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Prediction of the in vivo digestibility of whole crop wheat from in vitro digestibility, chemical composition, in situ rumen degradability, in vitro gas production and near infrared reflectance spectroscopy
Animal Feed Science and Technology 74 (1998) 259±272
Prediction of the in vivo digestibility of whole crop wheat from in vitro digestibility, chemical composition, in situ rumen degradability, in vitro gas production and near infrared reflectance spectroscopy A.T. Adesogana,b,*, E. Owena, D.I. Givensb a b
Department of Agriculture, The University of Reading, Earley Gate, PO Box 236, Reading RG6 6AT, UK Feed Evaluation and Nutritional Sciences, ADAS Drayton, Alcester Road, Stratford on Avon CV37 9RQ, UK Received 4 November 1997; accepted 6 April 1998
Abstract Twenty-six winter wheat forages (cultivars Slepjner, Hussar and Cadenza) harvested at three stages of maturity in each of two years, were conserved with or without Maxgrass additive or with urea (20 or 40 g kgÿ1 dry matter; DM) in 200 l barrels. The forages were analysed for in vivo digestibility in wethers, chemical composition, in vitro rumen fluid-pepsin digestibility, in vitro neutral detergent-cellulase plus gamannase digestibility (NCGD), in vitro fermentation gas production and in situ rumen degradability. Forages were also scanned using near-infrared reflectance spectroscopy (NIRS) and calibration equations developed for predicting in vivo digestibility. In vivo digestible organic matter content (DOMD) was poorly predicted by cell wall content (r20.19), NCGD (r20.41), rumen fluid DOMD (r20.41), rumen degradability (r20.44) and in vitro gas production (r20.26). Although crude protein content was a better predictor (r20.48), the relationship differed (P<0.05) with the year of harvest of the forages. In contrast, NIRS was a more accurate and consistent predictor of DOMD in vivo (r20. 87). This study indicates that traditional laboratory-based feed evaluation techniques are unsuitable for predicting the DOMD of WCW, but that NIRS holds promise. However, as only 26 forages were used to derive the calibration equation, further research is required using large (150) data sets to validate the promise shown by NIRS and enable its adoption by the advisory services. # 1998 Elsevier Science B.V. Keywords: Nutritive value; Prediction; Whole crop; Digestibility; In vitro
* Corresponding author. Tel.: +44 1970 624471; fax: +44 1970 611264; e-mail: [email protected] 0377-8401/98/$19.00 # 1998 Elsevier Science B.V. All rights reserved PII S 0 3 7 7 - 8 4 0 1 ( 9 8 ) 0 0 1 7 5 - 8
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1. Introduction Whole crop wheat (WCW) is produced by conserving the aerial part of the wheat plant under alkaline or acidic conditions. This forage is increasingly being used for winter feeding of ruminant livestock in areas of the UK which are marginal for maize production. However, many of the nutritional results sent to farmers on their WCW samples are only estimated using laboratory-based techniques, some of which were developed for grass silage. Yet, there is little information in the literature on the accuracy with which such laboratory techniques predict the nutritive value of WCW. Therefore, the use of such techniques may give misleading information that could adversely affect profitability. Previous work (Adesogan et al., 1997) which showed that the nutritive value of WCW varies considerably with treatment application and maturity, further emphasises the need to validate laboratory-based assessments of WCW digestibility with in vivo studies. Therefore, the aim of this study was to evaluate the suitability of predicting the in vivo digestibility of WCW from various less animal-dependent techniques. The study involved the use of fermented and urea-treated WCW harvested over the stages of maturity used in practice. 2. Materials and methods 2.1. Forages used The study involved the use of twenty-six WCW forages produced over 2 years from three varieties of wheat (Hussar, Cadenza and Slepjner). In each year, the forages were harvested at three stages of maturity (milk, soft cheese and dough) and conserved in 200 l barrels with Maxgrass ± an acid-based additive (5, 4 or 3 l tÿ1) or with urea (0, 20 or 40 g kgÿ1 dry matter (DM)). The reader is referred to the work of Adesogan et al. (1997) for further details on the forages. 2.2. Chemical composition All forages were analysed for DM, ash, crude protein (CP), ammonia nitrogen (NH3N), lactic acid, volatile fatty acids (VFA) and water soluble carbohydrates (WSC) by the methods of MAFF (1986), acid detergent fibre (ADF) (Goering and van Soest, 1970) and neutral detergent fibre plus amylase (NDFA) (Alderman, 1985). Starch content was determined by using glucose oxidase to measure glucose content after the enzymatic conversion of starch to glucose with amyloglucosidase. 2.3. In vitro and in vivo digestibility In vitro digestible organic matter content (DOMD) was measured using the neutral detergent-cellulase plus amylase and gamannase digestibility (NCGD) method of Dowman and Collins (1982) as modified by Dowman (1993) and the two-stage, rumen fluid-pepsin method of Tilley and Terry (1963). The first stage of the Tilley and Terry
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(1963) procedure was terminated by the addition of acidified pepsin to the tubes rather than by centrifugation. In vivo DOMD was measured using four mature wethers weighing 864.6 and 627.5 in years 1 and 2, respectively. Daily diets of 12 g DM kgÿ1 live weight of forage were offered to provide diets near the maintenance level of feeding. A 10-day diet acclimatisation period was followed by a 10-day balance period when feed intake, feed refusals, and faecal outputs were measured. 2.4. In vitro gas production Fermentation gas production was determined on freeze-dried, milled (1 mm) samples using the method of Theodorou et al. (1994). Samples were incubated in triplicate for 0, 4, 8, 12, 16, 20, 24, 28, 32, 40, 48, 60, 72 and 96 h. The regression-corrected gas volumes were fitted to the modified Gompertz model (Beuvink and Kogut, 1993) which is of the form: or os exp ÿDr t ÿ exp ÿDs t Y Y1 exp ÿ Dr Ds where Y is the cumulative gas production (ml), Y1 is upper asymptote representing total gas production (ml), mor is specific rapid rate of gas production rate (hÿ1), mos is the specific slow rate of gas production (hÿ1), Dr is the rapid fractional rate governing decay (hÿ1), Ds is the slow fractional rate governing decay (hÿ1) and t is incubation time (h). The parameters of the model including the maximal rate of gas production, lag phase and time at which 95% of asymptote is reached were calculated from the model as described by Beuvink and Kogut (1993). The data were fitted to the model using Sigmaplot version 2.01 (Jandel Scientific, USA) which uses the Marquand±Levenberg algorithm in an iterative procedure that aims to minimise the residual mean squares. 2.5. Water solubility and in situ rumen degradation The solubility of DM and nitrogen of the forages was determined by soaking chopped (2 cm) samples in cold water (228C) for 1 h and analysing the residue obtained after vacuum filtration through a Whatman No.9 filter paper. In situ rumen degradability was determined by incubating chopped (2 cm) samples (5 g DM) of each forage in duplicate for 0, 3, 8, 16, 24, 45, 72 and 96 h within each of three sheep. Following incubation, the residues were washed in a washing machine and the DM disappearance values for individual bags (pore size 43 mm) were fitted to the exponential model of érskov and McDonald (1979) which is of the form Y a b 1 ÿ eÿct where Y is the degradability (g kgÿ1), a is the immediately soluble fraction (g kgÿ1), b is the potentially but not immediately degradable fraction (g kgÿ1), c is the fractional rate of soluble degradation (hÿ1) of b and t is the incubation time (h). The equation of Weisbjerg et al. (1990) was used to correct for losses of fine particles from the pores of the bags. The data were fitted to the model using a Prime 2265 main frame computer which uses an algorithm involving an iterative procedure that aims to minimise the residual mean squares.
