Acta Agrestia Sinica ›› 2017, Vol. 25 ›› Issue (1): 165-171.DOI: 10.11733/j.issn.1007-0435.2017.01.024

Previous Articles     Next Articles

Nutritional Quality and Digestibility Evaluation of Alfalfa Hay Bale by Near Infrared Reflectance

XUE Zhu-lin, LIU Nan, ZHANG Ying-jun   

  1. Institute of Grassland Science, China Agricultural University, Beijing 100193, China
  • Received:2016-06-07 Revised:2016-11-03 Online:2017-02-15 Published:2017-05-06

近红外光谱法预测紫花苜蓿草捆的营养品质和消化率

薛祝林, 刘楠, 张英俊   

  1. 中国农业大学草地研究所, 北京 100193
  • 通讯作者: 刘楠,E-mail:liunan@cau.edu.cn
  • 作者简介:薛祝林(1988-),男,河南林州人,博士,研究方向为草地管理与牧草利用,E-mail:xzl2007.ok@163.com
  • 基金资助:

    基本科研业务费“6种牧草品质分析及近红外预测模型建立”(2014XJ005);农业财政项目“牧草种质资源品质分析及快速测定”(2130135);公益性行业科研专项“科尔沁沙地苜蓿高效种植技术研究与示范”(201403048-2)资助

Abstract:

The nutritional quality and digestibility of alfalfa (Medicago sativa) as superior forage is closely related to the development of animal husbandry. In order to explore the feasibility of predicting alfalfa nutrients and digestibility by near infrared spectroscopy (NIRS), a total of 229 samples were collected from the main areas for alfalfa cultivation in our country, and the calibrations of nutritional quality and digestibility of alfalfa were set up by modified partial least squares (MPLS) with different spectra treatment and mathematical parameter settings. The results showed that it was appropriate to analysis the real content of relative feed value (RFV), neutral detergent fiber (NDF), acid detergent fiber (ADF) and crude protein (CP) by NIRS. But there was a need to improve the accuracy of prediction by expanding calibration samples numbers for Hemicellulose and In vitro dry matter disappearance (IVDMD) which ranged from 2.5 to 3 at relative prediction deviation for cross validation (RPDCV), and they could be used only for a rough estimation. This study established the quantitative analysis model for alfalfa nutrients and digestibility and expanded the database of nutritional quality, which could greatly promote alfalfa production and circulation and support reliable data for the establishment of animal feed formula.

Key words: Near infrared spectrometry, Alfalfa, Bale, Quality, Digestibility

摘要:

优质牧草苜蓿(Medicago sativa)品质的优劣和消化率的高低能在很大程度上影响畜牧业的发展。为探讨近红外光谱技术(NIRS)预测苜蓿草捆中营养成分和消化率的可行性,本试验采集来自我国苜蓿主产区的苜蓿草捆样品229份,利用改进的偏最小二乘法(MPLS),结合不同光谱处理和数学参数设置,建立苜蓿营养品质和消化率的近红外预测模型。结果表明:相对饲喂价值(relative feed value,RFV),中性洗涤纤维(neutral detergent fiber,NDF),酸性洗涤纤维(acid detergent fiber,ADF)和粗蛋白(crude protein,CP)的模型能用于实际含量的分析;半纤维素(Hemicellulose)和干物质体外消化率(In vitro dry matter disappearance,IVDMD)的交叉验证相对分析误差(relative prediction deviation for cross validation,RPDCV)值介于2.5~3之间,能够用于粗略分析,需要对定标集样品进一步扩充和完善以提高预测的准确度。试验初步建立了苜蓿草捆品质的定量分析模型,补充了我国苜蓿草捆营养品质数据库,为苜蓿草产品的生产、流通及动物饲料配方的制定提供了数据支持。

关键词: 近红外光谱法, 紫花苜蓿, 草捆, 品质, 消化率

CLC Number: