Characterisation of feedstuffs for ruminants
using some physical parameters
Sylvie Giger-Reverdin
*Laboratoire de Nutrition et Alimentation (INRA), Institut National Agronomique Paris±Grignon, 16 rue Claude Bernard, 75231 Paris Cedex 05, France
Received 3 August 1999; received in revised form 14 April 2000; accepted 4 May 2000
Abstract
Various ingredients and roughages of known chemical composition have been analysed by several physical methods: particle density, particle size, water holding capacity, feed solubilisation and osmotic pressure. Some of these methods already exist and have been adapted and others have been newly developed. All of the methods are quite easy to perform and the results are very repeatable. The data obtained by the physical and chemical methods have been correlated together. The physical methods gave new information about the nutritive value of feedstuffs for ruminants and they can be used to differentiate between feedstuffs. They might explain part of the role played by rumen ¯ora on feedstuffs which is not taken into account by the chemical approach. #2000 Elsevier Science B.V. All rights reserved.
Keywords:Feedstuffs; Ruminants; Physical properties; Particle size; Osmotic pressure; Water holding capacity
1. Introduction
The physical characteristics of feedstuffs for ruminants are rarely measured, particularly in relation to their nutritional properties that could be taken into account in feed formulation. It is known that some of these characteristics might partly explain the interaction between rumen ¯ora and feedstuffs degradation. Particle density in¯uences their rate of passage from the rumen (Ehle, 1984; Martz and Belyea, 1986) and thus ruminal turnover rate of feeds and possibly their level of intake (Singh and Narang, 1991). Particle size of feedstuffs in¯uences the surface area available for micro-organisms attack (Owens and Goetsch, 1988) and thus their multiplication (Dehority and Orpin, 1988). It
86 (2000) 53±69
*Tel.:33-1-44081768; fax:33-1-44081853.
E-mail address: giger@inapg.inra.fr (S. Giger-Reverdin)
also plays a role in the rate of passage of feedstuffs through the digestive tract (Balch, 1950). Water holding capacity (WHC) has an impact on microbial colonisation and osmotic pressure is a factor that should be considered in the overall ecology of the rumen (Bennink et al., 1978). Most methods concerning physical characteristics had been applied either to roughages or to rations (Montgomery and Baumgardt, 1965; Allen et al., 1984; Hooper and Welch, 1985; Singh and Narang, 1991), or to foods for human nutrition (McConnell et al., 1974; Robertson and Eastwood, 1981).
The purpose of this paper is to describe easy-to-perform methods and to test them on a set of feedstuffs representative of the range used in ruminant diets thereby allowing discussion of their potential usefulness compared to more classical chemical methods.
2. Materials and methods
2.1. Feedstuffs
Twenty-four feedstuffs were tested. They can be grouped into six main classes:
cereals (oats, wheat, barley, maize, sorghum),
cereal by-products (wheat bran, brewers grains, corn gluten feed, corn gluten meal),
legumes (faba beans, peas, lupine seed),
oilmeals (soyabean meal, coconut meal, rapeseed meal, sunflower meal, palm kernel
meal),
agro-industrial by-products (soyabean hulls, citrus pulp, sugar beet pulp),
roughages (corn silage, corn stover, alfalfa hay, dehydrated alfalfa).
These ingredients were chosen to represent the wide range of dietary sources used in rations or in compound feedstuffs for ruminants. Moreover, a part of the choice was performed so that the ingredients differed as much as possible from a chemical point of view. All the samples were ground through a 1 mm screen. Wet samples (corn silage, citrus pulp) were oven-dried at 608C for 48 h.
2.2. Physical methods
2.2.1. Bulk density
Bulk density was determined by a modi®cation of the method of Montgomery and Baumgardt (1965), where manual swirling of the container between the palms was replaced by an automatic method, and the fourth step of feedstuff addition was not performed. The following procedure was used to estimate the bulk density of the dried ground samples:
A 100 ml glass graduated cylinder (2.7 cm internal diameter) was filled with sample to
the 50 ml mark and swirled for 15 s. Weight of the sample and volume occupied were recorded.
