Faecal near infrared reflectance spectroscopy (NIRS) to assess chemical composition, in vivo digestibility and intake of tropical grass by Creole cattle

Animal Feed Science and Technology 114 (2004) 19–29

Faecal near infrared reflectance spectroscopy (NIRS) to assess chemical composition, in vivo digestibility and intake of tropical grass by Creole cattle M. Boval a,∗ , D.B. Coates b , P. Lecomte c , V. Decruyenaere d , H. Archimède a a

Institut National de la Recherche Agronomique (INRA) Antilles-Guyane, Unité de Recherches Zootechniques, Domaine Duclos, 97170 Petit-Bourg, Guadeloupe, France b CSIRO Sustainable Ecosystems, Davies Laboratory, Townsville 4814, Australia c Centre de Coopération International de Recherches Agronomiques Pour le Développement (CIRAD-EMVT), Programme Productions Animales, Campus de Baillarguet, Cedex 5 Montpellier F-34398, France d Centre de Recherches Agronomiques de Gembloux, Section Système Agricoles (CRAGx), 100 Rue de Serpont, 6600 Libramont, Belgium Received 25 February 2002; received in revised form 23 December 2003; accepted 23 December 2003

Abstract Selective grazing by ruminant livestock and other herbivores causes difficulties in measuring or estimating the chemical composition and functional properties (digestibility and intake) of the diets of free grazers. New methodologies using faecal near infrared reflection spectroscopy (NIRS) offers scope to estimate diet quality in grazing animals once suitable calibration equations have been developed. This study was conducted to determine the potential of faecal NIRS to predict crude protein (CP), neutral detergent fibre (NDF), acid detergent fibre (ADF) and organic matter digestibility (OMD) of the diet of cattle grazing tropical grass pastures as well as organic matter intake (OMI). Reference data and faecal spectra were measured from a pen experiment in which 11 Creole steers (were individually housed and fed diets of fresh grass harvested from irrigated plots of (i) Digitaria decumbens and (ii) Dichanthium spp. The experiment ran for 70 days (14 days adaptation, 56 days measurement) and variation in diet quality was achieved by varying the age of grass regrowth after an initial mowing. Grass samples and faecal samples were bulked within each week of the measurement period within steer. The 88 dried and milled faecal samples (11 steers × 8 weeks) were scanned in a NIR Systems 5000 monochromator. Faecal spectra and reference data were used to calibrate and cross validate equations for predicting the various parameters using the Abbreviations: NIRS, faecal near infrared reflectance spectroscopy; CP, crude protein; NDF, neutral detergent fibre; ADF, acid detergent fibre; OMD, organic matter digestibility; OMI, organic matter intake; SEC, standard error of calibration ∗ Corresponding author. Tel.: +33-689-468471; fax: +33-689-468464. E-mail address: [email protected] (M. Boval). 0377-8401/$ – see front matter © 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.anifeedsci.2003.12.009

20

M. Boval et al. / Animal Feed Science and Technology 114 (2004) 19–29

Modified Partial Least Squares (MPLS) procedure. Derived standard errors of calibration (SEC) and coefficients of determination (R2 ) were 0.33 and 0.98% for dietary CP, 0.96% and 0.88 for NDF, 0.81% and 0.89 for ADF, 0.021 and 0.72 for OMD, and 4.62 g/kg of (body weight)0.75 and 0.61 for OMI. These values compared favourably with published faecal NIRS reports, where available for CP and OMD, and also with many published forage NIRS results and confirmed the potential of faecal NIRS as a technology for reliably predicting the chemical composition and functional properties of diets in grazing cattle. © 2004 Elsevier B.V. All rights reserved. Keywords: Faecal near infrared reflectance spectroscopy (NIRS); In vivo digestibility; Intake; Diet chemical composition; Tropical fresh grass

