Institute of Physiology and Animal Nutrition, Hungarian University of Agriculture and Life Sciences, Kaposvár, Hungary.
Department of Animal and Range Science, College of Agricultural Sciences, Arba Minch University, Arba Minch, Ethiopia.
Anim Sci J. 2023 Jan-Dec;94(1):e13823. doi: 10.1111/asj.13823.
Near-infrared (NIR) spectroscopy was employed to determine the differences between forage mixtures of winter cereals and Italian ryegrass and to evaluate fermentation characteristics of mixed silages. Forages were harvested on five phases (Cuts 1-5), with 1 week interval (n = 100). The yield of the last harvest (Cut 5) was ensiled and analyzed on four different days (D0, D7, D14, and D90) (n = 80). Principal component analysis based on the NIR data revealed differences according to the days of harvest, differences between winter cereals and Italian ryegrass forages, and differences in the fermentation stages of silages. The partial least square regression models for crude protein (CP), crude fiber (CF), and ash gave excellent determination coefficient in cross-validation (R > 0.9), while models for ether extract (EE) and total sugar content were weaker (R = 0.87 and 0.74, respectively). The values of root mean square error of cross-validation were 0.59, 0.76, 0.22, 0.31, and 2.36 %DM, for CP, CF, EE, ash, and total sugar, respectively. NIR proved to be an efficient tool in evaluating type and growth differences of the winter cereals and Italian ryegrass forage mixtures and the quality changes that occur during ensiling.
近红外（NIR）光谱用于确定冬小麦和黑麦草饲料混合物之间的差异，并评估混合青贮饲料的发酵特性。在五个阶段（第 1 到 5 茬）收获饲草，间隔 1 周（n=100）。最后一茬（第 5 茬）的产量被青贮并在四个不同的时间（D0、D7、D14 和 D90）（n=80）进行分析。基于 NIR 数据的主成分分析显示了收获时间的差异、冬小麦和黑麦草饲草之间的差异以及青贮发酵阶段的差异。粗蛋白（CP）、粗纤维（CF）和灰分的偏最小二乘回归模型在交叉验证中具有出色的决定系数（R > 0.9），而乙醚提取物（EE）和总糖含量的模型则较弱（R 分别为 0.87 和 0.74）。交叉验证的均方根误差值分别为 0.59、0.76、0.22、0.31 和 2.36%DM，对应 CP、CF、EE、灰分和总糖。NIR 被证明是评估冬小麦和黑麦草饲草混合物的类型和生长差异以及青贮过程中发生的质量变化的有效工具。
