2021
DOI: 10.1080/1828051x.2021.1918028
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Use of near-infrared spectroscopy and multivariate approach for estimating silage fermentation quality from freshly harvested maize

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 ¼ 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 … Show more

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Cited by 13 publications
(5 citation statements)
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“…Consistently with the MFA, the quantitative variables grouped for DM, EE and starch, and in the opposite, grouped for ash, CP, ADF, and lignin (sa), while WSC stands alone (Figure 2B). This finding confirms the positive relation between DM and starch [32], and data from the previous study, except WSC, where the spatial distribution of quantitative variables was studied with a Principal Component Analysis [15]. In the current study, we found an anomalous trend for the ratio starch/WSC during the three experimental years.…”
Section: Discussionsupporting
confidence: 91%
See 1 more Smart Citation
“…Consistently with the MFA, the quantitative variables grouped for DM, EE and starch, and in the opposite, grouped for ash, CP, ADF, and lignin (sa), while WSC stands alone (Figure 2B). This finding confirms the positive relation between DM and starch [32], and data from the previous study, except WSC, where the spatial distribution of quantitative variables was studied with a Principal Component Analysis [15]. In the current study, we found an anomalous trend for the ratio starch/WSC during the three experimental years.…”
Section: Discussionsupporting
confidence: 91%
“…These data can be used by farmers and maize growers to make better processing choices at the time of harvest and when processing the whole maize plant into the silos [11,12]. Due to big data availability, the use of Machine Learning (ML) techniques has become more common in agricultural research in establishing knowledge-based farming systems [13,14] and particularly for estimating silage fermentation quality from freshly harvested maize [15]. Maize is the most studied crop using ML because of its versatile usage and wide world diffusion, and even for its quality evaluation via the use of spectrometers [14].…”
Section: Introductionmentioning
confidence: 99%
“…To simplify the quality evaluation, various indexes have been considered to summarize the overall fermentative quality of silage, such as the Flieg-Zimmer score (FZs) [11], the Vanbelle and Bertin score [12,13], the German agricultural society (DLG) score [14,15], the homolactic index [16,17], the standardised quality score (SQS) [18], and the fermentative quality index (FQI) score [19]. The composition of the fresh ensiled plant, including the DM, crude protein (CP), fibre, N-free extract and water-soluble carbohydrates (WSC) content, affects the final quality of the silage [10,20,21]. Additionally, various factors can decrease the aerobic stability of silages, providing a chance for opportunistic microorganisms such as yeast (Saccharomyces, Candida, Cryptococcus, and Pichia), moulds, bacilli, and acetic acid bacteria to become active and generate heat while consuming nutrients from the silage, which can cause spoilage [11,22,23].…”
Section: Introductionmentioning
confidence: 99%
“…The pre-ensiled composition can be assessed to compute a prognostic risk score for the aerobic stability of maize silage [30,31]. Additionally, research has shown that post-silage quality can be partially linked to fresh maize's pre-silage characteristics, even using machine learning techniques under different harvesting and ensiling conditions [20]. Furthermore, using portable Near-Infrared Spectroscopy (NIRS) instruments permits onfarm rapid, ecologic and cheap analysis of forages [32][33][34].…”
Section: Introductionmentioning
confidence: 99%
“…Evaluate the effectiveness of a multivariate approach and multiple linear regression in predicting the potential of freshly harvested maize (FHM) to ensure silage fermentation quality based on the chemical composition of the harvested whole-plant maize [17].…”
mentioning
confidence: 99%