African Journal of
Agricultural Research

  • Abbreviation: Afr. J. Agric. Res.
  • Language: English
  • ISSN: 1991-637X
  • DOI: 10.5897/AJAR
  • Start Year: 2006
  • Published Articles: 6576

Full Length Research Paper

Near infrared reflectance spectroscopy (NIRS) prediction of herbage quality from forage and browse legumes, and natural pasture grass grown in Zimbabwe

  J. J. Baloyi1*, H. Hamudikuwanda2, N. Berardo3, M. Ordoardi4 and N. T. Ngongoni2  
  1Department of Animal Science, School of Agriculture, University of Venda, Private Bag X5050, Thohoyandou, 0950, Republic of South Africa. 2Department of Animal Science, University of Zimbabwe, P. O. Box MP 167, Mount Pleasant, Harare, Zimbabwe. 3Istituto Sperimentale, Per La Cerealicoltura, Via Stezzano 24, 24126 Bergamo, Italy. 4Istituto Sperimentale, Colture Foraggere, Viale Piacenza 29, 20075 Lodi, Italy.
Email: [email protected]

  •  Accepted: 15 March 2013
  •  Published: 31 March 2013

Abstract

 

Near infrared reflectance spectroscopy (NIRS) was used to predict the chemical composition of forage legumes Vigna unguiculata (cowpea), Desmodium uncinatum (Silverleaf desmodium), Stylosanthes guianensis (cv. Oxley fine stem stylo), natural pasture grass hay, Stylosanthes scabra (cv. Fitzroy) and an indigenous browse tree, Brachystegia spiciformis (Musasa). Crude protein (CP), ash, neutral detergent fibre (NDF), acid detergent fibre (ADF) and acid detergent lignin (ADL) were analyzed. A software for scanning, mathematical processing and statistical analysis was supplied with the spectrophotometer and used multiple linear regression (MLR). A set of 35 samples that was analyzed for calibration was selected using the software SELECT on the basis of the NIR spectra of 82 samples. The samples were scanned and screened using a FOSS NIR systems model 5000 monochromator. Equations for predicting chemical composition of the legumes were derived using scores from partial least squares (PLS) as independent variables. Cross-validation procedures indicated good correlations between laboratory values and NIRS estimates. Prediction using independent samples validated the model developed. NIRS calibrations obtained from this study could be utilized in current and future programmes of evaluating quality of forage and browse legumes for animal production.

 

Key words: Tropical forage legumes, near infrared reflectance spectroscopy, chemical composition.