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Prediction of the in vitro gas production and chemical composition of kikuyu grass by near-infrared reflectance spectroscopy
ANIMAL FEED XIENCE AND TECHNOLOGY
ELSEVIER
Animal Feed Science Technology 60 (1996) 5 l-67
Prediction of the in vitro gas production and chemical composition of kikuyu grass by near-infrared reflectance spectroscopy M. Herrero a7*, I. Murray b, R.H. Fawcett a, J.B. Dent a ’ Institute of Ecology and Resource Management, The University of Edinburgh, West Mains Road, Edinburgh EH9 3JG, UK b Animal and Feed Technology Department, Scottish Agricultural College, 581 King Street, Aberdeen AB9 1 UD, UK
Received 7 February 1995; accepted 16 October 1995
Abstract The objective of this study was to predict the in vitro gas production and the estimated metabolisable energy (ME), crude protein (CP) and neutral detergent tibre (NDF) concentrations of kikuyu grass (Pennisetum clandestinum) by near infrared reflectance spectroscopy (NIRS). A total of 288 samples collected in the Poas Region, Costa Rica were scanned (Population 1). The in vitro gas production and ME calibrations were done on a subset of samples in which gas production measurements (3, 6, 12, 24, 36, 48, 72 and 96 h incubations) had been previously carried out (Population 2) while 41 samples for the CP and NDF calibrations (Population 3) were selected on the basis of their H distances from Population 1. The parameters a, b, c and lag for the exponential equation p = a + b (1 - e- ‘(‘-lag)) (McDonald, 19811, where p is the volume of gas produced at time t, were fitted to the gas production data and an attempt was also made to predict them. The volumes of gas produced between 6 and 48 h were successfully calibrated and cross-validated. Coefficients of determination for the cross-validation (1 - RV) were 0.65, 0.74, 0.78, 0.70 and 0.60 for the volumes of gas produced at 6, 12, 24, 36 and 48 h respectively. The volumes of gas produced at 72 h could only be calibrated CR2 = 0.71) but not cross-validated, while the calibration results for the gas production at 3 and 96 h and the parameters for the exponential equation were poor. An analysis of the wavelength segments associated with the in vitro gas production indicated that the primary wavelength was always located between the 1664 and the 1696 nm spectral region regardless of incubation time. The estimated ME, CP and NDF concentrations were accurately calibrated and cross-validated.
* Corresponding author. Tel.: 131 667 1041; Fax.: 131 667 2601; E-mail: [email protected]. 0377-8401/96/$15.00
0 1996 Elsevier Science B.V. All rights reserved
SSDI 0377.8401(95)00924-8
M. Herrero et al./Animal Feed Science Technology 60 (1996) 51-67
52
Standard errors of cross-validation of 0.23 MJ kg-’ DM, 11.4 g kg-’ DM and 15.9 g kg-’ DM were obtained for the ME, CP and NDF concentrations respectively. Scatter correction for particle size improved the performance of most of the equations across all constituents. The effects of different calibration methods, maths treatments and the factors affecting the results are discussed. Keywords:
Techniques; Near-infrared analysis; Gas production; Tropical feedstuffs; Pennisetum clandestinum
1. Introduction Forages are the primary resource for the nutrition of ruminant livestock throughout the world. Therefore, the estimation of their nutritional value has been of primary importance for the prediction of animal performance and the subsequent development of the livestock industry in the past decades. Advisory and research services have usually used wet chemical methods to obtain relevant nutritional information about forages (i.e. Tilley and Terry, 1963; Goering and van Soest, 1970). However, some of these techniques have proved to be time-consuming, often expensive, and/or in some cases, inaccurate (Aastveit and Marum, 1991; Murray, 1993). More recently, the in vitro gas production technique (Menke et al., 1979; Menke and Steingass, 1988; Theodorou et al., 1994) has been used as a method for determining the nutritive value of feedstuffs. It has provided better predictions of the in vivo digestibility and the energetic value of forages than other in vitro techniques (Menke and Steingass, 1988; Khazaal et al., 1993) and it can be used to represent the fermentation dynamics of the incubated samples (BYtimmel and Idrskov, 1993; Moss, 1994). Hence, its importance also lies in its relationship with dry matter degradation characteristics, forage intake and animal performance (Bliimmel and 0rskov, 1993; Kibon and Plrskov, 1993; Khazaal et al., 1993). The technique is gaining popularity because it is a low cost, highly reproducible and easy method of obtaining a dynamic description of the nutritive value of a feedstuff while at the same time allowing for more samples to be analysed. Since the work of Norris et al. (19761, near-infrared reflectance spectroscopy (NIRS) has been applied to estimate the nutritional constituents of forages. It has been found that the technique can provide cheaper, rapid, more precise, and in most cases, more accurate predictions of crude protein (CP), the fibre fractions and in vivo or in vitro digestibility than other available methods (see Murray, 1993; for a review). NIRS has also been applied successfully to the prediction of dry matter degradability (Russell et al., 1989; Givens et al., 1992a), metabolisable energy concentrations (ME) (Givens et al., 1992b) and pasture intake (Norris et al., 1976; Ward et al., 1982; Eckman et al., 1983; Flinn et al., 1992). As the in vitro gas production is a relatively new technique for the determination of forage quality, it is not surprising that its prediction by NIRS has not been reported. With suitable calibrations for NIRS, the time and cost of taking such measurements could be reduced without losing accuracy and access to the information required. Furthermore, more samples could be done and the need for a source of rumen liquor would be eliminated, thus promoting the use of non-invasive techniques in animal production.
M. Herrero et al./Animal Feed Science Technology 60 (1996) 51-67
53
Kikuyu grass (Pennisetum clandestinum) is a stoloniferous and rhizomatous tropical pasture species originally from the Kenyan highlands. It is an important pasture species because it provides a large proportion of the summer and autumn grazing in subtropical regions of Australia, New Zealand and South Africa and is one of the predominant pasture species in tropical highland areas of Africa, Latin America and parts of Asia. The objective of this study was to predict the in vitro gas production, calculated ME, CP and neutral detergent fibre (NDF) concentrations of kikuyu grass by NIRS.
2. Materials and methods
2.1. Samples and wet chemical analyses Two hundred and eighty eight samples of kikuyu grass originating from four commercial dairy farms of the Poas highlands, Costa Rica, were used (Population 1). They consisted of a mixed set of regrowths ranging from 15 to 38 days of age and post-grazing samples obtained from rotationally-grazed paddocks. They were collected over four experimental periods comprising the wet (periods 1 and 2, 150 samples) and dry seasons (period 3, 72 samples) and the transition period between the wet and dry seasons (period 4, 66 samples). All the samples were dried at 60°C for at least 36 h and were then hammer milled though a 1 mm sieve before analysis. For the in vitro gas production and ME calibrations, a subset of 48 samples from Population 1 in which gas production measurements had been previously taken (Herrero et al., 19951, was used (Population 2). It consisted of three samples from each of the four farms in each of the four periods which were selected at random from the regrowths to allow for seasonal and farm variations. These were incubated for in vitro gas production estimations as described by Menke and Steingass (1988). The rumen fluid used was obtained from three sheep fed twice a day a ration of 600 g hay : grass pellets (2:l). Gas volume measurements (ml> were taken after 3, 6, 12, 24, 36, 48, 72 and 96 hours of incubation and the results were fitted to the exponential model of McDonald (1981), p = a + b(1 - e-c(r-‘ag) ), where p represents the in vitro gas production (ml) at time t, (a + b) was the potential gas production, and c the fractional rate of gas production per hour. The model allowed for the estimation of a lag phase (Zag) before rapid gas production began. ME was estimated as described by Menke and Steingass (1988) for dry forages, and more recently by Krishnamoorthy et al. (1995). The equation used was: ME = 2.43 + 0.1206GP
+ 0.0069CP
+ 0.0187EE
where: ME = metabolisable energy (MJ kg-’ DM), GP = gas production at 24 h (ml 200 mg- ’ DM), CP = crude protein (g kg-’ DM), EE = ether extract (g kg-’ DM), as estimated by the Weende system. For the CP and NDF calibrations (Population 3), a different approach was taken. The required number of samples for calibration was chosen using the SELECT algorithm available in the IS1 software (Infrasoft International, Port Matilda, PA, USA) and developed by Shenk and Westerhaus (1991b). The SELECT algorithm works by
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M. Herrero et al./ AnimalFeed
Science
Technology60 (I 996) 51-67
Table 1 Characteristics of the samples used for the calibration of in vitro gas production, calculated metabolisable energy (Population 2, n = 48) and crude protein and neutral detergent fibre (Population 3, n = 41) Constituent
Mean
Std. dev.
Range
Gas 3 h (ml) Gas 6 h (ml) Gas 12 h (ml) Gas 24 h (ml) Gas 36 h (ml) Gas 48 h (ml) Gas 72 h (ml) Gas 96 h (ml) ME (MJ kg- ’ DM) CP (g kg-’ DM) NDF (g kg - ’ DM) a+b(ml) c c/h) lag (h)
3.95 6.94 16.30 28.38 34.05 38.66 42.71 44.57 7.11 181.8 606.7 45.47 0.042 1.30
0.860 1.531 2.793 3.139 3.340 3.483 3.552 4.206 0.522 38.05 37.67 3.82 0.0069 0.502
1.70-5.75 2.90-10.45 9.90-22.45 20.70-34.95 26.20-39.70 29.20-45.95 31.70-51.70 28.26-52.87 6.20-8.11 82.3-250.9 543.3-692.2 33.91-54.90 0.029-0.067 0.20-2.90
ME = metabolisable energy; CP = crude protein; NDF = neutral detergent fibre; a + b = asymptotic gas production; c = rate of gas production; lag = lag phase.
