Monitoring nutrition in small ruminants with the aid of near infrared reflectance spectroscopy (NIRS) technology: A review

Small Ruminant Research 61 (2006) 1–11

Review article

Monitoring nutrition in small ruminants with the aid of near infrared reflectance spectroscopy (NIRS) technology: A review S. Landau∗ , T. Glasser, L. Dvash Institute of Field and Garden Crops, Department of Natural Resources, Agricultural Research Organization, The Volcani Center, P.O. Box 6, Bet Dagan 50250, Israel Received 18 August 2004; received in revised form 21 December 2004; accepted 21 December 2004 Available online 10 February 2005

Abstract This review aims to evaluate the contribution of near infrared reflectance spectroscopy (NIRS) to monitor nutrition in small ruminants, with particular emphasis on the use of feed spectra and fecal spectra. NIRS provides satisfactory accuracy in the analysis of the chemical constituents of feeds for small ruminants, e.g., crude protein and cell wall composition, and is sometimes better than in vitro procedures for predicting in vivo digestibility and the available energy in feeds. In addition, in vitro digestibility can be accurately estimated by NIRS. The effective rumen degradability of protein could potentially be accurately predicted by NIRS, which would eliminate the need for rumen-fistulated animals. Good accuracy in the prediction of tannins has been reported for narrow, single-species applications, as well as for broad arrays of browse species. The identification of NIR segments corresponding to undigested entities has potential to help in providing spectral markers of digestibility. Fecal output can easily be evaluated, using the NIRS-aided analysis of polyethylene glycol (PEG) administered as external indigestible marker. Analysis of NIR spectra of the feces enables the accurate prediction of the chemical characteristics of the feed (dry matter digestibility and crude protein, cell wall attributes, PEG-binding tannins) in stall-fed and grazing animals, and to some extent, of the botanical composition of diets at pasture. Thus, fecal NIRS methodology holds the potential to provide nutritional diagnoses for farmers raising small ruminant. © 2005 Elsevier B.V. All rights reserved. Keywords: Goat; Sheep; Deer; Feed spectra; Fecal spectra

1. Introduction Since the introduction of NIRS for measuring moisture in grains, in the 1960s, the number of NIRS-aided ∗

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analytical applications has greatly expanded. The technology relies on the differential absorption of light in certain segments of the spectral range between 1100 and 2500 nm, by various chemical bonds. The theory of near infrared spectroscopy (NIRS) has been thoroughly explained by Hrushka (1987) and the mathematical basis of NIRS calibrations has been described in detail by

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Martens and Naes (1987). Briefly: NIRS calibrations are based on the statistical analysis, termed “chemometrics”, of the relationship between mathematically transformed spectra that are plots of log10 reflectance reciprocal—log (1/R)—and the frequency of chemical bonds in an organic matrix, termed “reference values”, quantified by classical laboratory “wet chemistry” procedures. The widespread use of monochromators, allowing for continuous scanning over the whole NIR range, and the availability of user-friendly PC software applications of chemometrics, have greatly contributed to the expansion of NIRS technology. Moreover, NIRS has gained legitimacy in recent years, because it is nondestructive (a single wheat kernel analyzed by NIRS can be planted later and can germinate) and it does not involve the use of chemicals, once the calibration process has been completed. The recent reviews by Foley et al. (1998) and by Deaville and Flinn (2000) are recommended reading for newcomers to NIRS. In addition, a more recent document, posted on the Web in July 2003, intended to encourage the use of NIRS in deer nutrition (Dryden, 2003), presents simplified but accurate information on the main issues involved in the acquisition and analysis of NIR spectra. In particular, the question of sample variability testing, the mathematical transformations of spectra, and the methods of calibration and validation, are discussed. In most studies, the quality of NIRS calibrations is evaluated in terms of linearity and accuracy. Linearity is indicated by the coefficient of determination (R2 ), i.e., the proportion of variability in the reference data accounted for by the regression equation. The standard error of calibration (SEC) represents the variability in the difference between predicted values and reference values when the equation was developed from the calibration data set. After a calibration has been set up that features high R2 and SEC, a validation method is needed, in which predictive accuracy is evaluated. Even though accuracy is sometimes assessed from the slope of the validation equation, we have adopted other estimators of accuracy in the present review, i.e., the standard errors of prediction (SEP) and of crossvalidation (SECV). SEP represents the variability in the difference between predicted and reference values when the equation is applied to an external (i.e., not used in any step of the calibration) validation data set. The SECV represents the variability in the difference

