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Relationship between in situ degradation kinetics and in vitro gas production fermentation using different mathematical models
Animal Feed Science and Technology 151 (2009) 86–96
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Animal Feed Science and Technology journal homepage: www.elsevier.com/locate/anifeedsci
Relationship between in situ degradation kinetics and in vitro gas production fermentation using different mathematical models M.A.M. Rodrigues a,∗, J.W. Cone b, L.M.M. Ferreira a, M.C. Blok c, C.V.M. Guedes a a b c
CECAV-Universidade de Trás-os-Montes e Alto Douro, Department of Animal Science, Apartado 1013, 5001-801 Vila Real, Portugal Animal Nutrition Group, Department of Animal Sciences, Wageningen University, The Netherlands CVB, Product Board Animal Feed, The Hague, The Netherlands
a r t i c l e
i n f o
Article history: Received 17 March 2008 Received in revised form 2 October 2008 Accepted 12 December 2008 Keywords: Rumen degradation Gas production technique Mathematical models
a b s t r a c t In vitro and in situ studies were conducted to evaluate the influence of different mathematical models, used to fit gas production profiles of 15 feedstuffs, on estimates of nylon bag organic matter (OM) degradation kinetics. The gas production data were fitted to Exponential, Logistic, Gompertz and a Sigmoidal model. Using only gas production parameters allowed poor prediction of in situ degradation. It was not possible to estimate the washout (W) and degradable (D) in situ fractions for all models, with the exception of the Sigmoidal model with which the D fraction was poorly estimated (R2 = 0.28). The Exponential model did not show any estimation capability, and the Logistic and Gompertz models best predicted in situ degradation rate of OM (kd ) with R2 values of 0.65 and 0.62, respectively. The transformation of the in situ rate of degradation (kd ) to its half-life value of degradation ((ln 2/kd )100) provided an improvement of kd prediction in the Sigmoidal model, with R2 changing from 0.35 to 0.64. As to kd and fermentable organic matter
Abbreviations: A, estimated asymptotic gas production; ADFom, ADF expressed exclusive of residual ash; B, time of incubation at which half of the asymptotic gas production (A) has been formed; C (Sigmoidal model), sharpness of the switching characteristic for the gas production profile; C (Gompertz model), relative rate of gas production; c (Exponential model), c (Gompertz model), constant rate of gas production; CFat, crude fat; CP, crude protein; D (Gompertz model), constant factor of microbial efficiency; D (in situ degradation), degradable fraction; DM, dry matter; FOM, fermentable organic matter; kd , degradation rate; NDFom, NDF not assayed with stable amylase expressed exclusive of residual ash; OM, organic matter; R, fractional rate of substrate fermentation; SR, specific rate of gas production; TRmax G, time at which maximum rate of gas production is reached; U, undegradable fraction; W, washout fraction. ∗ Corresponding author. Tel.: +351 259 350 422; fax: +351 259 350 482. E-mail address: [email protected] (M.A.M. Rodrigues). 0377-8401/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.anifeedsci.2008.12.004
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(FOM) all estimations improved upon inclusion of chemical composition characteristics, such as sugars, crude protein (CP), neutral detergent fibre (NDFom) and crude fat (CFat). The Logistic and Gompertz models continued to better predict kd , with R2 values of 0.79 and 0.88, respectively, while the Sigmoidal model showed a higher capability to estimate FOM (R2 = 0.90). It should also be noticed that the estimation of the washout fraction (W) estimation was obtained with only sugar and starch contents (R2 = 0.62). There were only moderate relationships between in situ and gas production indicating that the methods do not describe the degradation of these feedstuffs in a similar way. © 2008 Elsevier B.V. All rights reserved.
1. Introduction While it might be assumed that fermentation characteristics of feedstuffs should be evaluated through in vivo trials, it is also obvious that in vivo experiments need a high number of animals, have high financial costs, are difficult to standardize and only the feed to be tested can be fed during trials. Alternatively, in vitro and in situ techniques have been developed to allow its utilization on a routine basis. Nowadays, the nylon bag technique (Meherez and Ørskov, 1977) is the standard technique in feed evaluation systems (Tamminga et al., 1994; NRC, 2001; Thomas, 2004). Nevertheless, some drawbacks have been pointed out (Mould, 2003) and a particular importance has been given to the excessive loss of particulate material that might lead to the disappearance of undegraded material from the nylon bags. In this way, the technique might be inappropriate for feedstuffs with high proportions of soluble material (starch, sugars) and/or high proportions of small particles (Cone et al., 1998, 1999). The development of alternative in vitro methods, such as the gas production technique, has lead to the assessment of several mathematical models to describe and interpret the fermentation characteristics of feeds (Pitt et al., 1999; Dhanoa et al., 2000; France et al., 2000; Calabrò et al., 2005). While there are enough data to validate gas production models regarding its potential to estimate rumen degradation (Khazaal et al., 1993; Beuvink et al., 1993; Sileshi et al., 1996; Cone et al., 1998, 1999; Lopez et al., 1998) a thorough comparison with the nylon bag technique is lacking and few studies have been conducted to compare the prediction capability of the models. Furthermore, comparisons found in literature have been conducted mainly with fibrous feeds and only a limited number of feedstuffs with high proportion of soluble and/or small particles have been studied. The aim of this study was to analyse the relationship between the in situ degradation characteristics of organic matter (OM) of several feedstuffs and the gas production parameters using different mathematical models. 2. Materials and methods 2.1. Feedstuffs and chemical analysis Investigations were carried out with 15 feed samples (Table 1). The ruminally protected solvent extracted rapeseed meal (Rumiraap) was obtained after treatment with formaldehyde and was provided by Schouten (Giessen, The Netherlands). Amygold and amygold extra steep are wet maize gluten feeds (DM content approximately 400 g kg−1 ) and were provided by Amylum (Vilvoorde, Belgium). Amygold extra steep has a higher protein content than amygold. Pressed beet pulp and brewery grians were dried at 70 ◦ C and all samples were ground to pass a 1 mm screen before use. Ash was determined after 3 h at 550 ◦ C (ISO 5984). Crude protein (CP) was determined using a Kjeldahl method (ISO 5983). Crude fat (CFat) was determined gravimetrically after 6 h extraction with petroleum-ether (ISO 6492). Reducing sugars were measured colorimetrically (Van Vuuren et
