Faecal near infrared reflectance spectroscopy to predict diet quality for sheep

Li, H.; Tolleson, Douglas R.; Stuth, Jerry W.; Bai, K. Vijaya; Mo, Futao; Kronberg, Scott L.

doi:10.1016/j.smallrumres.2005.10.017

Cited by 30 publications

(21 citation statements)

References 5 publications

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“…In rabbits, Núñez-Sánchez et al (2012) had an SECV of 1.7% (R 2 = 0.69). In ruminants, Li et al (2007) obtained an SEC value of 1.5% and SEP around 2.0% in sheep, whereas in cattle Boval et al (2004) had an SEC of 2.0% and Coates and Dixon (2011) had an SEC of 1.87%, which are less precise than the results obtained in the present study.…”

Section: Discussioncontrasting

confidence: 97%

Predicting feed digestibility from NIRS analysis of pig faeces

Bastianelli

Bonnal

Jaguelin-Peyraud

et al. 2015

Animal

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Digestibility is a key parameter in the evaluation of feeds; however, the measurements on animals require heavy experimental trials, which are hardly feasible when large numbers of determinations are required -for example, in genetic studies. This experiment aimed at investigating the possibility to predict digestibility from NIRS spectra measured on faeces. A total of 196 samples were available from a digestibility experiment investigating the effects of age and genetic background of Large White pigs fed the same diet, rich in fibre (NDF = 21.4% DM). Digestibility of dry matter (dDM), organic matter (dOM), nitrogen content (dN), energy (dE) and apparent digestible energy content (ADE) were calculated, as well as total N content of faeces (N). The faeces samples were submitted to reflectance NIRS analysis after freeze-drying and grinding. Calibration errors and validation errors were, respectively, 0.08 and 0.13% DM for total N in faeces, 0.97% and 1.08% for dDM, 0.79% and 1.04% for dOM, 1.04% and 1.47% for dN, 0.87% and 1.12% for dE and 167 and 213 kJ/kg DM for ADE. These results indicate that NIRS can account for digestibility differences due to animal factors, with an acceptable accuracy. NIRS appears to be a promising tool for large-scale evaluations of digestibility. It could also be used for the study of digestibility of different feeds, after appropriate calibration based on a wide range of feed types.

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Section: Discussioncontrasting

confidence: 97%

Predicting feed digestibility from NIRS analysis of pig faeces

Bastianelli

Bonnal

Jaguelin-Peyraud

et al. 2015

Animal

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“…Furthermore, the ratio of performance to deviation value for these nutrient fractions was more than 3, which attests the robustness of our calibration models. Unlike previous studies, which applied NIRS for determining the chemical composition of feed residues after in situ ruminal incubation (Berzaghi et al 1997), predicting diet quality using fecal samples from white-tailed deer (Showers et al 2006), and sheep (Li et al 2007), and prediction of chemical composition of South African Medicago sativa L. hay (Scholtz et al 2009), our results provide evidence for direct analyses of solid rumen contents by NIRS. This, in turn, provides a critical link between habitat quality, management, and animal well-being.…”

Section: Discussionmentioning

confidence: 55%

Multivariate calibration of near infrared spectra for predicting nutrient concentrations of solid moose rumen contents

Tigabu¹

2018

Silva Fenn.

