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Near Infrared Reflectance Spectroscopy for Analyzing Undried Silage1, 2
Near Infrared Reflectance Spectroscopy for Analyzing Undried Silage 1,2 J. B. REEVES,III, and T. H. BLOSSER Ruminant Nutrition Laboratory, Animal Science Institute Beltsville Agricultural Research Center Agricultural Research Service United States Department of Agriculture Beltsville, MD 20705 V. F. COLENBRANDER Department of Animal Sciences Purdue University West Lafayette, IN 47907 ABSTRACT
organic acids were considerably lower but were higher for the alfalfa than for the corn silages. Use of different procedures for selecting calibration and validation sets had varying influence on near infrared reflectance spectroscopy estimates depending on the component being analyzed.
Alfalfa, corn, small grain crop, and grass silage samples (n = 146) were collected from farms in Maryland, Virginia, and Indiana to determine the usefulness of near infrared reflectance spectroscopy for analyzing the feeding value of wet feedstuffs. Undried silages were analyzed for major components (DM, CP, ADF, NDF, pH, ADlN, NH 3 N, hot water insoluble N, and in vitro digestible DM) and for acetic, propionic, isobutyric, butyric, isovaleric, lactic, and valeric acids by conventional chemical laboratory procedures compared with near infrared reflectance spectroscopy. Validation sets consisting of one-third of the samples within a group were used to evaluate the accuracy of calibration equations. The coefficients of determination of near infrared analyzed values with conventional chemical assays of major components in the wet alfalfa silages ranged from .78 to .99, except for ADlN. These values ranged from .75 to .96 for wet corn silage components, except for ADIN and hot water insoluble N. Correlations between the two methods of analysis for most of the short-chain
INTRODUCTION
Forages are commonly evaluated on the basis of their CP, ADF, and NDF content. With silage, the pH and concentrations of certain short-chain organic acids also are useful in predicting quality. Determining the amounts of NH 3 N, amines, and other soluble organic N compounds also contributes to the ability to predict silage acceptability and nutritive value (5, 12, 20, 21). High ADIN indicates excessive heating during fermentation with resultant heat damage (8, 23). Although the use of near infrared reflectance spectroscopy (NIRS) is very attractive to animal producers and others involved in feeding farm animals, considerable research is needed to maximize the value of NIRS analyses for users (4, 10, 14, 15, 16, 18). Waldo and Jorgensen (22) stated that before NIRS can be used generally in forage and ruminant feed evaluation, it must be adapted to analysis of undried silage and high moisture grain. Oven drying of silage prior to chemical analysis creates analytical errors because of the loss of volatiles formed during the silage fermentation process (6, 20). Using NIRS directly on undried silage would save time and reduce analytical errors associated with oven drying. Use of NIRS may also offer users an opportunity to examine a wide variety of silage quality parameters at a relatively low cost (1, 3, 13).
Received November 16, 1987. Accepted August 23, 1988. 1 Mention of a trade name, proprietary product, or specific equipment does not constitute a guarantee or warranty by the US Department of Agriculture and does not imply its approval to the exclusion of other products that may be suitable. 'Published as Journal Paper Number 11385, Indiana Agricultural Experiment Station. 1989 J Dairy Sci 72:79-88
79
REEVES, III, ET AL.
80
Problems exist, both for chemical and NIRS analysis, in using undried materials. A problem common to both is preparing wet samples of sufficiently fine subdivision to permit accurate sampling (10). An additional problem with NIRS is the presence in the near infrared spectrum of several strong absorption bands caused by water (10). Although these bands may limit the use of NIRS for high moisture feedstuffs to certain spectral regions, NIRS has been used successfully with a wide variety of high moisture materials (1, 15). An additional error is introduced when silage samples are oven dried for analysis for moisture content. Silages contain a variety of components that are volatile at oven drying temperatures. This creates an error, which is inherent when converting analysis of undried silages to a moisture-free basis (6, 20). To study the analysis of undried silages with NIRS, a trial was designed with the following objectives: 1) to determine the accuracy of NIRS when analyzing silage samples of widely varying chemical composition; and 2) to ascertain NIRS accuracy in analyzing sample sets of homogeneous and heterogenous species composition. MATERIALS AND METHODS Sample Collection and Preparation
Silage samples (n = 146) were collected from farms in Maryland, Virginia, and Indiana. When the samples were collected, information was recorded about the date and stage of maturity at harvest, type of structure in which the forage was ensiled, use of additives, and the dairy producers' assessment of quality. The subsequent division of the samples into species groups was limited by the accuracy of producer responses concerning the samples. The samples were frozen on the day of collection. The frozen samples were ground in a commercial-type Hobart food chopper and then were refrozen. Frozen subsamples were subsequently finely chopped in a kitchen -type food chopper (Vita-Mix 3600, Vita-Mix Corporation, 8615 Usher Road, Cleveland, OH) just prior to analysis. The chopping procedure was facilitated by the addition of dry ice to the undried silage sample. Silage samples ground in this way were subsequently used for both chemical and NIRS analyses. Journal of Dairy Science Vol. 72, No.1, 1989
Chemical Procedures
All chemical analyses were conducted on the undried samples. Silage DM was determined by drying for 24 h at 100°C in a forced draft oven. The procedures developed by Goering and Van Soest (9) for ADF, NDF, ADIN, hot water insoluble nitrogen (HWIN), and in vitro digestible DM (IVDDM) were used. The procedures described by AOAC (2) for Nand NH 3 N were used for these components, except that the NH 3 N procedure was modified by addition of magnesium oxide and distillation into boric acid. Protein content was calculated by multiplying the Kjeldahl N by 6.25. To determine pH, 25 g of plant tissue were macerated in a blender with 225 ml of distilled water. The pH was measured with an electrometric pH meter. Prior to analysis for short-chain organic acids, 30 ml of water were added to 15 g of wet silage. Oxalic acid (.2 M, 15 ml) and 2-ethyl butyric acid (2 ml 50 roM, pH 7.0) were added to the silage and water slurry; pH was adjusted with concentrated HCI to 2.0. After 2 min of homogenization in a blender, the silage-containing slurry was filtered through glass wool, and the filtrate was used for analysis. Short-chain organic acids were determined by gas chromatography on a 80/120 mesh Carbopack B-DA/4% Carbowax (Supelco Inc., Bellefonte, PA, Catalog No. 1-1889) 2.0 M column treated with trimesic acid in methanol (1 giL). Column times and temperatures used were 16 min at 175°C, 20 min until 185°C, and 16 min at 18SoC Near Infrared Reflectance Spectroscopic Procedures
Undried, dry ice ground, silage samples were scanned at room temperature with a Pacific Scientific Model 6350 monochromater. Analysis was performed using public domain software on a DEC PDP 1134 computer by multiterm regression procedures (10). For the routine NIRS analyses, two-thirds of the silage samples in a given lot were used for a calibration set and one-third for a validation set. Samples in the validation set were not used in the calibration set or vice versa. The factors considered in the final selection of a calibration equation included high r 2 , low standard error of analysis (SEA), and low bias in the validation set; and
SILAGE ANALYSIS WITH NEAR INFRARED SPECTROSCOPY
high R 2 low standard error of calibration, and high F values in the calibration set. For the purpose of studying the usefulness of NIRS analysis, the samples collected were divided into three lots: alfalfa silages, (n = 60); corn silages, (n = 59); and all silages (n = 146). The latter group included, in addition to all the alfalfa and corn silage samples, 27 miscellaneous silages (cereal grain crops, forage grass, sorghum, soybean, and red clover). There was no consistent pattern, in the calibration equations selected, in the mathematical treatment (first or second derivative) or in the number of terms used for the same components among the three silage lots. One portion of this study was designed to determine if oven drying errors would influence selection of calibration equations for NIRS a.nalysis. To examine this facet of NIRS analySIS, the NIRS analyses and chemical analyses were compared using calibrations developed on undried samples and chemical analyses expressed on either an as-received or DM basis. One question that confronts those who use NIRS as an analytical procedure is how many samples should be available to create an accurate calibration set. Shenk et al. (17) suggested that a minimum of 50 samples would be required to derive a calibration across forage samples of mixed species. However, there is very little information in the literature repo~ting .on studies designed specifically to study cabbratlon and validation sets of varying sizes and using heterogeneous versus homogeneous sample sets. A related question is how many samples to include in a validation set for the appraisal of the usefulness of a calibration set. Available software makes it possible to select a validation set consisting of every second, third, or fourth etc. sample in the sample pool and to identify which sample number will be chosen to initiate the selection of validation samples. The NIRS analysis values for the various silage components listed in the tables, unless otherwi~e noted, are based on the selection of every thIrd sample for the validation set and the choice of the third sample as the first validation sample. Information on the effect of the method of selecting the calibration and validation sets on the NIRS estimation of silage composition is also presented.
