Evaluation of the factors affecting silage intake of dairy cows: a revision of the relative silage dry-matter intake index

P. Huhtanen; M. Rinne; J. Nousiainen

doi:10.1017/S175173110773673X

Evaluation of the factors affecting silage intake of dairy cows: a revision of the relative silage dry-matter intake index

Published online by Cambridge University Press: 17 May 2007

P. Huhtanen ,

M. Rinne and

J. Nousiainen

Show author details

P. Huhtanen*: Affiliation:
MTT Agrifood Research Finland, Animal Production Research, FI-31600, Jokioinen, Finland
M. Rinne: Affiliation:
MTT Agrifood Research Finland, Animal Production Research, FI-31600, Jokioinen, Finland
J. Nousiainen: Affiliation:
Valio Ltd, Farm Services, PO Box 10, FI-00039 Valio, Finland

Article contents

Abstract
Introduction
Material and methods
Results
Discussion
References

Abstract

An evaluation of the factors affecting silage dry-matter intake (SDMI) of dairy cows was conducted based on dietary treatment means. The data were divided into six subsets based on the silage treatments used in the experiments: concentration of digestible organic matter in dry matter (D-value) influenced by the maturity of grass ensiled (n = 81), fermentation quality influenced by silage additives (n = 240), dry matter (DM) concentration influenced by wilting of grass prior to ensiling (W; n = 85), comparison of silages made from primary growth or regrowth of grass (n = 46), and replacement of grass silage with legume (L; n = 53) or fermented whole-crop cereal (WC; n = 37) silages. The data were subjected to the mixed model regression analysis. Both silage D-value and fermentation quality significantly affected SDMI. The average effects of D-value and total acid (TA) concentration were 17.0 g and − 12.8 per 1 g/kg DM, respectively. At a given D-value, silage neutral-detergent fibre (NDF) concentration tended to decrease SDMI. Silage TA concentration was the best fermentation parameter predicting SDMI. Adding other parameters into the multivariate models did not improve the fit and the slopes of the other parameters remained insignificant. Total NDF intake was curvilinearly related to silage D-value the maximum intake being reached at a D-value of 640 g/kg DM. Results imply that physical fill is not limiting SDMI of highly digestible grass silages and that both physical and metabolic factors constrain total DM intake in an interactive manner. Silage DM concentration had an independent curvilinear effect on SDMI. Replacing primary growth silage with regrowth, L or WC silages affected SDMI significantly, the response to regrowth silage being linearly decreasing and to L and WC quadratically increasing. The outcome of factors affecting SDMI was used to update the relative SDMI index as follows: SDMI index = 100+10 × [(D-value − 680) × 0.0170 − (TA − 80) × 0.0128+(0.0198 × (DM − 250) − 0.00002364 × (DM2 − 250 2)) − 0.44 × a+4.13 × b − 2.58 × b2+5.90 × c − 6.14 × c2 − 0.0023 × (550 − NDF)], where a, b and c represent the proportions (0–1) of regrowth, L or WC silages from total silage DM. For the whole data set, one index unit corresponded to the default value of 0.10 kg in SDMI. The SDMI index explained proportionally 0.852 of the variation in SDMI with 0.34 kg DM per day residual. The updated SDMI index provides improved basis for the practical dairy cow ration formulation and economic evaluation.

Keywords

digestibility fermentation fibre food intake forage

Type: Full Papers
Information: animal , Volume 1 , Issue 5 , June 2007 , pp. 758 - 770

DOI: https://doi.org/10.1017/S175173110773673X [Opens in a new window]
Copyright: Copyright © The Animal Consortium 2007

Introduction

Variation in the performance of ruminant animals is more closely related to feed intake than to diet digestibility or efficiency of converting digestible energy to metabolisable or net energy (Mertens, Reference Mertens and Fahey1994). Consequently, accurate prediction of feed intake is a fundamental prerequisite for any effective ration formulation system for lactating dairy cows. Regulation of feed intake in ruminants involves multiple mechanisms that are poorly understood, and despite extensive research efforts over the past 30 to 40 years no generally accepted intake model has been developed. Limited success in developing the predictive models is at least partly due to complicated interactions between animal and feed characteristics. Many of the models use milk yield as an input variable (Vadiveloo and Holmes, Reference Vadiveloo and Holmes1979; Dulphy et al., Reference Dulphy, Faverdin, Jarrige and Jarrige1989; National Research Council, 2001; Keady et al., Reference Keady, Mayne, Offer, Thomas and Thomas2004) but usefulness of such models in ration formulation may be questioned, since the final output (milk yield) is used to predict input (feed intake).