1. Introduction Analysis of herbage samples using near infrared reflectance spectroscopy (NIRS) to determine chemical and functional properties, such as crude protein (CP) concentration and digestibility, are now well accepted as an alternative to laboratory chemical procedures. The advantages and limitations of NIRS as an analytical tool for determining forage quality have been reviewed extensively (Norris et al., 1976; Shenk and Westerhaus, 1985; Murray, 1986; Givens et al., 1997; Kitessa et al., 1999; Fahey and Hussein, 1999; Coleman et al., 1999). There are many published reports of predictive accurate calibration equations being developed for dried pasture samples and hays (Abrams et al., 1987; Lippke and Barton, 1988; Brown et al., 1990), grass silage (Sinnaeve et al., 1994; Park et al., 1998) and fresh forage (Norris et al., 1976; Shenk et al., 1977; Berardo et al., 1997). Attempts have also been made to predict voluntary intake of forages by NIRS, using dried samples (Norris et al., 1976; Ward et al., 1982; Redshaw et al., 1986; Coelho et al., 1988; Park et al., 1997). In grazing animals, the quality of selectively grazed diets may bear little relation to the quality of the herbage on offer. Even samples of pasture harvested to simulate the material selected by the grazing animal are not likely to provide an accurate representation of the diet, especially in all but very intensive grazing systems. Therefore, analysis of pasture samples will usually be of little use in determining diet quality in grazing livestock. However, the chemical composition of the undigested residues of forage diets are likely to be closely correlated with the chemical composition of herbage ingested, so that faeces should contain information about the characteristics of the diet (Coleman et al., 1995). Therefore, NIRS analysis of faeces may provide a means by which dietary properties can be reliably estimated. Research in this field was pioneered in Texas to evaluate diet quality of free-ranging cattle (Stuth et al., 1989; Lyons and Stuth, 1992; Leite and Stuth, 1995). More recently, its potential application was described by Coates (1999) in relation to the cattle grazing industry of northern Australia, and by Coleman et al. (1995, 1999). However, in relative terms, faecal NIRS as a technology for predicting diet quality of domestic ruminants is still in its infancy. Because of the nature of the technology, it tends to be regarded with some scepticism as a legitimate and reliable tool for widespread application and, therefore, would benefit from further research.

M. Boval et al. / Animal Feed Science and Technology 114 (2004) 19–29

21

The development of faecal NIRS calibration equations to estimate the chemical and functional properties of diets requires determination of relevant dietary attributes (i.e. reference values) for pairing with the NIR spectra of the faecal samples. In many cases, oesophageal fistula samples have been used to determine these dietary reference values. Apart from the errors associated with fistula samples not being truly representative of the ingested diet (Coates et al., 1987; Jones and Lascano, 1992), measures of digestibility necessarily have to be based on in vitro analysis. An alternative approach, based on in vivo data as reference values, was described by Lecomte et al. (1995) and Stilmant et al. (1999). In vivo data sets derived from hand feeding experiments have the potential to provide accurate reference values for developing faecal NIRS calibrations to predict functional properties, such as digestibility and intake. The objective of this study was to evaluate the potential of faecal NIR spectral data to predict chemical composition, organic matter (OM) digestibility (OMD) and intake of tropical fresh grass when in vivo data from stall-fed cattle were used as reference values in the calibration. 2. Materials and methods 2.1. Location The experiment was conducted at the experimental station of the National Agronomic Research Institute (INRA) in Guadeloupe (16◦ 16 N, 61◦ 30 W) from July to September of 1996. Temperatures ranged from 21 to 31 ◦ C and the mean rainfall was 93 mm per month. 2.2. Cattle and diets Twelve Creole steers (256 ± 35 kg body weight (BW) and 18 ± 1-month-old) were individually housed in metabolism cages to be fed fresh forage harvested from grass plots of either Dichanthium spp. or Digitaria decumbens for a period of 8 weeks after an initial adaptation period of 2 weeks. The amount of feed offered each day was set at approximately 20% above voluntary intake. One steer fed Dichanthium spp. proved unsuitable and so that the number in this group was reduced to 5. The grass plots were mown and fertilised (90 kg N/ha) 20 days before the feeding trial commenced and irrigated in accordance with local evapo-transpiration estimated from data collected at an adjacent automatic meteorological station (CIMEL) to ensure continuous growth. Each plot was subdivided into 56 subplots and, commencing 20 days after the plots were cut, the regrowth from one subplot of each grass was harvested each morning at 06.30 h and chopped (5 cm length) before being fed in two meals (08.00 and 13.00 h) to the steers. Stage of regrowth, therefore, ranged from 20 days regrowth on the first day of the measurement period to 75 days regrowth on the last day of feeding. Feed offered during the adaptation period came from 20 additional sub-plots of each grass species. These subplots were mown and irrigated to provide 20 days regrowth. Steers were offered water ad libitum and had continuous access to salt (NaCl). Ticks and internal parasites were controlled by fortnightly treatments with anthelmintic and acaricide.