81
RESULTS AND DISCUSSION Chemical Composition of Silage Samples
The silage samples collected in this study varied widely in their chemical composition. For the purpose of discussion in this paper, all components for which analyses were conducted, except for the short-chain organic acids, will be referred to as major components. The range of values, means, and SD for each component are shown in Table 1. Means and SD are shown in Tables 2 and 3 for both the calibration and the validation sets. Corn silages showed less variability than the alfalfa silages for most of the major components except NDF (Table 1). The alfalfa silages were higher in DM, CP, ADF, NH 3 N, HWIN, ADIN, and pH than the corn silages. Corn silage samples were higher in IVDDM. The alfalfa silages also varied more than the corn silages in concentration of several shortchain organic acids. Amounts of these acids found in the samples used in this study (see Tables 1 and 3) are typical of those reported by others (11, 12,20). Acetic and lactic acids were by far the most predominant organic acids found in all samples analyzed. Isovaleric and valeric acids were found at very low concentrations in all samples. The butyric acid content of alfalfa was higher than that of corn silage. High butyric acid in certain high moisture, hay crop silages has been reported by several investigators (11, 20), particularly when the fermentation is uncontrolled and no additives are used. A number of corn silage samples contained no detectable butyric, isovaleric, and valerie acids. This was also true of valeric acid in the alfalfa silages. Analysis of Silage Samples for Major Components
When the alfalfa silages used in this study were analyzed by NIRS, the calibration set R 2 for all major components were .80 or higher (Table 2). Most validation set r 2 were not quite as high as those from the calibration set, but these values were .78 or higher for all components except ADIN. Of particular interest were the very high r 2 for DM (.99) and HWIN (.96). The SEA for NIRS analysis of the silage
Journal of Dairy Science Vol. 72, No.1, 1989
82
REEVES, Ill, ET AL.
samples available for this study were similar to those reponed by others who analyzed forage samples of comparable heterogeneity (10, 14, 19) but were higher than those reponed for more homogeneous sample sets. The SEA for CP in the alfalfa silages was quite high. Numerous degradation products are formed during fermentation (6). These nitrogenous compounds include amines, NH 3 , amino acids, and amides. Little is known about how such compounds absorb in the NIR region of the spectrum and how this may affect analytical accuracy with NIRS. The SEA for ADF and NDF were higher than the SEA found in the literature for more homogeneous sample sets. Blosser et al. (4) reported lower SEA for ADF of .80 and for NDF of .95 with relatively pure tall fescue stands. The bias column in Table 2 shows the deviation of the mean value for NIRS analysis
from the chemistry laboratory mean for each component. The deviation of the NIRS from the chemical mean for ADF was higher than anticipated and could be a function of the limited number of samples available for this study. The other bias values shown in Table 2 are similar to values found in the literature for comparable components (10, 14, 19). The lack of range in composition of the corn silages explains the lower R 2 (except for ADF) for the corn silages compared to the alfalfa silages. Likewise, validation set r 2 were lower for all components in the corn silages with the exception of ADF and IVDDM. However, SEC and SEA were lower in the corn silages than in the alfalfa silages for CP, ADF, IVDDM, NH 3 N, HWIN, and ADIN, and biases were lower for DM, CP, ADF, and IVDDM. When the analytical values for the alfalfa and corn silages and 27 additional silage samples
TABLE 1. Chemical composition of alfalfa and corn silage samples. I Corn silages 3
Alfalfa silages' Range
Mean
Mean
so
to 12.4 to 40.0 to 65.3 to 89.8 to 4.07 to 5.26 4.22 to to 4.7
37.3 8.6 27.7 49.0 82.4 .80 3.66 .82 3.89
7.4 1.4 4.7 8.5 3.1 .50 .77 .54 .46
3.71 1.28 .43 .39 .22 8.57 .15
1.82 .48 .16 .06 .03 4.26 .04
.85 .40 .08 .07 .04 1.50 .05
Range
SO (%)
OM CP AOF NOF IVOOM 4 NH 3 N 5 HWIN 6 AOIN s pH Shan-chain organic acids Acetic Propionic Isobutyric Butyric Isovaleric Lactic Valerie 1
24.1 5.4 27.4 40.0 55.7 .33 3.64 .69 3.6
to 76.0
to 26.1 to 66.6 to 79.9 to 86.1 to 5.72 to 15.62 to 11.88 to 7.7
51.8 19.9 39.9 50.2 74.4 1.92 8.70 2.31 4.85
13.2 4.0 6.8 8.4 6.7 1.06 2.44 1.66 .56
.32 to 5.59 .03 to .90 .03 to .57 0 to 2.70 0 to .22 .38 to 10.58 0 to .24
1.58 .18 .16 .23 .06 2.97 .09
1.17 .17 .12 .48 .05 2.47 .09
Chemical composition, OM basis.
• n = 60. 3n =
59.
41n vitro digestible DM. S
Expressed as N X 6.25.
6
Hot water insoluble N, expressed as N X 6.25.
Journal of Dairy Science Vol. 72, No.1. 1989
19.6 6.5 13.2 37.3 72.7 .44 .92 .40 3.5 .39 0
to 57.0
to
to
.06 to 0 to 0 to 1.58 to 0 to
SILAGE ANALYSIS WITH NEAR INFRARED SPECTROSCOPY
83
TABLE 2. Accuracy of near infrared reflectance spectroscopy (NIRS) in analyzing undried silages for dry matter, fiber and nitrogen components, in vitro digestible dry matter (IVDDM), and pH. ' Validation set 3
Calibration set' Chern. lab.' Component
X
NIRS
SD
R
2
SEC'
(%)
NIRS
Chern. lab. SD
X
r
2
51.6 20.2 40.8 50.0 74.1 1.88 8.64 2.40 4.80
13.1 3.7 7.1 7.7 7.0 .97 2.36 1.93 .41
.98 .89 .84 .86 .80 .91
Corn silages DM CP ADF NDF IVDDM NH,N HWIN ADIN pH
37.0 8.5 28.4 50.0 82.0 .80 3.61 .86 3.92
All silages II DM CP ADF NDF IVDDM NH 3 N HWIN ADIN pH
43.6 13.9 34.5 51.4 78.1 1.37 6.04 1.55 4.35
52.3 19.4 38.2 50.6 75.1 2.00 8.82 2.12 4.96
13.8 4.6 6.2 9.9 6.2 1.24 2.66 .90 .79
.99 .88 .79 .90 .78 .90 .96 .68 .82
1.51 1.59 2.85 3.48 2.87 .40
.91 .82
1.64 1.21 2.82 2.82 3.16 .29 .66 .59 .18
8.2 1.4 4.6 8.4 3.2 .58 .81 .63 .53
.96 .83 .86 .85 .53 .82 .60 .12 .14
1.58 .58 1.69 3.22 2.18 .24 .54 .59 .49
38.1 8.7 26.1 46.9 83.2 .80 3.75 .74 3.8B
5.6
1.3 4.7 8.4 2.9 .30 .66 .26 .24
.96 .83 .87 .82 .82 .75 .42 .56
12.9 6.2 8.6 8.8 6.7 .99 3.11 1.21 .71
.97 .97 .91 .87 .87 .87 .95 .71 .85
2.07 1.02 2.59 3.18 2.39 .35 .68 .65 .28
42.2 14.8 36.0 51.8 78.0 1.48 5.92 1.94 4.40
12.4 6.0 8.7 10.5 7.1 .90 3.00 2.J 5 .65
.92
1
Chemical composition expressed on a DM basis.
2
Alfalfa and corn silages, n
3
Alfalfa silages, n = 20; corn silages, n
= 40; all silages, n = 98. = 19; all silages,
Bias'
Slopes
--(%)---
(%)
Alfalfa silages DM CP ADF NDF IVDDM NH 3 N 9 HWIN'o ADIN 9 pH
SEA"
.57 .42
.86 -.61 1.87 -.02 1.62 -.07 -.03 -.05 .07
.98 1.17 .96 1.20 1.02 .96 .85 .74 1.55
.72
1.32 .58 1.68 3.98 1.43 .16 .53 .18 .14
-.40 .12 -.03 -.93 .26 -.08 .15 .01 -.12
.90 1.14 1.03 .81 1.35 .80 1.61 .84 .75
.95 .96 .90 .87 .73 .82 .82 .59 .55
3.15 1.25 2.78 3.75 3.78 .39 1.28 1.40 .45
-.05 -.11 .70 .83 .28 -.05 -.42 .31 .09
.88 .98
.72
.92 .98 .90
.92 .96 1.20 .83
n = 48.
'Values based on conventional chemical analyses. • Standard error of calibration (SEM from the least squares regression of chemical laboratory values on NIRS values), = ,j[I;(x - X)' /(N - 1)1. "Standard error of analysis (SEM from the least squares regression of the chemical laboratory values on the NIl~S values in the validation set), calculated as the SEC was.
, Deviation of NIRS mean from chemistry laboratory mean. S
Slope of predicted versus actual means.
9
Expressed as N X 6.25.
10
Hot water insoluble N, expressed as N X 6.25.
II
Includes alfalfa, corn, and miscellaneous silage samples (e.g., small grain, sorghum, soybean, red clover,
etc.).
Journal of Dairy Science Vol. 72, No.1, 1989
84
REEVES, III, ET AL.
were pooled, the means of the various components tended to fall between the values for the alfalfa and the corn silages (Table 2). The SEA for most components in the all silages sample set were comparable to those for the alfalfa silages and the corn silages, with the exception of HWIN and ADIN. These SEA
values were appreciably higher in the all silages group. Analysis of Silages for Short-Chain Organic Acids
Concentrations of the various short-chain organic acids are shown in Table 3. Calibration
TABLE 3. Accuracy of near infrared reflectance spectroscopy (NIRS) in analyzing undried silages for shortchain organic acids. 1 Validation set 3
Calibration set 2 Shortchain acid
Chern. lab.'
X
SD
NIRS R
2
SEC s
(%)
NIRS
Chern. lab.
X
SD
r
2
SEA 6
1.25 .17 .13 .48 .05 2.44 .08
.84 .86 .61 .80 .32 .96 .23
.51 .06 .08 .22 .04 .50 .07
.13 .23 .06 2.91 .11
Corn silages Acetic Propionic Isobutyric Butyric Isovaleric Lactic Valerie
1.85 .50 .16 .07 .03 4.22 .04
.94 .44 .07 .08 .05 1.50 .05
.75 .60 .44 .36 .40 .59 .12
.47 .28 .06 .06 .04 .97 05
1.77 .43 .17 .04 .01 4.32 .03
All silages 9 Acetic Propionic Isobutyric Butyric Isovaleric Lactic Valerie
1. 79 .38 .17 .18 .04 3.62 .07
1.10 .42 .11 .32 .04 2.60 .08
.74 .65 .57 .68 .38 .71
.56 .25 .08 .18 .03 1.41 .07
1.83
.33
1
Chemical composition expressed on a DM basis.