To overcome the problems resulting from variation in animal and environmental factors, we presented a relative silage dry-matter intake (SDMI) index using the data from milk production studies and mixed model regression analysis (Huhtanen et al., Reference Huhtanen, Khalili, Nousiainen, Rinne, Jaakkola, Heikkilä and Nousiainen2002). This approach allows estimating the effects of silage characteristics on dry-matter (DM) intake when the animal and other dietary factors (e.g. concentrate supplementation and feeding management) are constant. Although based on data from different trials and a statistical method, our approach is similar to the attempts to adjust for differences between the animals by using a standard reference forage (Osbourn et al., Reference Osbourn, Terry, Outen and Cammel1974) or by estimating silage intake potential directly by near infra-red spectroscopy (NIRS; Agnew et al., Reference Agnew, Offer, McNamee and Park2001). The relative SDMI index (Huhtanen et al., Reference Huhtanen, Khalili, Nousiainen, Rinne, Jaakkola, Heikkilä and Nousiainen2002) was based on the concentration of digestible organic matter (OM) in silage DM (D-value) and some fermentation parameters, and it predicted differences in SDMI relatively accurately when D-value was affected by the stage of grass maturity at harvest and silage fermentation characteristics were modified by using different levels and types of silage additives.

However, in addition to D-value and fermentation characteristics, silage intake is influenced by silage DM concentration the SDMI responses often being greater for high DM silages than predicted from silage fermentation parameters (Gordon, Reference Gordon1981; Yan et al., Reference Yan, Patterson, Gordon and Porter1996; Kokkonen et al., Reference Kokkonen, Tuori, Leivonen and Syrjälä-Qvist2000). The intake of legume silages is often higher than that of grass silages (e.g. Heikkilä et al., Reference Heikkilä, Toivonen and Mela1992; Dewhurst et al., Reference Dewhurst, Fisher, Tweed and Wilkins2003) the effects being unpredictable from D-value and fermentation quality. Positive associative effects of feeding mixtures of whole-crop and grass silages have been reported although the D-value of whole-crop silages have been lower than that of grass silages (Hameleers, Reference Hameleers1998; Jaakkola et al., Reference Jaakkola, Saarisalo and Heikkilä2001 and Reference Jaakkola, Saarisalo and Heikkilä2003; Patterson and Kilpatrick, Reference Patterson and Kilpatrick2005). There is also evidence that cows consume less regrowth silages compared with primary growth silages (Peoples and Gordon, Reference Peoples and Gordon1989; Khalili et al., Reference Khalili, Sairanen, Nousiainen and Huhtanen2005; Kuoppala et al., Reference Kuoppala, Rinne, Nousiainen, Huhtanen, O'Mara, Wilkins, L't Mannetje, Lovett, Rogers and Boland2005a) but the comparisons of silage harvest have in most cases been confounded by variations in D-value, DM concentration and fermentation characteristics.

The first objective of the present study was to update the relative SDMI index with new research data and extend it to include the effects resulting from variation in silage DM concentration, different harvests (primary growth v. regrowth) and forage types (inclusion of legume or whole-crop silages). The second objective was to estimate the effects of some animal and diet related factors on quantitative intake responses to one intake index unit. Further, the possible mechanisms influencing the intake responses to different forage factors are discussed.

Material and methods

Data acquisition

Mean treatment values were collected from dairy cow studies using ad libitum feeding of grass silage, or grass silage partly or completely replaced with legume or whole-crop silages. The list of references used to collect the database is provided in Appendix 1 available at: http://www.animal-journal.eu/. The diets were supplemented with concentrate feeds differing both in amount and composition between but not within experiments. When several concentrate treatments were used within an experiment, they were considered as separate experiments in the statistical analysis. Approximately half of the experiments were conducted in UK, half in Finland and a few in Ireland and Sweden. For the Finnish experiments, also unpublished data was used. Variation in the design of experiments, milk yield, stage and number of lactation, feeding routines etc. between the experiments was substantial.