22

M. Boval et al. / Animal Feed Science and Technology 114 (2004) 19–29

2.3. Measurements The fresh weight of forage offered, and forage refusals, were measured daily and subsamples at approximately 300 g fresh weight were collected for dry matter (DM) determination. Total faecal output was collected each day at 07.30 h, weighed, and a sub-sample taken for DM determination. Forage and faeces sub-samples were pooled within week and steer before further processing and analysis. Organic matter intake (OMI), faecal OM output and OMD were calculated for each steer for each week. DM contents of pooled forage and faeces samples were determined by drying to constant weight at 80 ◦ C in a forced draught oven. Dried samples were ground through a 0.75 mm screen before NIRS and/or chemical analysis. The OM content was determined by ashing sub-samples for 8 h at 550 ◦ C. Neutral and acid detergent fibre (NDF and ADF) were estimated following the method of Van Soest et al. (1991) and were expressed without residual ash. NDF assay did not use sodium sulphite or amylase. Nitrogen content was determined using a Kjeldahl method (Nelson and Sommers, 1980). The CP, NDF and ADF of grass offered and refused were determined for each steer for each week and dietary composition was calculated from the composition of the grass offered and grass refused. Dried and milled faecal samples were sent to Libramont (Belgium) for NIRS analysis. Samples were allowed to equilibrate with laboratory atmosphere before being scanned at 2 nm intervals over the wavelength range 1100–2500 nm using a NIR System 5000 monochromator. Samples were scanned using closed cells and spectral data were recorded as log 1/(Reflectance values) (log 1/R). Mathematical treatment of the spectral data was performed using ISI software (Infrasoft International; Shenk, 1992). Calibration equations were developed for predicting dietary CP, NDF and ADF, OMD and OMI, using the Modified Partial Least Square procedure (MPLS) as this technique had been proven to be superior in earlier research (Shenk and Westerhaus, 1993; Park et al., 1997, 1998). The regression analysis used second order derivatised spectral data 2,5,5,1 with scatter correction using Standard Normal Variate and Detrend (SNV-D). Cross validation, based on splitting the sample population into three groups, was used to select the optimum number of terms (i.e. principal components or eigenvectors) without overfitting.

3. Results and discussion 3.1. Quality of forage consumed The increasing age of regrowth over the feeding period together with differences due to grass species provided a range in dietary attributes for use as reference values (Table 1). Forage fed during the first week (day 20–26 regrowth) was of very high quality, being high in CP, OMD and OMI, and low in NDF and ADF. Forage fed in the final week (day 69–75 regrowth) was substantially lower in CP but the decline in OMD and OMI was slight, as was the increase in NDF and ADF.

M. Boval et al. / Animal Feed Science and Technology 114 (2004) 19–29

23

Table 1 Chemical composition and functional properties of the trial diets averaged among steers within each group Weeks

1 2 3 4 5 6 7 8

OMI (g/kg BW0.75 )

CP (% OM)

NDF (% OM)

ADF (% OM)

OMD

Dd

D

Dd

D

Dd

D

Dd

D

Dd

D

13.2 14.0 13.3 11.3 10.1 8.9 8.9 8.0

14.1 13.2 10.5 9.6 8.4 8.8 7.5 7.7

77.6 78.3 76.7 78.0 77.8 79.9 81.7 80.2

70.9 73.0 73.4 73.7 74.9 74.2 73.2 75.8

40.6 40.6 39.6 42.9 42.6 43.7 43.7 44.0

36.5 37.5 37.2 37.7 37.8 37.7 37.8 40.3

0.68 0.70 0.67 0.65 0.65 0.62 0.57 0.57

0.69 0.68 0.65 0.65 0.61 0.63 0.65 0.64

80.1 88.0 82.3 78.9 80.2 76.7 74.1 76.2

75.0 75.6 72.1 76.2 68.4 76.3 73.0 71.2

Dd: Digitaria decumbens; D: Dichanthium spp.

