Prediction error and repeatability of near infrared reflectance spectroscopy applied to faeces samples in order to predict voluntary intake and digestibility of forages by ruminants

Decruyenaere, Virginie; Planchón, Viviane; Dardenne, Pierre; Stilmant, Didier

doi:10.1016/j.anifeedsci.2015.04.011

Cited by 16 publications

(19 citation statements)

References 25 publications

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“…The low correlation between OMD AIA and OMD faeces can be partly explained by the small NIRS calibration data set used for developing prediction equations for OMD faeces . Decruyenaere et al (2015) demonstrated the importance of precision of reference data to obtain reliable NIRS calibration equations. In their study, reference data sets for OMD faeces were approximately four times larger and they obtained for the best prediction models R 2 values of 0.9 and higher while the corresponding value in our study was 0.69 (Table 2).…”

Section: Comparison Of Methodsmentioning

confidence: 99%

Cow‐specific diet digestibility predictions based on near‐infrared reflectance spectroscopy scans of faecal samples

Mehtiö

Rinne

Nyholm

et al. 2015

J Animal Breeding Genetics

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This study was designed to obtain information on prediction of diet digestibility from near-infrared reflectance spectroscopy (NIRS) scans of faecal spot samples from dairy cows at different stages of lactation and to develop a faecal sampling protocol. NIRS was used to predict diet organic matter digestibility (OMD) and indigestible neutral detergent fibre content (iNDF) from faecal samples, and dry matter digestibility (DMD) using iNDF in feed and faecal samples as an internal marker. Acid-insoluble ash (AIA) as an internal digestibility marker was used as a reference method to evaluate the reliability of NIRS predictions. Feed and composite faecal samples were collected from 44 cows at approximately 50, 150 and 250 days in milk (DIM). The estimated standard deviation for cow-specific organic matter digestibility analysed by AIA was 12.3 g/kg, which is small considering that the average was 724 g/kg. The phenotypic correlation between direct faecal OMD prediction by NIRS and OMD by AIA over the lactation was 0.51. The low repeatability and small variability estimates for direct OMD predictions by NIRS were not accurate enough to quantify small differences in OMD between cows. In contrast to OMD, the repeatability estimates for DMD by iNDF and especially for direct faecal iNDF predictions were 0.32 and 0.46, respectively, indicating that developing of NIRS predictions for cow-specific digestibility is possible. A data subset of 20 cows with daily individual faecal samples was used to develop an on-farm sampling protocol. Based on the assessment of correlations between individual sample combinations and composite samples as well as repeatability estimates for individual sample combinations, we found that collecting up to three individual samples yields a representative composite sample. Collection of samples from all the cows of a herd every third month might be a good choice, because it would yield a better accuracy.

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Section: Comparison Of Methodsmentioning

confidence: 99%

Cow‐specific diet digestibility predictions based on near‐infrared reflectance spectroscopy scans of faecal samples

Mehtiö

Rinne

Nyholm

et al. 2015

J Animal Breeding Genetics

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“…The calibration process establishes a relationship between a spectrum and a reference property (e.g., composition parameter) through the creation of a spectrochemical prediction model (Shenk & Westerhaus, 1993). This model aims at making the most accurate and precise prediction for a parameter/variable of interest (Decruyenaere et al, 2015).…”

Section: Calibration and Validation Processmentioning

confidence: 99%

“…Therefore, it is necessary that the laboratory values (from wet chemistry procedures) and techniques be precise and accurate to develop suitable calibration equations; the lower the error in reference values, the better the precision of the model (Decruyenaere et al, 2015;Osborne, 2000). This is a key point in the quality of the NIRS technique used, as NIRS requires a large number of reference samples for instrument calibration (Pujol et al, 2007).…”

Section: Calibration and Validation Processmentioning

confidence: 99%

“…This happens because errors in predicting animal responses are usually greater than those in predicting chemical compositions (Norris et al, 1976). Therefore, by comparison with the usual chemical parameters, the calibration and validation equation values of in vivo characteristics are higher due the variation between animals (Decruyenaere et al, 2015).…”

Section: Digestibility and Gas Productionmentioning

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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“…It is also essential that researchers include in their assessment criteria factors that reflect the local context within which they work (Boval and Dixon 2012). In this respect, NIRS (Near-Infrared Spectroscopy) progress with portable devices that allow evaluation of in situ situations is important (Liu et al 2014;Decruyenaere et al 2015).…”

Section: Reorienting the Agricultural Sciences Of Tropical Grasslandsmentioning

confidence: 99%

Tropical grasslands: A pivotal place for a more multi-functional agriculture

Boval¹,

Angeon²,

Rudel³

2016

Ambio

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Tropical grasslands represent a pivotal arena for the sustainable intensification of agriculture in the coming decades. The abundant ecosystem services provided by the grasslands, coupled with the aversion to further forest destruction, makes sustainable intensification of tropical grasslands a high policy priority. In this article, we provide an inventory of agricultural initiatives that would contribute to the sustainable intensification of the tropical grassland agro-ecosystem, and we recommend a shift in the scientific priorities of animal scientists that would contribute to realization of a more agro-ecological and multi-functional agriculture in the world's tropical grasslands.

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Prediction error and repeatability of near infrared reflectance spectroscopy applied to faeces samples in order to predict voluntary intake and digestibility of forages by ruminants

Cited by 16 publications

References 25 publications

Cow‐specific diet digestibility predictions based on near‐infrared reflectance spectroscopy scans of faecal samples

Cow‐specific diet digestibility predictions based on near‐infrared reflectance spectroscopy scans of faecal samples

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

Tropical grasslands: A pivotal place for a more multi-functional agriculture

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