choosing a subset of samples with representative spectral characteristics of the whole population on the basis of their standardised H (Mahalanobis) distances. A minimum standardised H distance of 1.0 between neighbour samples was chosen, resulting in the algorithm selecting 41 representative samples from Population 1. These were analysed for Kjeldahl nitrogen (N) and CP was obtain as N X 6.25. NDF was determined by the method described by Goering and van Soest (1970). These data were used for the respective calibrations. The main characteristics of Populations 2 and 3 can be observed in Table 1. 2.2. NIRS scanning and calibration procedures All samples were scanned using an NIRSystems 6500 spectrometer (Perstorp Analytical, Bristol, UK) connected to a Viglen 90 MHz Pentium personal computer. Although this instrument also scanned the visible spectra, only the NIR region from 1100 to 2500 nm was used for calibration purposes. Spectra were collected at 2 nm intervals providing 700 data points for each sample. All data were recorded as absorbance values (log (l/R)), where R = reflectance, and they were analysed using the IS1 software previously mentioned. Before calibration, the three datasets (Populations 1 to 3) were ranked according to their H distances, as measured from the average spectrum of the file, using the CENTER algorithm (Shenk and Westerhaus, 1991a). This was done to check for samples with atypical spectral characteristics as judged by H distances greater than 3.0. Calibrations were performed for the volumes of gas produced at different time intervals, their fitted parameters from the McDonald (198 1) model, and for the estimated ME, CP and NDF concentrations. For each season, the parameters for the McDonald
M. Herrero et al./Animal Feed Science Technology 60 (1996) 51-67
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Table 2 Mathematicaltreatments used for the calibration equations W,l,l 1,50,10,1 2,12,2,2 3,20,10,1
1,4,4,1 2.4.u 2,20,10,1 3,30,10,1
1,10,5,1 2,4,2,1 2,30,10,1 3,30,20,1
1,10,10,1 2,6,2,1 2,40,10,1 4,4,2,2
1,20,10,1 2,8,2,1 2,30,20,1 4,10,10,1
1,30,10,1 2A8.2 3,10,1,1
1,40,10,1 2,10,1,1 3,10,10,1
(1981) equation were also calculated from the predicted gas productions and compared with those fitted from the actual values. Log l/R data were given different mathematical treatments consisting of first, second or third derivations with different segment gaps and smoothings for the calibration of each constituent. Table 2 presents the mathematical treatments used for this study, where the first number is the order of the derivative, the second one is the segment gap in data points over which the derivative was calculated and the third and fourth are the number of data points which were used for the running average smooth. Modified partial least squares (MPLS) (Shenk and Westerhaus, 1991b) or stepwise multiple linear regression (SMLR) were used as the calibration methods. They were carried out with and without the standard normal variate and detrending procedure of Barnes et al. (1989) to allow for calibrations with and without particle size scatter correction. Four cross-validation groups were used to validate the MPLS algorithms. All calibrations and cross-validations were done within the calibration sets only, as an independent set of samples for external validation was not available. As stated by Shenk and Westerhaus (1994), the internal cross-validation is a valid approach, it makes a more efficient use of samples and can replace an external validation. The SMLR equations were also used to determine the most important single wavelengths in the determination of gas production measurements as judged by their F statistic. All calibrations were allowed to include a maximum of four terms and two outlier elimination passes, although only one pass was sufficient in all the cases where it was required. Critical F, T and H values of 7.0, 2.5 and 3.0 were used respectively (Murray, 1993). Th e a d equacy of the calibration and cross-validation results for each equation were based on their standard errors (SEC and SECV, respectively) and their coefficients of determination (R2 and 1 - RV, respectively). As the purpose of this study was also to find suitable calibrations for the whole set of samples (Population 11, another measure for equation selection was the number of H outliers when the constituents were predicted in Population 1. A low number of H outliers would indicate the breadth of the equation for predictive purposes (Shenk and Westerhaus, 1994). According to Murray (personal observations), in routine forage evaluation by NIRS, the number of H outliers when predictions are carried out in a population should be less than 4%. Therefore, this was the threshold limit used in the present study. A further analysis of the spectral structure of the populations was carried out using the SYMMETRY routine (IS1 software) with three principal components (PCA). This was done to observe if seasonal influences existed on the spectral distribution of the samples.
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3. Results 3.1. In vitro gas production
The results for the calibration of in vitro gas production can be observed in Table 3. The SMLR algorithms calibrated the gas production data marginally better than the MPLS equations, as shown by their SEC and R2 values. However, with the MPLS equations, the volumes of gas produced at 6, 12, 24, 36, and 48 h were successfully calibrated and cross-validated. The volumes produced at 6 and 48 h had lower R2 and 1 - RV values than those produced at 12, 24 and 36 h, while the volume of gas produced at 72 h was poorly calibrated by the MPLS method. In vitro gas production at 3 and 96 h could not be calibrated satisfactorily by either of the two methods (MPLS or SMLR). Their R2 of calibration did not exceed 0.50 for any of the maths treatments with and without scatter correction. When the actual parameters rather than the gas volumes were calibrated, the results were also poor. The calibration statistics for the a, b, c and lag parameters had R2 values of less than 0.50, 0.40, 0.50 and 0.30 respectively, regardless of the maths treatments. The use of SMLR to determine the most important single wavelength segments associated with gas production demonstrated that, in the best equations, the primary wavelength was always between 1664 and 1696 nm regardless of the time of incubation, maths treatment or scatter correction (Table 4). However, when equations with longer
Table 3 Results of the best calibrations time intervals Incubation 6 6 6 12 12 12 24 24 24 36 36 36 48 48 48 72 72 72
time (h)
and cross-validations
for the static in vitro gas volumes produced
Method
Maths treatment
SC
SECV (ml)
SMLR MRLS MPLS SMLR MPLS MPLS SMLR MRLS MRLS SMLR SMLR MRLS MPLS SMLR SMLR SMLR SMLR SMLR
24~1 1,50,10,1 2,20,10,1
yes
0.94 0.99 1.49 1.60 1.55 1.58 1.92 2.19 _
2,6,2,l 1,50,10,1 1,40,10,1 2,10,1,1 1,50,10,1 1,40,10,1 1.49491 2,12,2,2 1,50,10,1 2,4,1,1 3,10,1,1 2,10,1,1 2,10,1,1 3,10,1,1 2,8,2,l
yes yes yes yes yes no yes yes yes no yes no no no yes no yes
-
I-RV 0.65 0.61 0.74 0.70 0.78 0.76 _ 0.70 0.60 _ _
at different
SEC(ml)
R2
0.68 0.80 0.81 1.19 1.36 1.40 1.34 1.38 1.42 1.45 1.57 1.63 1.56 1.60 1.72 1.91 2.00 2.03
0.80 0.73 0.72 0.82 0.77 0.75 0.81 0.80 0.79 0.79 0.78 0.77 0.79 0.79 0.76 0.71 0.69 0.68
SC = scatter correction; SECV = standard error of cross-validation; 1 - RV = R-square of cross-validation; SEC = standard error of calibration; R* = R-square of calibration. Results for the SMLR equations are for the calibration only.
57
M. Herrero et al./Animal Feed Science Technology 60 (1996) 51-67 Table 4 The three wavelengths different
incubation
explaining
the largest proportion
of the variation
in the amount of gas produced
at
times
Incubation time (h)
Maths (treatment)
6 12 24 36 48 72
2,4,1,1 ’ 2,6,2,1 a 2,10,1,1 1,4,4,1 a 3,10,1,1 2,10,1,1 d
Primary wavelength
Secondary wavelength
Tertiary wavelenth
(nm)
(nm)
(nm)
1672 1672 1668 1664 1680 1696
1448 2072 2336 2300 1208 1540
2192 2316 1624 1184 1640 2052
a Scatter corrected.
segment gaps (30-50 data points) were used, the primary wavelength extended up to the 1720 nm region. The first wavelength accounted for 4060% of the variation in the volumes of gas produced from 6 to 48 h and for only 25% of the variation for the volumes of gas at 72 h. To be able to detect changes and make valid comparisons of wavelength selection as fermentation progresses, the maths treatment has to be kept constant for all the time intervals. Therefore, the single best math treatment describing the gas volumes from 6 to 72 h in this study (2,10,1 ,l) was used. This math treatment was consistently present in the three best equations of each of the predicted gas volumes. The results (Table 5) suggest that although all wavelengths were in a similar region, the incubation times between 12 and 48 h had closely related primary wavelengths between 1664 and 1668 nm while there was a displacement towards higher wavelengths within the same region (1670- 1700 nm> at the early (6 h) and late (72 h) stages of fermentation. As cross-validation is not convenient on SMLR equations due to changes in the wavelengths selected for the different groups, a method to test if the predicted gas volumes within the calibration set were accurate was to calculate the a + 6, c and lag parameters in the McDonald (1981) equation from the predicted data and to compare them with the actual figures. These were calculated with the volumes of gas produced
Table 5 Effect of incubation time on wavelength treatment to predict static gas volumes Incubation time (h)
6 12 24 36 48 72
segment
selection
by SMLR equations
using a 2,10,1,1
Primary wavelength
Secondary wavelength
Tertiary wavelength
(nm)
(nm)
(nm)
1672 1664 1668 1668 1664 1696
2404 2336 2328 1216 1540 1540
1184 1620 1632 2372 1496 2052
maths
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Table 6 Comparison of observed vs. predicted parameters for in vitro gas production of kikuyu grass according to sampling period (All calibration samples, Population 2) Parameter
a
Period 1 mean (s.e.)
Period 2 mean k.e.>
Period 3 mean (s.e.1
Period 4 mean (s.e.)
a (ml)
Obs. Pred. Obs. Pred. Obs. Pred. Obs. Pred.
- 2.84 (0.248) - 2.62 (0.214) 47.99 (0.862) 47.73 (0.352) 0.047 (0.003) 0.046 (0.001) 1.32 (0.117) 1.24(0.116)
-2.18 (0.265) -2.51 (0.198) 48.87 (0.981) 48.93 (0.700) 0.042 (0.002) 0.041 (0.002) 1.40 (0.140) 1.30 (0.101)
- 2.02 (0.127) - 2.00 (0.205) 47.78 (1.600) 47.38 (1.250) 0.038 (0.001) 0.038 (0.001) 1.17 (0.079) 1.16 (0.142)
- 2.84 (0.241) - 3.02 (0.223) 46.93 (1.260) 47.14 (1.020) 0.042 (0.001) 0.042 (0.001) 1.50 (0.149) 1.61 (0.142)
b (ml)
c c/h)
lag 09
’ Obs. = observed, Pred. = predicted.