between predicted and reference values when the equation is applied sequentially to subsets of data from the calibration data set. The SECV procedure may give over-optimistic results, in particular if data are replicated, but is justified in situations with calibration samples that are randomly selected from a natural population (Naes et al., 2002). In addition, the SECV does not enable to evaluate bias, i.e., the mean difference between the predicted and actual values in a validation data set, and to correct predicted values accordingly. Even though the SEP is superior to the SECV when long-term robustness of NIR equations is a major concern, both are widely used as estimators of accuracy. When considering accuracy figures in this review, one has to take into account the range of values for which the NIRS calibration is set: a SECV of 3.0% is more acceptable for Neutral Detergent Fiber (NDF, ranging from 30 to 80%) in forage than for PEG-binding tannin (ranging from 0 to 25%) in browse. The aim of the present paper is to review the potential uses of NIRS in the determination of the chemical and botanical quality of small ruminants’ diets. There have been two main NIRS-aided approaches to this question. The first, most established approach relies on direct analysis of feed spectra, and is particularly useful when a representative sample of the diet can be easily obtained, or when group evaluations of intake or digestibility are sufficient. The second approach relies on the analysis of fecal spectra and is mostly relevant when representative diet samples cannot be easily obtained, for example, in grazing animals, or when knowledge of the intake of an individual animal in a group is required, as in efficiency studies.

2. Assessing nutritional value for small ruminants by using NIR spectra of the feed 2.1. Chemical composition of diets In the first report on forage quality testing by NIRS (Norris et al., 1976), the SEP values for the concentrations in dry matter (DM) of crude protein (CP), Neutral Detergent Fiber (NDF), Acid Detergent Fiber (ADF), and Acid Detergent Lignin (ADL) were 0.95, 3.1, 2.5, and 2.1%, respectively. Even though accuracy has not been re-evaluated later by using the same set of samples, it is probable that the improvement in

S. Landau et al. / Small Ruminant Research 61 (2006) 1–11 Table 1 Prediction of feed crude protein (% of DM) and Neutral Detergent Fiber (% of DM) by NIRS, effect of widening the calibration data set on linearity (R2 of validation) and accuracy (represented by SEP or SECV) Sample CP Lucerne forage onlya Four temperate legumesa Barley hayb Barley silageb Barley hay and silageb NDF Lucerne forage onlya Four temperate legumesa Barley hayb Barley silageb Barley hay and silageb a b

Reference mean (%)

SEP/SECV

Bias

R2

20.8

0.42

−0.3

0.98

18.0

1.0

−0.3

0.97

10.8 10.8 10.8

0.48 0.41 0.52

42.3

1.46

−1.0

0.95

41.9

2.23

1.0

0.98

46.4 46.4 46.4

2.46 1.54 2.34

0.96 0.97 0.95

0.73 0.92 0.92

Marten et al. (1984). Hsu et al. (2000).

