Method for determining feed quality

This invention relates to a method for quantifying biomechanical properties of animal feed based on a correlation between the chemical and biomechanical properties of the feed, and to methods for objectively measuring the quality of animal feed, such as fodders including hay, pastures and forages.
Diet is the major determinant of productivity of an animal. In the livestock industry, animals are farmed for meat, wool and other valuable products. The diet of farmed livestock is largely dictated by man and, given the effect of diet on animal production it is highly desirable to optimize the diet of livestock to gain maximum benefit from the natural resource.
Feed quality is one variable that has a major impact on animal productivity. In this respect, feed quality affects the amount of feed an animal will consume and the feeding value it gains from the feed consumed. In the case of cattle, sheep and other ruminants, feed quality depends on digestibility, chemical attributes (nutrient composition) and biomechanical attributes (namely how easy it is for an animal to chew the feed during ingestion and rumination).
It is generally accepted that there are constraints on the intake of feed by ruminant animals, that the amount of useful energy obtained by a ruminant animal may fail short of the amount that the animal can potentially use, and that this would result in reduced productivity. For example, the principal constraints to voluntary intake of fodders are resistance of fodder fibre to chewing and digestibility (provided that the intake is not otherwise constrained by low palatability, deleterious secondary compounds, or the inadequacy of essential nutrients). Differences between feeds, such as fodders, in their resistance to chewing are reflected in differences in biomechanical properties, including comminution energy, shear energy, compression energy, tensile strength, shear strength and intrinsic shear strength.
Hay is a common feed and its quality is significantly affected by factors such as seasonal differences, haymaking practices and pasture composition. It has been shown in one recent survey that in some years as little as 11% of hay produced was good enough to promote liveweight gain in weaner sheep. This possibility of wide variation in measures of hay quality is a matter of increasing concern, and has given rise to a demand for a method of objective quality assessment.
A hay quality system adopted in the United States of America uses a measure known as relative feed value (RFV) to distinguish between hays of different quality. The RFV is calculated from the dry matter digestibility, which is predicted from acid detergent fibre (ADF) content, and from the dry matter intake, which is predicted from neutral detergent fibre (NDF) content.
The RFV based system suffers from a number of disadvantages. For example, the ADF and NDF contents of fodders are determined by chemical methods which take several days to complete, and thus are expensive in terms of resources.
While objective quality assessment and product specification has become an integral part of the production and marketing in domestic and export markets for the Australian grain, wool, meat and dairy industries, performance-based quality standards are not presently in place for feeds such as hays and other fodders. Consequently;
(a) the feed buyer cannot be sure of getting value for money, and this is likely to become increasingly important in respect of export markets if other exporting countries are able to guarantee standards for their product;
(b) the feed producer cannot be sure of getting a higher price for a superior product;
(c) livestock producers are unable to objectively formulate rations or supplementary feeding regimes to achieve animal production targets; and
(d) the market for animal feed tends to be unstable.
Whilst the relationship between biomechanical properties of feed and feed quality is now accepted, there is a need for a convenient, inexpensive and relatively accurate assay method for feed to determine its quality. An accurate determination of feed quality allows for optimisation of feeding regimes and improved animal production for obvious economic gains.
It is an object of this invention to overcome or at least partially alleviate the aforementioned problems and/or reduce the uncertainties and concomitant problems of the prior art systems for measuring the biomechanical properties of feed and hence determining feed quality.
Thus, the present invention provides a method for determining a biomechanical property of a feed, the method comprising the steps of;
(a) subjecting the feed to infrared radiation to obtain spectral data: and
(b) using the spectral data to determine the biomechanical property;
whereby the biomechanical property of the feed is determined on the basis of the bond energies of the chemical constituents of the feed.
The spectral data may be used directly to determine the biomechanical property of the feed. Alternatively, the spectral data may be used to determine another property of the feed and the other property is used to determine the biomechanical property on the basis of a correlation between the other property and the biomechanical property.
When the biomechanical property is determined via another property, the other property is preferably a chemical property of the feed such as the ADF content or the NDF content or the lignin content.
There is a variety of biomechanical properties of the feed that may be determined. Preferably, the biomechanical properties are selected from the group comprising shear energy, compression energy, comminution energy, tensile strength, shear strength and intrinsic shear strength.
The spectral data may comprise a reflectance spectrum at a combination of wavelengths or over a predetermined range of wavelengths such as 700 nm-3000 nm, or more preferably 1100 nm-2500 nm. Preferably, the data obtained for the spectral range of 1850 nm-1970 nm is disregarded, this being the range over which water reflects strongly.
The spectral data may be recorded at one or more wavelength intervals throughout the spectral range. When the spectral data is a reflectance spectrum over a predetermined range it is preferably measured at 2 nm intervals over the range. Of course, if so desired the spectral data may be measured at intervals other than 2 nm.
When the spectral data is used to directly determine a biomechanical property, the biomechanical property is preferably determined by comparison of the spectral data with a calibration equation that reflects the relationship between reflectance and the biomechanical property. Preferably, the calibration curve is determined on the basis of laboratory data establishing a correlation between reflectance and the biomechanical property.
Thus, the present invention also provides a method for determining a biomechanical property of a feed, the method comprising the steps of;
(a) subjecting the feed to infrared radiation to obtain spectral data; and
(b) comparing the spectral data obtained in (a) with a calibration equation to determine the biomechanical property;
whereby the biomechanical property of the feed is determined on the basis of the bond energies of the chemical constituents of the feed.
The present invention also provides a method for determining feed quality, the method comprising the steps of;
(a) subjecting the feed to infrared radiation to obtain spectral data;
(b) using the spectral data to determine a biomechanical property of the feed; and
(c) using the value of the biomechanical property obtained in step (b) to determine feed quality;
whereby the biomechanical property of the feed and thus the feed quality is determined on the basis of the bond energies of the chemical constituents of the feed.
In one particular form, the method described immediately above may further comprise the determination of an additional property of the feed. The additional property may vary and preferably is selected from the group comprising the digestibility of the feed in vivo or in vitro, the ADF content or the NDF content, or the lignin content.
The present invention is based on research establishing a strong correlation between the bond energies as they relate to the physical structure, and the biomechanical properties of feed. Once this correlation is established the bond energies of the chemical constituents, and in turn the biomechanical properties of the feed, can be determined using infrared spectroscopy. The biomechanical properties quantified in this way are useful for accurately determining feed quality.
In this respect, research resulting in the present invention has shown that the biomechanical attributes of feeds such as cereal and legume hays, straws, and mature, dry subterranean clovers are much more strongly related to animal performance than are digestibility or chemical composition of the feeds.
Thus, comminution energy, the energy required to grind or comminute fodder material, has proved to be a very effective indicator of forage consumption constraint (FCC), which is the difference between the quantity of forage an animal should consume to satisfy its capacity to use energy (a theoretical maximum) and the actual voluntary dry matter intake achieved.
Shear energy, the energy required to shear fodder material, and compression energy, the energy required to compress fodder material, are two biomechanical feed characters of fodders that are closely related to comminution energy and which also are good predictors of FCC.
In this respect, feed quality can be assessed in a number of ways. The forage consumption constraint (FCC) is one convenient measure of feed quality and equates to the difference between the quantity of the fodder that the animal would be attempting to consume to satisfy its capacity to use energy (theoretical maximum intake) and the voluntary forage consumption (VFC).
Thus, the present invention also provides a method for determining feed quality, the method comprising the steps of:
(a) subjecting the feed to infrared radiation to obtain spectral data;
(b) using the spectral data to determine a biomechanical property of the feed, and
(c) using the value of the biomechanical property obtained in step (b) to determine the forage consumption constraint (FCC) or voluntary feed consumption (VFC) as a measure of feed quality;
whereby the biomechanical property of the feed and thus the feed quality is determined on the basis of the bond energies of the chemical constituents of the feed.
The present invention is based on the finding that variations in biomechanical properties such as shear energy, comminution energy and compression energy are reflected in NIR spectra of fodders. This finding, together with recognition of the value of biomechanical characters for the prediction of FCC (and, in turn, the prediction of voluntary feed consumption (VFC)) makes it possible for quicker, less expensive, more convenient and more reliable prediction of feed quality than hitherto known and predicted.
Accordingly, this invention provides a method of (i) assessing the suitability of a fodder, such as a forage, to meet a required animal performance; or (ii) predicting the VFC of a forage; or (iii) predicting the FCC of a forage, which method comprises subjecting a sample of the forage to NIR radiation and determining the reflectance at selected wavelengths.
It has been found that the biomechanical properties, such as shear and comminution energy values for a given fodder, correlate with the fodder's reflectance of infrared radiation. More specifically, the invention is based on research showing that:
(a) NIR wavelengths at which reflectance (R), namely the second derivative of the logarithm of the inverse of R, correlates significantly with the variation in energy required to shear fodder materials are 1168 nm, 1458 nm, 1598 nm, 1718 nm, 1828 nm and 2048 nm. For the prediction of fodder shear energy (y.sub.1, kJ.m.sup.-2) the following equation may be used:
y.sub.1 =19.95+10239.46R.sub.1168 +3623.49R.sub.1458 -4255.61R.sub.1598 -5319.88R.sub.1718 +5148.38R.sub.1826 +2452.05R.sub.2048
(b) NIR wavelengths at which the second derivative of the logarithm of the inverse of reflectance (R) correlates significantly with the variation in energy required to comminute fodder materials are 1138 nm, 2018 nm, 2128 nm and 2408 nm.
For the prediction of fodder comminute energy (y.sub.2, kJ.kg DM.sup.-1) the following equation is proposed:
y.sub.2 =231.42+18224.74R.sub.1138 -4955.12R.sub.2018 -3005.37R.sub.2128 +4290.18R.sub.2408
(c) NIR wavelengths at which the second derivative of the logarithm of the inverse of reflectance (R) correlates significantly with the variation in compression energy are 1268 nm, 1588 nm, 1728 nm, 2278 nm. For the prediction of compression energy (y.sub.3, kJ.kgDM.sup.-1) the following equation may be used:
y.sub.3 =-0.71-911.04R.sub.1268 +112.57R.sub.1558 -79.48R.sub.1728 -28.02R.sub.2278
(d) NIR wavelengths at which the second derivative of the logarithm of the inverse of reflectance (R) correlates significantly with variation in in vivo digestibility of dry matter (DMD) (y.sub.4, %) is 1158 nm, 1238 nm, 1668 nm, 1908 nm, 1918 nm, and 2248 nm. For prediction of the DMD (y.sub.4, %) of a fodder the following equation is proposed:
y.sub.4 =46.62+8162.72R.sub.1158 -8799.69R.sub.1238 +1249.01R.sub.1668 +519.46R.sub.1908 -367.08R.sub.1918 -161.84R.sub.2248
(e) NIR wavelength at which the second derivative of the logarithm of the inverse of reflectance (R) correlates significantly with variation in in vitro digestibility of dry matter (IVDMD) is 1698 nm, 1748 nm, 1908 nm, 1918 nm and 2158 nm. For prediction of the DMD (in vitro) of a fodder the following equation is proposed:
y.sub.5 =63.43-2186.89R.sub.1698 -1491.99R.sub.1748 +981.30R.sub.1908 -556.01R.sub.1918 +2003.05R.sub.2158
Accordingly, in a preferred method according to this invention, the infrared wavelengths at which reflectance is measured comprise one or more of the following: 1168 nm, 1458 nm, 1598 nm, 1718 nm, 1828 nm, 2048 nm, 1138 nm, 2018 nm, 2128 nm, 2408 nm, 1268 nm, 1588 nm, 1728 nm, 2278 nm, 1158 nm, 1238 nm, 1668 nm, 1908 nm, 2248 nm, 1698 nm, 1748 nm, 1918 nm and 2158 nm.