only up to 72 h excluding the volumes at 96 h, which showed poor calibration performance, while the parameters of the observed values were recalculated also excluding the volume of gas produced at 96 h to allow for a proper comparison. The NIR predicted values of the asymptote gas production fa + b), and the rate constant (c) were correlated with the observed parameters (r = 0.77 and 0.72 respectively) while a weaker correlation (r = 0.5 1) was observed for the lag phase (lug). The parameters a and b when compared separately showed correlations of 0.51 and 0.78, respectively. When results were expressed by period, the observed and predicted seasonal mean a, b, c and lug parameters did not differ significantly for each of the periods (Table 6). The equations presented in Table 3 were tested to assess the breadth of their predictions in Population 1. The number of H outliers was less than 4% for all of them, suggesting that they were broad enough for the prediction of gas volumes of the whole set. Although there is no evidence to suggest that there is a best maths treatment to calibrate a particular constituent (Murray, 1993; Shenk and Westerhaus, 19941, the 2,10,1,1 equation, as stated before, was consistently present in the three best SMLR equations for the calibrations of gas volumes from 6 to 72 h in this study. Surprisingly, the maths treatments that gave better results in the MPLS equations were all first derivatives calculated over rather long segment gaps (i.e. 1,50,10,1). The results on the gas volumes produced from 6 to 36 h, showed that as the gap was increased up to 50 data points in these first derivatives, the standard errors (SEC and SECV respectively) were reduced, increasing the accuracy of the calibration and cross-validation. A reduction in cross-validation performance was observed after the gap was extended further. Table 7 shows an example for the volume of gas produced after 24 h. 3.2. Metubolisuble
energy
The best algorithms for the prediction of ME were all MPLS equations. Scatter correction improved the SEC and SECV and the coefficients of determination CR2 and 1 - RV respectively) in six of the eight best prediction equations. All of the eight best
M. Herrero et al./Animal Table 7 Effect of increasing for the prediction
the segment gap in the first derivative of in vitro gas production
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Feed Science Technology 60 (1996) 51-67
scatter corrected
h4PLS calibration
equations
used
after 24 h
Maths treatment
SEC (ml)
R2
SECV (ml)
1-RV
1,10,10,1 1,20,10,1 1,30,10,1 1,40,10,1 1,50,10,1 1,60,10,1
1.80 1.80 1.54 1.42 1.38 1.37
0.67 0.67 0.76 0.79 0.80 0.81
2.08 2.05 1.84 1.58 1.53 1.72
0.60 0.62 0.70 0.76 0.78 0.72
SEC = standard error of calibration; R2 = R-square 1 - RV = R-square of cross-validation.
of calibration;
SECV = standard error of cross-validation;
prediction equations had SECV values lower than 0.25 MJ kg-’ DM and 1 - RVs above 0.79. Five of them were obtained with first derivative maths treatments while second (2,12,2,2; 2,8,2,1) and third (3,10,10,1) derivatives gave good results in two and one occasions respectively. As with the gas volumes, when these equations were used for the prediction of ME in Population 1, all of them had less than 4% of H statistic outliers, suggesting that they were broad enough to be used for predictive purposes in that particular set. Table 8 shows the calibration and cross-validation results for only the best 4 equations for the prediction of the ME concentrations of Population 2. 3.3. Crude protein and neutral detergent jibre The best predictive equations for CP and NDF were also obtained using MPLS and scatter correction for particle size. Table 9 shows the results obtained with three different maths treatments for each constituent. In general, the effect of different segment lengths did not improve significantly the accuracy of the equations. First derivatives with segment lengths of 4 to 40 data points and second and third derivatives with gaps up to 30 data points had similar SEC and SECV values for both constituents. The mathematically untreated log 1/R spectra (O,O,1,l> calibrated and cross-validated the CP data with satisfactory statistics.
Table 8 Calibration statistics of the best equations gas production (Population 2)
for the prediction
Maths treatment
SC
SEC (MJ kg-
1,30,10,1 1,10,5,1 1,20,20,1 1,4,4,1
yes yes yes
0.18 0.18 0.18 0.21
yes
SC = Scatter correction; error of cross-validation;
’ DM)
of ME concentration
as estimated
from in vitro
R2
SECV (MJ kg - I DM)
1-RV
0.88 0.87 0.88 0.84
0.23 0.23 0.24 0.24
0.83 0.82 0.81 0.80
SEC = standard error of calibration; R2 = R-square 1 - RV = R-square of cross-validation.
of calibration;
SECV = standard
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M. Herrero et al. /Animal Feed Science Technology 60 (I 996) 51-67
Table 9 Calibration statistics for the test equations predicting CP and NDF of kikuyu grass (Population 3) Constituent
Maths treatment
SC
SEC (g kg-’ DM)
R*
SECV (g kg- ’DM)
l-RV
CP CP CP NDF NDF NDF
2,12,2,2 1,10,5,1 O,OJJ 2,12,2,2 2,12,2,2 2,30,20,1
yes
8.2 7.8 8.8 13.9 14.5 14.7
0.97 0.97 0.97 0.91 0.90 0.90
11.4 11.8 12.1 15.9 16.1 16.6
0.94 0.94 0.94 0.88 0.87 0.87
yes yes no yes yes
SC = Scatter correction; SEC = standard error of calibration; R* = R-square of calibration; SECV = standard error of cross-validation; 1 - RV = R-square of cross-validation.
3.4. Seasonal eflects on population structures The population structure analysis based on the hyperspheres produced by the SYMMETRY programme suggested that Population 1 was asymmetrical and that it had a bimodal distribution when described by the first three PCAs (Fig. 1). The samples in the lower sector of the hypersphere (+ s> were the samples from periods 1 and 2 corresponding to the wet season, while the samples from the dry and transition seasons (Xs) (periods 3 and 4 respectively) clustered in the upper part of the cube. Further analyses indicated that the asymmetry was caused by differences in particle size. Wet season samples had a smaller particle size than samples from the dry and transition seasons. When the first eigenvector was replaced, the bimodal effect disappeared and the population regained its symmetry as observed in Fig. 2. The particle size effect was in the first PCA and the same trends were observed in the other two populations.
Fig. 1. Spectral symmetry of Population 1 based on the first 3 principal components (PCAs) of the
spectra.
M. Herrero et al./Animal Feed Science Technology 60 (1996) 51-67
i
6:
i_________ ______-V_+___,
’ // +* ’ /I I/ I/ /____--___________.___--/ Fig. 2. Effect of removing the particle size effects (first PCA) on the spectral symmetry of Population 1.
4. Discussion The results of the present study suggest that static measurements of the in vitro gas production of incubated kikuyu grass samples can be predicted by NIRS. The best predictions were obtained for the gas volumes produced between 12 and 48 h, where the widest variation in gas production between samples occurred. The 6 and 72 h recordings were also calibrated but with lower accuracy than with the other recordings, while the initial (3 h) and final (96 h) volumes could not be calibrated. The cause for the reduced calibration and cross validation performance of some of the static measurements could have been caused partly by a decrease in the variability of gas production at the later stages (> 72 h) which was caused by high rates of fermentation after 48 h in some samples. This means that although the shapes of the curves had a wide variation due to the fermentation dynamics before 48 h, the gas volumes closer to the asymptote were less variable between them. This is characteristic of some tropical forages (0rskov, personal communication). De Ruiter et al. (1988) and Givens et al. (199213) found that lack of variation could account for a reduction in the calibration performance of hemicellulose and ME respectively in some herbage populations. Although the variability in the amount of gas produced at 3 h was high, possibly its relationship with the lag phase prevented a successful calibration. The dynamic description of gas production as described by the parameters for the McDonald (1981) equation could not be calibrated successfully. Waters and Givens (1992) obtained similar results when attempting a calibration of the a, b and c constants for N degradation of 19 herbage samples. However, they obtained better results when they split the population into primary growths and regrowths, but very few samples of a more homogeneous material were used for each calibration. A possibility for the failure of a successful parameter calibration is that the in sacco degradation or in vitro fermentation characteristics of feedstuffs are usually represented by non-linear kinetics (France et al., 1993). Maybe the exponential nature of these equations altered the distribution and range of the parameters, thus preventing a good fit by the calibration
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methods used, which are all multivariate and linear. In this study the lack of fit of the asymptote (a + b) could also be explained by the factors reducing the variability of the gas production measurements at the later stages of fermentation described above. Although there are other models available to describe the kinetics of gas production (i.e. France et al., 1993, Krishnamoorthy et al., 1995), the McDonald (1981) model was chosen because the relationship of some of its parameters with intake, digestibility and degradation characteristics of forages had been documented (Bliimmel and 0rskov, 1993; Kibon and 0rskov, 1993; Khazaal et al., 1993). This does not imply that this is the best model for the description of gas production kinetics, however the scope of this work does not allow for a comparison of models nor to question the biological meaning of some of their parameters. Nevertheless, more research should be done in order to observe if the parameters of other models can be calibrated by NIRS, as our particular model selection could be a reason for the poor calibration statistics obtained. Another possibility for reduced calibration performance might be the random nature of the population used for calibration. Perhaps if the samples had been selected by their spectral H distances, the parameters could have been fitted better and the calibrations of the static gas volumes could have been improved. These subjects need further investigation with large, broad sample populations. In terms of the single wavelengths segments explaining the largest proportion of the variation in the volume of gas produced, the results give further evidence of the relationship of gas production measurements with in vivo digestibility, degradation characteristics and animal performance (Bliimmel and 0rskov, 1993; Khazaal et al., 1993; Kibon and Plrskov, 1993). The primary wavelengths explaining most of the variation in gas production were in the 1660-1700 nm region which is where the maximum spectral variation between samples was found. Several authors (Norris et al., 1976; Murray et al., 1987; Clark and Lamb, 1991; Givens et al., 1992a) have found this region to be closely linked to in vivo digestibility. Russell et al. (1989) found that the 48 h degradability of straws was highly correlated with this spectral region while Norris et al. (1976) reached similar conclusions when measuring the dry matter intake of forages. The results of these authors can be related to the work of Bliimmel and 0rskov (1993) who found that static measurements of gas production between 8 and 36 h were highly related to digestible DMI of forages and animal performance and that this relationship was not improved by using a dynamic description of fermentation. They would also support the findings of Menke and Steingass (1988) who stated that the ME concentration of different feedstuffs could be explained largely by the volume of gas produced at 24 h. Murray (1987) and Shenk et al. (1992) have shown that the 1660-1685 nm region responds to the first overtone of the aromatic C-H stretch for lignin, which is the best single chemical compound most inversely correlated with digestibility (Murray, 1993). The secondary wavelengths associated with the gas volumes produced between 6 and 24 h are C-H combination regions in which cellulose absorbs (Murray, 1987; Shenk et al., 1992). Murray et al. (1987) and Garrido et al. (1992) found similar secondary wavelengths explaining the digestibility of grass silages and cereal straws respectively. Coelho et al. (1988) and Marten et al. (1988) found the 1500 nm region, in which the secondary wavelength at the 48 and 72 h incubation times in this study where, to be associated with the in vitro rumen fluid or cellulase digestibility of forages.