88
Feedstuff
Ash (g kg−1 DM)
CP (g kg−1 DM)
CFat (g kg−1 DM)
Starch (g kg−1 DM)
Sugars (g kg−1 DM)
NDFom (g kg−1 DM)
ADFom (g kg−1 DM)
Lignin (sa) (g kg−1 DM)
Amygold Amygold (extra steep) Beet pulp (molassed) Beet pulp (pressed) Brewery grains Citrus pulp Lin seed hulls Lucerne meal Lupin Maize gluten feed Palm kernel expeller Rapeseed meal SEb Rumiraap Sunflower meal SEb Soybean meal
38 62 79 68 57 66 60 125 29 59 48 72 77 74 75
174 227 88 106 251 66 373 174 315 234 158 404 385 340 559
40 36 14 13 109 24 97 27 66 39 93 45 58 22 32
186 158 4 2 19 5 24 19 3 183 1 9 11 3 2
23 28 234 2 2 272 53 54 64 27 21 113 112 78 114
453 373 356 542 539 191 205 419 292 336 590 273 289 382 86
122 100 182 230 195 147 129 318 209 97 366 205 193 269 51
8 16 8 11 33 10 44 77 4 4 98 83 78 81 1
S.E.M.c
0.5
1.9
a
1.2
0.9
4.0
2.1
1.5
1.3
CP: crude protein; CFat: crude fat; NDFom: NDF not assayed with stable amylase expressed exclusive of residual ash; ADFom: ADF expressed exclusive of residual ash; Lignin (sa): lignin determined by solubilisation of ADF with sulphuric acid. b Solvent extracted. c
All samples analysed in duplicate in single samples.
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Table 1 Chemical composition (g kg−1 DM) of the feedstuffs.a .
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al., 1993) after extraction with 40% ethanol followed by treatment with 0.023 M HCl to hydrolyse the bonds between sugar molecules. Starch content was determined as glucose using the amyloglucosidase method (Keppler and Decker, 1970) after hydrolysing the starch by heating in boiling water bath in the presence of 2 M HCl (Cone, 1991). Neutral detergent fibre (NDFom), acid detergent fibre (ADFom) and lignin (sa) fractions were determined by the detergent procedures of Van Soest et al. (1991) and Robertson and Van Soest (1981). Both NDFom and ADFom were expressed without residual ash. Sodium sulphite and amylase were not used to determine NDFom. 2.2. In situ measurements Measurements of in situ organic matter were performed in three mature cows fistulated in the rumen using the nylon bag technique (Ørskov and McDonald, 1979). The cows weighed 600–700 kg and produced 20–25 kg milk per day. Approximately 5 g of 3 mm ground samples were incubated in the rumen of each cow for 3, 8, 16, 48 and 336 h. Nylon bags (Nybolt, PA 40/30, Zürich, Switzerland), had a medium pore size of 40 m. Cows were fed ad libitum a ration containing 720 g kg−1 forage (DM basis) including, on a DM basis, grass hay (23 g kg−1 ), grass silage (143 g kg−1 ), maize silage (437 g kg−1 ), pea silage (65 g kg−1 ), maize gluten meal (127 g kg−1 ), corncob silage (96 g kg−1 ), solvent extracted soybean meal (52 g kg−1 ), solvent extracted rapeseed meal (52 g kg−1 ) and minerals (5 g kg−1 ). Animals received two portions daily, fed as totally mixed rations. Incubations were conducted in duplicate for the 3 h period and in quadruplicate for the 336 h period. Triplicate incubations were performed for all the other periods. All feeds were incubated at the same time and a maximum of 35 bags per cow were introduced in one time. At the end of each incubation period, the bags were collected and washed in a washing machine (AEG, Öko Turnamat 2800, AEG-Electrolux, Stockholm, Sweden) for 40 min with cold tap water and then dried at 70 ◦ C for 24 h. The residues where ground to pass a 1 mm screen and were analysed for OM and CP. The washout fraction (W) was determined by washing with cold tap water. The residue after 336 h incubation was considered to be the undegradable fraction (U). The degradable fraction (D) was calculated as 100-W-U. Residues after different incubation periods were fitted to a first-order degradation model (Robinson et al., 1986) in order to calculate the rate of degradation (kd , fraction per h). The amount of fermentable organic matter (FOM) was calculated from in situ incubations (Cone et al., 1994), assuming a passage rate 0.06 h−1 . Calculations were conducted without a discrete lag-time. 2.3. Gas production incubations Two non-lactating cows, weighing on average 480 (±42 kg) fitted with ruminal canula (Bar Diamond Inc., Parma, ID, USA), were used in this experiment to collect the rumen fluid. Animals were fed on a diet composed of 1 kg of standard compound feed, containing about 150 g kg−1 starch, offered in the morning and ad libitum grass hay in the morning and afternoon. Animals had free access to water and to mineral-vitamin blocks. Rumen fluid samples were withdrawn 2 h after the morning feeding into pre-warmed insulated flasks previously filled with CO2 . Rumen fluid was strained through four layers of cheesecloth and kept at 39 ◦ C under CO2 . Rumen fluid used to measure gas production was mixed (1:2, v/v) with an anaerobic buffer/mineral solution as described by Cone et al. (1996). All laboratory handlings were carried out under continuous flushing with CO2 . Samples (500 mg) were accurately weighed into 250 ml serum bottles (Schott, Mainz, Germany) and incubated in 60 ml buffered rumen fluid. Each sample was incubated in one run in duplicate. The gas production was recorded for 72 h using a fully automated system (Cone et al., 1996). Gas curves were fitted to four mathematical models. An Exponential mono-phasic model (Ørskov and McDonald, 1979): Y = A(1 − e−ct ) where A = estimated asymptotic gas production; c = constant rate of gas production.