The data were divided into six subsets based on the silage treatments used in the experiment. The topics of the subsets were: digestibility influenced by the maturity of grass ensiled (D-value), silage fermentation quality influenced by silage additives (fermentation quality), silage DM concentration influenced by wilting of grass prior to ensiling (wilting), comparison of silages made from primary growth or regrowth of grass (harvest), and replacement of grass silage with legume (legume) or fermented whole-crop cereal (whole-crop) silages. Characterisation of the different subsets is presented in Table 1.

Table 1 Description of the sub-datasets derived from treatment means of previously conducted milk production experiments†. The reference numbers refer to Appendix 1 available at: http://www.animal-journal.eu/

† DM = dry matter; NDF = neutral detergent fibre.

The minimum prerequisite for an experiment to be included in the data set was that silage and total DM intake, milk production and adequate silage characterisation (plant species, DM concentration, in vivo or in vitro digestibility, and fermentation quality) were reported. The concentration of total acids (TA) was calculated by summing the concentrations of lactic acid (LA) and volatile fatty acids (VFA). Energy-corrected milk (ECM) yield was calculated according to Sjaunja et al. (Reference Sjaunja, Baevre, Junkkarinen, Pedersen, Setälä, Gaillon and Chabert1991). For the Finnish data, silage D-values based on in vitro pepsin-cellulase method were corrected with forage-specific equations (primary growth and regrowth grass, legume and whole-crop silages; Huhtanen et al., Reference Huhtanen, Nousiainen and Rinne2006). When silage neutral-detergent fibre (NDF) and indigestible NDF (iNDF) concentrations were not reported, estimates derived from regression equations based on the Finnish data sets (Huhtanen et al., Reference Huhtanen, Nousiainen and Rinne2006) were used.

Calculations and statistical analysis

The data were analysed using the mixed model procedure of SAS (Littell et al., Reference Littel, Milliken, Stroup and Wolfinger1996). The model was:

$Y = B _{0} + B _{1} X _{ ij } + b _{0} + b _{ i } X _{ ij } + B _{2} X _{ ij } + B _{3} X _{ ij } + e _{ ij }$

where B ₀+B ₁X _ij…+B ₃X _ij are the overall fixed effects, b ₀, b ₁ (intercept and slope) and e _ij are the random part of the model, i = 1…n studies and j = 1…k values within study. In multivariate models only the first independent variable was a random factor. Variation in experimental protocols, animal types and laboratory assays would contribute to study effects in these regressions. The best fit model was chosen based on the lowest residual mean square error (RMSE) and the lowest Akaike's corrected information criteria. In the tables RMSE and R ² values after adjusting for the random effect of study are presented. Rationale and further details of using mixed model analysis to integrate quantitative findings from multiple studies are described by St-Pierre (Reference St-Pierre2001).

The effects of a single silage characteristic influencing SDMI are often confounded by other factors (e.g. silages harvested at different stages of maturity may differ in the concentrations of DM and fermentation products) and therefore possible confounding factors were included in models to estimate e.g. the effects of D-value on intake at fixed concentrations of DM and fermentation products. Similarly, the silages were adjusted for the D-value and fermentation effects when the effects of silage DM concentration on SDMI were evaluated. When evaluating the effects of silage harvest (primary growth v. regrowth), the intakes were adjusted for differences in silage DM, D-value and fermentation characteristics.

The results from analysis of all six sub-datasets (D-value, fermentation quality, wilting, harvest, legume, and whole crop) were combined to create a relative SDMI index as described in our previous paper (Huhtanen et al., Reference Huhtanen, Khalili, Nousiainen, Rinne, Jaakkola, Heikkilä and Nousiainen2002). In the index, a difference of approximately 0.10 kg in SDMI is equal to one unit. The standard silage (100 units) was defined as follows: primary growth grass silage containing 250 g DM per kg, 680 g/kg DM digestible organic matter and 80 g/kg DM total acids. The lower limit for TA concentration for improving index scores was set to 50 g/kg DM.

Possible interactions between the index components were evaluated by bivariate regression models using all silage parameters as independent variables in addition to the estimated SDMI index. The residuals of the mixed model regression between SDMI and the index were plotted against all silage parameters using a simple regression of Y on X.

