Near infrared spectroscopy (NIRS) is a rapid and accurate method for analyzing the quality of cereals, and dried animal forage. However, one limitation of this method is its inability to measure fermentation parameters in dried and ground samples because they are volatile, and therefore, respectively lost during the drying process. In order to overcome this limitation, in this study, fresh coarse haylage was used to test the potential of NIRS to accurately determine chemical composition and fermentation parameters. Fresh coarse Italian ryegrass haylage samples were scanned at 1 nm intervals over a wavelength range of 680 to 2500 nm, and optical data were recorded as log 1/reflectance. Spectral data, together with first- and second-order derivatives, were analyzed using partial least squares (PLS) multivariate regressions; scatter correction procedures (standard normal variate and detrend) were used in order to reduce the effect of extraneous noise. Optimum calibrations were selected based on their low standard error of cross validation (SECV) values. Further, ratio of performance deviation, obtained by dividing the standard deviation of reference values by SECV values, was used to evaluate the reliability of predictive models. Our results showed that the NIRS method can predict chemical constituents accurately (correlation coefficient of cross validation, R2 cv, ranged from 0.76 to 0.97); the exception to this result was crude ash (R2 cv = 0.49 and RPD = 2.09). Comparison of mathematical treatments for raw spectra showed that second-order derivatives yielded better predictions than first-order derivatives. The best mathematical treatment for DM, ADF, and NDF, respectively was 2, 16, 16, whereas the best mathematical treatment for CP and crude ash, respectively was 2, 8, 8. The calibration models for fermentation parameters had low predictive accuracy for acetic, propionic, and butyric acids (RPD < 2.5). However, pH, and lactic and total acids were predicted with considerable accuracy (R2 cv 0.73 to 0.78; RPD values exceeded 2.5), and the best mathematical treatment for them was 1, 8, 8. Our findings show that, when fresh haylage is used, NIRS-based calibrations are reliable for the prediction of haylage characteristics, and therefore useful for the assessment of the forage quality.

This research was conducted to investigate the effects of feeding high and low forage diets with different forage sources on rumen fermentation characteristics and blood parameters of Holstein cows during the dry period. Eight Holstein cows were completely randomized assigned to two groups and repeated measurement was utilized in the analysis. Cows in two treatments were fed with diets with high (F:C = 70:30, 70F; forage source: mixed-sowing whole crop barley and Italian ryegrass silage, BIRG) and low (F:C = 55:45, 55F; forage source: tall fescue hay, TF) forage level. Rumen fluid pH was higher in 70F group. Levels of acetic acid, propionic acid, and butyric acid showed a similar pattern: from the lowest value at 07:30 h to the highest at 10:30 h and then decreased in both groups. The ratio of acetic acid to propionic acid was significantly higher (p < 0.05) in 55F group at 09:30 and 10:30 h. Rumen fluid NH3-N concentrations were significantly higher (p < 0.05) in 70F group at 09:30 and 10:30 h. Blood urea nitrogen was significantly higher (p < 0.05) in 70F group. It was concluded that BIRG based diet with a high forage level had no adverse effects on rumen fermentation, some blood chemical parameters, and immune system in dry Holstein cows and could be used as a forage source instead of imported TF.

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.

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

This study was carried out to explore the accuracy of near infrared spectroscopy (NIRS) for the prediction of chemical and fermentation parameters of whole crop winter rye silages. A representative population of 216 fresh winter rye silages was used as database for studying the possibilities of NIRS to predict chemical composition and fermentation parameters. Samples of silage were scanned at 1 nm intervals over the wavelength range 680~2,500 nm and the optical data recorded as log 1/Reflectance (log 1/R) and scanned in fresh condition. NIRS calibrations were developed by means of partial least-squares (PLS) regression. NIRS analysis of fresh winter rye silages provided accurate predictions of moisture, acid detergent fiber (ADF), neutral detergent fiber (NDF), crude protein (CP) and pH as well as lactic acid content with correlation coefficients of cross-validation (R2cv) of 0.96, 0.86, 0.79, 0.85, 0.82 and 0.78 respectively and standard error of cross-validation (SECV) of 1.89, 2.02, 2.79, 1.14, 1.47 and 0.46 % DM respectively. Results of this experiment showed the possibility of NIRS method to predict the chemical parameters of winter rye silages as routine analysis method in feeding value evaluation and for farmer advice.

본 시험은 효소제 및 효모추출물 급여가 사양성적, in vitro 반추위 발효 및 혈액성상에 미치는 영향을 구명하기 위해 23개월령의 한우 거세우 48두를 공시하여 사료용 첨가제로 농후사료의 0.1% 수준으로 급여하여 출하시까지 대조구(무첨가), 처리구 1(효소제), 처리구 2(효모추출물), 처리구 3(효소제+효모추출물)으로 구분하여 급여하였다. 사양성적에서는 급여 4개월과 8개월의 일당증체량과 사료요구율이 처리구 1에서 가장 향상되었고 (p<0.05), 대조구에서 가장 낮았다(p<0.05). 도축성적에서는 육량지수를 포함하여 처리구 1>처리구 3>처리구 2>대조구 순으로 나타났다. in vitro 배양액에 따른 처리구별 pH는 대조구>처리구 2>처리구 3>처리구 1 순으로 유의적(p<0.05)인 차이를 나타냈고, VFA와 사료소화율은 처리구 1과 3에서 높았고 (p<0.05), 대조구에서는 낮았다(p<0.05). 혈액성상에서는 면역 첨가제 급여군인 처리구 2와 3에서 급여기간에 따른 백혈구 수치가 유의적(p<0.05)으로 감소하였는데, 이는 면역글로블린 G의 유의적(p<0.05)인증가에 따라서 상대적으로 나타난 것으로 판단된다. 결과적으로, 한우 비육후기 거세우의 사료첨가제 급여 시험에서 처리구 1은 급여 4개월부터 대조구 대비 사양성적 및 도체성적을 향상시켰고, 처리구 2는 급여 2개월부터 면역에 관련된 면역글로블린 G의 혈액성상을 개선시키는 것으로 분석되었다.