2
Alfalfa and corn silages, n = 40; all silages, n = 98.
3
Alfalfa silages, n = 20; corn silages, n
1.27 .18
.33 .18 .21 .05 3.93 .07
Slopes
(%)
(%)
Alfalfa silages Acetic 1. 73 Propionic .19 Isobutyric .17 Butyric .24 Isovaleric .06 Lactic 3.00 Valerie .08
Bias'
.94 .18 .09 .49 .05 2.58 .11
.66 .71 .69 .91 .40 .73 .40
.66
.52 .37 .22 .16 .20 .66 .23
.33 .09 .04 .03 1.53 .05 1.08 .30 .14 .43 .05 2.49 .08
.57 .51
.33 .57 .26 .74 .27
.77 .10 .07 .19 .04 1.36 .09
-.11 -.04 -.06 .01 -.01 .20 .02
1.64
.59 .08 .07 .04 .89 .04
-.09 -.08 .01 -.02 -.01 -.24 -.02
.53 .56 .60 -.31 -.50 1.05 .75
.79 .27 .12 .29 .04 1.30 .07
-.05 -.10 -.01 .01 <.01 .01 <'01
.71 .56 .83 1.24 1.00 .87 1.01
.31
.58 .86 .63 .80 .88
.92
= 19; all silages, n = 48.
'Values based on conventional chemical analyses. S Standard error of calibration (SEM from the least squares regression of the chemical laboratorv values on NIRS values) = J[~(x - X)2 f(N - 1)1.
6 Standard error of analysis (SEM from the least squares regression of the chemical laboratory values on the NIRS values in the validation set) = J[~(x - X)2 f(N - 1)1.
'Deviation of NIRS mean from chemistry laboratory mean. S
Slope of predicted versus actual mean values.
Includes alfalfa, corn, and miscellaneous silage samples (e.g., small grain, grass, sorghum, soybean, red clover, etc.). 9
Journal of Dairy Science Vol. 72, No.1, 1989
SILAGE ANALYSIS WITH NEAR INFRARED SPECTROSCOPY
set R 2 for acetic, propionic, butyric, and lactic acids in the alfalfa silages were .84, .86, .80, and .96 and the validation set r 2 for the same acids were .66, .71, .91, and .73. The biases for the various silage acids were quite low. Correlations of NIRS with the chemistry laboratory analysis for isobutyric, isovaleric, and valeric acids were lower than for those acids found in greater amounts. One way of assessing the relative accuracy of NIRS for analyzing the various components is to divide the SEA by the mean for each component (7). It is apparent from Table 4 that NIRS analyses for the short-chain organic acids are not as accurate as for the major components. The best SEA/mean short-chain organic acid values are for the major silage acids, acetic and lactic. The correlations between the chemistry laboratory and NIRS analyses for the various silage acids in the all silages sample set were lower than for the alfalfa silages and higher than for the corn silages. Near Infrared Reflectance Spectroscopic Analyses from Equations Generated from Conventional Laboratory Values on Dry and Wet Basis
Table 4 presents the data resulting from a study of the accuracy of NIRS analysis using calibration equations prepared on a dry versus wet basis. Comparisons of these two procedures were made based on silage type, short-chain organic acids, and NH 3 N compared with other components, and on major silage components (i.e., > 5%) versus minor components. The data did not reveal systematic differences between the dry and wet based computations. Examination of the wavelengths used for calibration showed no trend on any basis examined. The r2 for CP, ADF, and NDF tended to be equal to or higher for the wet versus the dry basis for all three silage groups. However, for these same components, the SEA expressed as a percent of the mean was generally greater for the wet basis. This implies somewhat more accuracy for NIRS analysis for these components using calibration equations based on dry (i.e., corrected for moisture) analyses. There were no consistent trends (wet vs. dry) for NIRS analysis of short-chain organic acids and AN. In contrast to the situation with the major
85
components, the majority of the correlations between NIRS and chemical analyses for short-chain organic acids were higher when using equations based on the 100% dry analyses. There was little difference between the two procedures in accuracy of analysis as judged by the SEA:mean ratio. The examination of more data sets will be required to answer the question of how best to compute silage chemistry for NIRS. Comparison of Several Methods of Selecting Calibration and Validation Sets
Table 5, prepared from the 60 alfalfa and 146 all silage samples used in this study, shows how the choice of calibration and validation sets changed the magnitude of correlations and standard errors. For the alfalfa silages in this table, the calibration sets contained 40, 45, or 60 samples and the validation sets 20, 15, or no samples. For all silage samples, the calibration sets contained 98, 110, or 146 samples and the validation sets 48, 36, or no samples. It is quite clear from Table 5 that using limited numbers of samples, as in the alfalfa silage sample set, for development of calibration equations, had a substantial effect, particularly with certain components, on the magnitudes of the various statistics used to judge the validity of NIRS for analytical use. When the alfalfa silages were examined, and r 2 and SEA were used as measures of analytical consistency, any of the methods used in validation set selection produced repeatable results for such components as DM, NDF, AN, and HWIN. Somewhat more variable results were experienced with CP, ADF, and IVDDM, and even more inconsistency with ADIN and pH. When the considerably larger all-silage sample set was examined using the various procedures for calibration equation selection, variation was less in both r 2 and SEA values. Thus, in assessing the validity of NIRS for analyzing for several components, particularly with a limited number of samples, it is important to examine the analytical results using several methods of choosing calibration and validation sets. CONCLUSIONS
This study demonstrates that, when suitable calibration equations are prepared, NIRS is a Journal of Dairy Science Vol. 72, No.1, 1989
.ge.
'-
00
0\
I'
...,
0
0 ~. '<
TABLE 4. Comparison of near infrared reflectance spectroscopy (NIRS) with conventional chemical analyses of undried silages using calibration equations prepared from components either corrected to a dry basis or uncorrected (wet). Validation samples
rJl
"til· ,.I'"
Alfalfa silages!
<:
Dry
r' Dry
r'
SEA/X Wet
Wet
- - (%)
--
Fiber and nitrogen components CP .88 .93 ADF .79 .93 NDF .90 .92 NH 3 N 6 .90 .84 HWIN' .97 .96 .68 .68 ADIN 6
8.2 7.5 6.9 20.0 8.2 26.9
9.3 9.0 10.5 23.2 11.0 38.6
.83 .87 .82 .75 .42 .56
.92 .89 .84 .92 .80 .51
6.7 6.4 8.5 20.0 14.1 24.3
7.9 6.2 8.1 19.4 20.1 35.7
.96 .90 .87 .82 .82 .59
.95 .92 .88 .92 .86 .69
8.4 7.7 7.4 26.4 21.6 72.2
13.6 13.7 12.0 16.1 30.5 81.5
Short-chain organic acids Acetic .66 Propionic .71 Isobutyric .69 Butyric .91 Isovaleric .40 Lactic .73 .40 Valeric
60.6 55.6 53.8 82.6 66.7 46.7 81.8
49.1 50.0 66.7 100.0 66.7 42.9 80.0
.52 .37 .22 .16 .20 .66 .23
.30 .41 .23 .19 .18 .46 .31
33.3 72.1 47.1 175.0 400.0 20.6 133.3
27.7 60.0 50.0 150.0 200.0 23.3 200.0
.57 .51 .33 .57 .26 .74 .27
.38 .44 .27 .51 .48 .74 .33
43.2 81.9 66.7 138.1 80.0 33.1 100.0
35.7 75.0 57.1 137.5 100.0 28.2 100.0
Dry
Wet
SEA/X
Dry
12-
Wet
All silages 3
Corn silages' SEA 4 /X 5
r'
Dry
Wet
Dry
Wet
...:I
.N
Z P
........ \Q
00
\Q
.41 .47 .24 .89 .24 .67 .20
--(%)--
! Calibration set, n = 40; validation set, n = 20. , Calibration set, n
= 40; validation set, n = 19.
3
Calibration set, n = 98; validation set, n = 48.
4
Standard error of analysis, SEM of NIRS vs. conventional chemical analysis)
S
SEA/mean
6
Expressed as N X 6.25.
= J[k(X
=Standard error of analysis/conventional chemical analysis mean.
, Hot water insoluble N, expressed as N X 6.25.
-
X)' /(N - 1)].