Additional sample was added up to the 100 ml mark and the cylinder was swirled for
10 s.
The cylinder was refilled to 100 ml mark and swirled for 5 s. Total weight of sample and final volume were recorded.
As density is de®ned as the mass of the substrate over the mass of an equivalent volume of water, bulk density was equal to the weight of sample (expressed in mg) over the volume occupied (expressed in ml).
2.2.2. Median particle size
Median particle size of feedstuffs was measured by a dry-sieving method according to Melcion and de Monredon (1991). About 100 g of each feedstuff was weighed into the upper sieve and shaken from side to side for 15 min. For wheat bran, alfalfa hay, corn silage and corn stover, the largest particles clogged the sieve meshes. Therefore, 50 g of these feedstuffs was used. The sizes of the six sieves were 0.400, 0.315, 0.250, 0.200, 0.125 and 0.080 mm. Refusals from each sieve were weighed, and the percentage of each class was calculated. The cumulated percentage was calculated for each sieve from the largest sieve to the smallest. Results were plotted against the logarithm of sieve size (AFNOR, 1985). Median particle size (D50) was read directly as corresponding to a virtual screen which would retain 50% of the particles, and D16 and D84 were, respectively, those corresponding to 16 and 84% probability (Allen et al., 1984). In order to estimate, the distribution of the particles around the median particle size, a relative deviation was estimated as the difference between D16 and D84 divided by the median particle size and called RD50.
2.2.3. Water holding capacity
WHC of feedstuffs was measured using an adaptation of one of the methods proposed by Robertson and Eastwood (1981). The whole sample was used instead of its ®brous part. A mass of 2.5 g of sample was left to soak for 16±24 h in 250 ml of distilled water, so that water was in excess. The sample was then ®ltered on a fritted glass crucible (porosity 2) and the walls of the beaker were carefully rinsed. The wet sample was weighed after letting water decant for 10 min. WHC was the quantity of water retained by the sample and expressed as l kgÿ1sample dry matter.
2.2.4. Feed solubilisation
The ®ltrate sample collected after passage through the ®lter was oven-dried for 72 h at
1038C, weighed, and subsequently ashed at 5508C overnight and weighed. Dry matter
and ash solubilised were expressed as g lÿ1 or as percentage of initial weight of the
component considered.
2.2.5. Intrinsic osmotic pressure
2.5 g of sample was soaked in 50 ml of water for 24 h. Filtration was performed according to the WHC method already described, but without the addition of water. Osmotic pressure was measured on the ®ltrate using the freezing point depression technique with a Mark 3 Osmometer, manufactured by Fiske (USA).
2.3. Chemical methods
The feedstuffs were analysed by standard methods: dry matter was estimated from water content (AFNOR, 1982), ash (AFNOR, 1977), crude protein (ISO, 1997), enzymatic starch (AFNOR, 1997a) and fat (AFNOR, 1997b). Cell wall content was estimated by the neutral detergent ®bre (NDF) method of Van Soest and Wine (1967) as modi®ed by AFNOR (1997c). Lignocellulose (ADF) and lignin (ADL) were obtained using a sequential approach (AFNOR, 1997c) on the NDF residue as proposed by Giger et al. (1987).
2.4. Statistical analysis
The SAS package (SAS, 1987) was used for statistical calculations.
When analyses were in duplicate for a sample, a variance analysis including a feed effect was performed and the so-obtained residual standard deviation (RSD) was divided by the mean value in order to estimate the precision of the method involved. This unitless value which could be called coef®cient of variation would allow to estimate the respective abilities of the methods to discriminate the feedstuffs.
Regressions were performed to study the relationships between physical and chemical parameters. The link between the dependent variable and the one or more independent variables was analysed using the RSD of the dependent variable.
A principal component analysis (PCA) was used to examine the relationships among several quantitative variables, as described by Lebart and Fenelon (1971).
