Prediction of the digestibility of the primary growth of grass silages harvested at different stages of maturity from chemical composition and pepsin-cellulase solubility

Animal Feed Science and Technology 103 (2003) 97–111

Prediction of the digestibility of the primary growth of grass silages harvested at different stages of maturity from chemical composition and pepsin-cellulase solubility J. Nousiainen a , M. Rinne b , M. Hellämäki a , P. Huhtanen b,∗ a

Valio Ltd., Farm Services, P.O. Box 10, FIN-00039 Valio, Finland MTT Agrifood Research Finland, Animal Production Research, FIN-31600 Jokioinen, Finland

b

Received 5 December 2001; received in revised form 6 September 2002; accepted 11 September 2002

Abstract Relationships between silage chemical components or organic matter (OM) pepsin-cellulase solubility and in vivo organic matter digestibility (OMD) and D-value (digestible OM content in dry matter) were studied. Twenty-five silages were made from primary growth timothy-meadow fescue swards in 6 years (3–6 silages per year) at 6–10 days intervals. The silages were analyzed for chemical composition, in vitro pepsin-cellulase solubility and in vivo digestibility in sheep. Chemical components of silage were highly correlated with OMD and D-value, but the accuracy of the OMD and D-value prediction equations were not satisfactory for ration formulation. Acid detergent fibre was the best single predictor of chemical parameters explaining 0.80 of the variation in OMD (residual mean square error (RMSE) 27.2 g kg−1 ). Relationships between crude protein (CP) and lignin were improved, when the effects of year or year × component interaction were included in the model. For CP the intercepts and for lignin the slopes, were different between the years. Pepsin-cellulase solubility was superior to chemical parameters in predicting silage OMD and D-value. The monovariate regression equation between OM solubility and OMD was: OMD (g kg−1 ) = 97 + 0.87 × OM solubility (R 2 = 0.974; RMSE = 10.8 g kg−1 ). Prediction of D-value was improved by including ash as a second factor in bivariate regression analysis: D-value (g DOM (kg DM)−1 ) = 160 + 0.818 × OM solubility −1.09 × ash (R 2 = 0.974;

Abbreviations: ADF, acid detergent fibre; CP, crude protein; DM, dry matter; DOM, digestible organic matter; NDF, neutral detergent fibre; NIRS, near-infrared reflectance spectroscopy; OM, organic matter; OMD, organic matter digestibility; OMS, organic matter solubility; RMSE, root mean square error ∗ Corresponding author. Tel.: +358-3-41883631; fax: +358-3-41883661. E-mail address: [email protected] (P. Huhtanen). 0377-8401/02/$ – see front matter © 2002 Elsevier Science B.V. All rights reserved. PII: S 0 3 7 7 - 8 4 0 1 ( 0 2 ) 0 0 2 8 3 - 3

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RMSE = 9.7 g kg−1 ). It was concluded that OM pepsin-cellulase solubility has great potential in predicting silage OMD and D-value both precisely and accurately. © 2002 Elsevier Science B.V. All rights reserved. Keywords: Grass silage; Pepsin-cellulase; In vitro; In vivo; D-value; Digestibility

