Use of near-infrared spectroscopy and multivariate approach for estimating silage fermentation quality from freshly harvested maize. Issue 1 (1st January 2021)

• Record Type: Journal Article
• Authors: Serva, Lorenzo; Marchesini, Giorgio; Chinello, Maria; Contiero, Barbara; Tenti, Sandro; Mirisola, Massimo; Grandis, Daniel; Andrighetto, Igino
• Is Part Of: Italian journal of animal science. Volume 20:Issue 1(2021)
• Page Start: 859
• Page End: 871
• Publication Date: 2021-01-01
• Subjects: Precision feeding -- corn silage -- silage quality prediction -- machine learning; Animal culture -- Periodicals; Livestock -- Italy -- Periodicals; Veterinary medicine -- Italy -- Periodicals; Animal culture; Livestock; Veterinary medicine; Italy; Periodicals; Periodicals; 636.005
• DOI: 10.1080/1828051X.2021.1918028
• Languages: English
• ISSNs: 1828-051X

Abstract

Abstract: 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 < 0.0001) affected by year (negatively for 2018), level input field (positively for high level) and maturity stage (negatively for the late harvest). The most satisfying results were attained using a stepwise regression algorithm ( R 2 = 0.48), improved by the introduction of fixed effects ( R 2 = 0.55) and partial least square discriminant analysis (PLS-DA), which was assessed through the Mattew Correlation Coefficient (MCC) in validation (MCC = 0.57). Concluding, among the tested approaches, the use of linear regression after stepwise algorithm or the use of PLS could be of practical help for the farmers to the effective management of the ensiling process of maize plants, even though environmental conditions should be considered to improve the predictions. HIGHLIGHTS: The prediction of FQ at harvest would allow the farmer to tune up the ensiling process The prediction of FQ through the use of portable NIRs instruments was successful DM, starch and ADL were negatively related to FQ index