3.2. Faecal NIRS calibration equations 3.2.1. Dietary crude protein equation The standard error of calibration (SEC) of 0.33% and coefficient of determination (R2 ) of 0.98 were both indicative of very good calibration statistics (Table 2, Fig. 1). These values compare favourably with those reported by Lyons and Stuth (1992) and Leite and Stuth (1995) with SEC of 0.89 and 1.12% and R2 of 0.92 and 0.94, respectively. The standard error of cross validation (SEC-V) and the cross validation R2 (R2cv ) were only marginally higher than the SEC and R2 , indicating good performance statistics within the limits of the sample population. Cross validation statistics present a truer estimation of the performance of calibrations than the calibration statistics per se (Shenk and Westerhaus, 1993). 3.2.2. Dietary NDF and ADF equations Calibration statistics for dietary NDF and ADF (Table 2) were mostly acceptable with low SEC (0.96 and 0.81 for NDF and ADF, respectively) and relatively high R2 values (0.88 and 0.89, respectively). Cross validation results were indicative of good predictive performance. No published reports of faecal NIRS calibrations for predicting dietary NDF and ADF were found with which to make a direct comparison, but it is noteworthy that these SEC values Table 2 Mean, standard deviation and range of reference values for diet composition (CP, NDF, ADF), organic matter digestibility (OMD), organic matter intake (OMI) and calibration and validation statistics for faecal NIRS calibration equations developed from Creole steers fed with Digitaria decumbens or Dichanthium spp n

CP (%OM) NDF (% OM) ADF (% OM) OMD OMI (g/kg BW0.75 )

86 87 86 87 87

Mean

10.5 75.5 39.6 0.64 76.2

S.D.

2.28 2.73 2.46 0.04 6.40

Range

7.4–14.1 70.9–81.4 36.4–44.2 0.53–0.74 60.8–93.7

Calibration

Validation

SEC

R2

SEC-V

R2cv

0.33 0.96 0.81 0.02 4.62

0.98 0.88 0.89 0.72 0.61

0.50 1.22 0.98 0.02 5.29

0.95 0.80 0.84 0.69 0.52

24

M. Boval et al. / Animal Feed Science and Technology 114 (2004) 19–29

Predicted diet CP (% OM)

16

12

8

4 4

8

12

16

Actual diet CP (% OM) Fig. 1. Actual and NIRS predicted crude protein (CP) content of the diet of Creole cattle fed with Digitaria decumbens (䊉) or Dichanthium spp. (䊊). The line Y = X represents agreement between predicted and observed CPd.

are lower than those reported for forage NIRS equations. In forage NIRS, SEC values for NDF of 1.5% (Brown et al., 1990), 1.7% (Redshaw et al., 1986) and 3.3 and 4.5% (Park et al., 1998) and SEC values for ADF ranging from 1.0 to 2.8% (Norris et al., 1976; Shenk et al., 1979; Marten et al., 1983) were appreciably larger than those for our calibrations. The low SEC values in our study were probably a consequence of the characteristics of the sample population, especially with regard to the limited range of reference values and the lack of diversity with respect to diet (i.e. pasture species, season, year, location, soil type, environmental influences). 3.2.3. Digestibility equation Calibration statistics (SEC = 2.1; R2 = 0.72) and cross validation statistics (SEC-V = 2.2; R2cv = 0.69) for in vivo OMD were noticeably poorer for this functional property (Table 2, Fig. 2) than for those relating to the chemical composition of the diet (i.e. CP, NDF and ADF). No published in vivo data was found with which to compare our results. However, Coates (unpublished data) developed a faecal NIRS equation for predicting DM digestibility (DMD) using data from 54 in vivo trials covering a wide range of pasture grass and legume hays and obtained a SEC of 2.5% and a R2 of 0.89 (n = 187). Faecal NIRS equations for predicting OMD developed by Lyons and Stuth (1992) and Leite and Stuth (1995) used in vitro estimates of digestibility determined on oesophageal fistula samples as reference values. The resultant SEC values of 1.66 and 2.01%, respectively, were lower than the 2.2% from this study but it must be noted that in vivo estimates of digestibility derived from in vitro analysis are themselves subject to appreciable error due to the imperfect correlation between in vitro and in vivo digestibility. In particular, digestibility estimates derived from in vitro analysis are not subject to variation due to animal effects. In our study, between animal variation in observed OMD was as high as 12% and contributed to increased SEC and reduced R2 of the calibration. Purnomoadi et al. (1997) reported a SEC of 2.93% for