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A Logistic mono-phasic model (Schofield et al., 1994): A
Y=
1 + e(2−4SRt)
where A = estimated asymptotic gas production; SR = specific rate of gas production. A Gompertz mono-phasic model (Lavrenˇciˇc et al., 1997):
Y = A e(−C)e
(Dt)
where A = estimated asymptotic gas production; C = relative rate of gas production; D = constant factor of microbial efficiency. Constant rate of gas production (c) was calculated as c=
D C
A mono-phasic Sigmoidal model as described by Groot et al. (1996) Y=
A 1 + (B/t)
C
where A = estimated asymptotic gas production; B = time of incubation at which half of the asymptotic gas production has been formed; C = sharpness of the switching characteristic for the profile. The time at which maximum rate of gas production is reached (TRmax Gi ) was calculated according to Yang et al. (2005): TRmax G = B
C − 1 1/C C +1
Fractional rate of substrate fermentation (R) was calculated using the equation proposed by Groot et al. (1996) R=
CTRmax G(C−1) BC + TRmax GC
2.4. Statistical analysis All models were fitted to data by nonlinear regression using the NLIN procedure of SAS (1999). Multiple regression analyses were used to investigate the relationships between in situ parameters, gas production parameters and chemical composition data using the REG procedure of SAS (1999). The multiple regression equations were evaluated with the determination coefficient adjusted for the degrees of freedom (R2 ) and the residual standard deviation (R.S.D.). Equations were fitted by the least square method following a stepwise procedure with the 0.05 probability level for inclusion or deletion of variables. 3. Results 3.1. Chemical composition The chemical composition of the investigated feedstuffs is presented in Table 1. Crude protein ranged from 66 g kg−1 DM for citrus pulp to 559 g kg−1 DM for soybean meal. Starch ranged from 1 g kg−1 DM for palm kernel expeller to 186 g kg−1 DM for amygold. Sugars content ranged from 2 g kg−1 DM for beet pulp (pressed) and brewery grains to 272 g kg−1 DM for citrus pulp. Crude fat ranged from 13 g kg−1 DM in Beet Pulp (pressed) to 109 g kg−1 DM in brewery grains. Cell wall content (NDFom) ranged from 86 g kg−1 DM for soybean meal to 590 g kg−1 DM for palm kernel expeller.
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Table 2 The washout (W), undegradable (U) and degradable fractions (D) as well as degradation rates (kd ) of OM determined in situ, and fermentable organic matter (FOM, g kg−1 OM) of feedstuffs. Feedstuff
W
U
D
kd a (h−1 )
FOMb (g kg−1 OM)
Amygold Amygold (extra steep) Beet pulp (molassed) Beet pulp (pressed) Brewery grains Citrus pulp Lin seed hulls Lucerne meal Lupin Maize gluten feed Palm kernel expeller Rapeseed meal SEc Rumiraap Sunflower meal SE Soybean meal
0.21 0.27 0.32 0.00 0.01 0.43 0.02 0.23 0.27 0.42 0.11 0.21 0.17 0.19 0.27
0.04 0.02 0.04 0.03 0.19 0.02 0.11 0.27 0.02 0.03 0.19 0.16 0.16 0.27 0.00
0.075 0.70 0.64 0.97 0.80 0.56 0.88 0.49 0.71 0.55 0.71 0.63 0.67 0.55 0.73
0.0300 0.033 0.068 0.041 0.034 0.068 0.089 0.049 0.073 0.035 0.041 0.089 0.016 0.084 0.092
437 492 627 374 284 691 518 428 627 592 378 557 296 486 676
S.E.M.d
0.008
0.005
0.005
0.002
a b
Calculated without lag-period. FOM was calculated using the mean values of W, D and kd .
c
Solvent extracted. Incubations were conducted in duplicate for the 3 h period and in quadruplicate for the 336 h period. Triplicate incubations were performed for all the other periods. d
3.2. In situ degradation of OM The in situ degradation fractions and the kd values of the feedstuffs are presented in Table 2. The W fraction ranged from 0 for beet pulp (pressed) to 0.43 for citrus pulp. For the majority of feedstuffs the U fraction was very low, not exceeding 0.05. However, brewery grains, lin seed hulls, lucerne meal, palm kernel expeller, rapeseed meal, rumiraap, and sunflower meal presented values that varied between 0.11 and 0.27. The D fraction ranged from 0.49 for lucerne meal and 0.97 for beet pulp (pressed). Values for kd , ranged from 0.03 h−1 for amygold to 0.092 h−1 for soybean meal. The amount of FOM in the feedstuffs ranged from 28.4 g kg−1 for brewery grains to 691 g kg−1 for citrus pulp. 3.3. Gas production profiles fitted with mono-phasic models Gas production profiles showed variations in both rate and extent of fermentation among feedstuffs (Table 3), for all four mathematical models. The estimated asymptotic gas production (A) and the constant rate of gas production (c), obtained from data fitted with the Exponential model (253 ± 11.9 ml/g OM and 0.130 ± 0.0231 h−1 , respectively) were generally higher than the values obtained from data fitted with the Logistic (237 ± 11.2 ml/g OM and 0.066 ± 0.0027 h−1 , respectively) and the Gompertz (242 ± 11.2 ml/g OM and 0.068 ± 0.0031 h−1 , respectively) models, which presented a similar range of values within the two parameters. On the contrary, the asymptotic gas production (A) of Groot’s model was generally higher (279 ± 11.5 ml/g OM) than the values obtained with the Exponential model, with the exception of palm kernel expeller. The majority of data obtained for the fractional rate of substrate fermentation (R) were lower (0.090 ± 0.0043 h−1 ) than those obtained for the constant rate of gas production (c) in the Exponential model. 3.4. Estimation of in situ parameters The in situ degradation constants were estimated from gas production parameters alone or together with chemical composition data.
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Table 3 Parameters of gas production profiles fitted with mono-phasic models and residual standard deviation (R.S.D.).
