Prediction of the Chemical Composition and Fermentation Parameters of Pasture Silage by Near Infrered Reflectance Spectroscopy (NIRS)

Ibáñez, Sviatoslav; Alomar, C Daniel

doi:10.4067/s0718-58392008000400005

Cited by 10 publications

(11 citation statements)

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“…The quality of the reference-method analysis has a crucial effect on the accuracy of NIR calibrations (Conzen, 2006). This technology is broadly accepted as a fast and reliable method for evaluating the nutritional qual-ity of pasture silages (Ibáñez and Alomar, 2008;Restaino et al, 2009), as well as dried and fresh pastures (Cozzolino and Labandera, 2002;Alomar et al, 2009;Burns et al, 2013;Lobos-Ortega et al, 2013;Moscoso and Balocchi, 2016). It has also been used to evaluate how forage sample preparation affects nutrient composition analysis (Alomar et al, 2003), green forage intake and digestibility in ruminants (Decruyenaere et al, 2009), and water-soluble carbohydrates (WSC) in stolon samples of white clover (Inostroza et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Calibration models for the nutritional quality of fresh pastures by nearinfrared reflectance spectroscopy

Lobos¹,

Moscoso

Pavez³

2019

Ciencia e investigación agraria

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I. Lobos, C.J. Moscoso, and P. Pavez. 2019. Calibration models for the nutritional quality of fresh pastures by near-infrared reflectance spectroscopy. Cien. Inv. Agr. 46(3):234-242.High levels of animal performance and health depend on high-quality nutrition. Determining forage quality both reliably and quickly is essential for improving animal production. The present study describes the use of near infrared reflectance spectroscopy (NIRS) for the quantification of nutritional quality (dry matter (DM), water-soluble carbohydrates (WSC), crude protein (CP), in vitro dry matter digestibility (DMD), organic matter digestibility (OMD), neutral detergent fiber (NDF) and the WSC/CP ratio) in samples from fresh pastures in southern Chile (39° to 40° S). Calibration models were developed with wet chemistry and NIRS spectral data using partial least squares regression (PLSR). The coefficients of determination in the validation set ranged between 0.69 and 0.93, and the error of prediction varied from 0.064 to 2.89. The evaluation of the model confirmed the high predictive ability of NIRS for DM and CP and its low predictive ability for DMD, OMD, NDF and the WSC/CP ratio. It was not possible to obtain a model for WSC because it would have required an increased number of samples to improve the spectral variability and the R 2 value (> 80%).

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Section: Introductionmentioning

confidence: 99%

Calibration models for the nutritional quality of fresh pastures by nearinfrared reflectance spectroscopy

Lobos¹,

Moscoso

Pavez³

2019

Ciencia e investigación agraria

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“…Near infrared spectroscopy is a rapid, non-destructive and valid alternative technique, which represents a radical shift from conventional chemical methods, in which the whole matrix is characterized in terms of its absorption properties (Tassone et al, 2014). All the organic bonds, such as C-H, N-H, and O-H, have absorption bands in the near infrared (NIR) region (Osborne, 2000); this shows that NIRS can detect the bonds of fractions of fats, proteins, and carbohydrates in forage (Ibáñez & Alomar, 2008).…”

Section: Principles Of Nirsmentioning

confidence: 99%

“…The NIR region is located just outside the red band, with a wavelength range between 700 and 2500 nm in the electromagnetic spectrum; infrared (IR) light is emitted and absorbed by all biological compounds (Stuth et al, 2003). When a sample is scanned, the NIR spectrometer projects NIR light in the sample, and the radiant energy is absorbed by the sample molecules according to the frequency of a specific vibration, which results in a unique spectrum for that sample (Ibáñez & Alomar, 2008), which is then stored in a computer (Stuth et al, 2003).…”

Section: Principles Of Nirsmentioning

confidence: 99%

“…Near infrared spectroscopy is a very rapid technique with a low maintenance cost. It provides results at the time of analysis, which makes it a very interesting tool for animal feed control (Pujol et al, 2007) and a reliable tool in the determination of forage quality parameters (Castro, 2002;Ibáñez & Alomar, 2008;Molano et al, 2016).…”

Section: Principles Of Nirsmentioning

confidence: 99%

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Use of near infrared spectroscopy for the evaluation of forage for ruminants