NIR spectrometers and in chemometric methods led to increased accuracy, as outlined recently by Dryden (2003). Among NIRS-based predictions of forage quality, “narrow”, single-species calibrations are often, but not always, more accurate than “wide”, multi-species calibrations that are aimed to encompass a wider spectral variety (Table 1). In other words, a trade-off may exist between the robustness of a calibration, i.e., its ability to predict attributes in arrays of samples that exhibit wide variability, such as are typical of natural pastures, on one hand, and the accuracy of predictions, on the other hand. For instance, the SEP values of 0.4–0.5% for CP, NDF and ADF and of 1.6% for ADL which were obtained for the contents in clover fodder (Berardo, 1997) are lower than those of 0.57, 2.0, 1.9, and 1.9%, respectively, that were obtained for pasture species (Garcia-Cindad et al., 1993). Another example, mainly relevant to goats, concerns NIRS analyses of browse: SECV values for predictions of CP, NDF, ADF and lignin in tagasaste (Chamaecytisus proliferus), were 0.6, 1.6, 1.0, and 0.5% (Flinn et al., 1996), whereas Meuret et al. (1993) predicted the same

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attributes in a wide array of Mediterranean browse species with higher SECVs of 0.9, 2.1, 2.4, 1.5 and 2.1%, respectively. A NIRS calibration is considered ideal if it predicts chemical composition with an error of similar order to that achieved in the “wet chemistry”, i.e., the lab analytical procedures used to provide reference values. Such is the situation in some applications listed above, and NIRS is now considered standard technology for the measurement of CP and some cell wall parameters, such as ADF (Barton and Windham, 1988). 2.2. In vitro estimations of energy in feeds In vitro procedures are widely used to evaluate the energy content of forages for ruminants. Two questions need to be addressed: (i) does NIRS predict in vivo digestibility as accurately as in vitro procedures, and (ii) to what accuracy does NIRS predict in vitro digestibility. The accuracy of NIRS predictions of the organic matter digestibility (OMD) of grass silages was evaluated with 72 wether sheep by Park et al. (1997) in Ireland. The in vivo OMD of silages ranged between 53 and 80% and the SEP of NIRS-based determinations ranged between 2.4 and 2.8% of OMD. A review by Coleman et al. (1999) showed better prediction of in vivo digestibility of straw and grass silage with NIRS than with the pepsin–cellulase procedure. NIRS calibrations have also been superior to an in vitro pepsin–cellulase procedure in predicting the metabolizable energy content for sheep in whole-crop wheat (Adesogan et al., 1999), to a neutral detergent–cellulase procedure in predicting the in vivo digestibility of grass silage for sheep and cows (De la Roza et al., 2000), and to the Tilley and Terry (1963), pepsin–cellulase and neutral detergent–cellulase procedures in the prediction of in vivo digestibility of cereal straw (Givens et al., 1991). In contrast, De Boever et al. (1997) reported that NIRS predictions of the in vivo digestibility of corn silage in sheep, which averaged 75%, were less accurate than the Tilley and Terry (1963) or than a cellulase procedure; the respective SEP values were 1.5, 0.9, and 0.9% of digestibility. Values of SEP for in vitro DM digestibility in wide forage data sets vary between 3.5% for the NIRS prediction of Tilley and Terry (1963) in vitro DM digestibility (Norris et al., 1976) to 2% for a fungal cel-

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lulase procedure (Griggs et al., 1999). These figures, obtained with forages ranging from 34 to 87% in their DM in vitro digestibility are comparable to the lab error (2.8%) involved in predicting DM digestibility, using regression equations based on the chemical composition of feeds ranging from 65 to 91% in DM digestibility (Demarquilly et al., 1989). 2.3. Ruminal degradability and its kinetics NIRS has been shown to be potentially capable of predicting the ruminal degradability of DM from forages, but its predictive performance has not been outstanding. R2 values for quickly and slowly degradable fractions (which comprised 15–51 and 29–60%, respectively, of total DM) were 0.86 and 0.78, respectively; and the SECs were high, at 4.2 and 5.2%, respectively (Todorov et al., 1994). Atanassova et al. (2000) attempted to predict gas production, as a means to estimate of degradability of DM of hays and silages in sheep rumen liquor, and to characterize the kinetics of the process, by using NIRS. The estimates of total gas production were accurate for alfalfa, meadow hay and maize silage (SECV = 1.2 ml in a range of 22 to 43 ml per syringe), but not for alfalfa silage (SECV = 2.5 ml in a range of 13 to 36 ml per syringe). Apart from alfalfa hay, the prediction of kinetic parameters of degradation, particularly the time factor c, was poor to mediocre: R2 ranged from 0.33 for meadow hay to 0.80 for maize silage. Knowledge of the kinetics of ruminal degradability of dietary nitrogen, based on experiments with animals fitted with ruminal and duodenal cannulas has greatly contributed to ruminant diet formulation. NIRS accounted for 75% of the variance of protein outflowing from the rumen at 0.05 h−1 in sheep equipped with ruminal fistulae (Waters and Givens, 1992). Using rumen-fistulated cattle fed corn silage, De Boever et al. (2002) found that NIRS gave reasonably accurate predictions (R2 = 0.79) of the percentage of protein that escaped the rumen and of fermentable organic matter and 0.72, respectively) but not of the true protein digestibility in the intestine. Atanassova et al. (1996) found it feasible in sheep to distinguish between bacterial and feed protein in the duodenal content by means of NIRS-aided analysis of purine N in a range from 5.6 to 12.9 mg/g DM (R2 = 0.96,