Amygold Amygold (extra steep) Beet pulp (molassed) Beet pulp (pressed) Brewery grains Citrus pulp Lin seed hulls Lucerne meal Lupin Maize gluten feed Palm kernel expeller Rapeseed meal SEe Rumiraap Sunflower meal SEe Soybean meal S.E.M.f a b
Exponentiala
Logisticb
Gompertzc
Sigmoidald
A
c
R.S.D.
A
SR
R.S.D.
A
C
D
c
R.S.D.
A
B
C
R
R.S.D.
333 301 333 345 178 335 202 175 294 271 250 198 184 170 224
0.060 0.068 0.116 0.091 0.072 0.124 0.127 0.108 0.099 0.082 0.060 0.108 0.067 0.142 0.099
8.1 5.1 8.2 16.3 4.0 7.9 3.5 2.8 7.6 2.4 17.6 4.7 4.7 4.1 4.8
305 279 316 324 165 318 193 166 278 254 227 188 171 162 211
0.045 0.050 0.081 0.066 0.054 0.087 0.087 0.074 0.070 0.058 0.046 0.076 0.049 0.081 0.070
12.8 12.7 17.2 16.2 11.4 18.3 10.8 9.8 12.9 14.3 12.4 12.3 11.5 10.8 11.3
310 284 322 326 170 326 196 170 282 261 229 193 179 167 216
2.76 2.61 2.49 3.42 2.31 2.22 2.36 2.16 2.77 2.30 3.99 2.14 2.06 2.01 2.47
0.127 0.134 0.211 0.214 0.128 0.204 0.220 0.172 0.198 0.140 0.163 0.172 0.104 0.173 0.180
0.046 0.051 0.085 0.063 0.054 0.092 0.093 0.080 0.072 0.061 0.040 0.080 0.050 0.086 0.073
6.7 7.6 12.5 7.7 8.4 14.6 7.9 6.6 8.0 9.5 5.0 8.9 7.7 7.5 8.1
347 319 349 341 202 361 214 192 302 299 238 220 237 191 238
11.8 10.7 6.2 7.4 11.3 5.9 5.7 7.1 7.1 9.5 10.8 7.2 15.9 6.7 7.4
1.53 1.45 1.49 2.00 1.21 1.30 1.40 1.24 1.64 1.27 2.35 1.2 1.10 1.14 1.42
0.103 0.068 0.118 0.117 0.065 0.121 0.176 0.103 0.108 0.076 0.092 0.102 0.050 0.114 0.098
4.8 3.5 5.6 5.8 2.6 4.1 1.9 1.6 2.4 2.5 8.4 2.2 2.2 1.7 3.3
0.032
0.0022
0.0006
0.014
0.0012
4.2
0.0064
3.9
0.0007
4.0
4.8
0.17
A: estimated asymptotic gas production (ml/g OM); c: constant rate of gas production (h−1 ). A: estimated asymptotic gas production (ml/g OM); SR: specific rate of gas production (h−1 ).
A: estimated asymptotic gas production (ml/g OM); C: relative rate of gas production (h−1 ); D: constant factor of microbial efficiency; c: constant rate of gas production (h−1 ). A: estimated asymptotic gas production (ml/g OM); B: time of incubation at which half of the asymptotic gas production has been formed (h); C: sharpness of the switching characteristic for the profile; R: fractional rate of substrate fermentation (h−1 ). e Solvent extracted. c
d
f
All samples analysed in duplicate in single samples.
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Feedstuff
Table 4 Estimation of in situ parameters with gas production and chemical composition using mono-phasic models. Predicted variablea
Model
Fermentation parameters and chemical compositionb variables
Fermentation parameters Equation
R2
– – – – –
– – – – –
– – – – –
– – −3.6 + 138.9 × SR 15.1 + 523.4 × SR 46.4 − 462.9 × SR
– – 0.65 0.31 0.51
– – 0.0002 0.0177 0.0016
– – 1.5 10.0 6.5
– – −2.5 + 118.5 × c −5.9 + 483.9 × c + 0.09 × A 49.8 − 200.9 × D
– – 0.62 0.50 0.58
– – 0.0003 0.0063 0.0006
– – 1.6 9.3 6.1
– – 11.9 − 0.72 × B 76.6 − 3.1 × B −10.8 + 3.1 × B
– – 0.64 0.42 0.86
– – 0.0002 0.0055 <0.0001
– – 1.5 10.0 5.7
P
R.S.D.
Equation
R2
P
R.S.D.
5.1 + 0.14 × Sugars + 0.12 × Starch – 9.5 − 0.01 × NDFom 50.9 + 0.02 × Sugars − 0.06 × NDFom + 0.08 × c –
0.62 – 0.32 0.64 –
0.0012 – 0.0164 0.0025 –
8.2 – 2.1 7.9 –
5.1 + 0.12 × Sugars + 0.14 × Starch – −5.2 + 135.1 × SR + 0.007 × CP 30.5 + 255.3 × SR − 0.05 × NDFom + 0.09 × A –
0.62 – 0.79 0.71 –
0.0012 – <0.0001 0.0007 –
8.2 – 1.2 7.0 –
5.1 + 0.12 × Sugars + 0.14 × Starch – −12.3 + 146.5 × c + 0.009 × CP + 2.2 × C 29.2 + 236.2 × c + 0.09 × A − 0.05 × NDFom –
0.62 – 0.88 0.71 –
0.0012 – <0.0001 0.0007 –
8.2 – 0.9 7.0 –
5.1 + 0.12 × Sugars + 0.14 × Starch – 9.9 − 0.76 × B + 0.009 × CP 103.9 − 0.05 × NDFom − 3.8 × B + 0.06 × A − 184.5 × R –
0.62 – 0.90 0.90 –
0.0012 – <0.0001 <0.0001 –
8.2 – 0.8 4.1 –
c
Exponential
– – – – –
Logisticd W D kd FOM (ln 2/kd )100 Gompertze W D kd FOM (ln 2/kd )100 Sigmoidalf W D kd FOM (ln 2/kd )100
a W: washout fraction of organic matter determined wit the nylon bag technique; D: digestible organic matter fraction (100-W-U); kd : degradation rate of organic matter determined with the nylon bag technique; FOM: fermentable organic matter calculated from nylon bag data; (ln 2/kd )100: transformation of kd to a half-time value. b CP: crude protein; NDFom: NDF not assayed with stable amylase expressed exclusive of residual ash; CF: crude fat. c d
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W D kd FOM (ln 2/kd )100
c: constant rate of gas production (h−1 ). A: estimated asymptotic gas production (ml/g OM); SR: specific rate of gas production (h−1 ).