1. Introduction The prediction of organic matter digestibility (OMD) and digestible organic matter (DOM) content in dry matter (D-value, g DOM (kg DM)−1 ) of grass silage are essential measurements in the formulation of ruminant rations (Beever and Mould, 2000). In dairy cows the voluntary silage dry matter intake was improved by 15 g per day (Huhtanen et al., 2001) and milk production by 50 g per day (Rinne et al., 1999; Rinne, 2000) per 1 g (kg DM)−1 increase in silage D-value, respectively. In addition, metabolizable energy content of silage is calculated directly from D-value in the current feed evaluation systems used, e.g. in the UK (Beever et al., 2000) and the Nordic countries (Tuori et al., 1998). Several laboratory methods, including chemical analyses and in vitro techniques have been introduced to characterize grass silages and to predict silage OMD and D-value (Beever and Mould, 2000). In general, the regression estimates for silage OMD and D-value based on the total cell wall content (NDF), cell wall components (e.g. crude fibre, acid detergent fibre (ADF), modified ADF, lignin) or crude protein (CP) as predictors have been relatively poor, though widely used. In the UK, for example, estimation of silage D-value is often based on modified ADF (see Beever and Mould, 2000). However, relatively high (44.0 g (kg DM)−1 ) residual standard deviation was found for this method (Morgan, 1973). Instead, in vitro techniques based on ruminal fluid or cell wall degrading fungal enzymes have produced more precise and repeatable predictions for forage OMD (Jones and Theodorou, 2000). In routine feed evaluation near-infrared reflectance spectroscopy (NIRS) technique is widely used to predict grass silage composition and OMD with a relatively good accuracy, but valid reference methods are required to obtain robust calibrations for different forage materials (Deaville and Flinn, 2000). In vivo total tract digestibility in mature sheep may be the best reference method for NIRS. However, a reliable in vitro laboratory technique may offer a cheaper and less laborious calibration and control method for NIRS, provided it predicts satisfactorily in vivo OMD. Due to the problems in standardizing in vitro techniques based on rumen inoculum in measuring forage OMD, much effort has been placed to develop enzymatic methods (Jones and Theodorou, 2000). Indeed, systems using fungal (Thrichoderma sp.) cellulolytic enzymes after pre-treatment with neutral detergent or acid-pepsin have given OMD estimates correlating well with in vivo total tract digestibility (see Jones and Theodorou, 2000). Since enzyme solubility of forage OM does not quantitatively reflect the in vivo total tract OMD in sheep, mathematical correction equations have been used (Terry et al., 1978; Givens et al., 1989; Friedel, 1990). The aim of this study was to examine the validity of chemical composition and pepsincellulase solubility in predicting OMD and D-value of primary growth grass silages and to develop prediction equations for routine use.

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2. Material and methods 2.1. Silages The silages were harvested from primary growth of mixed timothy (Phleum pratense) meadow fescue (Festuca pratensis) swards in 1994 and 1996–2000 in Jokioinen, Finland (61◦ N). The swards were second year leys. A total of 25 silages were harvested, 3–6 silages per year. Within a year the silages were harvested from a single ley at 6–10 days intervals. The swards were cut with a flail-harvester and ensiled unwilted into 3 m3 pilot scale experimental silos. In 1994 the silages were made into bunker silos (capacity 70 ton). Formic acid-based additive was used for all silages at a rate of 4 l formic acid per ton. 2.2. In vivo digestibility Apparent in vivo digestibility of the silages was measured with sheep (mean LW 87.5 kg) by total collection of faeces. The digestibility trials were made according to complete (3 or 4 silages in a trial) or incomplete (more than 4 silages in a trial) Latin-square designs with 21 days periods and the last 7 days used for faecal collection. Digestibility estimates are based on the mean of three (6 silages) or four (19 silages) sheep. The silages were offered to sheep at approximately maintenance level (35 g DM kg−1 LW0.75 ) supplemented daily with 30 g of mineral mixture and 10 g of NaCl. The sheep were fed twice daily and had free access to drinking water. 2.3. In vitro pepsin-cellulase organic matter solubility 2.3.1. Pre-treatment with pepsin-HCl The enzymatic in vitro digestion method was a modification of that described by Friedel (1990). The oven-dried (12 h, 50 ◦ C) silage samples were ground to pass a 1 mm screen. The samples were placed (200 mg on air dry basis) in 50 ml Erlenmeyer flasks, 20 ml of pepsin-HCl solution (1000 ml 0.5 N HCl and 2 g pepsin, Merck No. 7190, 2000 FIP U g−1 ) was added and the flasks were shaken. Thereafter the flasks were closed with rubber stoppers and incubated for 24 h at 40 ◦ C. After the incubation the flasks were placed in a boiling water-bath for 10 min to stop the activity of pepsin. Then the samples were transferred quantitatively to filter crucibles (Laborexin Ltd., model P3, porosity 40 ␮m). The crucibles were placed in a cold-extraction unit (Tecator Fibretec System 1021) and dried under suction. 2.3.2. Cellulase incubation After the pepsin-HCl treatment each crucible was placed in a 50 ml glass beaker and 20 ml of cellulase-buffer solution was added to the crucible and 10 ml to the beaker. The cellulase-buffer solution (pH 4.6) contained (per 1000 ml) 16.64 g of Na2 HPO4 , 11.19 g of citric acid, 20 mg of tiomersalate (BDH No. 30416) and 5.0 g (Unizyme F, IFZ Biotechnologie GmbH, Germany, activity 0.9 FPU mg−1 ) or 3.3 g (Onozuka R-10, Yakult Pharmaceutical Ind. Co. Ltd., Japan, activity 11.7 U g−1 ) of crystalline cellulase from Trichoderma viride. Each cellulase batch was calibrated against feed samples of known in vivo digestibility. A standard silage sample was included in every incubation run to verify