To validate the robustness of the SDMI index model, the data were split experimental-wise into six subsets of 73 to 86 observations. Each subset was in turn left out and the SDMI index model was calculated with the remaining five sub-sets. The resultant model parameters were used to compute silage SDMI index for the observations in the excluded subset, and the procedure was repeated for all subsets (cross-validation). The cross-validation error (RMSE_CV) was obtained by regressing observed SDMI against predicted SDMI for all cross-validation subsets using a mixed model regression analysis with random study effect as described above.

The effects of some animal and diet related parameters on the index responses to SDMI were estimated by simple regression on animal factors (e.g. milk yield, days in milk, total DM intake) on the slope of regression [Y; fixed effect (B ₁)+ random effect (b ₁)].

Results

Silage digestibility as influenced by stage of maturity

Silage D-value was the best single predictor of SDMI when silages were harvested at varying stages of grass maturity (Table 2). The mean response in SDMI to improved D-value was 0.0175 kg per 1 g/kg in D-value. When primary growth (n = 55) and regrowth (n = 26) silages were analysed separately, the regression coefficients of D-value were similar (0.0168 v. 0.0164). Including silage DM or ammonia N concentration in the model slightly reduced the prediction error compared with D-value alone, but the coefficient of D-value did not markedly change.

Table 2 Relationships between silage parameters and silage DM intake in studies investigating the effects of maturity of ensiled grass (n=81)†

† D-value = digestible organic matter (g/kg DM), CP = crude protein (g/kg DM), DM = dry matter (g/kg), NH₃-N (g/kg total N), NDF = neutral detergent fibre (g/kg DM), iNDF = indigestible NDF (g/kg DM), pdNDF = potentially digestible NDF (g/kg DM).

The effect of silage crude protein (CP) concentration on SDMI was not significant when used in a bivariate model with D-value. Including silage NDF concentration in the model with D-value decreased the regression coefficient of D-value and NDF had a significantly negative coefficient. Silage DM intake was much more closely associated to D-value than NDF concentration. However, when NDF was divided into iNDF and potentially digestible NDF (NDF – iNDF; pdNDF), the precision of SDMI prediction improved markedly compared with NDF alone. Using OMD instead of D-value increased the prediction error, but when ash concentration was included in the model, the prediction error was similar to D-value.

When the data were classified according to the mean milk yield within the study (below or above 25 kg/day), the response to increased D-value tended to be higher (0.0210 v. 0.0142; P = 0.11) for the high yielding cows compared with the low yielding cows. The average energy-corrected milk (ECM) yields were 21.8 and 29.2 kg/day for the low and high yielding experiments, respectively.

Intake of NDF decreased (P < 0.05) with improved silage D-value (Table 3). The effect was curvilinear with the maximum NDF intake being observed at a D-value of 640 g/kg DM. Silage NDF intake was more closely associated with silage NDF concentration than D-value. Intake of NDF increased with increasing concentrations of silage iNDF and pdNDF, but was more closely associated with pdNDF than with iNDF. When NDF intake was predicted using two independent variables, i.e. NDF concentration and three different parameters describing the NDF quality, prediction error of NDF intake was clearly decreased compared with other models. When NDF and iNDF concentrations were used as independent variables, the coefficient for iNDF was strongly negative indicating that at the same NDF concentration, NDF intake will decrease with increasing iNDF concentration (or decreased potential NDF digestibility).

Table 3 Relationships between silage fibre fractions and D-value (g/kg DM) on total NDF intake (g/d) (n=81)†

† D-value = digestible organic matter (g/kg DM), pdNDF = potentially digestible NDF (g/kg DM), iNDF = indigestible NDF (g/kg DM), pdNDFD = potential NDF digestibility = pdNDF/NDF

Silage fermentation characteristics

The concentration of TA had the strongest effect of the fermentation parameters on SDMI (Table 4). However, when silage DM concentration and D-value were included in the model, the slope for TA decreased suggesting that the effects of increased in-silo fermentation on intake were derived from associated changes in silage DM concentration and D-value. None of the other fermentation parameters had a significant effect on SDMI; only propionic acid had a negative trend (P = 0.15). The effect of silage pH on SDMI was negative (P < 0.01), but the magnitude of the response was negligble within the practical pH range ( − 0.36 kg DM per one pH unit).