It will be understood that the foregoing are wavelengths at which the strongest correlations have been observed, and the possibility of useful correlations being observed at other wavelengths are highly likely.
Essentially, it can be shown that in the same way that a decrease in comminution energy is reflected by a decrease in forage consumption constraint, there is also a linear relationship between comminution energy or shear energy and the consumption constraint of a fodder. Thus, the use of NIR spectra, in conjunction with the equations detailed at paragraphs (a) to (e) above, permits estimation of the VFC of a fodder, which together with estimates of digestibility (conveniently obtained from NIR spectra) can be expected to provide a valuable basis for performance-based quality standards for fodders.
It is to be appreciated that the intention of this invention is to offer a quick, reliable and relatively inexpensive means of obtaining information from which the fodder producer and user, such as purchaser, might make informed judgements about the market value of a given fodder sample relative to alternatives, and of its suitability for a particular purpose.
Conceivably, fodder quality predictions obtained by the method of this invention could be a useful component of, or used in conjunction with, for example, Decision Support Software (DSS) packages designed to assist livestock management.
It is further envisaged that by combining NIR measurements made by a remote sensing system, such as Landsat, with data from a Geographical Information System, the invention will provide a means of making reliable predictions of pasture quality. These predictions, together with predictions of feed intake and animal performance, should then provide a useful basis for strategies of supplementary feeding to improve performance in grazing ruminants.
The present invention also provides for a spectrometer configured to determine biomechanical properties and/or quality of feed according to the methods of the present invention. Preferably, the spectrometer includes a data processing means which enables the spectrometer to receive a feed sample and quantify either or both the biomechanical properties of the feed and the quality of the feed. In one particular form the data processing means includes a calibration equation to facilitate the determination of the feed quality or biomechanical property.

BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 is a graph of spectra of samples in the validation and calibration sets.
FIGS. 2a-2e are graphs illustrating simple linear correlation coefficient between laboratory determined and NIR predicted values for each of the biomechanical characters and digestibility of dry matter of a sample in the validation set.
FIGS. 3a-3e are graphs illustrating simple linear correlation coefficient between laboratory determined and NIR predicted values for each of the biomechanical characters and digestibility of dry matter of a sample in the validation set.
FIGS. 4a-4e are graphs illustrating simple linear correlation coefficient between laboratory determined and NIR predicted values for each of the biomechanical characters and digestibility of dry matter of a sample in the validation set.
FIG. 5 is a graph of predicted voluntary feed consumption versus actual voluntary feed consumption.

The invention will now be described with reference to the following examples. The description of the examples is in no way to limit the generality of the preceding paragraphs.
EXAMPLES
The energy of molecular vibrations correspond to the energy of the infrared spectrum of the electromagnetic spectrum, and these molecular vibrations may be detected and measured in the wavelength range of the infrared spectrum. Functional groups in molecules have vibration frequencies that are characteristic of that functional group and that are within well-defined regions of the infrared spectrum.
For organic compounds the principal analytical features of the near infrared (NIR) spectrum are due to absorbance of radiant energy by bonds between hydrogen, carbon, nitrogen, oxygen or with sulphur, phosphorus and metal halides. When organic compounds are irradiated with infrared radiation at wavelengths between 700 and 3000 nm part of the incident radiation is absorbed and the remainder is reflected, refracted or transmitted by the sample. Most quantitative reflectance analyses are made in the wavelength range of 1100 to 2600 nm. The amount of energy absorbed or diffusely reflected at any given wavelength in this wavelength range is related to the chemical composition of the organic compound. NIR spectroscopy uses detectors to measure the amount of radiation that is diffusely reflected by the irradiated sample.
NIR spectroscopic analysis is an analytical procedure calibrated to a primary reference method. Calibration in NIR spectroscopy (NIRS) relies on similarities among the spectra, and analytical properties of interest in the reference samples. In this example the analytical properties of interest were the biomechanical characters of forages, and the procedure that was adopted in this example was as follows:
a) prediction of biomechanical characters of a range of grasses using NIR spectroscopy was established by developing a calibration equation(s) from laboratory determined values of a set of reference samples.
b) validation of the equation(s) either by using laboratory determined values of a separate set of samples, or by a cross-validation procedure using the laboratory determined values of the reference samples.
c) using the NIRS-predicted values for biomechanical characters of the forages and for digestibility of the forages, forage consumption constraint (FCC) was predicted, and in turn voluntary feed consumption (VFC) was predicted.
d) the predicted FCC and VFC were compared with actual data from groups of animals fed each of these forages.
Example A
Developing a Calibration Equation to Predict Biomechanical Properties of Herbage
The samples used in this example were a range of varieties of Panicum spp. harvested at a range of plant maturities throughout the growing season (Table 1). Each of the samples was dried and chaffed, and then fed to groups of sheep (8 sheep per group) which were penned individually, to determine in vivo dry matter digestibility (DMD), VFC and FCC. Samples of the hays were stored for laboratory analyses.
Biomechanical properties of the forages were determined using published methods; the energies required to shear or compress the forages according to Baker, Klein, de Boer and Purser (Genotypes of dry, mature subterranean clover differ in shear energy. Proceedings of the XVII International Grassland Congress 1993. pp 592-593.) and the energy required to comminute the forages according to Weston, R. H. and Davis, P (1991) `The significance of four forage characters as constraints to voluntary intake` p.33 in `Proceedings of The Third International Symposium on the Nutrition of Herbivores` (Compiled by: Wan Zahari, M., Amad Tajuddin, Z., Abdullah, N. and Wong, H. K. `Published by the Malaysian Society of Animal Production (MSAP). ISBN: 967-960-026-2). In vitro digestibility of dry matter (IVDMD) was determined by the pepsin-cellulase technique as modified by Klein and Baker (Composition of the fractions of dry, mature subterranean clover digested in vivo and in vitro. Proceedings of the XVII International Grassland Congress 1993. pp593-595.).
There are several ways to process samples for NIRS analysis, and in this example the samples were ground through a cyclone mill with a 1 mm screen and equilibrated at 25.degree. C. or at least 24h before NIRS analysis. The samples were scanned by monochomating near infrared reflectance spectrophotometer (Perstorp NIRS 6500) and the absorption spectra recorded for the range 1100 to 2500 nm at 2 nm intervals. One spectral range 1850 to 1970 nm, where water absorbs strongly, was disregarded in further analysis of the spectral data.
For NIRS analysis the samples were divided into two groups: one group to be used as a `calibration` set to establish a prediction equation, and a second group, the `validation` set, to be used to validate the prediction equation. There are a number of ways to select the samples for each set. In this example the samples were ranked according to each of the characters that were to be predicted and every other sample was selected for the calibration set (33 samples) and the validation set (32 samples). Thus, for each character that was evaluated, a different selection was made from the 65 samples to establish the respective calibration and validation sample sets.
The ranges, mean, median and variation in the laboratory-determined values for each of the characters of interest in the calibration and validation sets are listed in Table 2.
The software for scanning, mathematical processing and statistical analysis were supplied with the spectrophotometer by the manufacturers. The spectral data were transformed by taking the second derivative of the logarithm of the inverse of the reflectance (R) at each wavelength (d" log (1/R)). The similarities amongst the spectra (FIG. 1) of the samples in the validation and calibration sets were determined using principal components scores to rank the spectra according to the Mahalanobis distance from the average of the spectra. The Mahalanobis distance values were standardized by dividing them by their average value, and were denoted `global` H values (Table 3).
Calibration equations were developed using the calibration samples by regressing the data from the laboratory analyses of each biomechanical property against the corresponding transformed spectral data using the following mathematical methods:
a) Stepwise linear regression
b) Step-up linear regression
c) Principal components regression (PCR)
a) Partial least squares regression (PLS). and
e) Modified partial least squares regression (MPLS).
Stepwise calibrations were developed for each calibration set of samples using the mathematical treatments of the spectral data 2,2,2; 2,5,5; 2,10,5; and 2,10,10; where the first number denotes that the second derivative was used, the second indicates that second derivatives of the spectral data (determined at 2 nm intervals) were taken at intervals of 4, 10 or 20 nm, and the third indicates that the function was smoothed using the `boxcar` method over intervals of wavelength of 4, 10, or 20 nm (Table 4a). Likewise step-up calibrations were developed for each calibration set with up to 6 terms in each calibration equation using mathematical treatments 2,2,2; 2,5,5; 2,10,5; and 2,10,10 (Table 4b). Calibrations developed for each calibration set using principal components regression, partial least squares regression, or modified partial least squares regression each were developed using mathematical treatments 2,5,5 and 25 2,10,10 (Table 4c).
In developing the calibration equations in the stepwise and step-up regressions, only wavelengths with partial F-statistic of more than 8 were accepted for the models.
For each calibration using each calibration set the following calibration statistics were determined:
a) Squared multiple correlation coefficient (R.sup.2), an indication of the proportion of the variation in the calibration set that is adequately modelled by the calibration equation.
b) The standard error of calibration (SEC) together with its confidence interval (.+-.CL), which is the standard deviation for the residuals due to difference between the laboratory determined (reference) and the NIR predicted values for samples within the calibration set
Once the calibration equations were developed, each equation was validated by using it to predict the respective biomechanical property values for each sample in the validation sample set. For each calibration equation the following validation statistics were determined:
a) Simple linear correlation coefficient (r.sup.2) between the laboratory determined and NIR predicted values.
b) The bias (or systematic error) in the regression relationship between the laboratory determined (reference) and NIR predicted values.
c) The confidence limits of the bias in the regression relationship between the laboratory determined (reference) and NIR predicted values.
d) The standard error of prediction, corrected for bias (SEP(C)), which represents the unexplained error of the prediction, the deviation of the differences between laboratory determined and NIR predicted values.
e) The coefficient of determination, or slope (.beta.), and y-intercept (.alpha.) of the linear regression relationship between the laboratory determined and NIR predicted values.
f) The residual standard deviation (RSD) of the linear regression relationship between the laboratory determined and NIR predicted values.
In addition, the calibration equations were validated using a procedures of cross-validation. These are procedures where every sample in the calibration set was used once for prediction, and the standard error of validation corrected for bias (SEV(C), for stepwise and step-up regressions) and cross-validation (SECV, for multivariate regressions) can be determined.