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the activity of the enzyme and follow standard laboratory procedures. The samples were incubated in the cellulase-buffer solution at 40 ◦ C for 48 h and mixed twice daily. After 48 h the residues were washed in the cold-extraction unit, first three to four times with water and then three times with acetone and dried at 105 ◦ C for 16–18 h. The pepsin-cellulase residues and intact feed samples were ashed at 520 ◦ C for 3 h in a furnace. The solubility of OM was calculated as a difference between intact feed and pepsin-cellulase insoluble residue. 2.4. Analytical methods Dry matter content was determined by drying at 105 ◦ C for 18 h and OM was determined by ashing at 600 ◦ C for 12 h. Concentrations of NDF were determined according to Van Soest et al. (1991) in the presence of sodium sulfite but without ␣-amylase treatment and expressed without residual ash. ADF and lignin concentrations were determined according to Robertson and Van Soest (1981). Total N content in fresh samples was measured with Kjeldahl analysis using Cu as digestion catalyst and Tecator 1028 distillation unit (Nordion Instruments Ltd., Helsinki, Finland). Oven DM content of silages were corrected for the loss of volatiles according to Huida et al. (1986). Silage fermentation characteristics were analyzed using the methods described by Shingfield et al. (2001). 2.5. Calculations and statistical methods To compare the efficiency of digestion by the in vitro pepsin-cellulase method with in vivo digestibility in sheep true OM digestibility was estimated using the Lucas test (see Van Soest, 1994) for cell solubles (OM minus NDF). In the Lucas test the true digestibility of cell solubles is estimated as a slope of regression between digestible cell solubles and content of cell solubles in the diet. The negative intercept of the equation is an estimate of metabolic faecal output of organic matter. True OM digestibility in vivo was calculated as Digestible NDF + truly digestible cell solubles . Content of OM in the diet The relationships between in vivo OMD or D-value and laboratory measurements were examined by linear regression analysis. The relationships were compared using the proportion of variance accounted for by the model (R2 ) and residual mean square error (RMSE). The quadratic relationships between laboratory measurements and in vivo OMD and D-value were also investigated. The relationships between laboratory measurements and in vivo OMD and D-value within a year were investigated using MIXED procedure of SAS (Littel et al., 1996). As an example of the relationship between OMD and NDF the following model was used: OMD = constant + year + NDF + error with year as a random effect. When R2 was decreased by including year effect in the model, statistical significance of interaction between year and independent variable was tested by the model: OMD = constant + year + NDF + (year × NDF) + error, where year was a random factor. Further details of using the mixed model methodology are presented in a recent review by St-Pierre (2001).

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3. Results 3.1. Chemical composition and digestibility of silages Chemical composition, in vivo digestibility and cellulase solubility of the silages are in Table 1. The results show that both composition and digestibility of the silages were highly variable. All parameters were normally distributed. The silages were well and predominantly restrictively fermented as indicated by low concentrations of ammonia N and fermentation acids. 3.2. Prediction of OMD and D-value Table 2 shows the relationship between various laboratory measurements and in vivo organic matter digestibility (OMD). Crude protein (CP) was positively and different cell wall fractions negatively correlated to OMD. Of different cell wall fractions ADF was the best predictor of OMD over the years and lignin within the year. When year was included as a random factor into the model, variation accounted for improved considerably. However, interaction term (CP × year) did not reach statistical significance. The year effects were relatively small for NDF, ADF, cellulose and hemicellulose as indicated by only small improvements in R2 values when year factor was included in the model. On the other hand, Table 1 Chemical composition and in vivo digestibility of the silages (N = 25) Mean

S.D.