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2.6. Accuracy of prediction The value of any single laboratory measurement or multivariate model as a predictor was assessed by the coefficient of determination (r2) adjusted for degrees of freedom following analysis of variance (r2adj.). With multivariate models, variables were only retained in the model when they significantly (P<0.05) contributed to the variance accounted for. The accuracy of prediction of any individual equation was expressed in terms of its' minimum standard error of prediction (s) which relates to error at the mean value of the independent variable (Givens et al., 1989). The influence of year of harvest on the prediction relationships was also assessed by comparing whether the data from both years were best described using single, parallel or separate regression lines. This involved declaring a factor structure which classified the data according to the year of harvest and included the year of harvest along with the independent variable in the regression model. 2.7. Near infrared reflectance spectroscopy (NIRS) The organic matter digestibility (OMD) of the forages was initially predicted using the NIRS spectral model derived by Baker et al. (1994, Eq.(1)) for predicting the OMD of grass silage. Subsequently, a WCW-based calibration equation for the prediction of DOMD and OMD was developed as follows: All forages were scanned over the range 1100±2500 nm using an NIR Systems 5000 scanning monochromater (Intec 2, Basingstoke, UK) and NIR spectrometer linked to a personal computer. Spectral data were collected at wavelength intervals of 2 nm and the 700 data points obtained for each sample were stored in the computer as their absorbance, logarithm of the reciprocal reflectance values (log10(1/R)), where R is the percentage reflectance. The log(1/R) values were all converted to their respective standard normal variates (SNV) and spectral curvature was removed by the detrending (D) process (Barnes et al., 1989) using the Infrasoft International Software (ISI, Port Matilda, PA, USA) developed from that first described by Shenk et al. (1981). The SNV-D values were then derived as described by Norris et al. (1976) to improve their predictive ability. Calibration relationships between DOMD in vivo and the derived SNV-D values were developed using the ISI software which performs stepwise multiple linear regression (MSR) to provide an equation with up to nine spectral terms. The wavelength range was set at 1108±2490 nm ignoring the first and last four wavelengths and segments at every 8 nm within this range were used to develop the calibration equation. With the F-ratio for each term set at 7 (Windham et al., 1989), the selection of the best equation was based on the model with the largest R2, smallest standard error of calibration (SEC) and lowest number of spectral terms to avoid overfitting (Westerhaus, 1989). 3. Results 3.1. Animal and laboratory-based measurements The detailed results of the measurements on the forages were discussed by Adesogan et al. (1997), hence only summary data are presented in this paper (Appendices 1 and 2).
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Table 1 The most accurate (P<0.05) relationships for predicting the digestible organic matter content in vivo of WCW from monovariate models (g kgÿ1 corrected DM or as stated) Predictor (x)
Year
r2adj.a
sb
Equation
Crude protein NCGDc Fine particle lossesd Immediately soluble fractione Rumen fluid-pepsin digestibility Rumen fluid-pepsin digestibilitye NCGDc NCGDc,e Crude protein Effective degradabilityf,g Lactic acid Total volatile fatty acid content Effective degradabilityf
1 1 1 2 2 2 2 2 2 2 2 2 2
0.43 0.36 0.30 0.44 0.39 0.39 0.37 0.37 0.29 0.33 0.26 0.25 0.28
20.4 26.4 27.5 25.3 25.6 26.3 26.3 27.1 27.9 27.9 28.4 28.6 28.9
5870.238x 3270.456x 669ÿ0.308x 3806.24x 2210.686x 2160.689x 3310.508x 3290.502x 5980.243x 2675.53x 6120.306x 6090.272x 2606.15x
* * * ** ** * * ** * * * * *
a
Accountable variance adjusted for degrees of freedom. Residual standard deviation. c Neutral detergent-cellulase plus gamannase digestibility. d Particulate losses from rumen degradability bags. e Measured as g kgÿ1 oven DM. f Calculated at an assumed outflow rate of 0.02 hÿ1. g Corrected for fine particles losses after Weisbjerg et al. (1990). * P<0.05. ** P<0.01. b
3.2. Prediction of digestibility in vivo from in vitro digestibility In general, predictions of DOMD in vivo from the traditional laboratory-based methods were imprecise and inconsistent, though better in year 2. For instance, rumen fluid and NCGD methods only gave significant (P<0.05) predictions in year 2, but even then, the accuracy of prediction was relatively low (Table 1). The accountable variance of the equations tended to be marginally higher, when volatile constituents lost during oven drying were accounted for and assumed to be wholly digestible (Table 1). The inaccuracy of the rumen fluid/in vivo relationship which typifies that of the other in vitro techniques is depicted in Fig. 1. 3.3. Prediction of digestibility in vivo from chemical composition Generally predictions from chemical composition were also imprecise. The most accurate and consistent chemical predictor was the CP content which accounted for at least a third of the variability in DOMD in vivo in both years. The only other notable chemical predictors were VFA and lactic acid contents, but these only gave significant (P<0.05) predictions in year 2. No consistently accurate relationships were found between NDF digestibility and either ADF (r20.27), NDFA (r20.37) or lignified cell wall content (ADF/NDFA) (r20.27).
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Fig. 1. The relationships between the in vivo and rumen fluid-pepsin digestible organic matter content (DOMD) of WCW in both years (g kgÿ1 corrected DM)
3.4. Prediction of digestibility in vivo from in situ degradability Of the rumen degradability parameters, only the fine particle fraction, immediately soluble fraction and effective degradability gave significant (P<0.05) predictions of DOMD in vivo (Table 1). However, none of these predictions was consistent across years. For instance, the fine particle fraction which contained up to 500 g kgÿ1 starch only gave a significant prediction in year 1 (r20.37) when the starch content was quite variable (Appendix 1). Correction for such particulate losses only marginally improved the accuracy of the prediction from effective degradability (Table 1). 3.5. Prediction of digestibility in vivo from in vitro gas production None of the gas production parameters accounted for up to a third of the variability in DOMD in vivo and none of such predictions was significant (P<0.05). 3.6. Prediction of in vivo digestibility from multivariate predictors No multivariate combination of single predictors was significant (P<0.05) for predicting DOMD in vivo in year 2, but in year 1, two of such equations were found
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Table 2 The most accurate (P<0.05) relationships for predicting the digestible organic matter content in vivo of WCW from multivariate models (g kgÿ1 corrected DM) Year
1 1
Predictors x1
x2
Crude protein Starch
Total ash Rumen fluid-pepsin digestibility
R2 adj.a
sb
Equation (y)
0.61 0.46
16.7 19.8
713.80.238x1ÿ1.91x2 267.80.296x10.51x2
** *
a
Accountable variance adjusted for degrees of freedom. Residual standard deviation. * P<0.05 ** P<0.01. b
Table 3 The effect of year of harvest on the prediction of the digestible organic matter content in vivo of WCW from monovariate models Predictor
Effect of year of harvest on: Constant
Slope
Accountable variancea
Crude protein NCGDb Rumen fluid-pepsin digestibility NIRS-DOMDc Total volatile fatty acids Acid detergent fibre Water soluble carbohydrates Neutral detergent fibre plus amylase Ethanol Starch Hemicellulose
* * ns ns ns ns ns ns ns ns ns
* * ns ns ns ns ns ns ns ns ns
0.32 0.30 0.24 0.14 0.14 0.05 0.04 0.00 0.00 0.00 0.00
* P<0.05. ns : Not significant. a Includes effect of year of harvest. b NCGD, Neutral detergent cellulase plus gamannase digestibility. c Digestible organic matter content predicted from near-infrared reflectance spectroscopy using the grass-silage equation of Baker et al. (1994).
(Table 2). Combining total ash and CP contents in year 1 improved the accuracy of prediction over that from the single predictors. Starch and rumen fluid DOMD contents were also significantly (P<0.05) additive in year 1. 3.7. Effect of year of harvest on the prediction of digestibility in vivo Table 3 shows the effect of year of harvest on the predictive equations. When the data from both years were pooled, different relationships were required for predicting DOMD in vivo from CP content or NCGD. Although a single equation sufficed for
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Fig. 2. Calibration relationship for predicting the in vivo digestible organic matter content (DOMD) (g kgÿ1 corrected DM) of whole crop wheat forages from near-infrared reflectance spectroscopy (NIRS).
Table 4 The most accurate near-infrared spectroscopy-based equations for predicting the in vivo digestible organic matter content (DOMD) and organic matter digestibility (OMD) of Year One and Two forages (ranked by standard error of calibration (SEC)) Predicted term
Maths treatmenta
DOMD
2,4,1,1 2,4,2,1 2,8,2,1 1,4,4,1 2,6,2,1 0,0,1,1 2,4,2,1 2,8,1,1
OMD
1
Calibration statistics Accountable variance
SEC
Important wavelengths (nm)
0.87 0.74 0.66 0.67 0.72 0.62 0.63 0.62
13.0 18.8 18.9 21.6 1.9 2.2 2.3 2.3
2212, 2268, 2068, 2244, 1436, 1516, 1540, 2204,
1732, 1508, 1900, 1932, 2324 1348 1732, 2020,
Equation number 1988 1732 2148 2316 1684 2364
1 2 3 4 5 6 7 8
The first, second, third and fourth numbers in the maths treatment denote the order of the derivative function, the spectral segment length in data points over which the derivative was taken and the segment lengths over which the function was smoothed.
