Comparison of fecal crude protein and fecal near-infrared reflectance spectroscopy to predict digestibility of fresh grass consumed by sheep.

Organic matter digestibility (OMD), an essential criterion for the evaluation of the nutrition of ruminants, cannot be measured easily at pasture. Therefore, the objective of this study was to test and compare 2 methods of OMD prediction based on the fecal CP content (CPf) or near infrared reflectance spectroscopy (NIRS) applied to feces. First, published equations derived from fecal N (Eq. 1(CP), n = 40) and from fecal NIRS (Eq. 1(NIRS), n = 84) were used to predict OMD of an independent validation data set from which in vivo OMD, ranging from 58 to 74%, was measured for 4 regrowth stages of Digitaria decumbens. Second, to establish equations usable in grazing situations and to improve the efficiency of the predictions, new equations were calculated from a large data set (n = 174) using CPf (Eq. 2(CP)) or fecal NIRS (Eq. 2(NIRS)). By applying the CPf method, Eq. 2(CPf) (OMD, % = 88.4 - 263.9/CPf, % of OM; residual SD = 2.92, r(2) = 0.63) showed similar statistical parameters (P < 0.01) when compared with Eq. 1(CP) (OMD, % = 86.6 - 266.2/CPf, % of OM; residual SD = 2.95, r(2) = 0.79). When using fecal NIRS, Eq. 2(NIRS) showed decreased SE of calibration (SEC = 1.48) and of cross-validation (SECV = 1.75) and greater coefficient of determination of cross-validation (R(2)(CV) = 0.85) than the previously published Eq. 1(NIRS) (SEC = 1.78, SECV = 2.02, R(2)(CV) = 0.77). The validation of the 4 equations on the validation data set was satisfactory overall with an average difference between the predicted and the observed OMD ranging from 0.98 to 2.79 percentage units. The Eq. 2(NIRS) was nevertheless the most precise with a decreased residual SD of 2.53 and also the most accurate, because the SD of the average difference between predicted and observed OMD was the lowest. Therefore, fecal NIRS provided the most reliable estimates of OMD and is thus a useful tool to predict OMD at pasture. However, an adequate number of reference data are required to establish good calibration. Indeed, better calibration statistics were obtained by increasing the data set from 84 (Eq. 1(NIRS)) to 174 (Eq. 2(NIRS)). In contrast, using fecal N on a set of 84 or 174 points did not improve the prediction. Both methods are useful for predicting OMD at pasture in certain circumstances, using fecal NIRS when a large data set (n = 84 and n = 174) is available and fecal CP with smaller data sets (n = 40).

[1]  P. Lecomte,et al.  Faecal Indices Based on near Infrared Spectroscopy to Assess Intake, in vivo Digestibility and Chemical Composition of the Herbage Ingested by Sheep (Crude Protein, Fibres and Lignin Content) , 2007 .

[2]  E. Schlecht,et al.  Estimating the digestibility of Sahelian roughages from faecal crude protein concentration of cattle and small ruminants. , 2006, Journal of animal physiology and animal nutrition.

[3]  Serge Yan Landau,et al.  Monitoring nutrition in small ruminants with the aid of near infrared reflectance spectroscopy (NIRS) technology: A review , 2006 .

[4]  I. Murray,et al.  Nutritive Evaluation of Forages by near Infrared Reflectance Spectroscopy , 2005 .

[5]  K. Südekum,et al.  Relationship between fecal crude protein concentration and diet organic matter digestibility in cattle. , 2005, Journal of animal science.

[6]  J. Cone,et al.  Prediction of forage digestibility in ruminants using in situ and in vitro techniques , 2004 .

[7]  P. Lecomte,et al.  Faecal near infrared reflectance spectroscopy (NIRS) to assess chemical composition, in vivo digestibility and intake of tropical grass by Creole cattle , 2004 .

[8]  Jerry W. Stuth,et al.  Direct and indirect means of predicting forage quality through near infrared reflectance spectroscopy , 2003 .

[9]  M. Boval,et al.  The ability of faecal nitrogen to predict digestibility for goats and sheep fed with tropical herbage , 2003, The Journal of Agricultural Science.

[10]  Horacio,et al.  In vivo digestibility of kleingrass from fecal nitrogen excretion , 2003 .

[11]  M. Boval,et al.  Effect of regrowth age on intake and digestion of Digitaria decumbens consumed by Black-belly sheep. , 2000 .

[12]  M. Schlegel,et al.  Grazing methods and stocking rates for direct-seeded alfalfa pastures: II. Pasture quality and diet selection. , 2000, Journal of animal science.

[13]  M. Meuret,et al.  How forage characteristics influence behaviour and intake in small ruminants: a review. , 2000 .

[14]  G. G. Irish,et al.  Comparison of methods used to predict the in vivo digestibility of feeds in ruminants , 1999 .

[15]  F. J. Gordon,et al.  The use of near infrared reflectance spectroscopy /NIRS on undried samples of grass silage to predict chemical composition and digestibility parameters , 1998 .

[16]  F. J. Gordon,et al.  The use of Near Infrared Reflectance Spectroscopy on dried samples to predict biological parameters of grass silage , 1997 .

[17]  A. Xandé,et al.  Évaluation d'indicateurs fécaux pour prédire la digestibilité et les quantités ingérées de Dichanthium sp par des bovins créoles , 1996 .

[18]  J. Wehausen Fecal measures of diet quality in wild and domestic ruminants , 1995 .

[19]  John S. Shenk,et al.  Population Definition, Sample Selection, and Calibration Procedures for Near Infrared Reflectance Spectroscopy , 1991 .

[20]  R. H. Armstrong,et al.  The prediction of the in vivodigestibility of the diet of sheep and cattle grazing indigenous hill plant communities by in vitrodigestion, faecal nitrogen concentration or indigestible' acid‐detergent fibre , 1989 .

[21]  W. Horwitz Official Methods of Analysis , 1980 .

[22]  R. J. Lancaster Estimation of Digestibility of Grazed Pasture from Fæces Nitrogen , 1949, Nature.