3. Results
3.1. General considerations about chemical and physical compositions
Chemical parameters for each feedstuff are summarised in Table 1. For some feedstuffs, either starch or fat were not determined, because those feeds were assumed to contain only traces of these elements. The statistical parameters were determined without
them (Table 1), but in the PCA, they were assume to be equal to 0 g kgÿ1 DM. As
expected, feedstuffs showed a wide variability in chemical composition.
All physical determinations have been performed in duplicate on all feedstuffs, what allowed calculating a coef®cient of variation as de®ned before.
3.2. Bulk density
In fact, two types of bulk density were measured. The ®rst one was obtained after the ®rst swirling and was called density50, and the second one after the third swirling, called density100. They were directly proportional and their mean values (0.561 vs 0.541) did not statistically differ
Density1000:966 density50 r20:995;n24;RSD0:0110
Moreover, the coef®cient of variation of density100 (0.99%) was less than half that of density50 (2.05%). This means that the density100 was a more repeatable method than density50. This method with three steps of feedstuff addition and three swirlings was thus used, and called simply bulk density. Bulk density varied greatly between feedstuffs
(Table 2). Corn stover had the lowest density (0.138 kg lÿ1) and peas the highest
(0.788 kg lÿ1).
Bulk density was negatively correlated with the cell wall content parameters of feedstuffs The best relationship was with NDF content (Fig. 1):
Bulk density0:743ÿ0:589 NDF kg kgÿ1 DM
r20:644;n24;RSD0:0955
Therefore, for the same bulk density of around 0.5, NDF content varied from 206 g kgÿ1
DM (lupine seed) to 612 g kgÿ1 DM palm kernel meal, ADF explained 44.9% of the
variation of bulk density, and ADL, only 19.3%.
Table 1
Chemical parameters of feedstuffs (g kgÿ1DM)
Crude protein
Fat Starch NDF ADF ADL Ash
Oats 92 47 432 336 162 40 34
Wheat 103 17 727 122 35 13 19
Barley 141 20 571 210 75 11 26
Maize 104 40 721 108 31 8 16
Sorghum 114 36 707 103 65 25 19
Wheat bran 165 30 149 450 135 37 71
Brewers grains 300 57 48 505 201 53 36
Corn gluten feed 188 31 234 407 109 10 54
Corn gluten meal 650 24 239 5 4 1 14
Faba beans 294 15 426 120 90 12 40
Peas 233 13 495 110 85 6 35
Lupine seed 382 110 9 206 145 7 39
Soyabean meal 472 19 NDa 102 59 6 76
Coconut meal 146 29 ND 578 429 43 57
Rapeseed meal 415 11 ND 230 160 50 79
Sun¯ower meal 381 20 ND 430 286 88 77
Palm kernel meal 164 97 ND 612 381 103 46
Soyabean hulls 124 22 ND 622 446 19 51
Citrus pulp 81 25 ND 215 140 15 78
Sugar beet pulp 85 ND ND 427 222 16 93
Corn silage 73 ND 250 479 237 35 57
Corn stover 44 ND ND 776 437 36 75
Alfalfa hay 172 ND ND 553 365 85 108
Dehydrated alfalfa 157 ND ND 525 370 90 111
Statistical parameters
Mean 212 35 385 343 195 34 55
No. of feedstuffs 24 19 13 24 24 24 24
Standard deviation 152 27 251 214 142 30 28
aND: not determined.