Table 4 Relationships between silage fermentation characteristics and silage DM intake (n=240)†

† DM = dry matter (g/kg), NH₃-N (g/kg total N), TA = total acids (g/kg DM), LA = lactic acid (g/kg DM), AA = acetic acid (g/kg DM), PrA = propionic acid (g/kg DM), BA = butyric acid (g/kg DM), VFA = volatile fatty acids (g/kg DM).

Silage DM concentration

Silage DM intake increased quadratically with increasing silage DM concentration (Table 5). The maximum intake was predicted at a DM concentration of 419 g/kg. Because the effects of DM concentration on SDMI in response to wilting are partly confounded by changes in fermentation characteristics, parameters describing the fermentation quality were included in the model. This reduced the DM concentration at predicted maximum intake to 370, 337 and 350 g/kg when ammonia-N, ln(ammonia-N) or TA, respectively, was included in the model.

Table 5 Relationships between silage dry matter (DM) concentration and silage DM intake (n=83 for models including concentration of ammonia-N in total N (g/kg; NH₃-N) and 69 for models with the concentration of total acids (g/kg DM; TA))

Silage harvest and inclusion of legume and whole-crop silages

In most cases the effects of silage harvest (primary growth v. regrowth) are confounded by differences in DM concentration, D-value and fermentation characteristics. In the present data, D-value was markedly higher for the primary growth compared with the regrowth silages (686 v. 645 g/kg DM) but the mean differences in DM (287 v. 272 g/kg), ammonia N (50 v. 55 g/kg N) and TA concentrations (59 v. 55 g/kg DM), respectively, were relatively small.

When the intakes were corrected for the effects of D-value (0.017 kg per g/kg DM), TA concentration (0.0128 kg per g/kg DM) and DM concentration [DM effect (kg/day) = 0.042 DM − 4.63 × 10^− 5 DM² − 5.657] to correspond the values of the standard silage (680 and 80 g/kg DM and 250 g/kg, respectively), the difference in SDMI between the primary growth and regrowth silages decreased from 1.70 to 0.44 kg DM per day. However, the standard error of the estimate (0.37) for the harvest effect was relatively large, and it only tended (P = 0.10) to reach statistical significance.

Responses to replacing grass silages partially or totally with legume or whole-crop silages on SDMI could not be accurately predicted from differences in silage D-value, DM concentration or fermentation characteristics. When the cows were fed mixtures of grass and legume or whole-crop silages, the SDMI was generally higher than predicted from silage characteristics. The difference between the observed and predicted intake (corrected for differences in DM concentration, D-value and fermentation characteristics) for the silages containing legumes could be described by the equation:

$Y = 4.13(\pm 1.27) X - 2.59(\pm 0.1.22) X ^{2}\quad (RMSE = 0.789,\, R ^{2} = 0.467)$

where X is the proportion of legumes in silage DM. The maximum increase for legume silages was reached at the inclusion rate of 0.80 of total silage DM and its magnitude was 1.65 kg/day. For the whole-crop grass silage mixtures the corresponding equation was:

$Y = 5.90(\pm 0.89) X - 6.14(\pm 0.57) X ^{2}\quad (RMSE = 0.158,\, R ^{2} = 0.863)$

where X is the proportion of whole-crop silage in total silage DM. The maximum SDMI increase of 0.42 kg/day was obtained when the proportion of the whole-crop silage was 0.48 of total silage DM.

Silage DM intake index

The coefficients of D-value, TA and DM concentration, silage harvest and replacement of grass with legume or whole-crop silages were integrated manually into a relative SDMI index as follows:

$SDMI\,index = 100 + 10\times [(D\hyphen value - 680)\times 0.0170 -- (TA - 80)\times 0.0128 + (0.0198\times (DM - 250) - 0.00002364\times (DM^{2} - 250^{2})) - 0.44\times a \hairsp + 4.13\times b - 2.58\times b ^{2} + 5.90\times c - 6.14\times c \hairsp ^{2}]$

where D-value is digestible OM (g/kg DM), TA = total acids (g/kg DM), DM = dry matter (g/kg DM), a = proportion of regrowth grass silage of silage DM (0 – 1), b = proportion of legumes in silage DM (0 – 1) and c = proportion of whole-crop silage of total silage DM (0 – 1). When the residuals of the regression of SDMI index on SDMI were regressed against silage parameters, none of the parameters were significantly related to residuals (P>0.10; R ² < 0.005), i.e. the residuals were randomly distributed (data not shown).

