Optimizing near Infrared Reflectance Spectroscopy to Predict Nutritional Quality of Chickpea Straw for Livestock Feeding

Multidimensional improvement programs of chickpea require screening of a large number of genotypes for straw nutritive value. The ability of near infrared reflectance spectroscopy (NIRS) to determine the nutritive value of chickpea straw was identified in the current study. A total of 480 samples of...

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Bibliographic Details
Published in:Animals (Basel) 2021-11, Vol.11 (12), p.3409
Main Authors: Alemu, Tena, Wamatu, Jane, Tolera, Adugna, Beyan, Mohammed, Eshete, Million, Alkhtib, Ashraf, Rischkowsky, Barbara
Format: Article
Language:English
Subjects:
Animal husbandry
Ashes
Calcium
Calibration
Chickpeas
crop residue
Dry matter
Feeds
Gas production
Genotypes
Grain
Infrared reflection
Infrared spectroscopy
Lignin
Livestock
Magnesium
Manganese
Methods
Multiple regression analysis
Near infrared radiation
NIRS
Nitrogen
nutritional quality
Nutritive value
Oil and gas production
prediction error
Predictions
Proteins
Reflectance
Software
Spectra
Spectroscopy
Standard error
Statistical analysis
Straw
validation
Zinc
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Summary:Multidimensional improvement programs of chickpea require screening of a large number of genotypes for straw nutritive value. The ability of near infrared reflectance spectroscopy (NIRS) to determine the nutritive value of chickpea straw was identified in the current study. A total of 480 samples of chickpea straw representing a nation-wide range of environments and genotypic diversity (40 genotypes) were scanned at a spectral range of 1108 to 2492 nm. The samples were reduced to 190 representative samples based on the spectral data then divided into a calibration set (160 samples) and a cross-validation set (30 samples). All 190 samples were analysed for dry matter, ash, crude protein, neutral detergent fibre, acid detergent fibre, acid detergent lignin, Zn, Mn, Ca, Mg, Fe, P, and in vitro gas production metabolizable energy using conventional methods. Multiple regression analysis was used to build the prediction equations. The prediction equation generated by the study accurately predicted the nutritive value of chickpea straw (R of cross validation > 0.68; standard error of prediction < 1%). Breeding programs targeting improving food-feed traits of chickpea could use NIRS as a fast, cheap, and reliable tool to screen genotypes for straw nutritional quality.
ISSN:2076-2615
2076-2615
DOI:10.3390/ani11123409