Calibration equations for each biomechanical character were selected using the following criteria:
a) Lowest partial F-ratio, highest R.sup.2, lowest SEC and, for PCR, PLS and MPLS, lowest SEV(C) (or, for multivariate regressions, SECV)
b) Highest r.sup.2, lowest bias and .vertline.bias.vertline.<bias confidence limit. lowest SEP(C), .beta. closest to 1.0, .alpha. closest to 0, and lowest RSD. As well, SEP(C) was compared with the standard error of laboratory determined values amongst all 65 samples, listed in Table 5.
Calibration equations were similarly established to predict in vivo digestibility and in vitro digestibility. The coefficients for each wavelength in the selected calibration equations from stepwise or step-up regression analyses are listed in Table 6a, and those from multivariate analyses are listed in Table 6b.
Simple linear correlation coefficient (r.sup.2) between the laboratory determined and NIR predicted values for each of the biomechanical characters (energies required to shear, comminute or compress) and digestibility of dry matter determined in vivo or in vitro of the samples in the validation set are shown in FIGS. 2a-2e, 3a-3e, and 4a-4e. NIR predicted values are predicted using calibration equations that best met the criteria listed above.
Example B
Prediction of FCC and VFC Using NIR Determinations of Energy Required to Shear and In Vivo Digestibility
To demonstrate the prediction of voluntary feed consumption using NIR determined values for a biomechanical character and digestibility of forages, samples of Panicum spp. hay were selected which were common to both of the validation sample sets used to establish the NIR prediction equations for energy required to shear and in viva digestibility. The hays represented the range of varieties in the sample set, and are listed in Table 7. The samples were scanned by the same spectrophotometer that was used to establish the calibration equations, and the absorption spectra were recorded in the range 1100 to 2500 nm at 2 nm intervals. Values for energy required to shear and in vivo digestibility were predicted from calibration equations (Tables 4a 4b and 4c) using the recorded spectra data.
These values then were used to estimate FCC from the relationship between biomechanical character(s) and FCC of the range of forages used by Weston and Davis (1991). Energy required to shear the forages used by Weston and Davis was determined according to Baker et al. (1993). The relationship between the energy required to shear these forages (kJ/m.sup.2) and FCC (g organic matter (OM)/d/kg metabolic body weight (MBW)) was described by the relationship:
Energy required to shear (x)=-26.13+5.53(FCC(y))
where R=0.92; RSD=8.70; N=13; P<0.0001.
FCC from this relationship and in vivo digestibility predicted by NIR were then used to estimate VFC, as the difference between the animal's capacity to use energy (as defined by Weston and Davis, 1991) and FCC. These data are summarised in Table 8.
VFC predicted in this way explained most of the variation in actual VFC (R=0.87; RSD=5.04; P=0.023) (FIG. 5).
TABLE 1__________________________________________________________________________Description of herbage used in this example. Part of ProcessGenus Species Variety Common name plant undergone Stage of maturity Regrowth__________________________________________________________________________Panicum coloratum Bambatal Makarikari grass aerial dried and chaffed late bloom (9 weeks' regrowth) late bloom - regrowthPanicum coloratum Bambatal Makarikari grass aerial dried and chaffed late bloom (13 weeks' regrowth) late bloom - regrowthPanicum coloratum Bambatal Makarikari grass aerial dried and chaffed late bloom (4 weeks' regrowth) late bllom - regrowthPanicum coloratum Bambatal Makarikari grass aerial dried and chaffed mid bloom (1 month's mid bloom -regrowthPanicum coloratum Bambatal Makarikari grass aerial dried and chaffed mid bloom (10 weeks' regrowth) mid bloom - regrowthPanicum coloratum Bambatal Makarikari grass aerial dried and chaffed mid bloom (6 weeks' regrowth) mid bloom - regrowthPanicum coloratum Bambatal Makarikari grass aerial dried and chaffed vegetative regrowth (29 vegetative regrowthPanicum coloratum Kabulabula Makarikari grass aerial dried and chaffed late bloom late bloom CPI 16796Panicum coloratum Kabulabula Makarikari grass aerial dried and chaffed late bloom (4 weeks' regrowth) late bloom - regrowth CPI 16796Panicum coloratum Kabulabula Makarikari grass aerial dried and chaffed late bloom (19 weeks' regrowth) late bloom - regrowth CPI 16796Panicum coloratum Kabulabula Makarikari grass aerial dried and chaffed late bloom (14 weeks' regrowth) late bloom - regrowth CPI 16796Panicum coloratum Kabulabula Makarikari grass aerial dried and chaffed mid bloom 9 weeks' regrowth) mid bloom - regrowth CPI 16796Panicum coloratum Kabulabula Makarikari grass aerial dried and chaffed mid bloom 6 weeks' regrowth) mid bloom - regrowth CPI 16796Panicum coloratum Kabulabula Makarikari grass aerial dried and chaffed vegetative regrowth (28 vegetative regrowth CPI 16796Panicum coloratum var Burnett Makarikari grass aerial dried and chaffed early bloom (1 month's late bloom - regrowth MakarikariensePanicum coloratum var Burnett Makarikari grass aerial dried and chaffed late bloom (14 weeks late bloom - regrowth MakarikariensePanicum coloratum var Burnett Makarikari grass aerial dried and chaffed late bloom (4 weeks' regrowth) late bloom - regrowth MakarikariensePanicum coloratum var Burnett Makarikari grass aerial dried and chaffed mid bloom (8 weeks' regrowth) mid bloom - regrowth MakarikariensePanicum coloratum var Burnett Makarikari grass aerial dried and chaffed mid bloom (10 weeks' regrowth) mid bloom - regrowth MakarikariensePanicum coloratum var Burnett Makarikari grass aerial dried and chaffed vegetative regrowth (31 vegetative regrowth MakarikariensePanicum coloratum var Burnett Makarikari grass aerial dried and chaffed mid bloom (1 month's mid bloom - regrowth MakarikariensePanicum coloratum var Burnett Makarikari grass aerial dried and chaffed mid bloom (4 weeks' regrowth) mid bloom - regrowth MakarikariensePanicum maximum Coloniso Guinea grass aerial dried and chaffed late bloom (4 weeks' regrowth) late bloom - regrowthPanicum maximum Coloniso Guinea grass aerial dried and chaffed mid bloom (13 weeks' regrowth) mid bloom - regrowthPanicum maximum Coloniso Guinea grass aerial dried and chaffed mid bloom (10 weeks' regrowth) mid bloom - regrowthPanicum maximum Coloniso Guinea grass aerial dried and chaffed vegetative regrowth (4 vegetative regrowthPanicum maximum Coloniso Guinea grass aerial dried and chaffed vegetative regrowth (33 vegetative regrowthPanicum maximum Coloniso Guinea grass aerial dried and chaffed vegetative regrowth (28 vegetative regrowthPanicum maximum Coloniso Guinea grass aerial dried and chaffed vegetative regrowth (1 vegetative regrowthPanicum maximum Coloniso Guinea grass aerial dried and chaffed vegetative regrowth (6 vegetative regrowthPanicum maximum Hamll Guinea grass aerial dried and chaffed early bloom (1 month's early bloom -regrowthPanicum maximum Hamll Guinea grass aerial dried and chaffed early bloom (10 weeks' regrowth) early bloom - regrowthPanicum maximum Hamll Guinea grass aerial dried and chaffed late bloom (4 weeks' regrowth) late bloom - regrowthPanicum maximum Hamll Guinea grass aerial dried and chaffed mid bloom (13 weeks' regrowth) mid bloom - regrowthPanicum maximum Hamll Guinea grass leaf dried and chaffed 54 days' regrowth regrowthPanicum maximum Hamll Guinea grass leaf dried and chaffed 75 days' regrowth regrowthPanicum maximum Hamll Guinea grass leaf dried and chaffed 68 days' regrowth regrowthPanicum maximum Hamll Guinea grass aerial dried and chaffed vegetative (8 weeks' regrowth) vegetativePanicum maximum Hamll Guinea grass aerial dried and chaffed vegetative regrowth (9 vbegetative regrowthPanicum maximum Hamll Guinea grass aerial dried and chaffed vegetative regrowth (32 vegetative regrowthPanicum maximum var. Petrie Green Panlo aerial dried and chaffed late bloom (6 weeks' regrowth) late bloom - regrowth