[NIRS for Predicting Nutritive Value of Elymus sibiricus]

Area of Science:

• Agricultural Science
• Analytical Chemistry
• Forage Quality Analysis

Background:

• Siberian wildrye (Elymus sibiricus L.) is a vital pasture species on China's Qinghai-Tibet plateau, crucial for livestock fodder.
• Assessing forage nutritional value, including crude protein (CP), acid detergent fiber (ADF), neutral detergent fiber (NDF), and in vitro dry matter digestibility (IVDMD), is essential for animal husbandry.

Purpose of the Study:

• To develop and validate a Near Infrared Spectroscopy (NIRS) method for rapid and accurate determination of key nutritional parameters in Siberian wildrye.
• To assess the efficacy of NIRS combined with Partial Least Square Regression (PLSR) for predicting CP, ADF, NDF, and IVDMD.

Main Methods:

• Near Infrared Spectroscopy (NIRS) was employed for spectral data acquisition.
• Partial Least Square Regression (PLSR) modeling was utilized to establish calibration and prediction equations.
• Results were validated against traditional chemical analysis methods.

Main Results:

• Highly accurate prediction equations were developed for CP, ADF, NDF, and IVDMD, with high coefficients of determination (R² > 0.8907) and low standard errors of prediction (SEP < 1.4303 %DM).
• Strong correlations (r > 0.9438) were observed between NIRS predictions and chemical reference methods.
• Residual Predictive Deviation (RPD) values ranged from 3.02 to 12.63, indicating excellent predictive ability.

Conclusions:

• NIRS, coupled with chemometrics (PLSR), provides a sensitive, rapid, and accurate method for predicting the nutritional components (CP, ADF, NDF) and IVDMD of Siberian wildrye.
• This technique offers a viable alternative to traditional chemical analysis for routine forage quality assessment, supporting efficient livestock management in alpine regions.