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each of the remaining predictors evaluated, the accuracy of such predictions was very low. 3.8. Prediction of in vivo digestibility from near-infrared reflectance spectroscopy The grass silage equation of Baker et al. (1994) gave poor predictions of OMD in vivo in years 1 (r20.0, s28.08) and 2 (r20.30, s27.64). In contrast, the WCW calibration equation acurately predicted the in vivo OMD and DOMD of the forages (Fig. 2, Table 4), though spectral dispersion was relatively high between 1800 and 2000 nm. The best calibration equations accounted for 0.87 of the variability in DOMD in vivo (Eq. 1) and 0.72 of OMD in vivo (Eq. 5) (Table 5). The Mahalanobis distance (Shenk and Westerhaus, 1991) of all samples was less than 4, confirming their proximity to the population mean and the absence of outliers. This indicates that all the samples were spectrally similar and suggests that the year of harvest had little or no effect on the spectra.
4. Discussion It is surprising that the biological and chemical techniques evaluated gave such inaccurate predictions of DOMD in vivo which contradict previous suggestions (Leaver and Hill, 1992; Tetlow, 1992). Due to the dearth of similar predictive studies for WCW in the literature, the results were compared to published relationships for maize silage. It is interesting to note that the imprecision observed is similar to that obtained by Dowman and Collins (1982) for predicting the DOMD in vivo of maize silage from six in vitro digestibility techniques (r20.23±0.51). Furthermore, Givens et al., (1995, unpublished) found that the DOMD in vivo of 50 maize silages was inadequately predicted (r20.34) by the Tilley and Terry (1963) technique, though a moderate relationship was found with the NCGD technique (Givens et al., 1995). In addition to in vitro/in vivo differences in the grinding of husks (Tetlow, 1992), such inaccurate predictions for heterogeneous forages like maize silage and WCW may be related to differences in the sites, rates and extents of digestion of the starch and cell wall fractions. Since low ruminal pH inhibits cellulolytic organisms (Grant and Mertens, 1992), the acidic conditions imposed by starch digestion in such forages may have enhanced the post-ruminal digestion of the cell walls at the expense of intra-ruminal digestion. This suggestion is in accord with the observation that in dairy cows, the intra-ruminal digestion of organic matter decreased as the proportion of WCW in a grass silage diet increased (Abdalla et al., 1996). Although future studies need to verify that post-ruminal digestion is similarly increased in sheep fed WCW, the latter study suggests that in vitro techniques which do not accurately simulate post-ruminal digestion are of limited use for predicting DOMD in vivo of WCW. This is supported by the additivity of starch and rumen fluid DOMD in year 1, which indicates that the rumen fluid technique did not account for all the starch digestion. The additivity also agrees with suggestions of augmented starch fermentation in the caecum and small intestine of steers fed WCW (Deschard et al., 1988).
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The poor relationships may also be related to the low N solubility values (118±934 g kgÿ1, Adesogan, 1996) of some forages which suggests the presence of Maillaird products that are digestible in vitro but not in vivo (Tetlow and Mason, 1987). In addition, the small population used implies that values at the extreme of the relationships (Fig. 1) considerably influenced the position of the regression line and the resulting accuracy. Whether or not the prediction would be improved by using a larger population is debatable, since the relatively narrow range in the DOMD in vivo of the forages typifies that found in practice (Weller, 1992). It would have been interesting to determine the strength of the predictive relationships for isolated populations of fermented and ureatreated forages, but this was precluded by the fermentation of year 2 urea-treated forages (Adesogan et al., 1997). The inaccurate cell wall-based predictions agree with findings for maize silage by Givens et al. (1995) who noted that substantial variations in the proportions of cell wall components in maize silage were not reflected in the digestibility values obtained. The inaccuracy of the cell wall-based prediction for WCW may be related to the wide range of maturities over which WCW is harvested. While other forages are harvested across a narrow range of maturities within which NDF content increases with maturity, maturityrelated decreases in NDF content can occur in the WCW (Adesogan et al., 1997). Furthermore, the tendency for a decrease in digestibility with maturity in other forages is counteracted in WCW by the replacement of water soluble carbohydrates by starch with maturity. The imprecise prediction from the degradability parameters contrasts with results for hay (Khazaal et al., 1993) and may have been partly due to the fine particle losses. The lack of substantial improvements in prediction accuracy after correcting for such particulate losses, is probably because the Weisbjerg et al. (1990) model assumes similar degradation rates for the fine particles lost and the material remaining in the bag. This is invalid for WCW because of the different degradation rates of the grain and straw fractions (Givens et al., 1993). Since Hill and Leaver (1993) also noted that the rates of passage of these fractions differ, the use of single rate constants in this work may have contributed to the poor predictions from the degradability parameters. The poor prediction of DOMD in vivo from gas production contradicts the findings of Menke et al. (1979) for various feedstuffs. However, Menke et al. (1979) suggested that inaccurate gas production/DOMD in vivo relationships could result from in vitro/in vivo differences in the rates of degradation and outflow and improved post-ruminal utilisation of nutrients escaping fermentation as appears to be the case with WCW. Other factors which may be implicated include the indirect production of gas from the buffer or from microbial turnover, the dependence of gas production on the end products of fermentation and the microbial potential for switching between VFA and lactate production (Beuvink and Spoelstra, 1992). NIRS was the only promising method for predicting the digestibility of WCW. However, since only the WCW-based calibration equation produced accurate predictions, it should be noted that grass silage-based calibration equations may give erroneous results when used for WCW evaluation. The spectral dispersion at the moisture-sensitive region suggests that there was still some variation in the moisture content of the samples. Since
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including such moisture-sensitive peaks can disproportionately influence the spectra and affect the validity of prediction equations, further research is required to validate the promise of the NIRS technique for predicting the nutritive value of WCW. Such research should utilise undried or adequately dried samples, otherwise the use of a noise repeatability file in addition to the SNV-D process is recommended to eliminate sensitivity to residual moisture. There is also the need to use large diverse populations (>100 samples) to ensure that accurate, robust calibration and validation equations are developed. 5. Conclusions The chemical and biological techniques evaluated provided inaccurate predictions of the DOMD in vivo. This was thought to relate to the fact that the in vitro techniques primarily simulate ruminal digestion and therefore, do not sufficiently account for the differences in the sites, extents and rates of digestion of the cell wall and starch fractions of WCW. Thus, as with maize silage (Givens et al., 1995), future methods of characterising WCW will need to accurately simulate the intra-ruminal and post-ruminal digestion kinetics of the starch and cell wall fractions. Future work will also need to develop sample preparatory methods which prevent fine particle losses. It is noteworthy that although NIRS appears to be the most promising method for predicting the DOMD in vivo of WCW, grass silage-based equations may give erroneous results. Future work should validate the prediction accuracy of the WCW-based equation using large data sets (>100) that enable the production of robust calibration and validation equations. Such research is integral to the adoption of the NIRS technique for routine prediction of the nutritive value of WCW forages. Nevertheless, the cost implications of developing robust NIRS-based equations suggests that in the short term, attention should be focussed on modifying existing in vitro techniques (which primarily simulate ruminal digestion) to account for the postulated post ruminal digestion of WCW. Acknowledgements This programme was funded within the LINK Programme `Technologies for Sustainable Farming Systems' by Agricultural Genetics Co. Ltd., BOCM Pauls, Hi Spec Engineering Ltd., ICI Nutrition, J Bibby Agriculture Ltd., Maize Growers Association, Milk Marketing Board of England and Wales, Ministry of Agriculture, Fisheries and Food, Rumenco, Trouw Nutrition UK Ltd. and Zeneca Seeds UK Ltd. Appendix Tables 5 and 6
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Table 5 Laboratory analyses of whole crop wheat forages (g kgÿ1 oven DM or as stated) Year 1 (n12)
1
ÿ1
Oven DM (g kg ) Corrected DM (g kgÿ1) Crude proteina Total ash Water soluble carbohydrates Starch Neutral detergent fibre plus amylaseb Acid detergent fibreb Rumen fluid DOMDc NCGDd NIR-DOMDe Effective degradability 96 h gas production (ml gÿ1 OM)
Year 2 (n14)
Mean
s.e.
Range
Mean
s.e.
