Use of near-infrared spectroscopy and multivariate approach for estimating silage fermentation quality from freshly harvested maize

The study aimed to evaluate the most predictive traits of fresh maize and the most appropriate multivariate approach for estimating silage fermentation quality. The use of near infrared (NIRs) instruments allowed rapid, accurate and cheap analysis. Samples of fresh maize plant (n = 822) from hybrids...

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Bibliographic Details
Published in:Italian journal of animal science 2021-01, Vol.20 (1), p.859-871
Main Authors: Serva, Lorenzo, Marchesini, Giorgio, Chinello, Maria, Contiero, Barbara, Tenti, Sandro, Mirisola, Massimo, Grandis, Daniel, Andrighetto, Igino
Format: Article
Language:English
Subjects:
Algorithms
Carbohydrates
corn silage
Cultivars
Discriminant analysis
Dry matter
Environmental conditions
Fermentation
Hybrids
Infrared spectroscopy
Learning algorithms
Machine learning
Precision feeding
Predictions
Silage
silage quality prediction
Soil fertility
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Summary:The study aimed to evaluate the most predictive traits of fresh maize and the most appropriate multivariate approach for estimating silage fermentation quality. The use of near infrared (NIRs) instruments allowed rapid, accurate and cheap analysis. Samples of fresh maize plant (n = 822) from hybrids (Class Cultivar) of early and late classes, were harvested at three maturity stages: early, medium and late, in three areas (level input field) of 'low', 'medium' and 'high' soil fertility, along three consecutive years. Several algorithms of feature selection, regression, classification and machine learning, were tested. Maize silage fermentative quality was summarised through a Fermentative Quality Index (FQI). We found the most predictive traits as dry matter (DM), starch, and acid detergent lignin (ADL), with negative coefficients, or water-soluble carbohydrates (WSC) with a positive coefficient. FQI was significantly (p 
ISSN:1828-051X
1594-4077
1828-051X
DOI:10.1080/1828051X.2021.1918028