Use of near Infrared Reflectance Spectroscopy to Analyse Bovine Faecal Samples

Cozzolino, Daniel; Manna, Alejandro La; Martins, D. Vaz

doi:10.1255/jnirs.347

Cited by 12 publications

(13 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our validation statistics were close to or within the range of those reported previously using dried ground cattle faeces for starch [R 2 val = 0.89 (SEP = 1.09) compared with 0.91 (SEP = 0.57) (Fredin et al 2014)], N [R 2 val = 0.80 (SEP = 0.10) compared with 0.78 (SEP = 0.08) to 0.83 (SEP = 0.10) (Purnomoadi et al 1996;Cozzolino et al 2002)], NDF [R 2 val = 0.84 (SEP = 0.98) compared with 0.85 (SEP = 0.80) (Cozzolino et al 2002)], and ADF [R 2 val = 0.92 (SEP = 0.95) compared with 0.74 (SEP = 0.80) to 0.96 (SEP = 0.12) (Purnomoadi et al 1996;Cozzolino et al 2002)] but poorer for ADL [R 2 val = 0.87 (SEP = 0.96) compared to 0.94 (SEP = 0.15) (Purnomoadi et al 1996)]. The comparatively low R 2 val for N (0.80) is likely due to the small variation (2.24%-3.17%) in reference values for this faecal constituent.…”

Section: Populationsupporting

confidence: 89%

Characterization of the variation in the daily excretion of faecal constituents and digestibility predictions in beef cattle fed feedlot diets using near-infrared spectroscopy

Jancewicz

Penner

Swift³

et al. 2016

Can. J. Anim. Sci.

View full text Add to dashboard Cite

Six heifers were individually housed and assigned to once (FF1) or twice (FF2) daily feeding regimes over backgrounding and finishing periods. Following adaptation, total faecal collections were conducted at 4-h intervals and at 24-h intervals over 4 d, and near-infrared spectroscopy (NIRS) was used to predict faecal organic matter (OM), starch, nitrogen (N), neutral detergent fibre (NDF), acid detergent fibre (ADF), and acid detergent lignin (ADL). At each interval, NIRS calibrations were used to estimate faecal constituents and ADL to calculate apparent (aTTD) and estimated (eTTD) total tract digestibility. Faecal dry matter (DM) (%), NDF, and ADF varied among 4-h interval samples in the backgrounding period and faecal DM, starch, NDF, ADF, and ADL in the finishing period. Faecal starch was able to predict aTTD during both feeding periods (backgrounding: R2 = 0.96, P < 0.01; finishing: R2 = 0.98, P < 0.01). The NIRS calibrations for predicting aTTD using the 4-h interval samples or the 4-d–24-h composite were least accurate for NDF and ADF. Most 4-h interval samples could be used to predict eTTD of nutrients, and aside from starch in the finishing period, there were no differences in eTTD using faecal samples collected over 4-h intervals versus those collected over 4 d. Spot faecal samples collected at any time point from multiple cattle have the potential to predict digestibility. Timing of sampling after feeding must be standardized to predict starch digestibility during the finishing period, with samples between 0–4 h and 8–16 h generating estimates of both starch concentration and digestibility that were closest to that derived from 4-d–24-h composite samples.

show abstract

Section: Populationsupporting

confidence: 89%

Characterization of the variation in the daily excretion of faecal constituents and digestibility predictions in beef cattle fed feedlot diets using near-infrared spectroscopy

Jancewicz

Penner

Swift³

et al. 2016

Can. J. Anim. Sci.

View full text Add to dashboard Cite

show abstract

“…For these materials, the NDSol mean values (100-48.6 = 51.4 g 100 g -1 ) were about twice our values, whereas the SD associated with NDF were much lower than ours. These fi gures explain why the SEC associated with our NDSol was almost twice that reported for bovine manure (1.35 g 100 g -1 ) 33 or mushroom compost (1.33 g 100 g -1 ). 47 In our study, SECV for NDSol was similar to that of temperate plant materials (3.82 g 100 g -1 ) 23 or dairy manure (2.8 g 100 g -1 ), 31 but higher than that of mushroom compost (1.14 g 100 g -1 ) 47 and (0.57 g 100 g -1 b.w.).…”

Section: Total Organic Matter Characteristics: Ash C Dumas N Dumas supporting

confidence: 55%

“…The SEC value of NKj obtained in this study was about twice that reported for Mediterranean leaves and litters (0.08 g 100 g -1 ) 20 and compost (0.07 and 0.03 g 100 g -1 ), 28,47 but was lower than that reported for other heterogeneous materials such as bovine manure (0.74 g 100 g -1 ). 33 The SECV of NKj can be compared with other studies: dairy manures (4.42, 0.23 and 0.05 g 100 g -1 ), [30][31]49 or compost (0.08 g 100 g -1 DM and 0.03 g 100 g -1 bulk weight). 28,47 RPD helps in comparing the calibrations from different studies (different population abundance and material homogeneity).…”