SECV = 0.63 mg/g DM). In another study with sheep, Antoniewicz et al. (1995) found that NIRS-based measurements accounted for 87–99% of the variability of ruminal in sacco degradability values of lucerne forage and predicted effective CP degradability—in a range 70–96% – with a SECV of 2.6%. Hoffman et al. (1999) found that in situ protein fractions, including rapidly degraded protein, slowly degraded protein, and undegradable protein were predicted by NIRS with high linearity (R2 > 0.92), but the prediction of degradation rate was less satisfactory (R2 = 0.87). It is possible that difficulties arise in the application of NIRS-based procedures, which rely on multiple linear regressions for calibration, to phenomena that are not essentially linear, such as degradation kinetics. In many applications, SEP or SECV are very close to the error associated with the analytical procedures used to provide the reference values. NIRS seems to have been developed to the level of accuracy needed to evaluate the availability of energy in feeds for small ruminants, but the accuracy of NIRS-based predictions of the kinetics of in vitro and in situ digestion still needs to be improved. It is probable that NIRSaided estimates of the ruminal degradability of feed will increasingly replace those obtained in fistulated ruminants, a great step forward in improving animal welfare. 2.4. Anti-nutritional compounds Of special interest for goat nutrition is the quantification of the chemical composition of browse species, especially their contents of tannins and other secondary compounds. The total phenolics in tagasaste, ranging from 1.4 to 25.4% of DM was analyzed by NIRS with 1.2% accuracy and high linearity (Flinn et al., 1996). Satisfactory predictions of condensed tannins, with R2 ranging from 0.84 to 0.91 were obtained by means of narrow, single-species calibrations by Smith and Kelman (1997) for Lotus uliginosus Schkuhr, by Wheeler et al. (1996) for Leucaena leucocephala, and by Windham et al. (1988) for Sericea lespedeza (Table 2). Goodchild et al. (1998) used NIRS to assay total phenolics, total tannins, and condensed tannins in hays and straws of Vicia and Lathyrus spp.; the contents, in DM, were 0.45–3.4, 0.13–2.3, and 0.05–3.0%, respectively, and the SECVs were 0.17,

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Table 2 NIRS-aided analysis of tannin in roughage (% of DM) Species lespedezaa

Sericea Lotus uliginosusc

Vicia and Lathyrus spp.d Mediterranean browse (15 spp.)e a b c d e

Analyte

Range (mean)

Reference method

R2

SEP/SECV

Bias (slope)

Tannins Condensed tannins Condensed tannins PEG-binding tannins

2.6–12.5 (6.2) 0.5–18.0 (6.1)

Vanillin–HCl Butanol–HCl

0.90 0.82

1.5 1.3

−0.2 (n.d.)b 0.05 (0.94)

0.5–3.1

Butanol–HCl

0.93

0.23

1.7–20.7

Radioactive–PEG binding

0.96

1.7

0.14 (0.96)

Windham et al. (1988). n.d. - not determined. Smith and Kelman (1997). Goodchild et al. (1998). Landau et al. (2004a).