Range
518 539 179 70 12 228 458 297 566 622 618 498 508
36.8 35.1 23.7 2.3 1.9 21.2 19.7 15.1 9.2 13.4 9.1 15.7 38.9
319±747 346±751 107±373 61±87 2±24 95±377 306±586 198±408 513±611 528±696 566±660 416±588 338±760
442 455 164 81 13 189 500 297 594 590 635 451 621
26.8 26.5 22.0 6.0 3.1 27.5 17.3 10.7 9.0 11.7 9.4 12.7 45.7
248±546 261±559 89±394 48±115 5±43 23±424 412±655 238±387 525±655 508±675 587±711 379±542 478±1016
a
Measured on fresh material. Ash free. 3 Digestible organic matter content. d Neutral detergent cellulase plus gamannase digestibility. 5 DOMD predicted from a whole-crop wheat-based near-infrared reflectance spectroscopy calibration equation. b
Table 6 Digestibility in vivo of whole crop wheat forages Year 1 (n12)
a
ÿ1
DOMD (g kg
oven DM)
Digestibility coefficients Organic matter Dry matter Gross energy Starch Neutral detergent fibre a
Year 2 (n14)
Mean
s.e.
Range
Mean
s.e.
Range
611
9.51
558±653
626
9.11
561±708
0.009 0.009 0.009 0.006 0.028
0.602±0.704 0.582±0.680 0.578±0.691 0.940±0.999 0.362±0.695
0.009 0.009 0.010 0.018 0.008
0.598±0.742 0.586±0.732 0.642±0.755 0.748±0.997 0.579±0.6931
0.656 0.636 0.646 0.977 0.568
0.682 0.655 0.703 0.957 0.627
Digestible organic matter content.
References Abdalla, A.L., Sutton, J.D., Humphries, D.J., Phipps, R.H. (1996). Digestibility of diets of grass silage and whole-crop wheat in the rumen of lactating dairy cows. Anim. Sci. 63, 631 (Abstract). Adesogan, A.T., 1996. Prediction of the nutritive value of fermented and urea-treated whole crop wheat forages. Ph.D. Thesis, The University of Reading, UK. Adesogan, A.T., Givens, D.I., Owen, E., 1997. The chemical composition, digestibility and energy value of fermented and urea-treated whole crop wheat harvested at three stages of maturity. Grass Forage Sci. 53, 66±75.
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Alderman, G., 1985. Prediction of the energy value of compound feeds. In: Haresign, W., Cole, D.J.A. (Eds.), Recent Advances in Animal Nutrition. Butterworths, London, pp. 3±52. Baker, C.W., Givens, D.I., 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., Lister, S.J., 1989. Standard normal variate transformation and de-trending of nearinfrared diffuse reflectance spectra. Appl. Spectrosc. 43, 772±777. Beuvink, J.M.W., Spoelstra, S.F., 1992. Interactions between substrate, fermentation end-products buffering systems and gas production upon fermentation of different carbohydrates by mixed rumen micro-organisms in vitro. Appl. Microbiol. Biotechnol. 37, 505±509. Beuvink, J.M.W., Kogut, J., 1993. Modelling gas production kinetics of grass silages incubated with buffered rumen fluid. J. Anim. Sci. 71, 1041±1046. Deschard, G., Mason, V.C., Tetlow, R.M., 1988. Treatment of whole crop cereals with alkali. 4. Voluntary intake and growth in steers given wheat ensiled with sodium hydroxide, urea or ammonia. Anim. Feed. Sci. Technol. 19, 55±66. Dowman, M.G., Collins, F.C., 1982. The use of enzymes to predict the digestibility of animal feeds. J. Sci. Food. Agric. 33, 689±696. Dowman, M.G., 1993. Modifications to the neutral detergent cellulase digestibility method for the prediction of the metabolisable energy of compound feedstuffs containing palm kernel meal. J. Sci. Food. Agric. 61, 327±331. Givens, D.I., Everington, J.M., Adamson, A.H., 1989. The digestibility and metabolisable energy content of grass silage and their prediction from laboratory measurements. Anim. Feed. Sci. Technol. 24, 27±43. Givens, D.I., Moss, A.R., Adamson, A.H., 1993. The digestion and energy value of whole crop wheat treated with urea. Anim. Feed. Sci. Technol. 43, 51±64. Givens, D.I., Cottyn, B.G., Dewey, P.J.S., Steg, A., 1995. A comparison of the neutral detergent-cellulase method with other laboratory methods for predicting the digestibility in vivo of maize silages from three European countries. Anim. Feed. Sci. Technol. 54, 55±64. Goering, H.K., van Soest, P.J., 1970. Forage Fiber Analysis, Agriculture Handbook No. 379, Agriculture Research Service, US Dept. of Agriculture, Washington. Grant, R.J., Mertens, D.R., 1992. Influence of buffer pH and raw corn starch addition on in vitro fiber digestion kinetics. J. Dairy Sci. 75, 2762±2768. Hill, J., Leaver, J.D., 1993. The intake digestibility and rate of passage of whole crop wheat by growing heifers. Proc. 1993 Winter Meeting of the British Society of Animal Production, Scarborough. Khazaal, K., Detinho, M.T., Ribeiro, J.M., érskov, 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±112. Leaver, J.D., Hill, J., 1992. Feeding cattle on whole crop cereals. In: Stark, B.A., Wilkinson, J.M. (Eds.), Wholecrop Cereals, 2nd ed. Chalcombe, Canterbury, pp. 59±72. MAFF, 1986. The Analysis of Agricultural Materials, Reference Book 427, HMSO, London. Menke, K.H., Raab, L., Salewski, A., Steingass, H., Fritz, D., Schneider, W., 1979. The estimation of the digestibility and metabolisable energy content of ruminant feedingstuffs from gas production when they are incubated with rumen liquor in vitro. J. Agric. Sci. Camb. 93, 217±222. Norris, K.H., Barnes, R.F., Moore, J.E., Shenk, J.S., 1976. Predicting forage quality by infrared reflectance spectroscopy. J. Anim. Sci. 43, 889±897. érskov, E.R., McDonald, I., 1979. The estimation of protein degradability in the rumen from incubation measurements weighted according to rate of passage. J. Agric. Sci. 92, 499±503. Shenk, J.S., Landa, I., Hoover, H.R., Westerhaus, M.O., 1981. Description and evaluation of a near-infrared reflectance spectro-computer for forage and grain analysis. Crop Sci. 21, 355±358. Shenk, J.S., Westerhaus, M.O., 1991. Population definition, sample selection and calibration procedures for near-infrared reflectance spectroscopy. Crop Sci. 31, 469±474. Tetlow, R.M., 1992. A decade of research into whole crop cereals at Hurley. In: Wilkinson, J.M., Stark, B.A. (Eds.), Whole-Crop Cereals, 2nd ed. Chalcombe, Canterbury, pp. 1±20.
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Tetlow, R.M., Mason, V.C., 1987. Treatment of whole crop cereal with alkali. 1. The influence of sodium hydroxide and ensiling on the chemical composition and in vitro digestibility of rye, barley and wheat crops harvested at increasing maturity and dry matter content. Anim. Feed. Sci. Technol. 18, 257±269. Theodorou, M.K., Williams, B.A., Dhanoa, M.S., McAllan, A.B., 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., Terry, R.A., 1963. A two stage technique for the in vitro digestion of forage crops. J. Br. Grassl. Soc. 18, 104±111. Weisbjerg, M.R., Bhargava, P.K., Hvelplund, T., Madsen, J., 1990. Use of degradation curves in feed evaluation., Report No. 679, National Institute of Animal Production, Beretning fra Statens Husdyrbrugsforsog, Denmark. Weller, R.F., 1992. The national whole-crop cereals survey. In: Wilkinson, J.M., Stark, B.A. (Eds.), Whole-Crop Cereals, 2nd ed. Chalcombe, Canterbury, pp. 1±20. Westerhaus, M.O., 1989. Interpretation of regression statistics. In: Marten, G.S., Shenk, J.S., Barton, F.E., (Eds.), Near-infrared Reflectance Spectroscopy (NIRS): Analysis of Forage Quality. Agriculture Handbook No. 643 (revised), USDA, pp. 39±40. Windham, W.R., Mertens, D.R., Barton, F.E., 1989. Protocol for NIRS. Calibration: Sample selection and equation development and validation. In: Marten, G.C., Shenk, J.S., Barton, F.E. (Eds.), Near-infrared Reflectance Spectroscopy (NIRS): Analysis of Forage Quality. Agriculture Handbook No. 643 (revised), Suppl. I, USDA, pp. 96±103.