trichoglumePanicum maximum var. Petrie Green Panlo aerial dried and chaffed mid bloom (1 month's mid bloom - regrowth trichoglumePanicum maximum var. Petrie Green Panlo aerial dried and chaffed mid bloom (10 weeks' regrowth) mid bloom - regrowth trichoglumePanicum maximum var. Petrie Green Panlo aerial dried and chaffed mid bloom (14 weeks' regrowth) mid bloom - regrowth trichoglumePanicum maximum var. Petrie Green Panlo aerial dried and chaffed mid bloom (4 weeks' regrowth) mid bloom - regrowth trichoglumePanicum maximum var. Petrie Green Panlo aerial dried and chaffed mid bloom (15 weeks' regrowth) mid bloom - regrowth trichoglumePanicum maximum var. Petrie Green Panlo aerial dried and chaffed mid bloom (9 weeks' regrowth) mid bloom - regrowth trichoglumePanicum maximum var. Petrie Green Panlo aerial dried and chaffed mid bloom (1 month's) mid bloom - regrowth trichoglumePanicum maximum var. Petrie Green Panlo aerial dried and chaffed mid bloom (13 weeks' regrowth) mid bloom - regrowth trichoglumePanicum maximum var. Petrie Green Panlo aerial dried and chaffed mid bloom (4 weeks' regrowth) mid bloom - regrowth trichoglumePanicum maximum var. Petrie Green Panlo aerial dried and chaffed mid bloom (11 weeks' regrowth) mid bloom - regrowth trichoglumePanicum maximum var. Petrie Green Panlo aerial dried and chaffed vegetative regrowth (28 vegetative regrowth trichoglumePanicum maximum var. Petrie Green Panlo aerial dried and chaffed vegetative regrowth (32 vegetative regrowth trichoglumePanicum maximum var. Petrie Green Panlo aerial dried and chaffed vegetative regrowth (4 vegetative regrowth trichoglumePanicum maximum var. Petrie Green Panlo aerial dried and chaffed vegetative regrowth (4 vegetative regrowth trichoglume__________________________________________________________________________
TABLE 2__________________________________________________________________________Summary statistics for each calibration and validation set Energy required Energy required Energy required Digestibility of Digestibility of dry to shear to comminute to compress dry matter in vivo matter in vitro (kJ/m.sup.2) (kJ/kg DM) (kJ/kg DM) (%) (%)__________________________________________________________________________Energy required to shearCalibration setmean 15.48 134.9 3.70 55.7 53.3median 15.17 133.8 3.65 56.0 55.1maximum 20.95 216.5 4.39 64.0 63.0minimum 10.80 72.5 3.25 43.0 39.8standard deviation 2.572 37.50 0.265 5.73 6.97Validation setmean 15.43 130.9 3.78 55.6 52.7median 15.20 128.3 3.75 56.5 53.3maximum 20.43 205.2 4.24 64.0 63.0minimum 10.94 54.5 3.34 47.0 40.1standard deviation 2.444 37.50 0.229 5.36 7.01Energy required to comminuteCalibration setmean 15.01 133.1 3.69 55.7 52.8median 14.76 129.5 3.70 57.0 54.7maximum 19.97 216.5 4.18 64.0 63.0minimum 10.80 54.5 3.25 43.0 39.8standard deviation 2.444 38.82 0.227 5.64 7.06Validation setmean 15.92 132.9 3.79 55.6 53.2median 15.97 130.2 3.79 55.5 54.7maximum 20.95 205.2 4.39 64.0 62.5minimum 11.48 60.7 3.34 47.0 40.9standard deviation 2.490 36.20 0.263 5.47 6.92Energy required to compressCalibration setmean 15.28 128.1 3.74 56.3 53.8median 15.07 128.4 3.72 57.0 54.7maximum 19.97 204.0 4.39 64.0 63.0minimum 10.80 54.5 3.25 47.0 39.8standard deviation 2.477 38.00 0.261 5.15 6.64Validation setmean 15.64 138.0 3.74 55.0 52.2median 15.42 132.5 3.72 54.5 54.5maximum 20.95 216.5 4.24 64.0 62.0minimum 11.46 60.7 3.34 43.0 40.1standard deviation 2.530 36.39 0.240 5.87 7.26Digestibility of dry matter in vivoCalibration setmean 15.14 133.9 3.74 55.5 53.1median 15.17 128.4 3.72 56.0 55.1maximum 20.95 216.5 4.24 64.0 63.0minimum 10.80 60.7 3.25 43.0 40.1standard deviation 2.528 36.39 0.247 5.73 7.33Validation setmean 15.78 132.1 3.75 55.7 52.9median 15.20 134.7 3.72 56.5 54.4maximum 20.37 205.2 4.39 64.0 63.0minimum 10.94 54.5 3.34 47.0 39.8standard deviation 2.446 38.70 0.255 5.36 6.63Digestibility of dry matter in vitroCalibration setmean 14.70 131.3 3.75 55.6 53.0median 14.34 129.3 3.71 56.0 54.7maximum 19.36 216.5 4.39 64.0 63.0minimum 10.80 54.5 3.25 43.0 40.1standard deviation 2.235 42.58 0.241 5.78 6.94Validation setmean 16.19 134.6 3.74 55.6 53.0median 16.16 133.8 3.74 56.0 54.7maximum 20.95 194.8 4.24 64.0 63.0minimum 10.94 65.7 3.34 47.0 39.8standard deviation 2.538 31.84 0.260 5.33 7.05__________________________________________________________________________
TABLE 3______________________________________Mahalanobis distances Mean Median Range______________________________________For full sample set: 0.655 0.623 0.203-1.983For calibration sets for:Energy required to shear 0.588 0.549 0.171-1.646Energy required to comminute 0.718 0.676 0.350-1.553Energy required to compress 0.757 0.760 0.188-1.440Digestibility of dry matter in vivo 0.673 0.634 0.389-1.547Digestibility of dry matter in vitro 0.645 0.574 0.185-1.178______________________________________
TABLE 4a______________________________________Calibration and validation statistics______________________________________ Stepwise Regression 2,2,2 2,5,5 2,10,5 2,10,10______________________________________Energy required to shearLowest partial F-ratio 10.27 6.18 8.27 4.70R.sup.2 0.798 0.787 0.795 0.780SEC 1.155 1.188 1.166 1.207SEC CL 1.493 1.535 1.507 1.560SEV(C) 1.230 1.306 1.273 1.322r.sup.2 0.368 0.625 0.520 0.495Bias 0.690 0.710 0.700 0.720Bias CL 1.484 1.527 1.498 1.551SEP (C) 1.500 1.540 1.520 1.570Slope 0.604 0.617 0.598 0.758Intercept 6.340 5.440 5.640 3.710R.S.D. 1.627 1.627 1.484 1.476Bias CLne.Bias.vertline. -0.794 0.817 0.798 0.831.vertline.Bias.vertline. < Bias CL? Yes Yes Yes Yesnumber of terms 6 5 5 6Energy required to comminuteLowest partial F-ratio 5.54 4.45 16.55 10.89R.sup.2 0.910 0.802 0.818 0.831SEC 11.626 17.281 16.546 15.980SEC CL 1.493 1.535 1.507 1.560SEV(C) 13.103 18.040 17.587 17.100r.sup.2 0.363 0.429 0.374 0.213Bias 6.980 10.370 9.930 9.590Bias CL 14.941 22.209 21.264 20.537SEP (C) 15.110 22.460 21.510 20.770Slope 0.530 0.575 0.607 0.417Intercept 58.300 48.900 48.600 74.600R.S.D. 28.900 27.360 28.650 32.120Bias CLne.Bias.vertline. -7.961 -11.839 -11.334 -10.947.vertline.Bias.vertline. < Bias CL? Yes Yes Yes Yesnumber of terms 6 3 4 4Energy required to compressLowest partial F-ratio 5.05 4.44 7.90 16.19R.sup.2 0.784 0.500 0.525 0.534SEC 0.121 0.209 0.204 0.202SEC CL 1.493 1.535 1.507 1.560SEV(C) 0.135 0.224 0.217 0.215r.sup.2 0.069 0.113 0.008 0.067Bias 0.070 0.130 0.120 0.120Bias CL 0.156 0.269 0.262 0.260SEP (C) 0.160 0.270 0.270 0.260Slope 0.180 -0.080 0.314 0.211Intercept 3.060 4.030 2.580 2.960R.S.D. 0.229 0.229 0.227 0.232Bias CLne.Bias.vertline. -0.086 -0.139 -0.142 -0.140.vertline.Bias.vertline. < Bias CL? Yes Yes Yes Yesnumber of terms 6 4 4 4Digestibility of dry matter in vivoLowest partial F-ratio 7.63 20.68 4.28 6.08R.sup.2 0.934 0.917 0.914 0.921SEC 1.107 1.236 1.258 1.207SEC CL 1.493 1.535 1.507 1.560SEV(C) 1.215 1.368 1.341 1.284r.sup.2 0.654 0.881 0.878 0.876Bias 1.070 0.890 0.910 0.900Bias CL 0.156 0.269 0.262 0.260SEP (C) 2.320 1.940 1.980 1.960Slope 0.705 0.878 0.840 0.827Intercept 16.500 6.690 8.640 9.340R.S.D. 3.153 1.852 1.873 1.888Bias CLne.Bias.vertline. 0.914 0.621 0.648 0.640.vertline.Bias.vertline. < Bias CL? No No No Nonumber of terms 6 6 6 5______________________________________ Stepwise Regression 2,2,2,1 2,5,5 2,10,5 2,10,10______________________________________Digestibility of dry matter in vitroLowest partial F-ratio 7.68 11.84 4.33 6.31R.sup.2 0.935 0.933 0.915 0.922SEC 1.808 1.751 2.052 1.974SEC CL 1.493 1.535 1.507 1.560SEV(C) 1.984 1.981 2.186 2.100r.sup.2 0.699 0.847 0.743 0.736Bias 1.080 1.050 1.230 1.180Bias CL 2.324 2.250 2.637 2.537SEP (C) 2.340 2.280 2.670 2.570Slope 0.839 0.962 0.775 0.763Intercept 8.790 1.650 12.200 12.700R.S.D. 3.805 3.794 3.805 2.719Bias CLne.Bias.vertline. -1.244 -1.200 -1.407 -1.357.vertline.Bias.vertline. < Bias CL? Yes Yes Yes Yesnumber of terms 6 5 6 5______________________________________
