Prediction of the relative intake potential of grass silage by dairy cows

Data based on the mean treatment values from production studies in lactating dairy cows were used to estimate relationships between silage composition and silage dry matter intake (SDMI). Data from 21 studies were used to estimate relationships between D-value (g digestible organic matter in kg dry matter (DM)) and crude protein (CP) content, and SDMI. The silages were harvested at different maturities but using the same ensiling techniques within the study. Relationships between silage fermentation characteristics and SDMI were estimated using data from 47 studies. The silages were harvested at the same time from the same sward but using various additives. When factorial designs were used, silage data within each treatment (subexperiment) were recorded separately. The data sets included 125 D-value and 234 fermentation observations. Relationships between SDMI and silage parameters were analyzed using mixed model regression analyses with experiment as a fixed factor and subexperiment within experiment as a random factor. The ranges for silage CP concentration and D-value were 111 to 238 (S.D. 25.2) and 589 to 756 (S.D. 48.5) g/kg DM, respectively. D-value was a much better predictor of SDMI than CP (R2 within experiment 0.71 vs. 0.26). The effect of D-value on SDMI diminished with increasing level of concentrate supplementation (interaction P<0.001). D-value had a greater effect on SDMI when the total DM intake was high. SDMI was negatively correlated with concentrations of ammonia N, lactic acid, individual and total volatile fatty acids and total fermentation acids and positively correlated to the concentration of residual water soluble carbohydrates. Total acid concentration was the best SDMI predictor of individual fermentation parameters (R2 within experiment 0.41) followed by lactic acid and ammonia N. Use of quadratic regressions generally increased the variation accounted for by the model. The best multiple regression accounted for 0.51 of the variation in SDMI within experiment. To facilitate interpretation of data, a theoretical model is presented. An interplay between physical load and capacity to use energy determines SDMI of well-fermented silages over the whole range of D-values. With increasing extent of fermentation SDMI is constrained by nutrient imbalance, most probably amino acid to energy ratio at the tissue level, resulting from reduced microbial protein synthesis in the rumen. Low palatability can further constrain silage DM intake. For advisory silage evaluation the following model predicting relative SDMI is proposed: SDMI index=100+0.151×(D-value−690)−0.000531×(TA2−6400)−4.7650 [Ln(Ammonia N) −Ln(50)], where D-value and total acids (TA) are expressed as g/kg DM and ammonia N as g/kg total N, respectively. Regression coefficients are scaled to a mean SDMI of 10 kg DM/day. The values of 690, 80 and 50 are used as standard D-value (g/kg DM), total acid (g/kg DM) and ammonia N (g/kg N) for high quality restrictively fermented silages. The model parameters were limited to those which are currently available for Finnish farm silage analyses, i.e., by near infra-red reflectance spectra (D-value) and electrometric titration (fermentation parameters).

[1]  P. Huhtanen,et al.  Silages harvested at different stages of grass growth v. concentrate foods as energy and protein sources in milk production , 1999 .

[2]  A. J. Rook,et al.  Prediction of the voluntary intake of grass silages by beef cattle. 1. Linear regression analyses , 1990 .

[3]  F. Gordon,et al.  The use of sheep as a model to predict the relative intakes of silages by dairy cattle , 1994 .

[4]  R. J. Wilkins,et al.  The voluntary intake of silage by sheep:I. Interrelationships between silage composition and intake , 1971, The Journal of Agricultural Science.

[5]  R. C. Campling.  The intake of hay and silage by cows. , 1966 .

[6]  A. Rook,et al.  15 – FACTORS AFFECTING THE VOLUNTARY INTAKE OF ROUGHAGES BY THE DAIRY COW , 1988 .

[7]  T. Moisio,et al.  A titration method for silage assessment , 1989 .

[8]  A. Egan.  Nutritional status and intake regulation in sheep. VI. Evidence for variation in setting of an intake regulatory mechanism relating to the digesta content of the reticulorumen , 1970 .

[9]  R. Littell.  SAS System for Mixed Models , 1996 .

[10]  P. Huhtanen,et al.  Improving the feeding and health value of ensiled forages , 1996 .