Prediction of the in vivo digestibility of whole crop wheat from in vitro digestibility, chemical composition, in situ rumen degradability, in vitro gas production and near infrared reflectance spectroscopy A.T. Adesogana,b,*, E. Owena, D.I. Givensb a b
Department of Agriculture, The University of Reading, Earley Gate, PO Box 236, Reading RG6 6AT, UK Feed Evaluation and Nutritional Sciences, ADAS Drayton, Alcester Road, Stratford on Avon CV37 9RQ, UK Received 4 November 1997; accepted 6 April 1998
Abstract Twenty-six winter wheat forages (cultivars Slepjner, Hussar and Cadenza) harvested at three stages of maturity in each of two years, were conserved with or without Maxgrass additive or with urea (20 or 40 g kgÿ1 dry matter; DM) in 200 l barrels. The forages were analysed for in vivo digestibility in wethers, chemical composition, in vitro rumen fluid-pepsin digestibility, in vitro neutral detergent-cellulase plus gamannase digestibility (NCGD), in vitro fermentation gas production and in situ rumen degradability. Forages were also scanned using near-infrared reflectance spectroscopy (NIRS) and calibration equations developed for predicting in vivo digestibility. In vivo digestible organic matter content (DOMD) was poorly predicted by cell wall content (r20.19), NCGD (r20.41), rumen fluid DOMD (r20.41), rumen degradability (r20.44) and in vitro gas production (r20.26). Although crude protein content was a better predictor (r20.48), the relationship differed (P<0.05) with the year of harvest of the forages. In contrast, NIRS was a more accurate and consistent predictor of DOMD in vivo (r20. 87). This study indicates that traditional laboratory-based feed evaluation techniques are unsuitable for predicting the DOMD of WCW, but that NIRS holds promise. However, as only 26 forages were used to derive the calibration equation, further research is required using large (150) data sets to validate the promise shown by NIRS and enable its adoption by the advisory services. # 1998 Elsevier Science B.V. Keywords: Nutritive value; Prediction; Whole crop; Digestibility; In vitro
* Corresponding author. Tel.: +44 1970 624471; fax: +44 1970 611264; e-mail: [email protected] 0377-8401/98/$19.00 # 1998 Elsevier Science B.V. All rights reserved PII S 0 3 7 7 - 8 4 0 1 ( 9 8 ) 0 0 1 7 5 - 8
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1. Introduction Whole crop wheat (WCW) is produced by conserving the aerial part of the wheat plant under alkaline or acidic conditions. This forage is increasingly being used for winter feeding of ruminant livestock in areas of the UK which are marginal for maize production. However, many of the nutritional results sent to farmers on their WCW samples are only estimated using laboratory-based techniques, some of which were developed for grass silage. Yet, there is little information in the literature on the accuracy with which such laboratory techniques predict the nutritive value of WCW. Therefore, the use of such techniques may give misleading information that could adversely affect profitability. Previous work (Adesogan et al., 1997) which showed that the nutritive value of WCW varies considerably with treatment application and maturity, further emphasises the need to validate laboratory-based assessments of WCW digestibility with in vivo studies. Therefore, the aim of this study was to evaluate the suitability of predicting the in vivo digestibility of WCW from various less animal-dependent techniques. The study involved the use of fermented and urea-treated WCW harvested over the stages of maturity used in practice. 2. Materials and methods 2.1. Forages used The study involved the use of twenty-six WCW forages produced over 2 years from three varieties of wheat (Hussar, Cadenza and Slepjner). In each year, the forages were harvested at three stages of maturity (milk, soft cheese and dough) and conserved in 200 l barrels with Maxgrass ± an acid-based additive (5, 4 or 3 l tÿ1) or with urea (0, 20 or 40 g kgÿ1 dry matter (DM)). The reader is referred to the work of Adesogan et al. (1997) for further details on the forages. 2.2. Chemical composition All forages were analysed for DM, ash, crude protein (CP), ammonia nitrogen (NH3N), lactic acid, volatile fatty acids (VFA) and water soluble carbohydrates (WSC) by the methods of MAFF (1986), acid detergent fibre (ADF) (Goering and van Soest, 1970) and neutral detergent fibre plus amylase (NDFA) (Alderman, 1985). Starch content was determined by using glucose oxidase to measure glucose content after the enzymatic conversion of starch to glucose with amyloglucosidase. 2.3. In vitro and in vivo digestibility In vitro digestible organic matter content (DOMD) was measured using the neutral detergent-cellulase plus amylase and gamannase digestibility (NCGD) method of Dowman and Collins (1982) as modified by Dowman (1993) and the two-stage, rumen fluid-pepsin method of Tilley and Terry (1963). The first stage of the Tilley and Terry
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(1963) procedure was terminated by the addition of acidified pepsin to the tubes rather than by centrifugation. In vivo DOMD was measured using four mature wethers weighing 864.6 and 627.5 in years 1 and 2, respectively. Daily diets of 12 g DM kgÿ1 live weight of forage were offered to provide diets near the maintenance level of feeding. A 10-day diet acclimatisation period was followed by a 10-day balance period when feed intake, feed refusals, and faecal outputs were measured. 2.4. In vitro gas production Fermentation gas production was determined on freeze-dried, milled (1 mm) samples using the method of Theodorou et al. (1994). Samples were incubated in triplicate for 0, 4, 8, 12, 16, 20, 24, 28, 32, 40, 48, 60, 72 and 96 h. The regression-corrected gas volumes were fitted to the modified Gompertz model (Beuvink and Kogut, 1993) which is of the form: or os exp ÿDr t ÿ exp ÿDs t Y Y1 exp ÿ Dr Ds where Y is the cumulative gas production (ml), Y1 is upper asymptote representing total gas production (ml), mor is specific rapid rate of gas production rate (hÿ1), mos is the specific slow rate of gas production (hÿ1), Dr is the rapid fractional rate governing decay (hÿ1), Ds is the slow fractional rate governing decay (hÿ1) and t is incubation time (h). The parameters of the model including the maximal rate of gas production, lag phase and time at which 95% of asymptote is reached were calculated from the model as described by Beuvink and Kogut (1993). The data were fitted to the model using Sigmaplot version 2.01 (Jandel Scientific, USA) which uses the Marquand±Levenberg algorithm in an iterative procedure that aims to minimise the residual mean squares. 2.5. Water solubility and in situ rumen degradation The solubility of DM and nitrogen of the forages was determined by soaking chopped (2 cm) samples in cold water (228C) for 1 h and analysing the residue obtained after vacuum filtration through a Whatman No.9 filter paper. In situ rumen degradability was determined by incubating chopped (2 cm) samples (5 g DM) of each forage in duplicate for 0, 3, 8, 16, 24, 45, 72 and 96 h within each of three sheep. Following incubation, the residues were washed in a washing machine and the DM disappearance values for individual bags (pore size 43 mm) were fitted to the exponential model of érskov and McDonald (1979) which is of the form Y a b 1 ÿ eÿct where Y is the degradability (g kgÿ1), a is the immediately soluble fraction (g kgÿ1), b is the potentially but not immediately degradable fraction (g kgÿ1), c is the fractional rate of soluble degradation (hÿ1) of b and t is the incubation time (h). The equation of Weisbjerg et al. (1990) was used to correct for losses of fine particles from the pores of the bags. The data were fitted to the model using a Prime 2265 main frame computer which uses an algorithm involving an iterative procedure that aims to minimise the residual mean squares.