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The changes in the secondary and tertiary wavelengths as fermentation time increased are not well understood. However, following the observations of Dhanoa et al. (1995), it is possible to suggest that these changes are caused by the changes in the proportions of the substrates being fermented. In such a case, the lignin component, which is indigestible, would always increase relative to the other cell wall fractions as fermentation time increases, thus explaining why the primary wavelengths are always in the regions where lignin absorb. These observations can be partly explained by studies on the residues obtained after degradation at different time intervals (Barton et al., 1986; Givens et al., 1992a) which suggest that the spectral differences as incubation time increase are centered around the 1672 and 2252 nm spectral regions and are mostly explained by the lignin component. Murray (1989) stated that the 2266 nm region corresponds to lignocellulose absorption, while the 1666 nm band is specific for lignin. Therefore, the secondary and tertiary wavelengths would then depend on the amounts of residual cellulose and hemicellulose and other components bound to these fractions (i.e. N, other lesser insoluble carbohydrates) as fermentation advances. These amounts of residue would in turn depend on their proportions bound to lignin. Possibly the samples showing a slower but more constant fermentation had a higher proportion of cellulose bound to lignin, suggesting that the high fermentation rate after 48 h was caused by a slower but steadier release of cellulose from the cell walls. Certainly, more research is needed in this area to study if changes in substrate fermentation can be reflected in gas production measurements and can be predicted by NIRS. The fitted parameters from the predicted gas volumes suggest that, in this study, the calibration methods and maths treatments used were sensitive enough to deal successfully with spectral seasonal influences as shown by the similarity of the means between the predicted and the observed parameters for the different periods. The results were as good when described on a per farm basis (results not shown), suggesting that these estimations could be used for general advisory purposes where seasonal or farm means were required. This applies to rotational grazing systems with short rotation lengths, were animal performance is difficult to quantify over short periods and where the knowledge of the nutritive value of an individual paddock is of limited use as it cannot be related to current animal production. Although, when the predicted and observed results of all individual samples were compared, they showed good correlation coefficients. This is promising, since a better calibration of static gas production measurements in larger, broader and strategically selected populations (i.e. with the SELECT algorithm) would lead to more accurate parameter estimations on individual samples. The better calibration performance of some SMLR equations when compared with MPLS is not uncommon. However, Shenk and Westerhaus (1991b) and Baker et al. (1994) found that even at similar calibration performances, the MPLS equations provided slightly superior validations of herbage CP, ADF and in vitro digestibility and grass silage organic matter in vivo digestibility respectively. Although the primary objective of this study was not to compare the performance of the calibration methods, unfortunately a comparison in terms of their cross-validation or external validation error could not be performed due to the lack of another set of samples with gas production measurements to validate the SMLR equations. A surprising finding was the effect of the different maths treatments on the equation
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calibration and cross-validation performance for the gas volumes. Our results contrast with the general NIRS belief that the maths treatment has a small effect on equation performance. An improvement from 0.60 to 0.78 in the 1 - RV was found by increasing the gap from 10 to 50 data points using a first derivative. Whether the successful calibration and cross-validation performance of equations with wide gaps are robust enough for future validations and species of forages is not known. Although, some authors have successfully used wide segment lengths to calibrate different constituents (i.e. Abrams et al., 1987; Shenk and Westerhaus, 1991ab). The selection of an appropriate maths treatment is one of the most subjective and undefined areas of NIRS, and therefore deserves more research. As stated by Shenk and Westerhaus (1994), the selection of a mathematical treatment is still by trial and error. Few reports of the prediction of ME concentrations of herbage by NIRS are available in the literature. However, our calibration of the estimated ME concentrations is in agreement with the findings of Lindgren (1983, 1988) and Givens et al. (1992b) who found that the ME of mixed populations of temperate grasses could be calibrated and predicted by NIRS with accuracy using a second derivative maths treatment. The lower SEC and SECV of this study are probably related to the fact that ME concentrations were estimated from gas production measurements, while Lindgren (1983, 1988) and Givens et al. (1992b) estimated ME concentrations in vivo. E&man et al. (1983) also found slightly lower results when calibrating digestible energy as measured in sheep. In the case of CP and NDF, several authors (Shenk et al., 1979; Abrams et al., 1987; Coelho et al., 1988; Smith and Flinn, 1991; Abreu et al., 1991) have reported accurate predictions of both constituents by NIRS which confirm the findings of this study. Most of these authors have used first or second derivative maths treatments to obtain good results, however the raw log l/R data gave an accurate calibration and cross-validation for CP. Holechek et al. (1982) and Abrams et al. (1987) have made similar observations while working with samples from oesophageally fistulated animals grazing rangeland grasses and a broad set of forage samples respectively. The excellent calibration and cross-validation results for both constituents are in line with those published in the review of Murray (1993) and they can be partly explained by the selection of samples based on their H distances with the SELECT algorithm even when the cut off point was set to 1.0, and by the wide range observed in the concentrations of both constituents. This method of sample selection has proven to be very efficient (Shenk and Westerhaus, 1991b; Cosgrove et al., 1994) and was probably also responsible for the lack of effect of varying the maths treatments. In general terms the effect of scatter correction improved the calibration and cross-validation statistics for all the variables predicted. Baker and Barnes (19901, Givens et al. (1992a) and Baker et al. (1994) have found similar results when using second derivative treatments. The effect of using the detrending technique for particle size correction probably reduced the effects of the seasonal asymmetry found in the sample populations, therefore increasing the performance of the different equations. The lower particle size of the wet season samples was a reflection of their higher nutritive value (Shenk and Westerhaus, 1994).
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Acknowledgements We gratefully appreciate all the help provided by Messrs. C. Solano, R. Baars, L. Quiros and Dr. E. Perez from the School of Veterinary Medicine, National University, Costa Rica. Thanks are also due to Rhona Patterson for her expert manipulation of the samples during the NIRS procedures. Financial support from the Overseas Development Administration CODA) is also acknowledged.
References Aastveit, A.H. and Marum, P., 1991. On the effect of calibration accuracy and the accuracy of NIR spectroscopy with high levels of noise in the reference values. Appl. Spectr., 45: 109-l 15. Abrams, S.M., Shenk, J.S. Westerhaus, M.O. and Barton, F.E., 1987. Determination of forage quality by near infrared reflectance spectroscopy: efficacy of broad-based calibration equations. J. Dairy Sci., 70: 806-813. Abreu, J.M., Pace, M.T.A., Acamovic, T. and Murray, I., 1991. NIR calibration for nutritional value of a diverse set of Portuguese forage crops. In: I. Murray and IA. Cowe (Editors), Making Light Work: Advances in Near Infrared Reflectance Spectroscopy, Weinheim , VCH, pp. 309-313. Baker, C.W. and Barnes, R.J., 1990. The application of NIRS to forage evaluation in ADAS. In: J. Wiseman and D.J. Cole (Editors), Feedsuffs Evaluation, Butterworths, London, pp. 337-352. Baker, C.W., Givens, D.I. and Deaville, E.R., 1994. Prediction of organic matter digestibility in vivo of grass silage by near infrared reflectance spectroscopy: effect of calibration method, residual moisture and particle size. Anim. Feed Sci. Technol., 50: 17-26. Barnes, R.J., Dhanoa, M.S. and Lister, S.J., 1989. Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra. Appl. Spectrosc., 43: 772-777. Barton, F.E., Wolsing, J.H. and Vedder, H.M., 1986. Near infrared reflectance spectroscopy of untreated and ammonia treated barley straw. Anim. Feed Sci. Technol., 15: 189-196. Bliimmel, M. and Orskov, E.R., 1993. Comparison of in vitro gas production and nylon bag degradability of roughages in predicting feed intake in cattle. Anim. Feed Sci. Technol., 40: 109-l 19. Clark, D.H. and Lamb, R.C., 1991. Near infrared reflectance spectroscopy: a survey of wavelength selection to determine dry matter digestibility. J. Dairy Sci., 74: 2200-2205. Coelho, M., Hembry, F.G., Barton, F.E. and 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. Cosgrove, G.P., Bums, J.C., Fisher, D.S., Mueller, J.P. and Pond, K.R., 1994. Near infrared reflectance spectroscopy prediction of quality from masticated temperate forage species. Crop Sci., 34: 789-792. de Ruiter, J.M., Bums, J.C., McClure, W.F. and Timothy, D.H., 1988. Prediction of cell wall carbohydrates and quality in Panicum species by near infrared reflectance spectroscopy. Crop Sci., 28: 348-353. Dhanoa, MS., France, J., Siddons, R.C., Lopez, S. and Buchanan-Smith, J.G., 1995. A non-linear compartmental model to describe forage degradation kinetics during incubation in polyester bags in the rumen. Br. J. Nut., 73: 3-15. Eckman, D.D., She& J.S., Wangness, P.J. and Westerhaus, M.O., 1983. Prediction of sheep responses by near infrared reflectance spectroscopy. J. Dairy Sci., 66: 1983-1987. Flinn, PC., Windham, W.R. and Dove, H., 1992. Pasture intake by grazing sheep estimated using natural and dosed N-alkanes-A place for NIR? In: K.I. Hildrum, T. Is&son, T. Naes and A. Tandberg (Editors), Near Infrared Spectroscopy: Bridging the gap between Data Analysis and NIR Applications. Ellis Honvood, Chichester, pp. 173-178. France, J., Dhanoa, M.S., Theodorou, M.K. Lister, S.J., Davies, D.R. and Isac, D., 1993. A model to interpret gas accumulation profiles associated with in vitro degradation of ruminant feeds. J. Theor. Biol., 163: 99-111.