Table 2
Physical parameters of feedstuffs (bulk density, median particle size and WHC)a
Bulk densityb D50c RD50d WHC (l kgÿ1
DM of sample)
Cereals
Oats 0.469k,l 0.460d,e 2.860b,c 2.18i,j
Wheat 0.665e 0.315h,i,j,k 3.017b,c 1.98j
Barley 0.566i 0.380f,g 2.048b,c,d,e 2.28i,j
Maize 0.651f 0.530c 1.387d,e 1.62k
Sorghum 0.686d 0.400f 1.788c,d,e,j 1.50k
Cereal by-products
Wheat bran 0.340n 0.740b 3.162b 3.07e,f
Brewers grains 0.465k,l 0.235l 1.489d,e 4.07c,d
Corn gluten feed 0.604h 0.275j,k,l 1.127e 2.53h,i
Corn gluten meal 0.672e 0.255k,l 0.978E 2.01j
Legumes
Faba beans 0.701c 0.325h,i,j 2.190b,c,d,e 2.46h,i
Peas 0.788a 0.343g,h 4.080a 2.44h,i
Lupine seed 0.460l 0.335g,h,i 1.108e 3.79d
Oilmeals
Soyabean meal 0.701c 0.535c 2.972b,c 3.22e,f
Coconut meal 0.476k 0.480d 2.154b,c,d,e 4.08c,d
Rapeseed meal 0.651f 0.305h,i,j,k 1.739c,d,e 3.35e
Sun¯ower meal 0.473k,l 0.420e,f 2.107b,c,d,e 3.24e,f
Palm kernel meal 0.519j 0.450d,e 1.241d,e 2.70g,h
Agro-industrial by-products
Soyabean hulls 0.448m 0.480d 1.750c,d,e 5.23b
Citrus pulp 0.715b 0.270j,k,l 1.853c,d,e 4.33c
Sugar beet pulp 0.625g 0.390f 2.455b,c,d 5.37b
Roughages
Corn silage 0.318o 0.280i,j,k,l 1.873c,d,e 4.09c,d
Corn stover 0.138p 0.875a 4.127a 8.87a
Alfalfa hay 0.333n 0.260k,l 1.808c,d,e 3.80d
Dehydrated alfalfa 0.530j 0.280i,j,k,l 2.004b,c,d,e 2.87f,g
Statistical parameters
Mean (24 feedstuffs) 0.541 0.401 2.139 3.382
Standard deviation 0.157 0.155 0.853 1.567
Coefficient of variation (%) 0.99 4.63 15.90 3.97
aValues within columns with different superscript letters indicate a signi®cant feed effect atp<0.05. bBulk density: (mass of sample occupying 100 ml before swirling)/(mass of water occupying a volume
equal to that occupied by the sample after swirling).
cD50 (D16 and D84): median particle size of feeds or mesh size of an hypothetical screen which would
retain 50% (16 or 84) of the particles. dRD50: relative deviation of D50
(D16ÿD84)/D50.
In general, roughages which had high levels of cell wall content had low bulk density values, while cereals or grain legumes had low levels of cell wall contents and high bulk density values.
3.3. Median particle size
Although, the coef®cient of precision for D50 (Table 2) remained quite high (4.6%), it was smaller than that of Waldo et al. (1971) which was 14%. The residual deviation of D50 (RD50) varied a lot between feeds. This means that for the same D50 value, the distribution of particle size might vary a lot. For example, for about the same D50 (around 0.34), the three legumes had statistically different RD50 values, with a high value for peas (4.08), a medium one for faba beans (2.19) and a low one for lupine seed (1.11). In other terms, peas were more heterogeneous in terms of median particle size than lupine seed.
D50 was not signi®cantly related to any of the chemical parameters at the 5% threshold. The best chemical predictor of D50 (r20.136,n24, RSD0.148) was NDF. D50 was however correlated with bulk density,
D500:656ÿ0:472 bulk density r20:227;n24;RSD0:140
An increase in median particle size was associated with a decrease in density. Corn stover which had the lowest density and the highest median particle size had a large standardised residual in this equation and therefore had a large in¯uence on the coef®cients.
The relative deviation of D50 (RD50) increased with D50, which means that coarse feeds were more heterogeneous than ®ne ones. RD50 was correlated with fat content
RD502:51ÿ13:4 fat kg kgÿ1 DM r20:192;n24;RSD0:780
Fig. 1. Relationship between bulk density and NDF.
Lupine seed and palm kernel meal which had a high fat content and a low RD50 had a large in¯uence on the coef®cients of this last regression.
3.4. Water holding capacity
WHC varied greatly between feedstuffs (Table 2). It was quite low for cereals and high for roughages (and in particular, for corn stover) and for some by-products, such as soyabean hulls, citrus pulp and sugar beet pulp. Compared to the other methods, the coef®cient of precision was quite high (4.0%). WHC was highly correlated with cell wall material (NDF,r20.456,n24) and lignocellulose (ADF,r20.418,n24), but not with lignin (r20.013,n24). It increased with the cell wall content of feedstuffs (Fig. 2) and decreased with the bulk density (r20.396,n24).