TABLE 4b__________________________________________________________________________Calibration and validation statistics (Step-up regression)__________________________________________________________________________ Step-up Regression 2, 2, 2 Step-up Regression 2, 5, 5 1 term 2 terms 3 terms 4 terms 5 terms 6 terms 1 term 2 terms 3 terms 4 terms 5 terms 6__________________________________________________________________________ termsEnergy required to shearLowest partial F-ratio 29.33 13.83 6.65 5.29 5.89 3.23 25.70 21.99 5.71 1.60 4.92 1.70R.sup.2 0.470 0.625 0.684 0.725 0.766 0.784 0.436 0.663 0.709 0.715 0.778 0.792SEC 1.873 1.575 1.445 1.349 1.244 1.196 1.932 1.492 1.387 1.373 1.211 1.173SEC CL 2.420 2.035 1.867 1.743 1.608 1.546 2.497 1.928 1.792 1.774 1.565 1.516SEV (C) 1.973 1.672 1.561 1.476 1.390 1.318 2.022 1.571 1.476 1.470 1.357 1.319r.sup.2 0.371 0.344 0.310 0.205 0.202 0.168 0.375 0.531 0.557 0.557 0.631 0.635Bias 1.120 0.950 0.870 0.810 0.775 0.720 1.160 0.900 0.830 0.820 0.730 0.700Bias CL 2.407 2.024 1.857 1.734 1.599 1.537 2.483 1.917 1.783 1.765 1.556 1.507SEP (C) 2.430 2.050 1.880 1.750 1.620 1.550 2.510 1.940 1.800 1.780 1.570 1.520Slope 1.000 0.795 0.784 0.598 0.549 0.498 0.643 0.606 0.616 0.633 0.644 0.633Intercept -0.120 3.030 3.290 6.090 6.950 7.790 6.390 5.790 5.560 5.270 5.050 5.170R.S.D. 1.896 1.803 1.773 1.769 1.732 2.058 1.891 1.806 1.698 1.656 2.317 1.945.vertline.Bias.vertline. - Bias CL -1.287 -1.074 -0.987 -0.924 -0.824 -0.817 -1.323 -1.017 -0.953 -0.945 -0.826 -0.807.vertline.Bias.vertline. < Bias CL? Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes YesEnergy required to comminuteLowest partial F-ratio 81.33 12.82 9.62 6.10 6.71 2.92 67.30 8.96 2.73 4.91 5.06 4.26R.sup.2 0.715 0.794 0.840 0.864 0.887 0.894 0.974 0.741 0.755 0.782 0.810 0.830SEC 20.719 17.829 15.538 14.330 13.061 12.620 22.149 19.757 19.213 18.105 16.921 15.983SEC CL 2.420 2.035 1.867 1.743 1.608 1.546 2.497 1.928 1.792 1.774 1.565 1.516SEV (C) 21.511 18.353 16.378 15.230 13.967 13.633 22.769 20.547 20.096 19.092 18.262 17.484r.sup.2 0.322 0.424 0.421 0.411 0.371 0.373 0.183 0.199 0.148 0.099 0.114 0.098Bias 12.430 10.580 9.320 8.600 7.840 7.570 13.290 11.850 11.530 10.860 10.150 9.590Bias CL 26.627 22.656 19.969 18.416 16.785 16.219 28.465 25.391 24.692 23.268 21.746 20.541SEP (C) 26.940 22.920 20.200 18.630 16.980 16.410 28.790 25.680 24.980 23.540 22.000 20.780Slope 0.605 0.623 0.577 0.560 0.524 0.521 0.491 0.518 0.441 0.346 0.365 0.317Intercept 47.100 43.900 48.900 52.900 58.300 57.800 60.100 58.500 70.800 84.600 82.700 89.600R.S.D. 29.810 27.480 27.550 27.790 28.720 28.670 32.720 32.400 33.420 34.370 34.070 34.380.vertline.Bias.vertline. - Bias CL -14.197 -12.076 -9.816 -8.945 -8.649 -15.175 -13.541 -13.162 -12.408 -11.596 -10.951.vertline.Bias.vertline. < Bias CL? Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes YesEnergy required to compressLowest partial F-ratio 7.28 6.24 3.22 2.65 3.10 0.00 8.07 4.23 4.08 2.21 5.36 1.87R.sup.2 0.164 0.285 0.334 0.370 0.440 0.530 0.181 0.258 0.327 0.445 0.520 0.535SEC 0.270 0.250 0.241 0.235 0.221 0.203 0.268 0.255 0.243 0.220 0.205 0.202SEC CL 2.420 2.035 1.867 1.743 1.608 1.546 2.497 1.928 1.792 1.774 1.565 1.518SEV (C) 0.277 0.259 0.252 0.248 0.238 0.226 0.276 0.268 0.257 0.241 0.227 0.222r.sup.2 0.067 0.089 0.087 0.104 0.067 0.033 0.039 0.064 0.038 0.005 0.010 0.006Bias 0.160 0.150 0.140 0.140 0.130 0.120 0.160 0.150 0.150 0.130 0.120 0.120Bias CL 0.347 0.321 0.310 0.302 0.284 0.261 0.344 0.328 0.312 0.283 0.263 0.260SEP (C) 0.350 0.330 0.310 0.310 0.290 0.260 0.350 0.330 0.320 0.290 0.270 0.260Slope 0.367 0.394 0.345 0.341 0.280 0.156 0.267 0.295 0.198 0.068 0.085 0.063Intercept 2.380 2.270 2.460 2.470 2.690 3.160 2.750 2.640 3.010 3.490 3.420 3.510R.S.D. 0.235 0.232 0.235 0.239 0.239 0.239 0.236 0.233 0.230 0.229 0.233 0.239.vertline.Bias.vertline. - Bias CL -0.187 -0.171 -0.170 -0.162 -0.154 -0.141 -0.184 -0.178 -0.162 -0.153 -0.143 -0.140.vertline.Bias.vertline. < Bias CL? Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes YesDigestibility of dry matter in vivoLowest partial F-ratio 72.42 5.79 8.71 16.80 13.18 2.86 80.75 12.35 8.41 7.34 6.03 3.72R.sup.2 0.616 0.714 0.830 0.862 0.883 0.906 0.679 0.826 0.897 0.909 0.919 0.924SEC 3.555 -3.069 2.365 2.127 1.962 1.755 3.248 2.394 1.840 1.728 1.635 1.588SEC CL 2.420 2.035 1.867 1.743 1.608 1.546 2.497 1.928 1.792 1.774 1.565 1.516SEV (C) 3.666 3.250 2.447 2.312 2.152 1.956 3.328 2.457 1.972 1.884 1.828 1.782r.sup.2 0.785 0.712 0.684 0.787 0.884 0.768 0.755 0.740 0.884 0.893 0.876 0.869Bias 2.130 1.840 1.420 1.280 1.180 1.050 1.950 1.440 1.100 1.040 0.980 0.950Bias CL 0.347 0.321 0.310 0.302 0.284 0.261 0.344 0.328 0.312 0.283 0.263 0.260SEP (C) 4.620 3.990 3.070 2.770 2.550 2.280 4.220 3.110 2.390 2.250 2.130 2.060Slope 1.050 0.805 0.792 0.826 0.777 0.731 1.090 1.050 0.889 0.880 0.866 0.885Intercept -2.180 10.300 11.000 9.040 11.900 14.900 -5.290 -3.300 5.850 6.430 7.210 6.080R.S.D. 2.484 2.877 2.584 2.652 2.734 3.108 2.476 1.825 1.825 1.750 1.884 1.940.vertline.Bias.vertline. - Bias CL 1.783 1.519 1.110 0.978 0.896 0.789 1.606 1.112 0.788 0.757 0.717 0.690.vertline.Bias.vertline. < Bias CL? No No No No No No No No No No No NoDigestibility of dry matter in vitroLowest partial F-ratio 73.30 5.73 8.71 17.00 13.23 2.99 81.21 12.38 8.48 7.23 6.07 3.72R.sup.2 0.692 0.733 0.788 0.905 0.915 0.715 0.791 0.833 0.863 0.884 0.894SEC 3.913 3.645 2.251 2.610 2.177 2.058 3.768 3.222 2.883 2.818 2.407 2.294SEC CL 2.420 2.035 1.867 1.743 1.608 1.546 2.497 1.928 1.792 1.774 1.565 1.516SEV (C) 4.020 3.186 3.411 2.a781 2.324 2.203 3.855 3.360 3.063 2.809 2.615 2.490r.sup.2 0.731 0.694 0.687 0.644 0.685 0.671 0.735 0.856 0.845 0.849 0.801 0.800Bias 2.350 2.190 1.950 1.570 1.310 1.230 2.260 1.930 1.730 1.570 1.440 1.380Bias CL 5.029 4.684 2.893 3.354 2.798 2.645 4.842 4.141 3.705 3.362 3.093 2.948SEP (C) 5.090 4.740 4.230 3.390 2.830 2.680 4.900 4.190 3.750 3.400 3.130 2.980Slope 0.946 0.868 0.861 0.877 0.860 0.830 1.080 0.994 1.020 0.976 0.975 0.914Intercept 2.000 5.890 6.550 6.140 7.240 9.150 -4.700 -0.410 -1.970 0.240 0.240 4.040R.S.D. 3.565 3.601 3.842 3.882 4.143 3.895 3.576 2.637 2.733 2.694 3.097 3.103.vertline.Bias.vertline. - Bias CL -2.879 -2.494 -0.943 -1.784 -1.488 -1.415 -2.582 -2.211 -1.975 -1.792 -1.653 -1.568.vertline.Bias.vertline. < Bias CL? Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes__________________________________________________________________________ Step-up Regression 2,10,5 Step-up Regression 2, 10, 10 1 term 2 terms 3 terms 4 terms 5 terms 6 terms 1 term 2 terms 3 terms 4 terms 5 terms 6__________________________________________________________________________ termsEnergy required to shearLowest partial F-ratio 25.11 14.92 5.87 4.42 7.81 1.89 23.54 15.23 4.01 4.30 4.35 4.66R.sup.2 0.430 0.606 0.661 0.697 0.755 0.763 0.413 0.598 0.641 0.678 0.721 0.755SEC 1.942 1.613 1.496 1.415 1.273 1.252 1.970 1.631 1.541 1.460 1.358 1.274SEC CL 2.510 2.084 1.933 1.829 1.645 1.618 2.546 2.108 1.991 1.887 1.755 1.646SEV (C) 2.020 1.674 1.611 1.541 1.403 1.392 2.047 1.689 1.612 1.569 1.494 1.411r.sup.2 0.273 0.398 0.456 0.473 0.476 0.498 0.291 0.333 0.401 0.454 0.517 0.541Bias 1.170 0.970 0.900 0.850 0.760 0.750 1.180 0.980 0.920 0.880 0.810 0.760Bias CL 2.496 2.073 1.923 1.818 1.636 1.609 2.532 2.096 1.980 1.876 1.745 1.637SEP (C) 2.520 2.100 1.950 1.840 1.650 1.630 2.560 2.120 2.000 1.900 1.760 1.660Slope 0.706 0.723 0.717 0.707 0.610 0.616 0.737 0.581 0.639 0.653 0.715 0.709Intercept 4.150 3.950 4.320 4.260 5.620 5.440 3.720 5.850 5.040 5.040 4.130 4.160R.S.D. 2.170 2.193 2.343 2.367 2.524 2.375 2.179 1.607 1.666 1.644 1.889 1.893.vertline.Bias.vertline. - Bias CL -1.326 -1.103 -1.023 -0.968 -0.876 -0.859 -1.352 -1.116 -1.060 -0.996 -0.935 -0.877.vertline.Bias.vertline. < Bias CL? Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes YesEnergy required to comminuteLowest partial F-ratio 76.72 5.31 2.76 2.18 1.68 1.01 74.65 5.39 2.13 2.96 4.38 1.49R.sup.2 0.730 0.739 0.754 0.763 0.772 0.800 0.698 0.735 0.745 0.781 0.787 0.791SEC 21.158 19.825 19.267 18.887 18.518 17.344 21.345 19.977 19.611 18.982 17.929 17.768SEC CL 2.510 2.084 1.933 1.829 1.645 1.618 2.548 2.108 1.991 1.887 1.755 1.646SEV (C) 21.803 20.707 20.279 19.690 19.499 18.691 22.033 20.904 20.985 19.911 18.777 18.634r.sup.2 0.460 0.468 0.414 0.394 0.330 0.215 0.434 0.450 0.408 0.397 0.357 0.387Bias 12.690 11.890 11.560 11.330 11.110 10.410 12.810 11.990 11.700 11.390 10.760 10.660Bias CL 27.191 25.478 24.761 24.273 23.799 22.290 27.432 25.674 25.203 24.395 23.042 22.835SEP (C) 27.510 25.770 25.050 24.550 24.070 22.550 27.750 25.970 25.490 24.680 23.310 23.100Slope 0.793 0.737 0.688 0.649 0.598 0.468 0.776 0.729 0.694 0.645 0.622 0.633Intercept 18.300 24.500 31.800 39.600 48.100 67.100 20.200 25.600 30.800 39.000 43.600 42.200R.S.D. 26.610 26.420 27.720 28.170 29.640 32.080 27.230 26.850 27.860 28.100 29.030 28.350.vertline.Bias.vertline. - Bias CL -14.501 -13.588 -13.201 -12.943 -12.689 -11880 -14.622 -13.684 -13.503 -13.005 -12.282 -12.175.vertline.Bias.vertline. < Bias CL? Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes YesEnergy required to compressLowest partial F-ratio 8.16 2.24 8.06 4.06 1.97 4.98 6.50 4.94 4.91 1.75 3.54 3.83R.sup.2 0.183 0.214 0.364 0.457 0.532 0.592 0.147 0.243 0.397 0.412 0.461 0.512SEC 0.267 0.262 0.236 0.218 0.202 0.189 0.273 0.257 0.230 0.227 0.217 0.207SEC CL 2.510 2.084 1.933 1.829 1.645 1.618 2.546 2.108 1.991 1.887 1.755 1.646SEV (C) 0.278 0.275 0.252 0.235 0.218 0.210 0.283 0.273 0.250 0.250 0.247 0.235r.sup.2 0.010 0.028 0.052 0.076 0.086 0.053 0.006 0.057 0.045 0.052 0.035 0.029Bias 0.160 0.160 0.140 0.130 0.120 0.110 0.160 0.150 0.140 0.140 0.130 0.120Bias CL 0.343 0.337 0.303 0.280 0.260 0.243 0.351 0.330 0.300 0.290 0.280 0.270SEP (C) 0.350 0.340 0.310 0.280 0.260 0.250 0.360 0.330 0.300 0.290 0.280 0.270Slope 0.127 0.212 0.239 0.252 0.218 0.142 0.102 0.294 0.216 0.212 0.149 0.149Intercept 3.270 2.960 2.850 2.800 2.930 3.210 3.360 2.640 2.940 2.950 3.180 3.190R.S.D. 0.234 0.233 0.235 0.236 1.942 1.495 1.736 1.938 1.980 2.030 2.179 2.183.vertline.Bias.vertline. - Bias CL -0.183 -0.177 -0.163 -0.150 -0.140 -0.133 -0.191 -0.180 -0.156 -0.152 -0.149 -0.146.vertline.Bias.vertline. < Bias CL? Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes YesDigestibility of dry matter in vivoLowest partial F-ratio 91.59 9.46 9.77 7.10 2.58 4.12 93.60 6.52 16.07 4.36 4.68 4.94R.sup.2 0.700 0.867 0.902 0.916 0.927 0.935 0.675 0.815 0.898 0.912 0.922 0.927SEC 3.139 2.095 1.794 1.660 1.545 1.457 3.271 2.467 1.828 1.698 1.598 1.546SEC CL 2.510 2.084 1.933 1.829 1.645 1.618 2.546 2.108 1.991 1.887 1.755 1.646SEV (C) 3.332 2.282 1.997 1.830 1.694 1.607 3.357 2.572 2.016 1.905 1.840 1.787r.sup.2 0.777 0.856 0.888 0.871 0.887 0.881 0.828 0.831 0.809 0.836 0.877 0.892Bias 1.880 1.260 1.080 1.000 0.930 0.870 1.960 1.480 1.100 1.020 0.960 0.930Bias CL 0.343 0.337 0.303 0.280 0.260 0.243 0.351 0.330 0.296 0.292 0.279 0.266SEP (C) 4.080 2.720 2.330 2.160 2.010 1.890 4.250 3.210 2.380 2.210 2.080 2.010Slope 0.892 0.880 0.924 0.870 0.851 0.837 1.130 0.991 0.812 0.829 0.840 0.865Intercept 7.380 6.750 3.930 6.850 8.320 8.960 -6.490 0.210 9.960 9.030 8.710 7.270R.S.D. 2.531 2.034 1.791 1.927 1.799 1.848 2.222 2.201 2.344 2.172 1.876 1.761.vertline.Bias.vertline. - Bias CL 1.537 0.923 0.777 0.720 0.670 0.627 1.609 1.150 0.804 0.728 0.681 0.664.vertline.Bias.vertline. < Bias CL? No No No No No No No No No No No NoDigestibility of dry matter in vivoLowest partial F-ratio 94.12 8.62 16.01 4.48 4.61 5.01 92.14 9.60 9.70 10.55 2.67 4.41R.sup.2 0.744 0.784 0.856 0.871 0.886 0.901 0.740 0.797 0.842 0.881 0.888 0.900SEC 3.568 3.283 2.680 2.532 2.384 2.224 3.598 3.182 2.802 2.430 2.351 2.235SEC CL 2.510 2.084 1.933 1.829 1.645 1.618 2.546 2.108 1.991 1.887 1.755 1.646SEV (C) 3.633 3.371 2.785 2.667 2.563 2.364 3.655 3.280 2.892 2.818 2.525 2.416r.sup.2 0.828 0.816 0.813 0.802 0.819 0.851 0.823 0.807 0.810 0.844 0.818 0.823Bias 2.140 1.970 1.610 1.520 1.430 1.330 2.160 1.910 1.680 1.460 1.420 1.340Bias CL 4.585 4.219 3.444 3.254 3.064 2.858 4.621 4.089 3.601 3.123 3.034 2.872SEP (C) 4.640 4.270 3.480 3.290 3.100 2.890 4.680 4.140 3.640 3.160 3.070 2.910Slope 0.960 0.971 0.906 0.862 0.867 0.864 0.937 0.927 0.882 0.935 0.881 0.841Intercept 2.120 1.280 4.530 7.230 7.380 7.610 3.490 3.660 5.790 3.260 6.140 8.660R.S.D. 2.978 3.002 3.088 2.952 2.681 2.922 3.023 2.742 2.959 2.846 0.231 0.239.vertline.Bias.vertline. - Bias CL -2.445 -2.49 -1.834 -1.734 -1.634 -1.528 -2.461 -2.179 -1.921 -1.663 -1.614 -1.532.vertline.Bias.vertline. < Bias CL? Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes__________________________________________________________________________
TABLE 4c__________________________________________________________________________Calibration and validation statistics (multivariateregressions) PCR PLS MPLS 2, 5, 5 2, 10, 10 2, 5, 5 2, 10, 10 2, 5, 5 2, 10, 10__________________________________________________________________________Energy required to shearR.sup.2 0.847 0.752 0.639 0.601 0.601 0.582SEC 1.036 1.290 1.545 1.624 1.550 1.586SEC CL 1.199 1.493 1.788 1.879 1.793 1.835SECV 1.750 1.592 1.788 1.933 1.600 1.583r.sup.2 0.5441 0.4876 0.4938 0.4157 0.3080 0.3563Bias 0.620 0.770 0.930 0.970 0.930 0.950Bias CL 1.331 1.658 1.986 2.087 1.992 2.038SEP (C) 1.350 1.680 2.010 2.110 2.020 2.060Slope 0.6540 0.6850 0.7390 0.6270 0.5220 0.6000Intercept 5.2900 4.8100 3.7500 5.4500 7.3100 6.0200R.S.D. 1.671 1.776 1.761 1.892 2.065 1.992.vertline.Bias.vertline. - Bias CL -0.711 -0.888 -1.056 -1.117 -1.062 -1.088.vertline.Bias.vertline. < Bias CL? Yes Yes Yes Yes Yes YesEnergy required to comminuteR.sup.2 0.584 0.574 0.605 0.595 0.556 0.558SEC 23.378 23.682 22.788 23.075 24.164 24.101SEC CL 27.048 27.400 26.366 26.698 27.958 27.885SECV 26.030 26.121 25.548 25.683 26.409 26.252r.sup.2 0.349 0.337 0.332 0.325 0.33 0.328Bias 14.030 14.210 13.670 13.840 14.500 14.460Bias CL 30.044 30.435 29.286 29.655 31.055 30.974SEP (C) 30.390 30.790 29.620 30.000 31.410 31.330Slope 0.649 0.636 0.644 0.632 0.657 0.638Intercept 37.3 39.2 38.1 39.7 36.6 39.1R.S.D. 28.246 28.676 28.714 28.884 28.651 28.900.vertline.Bias.vertline. - Bias CL -16.014 -16.225 -15.616 -15.815 -16.555 -16.514.vertline.Bias.vertline. < Bias CL? Yes Yes Yes Yes Yes YesEnergy required to compressR.sup.2 0.251 0.160 0.231 0.208 0.038 0.040SEC 0.225 0.241 0.265 0.269 0.260 0.260SEC CL 0.260 0.279 0.307 0.311 0.301 0.301SECV 0.299 0.277 0.301 0.307 0.301 0.299r.sup.2 0.0220 0.0120 0.0130 0.0090 0.0060 0.0080Bias 0.140 0.140 0.160 0.160 0.160 0.160Bias CL 0.289 0.310 0.341 0.346 0.334 0.334SEP (C) 0.290 0.310 0.340 0.350 0.340 0.340Slope 0.2290 0.2270 0.1530 0.1330 0.2290 0.2590Intercept 2.8900 2.9000 3.1700 3.2500 2.8900 2.7700R.S.D. 0.235 0.236 0.236 0.237 0.237 0.237.vertline.Bias.vertline. - Bias CL -0.149 -0.170 -0.181 -0.186 -0.174 -0.174.vertline.Bias.vertline. < Bias CL? Yes Yes Yes Yes Yes YesDigestibility of dry matter in vivoR.sup.2 0.909 0.900 0.958 0.937 0.571 0.892SEC 1.638 1.711 1.109 1.356 3.756 1.911SEC CL 1.895 1.980 1.283 1.569 4.346 2.211SECV 2.159 2.075 1.957 1.776 3.797 2.180r.sup.2 0.9022 0.8865 0.8447 0.8457 0.6963 0.8671Bias 0.980 1.030 0.670 0.810 2.250 1.150Bias CL 2.105 2.199 1.425 1.743 4.827 2.456SEP (C) 2.130 2.220 1.440 1.760 4.880 2.480Slope 0.848 0.807 0.839 0.822 0.981 0.745Intercept 8.77 11.1 8.65 9.41 1.99 14.6R.S.D. 1.704 1.834 2.143 2.139 2.914 1.984.vertline.Bias.vertline. - Bias CL -1.125 -1.169 -0.755 -0.933 -2.577 -1.306.vertline.Bias.vertline. < Bias CL? Yes Yes Yes Yes Yes YesDigestibility of dry matter in vitroR.sup.2 0.820 0.790 0.780 0.760 0.420 0.490SEC 2.880 3.100 3.330 3.470 5.380 4.850SEC CL 3.332 3.587 3.853 4.015 6.225 5.611SECV 3.170 3.560 3.830 3.900 5.690 4.780r.sup.2 0.8120 0.7730 0.8530 0.8040 0.6910 0.6690Bias 1.730 1.860 2.000 2.080 3.230 2.910Bias CL 3.701 3.984 4.280 4.459 6.914 6.233SEP (C) 3.740 4.030 4.330 4.510 6.990 6.310Slope 0.9180 0.9840 0.9530 0.6120 1.1200 0.8650Intercept 3.4700 -0.3600 2.3100 4.6300 -7.5100 6.1800R.S.D. 3.053 3.363 2.836 3.089 3.911 4.002.vertline.Bias.vertline. - Bias CL -1.971 -2.124 -2.280 -2.379 -3.684 -3.323.vertline.Bias.vertline. < Bias CL? Yes Yes Yes Yes Yes Yes__________________________________________________________________________