---(%)---
::>;l t>1 t>1
<: t>1
..,
Y' t>1
;I>
r
TABLE 5. A comparison of silage analyses using several methods of selecting calibration and validation sets within the same sample pool. Calibration set
Validation set
Component
3rd'
3rd 4
3rd'
r'
SEC'
R'
4rh'
All'
3rd
3rd
3rd
4th
All
3rd
3rd
SEA' 3rd
4th
3rd
3rd
3rd
4th
en
-
'-
Alfalfa silages DM CP ADF NDF IVDDM' NH,N' HWlN IO ADIN' pH
.99 .90 .91 .89 .83 .87 .92 .67 .67
.98 .85 .82 .89 .73 .88 .90 .89 .89
.98 .89 .84 .87 .80 .91 .92 .91 .82
.99 .93 .82 .92 .84 .91 .89 .87 .82
.99 .90 .90 .94 .88 .96 .94 .91 .88
1.27 1.36 2.12 3.09 2.93 .40 .65 .44 .37
1.83 1.54 2.66 2.76 3.14 .38 .79 .64 .20
1.64 1.21 2.82 2.82 3.16 .29 .66 .59 .18
1.54 1.14 2.98 2.53 3.49 .31 .85 .67 .25
1.33 1.24 2.12 2.14 2.30 .21 .59 .50 .20
.96 .74 .62 .85 .69 .82 .91 .93 .76
.98 .89 .81 .88 .85 .95 .78 .52 .82
.99 .88 .79 .90 .78 .90 .96 .68 .82
.98 .66 .79 .88 .84 .86 .82 .39 .87
2.32 1.64 4.66 2.84 3.60 .44 .80 1.33 .20
1.90 1.40 3.45 3.00 3.30 .33 .91 .54 .20
1.51 1.59 VIS 3.48 2.87 .40 .72 .57 .42
1.97 1.85 2.95 2.67 3.49 .53 .90 .77 .20
All silages DM CP ADF NDF IVDDM NH,N HWIN ADIN Ph
.96 .97 .89 .89 .83 .88 .90 .63 .78
.96 .96 .91 .89 .86 .93 .84 .60 .63
.97 .97 .91 .87 .87 .87 .95 .71 .85
.96 .97 .86 .89 .84 .85 .89 .61 .70
.97 .97 .92 .90 .84 .92 .90 .67 .80
2.65 1.06 2.62 3.06 2.69 .32 .99 1.08 .32
2.53 1.17 2.81 3.40 2.68 .25 1.14 1.09 .40
2.31 1.02 2.57 3.18 2.39 .35 .68 .65 .28
2.53 1.09 3.26 3.05 2.80 .37 1.01 1.02 .39
2.25 1.03 2.44 3.01 2.74 .27 .95 .91 .31
.97 .96 .91 .86 .81 .92 .88 .60 .69
.95 .96 .86 .88 .72 .84 .96 .60 .87
.95 .96 .91 .87 .73 .82 .82 .59 .55
.98 .96 .89 .87 .80 .85 .88 .77 .87
2.23 1.14 2.88 3.84 3.31 .27 .95 .73 .39
2.39 1.25 2.78 2.59 3.16 .39 .66 .73 .26
3.15 1.25 2.78 3.75 3.78 .39 1.28 1.40 .45
2.18 1.20 2.79 3.63 2.90 .37 1.07 .71 .23
g
a
f!-
a, o
..
~.
en l"l
n' o l"l
'"<:
~
~ -< en t""
r;;
::e ::J
::t Z
t!1 )-
::t:l
Z "'1
::t:l
)-
::t:l
t!1
1
Standard error of calibration (SEM from the least squares regression of chemical laboratory values on NIRS values): chemical composition expressed on a DM basis, = .JII:(x - X)2 j(N - l}l.
1 Standard error of analysis (SEM from the least squares regression of the chemical laboratory values on the NtRS values in the validation set): chemical composition expressed on a dry matter basis, = '-"11:(x - X)' I(N - 1)1.
3
Every third sample included in validation set; first validation sample is #1; calibration set, alfalfa silages, n = 40, all silages, n = 98; validation set, alfalfa silages, n = 20, all silages, n = 48.
4
Every third sample included in validation set; first validation sample is #2; calibration set, alfalfa silages, n = 40, all silages, n = 98; validation set, alfalfa silages, n = 20, all silages, n = 48.
S Every third sample included in validation set; first validation sample is #3 (this validation set used in preparing Tables 1 and 3); calibration set, alfalfa silages, n = 40, all silages, n = 98; validation set, alfalfa silages, n = 20. all silages, n = 48.
6
Every fourth sample included in validation set; first validation sample is #4; calibration set, alfalfa silages, n = 45, all silages, n = 110; validation set, alfalfa silages, n = 15, all silages, n = 36.
'1
All samples in calibration set; no validation set prepared; calibration set, alfalfa silages, n = 60, all silages, n = 146.
8
In vitro digestible DM.
9
Expressed as N X 6.25.
--l
J'> Z
~
Cl t!1 ;J>
10
o
en
'"0
t!1
n ..., ::t:l
oen n o'"0 -<
Hot water insoluble N, expressed as N X 6.25.
9
.... .... '0
00 '0
00 '-l
88
REEVES, III, ET AL.
useful procedure in analyzing properly ground undried silages for several components. Results are particularly encouraging for most major components except ADIN. Correlations were lower and SEA higher when NIRS was used for analyzing short-chain organic acids. With alfalfa silages, which are considerably more variable in organic acid content, correlations between conventional chemistry and NJRS analyses were higher for acetic, butyric, lactic, and propionic acids than for the acids found in smaller amounts. The results also revealed that when NJRS is used for analysis of certain components, there is some advantage in creating calibration equations from more homogeneous sample sets, i.e., sample sets arranged according to species (e.g., alfalfa vs. corn) as contrasted with having calibration equations prepared from more heterogeneous sample sets. With small sample sets for creating calibration equations, it is advisable to examine several procedures for creating calibration and validation sets before arriving at a judgment concerning the suitability and accuracy of NJRS for analyzing a specific component. REFERENCES 1 Abrams, S. M., J. S. Shenk, and M. O. Westerhaus. 1983. Capability of near infrared reflectance spectroscopy (NIR) to determine silage quality. J. Dairy Sci. 66(Suppl. 1):183. (Abstr.) 2 Association of Official Analytical Chemists. 1980. Official methods of analysis. 13th ed. Assoc. Offic. Anal. Chern., Washington, DC. 3 Blosser, T. H. 1985. Future applications for NIRS: high-moisture feedstuffs, including silage. Pages 56-57 in Agric. Handbook No. 643, Agric. Res. Serv., USDA, Washington, DC. 4 Blosser, T. H., J. B. Reeves, III, and J. Bond. 1988. Factors affecting analysis of the chemical composition of tall fescue with near infrared reflectance spectroscopy. J. Dairy Sci. 71:398. 5 Buchanan-Smith, J. G. 1983. Constituents in silage associated with feed intake reduction. J. Dairy Sci. 66(Suppl. 1): 184. (Abstr.) 6 Clancy, M., P. J. Wangsness, and B. R. Baumgardt. 1977. Effects of moisture determination method on estimates of digestibilities and intakes of conserved alfalfa J. Dairy Sci. 60:216. 7 Clark, D. H., H. F. Mayland, and R. C. Lamb. 1987. Mineral analysis of forages with near infrared reflectance spectroscopy. Agron. J. 79:485. 8 Goering, H. K., C. H. Gordon, R. W. Hemken, D.
Journal of Dairy Science Vol. 72, No. 1,1989
R. Waldo, P. j. Van Soest, and L. W. Smith. 1972. Analytical estimates of nitrogen digestibility in heat damaged forages. J. Dairy Sci. 55:1775. 9 Goering, H. K., and P. J. Van Soest. 1970. Forage fiber analysis (apparatus, reagents, procedures, and some applications). Agtic. Handbook No. 379, Agric. Res. Serv., USDA, Washington, DC. 10 Marten, G. C., J. S. Shenk, and F. E. Barton, II, ed. 1985. Near infrared reflectance spectroscopy (NIRS): analysis of forage quality. Agric. Handbook No. 643, Agric. Res. Serv., USDA, Washington, DC. 11 Langston, C. W., H. Irvin, C. H. Gordon, C. Bouma, H. G. Wiseman, C. G. Melin, L. A. Moore, and J. R. McGalmont. 1958. Microbiology and chemistry of grass silage. USDA Tech. Bull. 1187. Agric. Res. Serv., USDA, Washington, DC. 12 McDonald, P. 1981. The biochemistry of silage. john Wiley and Sons, Ltd., New York, NY. 13 Moe, A. J., and S. B. Carr. 1985. Laboratory assays and near infrared reflectance spectroscopy for estimates of feeding value of corn silage. J. Dairy Sci. 68:2220. 14 Norris, K. H., R. F. Barnes, J. E. Moore, and j. S. Shenk. 1976. Predicting forage quality by infrared reflectance spectroscopy. j. Anim. Sci. 43 :889. 15 Polesello, A., and R. Giangiacomo. 1983. Application of near infrared spectrophotometry to the nondestructive analysis of foods: A review of experimental results. Pages 203-230 in Critical reviews in food science and nutrition. Vol. 18. CRC Press, Inc., Boca Raton, FL. 16 Rohweder, D. A. ed. 1985. Proceedings of invitational ECOP workshop on NIRS. November 13 to 15, Madison, WI. 17 Shenk, j. S., M. O. Westerhaus, and M. R. Hoover. 1977. Infrared analysis offorages. Pages 242-244, 252 in Grassland forage harvesting. 1 ASAE Publ. 1-78. Am. Soc. Agric. Eng., St. joseph, MI. 18 Shenk, j. S., M. O. Westerhaus. and M. R. Hoover. 1979. Analysis of forages by infrared reflectance. ]. Dairy Sci. 62:807. 19 Templeton, W. C., Jr., J. S. Shenk, K. H. Norris, G. W. Fissel, G. C. Marten, j. H. Elgin, Jr., and M. O. Westerhaus. 1983. Forage analysis with near infrared reflectance spectroscopy: Status and outline of federal research project. Pages 528-531 in Proc. XIV Int. Grass. Congr., Westview Press, Boulder, CO. 20 Van Soest, P. j. 1982. Nutritional ecology of the ruminant. 0 and B Books Inc., Corvallis, OR. 21 Wilkins, R. j., K. j. Hutchinson, R. F. Wilson, and C. E. Harris. 1971. The voluntary intake of silage by sheep. I. Interrelationships between silage composition and intake. ]. Agric. Sci., Camb. 77: 531. 22 Waldo, D. R., and N. A. jorgensen. 1981. Forages for high animal production: nutritional factors and effects of conservation. j. Daity Sci. 64: 1207. 23 Yu, Y., and j. W. Thomas. 1976. Estimation of the extent of heat damage in alfalfa haylages by laboratoty measurement. j. Anim. Sci. 42:766.
organic acids were considerably lower but were higher for the alfalfa than for the corn silages. Use of different procedures for selecting calibration and validation sets had varying influence on near infrared reflectance spectroscopy estimates depending on the component being analyzed.