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Garrido, A., Guzman, J.L., Gomez-Cabrera, A., Guerrero, J.E. and Murray I., 1992. NIR calibration performance in cereal straw. 2. Organic matter digestibility. In: K.I. Hi&urn, T. Issakson, T. Naes and A, Tandberg (Editors), Near Infra-red Spectroscopy: Bridging the gap between Data Analysis and NIR Applications. Ellis Horwood, Chichester, pp. 241-246. Givens, D.I., Baker, C.W. and Zamime, B., 1992a. Regions for normalised near infrared reflectance difference spectra related to the rumen digestion of straws. Anim. Feed Sci. Technol., 36: 1-12. Givens, D.I., Baker, C.W., Adamson, A.H. and Moss, A.R., 1992b. Influence of growth type and season on the prediction of the metabolisable energy content of herbage by near-infrared reflectance spectroscopy. Anim. Feed Sci. Technol., 37: 281-295. Goering, H.K. and van Soest, P.J., 1970. Forage fibre analysis (apparatus, reagents, procedures and some applications). US Department of Agriculture. Handbook No. 379. Herrero, M., Fawcett, R.H. and Dent, J.B., 1995. Seasonal differences in the metabolisable energy concentration of kikuyu grass as predicted from in vitro gas production measurements. Ann. Zoot. 44, Suppl., 1: 46. Holechek, J.L., She& J.S., Vavra, M. and Arthun, D., 1982. Prediction of forage quality using near infrared reflectance spectroscopy on oesophageal fistula samples from cattle on mountain range. J. Anim. Sci., 55: 971-975. Khazaal, K., Dentinho, M.T., Ribeiro, J.M. and Orskov, E.R., 1993. A comparison of gas production during incubation with rumen contents in vitro and nylon bag degradability as predictors of the apparent digestibility in vivo and the voluntary intake of hays. Anim. Prod., 57: 105-l 12. Kibon, A. and Orskov, E.R., 1993. The use of degradation characteristics of browse plants to predict intake and digestibility by goats. Anim. Prod., 57: 247-252. Krishnamoorthy, U., Soller, H., Steingrass, H. and Menke, K.H., 1995. Energy and protein evaluation of tropical feedstuffs for whole tract and ruminal digestion by chemical analysis and rumen innoculum studies in vitro. Anim. Feed. Sci. Technol., 52: 177-188. Lindgren, E., 1983. The use of near infrared reflectance spectroscopy for predicting energy value of grass. Swed. J. Agric. Res., 13: 229-234. Lindgren, E., 1988. Prediction of energy values and protein content of forages by near infrared reflectance spectroscopy. Swed. J. Agric. Res., 18: 21-26. Marten, G.C., Halgerson, J.L. and Sleper, D.A., 1988. Near infrared reflectance spectroscopy evaluation of ruminal fermentation and cellulase digestion of diverse forages. Crop Sci., 28: 163-167. McDonald, I., 1981. A revised model for the estimation of protein degradability in the rumen. J. Agric. Sci., 96: 251-252. Menke, K.H., Raab, L., Salewski, A., Fritz, D. and Schneider, W., 1979. The estimation of the digestibility and metabolizable energy content of ruminant feedstuffs from the gas production when they are incubated with rumen liquor. J. Agric. Sci., 93: 217-222. Menke, K.H. and Steingass, H., 1988. Estimation of the energetic feed value obtained from chemical analysis and in vitro gas production using rumen fluid. Anim. Res. Dev., 28: 7-55. Moss, A.R., 1994. Methane production by ruminants - Literature review of I. Dietary manipulation to reduce methane production and II. Laboratory procedures for estimating methane potential of diets. Nut. Abs. Revs. (Series B)., 64: 785-806. Murray, I., 1987. The NlR spectra of homologous series of organic compounds. Proc. Int. NIR/NIT Conf., Budapest, Hungary, pp. 13-28. Murray, I., 1989. Applications of NIRS in agriculture. In: M. Iwamoto and S. Kawano (Editors), Proceedings of the 2nd Int. NIRS Conf., Tsukuba, Japan. Korin Publishing, Japan, pp. 329-337. Murray, I., 1993. Forage analysis by near infra-red reflectance spectroscopy. In: A. Davies, R.D. Baker, S.A. Grant and A.S. Laildlaw (Editors), Sward Measurement Handbook. Br. Grassl. Sot., pp. 285-312. Murray, I., Kridis, M.S., Barber, G.D., Givens, D.I. and Offer, N.W., 1987. Grass silage in vivo digestibility prediction by NIR. In: C. Paul (Editor), Forage Quality Analysis by Near Infrared Reflectance Spectroscopy. CEC Monograph, Brussels, pp. 1 lo- 116. Norris, K.H., Barnes, RF., Moore, J.E. and Shenk, J.S., 1976. Predicting forage quality by infrared reflectance spectroscopy. J. Anim. Sci., 43: 889-897. Russell, J.D., Murray, I. and Fraser, A.R., 1989. Near and mid-infrared studies of the cell wall structure of cereal straw in relation to its rumen degradability. In: A. Chesson and E.R. 0rskov (Editors), Physicochemical Characterisation of Plant Residues for Industrial and Feed use. Elsevier, London, pp. 13-24.
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She& J.S. and Westerhaus, M.O., 199la. Population definition, sample selection, and calibration procedures for near infrared reflectance spectroscopy. Crop Sci., 31: 469-474. Shenk, J.S. and Westerhaus, M.O., 199lb. Population structuring of near infrared spectra and modified partial least squares regression. Crop Sci., 31: 1548-1555. Shenk, J.S. and Westerhaus, M.O., 1994. The application of near infrared reflectance spectroscopy (NIRS) to forage analysis. In: G.C. Fahey (Editor), Forage quality, evaluation and utilization. Crop Science Society of America. Lincoln, Nebraska, pp. 406-449. Shenk, J.S., Westerhaus, M.O. and Hoover, M.R., 1979. Analysis of forages by near infrared reflectance. J. Dairy Sci., 62: 807-812. Shenk, J.S., Workman, J.J. and Westerhaus, M.O., 1992. Application of NIR Spectroscopy to agricultural products. In: D.A. Bums and E.W. Ciurczak (Editors), Handbook of Near-Infrared Analysis, Marcel Dekker, New York, pp. 383-431. Smith, K.F. and Flinn, P.C., 1991. Monitoring the performance of a broad-based calibration for measuring the nutritive value of two independent populations of pasture using near infrared reflectance (NIR) spectroscopy. Aust. J. Exp. A&., 31: 205-210. Theodorou, M.K, Williams, B.A., Dhanoa, M.S., McAllan, A.B. and France, J., 1994. A simple gas production method using a pressure transducer to determine the fermentation kinetics of ruminant feeds. Anim. Feed Sci. Technol., 48: 185-197. Tilley. J.M.A. and Terry, R.A., 1963. A two stage technique for the in vitro digestion of forage crops. J. Br. Grassl. Sot., 18: 104-111. Ward, R.G., Wallace, J.D., Urquart, N.S. and Shenk, J.S., 1982. Estimates of intake and quality of grazed range forage by near-infrared reflectance spectroscopy. J. Anim. Sci., 54: 335-349. Waters, C.J. and Givens, D.I., 1992. Nitrogen degradability of fresh herbage: effect of maturity and growth type, and prediction from chemical composition and by near infrared reflectance spectroscopy. Anim. Feed Sci. Technol., 38: 335-349.
ELSEVIER
Animal Feed Science Technology 60 (1996) 5 l-67
Prediction of the in vitro gas production and chemical composition of kikuyu grass by near-infrared reflectance spectroscopy M. Herrero a7*, I. Murray b, R.H. Fawcett a, J.B. Dent a ’ Institute of Ecology and Resource Management, The University of Edinburgh, West Mains Road, Edinburgh EH9 3JG, UK b Animal and Feed Technology Department, Scottish Agricultural College, 581 King Street, Aberdeen AB9 1 UD, UK
Received 7 February 1995; accepted 16 October 1995
Abstract The objective of this study was to predict the in vitro gas production and the estimated metabolisable energy (ME), crude protein (CP) and neutral detergent tibre (NDF) concentrations of kikuyu grass (Pennisetum clandestinum) by near infrared reflectance spectroscopy (NIRS). A total of 288 samples collected in the Poas Region, Costa Rica were scanned (Population 1). The in vitro gas production and ME calibrations were done on a subset of samples in which gas production measurements (3, 6, 12, 24, 36, 48, 72 and 96 h incubations) had been previously carried out (Population 2) while 41 samples for the CP and NDF calibrations (Population 3) were selected on the basis of their H distances from Population 1. The parameters a, b, c and lag for the exponential equation p = a + b (1 - e- ‘(‘-lag)) (McDonald, 19811, where p is the volume of gas produced at time t, were fitted to the gas production data and an attempt was also made to predict them. The volumes of gas produced between 6 and 48 h were successfully calibrated and cross-validated. Coefficients of determination for the cross-validation (1 - RV) were 0.65, 0.74, 0.78, 0.70 and 0.60 for the volumes of gas produced at 6, 12, 24, 36 and 48 h respectively. The volumes of gas produced at 72 h could only be calibrated CR2 = 0.71) but not cross-validated, while the calibration results for the gas production at 3 and 96 h and the parameters for the exponential equation were poor. An analysis of the wavelength segments associated with the in vitro gas production indicated that the primary wavelength was always located between the 1664 and the 1696 nm spectral region regardless of incubation time. The estimated ME, CP and NDF concentrations were accurately calibrated and cross-validated.
* Corresponding author. Tel.: 131 667 1041; Fax.: 131 667 2601; E-mail: [email protected]. 0377-8401/96/$15.00
0 1996 Elsevier Science B.V. All rights reserved
SSDI 0377.8401(95)00924-8
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Standard errors of cross-validation of 0.23 MJ kg-’ DM, 11.4 g kg-’ DM and 15.9 g kg-’ DM were obtained for the ME, CP and NDF concentrations respectively. Scatter correction for particle size improved the performance of most of the equations across all constituents. The effects of different calibration methods, maths treatments and the factors affecting the results are discussed. Keywords:
Techniques; Near-infrared analysis; Gas production; Tropical feedstuffs; Pennisetum clandestinum
1. Introduction Forages are the primary resource for the nutrition of ruminant livestock throughout the world. Therefore, the estimation of their nutritional value has been of primary importance for the prediction of animal performance and the subsequent development of the livestock industry in the past decades. Advisory and research services have usually used wet chemical methods to obtain relevant nutritional information about forages (i.e. Tilley and Terry, 1963; Goering and van Soest, 1970). However, some of these techniques have proved to be time-consuming, often expensive, and/or in some cases, inaccurate (Aastveit and Marum, 1991; Murray, 1993). More recently, the in vitro gas production technique (Menke et al., 1979; Menke and Steingass, 1988; Theodorou et al., 1994) has been used as a method for determining the nutritive value of feedstuffs. It has provided better predictions of the in vivo digestibility and the energetic value of forages than other in vitro techniques (Menke and Steingass, 1988; Khazaal et al., 1993) and it can be used to represent the fermentation dynamics of the incubated samples (BYtimmel and Idrskov, 1993; Moss, 1994). Hence, its importance also lies in its relationship with dry matter degradation characteristics, forage intake and animal performance (Bliimmel and 0rskov, 1993; Kibon and Plrskov, 1993; Khazaal et al., 1993). The technique is gaining popularity because it is a low cost, highly reproducible and easy method of obtaining a dynamic description of the nutritive value of a feedstuff while at the same time allowing for more samples to be analysed. Since the work of Norris et al. (19761, near-infrared reflectance spectroscopy (NIRS) has been applied to estimate the nutritional constituents of forages. It has been found that the technique can provide cheaper, rapid, more precise, and in most cases, more accurate predictions of crude protein (CP), the fibre fractions and in vivo or in vitro digestibility than other available methods (see Murray, 1993; for a review). NIRS has also been applied successfully to the prediction of dry matter degradability (Russell et al., 1989; Givens et al., 1992a), metabolisable energy concentrations (ME) (Givens et al., 1992b) and pasture intake (Norris et al., 1976; Ward et al., 1982; Eckman et al., 1983; Flinn et al., 1992). As the in vitro gas production is a relatively new technique for the determination of forage quality, it is not surprising that its prediction by NIRS has not been reported. With suitable calibrations for NIRS, the time and cost of taking such measurements could be reduced without losing accuracy and access to the information required. Furthermore, more samples could be done and the need for a source of rumen liquor would be eliminated, thus promoting the use of non-invasive techniques in animal production.