TABLE 5__________________________________________________________________________Standard error of laboratory determination (SEL) Digestibility Digestibility Energy Energy Energy of dry of dry required to required to required to matter matter shear comminute compress in vivo in vitro (kJ/m.sup.2) (kJ/kg DM) (kJ/kg DM) (%) (%)__________________________________________________________________________Mean SEL (n = 65) 0.796 5.830 0.078 not available 0.314Median SEL 0.788 5.492 0.085 not available 0.270Maximum SEL 2.044 13.098 0.211 not available 1.126Minimum SEL 0.114 0.780 0.019 not available 0.005SEL CL (using mean SEL) 1.035 7.319 0.101 not available 0.408SEL CL (using median SEL) 1.024 7.140 0.111 not available 0.351__________________________________________________________________________
TABLE 6a__________________________________________________________________________Components of possible prediction equationsfrom stepwise and step-up regression analyses. Coefficient Wavelength Coefficient Wavelength__________________________________________________________________________ Energy required to shearRegression analysis Stepwise Step-upMathematical treatment 2, 2, 2 (6 terms) 2, 5, 5 (2 terms)__________________________________________________________________________ 19.95 28.09 2452.05 2048 1035.77 2048 -4255.81 1598 700.12 1958 3823.49 1458 -5319.88 1718 5148.38 1828 10239.46 1168__________________________________________________________________________ Energy required to compressRegression analysis Stepwise Step-upMathematical treatment 2, 10, 10 (4 terms) 2, 10, 5 (3 terms)__________________________________________________________________________ -0.71 2.49 -28.02 2278 -31.05 1728 112.57 1588 -108.89 1548 -79.48 1728 -105.95 1268 -911.04 1268__________________________________________________________________________ Energy required to comminuteRegression analysis Stepwise Step-upMathematical treatment 2, 10, 5 (4 terms) 2, 10, 5 (1 term)__________________________________________________________________________ 231.42 49.16 -3005.37 2128 -1521.33 2268 4290.19 2408 -4955.12 2018 18224.74 1138__________________________________________________________________________ Digestibility of dry matter in vivoRegression analysis Stepwise Step-upMathematical treatment 2, 5, 5 (6 terms) 2, 10, 5 (3 terms)__________________________________________________________________________ 48.62 49.15 -387.08 1918 -612.43 1698 -8799.69 1238 252.82 1418 8152.72 1158 -943.77 1618 1249.01 1668 519.46 1908 -181.84 2248__________________________________________________________________________ Digestibility of dry matter in vitroRegression analysis Stepwise Step-upMathematical treatment 2, 2, 5 (5 terms) 2, 10, 10 (4 terms)__________________________________________________________________________ 63.43 54.29 -558.01 1918 -1171.70 1698 981.30 1908 311.12 1418 -2186.89 1898 -2657.69 1618 2003.05 2158 -2319.81 1228 -1491.99 1748__________________________________________________________________________
TABLE 6b__________________________________________________________________________Components of possible prediction equations from multivariate regressionanalyses.__________________________________________________________________________Energy required to shear Energy required to compress Energy required to comminutePCR (2,5,5) PCR (2,5,5) PCR (2,5,5) PLS (2,5,5)Coefficient Wavelength Coefficient Wavelength Coefficient Wavelength Coefficient Wavelength__________________________________________________________________________-3.33 3.35 -22.8 -16.4418.1 1108 0.17 1108 93.07 1108 91.01 11081.76 1118 0.02 1118 6.5 1118 7.19 1118-0.2 1128 -0.01 1128 -7.27 1128 -5.63 1128-0.84 1138 -0.01 1138 -0.79 1138 -0.13 11381.15 1148 0.01 1148 3.98 1148 5.58 1148-0.85 1158 -0.01 1158 -10.39 1158 -8.55 11580.13 1168 0 1168 -4.8 1168 -4.72 11680.64 1178 0.03 1178 13.75 1178 13.27 1178-0.04 1188 0.04 1188 19.07 1188 17.38 1188-0.84 1198 0 1198 5.06 1198 0.44 1198-1.14 1208 -0.03 1208 -8.92 1208 -13.96 1208-1.71 1218 -0.05 1218 -20.83 1218 -23.8 1218-1.73 1228 -0.04 1228 -17.9 1228 -15.15 1228-0.85 1238 -0.01 1238 -3.26 1238 -0.82 12380.12 1248 0 1248 -0.85 1248 1.6 12480.5 1258 0 1258 -2.55 1258 -0.82 1258-0.9 1268 -0.01 1268 -4.67 1268 -3.84 1268-1.25 1278 0 1278 1.29 1278 1.1 1278-0.25 1288 0.01 1288 5.24 1288 4.93 12880.2 1298 0.02 1298 5.9 1298 7.12 1298-0.1 1308 0.03 1308 9.22 1308 10.4 1308-0.66 1318 0.04 1318 14.26 1318 16.14 13181.25 1328 0.06 1328 22.7 1328 23.58 13285 1338 0.07 1338 27.63 1338 27.65 13382.34 1348 0.03 1348 6.64 1348 10.27 1348-2.37 1358 -0.06 1358 -26.23 1358 -24.35 1358-10.62 1368 -0.15 1368 -66.36 1368 -60.7 1368-9.89 1378 -0.01 1378 -1.83 1378 2.2 1378-1.68 1388 0.16 1388 67.77 1388 67.91 13888.65 1398 0.3 1398 125.67 1398 115.99 139823.88 1408 0.49 1408 188.24 1408 174.79 140813.87 1418 0.2 1418 67.19 1418 62.69 1418-12.53 1428 -0.37 1428 -153.54 1428 -139.99 1428-0.01 1438 -0.39 1438 -145.28 1438 -137.82 1438-1.04 1448 -0.21 1448 -66.74 1448 -75.27 14482.09 1458 -0.13 1458 -37.31 1458 -47.38 1458-2.69 1468 -0.11 1468 -49.09 1468 -48.54 1468-8.05 1478 -0.14 1478 -70.4 1478 -59.94 1478-1.2 1488 -0.06 1488 -26.64 1488 -26.06 1488-2.08 1498 -0.01 1498 -1.87 1498 -3.2 1498-1.69 1508 0.01 1508 4.61 1508 3.33 15080.35 1518 0.07 1518 28.69 1518 26.91 15182.8 1528 0.12 1528 49.84 1528 48.03 1528-0.01 1538 0.08 1538 34.38 1538 33.02 1538-1.83 1548 0 1548 -3.32 1548 0.49 1548-3.7 1558 -0.04 1558 -21.79 1558 -17.62 1558-0.66 1568 -0.01 1568 -4.61 1568 -3.35 1568-1.99 1578 -0.04 1578 -13.08 1578 -15.39 1578-3.67 1588 -0.09 1588 -43.91 1588 -40.21 1588-5.26 1598 -0.07 1598 -3.02 1598 -32.77 15980.06 1608 -0.01 1608 -7.28 1608 -5.93 16082.89 1618 0.04 1618 17.4 1618 15 16180.22 1628 0.09 1628 34.68 1628 33.13 1628-0.98 1638 0.13 1638 49.47 1638 48.83 163810.23 1648 0.1 1648 53.35 1648 44.32 164810.67 1658 0 1658 -3.16 1658 -2.24 16586.52 1668 -0.22 1668 -82.09 1668 -80.94 1668-20.53 1678 -0.07 1678 -60.38 1678 -38.22 1678-6.15 1688 0.15 1688 55.65 1688 60.49 16887.4 1698 0.06 1698 48.43 1698 42.6 16984.76 1708 0.06 1708 34.19 1708 27.79 1708-19.73 1718 -0.09 1718 -54.88 1718 -46.36 1718-5.98 1728 -0.13 1728 -54.69 1728 -69.44 172819.24 1738 0.15 1738 78.27 1738 63.67 17386.42 1748 0.19 1748 90.94 1748 91.13 1748-3.1 1758 0.06 1758 21.41 1758 20.27 1758-4.03 1768 -0.1 1768 -47.2 1768 -48.58 1768-1.47 1778 -0.11 1778 -42.48 1778 -39.05 1778-0.44 1788 -0.09 1788 -36.22 1788 -33.03 17881.72 1798 -0.01 1798 -2.33 1798 -3.76 17982.76 1808 0.05 1808 20.97 1808 18.41 1808-3.79 1818 -0.01 1818 -6.58 1818 -5.17 1919-4.32 1828 -0.07 1828 -30.81 1828 -26.5 1828-2.97 1838 -0.05 1838 -17.29 1838 -17.64 18381.97 1848 0.03 1848 17.65 1848 15.59 1848-2.48 1858 0.06 1858 28.06 1858 28.11 1858-6.48 1868 0.08 1868 42.2 1868 40.46 1868-0.22 1878 0.23 1878 115.52 1878 108.98 1878-1.64 1888 0.44 1888 219.15 1888 208.1 188831.11 1898 0.2 1898 98.82 1898 99.42 18982.3 1908 -0.35 1908 -179.9 1908 -157.11 1908-25.69 1918 -0.47 1918 -251.3 1918 -220.58 1918-12.22 1928 -0.34 1928 -171.93 1928 -172.09 192815.11 1938 -0.14 1938 -55.38 1938 -77.27 193828.89 1948 -0.03 1948 -6.17 1948 -23.21 194827.72 1958 -0.04 1958 -16.65 1958 -19.03 19588.93 1968 0.05 1968 12.02 1968 22.46 1968-15.33 1978 0.23 1978 68.03 1978 88.88 1978-9.25 1988 0.29 1988 85.84 1988 103.6 1988-3.33 1998 0.27 1998 78.68 1998 98.03 19981.28 2008 0.32 2008 97.53 2008 115.14 200820.22 2018 0.25 2018 104.79 2018 99.25 201818.34 2028 0.08 2028 42.08 2028 33.39 20289.58 2038 -0.01 2038 23.98 2038 11.1 20385.59 2048 0.13 2048 101.85 2048 77.38 2048-8.64 2108 -0.26 2108 -108.06 2108 -107.49 2108-6.28 2118 -0.23 2118 -91.06 2118 -91.23 2118-4.49 2128 -0.23 2128 -97.55 2128 -93.37 2128-16.58 2138 -0.2 2138 -101.35 2138 -85.08 2138-9.08 2148 -0.01 2148 -13.78 2148 -9.31 2148-2.68 2158 0.08 2158 37.5 2158 36.99 21583.19 2168 0.26 2168 118.43 2168 110.03 21684.27 2178 0.26 2178 122.68 2178 109.87 2178-6.3 2188 0.16 2188 54.73 2188 58.98 2188-13.33 2198 0.15 2198 38.08 2198 53.78 2198-2.74 2208 0.35 2208 129.58 2208 147.19 220835.77 2218 0.36 2218 171.77 2218 149.55 221830.36 2228 0.42 2228 182.8 2228 168.64 222822.91 2238 0.22 2238 73.19 2238 88.39 223898.61 2248 -0.67 2248 -152.9 2248 -250.89 2248-15.77 2258 -0.47 2258 -184.5 2258 -189.12 2258-85.22 2268 -0.2 2268 -167.23 2268 -101.11 2268-35.1 2278 -0.45 2278 -214.87 2278 -180.14 227827.27 2288 0.22 2288 148 