Alfalfa, corn, small grain crop, and grass silage samples (n = 146) were collected from farms in Maryland, Virginia, and Indiana to determine the usefulness of near infrared reflectance spectroscopy for analyzing the feeding value of wet feedstuffs. Undried silages were analyzed for major components (DM, CP, ADF, NDF, pH, ADlN, NH 3 N, hot water insoluble N, and in vitro digestible DM) and for acetic, propionic, isobutyric, butyric, isovaleric, lactic, and valeric acids by conventional chemical laboratory procedures compared with near infrared reflectance spectroscopy. Validation sets consisting of one-third of the samples within a group were used to evaluate the accuracy of calibration equations. The coefficients of determination of near infrared analyzed values with conventional chemical assays of major components in the wet alfalfa silages ranged from .78 to .99, except for ADlN. These values ranged from .75 to .96 for wet corn silage components, except for ADIN and hot water insoluble N. Correlations between the two methods of analysis for most of the short-chain
INTRODUCTION
Forages are commonly evaluated on the basis of their CP, ADF, and NDF content. With silage, the pH and concentrations of certain short-chain organic acids also are useful in predicting quality. Determining the amounts of NH 3 N, amines, and other soluble organic N compounds also contributes to the ability to predict silage acceptability and nutritive value (5, 12, 20, 21). High ADIN indicates excessive heating during fermentation with resultant heat damage (8, 23). Although the use of near infrared reflectance spectroscopy (NIRS) is very attractive to animal producers and others involved in feeding farm animals, considerable research is needed to maximize the value of NIRS analyses for users (4, 10, 14, 15, 16, 18). Waldo and Jorgensen (22) stated that before NIRS can be used generally in forage and ruminant feed evaluation, it must be adapted to analysis of undried silage and high moisture grain. Oven drying of silage prior to chemical analysis creates analytical errors because of the loss of volatiles formed during the silage fermentation process (6, 20). Using NIRS directly on undried silage would save time and reduce analytical errors associated with oven drying. Use of NIRS may also offer users an opportunity to examine a wide variety of silage quality parameters at a relatively low cost (1, 3, 13).
Received November 16, 1987. Accepted August 23, 1988. 1 Mention of a trade name, proprietary product, or specific equipment does not constitute a guarantee or warranty by the US Department of Agriculture and does not imply its approval to the exclusion of other products that may be suitable. 'Published as Journal Paper Number 11385, Indiana Agricultural Experiment Station. 1989 J Dairy Sci 72:79-88
79
REEVES, III, ET AL.
80
Problems exist, both for chemical and NIRS analysis, in using undried materials. A problem common to both is preparing wet samples of sufficiently fine subdivision to permit accurate sampling (10). An additional problem with NIRS is the presence in the near infrared spectrum of several strong absorption bands caused by water (10). Although these bands may limit the use of NIRS for high moisture feedstuffs to certain spectral regions, NIRS has been used successfully with a wide variety of high moisture materials (1, 15). An additional error is introduced when silage samples are oven dried for analysis for moisture content. Silages contain a variety of components that are volatile at oven drying temperatures. This creates an error, which is inherent when converting analysis of undried silages to a moisture-free basis (6, 20). To study the analysis of undried silages with NIRS, a trial was designed with the following objectives: 1) to determine the accuracy of NIRS when analyzing silage samples of widely varying chemical composition; and 2) to ascertain NIRS accuracy in analyzing sample sets of homogeneous and heterogenous species composition. MATERIALS AND METHODS Sample Collection and Preparation
Silage samples (n = 146) were collected from farms in Maryland, Virginia, and Indiana. When the samples were collected, information was recorded about the date and stage of maturity at harvest, type of structure in which the forage was ensiled, use of additives, and the dairy producers' assessment of quality. The subsequent division of the samples into species groups was limited by the accuracy of producer responses concerning the samples. The samples were frozen on the day of collection. The frozen samples were ground in a commercial-type Hobart food chopper and then were refrozen. Frozen subsamples were subsequently finely chopped in a kitchen -type food chopper (Vita-Mix 3600, Vita-Mix Corporation, 8615 Usher Road, Cleveland, OH) just prior to analysis. The chopping procedure was facilitated by the addition of dry ice to the undried silage sample. Silage samples ground in this way were subsequently used for both chemical and NIRS analyses. Journal of Dairy Science Vol. 72, No.1, 1989
Chemical Procedures
All chemical analyses were conducted on the undried samples. Silage DM was determined by drying for 24 h at 100°C in a forced draft oven. The procedures developed by Goering and Van Soest (9) for ADF, NDF, ADIN, hot water insoluble nitrogen (HWIN), and in vitro digestible DM (IVDDM) were used. The procedures described by AOAC (2) for Nand NH 3 N were used for these components, except that the NH 3 N procedure was modified by addition of magnesium oxide and distillation into boric acid. Protein content was calculated by multiplying the Kjeldahl N by 6.25. To determine pH, 25 g of plant tissue were macerated in a blender with 225 ml of distilled water. The pH was measured with an electrometric pH meter. Prior to analysis for short-chain organic acids, 30 ml of water were added to 15 g of wet silage. Oxalic acid (.2 M, 15 ml) and 2-ethyl butyric acid (2 ml 50 roM, pH 7.0) were added to the silage and water slurry; pH was adjusted with concentrated HCI to 2.0. After 2 min of homogenization in a blender, the silage-containing slurry was filtered through glass wool, and the filtrate was used for analysis. Short-chain organic acids were determined by gas chromatography on a 80/120 mesh Carbopack B-DA/4% Carbowax (Supelco Inc., Bellefonte, PA, Catalog No. 1-1889) 2.0 M column treated with trimesic acid in methanol (1 giL). Column times and temperatures used were 16 min at 175°C, 20 min until 185°C, and 16 min at 18SoC Near Infrared Reflectance Spectroscopic Procedures
Undried, dry ice ground, silage samples were scanned at room temperature with a Pacific Scientific Model 6350 monochromater. Analysis was performed using public domain software on a DEC PDP 1134 computer by multiterm regression procedures (10). For the routine NIRS analyses, two-thirds of the silage samples in a given lot were used for a calibration set and one-third for a validation set. Samples in the validation set were not used in the calibration set or vice versa. The factors considered in the final selection of a calibration equation included high r 2 , low standard error of analysis (SEA), and low bias in the validation set; and
SILAGE ANALYSIS WITH NEAR INFRARED SPECTROSCOPY
high R 2 low standard error of calibration, and high F values in the calibration set. For the purpose of studying the usefulness of NIRS analysis, the samples collected were divided into three lots: alfalfa silages, (n = 60); corn silages, (n = 59); and all silages (n = 146). The latter group included, in addition to all the alfalfa and corn silage samples, 27 miscellaneous silages (cereal grain crops, forage grass, sorghum, soybean, and red clover). There was no consistent pattern, in the calibration equations selected, in the mathematical treatment (first or second derivative) or in the number of terms used for the same components among the three silage lots. One portion of this study was designed to determine if oven drying errors would influence selection of calibration equations for NIRS a.nalysis. To examine this facet of NIRS analySIS, the NIRS analyses and chemical analyses were compared using calibrations developed on undried samples and chemical analyses expressed on either an as-received or DM basis. One question that confronts those who use NIRS as an analytical procedure is how many samples should be available to create an accurate calibration set. Shenk et al. (17) suggested that a minimum of 50 samples would be required to derive a calibration across forage samples of mixed species. However, there is very little information in the literature repo~ting .on studies designed specifically to study cabbratlon and validation sets of varying sizes and using heterogeneous versus homogeneous sample sets. A related question is how many samples to include in a validation set for the appraisal of the usefulness of a calibration set. Available software makes it possible to select a validation set consisting of every second, third, or fourth etc. sample in the sample pool and to identify which sample number will be chosen to initiate the selection of validation samples. The NIRS analysis values for the various silage components listed in the tables, unless otherwi~e noted, are based on the selection of every thIrd sample for the validation set and the choice of the third sample as the first validation sample. Information on the effect of the method of selecting the calibration and validation sets on the NIRS estimation of silage composition is also presented.