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2.6. Accuracy of prediction The value of any single laboratory measurement or multivariate model as a predictor was assessed by the coefficient of determination (r2) adjusted for degrees of freedom following analysis of variance (r2adj.). With multivariate models, variables were only retained in the model when they significantly (P<0.05) contributed to the variance accounted for. The accuracy of prediction of any individual equation was expressed in terms of its' minimum standard error of prediction (s) which relates to error at the mean value of the independent variable (Givens et al., 1989). The influence of year of harvest on the prediction relationships was also assessed by comparing whether the data from both years were best described using single, parallel or separate regression lines. This involved declaring a factor structure which classified the data according to the year of harvest and included the year of harvest along with the independent variable in the regression model. 2.7. Near infrared reflectance spectroscopy (NIRS) The organic matter digestibility (OMD) of the forages was initially predicted using the NIRS spectral model derived by Baker et al. (1994, Eq.(1)) for predicting the OMD of grass silage. Subsequently, a WCW-based calibration equation for the prediction of DOMD and OMD was developed as follows: All forages were scanned over the range 1100±2500 nm using an NIR Systems 5000 scanning monochromater (Intec 2, Basingstoke, UK) and NIR spectrometer linked to a personal computer. Spectral data were collected at wavelength intervals of 2 nm and the 700 data points obtained for each sample were stored in the computer as their absorbance, logarithm of the reciprocal reflectance values (log10(1/R)), where R is the percentage reflectance. The log(1/R) values were all converted to their respective standard normal variates (SNV) and spectral curvature was removed by the detrending (D) process (Barnes et al., 1989) using the Infrasoft International Software (ISI, Port Matilda, PA, USA) developed from that first described by Shenk et al. (1981). The SNV-D values were then derived as described by Norris et al. (1976) to improve their predictive ability. Calibration relationships between DOMD in vivo and the derived SNV-D values were developed using the ISI software which performs stepwise multiple linear regression (MSR) to provide an equation with up to nine spectral terms. The wavelength range was set at 1108±2490 nm ignoring the first and last four wavelengths and segments at every 8 nm within this range were used to develop the calibration equation. With the F-ratio for each term set at 7 (Windham et al., 1989), the selection of the best equation was based on the model with the largest R2, smallest standard error of calibration (SEC) and lowest number of spectral terms to avoid overfitting (Westerhaus, 1989). 3. Results 3.1. Animal and laboratory-based measurements The detailed results of the measurements on the forages were discussed by Adesogan et al. (1997), hence only summary data are presented in this paper (Appendices 1 and 2).
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Table 1 The most accurate (P<0.05) relationships for predicting the digestible organic matter content in vivo of WCW from monovariate models (g kgÿ1 corrected DM or as stated) Predictor (x)
Year
r2adj.a
sb
Equation
Crude protein NCGDc Fine particle lossesd Immediately soluble fractione Rumen fluid-pepsin digestibility Rumen fluid-pepsin digestibilitye NCGDc NCGDc,e Crude protein Effective degradabilityf,g Lactic acid Total volatile fatty acid content Effective degradabilityf
1 1 1 2 2 2 2 2 2 2 2 2 2
0.43 0.36 0.30 0.44 0.39 0.39 0.37 0.37 0.29 0.33 0.26 0.25 0.28
20.4 26.4 27.5 25.3 25.6 26.3 26.3 27.1 27.9 27.9 28.4 28.6 28.9
5870.238x 3270.456x 669ÿ0.308x 3806.24x 2210.686x 2160.689x 3310.508x 3290.502x 5980.243x 2675.53x 6120.306x 6090.272x 2606.15x
* * * ** ** * * ** * * * * *
a
Accountable variance adjusted for degrees of freedom. Residual standard deviation. c Neutral detergent-cellulase plus gamannase digestibility. d Particulate losses from rumen degradability bags. e Measured as g kgÿ1 oven DM. f Calculated at an assumed outflow rate of 0.02 hÿ1. g Corrected for fine particles losses after Weisbjerg et al. (1990). * P<0.05. ** P<0.01. b
3.2. Prediction of digestibility in vivo from in vitro digestibility In general, predictions of DOMD in vivo from the traditional laboratory-based methods were imprecise and inconsistent, though better in year 2. For instance, rumen fluid and NCGD methods only gave significant (P<0.05) predictions in year 2, but even then, the accuracy of prediction was relatively low (Table 1). The accountable variance of the equations tended to be marginally higher, when volatile constituents lost during oven drying were accounted for and assumed to be wholly digestible (Table 1). The inaccuracy of the rumen fluid/in vivo relationship which typifies that of the other in vitro techniques is depicted in Fig. 1. 3.3. Prediction of digestibility in vivo from chemical composition Generally predictions from chemical composition were also imprecise. The most accurate and consistent chemical predictor was the CP content which accounted for at least a third of the variability in DOMD in vivo in both years. The only other notable chemical predictors were VFA and lactic acid contents, but these only gave significant (P<0.05) predictions in year 2. No consistently accurate relationships were found between NDF digestibility and either ADF (r20.27), NDFA (r20.37) or lignified cell wall content (ADF/NDFA) (r20.27).
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Fig. 1. The relationships between the in vivo and rumen fluid-pepsin digestible organic matter content (DOMD) of WCW in both years (g kgÿ1 corrected DM)
3.4. Prediction of digestibility in vivo from in situ degradability Of the rumen degradability parameters, only the fine particle fraction, immediately soluble fraction and effective degradability gave significant (P<0.05) predictions of DOMD in vivo (Table 1). However, none of these predictions was consistent across years. For instance, the fine particle fraction which contained up to 500 g kgÿ1 starch only gave a significant prediction in year 1 (r20.37) when the starch content was quite variable (Appendix 1). Correction for such particulate losses only marginally improved the accuracy of the prediction from effective degradability (Table 1). 3.5. Prediction of digestibility in vivo from in vitro gas production None of the gas production parameters accounted for up to a third of the variability in DOMD in vivo and none of such predictions was significant (P<0.05). 3.6. Prediction of in vivo digestibility from multivariate predictors No multivariate combination of single predictors was significant (P<0.05) for predicting DOMD in vivo in year 2, but in year 1, two of such equations were found
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Table 2 The most accurate (P<0.05) relationships for predicting the digestible organic matter content in vivo of WCW from multivariate models (g kgÿ1 corrected DM) Year
1 1
Predictors x1
x2
Crude protein Starch
Total ash Rumen fluid-pepsin digestibility
R2 adj.a
sb
Equation (y)
0.61 0.46
16.7 19.8
713.80.238x1ÿ1.91x2 267.80.296x10.51x2
** *
a
Accountable variance adjusted for degrees of freedom. Residual standard deviation. * P<0.05 ** P<0.01. b
Table 3 The effect of year of harvest on the prediction of the digestible organic matter content in vivo of WCW from monovariate models Predictor
Effect of year of harvest on: Constant
Slope
Accountable variancea
Crude protein NCGDb Rumen fluid-pepsin digestibility NIRS-DOMDc Total volatile fatty acids Acid detergent fibre Water soluble carbohydrates Neutral detergent fibre plus amylase Ethanol Starch Hemicellulose
* * ns ns ns ns ns ns ns ns ns
* * ns ns ns ns ns ns ns ns ns
0.32 0.30 0.24 0.14 0.14 0.05 0.04 0.00 0.00 0.00 0.00
* P<0.05. ns : Not significant. a Includes effect of year of harvest. b NCGD, Neutral detergent cellulase plus gamannase digestibility. c Digestible organic matter content predicted from near-infrared reflectance spectroscopy using the grass-silage equation of Baker et al. (1994).
(Table 2). Combining total ash and CP contents in year 1 improved the accuracy of prediction over that from the single predictors. Starch and rumen fluid DOMD contents were also significantly (P<0.05) additive in year 1. 3.7. Effect of year of harvest on the prediction of digestibility in vivo Table 3 shows the effect of year of harvest on the predictive equations. When the data from both years were pooled, different relationships were required for predicting DOMD in vivo from CP content or NCGD. Although a single equation sufficed for
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Fig. 2. Calibration relationship for predicting the in vivo digestible organic matter content (DOMD) (g kgÿ1 corrected DM) of whole crop wheat forages from near-infrared reflectance spectroscopy (NIRS).
Table 4 The most accurate near-infrared spectroscopy-based equations for predicting the in vivo digestible organic matter content (DOMD) and organic matter digestibility (OMD) of Year One and Two forages (ranked by standard error of calibration (SEC)) Predicted term
Maths treatmenta
DOMD
2,4,1,1 2,4,2,1 2,8,2,1 1,4,4,1 2,6,2,1 0,0,1,1 2,4,2,1 2,8,1,1
OMD
1
Calibration statistics Accountable variance
SEC
Important wavelengths (nm)
0.87 0.74 0.66 0.67 0.72 0.62 0.63 0.62
13.0 18.8 18.9 21.6 1.9 2.2 2.3 2.3
2212, 2268, 2068, 2244, 1436, 1516, 1540, 2204,
1732, 1508, 1900, 1932, 2324 1348 1732, 2020,
Equation number 1988 1732 2148 2316 1684 2364
1 2 3 4 5 6 7 8
The first, second, third and fourth numbers in the maths treatment denote the order of the derivative function, the spectral segment length in data points over which the derivative was taken and the segment lengths over which the function was smoothed.