Section: Total Organic Matter Characteristics: Ash C Dumas N Dumas mentioning

confidence: 99%

“…In soil and manured soil, [30][31][32] potential mineralisable C and N have been determined, along with the fi bre content in manure. 31,33 Dynamics of C and N have also been studied by NIR through potential mineralisable N in soil from Northern [34][35][36] and Southern environments 37 and soil biomass and enzymatic activities 38,39 as important descriptors of their dynamics.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Prediction by near Infrared Spectroscopy of the Composition of Plant Raw Materials from the Organic Fertiliser Industry and of Crop Residues from Tropical Agrosystems

Thuriès¹,

Bastianelli

Davrieux

et al. 2005

Journal of Near Infrared Spectroscopy

View full text Add to dashboard Cite

The dynamics of carbon (C) and nitrogen (N) of plant residues and organic fertilisers are of great interest for agricultural and global warming studies. The proportion of the fractions obtained from biochemical analyses (fibres by sequential Van Soest analysis) can be used for predicting both C and N transformation of organic materials in soils. Considering the expensive and time-consuming Van Soest method, the principal aim of this study was to elaborate near infrared (NIR) calibrations for fibres, in order to use them for consecutive studies (for example, our works on transformation of added organics or TAO model). A wide set of organic fertilisers and their raw materials was sampled, including plant materials originating from temperate (especially Mediterranean) and tropical regions. The particular objective of this work was to build NIR calibrations for fibre fractions, along with C and N content, in plant materials used in the organic fertiliser industry and green house gases mitigating strategies. The second particular objective was to test for two levels of validation of the equations previously elaborated: (1) validation with a set of randomly chosen samples that was not considered during the calibration step, (2) extrapolation of the predictive capacity of the equations when applying them to outliers that were previously discarded. The fibres were the best predicted parameters, as R² = 0.95, 0.91, 0.97, 0.97 for neutral detergent soluble, hemicelluloses, cellulose and lignin, respectively, whereas the characteristics of total organic matter had R² varying from 0.87 (N Kjeldahl) to 0.94 (C Dumas). The accuracy of the calibrations developed for fibres was confirmed by the first level of validation, since the standard errors of prediction were close to the corresponding standard errors of cross-validation and the standard errors of calibration. Nevertheless, the calibrations developed for ash and C Dumas were not so good. Surprisingly, at the second level of validation, some outliers were not so badly predicted. This can illustrate the robustness of the calibrations for cellulose, lignin and, to a lesser extent, N Dumas which are key parameters for our modelling works on C and N transformation of added organics in soils.

show abstract

“…[16][17][18][19] The easiest thing to find, and therefore to survey is faeces, which are rich in chemical information that can be captured by NIR spectroscopy and reflect the dynamic and inherent physiology of the animal. [20][21][22] As an example, DNA analysis has been employed for population surveys of the Amur leopard (Panthera pardus orientalis) and Siberian tiger (Panthera tigris altaica) with some success. However, information from genetic material can be limited by the quality and quantity of DNA in the sample, as well as false alleles and allelic dropout in fragmented DNA obtained from in situ sources of biomaterials.…”

Section: Nir Spectroscopy Is Inherently Suited To Address Variation Imentioning

confidence: 99%

Near Infrared Spectroscopy in Wildlife and Biodiversity

Vance

Tolleson

Kinoshita

et al. 2016

Journal of Near Infrared Spectroscopy

View full text Add to dashboard Cite

Near infrared (NIR) spectroscopy has been used to answer a wide variety of questions in wildlife and biodiversity research. Whereas agricultural systems and manufacturing seek to limit variation in production systems, wildlife and biodiversity research must embrace it. Variation amongst individuals is the material on which natural selection operates and NIR spectroscopy provides a means to catalogue this variation and to use it in broader ecological and evolutionary analyses and for practical conservation outcomes. In this review we describe how NIR spectroscopy has been applied in wildlife and biodiversity research to obtain data that we could not obtain otherwise. Here we describe a range of applications for which NIR spectroscopy has been applied to questions in taxonomy, physiology, habitat evaluation and population monitoring and highlight new approaches that will allow NIR spectroscopy to be used more widely in wildlife and ecological studies.

show abstract

Use of near Infrared Reflectance Spectroscopy to Analyse Bovine Faecal Samples

Cited by 12 publications

References 12 publications

Characterization of the variation in the daily excretion of faecal constituents and digestibility predictions in beef cattle fed feedlot diets using near-infrared spectroscopy

Characterization of the variation in the daily excretion of faecal constituents and digestibility predictions in beef cattle fed feedlot diets using near-infrared spectroscopy

Prediction by near Infrared Spectroscopy of the Composition of Plant Raw Materials from the Organic Fertiliser Industry and of Crop Residues from Tropical Agrosystems

Near Infrared Spectroscopy in Wildlife and Biodiversity

Contact Info

Product

Resources

About