81
RESULTS AND DISCUSSION Chemical Composition of Silage Samples
The silage samples collected in this study varied widely in their chemical composition. For the purpose of discussion in this paper, all components for which analyses were conducted, except for the short-chain organic acids, will be referred to as major components. The range of values, means, and SD for each component are shown in Table 1. Means and SD are shown in Tables 2 and 3 for both the calibration and the validation sets. Corn silages showed less variability than the alfalfa silages for most of the major components except NDF (Table 1). The alfalfa silages were higher in DM, CP, ADF, NH 3 N, HWIN, ADIN, and pH than the corn silages. Corn silage samples were higher in IVDDM. The alfalfa silages also varied more than the corn silages in concentration of several shortchain organic acids. Amounts of these acids found in the samples used in this study (see Tables 1 and 3) are typical of those reported by others (11, 12,20). Acetic and lactic acids were by far the most predominant organic acids found in all samples analyzed. Isovaleric and valeric acids were found at very low concentrations in all samples. The butyric acid content of alfalfa was higher than that of corn silage. High butyric acid in certain high moisture, hay crop silages has been reported by several investigators (11, 20), particularly when the fermentation is uncontrolled and no additives are used. A number of corn silage samples contained no detectable butyric, isovaleric, and valerie acids. This was also true of valeric acid in the alfalfa silages. Analysis of Silage Samples for Major Components
When the alfalfa silages used in this study were analyzed by NIRS, the calibration set R 2 for all major components were .80 or higher (Table 2). Most validation set r 2 were not quite as high as those from the calibration set, but these values were .78 or higher for all components except ADIN. Of particular interest were the very high r 2 for DM (.99) and HWIN (.96). The SEA for NIRS analysis of the silage
Journal of Dairy Science Vol. 72, No.1, 1989
82
REEVES, Ill, ET AL.
samples available for this study were similar to those reponed by others who analyzed forage samples of comparable heterogeneity (10, 14, 19) but were higher than those reponed for more homogeneous sample sets. The SEA for CP in the alfalfa silages was quite high. Numerous degradation products are formed during fermentation (6). These nitrogenous compounds include amines, NH 3 , amino acids, and amides. Little is known about how such compounds absorb in the NIR region of the spectrum and how this may affect analytical accuracy with NIRS. The SEA for ADF and NDF were higher than the SEA found in the literature for more homogeneous sample sets. Blosser et al. (4) reported lower SEA for ADF of .80 and for NDF of .95 with relatively pure tall fescue stands. The bias column in Table 2 shows the deviation of the mean value for NIRS analysis
from the chemistry laboratory mean for each component. The deviation of the NIRS from the chemical mean for ADF was higher than anticipated and could be a function of the limited number of samples available for this study. The other bias values shown in Table 2 are similar to values found in the literature for comparable components (10, 14, 19). The lack of range in composition of the corn silages explains the lower R 2 (except for ADF) for the corn silages compared to the alfalfa silages. Likewise, validation set r 2 were lower for all components in the corn silages with the exception of ADF and IVDDM. However, SEC and SEA were lower in the corn silages than in the alfalfa silages for CP, ADF, IVDDM, NH 3 N, HWIN, and ADIN, and biases were lower for DM, CP, ADF, and IVDDM. When the analytical values for the alfalfa and corn silages and 27 additional silage samples
TABLE 1. Chemical composition of alfalfa and corn silage samples. I Corn silages 3
Alfalfa silages' Range
Mean
Mean
so
to 12.4 to 40.0 to 65.3 to 89.8 to 4.07 to 5.26 4.22 to to 4.7
37.3 8.6 27.7 49.0 82.4 .80 3.66 .82 3.89
7.4 1.4 4.7 8.5 3.1 .50 .77 .54 .46
3.71 1.28 .43 .39 .22 8.57 .15
1.82 .48 .16 .06 .03 4.26 .04
.85 .40 .08 .07 .04 1.50 .05
Range
SO (%)
OM CP AOF NOF IVOOM 4 NH 3 N 5 HWIN 6 AOIN s pH Shan-chain organic acids Acetic Propionic Isobutyric Butyric Isovaleric Lactic Valerie 1
24.1 5.4 27.4 40.0 55.7 .33 3.64 .69 3.6
to 76.0
to 26.1 to 66.6 to 79.9 to 86.1 to 5.72 to 15.62 to 11.88 to 7.7
51.8 19.9 39.9 50.2 74.4 1.92 8.70 2.31 4.85
13.2 4.0 6.8 8.4 6.7 1.06 2.44 1.66 .56
.32 to 5.59 .03 to .90 .03 to .57 0 to 2.70 0 to .22 .38 to 10.58 0 to .24
1.58 .18 .16 .23 .06 2.97 .09
1.17 .17 .12 .48 .05 2.47 .09
Chemical composition, OM basis.
• n = 60. 3n =
59.
41n vitro digestible DM. S
Expressed as N X 6.25.
6
Hot water insoluble N, expressed as N X 6.25.
Journal of Dairy Science Vol. 72, No.1. 1989
19.6 6.5 13.2 37.3 72.7 .44 .92 .40 3.5 .39 0
to 57.0
to
to
.06 to 0 to 0 to 1.58 to 0 to
SILAGE ANALYSIS WITH NEAR INFRARED SPECTROSCOPY
83
TABLE 2. Accuracy of near infrared reflectance spectroscopy (NIRS) in analyzing undried silages for dry matter, fiber and nitrogen components, in vitro digestible dry matter (IVDDM), and pH. ' Validation set 3
Calibration set' Chern. lab.' Component
X
NIRS
SD
R
2
SEC'
(%)
NIRS
Chern. lab. SD
X
r
2
51.6 20.2 40.8 50.0 74.1 1.88 8.64 2.40 4.80
13.1 3.7 7.1 7.7 7.0 .97 2.36 1.93 .41
.98 .89 .84 .86 .80 .91
Corn silages DM CP ADF NDF IVDDM NH,N HWIN ADIN pH
37.0 8.5 28.4 50.0 82.0 .80 3.61 .86 3.92
All silages II DM CP ADF NDF IVDDM NH 3 N HWIN ADIN pH
43.6 13.9 34.5 51.4 78.1 1.37 6.04 1.55 4.35
52.3 19.4 38.2 50.6 75.1 2.00 8.82 2.12 4.96
13.8 4.6 6.2 9.9 6.2 1.24 2.66 .90 .79
.99 .88 .79 .90 .78 .90 .96 .68 .82
1.51 1.59 2.85 3.48 2.87 .40
.91 .82
1.64 1.21 2.82 2.82 3.16 .29 .66 .59 .18
8.2 1.4 4.6 8.4 3.2 .58 .81 .63 .53
.96 .83 .86 .85 .53 .82 .60 .12 .14
1.58 .58 1.69 3.22 2.18 .24 .54 .59 .49
38.1 8.7 26.1 46.9 83.2 .80 3.75 .74 3.8B
5.6
1.3 4.7 8.4 2.9 .30 .66 .26 .24
.96 .83 .87 .82 .82 .75 .42 .56
12.9 6.2 8.6 8.8 6.7 .99 3.11 1.21 .71
.97 .97 .91 .87 .87 .87 .95 .71 .85
2.07 1.02 2.59 3.18 2.39 .35 .68 .65 .28
42.2 14.8 36.0 51.8 78.0 1.48 5.92 1.94 4.40
12.4 6.0 8.7 10.5 7.1 .90 3.00 2.J 5 .65
.92
1
Chemical composition expressed on a DM basis.
2
Alfalfa and corn silages, n
3
Alfalfa silages, n = 20; corn silages, n
= 40; all silages, n = 98. = 19; all silages,
Bias'
Slopes
--(%)---
(%)
Alfalfa silages DM CP ADF NDF IVDDM NH 3 N 9 HWIN'o ADIN 9 pH
SEA"
.57 .42
.86 -.61 1.87 -.02 1.62 -.07 -.03 -.05 .07
.98 1.17 .96 1.20 1.02 .96 .85 .74 1.55
.72
1.32 .58 1.68 3.98 1.43 .16 .53 .18 .14
-.40 .12 -.03 -.93 .26 -.08 .15 .01 -.12
.90 1.14 1.03 .81 1.35 .80 1.61 .84 .75
.95 .96 .90 .87 .73 .82 .82 .59 .55
3.15 1.25 2.78 3.75 3.78 .39 1.28 1.40 .45
-.05 -.11 .70 .83 .28 -.05 -.42 .31 .09
.88 .98
.72
.92 .98 .90
.92 .96 1.20 .83
n = 48.
'Values based on conventional chemical analyses. • Standard error of calibration (SEM from the least squares regression of chemical laboratory values on NIRS values), = ,j[I;(x - X)' /(N - 1)1. "Standard error of analysis (SEM from the least squares regression of the chemical laboratory values on the NIl~S values in the validation set), calculated as the SEC was.
, Deviation of NIRS mean from chemistry laboratory mean. S
Slope of predicted versus actual means.
9
Expressed as N X 6.25.
10
Hot water insoluble N, expressed as N X 6.25.
II
Includes alfalfa, corn, and miscellaneous silage samples (e.g., small grain, sorghum, soybean, red clover,
etc.).
Journal of Dairy Science Vol. 72, No.1, 1989
84
REEVES, III, ET AL.
were pooled, the means of the various components tended to fall between the values for the alfalfa and the corn silages (Table 2). The SEA for most components in the all silages sample set were comparable to those for the alfalfa silages and the corn silages, with the exception of HWIN and ADIN. These SEA
values were appreciably higher in the all silages group. Analysis of Silages for Short-Chain Organic Acids
Concentrations of the various short-chain organic acids are shown in Table 3. Calibration
TABLE 3. Accuracy of near infrared reflectance spectroscopy (NIRS) in analyzing undried silages for shortchain organic acids. 1 Validation set 3
Calibration set 2 Shortchain acid
Chern. lab.'
X
SD
NIRS R
2
SEC s
(%)
NIRS
Chern. lab.
X
SD
r
2
SEA 6
1.25 .17 .13 .48 .05 2.44 .08
.84 .86 .61 .80 .32 .96 .23
.51 .06 .08 .22 .04 .50 .07
.13 .23 .06 2.91 .11
Corn silages Acetic Propionic Isobutyric Butyric Isovaleric Lactic Valerie
1.85 .50 .16 .07 .03 4.22 .04
.94 .44 .07 .08 .05 1.50 .05
.75 .60 .44 .36 .40 .59 .12
.47 .28 .06 .06 .04 .97 05
1.77 .43 .17 .04 .01 4.32 .03
All silages 9 Acetic Propionic Isobutyric Butyric Isovaleric Lactic Valerie
1. 79 .38 .17 .18 .04 3.62 .07
1.10 .42 .11 .32 .04 2.60 .08
.74 .65 .57 .68 .38 .71
.56 .25 .08 .18 .03 1.41 .07
1.83
.33
1
Chemical composition expressed on a DM basis.