WHC1:684:96 NDF kg kgÿ1 DM
r20:456;n24;RSD1:171
For the same level of cell wall material, feedstuffs with a high pectin content, such as sugar beet pulp or citrus pulp, had a higher WHC than feedstuffs with low pectin levels. WHC can be accurately predicted by a combination of three chemical variables
(holocelluloseNDF minus ADL, ADL and starch) expressed in kg kgÿ1DM.
WHC3:225:59 holocellulose ÿ23:5 ADLÿ2:46 starch
r20:764;n24;RSD0:838
Since NDF includes ADL, and in addition, NDF and holocellulose have the same coef®cients of regression in equations which include ADL and starch, it is better to use the equation which uses holocellulose. This means that an increase in NDF or in
Fig. 2. Relationship between WHC and NDF.
holocellulose increases WHC, and in the equation which uses holocellulose, the other variables (ADL or starch) decrease WHC.
3.5. Solubilisation of feedstuffs
The soluble dry matter content (DMSol) varied greatly between feedstuffs (Table 3). It was very low for corn gluten meal (4.3% of initial dry matter), and quite high for citrus pulp (40.3%).
The percentage of initial ash which was solubilised varied between 31% (corn gluten meal) and 93% (maize). It was not signi®cantly correlated with ash content. Therefore, the quantity of solubilised ash expressed as g lÿ1depended both on ash content (% per DM) and of the percentage of initial ash which was solubilised. It varied from 0.04 g lÿ1 (corn gluten meal) to 0.67 g lÿ1(dehydrated alfalfa) and was directly proportional to ash content (Fig. 3):
Quantity solubilised ash g lÿ1 5:01 ash kg kgÿ1 DM r20:699;n24;RSD0:091
The quantity of solubilised organic matter was not signi®cantly correlated with chemical composition (except for ash), nor was it correlated with non-structural carbohydrates. It was however related to the quantity of solubilised ash (Fig. 4). Some feedstuffs were rich in highly soluble organic matter (citrus pulp or lupine seed), and others were rich in highly soluble ash (corn stover):
Quantity solubilised OM g lÿ1 0:8612:20 quantity solubilised ash g lÿ1
r2 0:193;n24;RSD0:748
Some feedstuffs contain large amounts of soluble organic matter and ash, such as alfalfa hay or dehydrated alfalfa. Cereals and their by-products were only poorly soluble (especially maize and sorghum).
3.6. Intrinsic osmotic pressure
Osmotic pressure had a good precision coef®cient, as de®ned before. Intrinsic osmotic pressure was high for forages, especially for alfalfa hay or dehydrated alfalfa, and low for cereals. It was highly correlated with ash (r20.493, n24) and especially with the quantity of solubilised ash (r20.716, n24) as shown in Fig. 5:
Osmotic pressure mOsm kgÿ1 H2O 13:1141 quantity solubilised ash g lÿ1
r20:699;n24;RSD14:8
However, the RSD of the variations in osmotic pressure decreased signi®cantly when the quantity of solubilised organic matter was also included as explicative variate:
Osmotic pressure109 quantity solubilised ash
14:8 quantity solubilised organic matter
r20:873;n24;RSD10:1
In the last equation, the impact of one point of solubilised ash was seven times greater than the effect of one point of solubilised organic matter, even if the quantity of solubilised organic matter is on average six times greater than the quantity of solubilised ash.