Minimum

Maximum

213 4.07

24.9 0.17

171 3.81

276 4.43

Chemical composition (g kg−1 DM) Ash Crude protein Neutral detergent fibre Acid detergent fibre Lignin Cell solubles Water soluble carbohydrates Lactic acid Acetic acid Butyric acid Ammonia N (g kg−1 N)

72 156 578 328 34 350 40 51 21 0.5 51

8.1 33.9 75.4 43.4 10.5 71.4 17.3 14.4 8.5 0.7 20.2

60 112 402 249 19.2 265 17.0 32 9 0 28

91 239 669 390 55.0 507 77.5 93 49 2.2 116

In vivo digestibility (g kg−1 ) Organic matter Neutral detergent fibre Crude protein

734 744 739

66.6 76.3 47.1

613 597 656

840 869 820

In vivo D-valuea OM solubility (g kg−1 )b

679 733

60.6 73.9

568 611

769 855

Dry matter pH

a b

(g kg−1 )

D-value (g digestible OM kg−1 DM). Pepsin-cellulase solubility.

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Variableb

Intercept

S.E.

P-value

B

S.E.

P-value

CP CP, year NDF NDF, year ADF ADF, year Lig Lig, year Cel Cel, year Hcel Hcel, year NDF, lig NDF, lig, year ADF, lig ADF, lig, year NDF, CP NDF, CP, year ADF, CP ADF, CP, year OMS OMS, year

480 460 1192 1192 1199 1198 931 946 1234 1234 1141 1141 1088 1055 1115 1081 1041 618 1109 927 97 113

36.4 25.7 47.5 47.5 40.5 40.3 21.1 19.4 54.7 54.7 59.6 59.6 49.9 41.8 48.9 41.0 149.5 163.5 127.0 151.7 21.4 16.7

<0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.013 <0.001 0.002 <0.001 <0.001

1.63 1.78 −0.79 −0.79 −1.42 −1.41 −5.71 −6.22 −1.70 −1.70 −1.63 −1.63 −0.43 −0.31 −0.91 −0.71 −0.64 −0.18 −1.26 −0.91 0.87 0.85

0.228 0.134 0.081 0.081 0.123 0.122 0.588 0.477 0.185 0.185 0.237 0.237 0.127 0.109 0.227 0.196 0.167 0.180 0.248 0.293 0.029 0.022

<0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.003 0.010 0.001 0.002 0.001 0.344 <0.001 <0.001 <0.001 <0.001

a b

C

−3.1 −4.1 −2.4 −3.4 0.4 1.4 0.2 0.7

S.E.

0.92 0.83 0.94 0.86 0.37 0.40 0.32 0.37

P-value

0.003 <0.001 0.018 0.001 0.299 0.003 0.466 0.092

RMSE

Adjusted R2

37.9 19.3 30.1 30.1 26.1 25.7 30.1 20.0 31.4 31.4 38.9 38.9 24.4 17.7 22.7 16.4 29.4 20.0 25.7 20.5 10.8 7.2

0.676 0.916 0.796 0.796 0.847 0.852 0.796 0.910 0.777 0.777 0.659 0.659 0.865 0.929 0.883 0.939 0.806 0.910 0.857 0.906 0.974 0.989

Regression equation: A, intercept; B, regression coefficient of the first parameter; C, regression coefficient of the second parameter; year is a random factor. CP, crude protein; NDF, neural detergent fibre; ADF, acid detergent fibre; lig, lignin; cel, cellulose; hcel, hemicellulose; OMS, organic matter in vitro solubility.

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Table 2 Predictions of in vivo organic matter digestibility from silage composition (g kg−1 DM) or in vitro OM solubility (g kg−1 ) by linear regression models (Y = A+BX1 +CX2 )a

