, 경기, 경북, 전북지역 은 약 30%의 수량감소가 예상되어 전국적으로 약 19%의 수량 감소가 예상되었다.

This study was conducted to assess the potential of using NIRS to accurately determine the chemical composition and fermentation parameters in fresh coarse sorghum and sudangrass silage. Near Infrared Spectroscopy (NIRS) has been increasingly used as a rapid and accurate method to analyze the quality of cereals and dried animal forage. However, silage analysis by NIRS has a limitation in analyzing dried and ground samples in farm-scale applications because the fermentative products are lost during the drying process. Fresh coarse silage samples were scanned at 1 nm intervals over the wavelength range of 680~2500 nm, and the optical data were obtained as log 1/Reflectance (log 1/R). The spectral data were regressed, using partial least squares (PLS) multivariate analysis in conjunction with first and second order derivatization, with a scatter correction procedure (standard normal variate and detrend (SNV&D)) to reduce the effect of extraneous noise. The optimum calibrations were selected on the basis of minimizing the standard error of cross validation (SECV). The results of this study showed that NIRS predicted the chemical constituents with a high degree of accuracy (i.e. the correlation coefficient of cross validation (R²cv) ranged from 0.86~0.96), except for crude ash which had an R² cv of 0.68. Comparison of the mathematical treatments for raw spectra showed that the second-order derivatization procedure produced the best result for all the treatments, except for neutral detergent fiber (NDF). The best mathematical treatment for moisture, acid detergent fiber (ADF), crude protein (CP) and pH was 2,16,16 respectively while the best mathematical treatment for crude ash, lactic acid and total acid was 2,8,8 respectively. The calibrations of fermentation products produced poorer calibrations (RPD < 2.5) with acetic and butyric acid. The pH, lactic acid and total acids were predicted with considerable accuracy at R²cv 0.72~0.77. This study indicated that NIRS calibrations based on fresh coarse sorghum and sudangrass silage spectra have the capability of assessing the forage quality control

A study was conducted to determine the effects of the cattle manure (CM) application on the botanical composition and micro-mineral contents (Fe, Mn, Cu, Zn) of grazing pasture at the experimental field of Livestock Division, Subtropical Animal Experiment Station, National Institute of Animal Science from year 2003 to 2005. The experiment was arranged in a randomized complete block design with three replications. The treatment consisted of T1: 100% chemical fertilizer (CF 100%), T2: 50% CF +50% CM, T3: 25% CF +75% CM, T4: 100% cattle manure (CM 100%), T5: 100% CM (1st yr.)+ 100% CF (2nd yr.) + 100% CM (3rd yr.), T6: 100% CM (1st yr.)+ 100% CF (2nd yr.)+ 100% CF (3rd yr.). The botanical composition of grassland for grass, legumes, and weeds showed that the rate of legumes was increased in all treatments. The weeds rate in T4 was the highest in comparison to the other treatments. For micro-mineral contents T5 showed the highest average Fe contents of 262.08 ppm and T1 showed the lowest (199.20 ppm). Mn contents was the highest in T1 among the other treatments. Zn contents was the highest in T3 as compared with other treatments. Cu contents was the highest in T6 as compared with other treatments. The results of this experiments indicated that micro-mineral contents of change was effect of legumes increased than treatment