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each of the remaining predictors evaluated, the accuracy of such predictions was very low. 3.8. Prediction of in vivo digestibility from near-infrared reflectance spectroscopy The grass silage equation of Baker et al. (1994) gave poor predictions of OMD in vivo in years 1 (r20.0, s28.08) and 2 (r20.30, s27.64). In contrast, the WCW calibration equation acurately predicted the in vivo OMD and DOMD of the forages (Fig. 2, Table 4), though spectral dispersion was relatively high between 1800 and 2000 nm. The best calibration equations accounted for 0.87 of the variability in DOMD in vivo (Eq. 1) and 0.72 of OMD in vivo (Eq. 5) (Table 5). The Mahalanobis distance (Shenk and Westerhaus, 1991) of all samples was less than 4, confirming their proximity to the population mean and the absence of outliers. This indicates that all the samples were spectrally similar and suggests that the year of harvest had little or no effect on the spectra.
4. Discussion It is surprising that the biological and chemical techniques evaluated gave such inaccurate predictions of DOMD in vivo which contradict previous suggestions (Leaver and Hill, 1992; Tetlow, 1992). Due to the dearth of similar predictive studies for WCW in the literature, the results were compared to published relationships for maize silage. It is interesting to note that the imprecision observed is similar to that obtained by Dowman and Collins (1982) for predicting the DOMD in vivo of maize silage from six in vitro digestibility techniques (r20.23±0.51). Furthermore, Givens et al., (1995, unpublished) found that the DOMD in vivo of 50 maize silages was inadequately predicted (r20.34) by the Tilley and Terry (1963) technique, though a moderate relationship was found with the NCGD technique (Givens et al., 1995). In addition to in vitro/in vivo differences in the grinding of husks (Tetlow, 1992), such inaccurate predictions for heterogeneous forages like maize silage and WCW may be related to differences in the sites, rates and extents of digestion of the starch and cell wall fractions. Since low ruminal pH inhibits cellulolytic organisms (Grant and Mertens, 1992), the acidic conditions imposed by starch digestion in such forages may have enhanced the post-ruminal digestion of the cell walls at the expense of intra-ruminal digestion. This suggestion is in accord with the observation that in dairy cows, the intra-ruminal digestion of organic matter decreased as the proportion of WCW in a grass silage diet increased (Abdalla et al., 1996). Although future studies need to verify that post-ruminal digestion is similarly increased in sheep fed WCW, the latter study suggests that in vitro techniques which do not accurately simulate post-ruminal digestion are of limited use for predicting DOMD in vivo of WCW. This is supported by the additivity of starch and rumen fluid DOMD in year 1, which indicates that the rumen fluid technique did not account for all the starch digestion. The additivity also agrees with suggestions of augmented starch fermentation in the caecum and small intestine of steers fed WCW (Deschard et al., 1988).
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The poor relationships may also be related to the low N solubility values (118±934 g kgÿ1, Adesogan, 1996) of some forages which suggests the presence of Maillaird products that are digestible in vitro but not in vivo (Tetlow and Mason, 1987). In addition, the small population used implies that values at the extreme of the relationships (Fig. 1) considerably influenced the position of the regression line and the resulting accuracy. Whether or not the prediction would be improved by using a larger population is debatable, since the relatively narrow range in the DOMD in vivo of the forages typifies that found in practice (Weller, 1992). It would have been interesting to determine the strength of the predictive relationships for isolated populations of fermented and ureatreated forages, but this was precluded by the fermentation of year 2 urea-treated forages (Adesogan et al., 1997). The inaccurate cell wall-based predictions agree with findings for maize silage by Givens et al. (1995) who noted that substantial variations in the proportions of cell wall components in maize silage were not reflected in the digestibility values obtained. The inaccuracy of the cell wall-based prediction for WCW may be related to the wide range of maturities over which WCW is harvested. While other forages are harvested across a narrow range of maturities within which NDF content increases with maturity, maturityrelated decreases in NDF content can occur in the WCW (Adesogan et al., 1997). Furthermore, the tendency for a decrease in digestibility with maturity in other forages is counteracted in WCW by the replacement of water soluble carbohydrates by starch with maturity. The imprecise prediction from the degradability parameters contrasts with results for hay (Khazaal et al., 1993) and may have been partly due to the fine particle losses. The lack of substantial improvements in prediction accuracy after correcting for such particulate losses, is probably because the Weisbjerg et al. (1990) model assumes similar degradation rates for the fine particles lost and the material remaining in the bag. This is invalid for WCW because of the different degradation rates of the grain and straw fractions (Givens et al., 1993). Since Hill and Leaver (1993) also noted that the rates of passage of these fractions differ, the use of single rate constants in this work may have contributed to the poor predictions from the degradability parameters. The poor prediction of DOMD in vivo from gas production contradicts the findings of Menke et al. (1979) for various feedstuffs. However, Menke et al. (1979) suggested that inaccurate gas production/DOMD in vivo relationships could result from in vitro/in vivo differences in the rates of degradation and outflow and improved post-ruminal utilisation of nutrients escaping fermentation as appears to be the case with WCW. Other factors which may be implicated include the indirect production of gas from the buffer or from microbial turnover, the dependence of gas production on the end products of fermentation and the microbial potential for switching between VFA and lactate production (Beuvink and Spoelstra, 1992). NIRS was the only promising method for predicting the digestibility of WCW. However, since only the WCW-based calibration equation produced accurate predictions, it should be noted that grass silage-based calibration equations may give erroneous results when used for WCW evaluation. The spectral dispersion at the moisture-sensitive region suggests that there was still some variation in the moisture content of the samples. Since
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including such moisture-sensitive peaks can disproportionately influence the spectra and affect the validity of prediction equations, further research is required to validate the promise of the NIRS technique for predicting the nutritive value of WCW. Such research should utilise undried or adequately dried samples, otherwise the use of a noise repeatability file in addition to the SNV-D process is recommended to eliminate sensitivity to residual moisture. There is also the need to use large diverse populations (>100 samples) to ensure that accurate, robust calibration and validation equations are developed. 5. Conclusions The chemical and biological techniques evaluated provided inaccurate predictions of the DOMD in vivo. This was thought to relate to the fact that the in vitro techniques primarily simulate ruminal digestion and therefore, do not sufficiently account for the differences in the sites, extents and rates of digestion of the cell wall and starch fractions of WCW. Thus, as with maize silage (Givens et al., 1995), future methods of characterising WCW will need to accurately simulate the intra-ruminal and post-ruminal digestion kinetics of the starch and cell wall fractions. Future work will also need to develop sample preparatory methods which prevent fine particle losses. It is noteworthy that although NIRS appears to be the most promising method for predicting the DOMD in vivo of WCW, grass silage-based equations may give erroneous results. Future work should validate the prediction accuracy of the WCW-based equation using large data sets (>100) that enable the production of robust calibration and validation equations. Such research is integral to the adoption of the NIRS technique for routine prediction of the nutritive value of WCW forages. Nevertheless, the cost implications of developing robust NIRS-based equations suggests that in the short term, attention should be focussed on modifying existing in vitro techniques (which primarily simulate ruminal digestion) to account for the postulated post ruminal digestion of WCW. Acknowledgements This programme was funded within the LINK Programme `Technologies for Sustainable Farming Systems' by Agricultural Genetics Co. Ltd., BOCM Pauls, Hi Spec Engineering Ltd., ICI Nutrition, J Bibby Agriculture Ltd., Maize Growers Association, Milk Marketing Board of England and Wales, Ministry of Agriculture, Fisheries and Food, Rumenco, Trouw Nutrition UK Ltd. and Zeneca Seeds UK Ltd. Appendix Tables 5 and 6
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Table 5 Laboratory analyses of whole crop wheat forages (g kgÿ1 oven DM or as stated) Year 1 (n12)
1
ÿ1
Oven DM (g kg ) Corrected DM (g kgÿ1) Crude proteina Total ash Water soluble carbohydrates Starch Neutral detergent fibre plus amylaseb Acid detergent fibreb Rumen fluid DOMDc NCGDd NIR-DOMDe Effective degradability 96 h gas production (ml gÿ1 OM)
Year 2 (n14)
Mean
s.e.
Range
Mean
s.e.