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the effect of lignin on OMD depended on year as indicated by a significant (P = 0.03) lignin × year interaction. The prediction power of bivariate regressions including CP and NDF or ADF did not improve compared to NDF or ADF alone, and the effect of CP did not reach significance. However, when the random effect of year was included in the model, the R2 values did not exceed that obtained with the model including CP alone. The increases in the content of NDF or ADF in silage had smaller effects on OMD at low compared to high concentrations. For example, increases in silage NDF content had only small effects on digestibility when it was below 500 g (kg DM)−1 . Positive relationship between silage CP content and OMD reduced with increasing CP content. In vitro pepsin-cellulase OM solubility was markedly better in predicting OMD than CP and different cell wall fractions. Compared to the best combinations of the chemical parameters RMSE values were less than 50%, irrespective if the year effect was or was not included in the model. In vitro OM solubility accounted for 0.974 of the variation in OMD with RMSE of 10.8 g kg−1 . Including the year effect in the model improved the OMD prediction slightly. The relationship between in vitro OM solubility and in vivo OMD tended (interaction P = 0.12) to be dependent on the year. 3.3. Prediction of D-value Predictions of D-value from chemical composition are in Table 3. Generally the relationships between CP or cell wall fractions and in vivo D-value were similar to those observed for OMD. The best single predictor in terms of the highest R2 and lowest RMSE was ADF, whereas CP and hemicellulose gave the poorest predictions. Including the year effect in the model improved markedly the prediction for CP and lignin, but not for the other cell wall fractions. Predictions of D-value were always improved by including ash in equations. As an example R2 increased from 0.804 to 0.858 and RMSE decreased from 26.5 to 22.6 g kg−1 using bivariate regression with ADF and ash as independent variables compared to single regression of ADF. Silage D-value was predicted considerably better from in vitro pepsin-cellulase OM solubility than from chemical parameters (Table 4). Including the year effect in the model reduced prediction error from 12.8 to 7.8 g kg−1 , but the interaction term (year × OM solubility) was not significant (P = 0.28). Bivariate analysis including chemical parameters improved prediction accuracy of D-value in vivo. The following prediction equation of D-value was calculated including ash as a second factor in bivariate regression analysis: D-value (g DOM (kg DM)−1 ) = 160 + 0.818 × OM solubility −1.09 × ash (R 2 = 0.974; RMSE = 9.7). Prediction of D-value was even slightly improved with CP as a second factor in the model, but the regression coefficient of CP was significantly negative in this equation. However, when random effect of the year was included CP did not improve the accuracy of prediction compared to in vitro OM solubility alone. The regression coefficient for ash was as expected at (approximately 1.0). 3.4. True digestibility of silages True digestibility of cell solubles was estimated using the Lucas test. The test showed that cell solubles behaved uniformly, and therefore true digestibility could be estimated

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Table 3 Predictions of in vivo D-value (g digestible OM kg−1 DM) from silage composition (g kg−1 DM) by linear regression models (Y = A + BX1 + CX2 )a Intercept

S.E.

P-value

B

S.E.

P-value

CP CP, ash CP, year NDF NDF, ash NDF, year ADF ADF, ash ADF, year Lig Lig, ash Lig, year Cel Cel, year Hcel Hcel, year ADF, lig ADF, lig, year NDF, lig NFG, lig, year NDF, CP NDF, CP, year ADF, CP ADF, CP, year

461 576 442 1080 1324 1080 1089 1287 1086 855 940 868 1119 1119 1031 1031 1009 973 980 947 971 540 1050 836

35.6 63.5 25.4 48.0 92.9 48.0 41.3 77.2 40.2 20.2 68.0 18.3 53.9 53.9 58.2 58.2 50.7 41.5 51.5 41.5 152.9 163.2 130.6 157.3

<0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.021 <0.001 0.003

1.41 1.71 1.55 −0.69 −0.82 −0.69 −1.24 −1.42 −1.23 −5.06 −5.38 −5.49 −1.49 −1.49 −1.40 −1.40 −0.76 −0.55 −0.34 −0.23 −0.58 −0.11 −1.17 −0.76

0.22 0.25 0.13 0.08 0.08 0.08 0.12 0.12 0.12 0.56 0.61 0.44 0.18 0.18 0.23 0.23 0.24 0.20 0.13 0.11 0.17 0.18 0.26 0.30

<0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.028 0.013 0.017 0.050 0.003 0.552 <0.001 0.022

a b

C

S.E.