A: estimated asymptotic gas production (ml/g OM); C: relative rate of gas production (h−1 ); D: constant factor of microbial efficiency; c: constant rate of gas production (h−1 ). A: estimated asymptotic gas production (ml/g OM); B: time of incubation at which half of the asymptotic gas production has been formed (h); R: fractional rate of substrate fermentation (h−1 ). e f
93
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The stepwise linear regression indicated that gas production alone was a poor predictor of in situ degradation (Table 4). The Exponential model did not show any estimation capability and the in situ degradable fraction (D) was not estimated by any of the models. As to the degradation rate of OM (kd ) and FOM all estimations improved upon inclusion of chemical composition characteristics, crude protein and neutral detergent fibre. It should also be noticed that the estimation of the washout fraction (W) estimation was obtained with only sugar and starch contents (R2 = 0.62; R.S.D. = 8.2). 4. Discussion Results presented in this study showed that the utilization of different mathematical models to adjust data from in vitro gas production influenced the overall prediction capability of in situ parameters, showing that for these feedstuffs the Exponential model was not suitable to estimate these parameters. Furthermore, differences occurred between the models with kd being better predicted by the Logistic model while FOM was better estimated by the Gompertz model. However highest correlations were obtained with the Sigmoidal model (R2 = 0.86) when kd was transformed to its half-life value. Similar results were described by Cone et al. (2002) where an increase from R2 = 0.42 to R2 = 0.53 was obtained when this transformation was applied. These relationships are not in accordance to the data reported by Khazaal et al. (1993) working with hays, Blümmel and Ørskov (1993) working with straws and Sileshi et al. (1996) working with tropical forages, who observed poor relationships between the rate of DM degradation and the rate of gas production. Nevertheless, Cone et al. (2002) working with feedstuffs similar to the ones used in our study reported moderate estimations of kd and FOM. Other studies in grass (Cone et al., 1998), grass silage (Cone et al., 1999) and hay (López et al., 1999) have also showed good relationships between in situ degradation characteristics and in vitro gas production. Differences between reported data can be attributed to several factors that according to Valentin et al. (1999) might be due to methodology, substrates as well as to the mathematical model used. In this way, it seems that while statistical comparisons between models are necessary to evaluate the robustness of fitting it should be noticed that when these models are used to evaluate the degradation characteristics in the rumen of several feedstuffs, special attention should be given to the possible dissimilarity between the obtained parameters. According to Calabrò et al. (2005) the examination of possible discrepancies among models in values of the main degradation parameters derived is essential as it will determine the possibility to conduct comparative ranking studies of feeds. Lopez et al. (1998) also mentioned that the choice of the mathematical model could result in different parameter estimates affecting the comparison of feeds. There was a lack of predictive capability of all models as to the W and D fractions of in situ incubations. While it is accepted that in spite of methodological differences both methods (nylon bag technique and gas production) give information on the degradation rate of substrates (Cone et al., 1998), it is also true that the problems inherent to these methods are related to the difficulties in comparing the early stages of the degradation process. In the gas production, gas is originated from the degradation of both soluble and insoluble substrates whereas in the in situ method, the disappearance of only the insoluble material is measured. In addition, the W fraction is assumed to be composed by the completely and rapidly degraded fraction; however, this assumption is not totally correct (Dhanoa et al., 1999). The relationship between the W fraction and the sugar and starch contents (R2 = 0.62) are in accordance to what was mentioned before. In fact, this relationship indicates that the W fraction assumes that both these constituents are soluble and immediately degraded, but this is not entirely true. Furthermore, while all potential fermentable components are measured in the gas production method, part of them might be lost in the nylon bag technique and thus are not included in the D fraction. Cone et al. (2006) refer to this subject highlighting that this might be the case of starch. In addition, these results also point out that the inclusion of starch into an immediately fermentable fraction is incorrect. Results presented by Chai et al. (2004) indicate that the majority of starch degradation normally occurs between 6 and 24 h, thus concluding that fermentation of starch and other non-soluble components occurred simultaneously. For all models, the inclusion of chemical characteristics improved the estimation of in situ parameters. For all models CP and NDFom were the main constants included in the regression equations. In the gas production method CP fermentation has a negative influence on gas production (Cone and Van