2
Alfalfa and corn silages, n = 40; all silages, n = 98.
3
Alfalfa silages, n = 20; corn silages, n
1.27 .18
.33 .18 .21 .05 3.93 .07
Slopes
(%)
(%)
Alfalfa silages Acetic 1. 73 Propionic .19 Isobutyric .17 Butyric .24 Isovaleric .06 Lactic 3.00 Valerie .08
Bias'
.94 .18 .09 .49 .05 2.58 .11
.66 .71 .69 .91 .40 .73 .40
.66
.52 .37 .22 .16 .20 .66 .23
.33 .09 .04 .03 1.53 .05 1.08 .30 .14 .43 .05 2.49 .08
.57 .51
.33 .57 .26 .74 .27
.77 .10 .07 .19 .04 1.36 .09
-.11 -.04 -.06 .01 -.01 .20 .02
1.64
.59 .08 .07 .04 .89 .04
-.09 -.08 .01 -.02 -.01 -.24 -.02
.53 .56 .60 -.31 -.50 1.05 .75
.79 .27 .12 .29 .04 1.30 .07
-.05 -.10 -.01 .01 <.01 .01 <'01
.71 .56 .83 1.24 1.00 .87 1.01
.31
.58 .86 .63 .80 .88
.92
= 19; all silages, n = 48.
'Values based on conventional chemical analyses. S Standard error of calibration (SEM from the least squares regression of the chemical laboratorv values on NIRS values) = J[~(x - X)2 f(N - 1)1.
6 Standard error of analysis (SEM from the least squares regression of the chemical laboratory values on the NIRS values in the validation set) = J[~(x - X)2 f(N - 1)1.
'Deviation of NIRS mean from chemistry laboratory mean. S
Slope of predicted versus actual mean values.
Includes alfalfa, corn, and miscellaneous silage samples (e.g., small grain, grass, sorghum, soybean, red clover, etc.). 9
Journal of Dairy Science Vol. 72, No.1, 1989
SILAGE ANALYSIS WITH NEAR INFRARED SPECTROSCOPY
set R 2 for acetic, propionic, butyric, and lactic acids in the alfalfa silages were .84, .86, .80, and .96 and the validation set r 2 for the same acids were .66, .71, .91, and .73. The biases for the various silage acids were quite low. Correlations of NIRS with the chemistry laboratory analysis for isobutyric, isovaleric, and valeric acids were lower than for those acids found in greater amounts. One way of assessing the relative accuracy of NIRS for analyzing the various components is to divide the SEA by the mean for each component (7). It is apparent from Table 4 that NIRS analyses for the short-chain organic acids are not as accurate as for the major components. The best SEA/mean short-chain organic acid values are for the major silage acids, acetic and lactic. The correlations between the chemistry laboratory and NIRS analyses for the various silage acids in the all silages sample set were lower than for the alfalfa silages and higher than for the corn silages. Near Infrared Reflectance Spectroscopic Analyses from Equations Generated from Conventional Laboratory Values on Dry and Wet Basis
Table 4 presents the data resulting from a study of the accuracy of NIRS analysis using calibration equations prepared on a dry versus wet basis. Comparisons of these two procedures were made based on silage type, short-chain organic acids, and NH 3 N compared with other components, and on major silage components (i.e., > 5%) versus minor components. The data did not reveal systematic differences between the dry and wet based computations. Examination of the wavelengths used for calibration showed no trend on any basis examined. The r2 for CP, ADF, and NDF tended to be equal to or higher for the wet versus the dry basis for all three silage groups. However, for these same components, the SEA expressed as a percent of the mean was generally greater for the wet basis. This implies somewhat more accuracy for NIRS analysis for these components using calibration equations based on dry (i.e., corrected for moisture) analyses. There were no consistent trends (wet vs. dry) for NIRS analysis of short-chain organic acids and AN. In contrast to the situation with the major
85
components, the majority of the correlations between NIRS and chemical analyses for short-chain organic acids were higher when using equations based on the 100% dry analyses. There was little difference between the two procedures in accuracy of analysis as judged by the SEA:mean ratio. The examination of more data sets will be required to answer the question of how best to compute silage chemistry for NIRS. Comparison of Several Methods of Selecting Calibration and Validation Sets
Table 5, prepared from the 60 alfalfa and 146 all silage samples used in this study, shows how the choice of calibration and validation sets changed the magnitude of correlations and standard errors. For the alfalfa silages in this table, the calibration sets contained 40, 45, or 60 samples and the validation sets 20, 15, or no samples. For all silage samples, the calibration sets contained 98, 110, or 146 samples and the validation sets 48, 36, or no samples. It is quite clear from Table 5 that using limited numbers of samples, as in the alfalfa silage sample set, for development of calibration equations, had a substantial effect, particularly with certain components, on the magnitudes of the various statistics used to judge the validity of NIRS for analytical use. When the alfalfa silages were examined, and r 2 and SEA were used as measures of analytical consistency, any of the methods used in validation set selection produced repeatable results for such components as DM, NDF, AN, and HWIN. Somewhat more variable results were experienced with CP, ADF, and IVDDM, and even more inconsistency with ADIN and pH. When the considerably larger all-silage sample set was examined using the various procedures for calibration equation selection, variation was less in both r 2 and SEA values. Thus, in assessing the validity of NIRS for analyzing for several components, particularly with a limited number of samples, it is important to examine the analytical results using several methods of choosing calibration and validation sets. CONCLUSIONS
This study demonstrates that, when suitable calibration equations are prepared, NIRS is a Journal of Dairy Science Vol. 72, No.1, 1989
.ge.
'-
00
0\
I'
...,
0
0 ~. '<
TABLE 4. Comparison of near infrared reflectance spectroscopy (NIRS) with conventional chemical analyses of undried silages using calibration equations prepared from components either corrected to a dry basis or uncorrected (wet). Validation samples
rJl
"til· ,.I'"
Alfalfa silages!
<:
Dry
r' Dry
r'
SEA/X Wet
Wet
- - (%)
--
Fiber and nitrogen components CP .88 .93 ADF .79 .93 NDF .90 .92 NH 3 N 6 .90 .84 HWIN' .97 .96 .68 .68 ADIN 6
8.2 7.5 6.9 20.0 8.2 26.9
9.3 9.0 10.5 23.2 11.0 38.6
.83 .87 .82 .75 .42 .56
.92 .89 .84 .92 .80 .51
6.7 6.4 8.5 20.0 14.1 24.3
7.9 6.2 8.1 19.4 20.1 35.7
.96 .90 .87 .82 .82 .59
.95 .92 .88 .92 .86 .69
8.4 7.7 7.4 26.4 21.6 72.2
13.6 13.7 12.0 16.1 30.5 81.5
Short-chain organic acids Acetic .66 Propionic .71 Isobutyric .69 Butyric .91 Isovaleric .40 Lactic .73 .40 Valeric
60.6 55.6 53.8 82.6 66.7 46.7 81.8
49.1 50.0 66.7 100.0 66.7 42.9 80.0
.52 .37 .22 .16 .20 .66 .23
.30 .41 .23 .19 .18 .46 .31
33.3 72.1 47.1 175.0 400.0 20.6 133.3
27.7 60.0 50.0 150.0 200.0 23.3 200.0
.57 .51 .33 .57 .26 .74 .27
.38 .44 .27 .51 .48 .74 .33
43.2 81.9 66.7 138.1 80.0 33.1 100.0
35.7 75.0 57.1 137.5 100.0 28.2 100.0
Dry
Wet
SEA/X
Dry
12-
Wet
All silages 3
Corn silages' SEA 4 /X 5
r'
Dry
Wet
Dry
Wet
...:I
.N
Z P
........ \Q
00
\Q
.41 .47 .24 .89 .24 .67 .20
--(%)--
! Calibration set, n = 40; validation set, n = 20. , Calibration set, n
= 40; validation set, n = 19.
3
Calibration set, n = 98; validation set, n = 48.
4
Standard error of analysis, SEM of NIRS vs. conventional chemical analysis)
S
SEA/mean
6
Expressed as N X 6.25.
= J[k(X
=Standard error of analysis/conventional chemical analysis mean.
, Hot water insoluble N, expressed as N X 6.25.
-
X)' /(N - 1)].