P-value

−2.24

1.06

0.045

−2.32

0.79

0.008

−1.94

0.67

0.008

−1.02

0.78

0.204

−2.31 −3.30 −2.99 −3.96 0.28 1.32 0.10 0.61

0.98 0.88 0.95 0.83 0.38 0.40 0.33 0.39

0.028 0.002 0.005 <0.001 0.464 0.004 0.755 0.131

RSME

Adjusted R2

37.1 33.8 19.2 30.4 25.8 30.4 26.5 22.6 24.8 28.8 27.7 18.5 30.9 30.9 37.9 37.9 23.7 16.5 25.2 17.4 30.0 19.7 26.5 20.4

0.633 0.682 0.897 0.753 0.815 0.753 0.804 0.858 0.828 0.769 0.786 0.904 0.734 0.734 0.599 0.599 0.843 0.924 0.823 0.916 0.749 0.892 0.805 0.884

Regression equation: A, intercept; B, regression coefficient of the first parameter; C, regression coefficient of the second parameter; year is a random factor. CP, crude protein; NDF, neural detergent fibre; ADF, acid detergent fibre; lig, lignin; cel, cellulose; hcel, hemicellulose; OMS, organic matter in vitro solubility.

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Variableb

Variableb

Intercept

S.E.

P-value

B

S.E.

P-value

OMS OMS, year OMS, ash OMS, ash, year OMS, CP OMS, CP, year OMS, NDF OMS, NDF, year OMS, ADF OMS, ADF, year OMS, lig OMS, lig, year

113 133 160 167 37 47 −60 −44 −51 −116 158 146

25.4 18.8 22.8 19.4 24.2 26.0 106.7 65.6 129.0 78.6 73.5 72.6

<0.001 0.001 < 0.001 <0.001 0.139 0.129 0.581 0.529 0.699 0.200 0.043 0.101

0.774 0.746 0.818 0.797 0.998 0.974 0.901 0.878 0.896 0.934 0.730 0.736

0.035 0.024 0.029 0.025 0.053 0.059 0.083 0.051 0.100 0.061 0.076 0.073

<0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001

a b

C

−1.092 −0.964 −0.565 −0.517 0.138 0.143 0.226 0.346 −0.358 −0.102

S.E.

0.270 0.297 0.119 0.132 0.083 0.051 0.175 0.106 0.553 0.572

P-value

RSME

Adjusted R2

0.001 0.005 <0.001 0.001 0.110 0.012 0.209 0.005 0.525 0.860

12.8 7.8 9.7 6.8 9.0 7.8 12.1 6.3 12.3 5.9 12.7 7.7

0.954 0.983 0.974 0.987 0.978 0.983 0.960 0.990 0.958 0.990 0.955 0.984

Regression equation: A, intercept; B, regression coefficient of the first parameter; C, regression coefficient of the second parameter; year is a random factor. CP, crude protein; NDF, neural detergent fibre; ADF, acid detergent fibre; lig, lignin; cel, cellulose; hcel, hemicellulose; OMS, organic matter in vitro solubility.

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Table 4 Predictions of in vivo D-value (g digestible OM kg−1 DM) from silage composition (g kg−1 DM) by linear regression models (Y = A + BX1 + CX2 )a

105

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Fig. 1. Relationship between the content of neutral detergent (ND) solubles and digestible ND solubles in silages: ND solubles = organicmatter − NDF.

from the slope of regression between the total and digestible contents in the diet (Fig. 1). True digestibility of cell solubles was 1.021 (S.E. = 0.0125) and estimated faecal output of metabolic OM 101 g per kg DM intake (S.E. = 4.5). Using the Lucas test for cell solubles and digestible NDF determined in vivo, the mean true OM digestibility of the silages was 843 g kg−1 . This value was on average 110 g kg−1 higher than in vitro OM solubility. The following relationship was calculated between in vivo true OM digestibility (TOMD) and OM solubility in vitro: TOMD = 205 (±17.8) + 0.87 (±0.024) OMS (R 2 = 0.982; RMSE = 9.0 g kg−1 ). 4. Discussion 4.1. Relationship between chemical composition and OM digestibility The main objective of the present study was to develop equations to predict OM digestibility and D-value of silages for routine use. To reduce the effects of geographical location, plant species, fertilization, soil type, harvest (primary versus regrowth) and ensiling technologies the silages were produced on the same experimental farm, but in different years to investigate the potential of the pepsin-cellulase method in predicting OMD and D-value of grass silage. For this purpose both the time span of silage harvests (6 years) and number of silages (25) is representative enough. Although the silages were produced in uniform conditions, the relationships between chemical parameters and digestibility were not good enough for practical prediction equations. Crude protein content was significantly correlated to OMD and D-value, but this