M. Boval et al. / Animal Feed Science and Technology 114 (2004) 19–29

25

0.75

Predicted OMD

0.70 0.65 0.60 0.55 0.50 0.50

0.55

0.60

0.65

0.70

0.75

Actual OMD Fig. 2. Actual and NIRS predicted organic matter digestibility (OMD) for Creole cattle fed with Digitaria decumbens (䉬) or Dichanthium spp. (䉫). The line Y = X represents agreement between predicted and observed CPd.

predicting in vivo digestibility. The prediction was based on differences between spectra of feeds and faeces. 3.2.4. Intake equation There was a further deterioration in calibration and cross validation statistics for this functional property compared with the other attributes studied (Table 2). This is consistent with previous reports based on forage NIRS predictions of OMI where SECs (and R2 ) for OMI of 9.6 (0.72), 7.3 (0.71) and 3.4 (0.90) g/kg BW0.75 were reported for cattle consuming arid and semi-arid forages (Ward et al., 1982), temperate grasses and legumes (Redshaw et al., 1986), and silage (Park et al., 1997), respectively. Coleman et al. (1999) synthesised SECs for OMI prediction from faecal NIRS and reported values of 0.7, 1.4 and 2.9 g/kg BW for cattle fed fresh herbage, hays or mixed tropical grasses, respectively. The latter values are close to the SEC of 1.15 that we found when expressed in g/kg BW. 3.2.5. Predictive accuracy of faecal NIRS equations Results from this study, like those of Lyons and Stuth (1992) and Leite and Stuth (1995), show that faecal NIRS calibration equations can be developed for predicting dietary attributes with acceptable accuracy. The SEC and R2 of the calibration equations that we developed for the various dietary attributes compare favourably with the few published values in the scientific literature. There are, however, many reports describing forage NIRS analysis for predicting these same attributes on pasture, hay and silage samples. The predictive accuracy of our faecal NIRS equations compared with forage NIRS equations is surprisingly good, considering the indirect nature of faecal NIRS where the prediction is based on the spectral characteristics of faecal material which represents the undigested residues of the diet together with various endogenous components including synthesised microbial residues. In fact, the performance statistics of our equations were substantially

26

M. Boval et al. / Animal Feed Science and Technology 114 (2004) 19–29

better than those reported for the same attributes predicted by forage NIRS, excepting those reported for silage by Park et al. (1997, 1998). The superior performance statistics of faecal NIRS equations for predicting dietary attributes developed in this study, compared with poorer performance statistics reported on many occasions for forage NIRS equations for predicting similar attributes, are quite unexpected and demand consideration. A possible reason for the inferior performance statistics in the earlier reports of forage NIRS equations (e.g. Norris et al., 1976; Shenk et al., 1979; Ward et al., 1982; Marten et al., 1983; Redshaw et al., 1986) may be associated with instrumentation and software capability and the subsequent advances that have been made in these areas in recent years (e.g. Park et al., 1997, 1998). The low SEC values for all attributes except OMI in the current study may be the result of a sample set that does not meet the usual criteria recommended for developing robust calibration equations (i.e. equations that will provide accurate predictions on “unknown” samples from the target population on a continuing basis). Thus, the diets (i.e. reference values) and resultant faecal samples (i.e. spectral data) were derived from just two grass species growing at a single location and over a very short time span (8 weeks). This contrasts with sampling procedures recommended for developing forage NIRS equations where the calibration set should be structured to cover the diversity likely to be encountered in the target population. Not only should the calibration samples encompass the full range of attribute values, but spectral diversity associated with different plant species and/or mixtures together with environmental influences, such as soil, climatic factors, fertilizer regime and other management factors, should be incorporated. Thus, it is generally recommended that calibration sets incorporate samples representing the full range of factors that give rise to spectral diversity: plant species, geographical location, soil type, management regime, season and year. It would seem logical for these same criteria to apply to faecal NIRS calibration sets. The samples in our study covered only a modest range of attribute values, a very limited diversity with respect to plant species and animal genotype, and no diversity with respect to soil type, location, season, year or management. Thus, it may be reasonable to expect calibration equations with low SEC and SEC-V values as well as high R2 and R2cv from such a sample set, but the ongoing predictive performance of such equations would likely be unacceptable. While the cross validation procedure is meant to provide assessment of the predictive performance on “unknown samples”, the assessment was constrained by the limited diversity within the sample set. Enlarging the calibration set would probably have opposing effects on calibration statistics. On the one hand, SEC values would probably tend to increase as a result of increased spectral diversity unrelated to attribute value, while the optimum number of terms used in the regression analysis usually increases as sample number increases and calibration SEC and R2 values usually improve as the number of terms increase. There is little doubt that the predictive performance of equations would improve as the calibration set was expanded to meet the recommended criteria for developing robust equations. Nevertheless, we did not set out to develop robust, useable calibration equations. Rather, the objective was to evaluate the potential of faecal NIRS, as an alternative to other technologies, for predicting chemical and functional properties of forage diets as a prelude to further development. We also wanted to evaluate the use of reference data derived from pen feeding experiments, rather than reference data derived from oesophageal fistula sampling as used by Lyons and Stuth (1992) and Leite and Stuth (1995).