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Gelder, 1999) as the amount of gas produced, because of a shift in the equilibrium in the carbonate buffer upon production of fatty acids, is reduced by the formation of NH4 when NH3 is released from protein fermentation (Menke and Steingass, 1988). The amount of CP (varied from 66 g kg−1 DM on citrus pulp to 559 g kg−1 DM on soybean meal) as well as the expected differences between its degradation within the feedstuffs used in this experiment point out that the inclusion of CP in the estimation of kd might be the result of correcting for differences between the degraded in situ CP and the rate of gas produced due to protein fermentation in the gas production method. The inclusion of NDFom in FOM estimation is the result of the proportion of gas being produced by the insoluble but degradable fraction that in the case of these feedstuffs is mainly represented by NDFom. In our study NDF was negatively related to FOM and this is consistent to what was mentioned before. 5. Conclusions The results show moderate relationships between in situ and gas production indicating that the methods do not describe the degradation of these feedstuffs in a similar way. As several advantages are generally pointed out to the gas production one possible research area that can be explored would be the application of this technique to directly estimate the fermentable metabolizable energy (FME) and rumen undegraded protein (RUP) through the use on in vivo and in vitro trials. Some of these experiments are now being conducted by this research team. References Beuvink, J.M.W., De Visser, H., Klop, A., 1993. In vitro gas production kinetics of different maize products: a comparison to nylon-bag degradation kinetics. In: Beuvink, J.M.W. (Ed.), Measuring and Modelling In Vitro Gas Production Kinetics to Evaluate Ruminal Fermentations of Feedstuffs. DLO-Research Institute for Livestock Feeding and Nutrition (IVVO-DLO) Report. Lelystad, The Netherlands, pp. 31–41. Calabrò, S., López, S., Piccolo, V., Dijkstra, J., Dhanoa, M.S., France, J., 2005. Comparative análysis of gas production profiles obtained with buffalo and sheep ruminal fluid as the source of inoculum. Anim. Feed Sci. Technol. 123 (124), 51–65. Chai, W.Z., Van Gelder, A.H., Cone, J.W., 2004. Relationship between gas production and starch degradation in feed samples. Anim. Feed Sci. Technol. 114, 195–204. Cone, J.W., 1991. Degradation of starch in feed concentrates by enzymes, rumen fluid and rumen enzyme. J. Sci. Food Agric. 54, 22–34. Cone, J.W., Van Gelder, A.H., Veerman, E.T., Van Vuuren, A.M., 1994. In vitro estimation of rumen fermentable organic matter using rumen fluid and a cell free preparation of rumen fluid. Neth. J. Agric. Sci. 42, 343–356. Cone, J.W., Van Gelder, A.H., Visscher, G.J.W., Oudshoorn, L., 1996. Influence of rumen fluid and substrate concentration on fermentation kinetics measured with a fully automated time related gas production apparatus. Anim. Feed Sci. Technol. 61, 113–128. Cone, J.W., Van Gelder, A.H., Valk, H., 1998. Prediction of nylon bag degradation characteristics of grass samples with the gas production technique. J. Sci. Food Agric. 77, 421–426. Cone, J.W., Van Gelder, A.H., 1999. The influence of protein fermentation on gas production profiles. Anim. Feed Sci. Technol. 76, 251–264. Cone, J.W., Van Gelder, A.H., Soliman, I.A., De Visser, H., Van Vuuren, A.M., 1999. Different techniques to study rumen fermentation characteristics of maturing grass and grass silage. J. Dairy Sci. 82, 957–966. Cone, J.W., Van Gelder, A.H., Bchmann, H., Hindle, V.A., 2002. Comparison of organic matter degradation in several feedstuffs in the rumen as determined with the nylon bag and gas production techniques. Anim. Feed Sci. Technol. 96, 55–67. Cone, J.W., Van Gelder, A.H., Chai, W.Z., 2006. Fermentation behaviour of the nylon bag washout and degradable fractions determined with the gas production technique. Anim. Feed Sci. Technol. 127, 319–326. Dhanoa, M.S., France, J., López, S., Dijkstra, J., Lister, S.J., Davies, D.R., Bannink, A., 1999. Correcting the calculation of the extent of degradation to account for particulate matter loss at zero time when applying the polyester bag method. J. Anim. Sci. 77, 3385–3391. Dhanoa, M.S., López, S., Dijkstra, J., Davies, D.R., Sanderson, R., Williams, B.A., Sileshi, Z., France, J., 2000. Estimating the extent of degradation of ruminant feeds from a description of their gas production profiles observed in vitro: comparison of models. Br. J. Nutr. 83, 131–142. France, J., Dijkstra, J., Dhanoa, M.S., López, S., Bannink, A., 2000. Estimating the extent of degradation of ruminant feeds from a description of their gas production profiles observed in vitro: derivation of models and other mathematical considerations. Br. J. Nutr. 83, 143–150. Groot, J.C.J., Cone, J.W., Williams, B.A., Debersaques, F.M.A., Latinga, E.A., 1996. Multiphasic analysis of gas production kinetics for in vitro fermentation of ruminant feeds. Anim. Feed Sci. Technol. 64, 77–89. Keppler, D., Decker, K., 1970. Glycogen. Determination with amyloglucosidase. In: Bergmeyer, H.U. (Ed.), Methoden der Enzymatischen Analyse, vol. II. Weinheim, Germany, Verlag Chemie, pp. 1127–1131. Khazaal, K., Dentinho, M.T., Ribeiro, J.M., Ørskov, E.R., 1993. A comparison of gas production during incubation with rumen contents in vitro and nylon bag degradability as predictors of the apparent digestibility in vivo and the voluntary feed intake of hays. Anim. Prod. 57, 105–112.