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relationship reflects a negative association between decreasing CP and increasing lignification with advancing maturity rather than direct effect of CP on digestibility. Improved accuracy of prediction by including random effects of the year in the model suggest that the relationship between CP and OMD varied between the years. The slopes were rather similar between the years as also indicated by the low P-value (P = 0.57) for the interaction term, but there were large differences in OMD between the years at the same CP content (up to 100 g kg−1 ). This could be associated to environmental factors influencing DM yield. High DM yield at a certain stage of maturity is likely to be related to low CP content, when constant rates of N fertilizers are applied. Lack of a direct effect of CP content on OMD was also shown by numerically small and non-significant regression coefficients in bivariate analysis including CP and NDF or ADF as independent variables. In the present study ADF was the best single OMD predictor of the fibre fractions analyzed. In other studies modified ADF has explained the variation in OMD better than other cell wall fractions (Givens et al., 1989, 1990, 1993a,b; Moss and Givens, 1990). However, the ranking order of other fibre fractions in predicting OMD has been inconsistent. Although our silages were produced in the same conditions, the prediction errors were relatively large. The validity of chemical components in predicting silage OMD could further be questioned considering the different relationships between fibre fractions and OMD for spring and autumn harvested forages (Givens et al., 1993a) and that the digestibility of grasses grown at different temperatures may change at different rates without concomitant changes in fibre fractions (Deinum et al., 1968). Van Soest et al. (1978) showed that neither NDF nor ADF satisfactorily predicted digestibility of secondary growth forages because of the lack of positive association between cellulose and lignin. In the present study the decrease in OMD per 1 g (kg DM)−1 increase in silage lignin content varied from 3.8 to 9.6 g (kg DM)−1 between the years. Significant annual variation in the slope between silage lignin content and OMD suggests that lignification may affect forage digestibility differently depending on the environmental conditions even at the same location. Although the model including the effects of lignin, year and year × lignin interaction predicted OMD with reasonable precision (RMSE = 12.6 g kg−1 ), it cannot be used for predictive purposes, because the year and year × lignin effects can only be estimated afterwards. Taking further into account different relationships between fibre fractions and OMD for different plant species (Clancy and Wilson, 1966; Minson, 1982), developing useful practical OMD prediction equations from chemical composition of forages seems to be difficult. 4.2. Relationship between pepsin-cellulase solubility and in vivo digestibility The cellulase method was superior to chemical components in predicting OMD or D-value of silages. This has also been shown earlier for grass silage (Barber et al., 1984; Givens et al., 1989, 1993a), hay (Moss and Givens, 1990) and fresh herbage (Givens et al., 1993a). Comparisons in prediction precision between rumen fluid in vitro incubations and enzymatic techniques have produced variable results. Terry et al. (1978) reported slightly smaller residual standard error (refers to RMSE in the present study) in DM digestibility for rumen fluid-pepsin than pepsin-cellulase method for grasses (14.6 g kg−1 versus 18.0 g kg−1 ), but for legumes rumen fluid-pepsin was considerably better (19.1 g kg−1 versus 31.7 g kg−1 ). For the rumen fluid method a single regression equation could be applied for both grasses and