When the possible effects of the silage variables not included in the index calculation were evaluated using them as a second independent variable in addition to SDMI index, the effect of NDF concentration became significant (P < 0.01) and all criteria of the goodness of fit of the equation were improved. Although quantitatively the effect was not very large, the following term describing the effect of NDF concentration was included in the index: – 0.0023 × (NDF – 550).

The relationships between the SDMI index and silage DM intake estimated by the mixed model are shown in Table 6. The slope of regression (0.0866) was significantly (P < 0.01) different from the default value of 0.10 kg DM per one index unit suggesting that there were some interactions between the components of the index. The deviation was greater when the effects of forage type were excluded from the model indicating that the slope bias may be associated to legume and whole-crop silages. Indeed, when the index based only on grass silage data were used for legume and whole-crop data, the relationship between SDMI index and intake was not significant (P = 0.38). However, when the effects of forage types were included in the index, the prediction error decreased. The slope increased and became highly significant, but still remained clearly below the default value. When the silages containing proportionally more than 0.50 of legume or whole-crop silages were excluded from the data, the prediction error clearly decreased and was not different from grass silage data.

Table 6 Relationships between the relative silage dry-matter (DM) intake index (SDMI-ind) and SDMI

† The effects of inclusion of legume and whole-crop silages excluded from the index.

When individual components of the index were eliminated and SDMI was predicted using the ‘reduced’ index, the prediction error increased most when D-value or fermentation quality was eliminated followed by the DM concentration and the harvest effect. Eliminating NDF or proportions of legume and whole-crop silages only slightly increased the prediction error (Figure 1).

Figure 1 The effect of including new components to the silage dry-matter (DM) intake index on residual mean-square error (RMSE) of silage DM intake (Exp = RMSE after excluding random study effect; FQ = fermentation quality; W-Crop = whole crop).

The cross-validation errors were only marginally higher than RMSE for all three data sets tested indicating a good robustness of the model (Table 7). The good performance of the model is consistent with the small range in regression coefficients between the two major components of the index, D-value and TA concentration v. SDMI. Within the six subsets, the slope of D-value ranged from 0.0150 to 0.0187 kg per 1 g/kg DM and that of TA from − 0.0113 to − 0.0137 per 1 g/kg DM, respectively. The effects of legume and whole-crop silages were not included in calibration and cross-validation models.

Table 7 Results of the cross-validation of silage dry-matter index (SDMI-ind) using whole data (A), data excluding diets using silages with >0.50 legume and whole-crop (B), and data from diets based on silages made from grass only (C)

† Legume and whole-crop components included in the index.

‡ Legume and whole-crop components excluded from the index.

The animal and diet related effects on the magnitude of intake responses to one SDMI index unit are presented in Table 8. The SDMI response to one index unit increased with milk yield, DIM and LW and decreased with increasing proportion of concentrate in the diet.

Table 8 The animal and diet related effects on the magnitude of intake responses of one silage dry-matter intake index unit

† ECM = energy corrected milk; DIM = days in milk; LW = live weight; Cprop = proportion of concentrate in the diet.

Discussion

Silage digestibility as influenced by stage of maturity

The present evaluation confirmed that SDMI is strongly influenced by silage D-value in accordance with earlier reports (Offer et al., Reference Offer, Percival, Dewhurst and Thomas1998; Huhtanen et al., Reference Huhtanen, Khalili, Nousiainen, Rinne, Jaakkola, Heikkilä and Nousiainen2002). Silage D-value predicted SDMI much more precisely than NDF concentration. Unfortunately NDF concentration was not analysed in earlier studies investigating the maturity effects of ensiled grass, but the decrease in prediction error was similar when based on data with analysed NDF concentration (RMSE = 0.159 v. 0.308) or on the whole data with estimated NDF concentration for 35 silages (RMSE = 0.207 v. 0.452). In both cases, silage iNDF concentration was more closely related to SDMI than total NDF concentration demonstrating the importance of NDF quality on SDMI. The regression coefficients of the bivariate model with iNDF and pdNDF as independent variables ( − 0.0186 v. − 0.005) may reflect relative differences in the turnover rates of these fibre fractions. Turn-over rate of pdNDF is much faster than that of iNDF (Rinne et al., Reference Rinne, Huhtanen and Jaakkola2002; Huhtanen et al., Reference Huhtanen, Asikainen, Arkkila and Jaakkola2006), because pdNDF disappears from rumen both by digestion and passage but iNDF can disappear only by passage.