0.18 and 0.23%, respectively. Even though NIRS methods failed to predict the biological activity of tannins on microbial fermentation estimated in vitro (Menke et al., 1979), a calibration, in a wide array of Mediterranean browse, for PEG-binding tannin, which serves as an estimator of the biological activity of tannins, was published recently (Landau et al., 2004a); it exhibited high linearity (R2 = 0.96) and acceptable accuracy (SEP = 1.7%). Another important contribution of NIRS to browsing goats’ nutrition is the determination of alkaloids that limit browse utilization (Clark et al., 1987). However, NIRS calibrations of other compounds, such as terpenes and nitrates, which deter animals from grazing or are poisonous, have still to be carried out. 2.5. Voluntary intake The error associated with prediction of forage intake from direct NIR spectra of feeds was initially found to be 7.8 g kg−1 BW0.75 (Norris et al., 1976). In more recent studies in sheep, SEP of the voluntary intake of DM from grass silage that ranged from 45 to 113 g kg−1 BW0.75 was 4.8 g kg−1 BW0.75 (Park et al., 1997), and that of a variety of grass and legume silages was 6 g kg−1 BW0.75 (Paul et al., 2001). SEP values compiled by Coleman et al. (1999) from four studies in sheep fed C3 and C4 grasses ranged between 6.3 and 10.6 g kg−1 BW0.75 . Overall, this level of accuracy is comparable with standard errors in the predictions of intake in sheep based on the in vivo digestibil-

ity and the nitrogen content of forages given as sole food (9.0 g kg−1 BW0.75 , as summarized by Ketelaars and Tolkamp, 1991; 5.4 to 11.8 g kg−1 BW0.75 , as reviewed by Poppi, 1996). Offer et al. (1998) concluded that forage intake by sheep was more accurately predicted by NIRS than by any combination of “classical” chemical predictors (DM, CP, OMD, NDF, ADF, NH3 , pH or ether extract). 2.6. Botanical composition of the diet with esophageal extrusa Because of the selective feeding behavior of grazing small ruminants, the determination of the botanical composition of their diet is an unavoidable step towards gaining understanding of their diet quality, particularly in heterogeneous environments. Esophageally fistulated animals have frequently been used for that purpose. When the micro-histology of esophageal extrusa samples from sheep was used as a reference source, NIRS was only partly successful in evaluating their botanical composition: the percentages of grasses and forbs (73 and 27% on average) were predicted with an error of 8.6%, and accurate prediction of individual species was possible only for big bluestem that formed 50% of the diet (Volesky and Coleman, 1996). Individual species differ in their detectability by NIRS in esophageal extrusa mixtures: proportions of alfalfa in mixtures of hays were more accurately predicted than proportions of bluestem and Bermuda grass in grazed materials, even though the actual proportions were similar (Coleman et al., 1985).