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Lavrenˇciˇc, A., Stefanon, B., Susmel, P., 1997. An evaluation of the Gompertz model in degradability studies of forage chemical components. Anim. Sci. 64, 423–431. Lopez, S., Carro, M.D., Gonzales, J.S., Ovejero, F.J., 1998. Comparison of different in vitro and in situ methods to estimate the extent and rate of degradation of hays in the rumen. Anim. Feed Sci. Technol. 73, 99–113. López, S., France, J., Dhanoa, M.S., Mould, F., Dijkstra, J., 1999. Comparison of mathematical models to describe disappearance curves obtained using the polyester bag technique for incubating feeds in the rumen. J. Anim. Sci. Technol. 77, 1875–1888. Meherez, A.Z., Ørskov, E.R., 1977. A study of the artificial fibre bag technique for determining the digestibility of feeds in the rumen. J. Agric. Sci. Camb. 88, 645–650. Menke, K.H., Steingass, H., 1988. Estimation of the energetic feed value obtained from chemical analysis and gas production using rumen fluid. Anim. Res. Dev. 28, 7–55. Mould, F.L., 2003. Predicting feed quality—chemical analysis and in vitro evaluation. Field Crops Res. 84, 31–44. NRC, 2001. Nutrient Requirements of Dairy Cattle. National Academy Press, Washington, DC, USA, 381 pp. Ørskov, E.R., McDonald, I., 1979. The estimation of protein degradability in the rumen from incubation measurements weighted according to the rate of passage. J. Agric. Sci. 92, 449–503. Pitt, R.E., Cross, T.L., Pell, A.N., Schofield, P., Doane, P.H., 1999. Use of in vitro gas production models in ruminal kinetics. Math. Biosci. 159, 145–163. Robertson, J.B., Van Soest, P.J., 1981. The detergent system of analysis and its application in human foods. In: James, W.P.T., Theander, O. (Eds.), The Analysis of Dietary Fiber in Food. Marcell Dekker Inc., New York, NY, USA, pp. 123–158. Robinson, P.H., Fadel, J.G., Tamminga, S., 1986. Evaluation of mathematical models to describe neutral detergent residue in terms of its susceptibility to degradation in the rumen. Anim. Feed Sci. Technol. 15, 249–271. SAS, 1999. SAS/STAT User’s Guide, Version 8. SAS Institute, Cary, NC, USA. Schofield, P., Pitt, R.E., Pell, A.N., 1994. Kinetics of fibre digestion from in vitro gas production. J. Anim. Sci. 72, 2980–2991. Sileshi, Z., Owen, E., Dhanoa, M.S., Theodorou, M.K., 1996. Prediction of in situ rumen dry matter disappearance of Ethiopian forages from an in vitro gas production technique using a pressure transducer, chemical analyses or in vitro digestibility. Anim. Feed Sci. Technol. 61, 73–87. Tamminga, S., Van Straalen, W.N., Subunel, A.P.J., Meijer, R.G.M., Steg, A., Wever, C.J.G., Block, M.C., 1994. The Dutch protein evaluation system: the DVE/OEB system. Liv. Prod. Sci. 40, 139–155. Thomas, C., 2004. Feed into Milk: A New Applied Feeding System for Dairy Cows. Nottingham University Press, Nottingham, UK, p. 72. Valentin, S.F., Williams, P.E.V., Forbes, J.M., Sauvant, D., 1999. Comparison of the in vitro gas production technique and the nylon bag degradability technique to measure short- and long-term processes of degradation of maize silage in dairy cows. Anim. Feed Sci. Technol. 78, 81–89. Van Soest, P.J., Robertson, J.B., Lewis, B.A., 1991. Symposium: carbohydrate methodology, metabolism, and nutritional implications in dairy cattle. Methods for dietary fiber, neutral detergent fiber, and nonstarch polysaccharides in relation to animal nutrition. J. Dairy Sci. 74, 3583–3597. Van Vuuren, A.M., Van der Koelen, C.J., Valk, H., De Visser, H., 1993. Effects of partial replacement of ryegrass by low protein feeds on rumen fermentation and nitrogen loss by dairy cows. J. Dairy Sci. 76, 2982–2993. Yang, H.J., Tamminga, S., Williams, B.A., Dijkstra, J., Boer, H., 2005. In vitro gas and volatile fatty acids production profiles of barley and maize and their soluble and washout fractions after feed processing. Anim. Feed Sci. Technol. 120, 125–140.
Contents lists available at ScienceDirect
Animal Feed Science and Technology journal homepage: www.elsevier.com/locate/anifeedsci
Relationship between in situ degradation kinetics and in vitro gas production fermentation using different mathematical models M.A.M. Rodrigues a,∗, J.W. Cone b, L.M.M. Ferreira a, M.C. Blok c, C.V.M. Guedes a a b c
CECAV-Universidade de Trás-os-Montes e Alto Douro, Department of Animal Science, Apartado 1013, 5001-801 Vila Real, Portugal Animal Nutrition Group, Department of Animal Sciences, Wageningen University, The Netherlands CVB, Product Board Animal Feed, The Hague, The Netherlands
a r t i c l e
i n f o
Article history: Received 17 March 2008 Received in revised form 2 October 2008 Accepted 12 December 2008 Keywords: Rumen degradation Gas production technique Mathematical models
a b s t r a c t In vitro and in situ studies were conducted to evaluate the influence of different mathematical models, used to fit gas production profiles of 15 feedstuffs, on estimates of nylon bag organic matter (OM) degradation kinetics. The gas production data were fitted to Exponential, Logistic, Gompertz and a Sigmoidal model. Using only gas production parameters allowed poor prediction of in situ degradation. It was not possible to estimate the washout (W) and degradable (D) in situ fractions for all models, with the exception of the Sigmoidal model with which the D fraction was poorly estimated (R2 = 0.28). The Exponential model did not show any estimation capability, and the Logistic and Gompertz models best predicted in situ degradation rate of OM (kd ) with R2 values of 0.65 and 0.62, respectively. The transformation of the in situ rate of degradation (kd ) to its half-life value of degradation ((ln 2/kd )100) provided an improvement of kd prediction in the Sigmoidal model, with R2 changing from 0.35 to 0.64. As to kd and fermentable organic matter
Abbreviations: A, estimated asymptotic gas production; ADFom, ADF expressed exclusive of residual ash; B, time of incubation at which half of the asymptotic gas production (A) has been formed; C (Sigmoidal model), sharpness of the switching characteristic for the gas production profile; C (Gompertz model), relative rate of gas production; c (Exponential model), c (Gompertz model), constant rate of gas production; CFat, crude fat; CP, crude protein; D (Gompertz model), constant factor of microbial efficiency; D (in situ degradation), degradable fraction; DM, dry matter; FOM, fermentable organic matter; kd , degradation rate; NDFom, NDF not assayed with stable amylase expressed exclusive of residual ash; OM, organic matter; R, fractional rate of substrate fermentation; SR, specific rate of gas production; TRmax G, time at which maximum rate of gas production is reached; U, undegradable fraction; W, washout fraction. ∗ Corresponding author. Tel.: +351 259 350 422; fax: +351 259 350 482. E-mail address: [email protected] (M.A.M. Rodrigues). 0377-8401/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.anifeedsci.2008.12.004
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(FOM) all estimations improved upon inclusion of chemical composition characteristics, such as sugars, crude protein (CP), neutral detergent fibre (NDFom) and crude fat (CFat). The Logistic and Gompertz models continued to better predict kd , with R2 values of 0.79 and 0.88, respectively, while the Sigmoidal model showed a higher capability to estimate FOM (R2 = 0.90). It should also be noticed that the estimation of the washout fraction (W) estimation was obtained with only sugar and starch contents (R2 = 0.62). There were only moderate relationships between in situ and gas production indicating that the methods do not describe the degradation of these feedstuffs in a similar way. © 2008 Elsevier B.V. All rights reserved.