M. Boval et al. / Animal Feed Science and Technology 114 (2004) 19–29

27

Reference data from pen feeding experiments is more accurate than data from oesophageal fistula sampling, especially for digestibility determinations where the oesophageal fistula method can only provide in vitro estimates. As well as the known errors associated with in vitro techniques, in vitro estimates of digestibility cannot incorporate the variation in in vivo digestibility due to individual animal effects (i.e. between animal variation). In addition, oesophageal fistula sampling cannot provide the intake reference values available from pen feeding experiments. For example the calibration statistics for dietary CP in our study were substantially better than those reported by Lyons and Stuth (1992) and Leite and Stuth (1995). While one can only speculate on possible reasons, it is acknowledged that accuracy with respect to reference values is a critical factor in development of usable calibration equations. It seems reasonable to conclude that the acceptable calibration statistics in our study were due in part to accurate reference values. Results of this study also suggest that faecal NIRS is a viable alternative to other technologies used for estimating dietary attributes. For example the faecal index technique has been used by various researchers, as a method to predict dietary N and digestibility with good accuracy (Nunez-Hernandez et al., 1992; Wehausen, 1995; Boval et al., 1996, 2003). However, faecal NIRS has definite advantages with respect to the number of parameters that can be predicted from a single analysis, together with the simplicity and rapidity of the technique with respect to sampling procedures and the processing and analysis of samples.

4. Conclusions Results of this study confirm previous reports regarding the potential of faecal NIRS to accurately predict chemical and functional properties of the diet of grazing cattle and demonstrate that accurate in vivo data derived from pen-feeding trials may offer the best means for the further development of robust faecal NIRS technology.

Acknowledgements We gratefully acknowledge O. Coppry and the help of G. Saminadin for their great technical participation. This study has been supported by the “Region Guadeloupe” and the “European Community” (FEOGA).

References Abrams, S.M., Shenk, J.S., Westerhaus, M.O., Barton, F.E., 1987. Determination of forage quality by near infrared reflectance spectroscopy: efficacy of broad-based calibration equation. J. Dairy Sci. 70, 806–813. Berardo, N., Dzowela, B.H., Hove, L., Odoardi, M., 1997. Near Infrared calibration of chemical constituents of Cajanus cajan (pigeon pea) used as forage. Anim. Feed. Sci. Technol. 69, 201–206. Boval, M., Peyraud, J.L., Xandé, A., Aumont, G., Coppry, O., Saminadin, G., 1996. Evaluation of faecal indicators to predict digestibility and voluntary intake of Dichanthium spp. by cattle. Ann. Zootech. 45, 121–134. Boval, M., Archimède, H., Fleury, J., Xandé, A., 2003. The ability of faecal nitrogen to predict digestibility for goats and sheep fed with tropical herbage. J. Agric. Sci. (Cambridge) 140, 1–8.