Table 3
Physical parameters of feedstuffs (solubilisation and intrinsic osmotic pressure)a
DMSol
Wheat 12.54h,i 1.080h 62.57c,d,e,f 0.102l,m 24.0j,k
Barley 13.20h,i 1.154h 68.32c,d,e 0.155k 36.0h,i
Maize 8.76j 0.750i 92.70a 0.127l 20.0k,l
Sorghum 6.74k 0.581j 57.24d,e,f,g 0.093m 17.8k,l
Cereal by-products
Wheat bran 22.43e 1.994e 87.22a,b 0.550c 73.5c,d
Brewers grains 5.98k 0.555j 34.81h 0.116l,m 25.0j,k
Corn gluten feed 24.16d 2.155d 73.04b,c,d 0.352f 83.3b,c
Corn gluten meal 4.30l 0.389k 31.39h 0.040n 11.5l
Legumes
Faba beans 28.27c 2.427c 68.09c,d,e 0.234h,i 50.5g
Peas 29.98c 2.595c 77.49a,b,c 0.234h,i 68.0d,e
Lupine seed 36.85b 3.353a 79.77a,b,c 0.283g 76.3b,c,d
Oilmeals
Soyabean meal 29.15c 2.533c 61.23c,d,e,f 0.405e 63.0e,f
Coconut meal 16.73g 1.465g 55.81d,e,f,g 0.279g 43.0h
Rapeseed meal 28.40c 2.539c 37.09h 0.262g,h 58.0f
Sun¯ower meal 20.74f 1.842f 37.29h 0.255g,h 38.0h
Palm kernel meal 11.68i 1.066h 49.81e,f,g,h 0.209i,j 26.3i,j,k
Agro-industrial by-products
Soyabean hulls 13.92h 1.189h 45.56f,g,h 0.199j 32.5h,i,j
Citrus pulp 40.30a 3.387a 40.99g,h 0.269g 79.0b,c
Sugar beet pulp 16.24g 1.446g 33.67h 0.279g 40.0h
Roughages
Corn silage 16.00g 1.483g 64.40c,d,e 0.340f 85.0b
Corn stover 12.77h,i 1.171h 69.84c,d,e 0.481d 59.5e,f
Alfalfa hay 35.65b 3.091b 61.55c,d,e,f 0.577b 102.5a
Dehydrated alfalfa 28.00c 2.532c 66.46c,d,e 0.668a 102.5a
Statistical parameters
Mean (24 feedstuffs) 19.82 1.745 58.31 0.277 52.0
Standard deviation 10.31 0.897 17.55 0.163 27.0
Coefficient of variation (%) 3.40 3.35 9.71 3.88 6.91
aValues within columns with different superscript letters indicate a signi®cant feed effect atp<0.05. bDmsol: soluble dry matter in an excess of water.
cAshsol: soluble ash in an excess of water.
3.7. Relationships between parameters
A PCA was performed with the 24 feedstuffs on 14 variables, seven of which were physical (bulk density, D50, RD50, WHC, quantities of solubilised organic matter and ash, osmotic pressure) while the seven remaining variables were chemical (crude protein, starch, fat, NDF, ADF, ADL, ash).
The ®rst two components explained about 60% of the variance (Fig. 6). The ®rst component exhibited the opposition between cell wall components (NDF, ADF and ADL) plus ash and cell constituents (crude protein, starch and fat). The second component was
Fig. 3. Relationship between solubilised ash content and ash content.
Fig. 4. Relationships between quantities of solubilised organic matter and ash.
mainly explained by physical parameters: particle size (D50 and RD50) and WHC had an opposite in¯uence compared to solubilised organic matter or ash, osmotic pressure and density. In this plot of the ®rst two components, it is also interesting to point out the opposite effects of bulk density with median particle size, WHC and NDF. There were also links between WHC and cell wall components on one hand, and of osmotic pressure with solubilised organic matter and ash on the other hand.
The projection of the feedstuffs on the plan of the ®rst two components highlighted differences between feedstuffs (Fig. 7). When plotting composition and feedstuffs on the same e ®rst plots, cereals were close to the cell constituents points (starch, fat, crude protein) and opposed to roughages. This graphically shows the high cell constituents
Fig. 5. Relationship between intrinsic osmotic pressure and quantity of solubilised ash.
Fig. 6. Plot of the two ®rst components of PCA.
contents of cereals and low ones for roughages. Corn stover was very different from the other feedstuffs, with its high WHC and its high cell wall content, but its low bulk density. Alfalfa hay and dehydrated alfalfa had a high osmotic pressure and were highly soluble.
