책임운영기관 농촌진흥청 국립축산과학원 (로고)

Near infrared reflectance spectroscopy (NIRS) is widely used to assess the nutrient composition of forages. In forage, the leaf to stem ratio of alfalfa greatly affects its forage quality, with a high ratio of leaf indicated as high quality. This study aimed to evaluate the predictability of the alfalfa leaf-to-stem ratio and feed value using NIRS. Alfalfa hay was manually separated into leaves and stems by hand and the analysis samples were then made in the controlled range between 0 and 100%. Calibration models (n=320) were developed using modified partial least squares regression (MPLS) based on cross-validation. The optimal calibrations were selected based on the highest coefficients of determination in cross-validation (R2 ) and the lowest standard error of cross-validation (SECV). The prediction accuracy for the leaf-to-stem ratio (SECV, 5.95 vs. 5.71%; R2 , 0.91 vs. 0.91) in alfalfa hay was comparable. For leaves, the standard error of calibration (SEC) was 4.94% (R2 =0.94), and for stems, it was 4.81% (R2 =0.94). The leaves and stems of the SEC were 4.94% (R2 =0.94) and 4.81% (R2 =0.94), respectively. The prediction accuracy for feed value, based on the leaf-to-stem ratio, predicted SECV values of 0.92% (R2 =0.88) for crude protein (CP) content, 1.92% (R2 =0.91) for neutral detergent fiber (NDF) content, 1.36% (R2 =0.91) for total digestibility nutrient (TDN) content, and 9.86 (R2 =0.81) for relative feed value (RFV). The results of this study demonstrate the potential of the NIRS method as a reliable tool for predicting the leaf-to-stem ratio of alfalfa hay, and show available techniques for routine feed value evaluation.