28

M. Boval et al. / Animal Feed Science and Technology 114 (2004) 19–29

Brown, W.F., Moore, J.E., Kunkle, W.E., Chambliss, C.G., Portier, K.M., 1990. Forage testing using Near Infrared Reflectance Spectroscopy. J. Anim. Sci. 68, 1416–1427. Coates, D.B., 1999. Faecal spectroscopy (NIRS) for nutritional profiling of grazing cattle. In: Proceedings of the Fifth International Rangeland Congress, vol. 1, 19–23rd July, Townsville, Qld., Australia, pp. 466–467. Coates, D.B., Schachenmann, P., Jones, R.J., 1987. Reliability of extrusa samples collected from steers fistulated at the oesophagus to estimate the diet of resident animals in grazing experiments. Aust. J. Exp. Agric. 27, 739–745. Coelho, M., Hembry, F.G., Barton, F.E., Saxton, A.M., 1988. A comparison of microbial, enzymatic, chemical and near-infrared reflectance spectroscopy methods in forage evaluation. Anim. Feed. Sci. Technol. 20, 219–231. Coleman, S.W., Stuth, J.W., Holloway, J.W., 1995. Prediction of intake by Near-Infrared Spectroscopic analysis of fecal samples. In: Owens, F.N., Gill, D., Lusby, K., McCollum, T. (Eds.), Proceedings of the Symposium on Intake by Feedlot Cattle. Oklahama, Agric. Exp. Sta., pp. 145–155. Coleman, S.W., Lippke, H., Gill, M., 1999. Estimating the nutritive potential of forages. In: Jung, H.-J.G., Fahey G.C. (Eds.), Nutritional Ecology of Herbivores Proceedings of the Fifth International Symposium on the Nutrition of Herbivores. American Society of Animal Science, Savoy, IL, USA, pp. 647–695. Fahey, G.C., Hussein, H.S., 1999. Forty years of forage quality research: accomplishments and impact from an animal nutrition perspective. Crop Sci. 39, 4–12. Givens, D.I., De Boever, J.L., Deaville, E.R., 1997. The principles, practices and some future applications of near infrared spectroscopy for predicting the nutritive value of foods for animal and humans. Nutr. Res. Rev. 10, 83–114. Jones, R.J., Lascano, C.E., 1992. Oesophageal fistulated cattle can give unreliable estimates of the proportion of legume in the diets of resident animals grazing tropical pastures. Grass Forage Sci. 47, 128–132. Kitessa, S., Flinn, P.C., Irish, G.G., 1999. Comparison of methods used to predict the in vivo digestibility of feeds in ruminants. Aust. J. Agric. Res. 50, 825–841. Lecomte, Ph., Decruyenaere, V., Limbourg, P., Dardenne, P., 1995. Approche préliminaire sur l’intérˆet de la spectrométrie proche infra rouge pour la description cinétique de l’utilisation de l’herbe au pˆaturage. Ann. Zootech. 44 (Suppl.), 41. Leite, E.R., Stuth, J.W., 1995. Fecal NIRS equations to assess diet quality of free-ranging goats. Small Ruminant Res. 15, 223–230. Lippke, H., Barton, F.E., 1988. Near infrared reflectance spectroscopy for predicting intake of digestible organic matter by cattle. J. Dairy Sci. 71, 2986–2991. Lyons, R.K., Stuth, J.W., 1992. Fecal NIRS equations for predicting diet quality of free-ranging cattle. J. Range Manage. 45 (3), 238–244. Marten, G.C., Halgerson, J.L., Cherney, J.H., 1983. Quality prediction of small grain forage by nearinfrared reflectance spectroscopy. Crop Sci. 23, 94–96. Murray, I., 1986. Near infrared reflectance analysis of forages. In: Haresign, W., Cole, D.J.A. (Eds.), Recent Advances in Animal Nutrition. Butterworths, London, pp. 141–156. Nelson, D.W., Sommers, L.E., 1980. Total nitrogen analysis of soil and plant tissues. J. Assoc. Off. Anal. Chem. 63, 770–780. Norris, K.H., Barnes, R.F., Moore, J.E., Shenk, J.S., 1976. Predicting forage quality by infrared reflectance spectroscopy. J. Anim. Sci. 43 (4), 889–897. Nunez-Hernandez, G., Holechek, J.L., Arthun, D., Tembo, A., Wallace, J.D., Galyean, M.L., Cardenas, M., Valdez, R., 1992. Evaluation of fecal indicators for assessing energy and nitrogen status of cattle and goats. J. Range Manage. 45 (2), 143–147. Park, R.S., Gordon, F.J., Agnew, R.E., Barnes, R.J., Steen, R.W.J., 1997. The use of near infrared reflectance spectroscopy on dried samples to predict biological parameters of grass silage. Anim. Feed. Sci. Technol. 68, 235–246. Park, R.S., Agnew, R.E., Gordon, F.J., Steen, R.W.J., 1998. The use of near infrared reflectance spectroscopy (NIRS) on undried samples of grass silage to predict chemical composition and digestibility parameters. Anim. Feed. Sci. Technol. 72, 155–167. Purnomoadi, A., Kurihara, M., Nishida, T., Terada, F., Abe, A., 1997. Prediction of feed digestibility using differences in NIRS spectra between feeds and feces at a determined region of wavelength. Anim. Sci. Technol. (Jpn. ) 69 (3), 253–259.