4. Discussion
Bulk density has seldom been measured, the only method for dry feedstuffs described in the literature was that of Montgomery and Baumgardt (1965). The main drawback of the above method was the manual swirling of the cylinder that seemed to be highly dependent on the manipulator. Thus, the measurement of the volume occupied by the sample after the third swirling seemed more appropriate than that measured after a fourth addition of sample as proposed by Montgomery and Baumgardt (1965), because, these authors did not treat the last grams of sample added in the same manner as the initial sample. The adaptation of the method proposed in this paper included automatic swirling and was very easy to perform. The precision of the method estimated by the coef®cient of variation (0.99%) was quite good compared to that of the functional speci®c gravity measured in water or in ionic solutions by the pycnometric technique (Hooper and Welch, 1985). Bulk density, in the present experiment, was determined in an arbitrary way to obtain relative, and not absolute values. Moreover, it distinguished between feedstuffs. All the densities were lower than 0.8 kg lÿ1, and therefore less than that of water.
It is important to have an accurate estimation of the bulk density of feedstuffs in the rumen, as it in¯uences transit in the rumen, and thus dry matter intake (Montgomery and Baumgardt, 1965; Seoane et al., 1981; Ehle, 1984; Wattiaux, 1993). When the three roughages (alfalfa hay, corn silage and corn stover) are considered the measured density was negatively related to their mean ®ll value estimated from the literature (Jarrige,
Fig. 7. Plot of the two ®rst components of PCA.
1988). This means that feedstuffs with low bulk density, which also have a high NDF content, might have more effect on rumen ®ll than feedstuffs with high bulk density. This agrees with the observations made by Singh and Narang (1991) on Indian forages. Moreover, it must be borne in mind that the bulk density was measured on sieved feedstuffs which may not accurately represent actual particle distribution of the raw feedstuffs as fed. Sieving also destroys cell wall structure, and according to the ``Hotel Theory'' of Van Soest (1975) increases the bulk density of feedstuffs because the number of empty interior cellular spaces, which can be ®lled with gas, water or even smaller particles, decrease.
Particle size of a feed can be estimated by the measurement of one or of several geometric characteristics, such as length, diameter, surface or volume, which are grouped in the general term of particle size (Vergnes, 1959). Sieving is a well established method which is often used (Allen et al., 1984). As already stressed by Rodrigue and Allen (1960) or Allen et al. (1984) for forages and faeces, grinding using a ®xed size screen does not produce particles of uniform size. This is of particular interest when trying to explain the results obtained with the in sacco method, because it might help explain particle losses from the bags (Michalet-Doreau and Cerneau, 1992). When particle size of feedstuffs increases, the ®brosity index also increases. The measurement of particle size is of nutritional interest, because the ®brosity index is one of the estimators of the risk of digestive disorders due to feedstuffs (Sauvant et al., 1990). Particle size and bulk density were poorly correlated for the 24 feeds of heterogeneous nature. Moreover, Maarou® et al. (1999) showed no signi®cant correlation between mean size diameter and densities obtained by more sophisticated methods when studying different granulometric fractions of pea seeds.
The WHC of a feedstuff is the amount of water retained in a feedstuff when water is in excess (McConnell et al., 1974) and thus a measure of its ability to immobilise water within its matrix (Singh and Narang, 1991). WHC is usually obtained by centrifugation at high speed (McConnell et al., 1974; Robertson and Eastwood, 1981; Seoane et al., 1981; Singh and Narang, 1991), and sometimes by ®ltration (Robertson and Eastwood, 1981). The ®ltration method is easier to perform, and follows more closely the conditions likely to be found in the gastro-intestinal tract, and thus mimics the physiological reality (Robertson and Eastwood, 1981). Comparison of the centrifugation and the ®ltration methods gave the same hierarchy among feedstuffs, which was the main objective of this work. Feedstuffs with a high WHC have generally low bulk density values. This could be due to the fact that feedstuffs which have low bulk density could have numerous gas pockets within their cell wall matrix, and these pockets might retain water when it is in excess, such as in the rumen. These feedstuffs might also have a high ®ll effect (Seoane et al., 1981) and have a low transit rate. WHC ranged from 1.5 to 8.9 l kgÿ1dry matter, which means that there is a ratio of 1±6 for the extreme values, which was higher that observed by Singh and Narang (1991), but agrees well with the data of McConnell et al. (1974) for similar samples used in human nutrition (maize, oats, bran or peas). Moreover, the high correlation of WHC with NDF is in agreement with previous papers (Singh and Narang, 1991), as is the effect of pectin on WHC (McBurney et al., 1985).
