Comparison of NIRS and Wet Chemistry Methods for the Nutritional Analysis of Haylages for Horses

Harris, P A; Nelson, Sarah; Carslake, Harry; Argo, Caroline McG.; Wolf, R.P.; Fabri, F. Bob; Brolsma, Karst M.; Oostrum, Martijn J. van; Ellis, A. D.

doi:10.1016/j.jevs.2018.08.013

Cited by 23 publications

(12 citation statements)

References 14 publications

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“…To limit this variability and always guarantee the animals a high quality ration, it is necessary to frequently measure feed characteristics as recommended within the view of precision feeding (Wathes et al 2008;Mottram 2016). In this regard, repeated measures are made feasible by the use of Near Infra-red spectroscopy (NIRs) technique, a type of analysis that is more and more extensively adopted in agriculture (Fern andez-Ahumada et al 2008) for its low costs, high speed and ease of use and for the availability of portable devices which allow performing the analyses in real-time at field level (Marchesini et al 2017;Harris et al 2018). Among the ingredients used in the ration of dairy cows, maize silage is one of the most important (Grant and Adesogan 2018) and is characterised by a great variability both in composition and fermentation quality (Grant and Ferraretto 2018;Marchesini et al 2019), which if not adequate, could reduce DM intake (DMI) and production (Gerlach et al 2013) and lead to poor health (Borreani et al 2008).…”

Section: Introductionmentioning

confidence: 99%

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

Serva

Marchesini

Chinello

et al. 2021

Italian Journal of Animal Science

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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 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 ¼ 0.48), improved by the introduction of fixed effects (R 2 ¼ 0.55) and partial least square discriminant analysis (PLS-DA), which was assessed through the Mattew Correlation Coefficient (MCC) in validation (MCC ¼ 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. HIGHLIGHTSThe 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 ARTICLE HISTORY

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Section: Introductionmentioning

confidence: 99%

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

Serva

Marchesini

Chinello

et al. 2021

Italian Journal of Animal Science

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“…Data from subsamples and scanned duplicates were averaged to perform the statistical analyses. Due to the large number of samples involved and the need to perform the analyses immediately after the harvest, or upon opening of the vacuum‐packed bags, this study was made possible thanks to the use of NIR spectroscopy, which despite being less accurate than wet chemistry, is reported to be precise and repeatable (Harris et al, ), because allows a great number of scans which decreases the error in the estimates.…”

Section: Methodsmentioning

confidence: 99%

Effect of maturity stage at harvest on the ensilability of maize hybrids in the early and late FAO classes, grown in areas differing in yield potential

Marchesini

Serva

Chinello

et al. 2019

Grass and Forage Science

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The aim of this study was to assess the effects of earliness (according to FAO class), maturity stage at harvest and environmental conditions on the ensilability of maize hybrids described by their fermentation products and by a fermentation quality index (FQI). Maize hybrids belonging to early (n = 14) and late (n = 15) FAO classes were grown in low, medium and high potential yield areas and harvested at an early (EH), medium (MH) and late maturity stage (LH), that is, at 1/3, 2/3 and 5 d after the 2/3 milk line stage, respectively, according to a split‐plot design. Upon harvest, each sample (n = 522) was analysed for dry matter (DM) and water‐soluble carbohydrates (WSC) before being ensiled in vacuum‐packed bags (n = 1,044). After 60 days of conservation, samples were analysed for DM and fermentation products. In the pre‐ensiling phase, DM was higher in early hybrids (p = .001), low yield areas (p < .001) and at LH maturity (p < .001), whereas WSC contents were higher in early hybrids (p < .001), medium yield areas (p < .001) and at EH maturity (p < .001). With regard to silages, early hybrids had a higher FQI (p < .001), which was highest in areas with a high yield potential (p < .01) and at EH maturity (p < .01). Late hybrids proved to be better suited for low yield areas compared with early hybrids (p < .01) and had a higher FQI at EH and MH than at LH maturity (p < .01).

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“…For an acceptable prediction of nutritional value, any NIRS apparatus must first be calibrated against a set of standard reference samples analyzed by wet chemistry or another conventional method and then validated using additional samples. Many studies across all regions for various feed types have demonstrated the predictive performance of NIRS (Harris et al., 2018) and scientists continue to test various models to make further enhancements (Wajizah & Munawar, 2020). Since its development in the 1970s, NIRS has shown a lot of potential benefits for routine feedstuff analysis in laboratories all over the world.…”

Section: The Potential Of Nirs For Improving Livestock Nutrition In Ssamentioning

confidence: 99%

Near‐infrared reflectance spectroscopy for forage nutritive value analysis in sub‐Saharan African countries

et al. 2021

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Limited supply of quality feed is the most common problem limiting livestock productivity in sub‐Saharan Africa (SSA). Routine feed evaluation is indispensable for formulating balanced rations, feed characterization, safety, and minimizing the environmental impact of livestock. Traditional wet chemistry has not met this demand in SSA because it is time consuming, expensive, reliant on imported reagents and equipment that requires regular maintenance. Near infrared reflectance spectroscopy (NIRS) is a rapid and accurate alternative. The NIRS can help meet the need to characterize locally available forages and feeds on the continent, thus allowing formulation of optimally balanced and safe rations, facilitating establishment of nutritive value‐based pricing, and improving feed marketing and environmental stewardship. Though several NIRS systems have been purchased in many SSA countries, few are currently used. Reasons include high upfront costs, lack of requisite technical capacity, lack of access to comprehensive wet chemistry‐based databases to develop and validate robust and accurate predictive equations, lack of access to or relevance of existing validated equations, and limited awareness about the value of NIRS. Recently developed portable devices can dramatically reduce cost, while providing flexibility and comparable accuracy to benchtop systems. Formation of NIRS consortia and communities of practice including public–private partnerships that link equipment, pool resources, and provide periodic training and troubleshooting, can address many of these problems. This paper elaborates the potential for using NIRS to improve feed analysis in SSA countries, the reasons for the low use of existing systems, and strategies to improve the adoption and use of NIRS.

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Comparison of NIRS and Wet Chemistry Methods for the Nutritional Analysis of Haylages for Horses

Cited by 23 publications

References 14 publications

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

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

Effect of maturity stage at harvest on the ensilability of maize hybrids in the early and late FAO classes, grown in areas differing in yield potential

Near‐infrared reflectance spectroscopy for forage nutritive value analysis in sub‐Saharan African countries

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