---(%)---
::>;l t>1 t>1
<: t>1
..,
Y' t>1
;I>
r
TABLE 5. A comparison of silage analyses using several methods of selecting calibration and validation sets within the same sample pool. Calibration set
Validation set
Component
3rd'
3rd 4
3rd'
r'
SEC'
R'
4rh'
All'
3rd
3rd
3rd
4th
All
3rd
3rd
SEA' 3rd
4th
3rd
3rd
3rd
4th
en
-
'-
Alfalfa silages DM CP ADF NDF IVDDM' NH,N' HWlN IO ADIN' pH
.99 .90 .91 .89 .83 .87 .92 .67 .67
.98 .85 .82 .89 .73 .88 .90 .89 .89
.98 .89 .84 .87 .80 .91 .92 .91 .82
.99 .93 .82 .92 .84 .91 .89 .87 .82
.99 .90 .90 .94 .88 .96 .94 .91 .88
1.27 1.36 2.12 3.09 2.93 .40 .65 .44 .37
1.83 1.54 2.66 2.76 3.14 .38 .79 .64 .20
1.64 1.21 2.82 2.82 3.16 .29 .66 .59 .18
1.54 1.14 2.98 2.53 3.49 .31 .85 .67 .25
1.33 1.24 2.12 2.14 2.30 .21 .59 .50 .20
.96 .74 .62 .85 .69 .82 .91 .93 .76
.98 .89 .81 .88 .85 .95 .78 .52 .82
.99 .88 .79 .90 .78 .90 .96 .68 .82
.98 .66 .79 .88 .84 .86 .82 .39 .87
2.32 1.64 4.66 2.84 3.60 .44 .80 1.33 .20
1.90 1.40 3.45 3.00 3.30 .33 .91 .54 .20
1.51 1.59 VIS 3.48 2.87 .40 .72 .57 .42
1.97 1.85 2.95 2.67 3.49 .53 .90 .77 .20
All silages DM CP ADF NDF IVDDM NH,N HWIN ADIN Ph
.96 .97 .89 .89 .83 .88 .90 .63 .78
.96 .96 .91 .89 .86 .93 .84 .60 .63
.97 .97 .91 .87 .87 .87 .95 .71 .85
.96 .97 .86 .89 .84 .85 .89 .61 .70
.97 .97 .92 .90 .84 .92 .90 .67 .80
2.65 1.06 2.62 3.06 2.69 .32 .99 1.08 .32
2.53 1.17 2.81 3.40 2.68 .25 1.14 1.09 .40
2.31 1.02 2.57 3.18 2.39 .35 .68 .65 .28
2.53 1.09 3.26 3.05 2.80 .37 1.01 1.02 .39
2.25 1.03 2.44 3.01 2.74 .27 .95 .91 .31
.97 .96 .91 .86 .81 .92 .88 .60 .69
.95 .96 .86 .88 .72 .84 .96 .60 .87
.95 .96 .91 .87 .73 .82 .82 .59 .55
.98 .96 .89 .87 .80 .85 .88 .77 .87
2.23 1.14 2.88 3.84 3.31 .27 .95 .73 .39
2.39 1.25 2.78 2.59 3.16 .39 .66 .73 .26
3.15 1.25 2.78 3.75 3.78 .39 1.28 1.40 .45
2.18 1.20 2.79 3.63 2.90 .37 1.07 .71 .23
g
a
f!-
a, o
..
~.
en l"l
n' o l"l
'"<:
~
~ -< en t""
r;;
::e ::J
::t Z
t!1 )-
::t:l
Z "'1
::t:l
)-
::t:l
t!1
1
Standard error of calibration (SEM from the least squares regression of chemical laboratory values on NIRS values): chemical composition expressed on a DM basis, = .JII:(x - X)2 j(N - l}l.
1 Standard error of analysis (SEM from the least squares regression of the chemical laboratory values on the NtRS values in the validation set): chemical composition expressed on a dry matter basis, = '-"11:(x - X)' I(N - 1)1.
3
Every third sample included in validation set; first validation sample is #1; calibration set, alfalfa silages, n = 40, all silages, n = 98; validation set, alfalfa silages, n = 20, all silages, n = 48.
4
Every third sample included in validation set; first validation sample is #2; calibration set, alfalfa silages, n = 40, all silages, n = 98; validation set, alfalfa silages, n = 20, all silages, n = 48.
S Every third sample included in validation set; first validation sample is #3 (this validation set used in preparing Tables 1 and 3); calibration set, alfalfa silages, n = 40, all silages, n = 98; validation set, alfalfa silages, n = 20. all silages, n = 48.
6
Every fourth sample included in validation set; first validation sample is #4; calibration set, alfalfa silages, n = 45, all silages, n = 110; validation set, alfalfa silages, n = 15, all silages, n = 36.
'1
All samples in calibration set; no validation set prepared; calibration set, alfalfa silages, n = 60, all silages, n = 146.
8
In vitro digestible DM.
9
Expressed as N X 6.25.
--l
J'> Z
~
Cl t!1 ;J>
10
o
en
'"0
t!1
n ..., ::t:l
oen n o'"0 -<
Hot water insoluble N, expressed as N X 6.25.
9
.... .... '0
00 '0
00 '-l
88
REEVES, III, ET AL.
useful procedure in analyzing properly ground undried silages for several components. Results are particularly encouraging for most major components except ADIN. Correlations were lower and SEA higher when NIRS was used for analyzing short-chain organic acids. With alfalfa silages, which are considerably more variable in organic acid content, correlations between conventional chemistry and NJRS analyses were higher for acetic, butyric, lactic, and propionic acids than for the acids found in smaller amounts. The results also revealed that when NJRS is used for analysis of certain components, there is some advantage in creating calibration equations from more homogeneous sample sets, i.e., sample sets arranged according to species (e.g., alfalfa vs. corn) as contrasted with having calibration equations prepared from more heterogeneous sample sets. With small sample sets for creating calibration equations, it is advisable to examine several procedures for creating calibration and validation sets before arriving at a judgment concerning the suitability and accuracy of NJRS for analyzing a specific component. REFERENCES 1 Abrams, S. M., J. S. Shenk, and M. O. Westerhaus. 1983. Capability of near infrared reflectance spectroscopy (NIR) to determine silage quality. J. Dairy Sci. 66(Suppl. 1):183. (Abstr.) 2 Association of Official Analytical Chemists. 1980. Official methods of analysis. 13th ed. Assoc. Offic. Anal. Chern., Washington, DC. 3 Blosser, T. H. 1985. Future applications for NIRS: high-moisture feedstuffs, including silage. Pages 56-57 in Agric. Handbook No. 643, Agric. Res. Serv., USDA, Washington, DC. 4 Blosser, T. H., J. B. Reeves, III, and J. Bond. 1988. Factors affecting analysis of the chemical composition of tall fescue with near infrared reflectance spectroscopy. J. Dairy Sci. 71:398. 5 Buchanan-Smith, J. G. 1983. Constituents in silage associated with feed intake reduction. J. Dairy Sci. 66(Suppl. 1): 184. (Abstr.) 6 Clancy, M., P. J. Wangsness, and B. R. Baumgardt. 1977. Effects of moisture determination method on estimates of digestibilities and intakes of conserved alfalfa J. Dairy Sci. 60:216. 7 Clark, D. H., H. F. Mayland, and R. C. Lamb. 1987. Mineral analysis of forages with near infrared reflectance spectroscopy. Agron. J. 79:485. 8 Goering, H. K., C. H. Gordon, R. W. Hemken, D.
Journal of Dairy Science Vol. 72, No. 1,1989
R. Waldo, P. j. Van Soest, and L. W. Smith. 1972. Analytical estimates of nitrogen digestibility in heat damaged forages. J. Dairy Sci. 55:1775. 9 Goering, H. K., and P. J. Van Soest. 1970. Forage fiber analysis (apparatus, reagents, procedures, and some applications). Agtic. Handbook No. 379, Agric. Res. Serv., USDA, Washington, DC. 10 Marten, G. C., J. S. Shenk, and F. E. Barton, II, ed. 1985. Near infrared reflectance spectroscopy (NIRS): analysis of forage quality. Agric. Handbook No. 643, Agric. Res. Serv., USDA, Washington, DC. 11 Langston, C. W., H. Irvin, C. H. Gordon, C. Bouma, H. G. Wiseman, C. G. Melin, L. A. Moore, and J. R. McGalmont. 1958. Microbiology and chemistry of grass silage. USDA Tech. Bull. 1187. Agric. Res. Serv., USDA, Washington, DC. 12 McDonald, P. 1981. The biochemistry of silage. john Wiley and Sons, Ltd., New York, NY. 13 Moe, A. J., and S. B. Carr. 1985. Laboratory assays and near infrared reflectance spectroscopy for estimates of feeding value of corn silage. J. Dairy Sci. 68:2220. 14 Norris, K. H., R. F. Barnes, J. E. Moore, and j. S. Shenk. 1976. Predicting forage quality by infrared reflectance spectroscopy. j. Anim. Sci. 43 :889. 15 Polesello, A., and R. Giangiacomo. 1983. Application of near infrared spectrophotometry to the nondestructive analysis of foods: A review of experimental results. Pages 203-230 in Critical reviews in food science and nutrition. Vol. 18. CRC Press, Inc., Boca Raton, FL. 16 Rohweder, D. A. ed. 1985. Proceedings of invitational ECOP workshop on NIRS. November 13 to 15, Madison, WI. 17 Shenk, j. S., M. O. Westerhaus, and M. R. Hoover. 1977. Infrared analysis offorages. Pages 242-244, 252 in Grassland forage harvesting. 1 ASAE Publ. 1-78. Am. Soc. Agric. Eng., St. joseph, MI. 18 Shenk, j. S., M. O. Westerhaus. and M. R. Hoover. 1979. Analysis of forages by infrared reflectance. ]. Dairy Sci. 62:807. 19 Templeton, W. C., Jr., J. S. Shenk, K. H. Norris, G. W. Fissel, G. C. Marten, j. H. Elgin, Jr., and M. O. Westerhaus. 1983. Forage analysis with near infrared reflectance spectroscopy: Status and outline of federal research project. Pages 528-531 in Proc. XIV Int. Grass. Congr., Westview Press, Boulder, CO. 20 Van Soest, P. j. 1982. Nutritional ecology of the ruminant. 0 and B Books Inc., Corvallis, OR. 21 Wilkins, R. j., K. j. Hutchinson, R. F. Wilson, and C. E. Harris. 1971. The voluntary intake of silage by sheep. I. Interrelationships between silage composition and intake. ]. Agric. Sci., Camb. 77: 531. 22 Waldo, D. R., and N. A. jorgensen. 1981. Forages for high animal production: nutritional factors and effects of conservation. j. Daity Sci. 64: 1207. 23 Yu, Y., and j. W. Thomas. 1976. Estimation of the extent of heat damage in alfalfa haylages by laboratoty measurement. j. Anim. Sci. 42:766.