As mentioned by Singh and Narang (1991), legumes have a higher solubility than non-legumes. Solubility might be an estimation of nutrient availability (Dehority and Johnson,
1964). But, some soluble substances, as e.g., Maillard products, soluble tanins and other phenolics, are not digestible (Van Soest, 1996), and are therefore not used by rumen bacteria. It should be noted that the quantities of soluble ash and organic matter were not very well correlated. Quantity of organic matter solubilised was not explained by chemical composition because the parameters used did not have the same potential of solubility for the feedstuffs used. Moreover, pectin was not measured and it could be a factor of explanation of high solubility of some feeds as citrus pulp or soyabean hulls.
Intrinsic osmotic pressure of feedstuffs was measured on a 2.5 g sample soaked in 50 ml of water, which corresponds with a 5 kg sample in 100 l. In order to compare these values with in vivo data obtained in dairy cows, they have been multiplied by four to give the theoretical intrinsic values for 20 kg of feed samples in a rumen of 100 l. Some feedstuffs have an intrinsic osmotic pressure higher than 300 mOsm lÿ1, such as wheat bran (300), corn gluten feed (330), lupine (300), citrus pulp (320), corn silage (340) and
about 400 mOsm lÿ1 for alfalfa hay and dehydrated alfalfa. If according to different
authors (Warner and Stacy, 1965; Bergen, 1972; Bennink et al., 1978; Owens and Goetsch, 1988), the normal value for ruminal osmolality might be assumed to be around
300 mOsm lÿ1 (260±340 mOsm lÿ1) and might increase up to 400 mOsm lÿ1 after a
meal, this means that the feedstuffs alone mentioned above could induce osmotic pressures close to those observed in vivo after a meal. This needs to be taken into account, because postprandial changes in osmotic pressure were primarily due to changes in concentrations of volatile fatty acids in the rumen (Bennink et al., 1978) and thus in¯uenced by the degradation rate of feedstuffs. If these two phenomena are additive, these feedstuffs might induce a problem in vivo, as values of ruminal osmolality higher
than 350 mOsm lÿ1(Welch, 1982) could modify rumen motility and the absorption of
water and fermentation products (Engelhardt, 1970), and thus decrease feed intake (Ternouth, 1967; Bergen, 1972).
5. Conclusion
The nutritive value of feedstuffs is based on chemical analysis that on its own is unable to explain some facts observed in vivo, such as acidosis or loss of appetite. As the chemical parameters and the physical ones described in this paper gave different information about feeds, the proposed methods might allow a new approach for feeding and help to reduce these problems. They are quite easy to perform and to standardise. Data obtained in different runs did not differ for a given feedstuff. The main drawback of these methods concerns the need to grind the samples, which is necessary to obtain repeatable data. Therefore, the feedstuffs are not analysed as fed. These methods need to be tested more extensively in vitro and in vivo in order to test their usefulness. For example, bulk density, intrinsic osmotic pressure and WHC needed to be more extensively related to voluntary intake, particle size to risk of digestive disorders, solubility to nutrient availability. Some of the data issued from these methods could then be integrated into systems to predict the nutritive value of feeds, when the information given is not redundant compared to that of chemical analysis.
Acknowledgements
The author is indebted Dr. C. Duvaux-Ponter, Dr. J.P. Melcion and Professor D. Sauvant for the constructive remarks in writing this paper and also to Dr. A.A. Ponter for his useful comments in re-reading the English. The author thanks Mr. Devillers from Radiometer S.A. for providing an osmometer for this study.
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