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Tigabu M., Felton A.M. (2018). Multivariate calibration of near infrared spectra for predicting nutrient concentrations of solid moose rumen contents. Silva Fennica vol. 52 no. 1 article id 7822. 14 p. https://doi.org/10.14214/sf.7822 Highlights• Multivariate calibrations were established for predicting nutrient concentrations of solid moose rumen contents by near infrared spectroscopy (NIRS).• Crude protein, available protein and ash contents were accurately predicted.• Prediction of microbial nitrogen, ash, acid-detergent fiber, neutral detergent fiber and lignin were satisfactory.• The results demonstrate that NIRS offers quick and inexpensive procedure to quantify nutrient concentrations of solid rumen contents. AbstractThis study aimed at establishing calibrations to predict nutrient concentrations of solid moose (Alces alces L.) rumen content using near infrared spectroscopy (NIRS), as an alternative to expensive chemical analyses. NIR reflectance spectra of 148 dry pulverized samples were recorded. The scanned samples were then analyzed for crude protein, available protein, microbial nitrogen (N), ash, acid-detergent fiber (ADF), neutral detergent fiber (NDF) and lignin contents following standard chemical analysis procedures. The calibration models were derived by Orthogonal Projection to Latent Structure (OPLS) and validated using external prediction sets. The calibration models accurately predicted crude protein, available protein and ash contents (R 2 = 0.99, 0.96, and 0.92, prediction error = 0.39, 0.72 and 0.53% dry matter, respectively) while NDF (R 2 = 0.92; prediction error = 2.23% dry matter) and ADF (R 2 = 0.89; prediction error = 1.94% dry matter) were predicted with sufficient accuracy and that of microbial-N (R 2 = 0.81; prediction error = 1.25 mg yeast-RNA g -1 dry matter) and lignin (R 2 = 0.84; prediction error = 1.05% dry matter) were acceptable. The ratio of performance to deviation values were > 3.0 for crude protein and available protein, between 3.0 and 2.5 for ADF, NDF and lignin, and 2.32 for microbial-N; attesting the robustness of the calibration models. It can be concluded that NIR spectroscopy offers a quick and inexpensive procedure for prediction of nutrient concentrations of solid rumen contents in wild herbivores.

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“…During the past 20 years, near infrared reflectance spectroscopy (NIRS) scanning of livestock feces has emerged as a reliable tool for assessing the quality of forage grazed by ruminants (Dixon and Coates, 2010;Leite and Stuth, 1995;Li et al, 2007;Lyons and Stuth, 1992;Stuth et al, 1991). Remote sensing-based approaches for forage quality estimation will require additional study to be effective in an EWS framework.…”

Section: Early Warning Systemsmentioning

confidence: 99%

A glimpse of the future in animal nutrition science. 2. Current and future solutions

Tedeschi

Fonseca

Muir³

et al. 2017

R. Bras. Zootec.

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-Despite tremendous advancements in the livestock sector, additional opportunities exist to improve even further livestock production around the globe. Forecasting is not an exact science and it relies heavily on past and current knowledge. Improvements in the nutritional sciences (both human and animal) include a better understanding of agents that cause deterioration of human health, improving the quality of animal products, applying effective fetal programming, developing new feeds and feeding strategies, and revisiting longstanding technologies. Improvements in the understanding of the rumen microbiome will enable scientists to increase the fermentation efficiency and, hopefully, select microbial species of greater interest. Improvements in remote sensing and ground-based instrumentation, telecommunications, and weather forecasting technologies will aid in the continued improvements of early warning systems to assist livestock producers in reducing risk and adapting to the changing environment. Broad utilization of sensor technologies will allow scientists to collect real-time data and, when combined with mathematical modeling, decision support systems will become an indispensable managerial tool for livestock production with the possibility to automate low-level decisions on the farm, such as supplementation schedules, sorting of animals, and early detection of disease and outbreaks. The identification of feed efficient animals may be the single most impactful advancement towards long-term livestock sustainability and the promise of feeding the world animal products. We contend that education across societal levels is the first step to solve current and future challenges of the livestock industry. The dilemma has been who will take the first step forward.

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Faecal near infrared reflectance spectroscopy to predict diet quality for sheep

Cited by 30 publications

References 5 publications

Predicting feed digestibility from NIRS analysis of pig faeces

Predicting feed digestibility from NIRS analysis of pig faeces

Multivariate calibration of near infrared spectra for predicting nutrient concentrations of solid moose rumen contents

A glimpse of the future in animal nutrition science. 2. Current and future solutions

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