Although SDMI increased with increasing silage D-value, the effect of D-value on NDF intake was curvilinear with the maximum NDF intake being observed at a D-value of 640 g/kg DM. Declining NDF intake with high silage D-values strongly suggests that factors other than rumen fill were limiting intake. Indeed, rumen NDF pool size has decreased with improved digestibility of grass silage (Bosch et al., Reference Bosch, Lammers-Wienhoven, Bangma, Boer and Van Adrichem1992; Rinne et al., Reference Rinne, Huhtanen and Jaakkola2002; Kuoppala et al., Reference Kuoppala, Rinne, Ahvenjärvi, Nousiainen and Huhtanen2004) suggesting that cows do not use all the rumen capacity when highly digestible grass silages are fed. Greater response in SDMI to increased D-value in high producing cows also suggests that rumen fill was not the limiting factor, and the cows were able to increase the intake of highly digestible silages when their energy demand increased.

Intake of digestible OM was strongly and linearly associated with silage D-value (Figure 2a), whereas NDF intake showed a quadratic pattern with the maximum intake at D-value of 640 g/kg DM. Intake of NDF increased with increasing silage NDF concentration, but this effect was not strong enough to maintain the intake of digestible OM, and consequently energy intake decreased with increasing fibre concentration in silage. Figure 2b clearly demonstrates that the potential to consume energy was not the only factor limiting the intake of grass silage-based diets, because the cows were not able to maintain energy intake.

Figure 2 The effects of silage D-value and neutral-detergent fibre (NDF) concentration on silage digestible organic matter (DOM) and NDF intake. The values are adjusted for random study effects.

The present data do not support the biphasic mechanisms of intake regulation (e.g. Mertens, Reference Mertens and Fahey1994), since no set point either for physical or metabolic feedback mechanisms could be detected. However, the data do not preclude that rumen fill has an effect on intake regulation, but it showed that it is not the only mechanism. The present data and data from rumen evacuation studies (Bosch et al., Reference Bosch, Lammers-Wienhoven, Bangma, Boer and Van Adrichem1992; Rinne et al., Reference Rinne, Huhtanen and Jaakkola2002; Kuoppala et al., Reference Kuoppala, Rinne, Ahvenjärvi, Nousiainen and Huhtanen2004) support the integration of physical and metabolic constraints on ruminant feed intake.

Fisher et al. (Reference Fisher, Burns and Pond1987) proposed a model integrating the physical and metabolic constraints on ruminant feed intake. They achieved this by using a double exponential term expressing intake as a function of rumen distension and nutrient flow intending to relate the strength of each stimulus in relation to the other. Huhtanen (Reference Huhtanen, Lechie and Thomas2002) tested this model for the data of Rinne et al. (Reference Rinne, Jaakkola, Kaustell, Heikkilä and Huhtanen1999) and found that the model predicted the differences in intake of the 16 diets accurately, while the biphasic model or models assuming that only rumen fill or capacity to use energy limit intake were unable to predict the intakes with reasonable accuracy. The conceptual model of Weston (Reference Weston1996) recognises the upper physiological limits of energy disposal and maximum rumen digesta. Over a wide range of forage qualities neither the rumen digesta upper ceiling nor the capacity to use energy limit intake alone.

The close relationship between silage D-value and the intake of digestible OM from silage (and also from the total diet, since the concentrate level was similar within each study) supports indirectly the theory proposed by Ketelaars and Tolkamp (Reference Ketelaars and Tolkamp1992) and Tolkamp and Ketelaars (Reference Tolkamp and Ketelaars1992). According to their hypothesis, the feed intake is adjusted by the animal to maximise the efficiency of oxygen utilisation and they showed that intake was closely related to q-value (metabolizable energy/gross energy) of the diet. The D-value of silage is almost completely correlated to q-value, since variation in silage gross energy and methane and urinary losses are relatively small. Furthermore, methane and urine losses are likely to be negatively correlated as proportionally higher urinary and lower methane losses of digestible energy are expected for early harvested high N silages.