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Kikuyu grass (Pennisetum clandestinum) is a stoloniferous and rhizomatous tropical pasture species originally from the Kenyan highlands. It is an important pasture species because it provides a large proportion of the summer and autumn grazing in subtropical regions of Australia, New Zealand and South Africa and is one of the predominant pasture species in tropical highland areas of Africa, Latin America and parts of Asia. The objective of this study was to predict the in vitro gas production, calculated ME, CP and neutral detergent fibre (NDF) concentrations of kikuyu grass by NIRS.
2. Materials and methods
2.1. Samples and wet chemical analyses Two hundred and eighty eight samples of kikuyu grass originating from four commercial dairy farms of the Poas highlands, Costa Rica, were used (Population 1). They consisted of a mixed set of regrowths ranging from 15 to 38 days of age and post-grazing samples obtained from rotationally-grazed paddocks. They were collected over four experimental periods comprising the wet (periods 1 and 2, 150 samples) and dry seasons (period 3, 72 samples) and the transition period between the wet and dry seasons (period 4, 66 samples). All the samples were dried at 60°C for at least 36 h and were then hammer milled though a 1 mm sieve before analysis. For the in vitro gas production and ME calibrations, a subset of 48 samples from Population 1 in which gas production measurements had been previously taken (Herrero et al., 19951, was used (Population 2). It consisted of three samples from each of the four farms in each of the four periods which were selected at random from the regrowths to allow for seasonal and farm variations. These were incubated for in vitro gas production estimations as described by Menke and Steingass (1988). The rumen fluid used was obtained from three sheep fed twice a day a ration of 600 g hay : grass pellets (2:l). Gas volume measurements (ml> were taken after 3, 6, 12, 24, 36, 48, 72 and 96 hours of incubation and the results were fitted to the exponential model of McDonald (1981), p = a + b(1 - e-c(r-‘ag) ), where p represents the in vitro gas production (ml) at time t, (a + b) was the potential gas production, and c the fractional rate of gas production per hour. The model allowed for the estimation of a lag phase (Zag) before rapid gas production began. ME was estimated as described by Menke and Steingass (1988) for dry forages, and more recently by Krishnamoorthy et al. (1995). The equation used was: ME = 2.43 + 0.1206GP
+ 0.0069CP
+ 0.0187EE
where: ME = metabolisable energy (MJ kg-’ DM), GP = gas production at 24 h (ml 200 mg- ’ DM), CP = crude protein (g kg-’ DM), EE = ether extract (g kg-’ DM), as estimated by the Weende system. For the CP and NDF calibrations (Population 3), a different approach was taken. The required number of samples for calibration was chosen using the SELECT algorithm available in the IS1 software (Infrasoft International, Port Matilda, PA, USA) and developed by Shenk and Westerhaus (1991b). The SELECT algorithm works by
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Table 1 Characteristics of the samples used for the calibration of in vitro gas production, calculated metabolisable energy (Population 2, n = 48) and crude protein and neutral detergent fibre (Population 3, n = 41) Constituent
Mean
Std. dev.
Range
Gas 3 h (ml) Gas 6 h (ml) Gas 12 h (ml) Gas 24 h (ml) Gas 36 h (ml) Gas 48 h (ml) Gas 72 h (ml) Gas 96 h (ml) ME (MJ kg- ’ DM) CP (g kg-’ DM) NDF (g kg - ’ DM) a+b(ml) c c/h) lag (h)
3.95 6.94 16.30 28.38 34.05 38.66 42.71 44.57 7.11 181.8 606.7 45.47 0.042 1.30
0.860 1.531 2.793 3.139 3.340 3.483 3.552 4.206 0.522 38.05 37.67 3.82 0.0069 0.502
1.70-5.75 2.90-10.45 9.90-22.45 20.70-34.95 26.20-39.70 29.20-45.95 31.70-51.70 28.26-52.87 6.20-8.11 82.3-250.9 543.3-692.2 33.91-54.90 0.029-0.067 0.20-2.90
ME = metabolisable energy; CP = crude protein; NDF = neutral detergent fibre; a + b = asymptotic gas production; c = rate of gas production; lag = lag phase.
choosing a subset of samples with representative spectral characteristics of the whole population on the basis of their standardised H (Mahalanobis) distances. A minimum standardised H distance of 1.0 between neighbour samples was chosen, resulting in the algorithm selecting 41 representative samples from Population 1. These were analysed for Kjeldahl nitrogen (N) and CP was obtain as N X 6.25. NDF was determined by the method described by Goering and van Soest (1970). These data were used for the respective calibrations. The main characteristics of Populations 2 and 3 can be observed in Table 1. 2.2. NIRS scanning and calibration procedures All samples were scanned using an NIRSystems 6500 spectrometer (Perstorp Analytical, Bristol, UK) connected to a Viglen 90 MHz Pentium personal computer. Although this instrument also scanned the visible spectra, only the NIR region from 1100 to 2500 nm was used for calibration purposes. Spectra were collected at 2 nm intervals providing 700 data points for each sample. All data were recorded as absorbance values (log (l/R)), where R = reflectance, and they were analysed using the IS1 software previously mentioned. Before calibration, the three datasets (Populations 1 to 3) were ranked according to their H distances, as measured from the average spectrum of the file, using the CENTER algorithm (Shenk and Westerhaus, 1991a). This was done to check for samples with atypical spectral characteristics as judged by H distances greater than 3.0. Calibrations were performed for the volumes of gas produced at different time intervals, their fitted parameters from the McDonald (198 1) model, and for the estimated ME, CP and NDF concentrations. For each season, the parameters for the McDonald
M. Herrero et al./Animal Feed Science Technology 60 (1996) 51-67
55
Table 2 Mathematicaltreatments used for the calibration equations W,l,l 1,50,10,1 2,12,2,2 3,20,10,1
1,4,4,1 2.4.u 2,20,10,1 3,30,10,1
1,10,5,1 2,4,2,1 2,30,10,1 3,30,20,1
1,10,10,1 2,6,2,1 2,40,10,1 4,4,2,2
1,20,10,1 2,8,2,1 2,30,20,1 4,10,10,1
1,30,10,1 2A8.2 3,10,1,1
1,40,10,1 2,10,1,1 3,10,10,1
(1981) equation were also calculated from the predicted gas productions and compared with those fitted from the actual values. Log l/R data were given different mathematical treatments consisting of first, second or third derivations with different segment gaps and smoothings for the calibration of each constituent. Table 2 presents the mathematical treatments used for this study, where the first number is the order of the derivative, the second one is the segment gap in data points over which the derivative was calculated and the third and fourth are the number of data points which were used for the running average smooth. Modified partial least squares (MPLS) (Shenk and Westerhaus, 1991b) or stepwise multiple linear regression (SMLR) were used as the calibration methods. They were carried out with and without the standard normal variate and detrending procedure of Barnes et al. (1989) to allow for calibrations with and without particle size scatter correction. Four cross-validation groups were used to validate the MPLS algorithms. All calibrations and cross-validations were done within the calibration sets only, as an independent set of samples for external validation was not available. As stated by Shenk and Westerhaus (1994), the internal cross-validation is a valid approach, it makes a more efficient use of samples and can replace an external validation. The SMLR equations were also used to determine the most important single wavelengths in the determination of gas production measurements as judged by their F statistic. All calibrations were allowed to include a maximum of four terms and two outlier elimination passes, although only one pass was sufficient in all the cases where it was required. Critical F, T and H values of 7.0, 2.5 and 3.0 were used respectively (Murray, 1993). Th e a d equacy of the calibration and cross-validation results for each equation were based on their standard errors (SEC and SECV, respectively) and their coefficients of determination (R2 and 1 - RV, respectively). As the purpose of this study was also to find suitable calibrations for the whole set of samples (Population 11, another measure for equation selection was the number of H outliers when the constituents were predicted in Population 1. A low number of H outliers would indicate the breadth of the equation for predictive purposes (Shenk and Westerhaus, 1994). According to Murray (personal observations), in routine forage evaluation by NIRS, the number of H outliers when predictions are carried out in a population should be less than 4%. Therefore, this was the threshold limit used in the present study. A further analysis of the spectral structure of the populations was carried out using the SYMMETRY routine (IS1 software) with three principal components (PCA). This was done to observe if seasonal influences existed on the spectral distribution of the samples.
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3. Results 3.1. In vitro gas production
The results for the calibration of in vitro gas production can be observed in Table 3. The SMLR algorithms calibrated the gas production data marginally better than the MPLS equations, as shown by their SEC and R2 values. However, with the MPLS equations, the volumes of gas produced at 6, 12, 24, 36, and 48 h were successfully calibrated and cross-validated. The volumes produced at 6 and 48 h had lower R2 and 1 - RV values than those produced at 12, 24 and 36 h, while the volume of gas produced at 72 h was poorly calibrated by the MPLS method. In vitro gas production at 3 and 96 h could not be calibrated satisfactorily by either of the two methods (MPLS or SMLR). Their R2 of calibration did not exceed 0.50 for any of the maths treatments with and without scatter correction. When the actual parameters rather than the gas volumes were calibrated, the results were also poor. The calibration statistics for the a, b, c and lag parameters had R2 values of less than 0.50, 0.40, 0.50 and 0.30 respectively, regardless of the maths treatments. The use of SMLR to determine the most important single wavelength segments associated with gas production demonstrated that, in the best equations, the primary wavelength was always between 1664 and 1696 nm regardless of the time of incubation, maths treatment or scatter correction (Table 4). However, when equations with longer
Table 3 Results of the best calibrations time intervals Incubation 6 6 6 12 12 12 24 24 24 36 36 36 48 48 48 72 72 72
time (h)
and cross-validations
for the static in vitro gas volumes produced
Method
Maths treatment
SC
SECV (ml)
SMLR MRLS MPLS SMLR MPLS MPLS SMLR MRLS MRLS SMLR SMLR MRLS MPLS SMLR SMLR SMLR SMLR SMLR
24~1 1,50,10,1 2,20,10,1
yes
0.94 0.99 1.49 1.60 1.55 1.58 1.92 2.19 _
2,6,2,l 1,50,10,1 1,40,10,1 2,10,1,1 1,50,10,1 1,40,10,1 1.49491 2,12,2,2 1,50,10,1 2,4,1,1 3,10,1,1 2,10,1,1 2,10,1,1 3,10,1,1 2,8,2,l
yes yes yes yes yes no yes yes yes no yes no no no yes no yes
-
I-RV 0.65 0.61 0.74 0.70 0.78 0.76 _ 0.70 0.60 _ _
at different
SEC(ml)
R2
0.68 0.80 0.81 1.19 1.36 1.40 1.34 1.38 1.42 1.45 1.57 1.63 1.56 1.60 1.72 1.91 2.00 2.03
0.80 0.73 0.72 0.82 0.77 0.75 0.81 0.80 0.79 0.79 0.78 0.77 0.79 0.79 0.76 0.71 0.69 0.68
SC = scatter correction; SECV = standard error of cross-validation; 1 - RV = R-square of cross-validation; SEC = standard error of calibration; R* = R-square of calibration. Results for the SMLR equations are for the calibration only.