[11]  F. Gordon,et al.  The prediction of intake potential and organic matter digestibility of grass silages by near infrared spectroscopy analysis of undried samples , 1996, Proceedings of the British Society of Animal Science.

[12]  V. F. Kristensen,et al.  Effect of cutting time on yield and quality of grass and on silage intake and milk production in dairy cows. , 1990 .

[13]  P. Huhtanen,et al.  The effects of forage preservation method and proportion of concentrate on digestion of cell wall carbohydrates and rumen digesta pool size in cattle , 1993 .

[14]  R. Baumont,et al.  The effect of protein degradation products in grass silages on feed intake and intake behaviour in sheep , 1995, British Journal of Nutrition.

[15]  J. Buchanan-Smith.  An investigation into palatability as a factor responsible for reduced intake of silage by sheep , 1990 .

[16]  C. Thomas.  19 – CONCENTRATE SUPPLEMENTATION OF SILAGE FOR DAIRY COWS , 1988 .

[17]  S. Lister,et al.  Prediction of voluntary intake of grass silages by lactating cows offered concentrates at a flat rate , 1991 .

[18]  J. Choung,et al.  Effects of addition of lactic acid and post-ruminal supplementation with casein on the nutritional value of grass silage for milk production in dairy cows , 1993 .

[19]  P. Huhtanen.  SUPPLY OF NUTRIENTS AND PRODUCTIVE RESPONSES IN DAIRY COWS GIVEN DIETS BASED ON RESTRICTIVELY FERMENTED SILAGE , 1998 .

[20]  F. J. Gordon,et al.  The use of near infrared reflectance spectroscopy /NIRS on undried samples of grass silage to predict chemical composition and digestibility parameters , 1998 .

[21]  P. Huhtanen,et al.  Digestive processes of dairy cows fed silages harvested at four stages of grass maturity. , 2002, Journal of Animal Science.

[22]  P. Garnsworthy,et al.  9 – OPTIMIZING COMPOUND FEED USE IN DAIRY COWS WITH HIGH INTAKES OF SILAGE , 1989 .

[23]  S. Spoelstra,et al.  Mechanisms of adaptation in sheep to overcome silage intake depression induced by biogenic amines , 1997, British Journal of Nutrition.

[24]  D. G. Chamberlain.  The silage fermentation in relation to the utilization of nutrients in the rumen , 1987 .

[25]  R. Miller,et al.  Ruminant Utilization of Silage in Relation to Hay, Pellets, and Hay Plus Grain. II. Rumen Content, Dry Matter Passage, and Water Intake , 1965 .

[26]  N. Offer.  A comparison of the effects on voluntary intake by sheep of dietary addition of either silage juices or lactic acid solutions of the same neutralizing value , 1997 .

[27]  R. Dewhurst,et al.  Prediction of the voluntary intake potential of grass silage by sheep and dairy cows from laboratory silage measurements. , 1998 .

[28]  J. Murphy,et al.  A note on the preferences for, and rate of intake of, grass silages by dairy cows , 1998 .

[29]  P. Huhtanen,et al.  Effects of and interactions between the extent of silage fermentation and protein supplementation in lactating dairy cows , 1998 .

[30]  R. Weston.  Some aspects of constraint to forage consumption by ruminants , 1996 .

[31]  C. Cadórniga,et al.  Protein and Energy Supplementation of High Alfalfa Silage Diets During Early Lactation , 1993 .

[32]  B. Baumgardt,et al.  Effect of Silage Extract on Voluntary Intake, Rumen Fluid Constituents, and Rumen Motility , 1977 .

[33]  C. Thomas.  14 – FACTORS AFFECTING SUBSTITUTION RATES IN DAIRY COWS ON SILAGE BASED RATIONS , 1987 .

[34]  P. V. Soest.  Nutritional Ecology of the Ruminant , 1994 .

[35]  R. E. Agnew,et al.  FACTORS AFFECTING THE INTAKE OF GRASS SILAGE BY CATTLE AND PREDICTION OF SILAGE INTAKE , 1998 .

[36]  R. Baumont,et al.  The influence of ammonia and amines on grass silage intake and intake behaviour in dairy cows. , 1995 .