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3. Assessing nutritional value for small ruminants by using fecal NIR spectra 3.1. Evaluation of fecal output, digestibility, and DM intake by using digestibility markers The assessment of individual intake of group-fed animals (or grazing animals) requires a two-step process: the determination of fecal output, using an external indigestible marker, and the determination of digestibility of a representative sample of the diet. The n-alkanes are the most widespread organic external markers used to determine fecal output: plant n-alkanes can potentially be quantified by NIRS and serve as markers of fecal output and digestibility (Flinn et al., 1992), but the detection level of alkanes and the sensitiveness of their analysis by NIRS are inferior to those of gas chromatography, so that using n-alkane as marker of fecal output for analysis by NIRS would be a very expensive procedure because high doses would be necessary, and purified alkanes are expensive. As alternative, the analysis of polyethylene glycol (PEG) in goat feces by NIRS has recently been shown to be accurate (SE 0.3% in a range of 0–10%), and PEG dosing offers an inexpensive alternative for the measurement of fecal output (Landau et al., 2002a). In goats that consume tannin-rich diets (Landau et al., 2002b) and that receive PEG to alleviate the negative effects of dietary tannins, the calibration equations of PEG in feces must rely on NIR segments in which tannins and PEG do not interfere, i.e., at wavelengths greater than 2280 nm. It is worth noting that, even when a representative sample of the diet is not available, a good evaluation of fecal output has significant biological value: Nunez-Hernandez et al. (1992) found that fecal output explained 86% of the variation in the intake of organic matter, on liveweight basis, of goats fed a wide variety of feeds. Once fecal output has been estimated, a representative sample, if one is available, must be used to evaluate digestibility, in order to assess intake. An indigestible internal marker, such as lignin, may be analyzed in food and feces, using NIRS, as done in cattle by Purnomoadi et al. (1996). A more elegant alternative, based on the existence of spectral “peaks of indigestibility” is theoretically possible but has not yet been implemented. Relationships between log (1/R) and various estimates of digestibility and ruminal degradability (Table 3) enable the identification of two NIR regions of low di-

gestibility: 1670–1690 nm and 2240–2290 nm. There may be spectral zones that characterize very low or even zero digestibility, and the absorbance values at such peaks could probably be used as “spectral markers”. In spite of its potential, no implementation of this method has been reported, probably because mathematical treatment of groups of spectra is still cumbersome. The recent publication by Reeves and Delwiche (2004) of SAS procedures for NIRS calibrations makes mathematical treatment of spectral data easier for users, and may be a significant step forward in the search for spectral markers of digestibility. 3.2. Using fecal NIR spectra to determine diet chemical composition and intake One of the most exciting recent applications of NIRS to small ruminant nutrition seems to be nutritional monitoring of grazing animals through the analysis of fecal spectra. The biological significance of fecal chemistry was reported before fecal NIRS was practiced (Nunez-Hernandez et al., 1992), but the pioneering research carried out in Texas A&M University has greatly increased our awareness of the value of feces for nutritional profiling. Lyons and Stuth (1992) used esophageally fistulated beef cows to calibrate dietary CP, and OM digestibility (%) against the log (1/R) of 344 fecal spectra, scanned at discrete wavelengths, and found that the percentages of variation of dietary CP and OM digestibility that were accounted for by the calibrations was 0.93 and 0.80, with errors of 0.9 and 1.6%. A similar technology was used for goats by Leite and Stuth (1995), who used 163 and 86 fecal spectra for the calibration of dietary CP and in vitro OM digestibility, respectively. The percentages of variation in dietary CP and OM digestibility that were accounted for by the calibrations were 0.94 and 0.92, with errors of 1.3 and 2.1%, respectively (Table 4); validations, based on sets of external data were satisfactory, with R2 ranging from 0.80 to 0.88, and errors 0.80 and 1.9% for CP and OM digestibility. Good predictions were also obtained for white-tailed deer (Odocoileus virginianus) fed 76 diets representative of their natural habitat (Showers, 1997): values of R2 and SEP for CP and digestible organic matter were 0.94 and 0.87%, and 0.85 and 2.9%, respectively. Showers (1997) also used fecal NIRS to predict the dietary content of phosphorus in deer diets (Table 4). This approach eventually led to the creation

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Table 3 NIR regions in feed associated with low ruminal degradation or total apparent digestion Species

Reference

Fresh grass, grass and maize silage

Low ruminal degradability

Wavelengths (nm)