57
M. Herrero et al./Animal Feed Science Technology 60 (1996) 51-67 Table 4 The three wavelengths different
incubation
explaining
the largest proportion
of the variation
in the amount of gas produced
at
times
Incubation time (h)
Maths (treatment)
6 12 24 36 48 72
2,4,1,1 ’ 2,6,2,1 a 2,10,1,1 1,4,4,1 a 3,10,1,1 2,10,1,1 d
Primary wavelength
Secondary wavelength
Tertiary wavelenth
(nm)
(nm)
(nm)
1672 1672 1668 1664 1680 1696
1448 2072 2336 2300 1208 1540
2192 2316 1624 1184 1640 2052
a Scatter corrected.
segment gaps (30-50 data points) were used, the primary wavelength extended up to the 1720 nm region. The first wavelength accounted for 4060% of the variation in the volumes of gas produced from 6 to 48 h and for only 25% of the variation for the volumes of gas at 72 h. To be able to detect changes and make valid comparisons of wavelength selection as fermentation progresses, the maths treatment has to be kept constant for all the time intervals. Therefore, the single best math treatment describing the gas volumes from 6 to 72 h in this study (2,10,1 ,l) was used. This math treatment was consistently present in the three best equations of each of the predicted gas volumes. The results (Table 5) suggest that although all wavelengths were in a similar region, the incubation times between 12 and 48 h had closely related primary wavelengths between 1664 and 1668 nm while there was a displacement towards higher wavelengths within the same region (1670- 1700 nm> at the early (6 h) and late (72 h) stages of fermentation. As cross-validation is not convenient on SMLR equations due to changes in the wavelengths selected for the different groups, a method to test if the predicted gas volumes within the calibration set were accurate was to calculate the a + 6, c and lag parameters in the McDonald (1981) equation from the predicted data and to compare them with the actual figures. These were calculated with the volumes of gas produced
Table 5 Effect of incubation time on wavelength treatment to predict static gas volumes Incubation time (h)
6 12 24 36 48 72
segment
selection
by SMLR equations
using a 2,10,1,1
Primary wavelength
Secondary wavelength
Tertiary wavelength
(nm)
(nm)
(nm)
1672 1664 1668 1668 1664 1696
2404 2336 2328 1216 1540 1540
1184 1620 1632 2372 1496 2052
maths
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Table 6 Comparison of observed vs. predicted parameters for in vitro gas production of kikuyu grass according to sampling period (All calibration samples, Population 2) Parameter
a
Period 1 mean (s.e.)
Period 2 mean k.e.>
Period 3 mean (s.e.1
Period 4 mean (s.e.)
a (ml)
Obs. Pred. Obs. Pred. Obs. Pred. Obs. Pred.
- 2.84 (0.248) - 2.62 (0.214) 47.99 (0.862) 47.73 (0.352) 0.047 (0.003) 0.046 (0.001) 1.32 (0.117) 1.24(0.116)
-2.18 (0.265) -2.51 (0.198) 48.87 (0.981) 48.93 (0.700) 0.042 (0.002) 0.041 (0.002) 1.40 (0.140) 1.30 (0.101)
- 2.02 (0.127) - 2.00 (0.205) 47.78 (1.600) 47.38 (1.250) 0.038 (0.001) 0.038 (0.001) 1.17 (0.079) 1.16 (0.142)
- 2.84 (0.241) - 3.02 (0.223) 46.93 (1.260) 47.14 (1.020) 0.042 (0.001) 0.042 (0.001) 1.50 (0.149) 1.61 (0.142)
b (ml)
c c/h)
lag 09
’ Obs. = observed, Pred. = predicted.
only up to 72 h excluding the volumes at 96 h, which showed poor calibration performance, while the parameters of the observed values were recalculated also excluding the volume of gas produced at 96 h to allow for a proper comparison. The NIR predicted values of the asymptote gas production fa + b), and the rate constant (c) were correlated with the observed parameters (r = 0.77 and 0.72 respectively) while a weaker correlation (r = 0.5 1) was observed for the lag phase (lug). The parameters a and b when compared separately showed correlations of 0.51 and 0.78, respectively. When results were expressed by period, the observed and predicted seasonal mean a, b, c and lug parameters did not differ significantly for each of the periods (Table 6). The equations presented in Table 3 were tested to assess the breadth of their predictions in Population 1. The number of H outliers was less than 4% for all of them, suggesting that they were broad enough for the prediction of gas volumes of the whole set. Although there is no evidence to suggest that there is a best maths treatment to calibrate a particular constituent (Murray, 1993; Shenk and Westerhaus, 19941, the 2,10,1,1 equation, as stated before, was consistently present in the three best SMLR equations for the calibrations of gas volumes from 6 to 72 h in this study. Surprisingly, the maths treatments that gave better results in the MPLS equations were all first derivatives calculated over rather long segment gaps (i.e. 1,50,10,1). The results on the gas volumes produced from 6 to 36 h, showed that as the gap was increased up to 50 data points in these first derivatives, the standard errors (SEC and SECV respectively) were reduced, increasing the accuracy of the calibration and cross-validation. A reduction in cross-validation performance was observed after the gap was extended further. Table 7 shows an example for the volume of gas produced after 24 h. 3.2. Metubolisuble
energy
The best algorithms for the prediction of ME were all MPLS equations. Scatter correction improved the SEC and SECV and the coefficients of determination CR2 and 1 - RV respectively) in six of the eight best prediction equations. All of the eight best
M. Herrero et al./Animal Table 7 Effect of increasing for the prediction
the segment gap in the first derivative of in vitro gas production
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Feed Science Technology 60 (1996) 51-67
scatter corrected
h4PLS calibration
equations
used
after 24 h
Maths treatment
SEC (ml)
R2
SECV (ml)
1-RV
1,10,10,1 1,20,10,1 1,30,10,1 1,40,10,1 1,50,10,1 1,60,10,1
1.80 1.80 1.54 1.42 1.38 1.37
0.67 0.67 0.76 0.79 0.80 0.81
2.08 2.05 1.84 1.58 1.53 1.72
0.60 0.62 0.70 0.76 0.78 0.72
SEC = standard error of calibration; R2 = R-square 1 - RV = R-square of cross-validation.
of calibration;
SECV = standard error of cross-validation;
prediction equations had SECV values lower than 0.25 MJ kg-’ DM and 1 - RVs above 0.79. Five of them were obtained with first derivative maths treatments while second (2,12,2,2; 2,8,2,1) and third (3,10,10,1) derivatives gave good results in two and one occasions respectively. As with the gas volumes, when these equations were used for the prediction of ME in Population 1, all of them had less than 4% of H statistic outliers, suggesting that they were broad enough to be used for predictive purposes in that particular set. Table 8 shows the calibration and cross-validation results for only the best 4 equations for the prediction of the ME concentrations of Population 2. 3.3. Crude protein and neutral detergent jibre The best predictive equations for CP and NDF were also obtained using MPLS and scatter correction for particle size. Table 9 shows the results obtained with three different maths treatments for each constituent. In general, the effect of different segment lengths did not improve significantly the accuracy of the equations. First derivatives with segment lengths of 4 to 40 data points and second and third derivatives with gaps up to 30 data points had similar SEC and SECV values for both constituents. The mathematically untreated log 1/R spectra (O,O,1,l> calibrated and cross-validated the CP data with satisfactory statistics.
Table 8 Calibration statistics of the best equations gas production (Population 2)
for the prediction
Maths treatment
SC
SEC (MJ kg-
1,30,10,1 1,10,5,1 1,20,20,1 1,4,4,1
yes yes yes
0.18 0.18 0.18 0.21
yes
SC = Scatter correction; error of cross-validation;
’ DM)
of ME concentration
as estimated
from in vitro
R2
SECV (MJ kg - I DM)
1-RV
0.88 0.87 0.88 0.84
0.23 0.23 0.24 0.24
0.83 0.82 0.81 0.80
SEC = standard error of calibration; R2 = R-square 1 - RV = R-square of cross-validation.
of calibration;
SECV = standard
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Table 9 Calibration statistics for the test equations predicting CP and NDF of kikuyu grass (Population 3) Constituent
Maths treatment
SC
SEC (g kg-’ DM)
R*
SECV (g kg- ’DM)
l-RV
CP CP CP NDF NDF NDF
2,12,2,2 1,10,5,1 O,OJJ 2,12,2,2 2,12,2,2 2,30,20,1
yes
8.2 7.8 8.8 13.9 14.5 14.7
0.97 0.97 0.97 0.91 0.90 0.90
11.4 11.8 12.1 15.9 16.1 16.6
0.94 0.94 0.94 0.88 0.87 0.87
yes yes no yes yes
SC = Scatter correction; SEC = standard error of calibration; R* = R-square of calibration; SECV = standard error of cross-validation; 1 - RV = R-square of cross-validation.
3.4. Seasonal eflects on population structures The population structure analysis based on the hyperspheres produced by the SYMMETRY programme suggested that Population 1 was asymmetrical and that it had a bimodal distribution when described by the first three PCAs (Fig. 1). The samples in the lower sector of the hypersphere (+ s> were the samples from periods 1 and 2 corresponding to the wet season, while the samples from the dry and transition seasons (Xs) (periods 3 and 4 respectively) clustered in the upper part of the cube. Further analyses indicated that the asymmetry was caused by differences in particle size. Wet season samples had a smaller particle size than samples from the dry and transition seasons. When the first eigenvector was replaced, the bimodal effect disappeared and the population regained its symmetry as observed in Fig. 2. The particle size effect was in the first PCA and the same trends were observed in the other two populations.
Fig. 1. Spectral symmetry of Population 1 based on the first 3 principal components (PCAs) of the
spectra.
M. Herrero et al./Animal Feed Science Technology 60 (1996) 51-67
i
6:
i_________ ______-V_+___,
’ // +* ’ /I I/ I/ /____--___________.___--/ Fig. 2. Effect of removing the particle size effects (first PCA) on the spectral symmetry of Population 1.