M. Boval et al. / Animal Feed Science and Technology 114 (2004) 19–29

29

Redshaw, E.S., Mathison, G.W., Milligan, L.P., Weisenburger, R.D., 1986. Near infrared reflectance spectroscopy for predicting forage composition and voluntary consumption and digestibility in cattle and sheep. Can. J. Anim. Sci. 66, 103–115. Shenk, J.S., 1992. NIRS analysis of natural agricultural products. In: Hildrum, K.I., Isakson, T., Naes, T., Tandberg, A. (Eds.), Near Infra-red Spectroscopy. Bridging the Gap Between Data Analysis and NIR Applications. Ellis Horwood, Chichester, pp. 235–240. Shenk, J.S., Westerhaus, M.O., 1985. Accuracy of NIRS intruments to analyse forage and grain. Crop Sci. 25, 1120–1122. Shenk, J.S., Westerhaus, M.O., 1993. Monograph: Analysis of Agriculture and Food Products by Near Infrared Reflectance Spectroscopy. Infrasoft International, Port Matilda, PA, USA, p. 103. Shenk, J.S., Westerhaus, M.O., Hoover, M.R., 1979. Analysis of forages by infrared reflectance. J. Dairy Sci. 62, 807–812. Shenk, J.S., Norris, K.H., Barnes, R.F., Fissel, G.W., 1977. Forage and feedstuff analysis with an infrared reflectance spectro-computer system. In: Proceedings of the XIIth International Grassland Congress, Leipzig, Germany, pp. 454–463. Sinnaeve, G., Dardenne, R., Agneessens, R., Biston, R., 1994. The use of near infrared spectroscopy for the analysis of fresh grass silage. J. Near Infrared Spectrosc. 2, 79–84. Stilmant, D., Lecomte, Ph., Decruyenaere, V., Limbourg, P., Dardenne, P., 1999. Approach on a continuous description of daily weight gain of grazing bulls related to NIR predictions of intake and quality of the grass. In: Proceedings of the Fifth International Symposium of Nutrition of Herbivores, 11–16 April, San Antonio, TX, http://cnrit.tamu.edu/conf/isnh/. Stuth, J.W., Kapes, E.D., Lyons, R.K., 1989. Use of near infrared spectroscopy to assess nutritional status of cattle diets on rangeland. In: Proceedings of the XVIIth International Grassland Congress, Nice, France, pp. 889–890. Van Soest, P.J., Robertson, J.B., Lewis, B.A., 1991. Methods for dietary fibre, neutral detergent fibre and non starch polysaccharides in relation to animal nutrition. J. Dairy Sci. 74, 3583–3597. Ward, R.G., Smith, G.S., Wallace, J.D., Urquhart, N.S., Shenk, J.S., 1982. Estimates of intake and quality of grazed range forage by near infrared reflectance spectroscopy. J. Anim. Sci. 54 (2), 399–402. Wehausen, J.D., 1995. Faecal measures of diet quality in wild domestic ruminants. J. Wildl. Manage. 59, 816–823.