1620–1690 2170–2190 Species as above, extracted lignin Low ruminal degradability 1670, 2266 Legume and grass hays Low apparent digestion 1714, 2256, 2306, 2346, 2382 Grass, maize, lucerne, whole wheat silages Low extent of cell wall degradation 1676, 2228 As above Lignin 1672, 2246 As above NDF 1680, 2214

in Texas of a nutrition diagnosis service for livestock producers (http://cnrit.tamu.edu/ganlab); the service is economically beneficial to beef cattle, goat and deer farmers (Eilers, 1999). A similar application for cattle has been developed in Australia (Coates, 1999); the data set included more than 400 spectra of feces and extrusa from grazing, and above 200 spectra of feces from confined animals. Equations were calibrated for dietary CP (%) and pepsin–cellulase in vitro DMD (%). Dietary CP (%) was accurately predicted for all animals, but the prediction of in vitro DMD for grazing animals was inferior to that for penned animals (R2 = 0.80 and R2 = 0.94, respectively). Flinn et al. (1992) pioneered the use of fecal NIRS to predict voluntary DM intake in sheep; their data base included 80 fecal samples and n-alkanebased estimates of intake as reference values. R2 and SEP were 0.80 and 140 g d−1 for sheep consuming 430–1460 g d−1 of herbage. More recently, in the WestIndies, fecal NIRS provided intake estimates that were

Authors Deaville and Givens (1998) Deaville and Givens (1998) Coleman and Murray (1993) Wilman et al. (2000) Wilman et al. (2000) Wilman et al. (2000)

very similar to the actual values of intake by grass-fed confined sheep, and were more accurate than estimates obtained by the n-alkane method (Decruyenaere et al., 2003). Coates (1999) calibrated fecal NIRS equations to predict feed intake for cattle in absolute (g kg−1 BW) terms, and not only in terms of dietary composition in percentages. Boval et al. (2003) recently used the same approach with confined Creole cattle. In both studies, R2 values were relatively low (0.73 and 0.51, respectively) but the accuracy of DM intake was acceptable (2.7 g kg−1 BW and 5.3 g kg−1 BW0.75 , respectively). More recently, fecal NIRS was used to predict the chemical composition of goats’ diets that consisted of hay and concentrate only (60 pairs of fecal spectra and dietary composition) or associated with three species of Mediterranean browse, namely, Pistacia lentiscus L., Phyllirea latifolia L. and Pinus brutia Ten. (143 pairs of fecal spectra and dietary reference values; Glasser, 2004; Landau et al., 2004b). R2 values for predictions of the percentage of CP (%), Tilley and Terry (1963) in

Table 4 The prediction of some dietary attributes by fecal NIRS in small ruminants R2

SECV/SEP

Authors

4.3–25.1 7.7–16.9

0.94 0.98

1.28 0.53

Leite and Stuth (1995) Landau et al. (2004b)

40.9-71.8 41.3–80.0 28.5–50.1 0.29–15.6

0.92 0.98 0.94 0.96

2.10 1.65 1.53 1.07

Leite and Stuth (1995) Landau et al. (2004b) Landau et al. (2004b) Landau et al. (2004b)

0.94 0.85 0.91

0.87 2.89 0.02

Showers (1997) Showers (1997) Showers (1997)

Range of dataset Goats CP (% of DM) In vitro OMD (% of DM) In vitro DMD (% of DM) NDF (% of DM) PEG-binding tannin (% of DM) Deer CP (% of DM) In vitro OMD (% of DM) Phosphorus (% of DM)