Paz¹,

Silva²,

Rêgo³

2019

RCA

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Near infrared spectroscopy (NIRS) is a technology that has been applied to evaluate the quality of forage for ruminants. This paper describes how the NIRS technique has been applied to the evaluation of fresh, dried and ground forage with, for example, laboratory bench equipment, portable equipment, and the use in drones and tractors. The technology has been widely implemented in the evaluation of chemical composition (dry matter, crude protein, neutral and acid detergent fiber, lignin, and ether extract), digestibility, gas production, intake and other parameters of forage quality with the benefits of not destroying samples, not using reagents and providing rapid analyses, among others. RESUMO: Espectroscopia do infravermelho próximo (NIRS) é uma tecnologia que tem sido aplicada na avaliação da qualidade da forragem de ruminantes. Neste artigo é descrito de que forma a técnica NIRS tem sido aplicada na avaliação de forragens frescas em pasto ou secas e moídas, tanto com o equipamento de bancada no laboratório quanto com o equipamento portátil e sua utilização em drones e tratores, por exemplo. A tecnologia tem sido amplamente implementada na avaliação da composição química (matéria seca, proteína bruta, fibra em detergente neutro e ácido, lignina, extrato etéreo), da digestibilidade, da produção de gás, do consumo e de outros parâmetros de qualidade da forragem, com os benefícios de não destruir amostras, não utilizar reagentes, fornecer rápido resultado das análises, entre outros.

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“…A complementary approach is the use of near infrared reflectance spectroscopy (NIRS) which has been widely established as a fast, multiple, precise and accurate technique to predict composition and digestibility traits. However, to obtain robust calibrations, a large number of samples with their spectral data is required, covering a wide range of variation in digestibility and reliable reference values to develop prediction equations with an acceptable degree of certainty (Alomar and Fuchslocher, 1998;Rinne et al, 2006;Ibáñez and Alomar, 2008).…”

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confidence: 99%

Use of Cellulases to Predict in vivo Digestible Organic Matter (D value) in Pasture Silages

Barchiesi-Ferrari

Alomar

Miranda

2011

Chilean J. Agric. Res.

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In pasture-based dairy herds where silage is a widely adopted supplement, optimized feeding requires reliable estimations of nutritional quality of this conserved forage. Metabolizable energy, an important nutritional fraction, can be predicted from digestibility-related traits, such as the digestible organic matter contained in the dry matter (D-value). The aim of the present study was to evaluate the prediction of D-value and dry matter digestibility (DMD) of grass silages made from four different pastures and maturity stages, using the pepsin-cellulase method. Fungal cellulase was used, applying different enzyme concentrations, incubation times and types of final wash. The silages were prepared from permanent pasture (Dactylis glomerata L., Lolium perenne L., Bromus catharticus Vahl var. catharticus, Trifolium repens L. and Holcus lanatus L.), rotation pasture (Lolium multiflorum Lam. cv. Tama), oats (Avena sativa L.), and mixed pasture (L. perenne-T. repens). These were harvested at three different physiological stages (vegetative, ear emergence and dough grain). The treatment using an incubation time of 24 h, a cellulase concentration of 6.25 g L-1 and final wash with water (Treatment 3) presented the best prediction capacity of the in vivo D-value (R 2 = 0.78) and in vivo DMD (R 2 = 0.71). In vivo D-value prediction improved (R 2 = 0.8) when a chemical determination (crude fibre, gross energy, neutral detergent fibre, total ash or acid detergent fibre) was included in addition (multiple regression) to D-value obtained with cellulases (Treatment 3). Results of DMD obtained with cellulases show good precision, but underestimate in vivo values, and are closer to those obtained with ruminal fluid. Suitable equations could be used to improve accuracy.

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Prediction of the Chemical Composition and Fermentation Parameters of Pasture Silage by Near Infrered Reflectance Spectroscopy (NIRS)

Cited by 10 publications

References 10 publications

Calibration models for the nutritional quality of fresh pastures by nearinfrared reflectance spectroscopy

Calibration models for the nutritional quality of fresh pastures by nearinfrared reflectance spectroscopy

Use of near infrared spectroscopy for the evaluation of forage for ruminants

Use of Cellulases to Predict in vivo Digestible Organic Matter (D value) in Pasture Silages

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