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legumes, but separate regressions were required when the enzyme technique was applied. Givens et al. (1990, 1993a,b) reported higher R2 for cellulase compared to a rumen fluid method, but other studies from the same laboratory (Givens et al., 1989; Moss and Givens, 1990) showed better correlation to in vivo OMD for rumen fluid-pepsin method compared to cellulase method. The usefulness of the cellulase method was also confirmed by Pulli (1976), who showed that simple cellulase solubility was well related to in vitro (rumen inoculum) digestibility of grasses and legumes. Although the methods using microbial inoculum have been widely and successfully used, there are some inherent problems in the use of these techniques. Jones and Theodorou (2000) specified the following problems in the use of rumen inoculum method: (1) activity of rumen fluid is lost or altered by preservation methods and therefore fresh rumen fluid must be accessible; (2) donor animals need to be maintained on a standard feeding regime to minimize variability in inoculum; (3) technical difficulties (e.g. maintaining anaerobic conditions, filtration problems) in using rumen fluid; and (4) increased concern of using surgically modified animals. In vitro enzyme techniques include pre-treatment with pepsin-HCl or neutral detergent. Comparisons of these pre-treatments have produced variable results with relatively small differences between the methods in precision of predicting in vivo OMD (see Jones and Theodorou, 2000). Precision of the in vitro method to predict silage OMD or D-value was markedly better in our study compared to earlier studies (e.g. Jones and Hayward, 1973; Terry et al., 1978; Barber et al., 1984; Givens et al., 1989, 1993a). This is probably associated with the more uniform material ensiled. This suggestion is supported by Terry et al. (1978), who found smaller residual standard deviation (RMSE) within species and growth phases compared to prediction equations for all grasses or all forages. True in vivo OM digestibility was considerably (110 g kg−1 ) higher than pepsin-cellulase OM solubility, which suggests that the extent of solubilization was not as complete as in vivo. Van Soest (1994) suggested that digestion with pepsin-cellulase may be considered as true digestibility estimate, because no microbial matter is produced. The slope between OM solubility and true OMD was 0.87, which indicates that the enzymes solubilized highly digestible silages more efficiently than silages of lower digestibility. However, in our study pepsin-cellulase solubility was very similar to in vivo apparent OMD, whereas, e.g. Jones and Hayward (1973) reported considerably lower solubility compared to in vivo values. Regression slopes between in vitro and in vivo digestibility have been smaller for pepsin-cellulase method compared to incubation in rumen fluid (e.g. Terry et al., 1978; Givens et al., 1990, 1993a), which also suggests that enzymatic methods solubilize less forage DM than rumen micro-organisms do and emphasizes the need for mathematical correction equations. Although the extent of digestion by pepsin-cellulase is lower than in vivo, it can be used for prediction of in vivo OMD because of the high correlation between in vitro OM solubility and in vivo OMD. In the present study, the slope of 0.87 between OM solubility and in vivo OMD was higher than values reported by Jones and Hayward (1973), Terry et al. (1978), de Boever et al. (1988), and Givens et al. (1990). The reasons for the higher slope in our study are not clear, but may be partly related to 48 h cellulase incubation instead of 24 h used in most of the other studies (e.g. Jones and Hayward, 1973; de Boever et al., 1988; Givens et al., 1989, 1993a). Higher solubility with increased incubation time found

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by McQueen and Van Soest (1975) is in line with this suggestion. Another reason for the higher slope in our study could be that the cell walls of grasses grown in the northern latitudes are more easily accessible to enzymatic digestion. Deinum et al. (1968) showed that light intensity and temperature during growing season influenced digestibility without accompanied changes in crude fibre or ADF. Prediction of D-value from OM solubility was considerably improved by including ash content in the bivariate regression. This has also been reported before by de Boever et al. (1988) and Givens et al. (1989). This is not very surprising, since DOM content in DM is directly influenced by ash (or OM) content in DM. Prediction error for D-value of the model including OM solubility and ash content was very small (9.7 g kg−1 ) and acceptable for practical prediction of feeding value of grass silages. Bivariate analysis including OM solubility and CP resulted in even smaller prediction error of D-value (9.0 g kg−1 ), but this was probably more associated to the year effect. Also including the cell wall fractions in the model improved the precision of the predictions, especially when the year effect was in the model (Table 4). However, the year effects can only be estimated afterwards, and therefore cannot be used in practical prediction equations. Givens et al. (1990) reported that cellulase-based in vitro methods are less influenced by external factors such as year, location, predominant variety and age of the ley than inoculum in vitro or chemical methods.

5. Conclusions The pepsin-cellulase method was superior to chemical components in predicting OMD and D-value of grass silage harvested from the primary growth of timothy-meadow fescue swards. A bivariate regression model including OM solubility and ash content predicted silage D-value very precisely and accurately. Parameter values of the prediction equation were different compared to the published work suggesting that the equations need to be validated in the environmental conditions and laboratories they are to be applied at. It is likely that the degree of precision in practice will be lower than reported, because the effects of plant species, location, age of sward etc. may increase prediction errors. However, this work has demonstrated a great potential of the pepsin-cellulase method in predicting silage D-value. Results also suggest that datasets for NIRS calibration can be produced by this method. However, more research is needed to study the relationships between OM solubility and OMD for silages made of legumes, regrowth grasses and whole crop cereals.

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