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vitro DMD (%), NDF (%), ADF (%), and PEG-binding tannins ranged between 0.85 and 0.99 (Table 4). As found previously by Coates (1999) in cattle, fecal NIRS spectra were less useful in predicting intake (g d−1 ) of these constituents than in predicting their percentage composition. 3.3. Botanical composition of diets Coates (1999) successfully used fecal NIRS to evaluate the proportion of C3 /C4 grasses, as determined from carbon isotope analysis in the diet. Within the framework of a project aimed to limit the expansion of leafy surge (Euphorbia esula) in Texas, the predicted percentage of the plant in the diet of grazing small ruminants, which ranged from 15 to 87%, was found to be estimated more accurately by fecal NIRS than by the classical micro-histological method: the SEP was approximately 5% (Walker et al., 1998). Using the same approach, Walker et al. (2002) determined the percentages of Artemisia tridentata Nutt., which ranged from 0 to 30%, in sheep diets. The accuracy was high (R2 = 0.96; SEP = 1.6%) when the samples used for validation belonged to the set used in calibration, but lower when they were from another population. Friedman (2002) applied principal component analysis to fecal NIRS spectra to determine the location of sheep grazing in safflower, barley or spatially mixed safflower-barley stands: all fecal samples from pure stands, and 95% of those from the mixed stand were correctly classified. Agreil and Meuret (2004) followed a similar approach in feeding behavior studies and used the spectral distance between fecal samples to verify that sheep under observation behaved naturally and selected diets that did not differ from those selected by unobserved counterparts. Elucidation of the botanical composition of diets that comprised three browse species, hay and concentrate was shown to be feasible for confined goats (Glasser, 2004). Calibrations for the dietary percentages of hay, concentrate, total browse, P. lentiscus, P. latifolia, P. brutia, showed R2 values ranging between 0.94 and 0.99; respective values for SECV were 5.5, 4.5, 6.1, 7.1, 7.0, and 6.5% of DM. For daily intake of these constituents (g DM kg−1 BW0.75 ), R2 values ranged between 0.84 and 0.96 with SECVs of 5.1, 2.9, 10.2, 6.3, 10.7, and 8.7 g DM kg−1 BW0.75 , respectively.

The main issue that makes the usefulness of fecal NIRS in predicting botanical composition questionable is whether the spectral variety concealed in the feces used for calibrations is relevant to application under field conditions. The high specificity of NIR spectra complicates the application of fecal NIRS to the huge and ever-changing variety of ranging goats’ diets. To date, reference values have been obtained from confined animals fed the main (target), but not all species available on the range (Walker et al., 2002; Landau et al., 2004b). High values for R2 and SECV may be obtained during the calibration process, but some of those calibrations are not enough robust to predict the intake of targeted species on rangelands (Glasser and Landau, in preparation). According to Coleman et al. (1995), NIRS equations cannot be extrapolated beyond the conditions represented in calibration samples, but nevertheless, attempts have been made to overcome this limitation by using statistical procedures. First, based on the assumptions of Naes et al. (2002), it is clear that validation with an external data set is a pre-requisite for justifiable use of fecal NIRS in predicting the botanical composition of diets. Second, the selection of fecal samples for calibration must be carefully carried out. In particular, Taylor et al. (2003) showed that by including in the calibration sample set “zero fecals”, i.e., samples from goats that had not consumed the species targeted for prediction, the slope of prediction was closer to 1, linearity was increased and SEP was decreased, i.e., the robustness of fecal NIRS for botanical predictions was strengthened. This was in agreement with Glasser (2004), who showed that an optimum for accuracy in the prediction of goat dietary attributes was reached when 20% addition of “zero samples” was made to the calibration data. The use of the H statistic, an estimate of spectral distance between samples, to select better the samples used in calibrations may improve the usefulness of fecal NIRS for dietary botanical prediction and is under study (Walker et al., 2002).

4. Conclusions The use of NIRS in feed analysis has gained recognition because its level of accuracy has reached that required by the feed industry. The definition of the accuracy of measurement of the quality of grazing animals’ diets or that of the intake of individuals among

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group-fed animals is more hazardous, because NIRS is calibrated against values that are themselves assessed, and not directly measured. The selective behavior of small ruminants, often stocked in heterogeneous environments, makes reference values undependable for calibration. The use of NIRS in the prediction of dietary quality for small ruminants is therefore developing slowly, and the bottleneck in the development process is the establishment of reliable data sets, with adequate variability, for calibration. It is hoped that scientists will overcome these drawbacks by co-operative NIRS networking. Another conclusion of this review is that a NIR spectrometer is probably the most indispensable and versatile instrument in a laboratory involved in research into small ruminant nutrition. Acknowledgements Contribution from the Agricultural Research Organization, the Volcani Center, Institute of Field and Garden Crops, Bet Dagan, Israel, No. 141/2002.

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