2288 122.32 22884.27 2298 0.84 2298 352.88 2298 341.16 2298-13.06 2308 0.29 2308 78.88 2308 63.66 23080.67 2318 -0.48 2318 -208.27 2318 -207.81 2318-13.34 2328 -0.47 2328 -167.95 2328 -150.99 2328-23.3 2338 -0.2 2338 -79.26 2338 -66.98 23380.66 2348 0.15 2348 62.44 2348 40.83 23487.98 2358 -0.07 2358 -23.09 2358 -30.25 2358-15.62 2368 0.08 2368 25.53 2368 41.9 2368-16.39 2378 0.05 2378 -3.86 2378 12.67 2378-4.44 2388 -0.05 2388 -33.15 2388 -26.65 238821.16 2398 0.2 2398 98.53 2398 79.92 239849.9 2408 0.22 2408 130.87 2408 89.16 240822.34 2418 0.29 2418 120.05 2418 107.71 2418-1.47 2428 0.22 2428 72.57 2428 84.86 242817.19 2438 0.03 2438 6.72 2438 3.78 243815.21 2448 0.11 2448 55.93 2448 45.74 2448-14.12 2458 0.16 2458 59.89 2458 71.29 2458-24.15 2468 -0.04 2468 -31.79 2468 -13.92 2468__________________________________________________________________________Digestibility of dry matter in vitro Digestibility of dry matter in vivoPLS (2,5,5) PCR (2,5,5) PLS (2,5,5)Coefficient Wavelength Coefficient Wavelength Coefficient Wavelength__________________________________________________________________________59.77 40.56 63.64-96.26 1108 -161.5 1108 -78.7 11087.55 1118 12.91 1118 4 111812.25 1128 22.04 1128 8.68 11286.85 1138 19.51 1138 3.94 11386.74 1148 8.19 1148 7.08 114811.89 1158 5.29 1158 6.24 11584.45 1168 -0.92 1168 0.43 1168-10.08 1178 -11.1 1178 -5.6 1178-29.6 1188 -37.4 1188 -15.1 1188-20.43 1198 -26.13 1198 -10.61 1198-20.05 1208 -38.03 1208 -15.98 1208-15.65 1218 -35.64 1218 -13.18 12182.41 1228 -13.39 1228 2.03 12286.62 1238 0.89 1238 6.37 12388.8 1248 14.13 1248 7.08 12487.47 1258 17.48 1258 5.09 1258-1.32 1268 -7.44 1268 -0.25 1268-7.39 1278 -22.67 1278 -4.08 1278-0.79 1288 -1.05 1288 0.15 12883.48 1298 9.23 1298 3.72 12985.1 1308 13.17 1308 4.38 13088.23 1318 16.6 1318 6.03 13188.48 1328 25.91 1328 9.03 132817.78 1338 40.71 1338 13 133824.61 1348 51.12 1348 13.51 1348-0.07 1358 4.6 1358 0.20 1358-23.89 1368 -88.33 1368 -13.81 1368-29.88 1378 -68.78 1378 -8.13 1378-18.97 1388 6.08 1388 -1.4 138823.92 1398 32.14 1398 18.06 139860.7 1408 78.51 1408 32.08 140855.51 1418 90.64 1418 11.94 1418-0.3 1428 -1.47 1428 -14.78 1428-28.21 1438 -11.15 1438 -19.94 1438-32.57 1448 10.5 1448 -19.24 1448-28.08 1458 18.31 1458 -18.15 14581.48 1468 -3.82 1468 3.7 146818.21 1478 -18.48 1478 10.65 1478-0.65 1488 -25.14 1488 -0.75 1488-22.99 1498 -74.43 1498 -7.65 1498-23.44 1508 -75.49 1508 -9.41 1508-15.74 1518 -58.78 1518 -8.06 1518-9.09 1528 -25.77 1528 -5.21 1528-2.8 1538 -7.83 1538 -3.83 153810.62 1548 17.83 1548 4.58 154828.76 1558 52.96 1558 15.5 15585.02 1568 21.09 1588 15.5 1558-6.6 1578 -27.42 1578 -3.09 1578-18.09 1588 -42.59 1588 -5.77 1588-9.26 1598 -63.69 1598 -8.48 1598-6.66 1608 -3.06 1608 -0.65 1608-3.82 1618 -1.82 1618 -0.67 1618-3.58 1628 -2.81 1628 0.24 16280.55 1638 -9.18 1638 3.42 16380.5 1648 13.81 1648 0.66 164823.87 1658 61.39 1658 13.12 165854.92 1668 159.6 1668 28.91 166878.64 1678 101.05 1678 44.01 1678-13.9 1688 -79.38 1688 -8.19 1688-74.54 1698 -77.73 1698 -34 1698-38.63 1708 -43.87 1708 -18.38 1708-24.48 1718 21.34 1718 2.17 1718-17.91 1728 -25.8 1728 -31.77 1728-43.01 1738 -40.51 1738 -21.21 1738'29.25 1748 -28.11 1748 -5.24 1748-16.33 1758 -4.55 1758 -11.67 17580.43 1768 2.68 1768 -0.49 176828.21 1778 20.91 1778 21.37 177818.92 1788 13.59 1788 13.25 17880.54 1798 9.35 1798 -0.93 1798-2.44 1808 7.17 1808 -4.5 1808-2.72 1818 -15.30 1818 -2.3 1818-5.84 1828 -29.4 1828 -3.37 1828-4.37 1838 -16.21 1838 -0.05 1838-8.79 1848 2.3 1848 -2.23 1848-7.72 1858 -0.87 1858 -3.79 1858-29.93 1868 -21.29 1868 -9.61 1868-98.16 1878 -82.33 1878 -34.58 1878-116.18 1888 -102.15 1888 -52.89 1888117.59 1898 211.27 1898 38.2 1898185 1908 204.51 1908 68.78 190833.91 1918 -3.12 1918 28.1 1918-35.31 1928 18.14 1928 -3.79 1928-44.59 1938 35.39 1938 -19.45 1938-9.28 1948 -8.24 1948 -8.41 194835.73 1958 -11.32 1948 15.95 195828.56 1968 -37.15 1968 13.49 196810.68 1978 -44.28 1978 2.03 197810.98 1988 -61.81 1988 0.57 198865.12 1998 -72.2 1998 31.07 199883.13 2008 -63.31 2008 38.57 20087.23 2018 37.37 2018 -1.21 20180.99 2028 183.21 2028 -9.38 2028-10.85 2038 156.66 2038 -14.51 2038-84.48 2048 -2.09 2048 -54.72 2048-122.91 2058 -178.03 2058 -68.29 2058-35.85 2068 -104.9 2068 -1.09 206834.93 2078 -7.7 2078 37.65 207828.83 2088 82.28 2088 27.17 208818.03 2098 54.28 2098 18.01 20985.09 2108 14.14 2108 15.52 2108-9.58 2118 -40.31 2118 7.88 21189.79 2128 2.34 2128 13.25 212823.04 2138 28.94 2138 18.49 2138-10.93 2148 -31.56 2148 -8.42 2148-10.97 2158 3.05 2158 -9.12 2158-41.78 2168 -68.48 2168 -27.35 2168-46.69 2178 -107.52 2178 -32.56 2178-14.5 2188 -54.54 2188 -12.58 2188-0.14 2198 -11.17 2198 4.5 2198-7.15 2208 -2.14 2208 4.87 2208-48.05 2218 -43.68 2218 -30.89 2218-18.22 2228 -0.01 2228 -19.94 222888.33 2238 100.18 2238 14.80 2238-24.11 2248 53.32 2248 -53.79 224855.09 2258 81.52 2258 18.08 2258110.06 2268 48.93 2268 90.27 226852.18 2278 -9.18 2278 83.96 2278-89.38 2288 25.1 2288 -35.59 2288-109.09 2298 -47.83 2298 -77.98 2298-54.11 2308 -23.3 2308 -61.38 230817.63 2318 -73.92 2318 22.34 231823.71 2328 -23.74 2328 46.6 232862.19 2338 13.84 2338 37.70 2338-58.18 2348 -21.67 2348 -48.16 2348-21.28 2358 -77.29 2358 -7.88 23584.01 2368 -88.75 2368 18.34 236832.53 2378 28.42 2378 17.8 237835.58 2388 68.01 2388 1.95 2388-21.25 2398 22.17 2398 -27.54 2398-70.01 2408 -28.96 2408 -50.39 2408-18.88 2418 8.02 2418 -18.09 241861.66 2428 75.99 2428 17.75 242814.94 2438 58.83 2438 -3.66 2438-11.88 2448 8.46 2448 -11.21 24485.35 2458 -4.33 2458 3.97 245810.49 2468 -33.25 2468 11.29 2468__________________________________________________________________________
TABLE 7__________________________________________________________________________Descriptions of forages used in Table 8.Samplein Part ofTable 8 Genus Species Variety Common name plant Process undergone Stage of maturity Regrowth__________________________________________________________________________1 Panicum coloratum Kabutabula Makarikari grass aerial dried and chaffed mid bloom (9 weeks' regrowth) mid bloom - CPI 16796 regrowth2 Pancium maximum Colonlao Guinea grass aerial dreid and chaffed vegetative regrowth (4 vegetative regrowth3 Panicum coloratum Babbalsi Makarlkari grass aerial dried and chaffed mid bloom (1 month's mid bloom - regrowth4 Panicum maximum Hamii Guinea grass aerial dried and chaffed early bloom (1 month's early bloom - regrowth5 Pancium coloratum Burnett Makarikari grass aerial dried and chaffed mid bloom (6 weeks' regrowth) mid bloom - var regrowth Makeri- kariense6 Pancium maximum Petrie Green Panic aerial dried and chaffed mid bloom (4 weeks' regrowth) mid bloom - var. regrowth tricho- glume__________________________________________________________________________
TABLE 8__________________________________________________________________________Examples of energy required to shear, digestibility of dry matter invivo,forage consumption constraint (FCC), and voluntary feed consumption(VFC) Energy required to Digestibility of drySample shear, matter in vivo,in predicted using NIR.sup.1 predicted using NIR.sup.2 Predicted FCC.sup.3 Predicted VFC.sup.5 Actual VFC Actual VFCTable 7 (kJ/m.sup.2) (%) (g OM/d/MBW).sup.4 (g OM/d/MBW) (g OM/d/MBW) (g OM/d)__________________________________________________________________________1 20.51 51.29 86.85 32.52 30.77 5342 16.70 54.73 66.92 44.95 39.47 6863 13.75 56.69 51.49 56.51 48.79 8484 13.18 59.59 48.41 54.34 53.58 9315 17.52 65.18 71.21 39.74 43.66 7596 16.63 55.88 66.55 43.01 45.66 793__________________________________________________________________________ .sup.1 Predicted using the calibration equation from stepwise regression analysis (Table 6a). .sup.2 Predicted using the calibration equation from stepwise regression analysis (Table 6a). .sup.3 Predicted using predicted energy required to shear, and the relationship between energy required to shear and FCC. .sup.4 Calculated from predicted FCC and predicted digestibility of dry matter in vivo. .sup.5 Abbreviations used: organic matter (OM), metabolic body weight (MBW) = BW.sup.0.75

Method for determining feed quality

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