Range
518 539 179 70 12 228 458 297 566 622 618 498 508
36.8 35.1 23.7 2.3 1.9 21.2 19.7 15.1 9.2 13.4 9.1 15.7 38.9
319±747 346±751 107±373 61±87 2±24 95±377 306±586 198±408 513±611 528±696 566±660 416±588 338±760
442 455 164 81 13 189 500 297 594 590 635 451 621
26.8 26.5 22.0 6.0 3.1 27.5 17.3 10.7 9.0 11.7 9.4 12.7 45.7
248±546 261±559 89±394 48±115 5±43 23±424 412±655 238±387 525±655 508±675 587±711 379±542 478±1016
a
Measured on fresh material. Ash free. 3 Digestible organic matter content. d Neutral detergent cellulase plus gamannase digestibility. 5 DOMD predicted from a whole-crop wheat-based near-infrared reflectance spectroscopy calibration equation. b
Table 6 Digestibility in vivo of whole crop wheat forages Year 1 (n12)
a
ÿ1
DOMD (g kg
oven DM)
Digestibility coefficients Organic matter Dry matter Gross energy Starch Neutral detergent fibre a
Year 2 (n14)
Mean
s.e.
Range
Mean
s.e.
Range
611
9.51
558±653
626
9.11
561±708
0.009 0.009 0.009 0.006 0.028
0.602±0.704 0.582±0.680 0.578±0.691 0.940±0.999 0.362±0.695
0.009 0.009 0.010 0.018 0.008
0.598±0.742 0.586±0.732 0.642±0.755 0.748±0.997 0.579±0.6931
0.656 0.636 0.646 0.977 0.568
0.682 0.655 0.703 0.957 0.627
Digestible organic matter content.
References Abdalla, A.L., Sutton, J.D., Humphries, D.J., Phipps, R.H. (1996). Digestibility of diets of grass silage and whole-crop wheat in the rumen of lactating dairy cows. Anim. Sci. 63, 631 (Abstract). Adesogan, A.T., 1996. Prediction of the nutritive value of fermented and urea-treated whole crop wheat forages. Ph.D. Thesis, The University of Reading, UK. Adesogan, A.T., Givens, D.I., Owen, E., 1997. The chemical composition, digestibility and energy value of fermented and urea-treated whole crop wheat harvested at three stages of maturity. Grass Forage Sci. 53, 66±75.
A.T. Adesogan et al. / Animal Feed Science and Technology 74 (1998) 259±272
271
Alderman, G., 1985. Prediction of the energy value of compound feeds. In: Haresign, W., Cole, D.J.A. (Eds.), Recent Advances in Animal Nutrition. Butterworths, London, pp. 3±52. Baker, C.W., Givens, D.I., 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., Lister, S.J., 1989. Standard normal variate transformation and de-trending of nearinfrared diffuse reflectance spectra. Appl. Spectrosc. 43, 772±777. Beuvink, J.M.W., Spoelstra, S.F., 1992. Interactions between substrate, fermentation end-products buffering systems and gas production upon fermentation of different carbohydrates by mixed rumen micro-organisms in vitro. Appl. Microbiol. Biotechnol. 37, 505±509. Beuvink, J.M.W., Kogut, J., 1993. Modelling gas production kinetics of grass silages incubated with buffered rumen fluid. J. Anim. Sci. 71, 1041±1046. Deschard, G., Mason, V.C., Tetlow, R.M., 1988. Treatment of whole crop cereals with alkali. 4. Voluntary intake and growth in steers given wheat ensiled with sodium hydroxide, urea or ammonia. Anim. Feed. Sci. Technol. 19, 55±66. Dowman, M.G., Collins, F.C., 1982. The use of enzymes to predict the digestibility of animal feeds. J. Sci. Food. Agric. 33, 689±696. Dowman, M.G., 1993. Modifications to the neutral detergent cellulase digestibility method for the prediction of the metabolisable energy of compound feedstuffs containing palm kernel meal. J. Sci. Food. Agric. 61, 327±331. Givens, D.I., Everington, J.M., Adamson, A.H., 1989. The digestibility and metabolisable energy content of grass silage and their prediction from laboratory measurements. Anim. Feed. Sci. Technol. 24, 27±43. Givens, D.I., Moss, A.R., Adamson, A.H., 1993. The digestion and energy value of whole crop wheat treated with urea. Anim. Feed. Sci. Technol. 43, 51±64. Givens, D.I., Cottyn, B.G., Dewey, P.J.S., Steg, A., 1995. A comparison of the neutral detergent-cellulase method with other laboratory methods for predicting the digestibility in vivo of maize silages from three European countries. Anim. Feed. Sci. Technol. 54, 55±64. Goering, H.K., van Soest, P.J., 1970. Forage Fiber Analysis, Agriculture Handbook No. 379, Agriculture Research Service, US Dept. of Agriculture, Washington. Grant, R.J., Mertens, D.R., 1992. Influence of buffer pH and raw corn starch addition on in vitro fiber digestion kinetics. J. Dairy Sci. 75, 2762±2768. Hill, J., Leaver, J.D., 1993. The intake digestibility and rate of passage of whole crop wheat by growing heifers. Proc. 1993 Winter Meeting of the British Society of Animal Production, Scarborough. Khazaal, K., Detinho, M.T., Ribeiro, J.M., érskov, 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±112. Leaver, J.D., Hill, J., 1992. Feeding cattle on whole crop cereals. In: Stark, B.A., Wilkinson, J.M. (Eds.), Wholecrop Cereals, 2nd ed. Chalcombe, Canterbury, pp. 59±72. MAFF, 1986. The Analysis of Agricultural Materials, Reference Book 427, HMSO, London. Menke, K.H., Raab, L., Salewski, A., Steingass, H., Fritz, D., Schneider, W., 1979. The estimation of the digestibility and metabolisable energy content of ruminant feedingstuffs from gas production when they are incubated with rumen liquor in vitro. J. Agric. Sci. Camb. 93, 217±222. Norris, K.H., Barnes, R.F., Moore, J.E., Shenk, J.S., 1976. Predicting forage quality by infrared reflectance spectroscopy. J. Anim. Sci. 43, 889±897. érskov, E.R., McDonald, I., 1979. The estimation of protein degradability in the rumen from incubation measurements weighted according to rate of passage. J. Agric. Sci. 92, 499±503. Shenk, J.S., Landa, I., Hoover, H.R., Westerhaus, M.O., 1981. Description and evaluation of a near-infrared reflectance spectro-computer for forage and grain analysis. Crop Sci. 21, 355±358. Shenk, J.S., Westerhaus, M.O., 1991. Population definition, sample selection and calibration procedures for near-infrared reflectance spectroscopy. Crop Sci. 31, 469±474. Tetlow, R.M., 1992. A decade of research into whole crop cereals at Hurley. In: Wilkinson, J.M., Stark, B.A. (Eds.), Whole-Crop Cereals, 2nd ed. Chalcombe, Canterbury, pp. 1±20.
272
A.T. Adesogan et al. / Animal Feed Science and Technology 74 (1998) 259±272
Tetlow, R.M., Mason, V.C., 1987. Treatment of whole crop cereal with alkali. 1. The influence of sodium hydroxide and ensiling on the chemical composition and in vitro digestibility of rye, barley and wheat crops harvested at increasing maturity and dry matter content. Anim. Feed. Sci. Technol. 18, 257±269. Theodorou, M.K., Williams, B.A., Dhanoa, M.S., McAllan, A.B., 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., Terry, R.A., 1963. A two stage technique for the in vitro digestion of forage crops. J. Br. Grassl. Soc. 18, 104±111. Weisbjerg, M.R., Bhargava, P.K., Hvelplund, T., Madsen, J., 1990. Use of degradation curves in feed evaluation., Report No. 679, National Institute of Animal Production, Beretning fra Statens Husdyrbrugsforsog, Denmark. Weller, R.F., 1992. The national whole-crop cereals survey. In: Wilkinson, J.M., Stark, B.A. (Eds.), Whole-Crop Cereals, 2nd ed. Chalcombe, Canterbury, pp. 1±20. Westerhaus, M.O., 1989. Interpretation of regression statistics. In: Marten, G.S., Shenk, J.S., Barton, F.E., (Eds.), Near-infrared Reflectance Spectroscopy (NIRS): Analysis of Forage Quality. Agriculture Handbook No. 643 (revised), USDA, pp. 39±40. Windham, W.R., Mertens, D.R., Barton, F.E., 1989. Protocol for NIRS. Calibration: Sample selection and equation development and validation. In: Marten, G.C., Shenk, J.S., Barton, F.E. (Eds.), Near-infrared Reflectance Spectroscopy (NIRS): Analysis of Forage Quality. Agriculture Handbook No. 643 (revised), Suppl. I, USDA, pp. 96±103.