4. Discussion The results of the present study suggest that static measurements of the in vitro gas production of incubated kikuyu grass samples can be predicted by NIRS. The best predictions were obtained for the gas volumes produced between 12 and 48 h, where the widest variation in gas production between samples occurred. The 6 and 72 h recordings were also calibrated but with lower accuracy than with the other recordings, while the initial (3 h) and final (96 h) volumes could not be calibrated. The cause for the reduced calibration and cross validation performance of some of the static measurements could have been caused partly by a decrease in the variability of gas production at the later stages (> 72 h) which was caused by high rates of fermentation after 48 h in some samples. This means that although the shapes of the curves had a wide variation due to the fermentation dynamics before 48 h, the gas volumes closer to the asymptote were less variable between them. This is characteristic of some tropical forages (0rskov, personal communication). De Ruiter et al. (1988) and Givens et al. (199213) found that lack of variation could account for a reduction in the calibration performance of hemicellulose and ME respectively in some herbage populations. Although the variability in the amount of gas produced at 3 h was high, possibly its relationship with the lag phase prevented a successful calibration. The dynamic description of gas production as described by the parameters for the McDonald (1981) equation could not be calibrated successfully. Waters and Givens (1992) obtained similar results when attempting a calibration of the a, b and c constants for N degradation of 19 herbage samples. However, they obtained better results when they split the population into primary growths and regrowths, but very few samples of a more homogeneous material were used for each calibration. A possibility for the failure of a successful parameter calibration is that the in sacco degradation or in vitro fermentation characteristics of feedstuffs are usually represented by non-linear kinetics (France et al., 1993). Maybe the exponential nature of these equations altered the distribution and range of the parameters, thus preventing a good fit by the calibration
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M. Herrero et al./Animal Feed Science Technology 60 (1996) 51-67
methods used, which are all multivariate and linear. In this study the lack of fit of the asymptote (a + b) could also be explained by the factors reducing the variability of the gas production measurements at the later stages of fermentation described above. Although there are other models available to describe the kinetics of gas production (i.e. France et al., 1993, Krishnamoorthy et al., 1995), the McDonald (1981) model was chosen because the relationship of some of its parameters with intake, digestibility and degradation characteristics of forages had been documented (Bliimmel and 0rskov, 1993; Kibon and 0rskov, 1993; Khazaal et al., 1993). This does not imply that this is the best model for the description of gas production kinetics, however the scope of this work does not allow for a comparison of models nor to question the biological meaning of some of their parameters. Nevertheless, more research should be done in order to observe if the parameters of other models can be calibrated by NIRS, as our particular model selection could be a reason for the poor calibration statistics obtained. Another possibility for reduced calibration performance might be the random nature of the population used for calibration. Perhaps if the samples had been selected by their spectral H distances, the parameters could have been fitted better and the calibrations of the static gas volumes could have been improved. These subjects need further investigation with large, broad sample populations. In terms of the single wavelengths segments explaining the largest proportion of the variation in the volume of gas produced, the results give further evidence of the relationship of gas production measurements with in vivo digestibility, degradation characteristics and animal performance (Bliimmel and 0rskov, 1993; Khazaal et al., 1993; Kibon and Plrskov, 1993). The primary wavelengths explaining most of the variation in gas production were in the 1660-1700 nm region which is where the maximum spectral variation between samples was found. Several authors (Norris et al., 1976; Murray et al., 1987; Clark and Lamb, 1991; Givens et al., 1992a) have found this region to be closely linked to in vivo digestibility. Russell et al. (1989) found that the 48 h degradability of straws was highly correlated with this spectral region while Norris et al. (1976) reached similar conclusions when measuring the dry matter intake of forages. The results of these authors can be related to the work of Bliimmel and 0rskov (1993) who found that static measurements of gas production between 8 and 36 h were highly related to digestible DMI of forages and animal performance and that this relationship was not improved by using a dynamic description of fermentation. They would also support the findings of Menke and Steingass (1988) who stated that the ME concentration of different feedstuffs could be explained largely by the volume of gas produced at 24 h. Murray (1987) and Shenk et al. (1992) have shown that the 1660-1685 nm region responds to the first overtone of the aromatic C-H stretch for lignin, which is the best single chemical compound most inversely correlated with digestibility (Murray, 1993). The secondary wavelengths associated with the gas volumes produced between 6 and 24 h are C-H combination regions in which cellulose absorbs (Murray, 1987; Shenk et al., 1992). Murray et al. (1987) and Garrido et al. (1992) found similar secondary wavelengths explaining the digestibility of grass silages and cereal straws respectively. Coelho et al. (1988) and Marten et al. (1988) found the 1500 nm region, in which the secondary wavelength at the 48 and 72 h incubation times in this study where, to be associated with the in vitro rumen fluid or cellulase digestibility of forages.
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Feed Science Technology 60 (1996) 51-67
63
The changes in the secondary and tertiary wavelengths as fermentation time increased are not well understood. However, following the observations of Dhanoa et al. (1995), it is possible to suggest that these changes are caused by the changes in the proportions of the substrates being fermented. In such a case, the lignin component, which is indigestible, would always increase relative to the other cell wall fractions as fermentation time increases, thus explaining why the primary wavelengths are always in the regions where lignin absorb. These observations can be partly explained by studies on the residues obtained after degradation at different time intervals (Barton et al., 1986; Givens et al., 1992a) which suggest that the spectral differences as incubation time increase are centered around the 1672 and 2252 nm spectral regions and are mostly explained by the lignin component. Murray (1989) stated that the 2266 nm region corresponds to lignocellulose absorption, while the 1666 nm band is specific for lignin. Therefore, the secondary and tertiary wavelengths would then depend on the amounts of residual cellulose and hemicellulose and other components bound to these fractions (i.e. N, other lesser insoluble carbohydrates) as fermentation advances. These amounts of residue would in turn depend on their proportions bound to lignin. Possibly the samples showing a slower but more constant fermentation had a higher proportion of cellulose bound to lignin, suggesting that the high fermentation rate after 48 h was caused by a slower but steadier release of cellulose from the cell walls. Certainly, more research is needed in this area to study if changes in substrate fermentation can be reflected in gas production measurements and can be predicted by NIRS. The fitted parameters from the predicted gas volumes suggest that, in this study, the calibration methods and maths treatments used were sensitive enough to deal successfully with spectral seasonal influences as shown by the similarity of the means between the predicted and the observed parameters for the different periods. The results were as good when described on a per farm basis (results not shown), suggesting that these estimations could be used for general advisory purposes where seasonal or farm means were required. This applies to rotational grazing systems with short rotation lengths, were animal performance is difficult to quantify over short periods and where the knowledge of the nutritive value of an individual paddock is of limited use as it cannot be related to current animal production. Although, when the predicted and observed results of all individual samples were compared, they showed good correlation coefficients. This is promising, since a better calibration of static gas production measurements in larger, broader and strategically selected populations (i.e. with the SELECT algorithm) would lead to more accurate parameter estimations on individual samples. The better calibration performance of some SMLR equations when compared with MPLS is not uncommon. However, Shenk and Westerhaus (1991b) and Baker et al. (1994) found that even at similar calibration performances, the MPLS equations provided slightly superior validations of herbage CP, ADF and in vitro digestibility and grass silage organic matter in vivo digestibility respectively. Although the primary objective of this study was not to compare the performance of the calibration methods, unfortunately a comparison in terms of their cross-validation or external validation error could not be performed due to the lack of another set of samples with gas production measurements to validate the SMLR equations. A surprising finding was the effect of the different maths treatments on the equation
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M. Herrero et al. /Animal Feed Science Technology 60 (I 996) 51-67
calibration and cross-validation performance for the gas volumes. Our results contrast with the general NIRS belief that the maths treatment has a small effect on equation performance. An improvement from 0.60 to 0.78 in the 1 - RV was found by increasing the gap from 10 to 50 data points using a first derivative. Whether the successful calibration and cross-validation performance of equations with wide gaps are robust enough for future validations and species of forages is not known. Although, some authors have successfully used wide segment lengths to calibrate different constituents (i.e. Abrams et al., 1987; Shenk and Westerhaus, 1991ab). The selection of an appropriate maths treatment is one of the most subjective and undefined areas of NIRS, and therefore deserves more research. As stated by Shenk and Westerhaus (1994), the selection of a mathematical treatment is still by trial and error. Few reports of the prediction of ME concentrations of herbage by NIRS are available in the literature. However, our calibration of the estimated ME concentrations is in agreement with the findings of Lindgren (1983, 1988) and Givens et al. (1992b) who found that the ME of mixed populations of temperate grasses could be calibrated and predicted by NIRS with accuracy using a second derivative maths treatment. The lower SEC and SECV of this study are probably related to the fact that ME concentrations were estimated from gas production measurements, while Lindgren (1983, 1988) and Givens et al. (1992b) estimated ME concentrations in vivo. E&man et al. (1983) also found slightly lower results when calibrating digestible energy as measured in sheep. In the case of CP and NDF, several authors (Shenk et al., 1979; Abrams et al., 1987; Coelho et al., 1988; Smith and Flinn, 1991; Abreu et al., 1991) have reported accurate predictions of both constituents by NIRS which confirm the findings of this study. Most of these authors have used first or second derivative maths treatments to obtain good results, however the raw log l/R data gave an accurate calibration and cross-validation for CP. Holechek et al. (1982) and Abrams et al. (1987) have made similar observations while working with samples from oesophageally fistulated animals grazing rangeland grasses and a broad set of forage samples respectively. The excellent calibration and cross-validation results for both constituents are in line with those published in the review of Murray (1993) and they can be partly explained by the selection of samples based on their H distances with the SELECT algorithm even when the cut off point was set to 1.0, and by the wide range observed in the concentrations of both constituents. This method of sample selection has proven to be very efficient (Shenk and Westerhaus, 1991b; Cosgrove et al., 1994) and was probably also responsible for the lack of effect of varying the maths treatments. In general terms the effect of scatter correction improved the calibration and cross-validation statistics for all the variables predicted. Baker and Barnes (19901, Givens et al. (1992a) and Baker et al. (1994) have found similar results when using second derivative treatments. The effect of using the detrending technique for particle size correction probably reduced the effects of the seasonal asymmetry found in the sample populations, therefore increasing the performance of the different equations. The lower particle size of the wet season samples was a reflection of their higher nutritive value (Shenk and Westerhaus, 1994).
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Acknowledgements We gratefully appreciate all the help provided by Messrs. C. Solano, R. Baars, L. Quiros and Dr. E. Perez from the School of Veterinary Medicine, National University, Costa Rica. Thanks are also due to Rhona Patterson for her expert manipulation of the samples during the NIRS procedures. Financial support from the Overseas Development Administration CODA) is also acknowledged.
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