1. Introduction While it might be assumed that fermentation characteristics of feedstuffs should be evaluated through in vivo trials, it is also obvious that in vivo experiments need a high number of animals, have high financial costs, are difficult to standardize and only the feed to be tested can be fed during trials. Alternatively, in vitro and in situ techniques have been developed to allow its utilization on a routine basis. Nowadays, the nylon bag technique (Meherez and Ørskov, 1977) is the standard technique in feed evaluation systems (Tamminga et al., 1994; NRC, 2001; Thomas, 2004). Nevertheless, some drawbacks have been pointed out (Mould, 2003) and a particular importance has been given to the excessive loss of particulate material that might lead to the disappearance of undegraded material from the nylon bags. In this way, the technique might be inappropriate for feedstuffs with high proportions of soluble material (starch, sugars) and/or high proportions of small particles (Cone et al., 1998, 1999). The development of alternative in vitro methods, such as the gas production technique, has lead to the assessment of several mathematical models to describe and interpret the fermentation characteristics of feeds (Pitt et al., 1999; Dhanoa et al., 2000; France et al., 2000; Calabrò et al., 2005). While there are enough data to validate gas production models regarding its potential to estimate rumen degradation (Khazaal et al., 1993; Beuvink et al., 1993; Sileshi et al., 1996; Cone et al., 1998, 1999; Lopez et al., 1998) a thorough comparison with the nylon bag technique is lacking and few studies have been conducted to compare the prediction capability of the models. Furthermore, comparisons found in literature have been conducted mainly with fibrous feeds and only a limited number of feedstuffs with high proportion of soluble and/or small particles have been studied. The aim of this study was to analyse the relationship between the in situ degradation characteristics of organic matter (OM) of several feedstuffs and the gas production parameters using different mathematical models. 2. Materials and methods 2.1. Feedstuffs and chemical analysis Investigations were carried out with 15 feed samples (Table 1). The ruminally protected solvent extracted rapeseed meal (Rumiraap) was obtained after treatment with formaldehyde and was provided by Schouten (Giessen, The Netherlands). Amygold and amygold extra steep are wet maize gluten feeds (DM content approximately 400 g kg−1 ) and were provided by Amylum (Vilvoorde, Belgium). Amygold extra steep has a higher protein content than amygold. Pressed beet pulp and brewery grians were dried at 70 ◦ C and all samples were ground to pass a 1 mm screen before use. Ash was determined after 3 h at 550 ◦ C (ISO 5984). Crude protein (CP) was determined using a Kjeldahl method (ISO 5983). Crude fat (CFat) was determined gravimetrically after 6 h extraction with petroleum-ether (ISO 6492). Reducing sugars were measured colorimetrically (Van Vuuren et
88
Feedstuff
Ash (g kg−1 DM)
CP (g kg−1 DM)
CFat (g kg−1 DM)
Starch (g kg−1 DM)
Sugars (g kg−1 DM)
NDFom (g kg−1 DM)
ADFom (g kg−1 DM)
Lignin (sa) (g kg−1 DM)
Amygold Amygold (extra steep) Beet pulp (molassed) Beet pulp (pressed) Brewery grains Citrus pulp Lin seed hulls Lucerne meal Lupin Maize gluten feed Palm kernel expeller Rapeseed meal SEb Rumiraap Sunflower meal SEb Soybean meal
38 62 79 68 57 66 60 125 29 59 48 72 77 74 75
174 227 88 106 251 66 373 174 315 234 158 404 385 340 559
40 36 14 13 109 24 97 27 66 39 93 45 58 22 32
186 158 4 2 19 5 24 19 3 183 1 9 11 3 2
23 28 234 2 2 272 53 54 64 27 21 113 112 78 114
453 373 356 542 539 191 205 419 292 336 590 273 289 382 86
122 100 182 230 195 147 129 318 209 97 366 205 193 269 51
8 16 8 11 33 10 44 77 4 4 98 83 78 81 1
S.E.M.c
0.5
1.9
a
1.2
0.9
4.0
2.1
1.5
1.3
CP: crude protein; CFat: crude fat; NDFom: NDF not assayed with stable amylase expressed exclusive of residual ash; ADFom: ADF expressed exclusive of residual ash; Lignin (sa): lignin determined by solubilisation of ADF with sulphuric acid. b Solvent extracted. c
All samples analysed in duplicate in single samples.
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Table 1 Chemical composition (g kg−1 DM) of the feedstuffs.a .
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al., 1993) after extraction with 40% ethanol followed by treatment with 0.023 M HCl to hydrolyse the bonds between sugar molecules. Starch content was determined as glucose using the amyloglucosidase method (Keppler and Decker, 1970) after hydrolysing the starch by heating in boiling water bath in the presence of 2 M HCl (Cone, 1991). Neutral detergent fibre (NDFom), acid detergent fibre (ADFom) and lignin (sa) fractions were determined by the detergent procedures of Van Soest et al. (1991) and Robertson and Van Soest (1981). Both NDFom and ADFom were expressed without residual ash. Sodium sulphite and amylase were not used to determine NDFom. 2.2. In situ measurements Measurements of in situ organic matter were performed in three mature cows fistulated in the rumen using the nylon bag technique (Ørskov and McDonald, 1979). The cows weighed 600–700 kg and produced 20–25 kg milk per day. Approximately 5 g of 3 mm ground samples were incubated in the rumen of each cow for 3, 8, 16, 48 and 336 h. Nylon bags (Nybolt, PA 40/30, Zürich, Switzerland), had a medium pore size of 40 m. Cows were fed ad libitum a ration containing 720 g kg−1 forage (DM basis) including, on a DM basis, grass hay (23 g kg−1 ), grass silage (143 g kg−1 ), maize silage (437 g kg−1 ), pea silage (65 g kg−1 ), maize gluten meal (127 g kg−1 ), corncob silage (96 g kg−1 ), solvent extracted soybean meal (52 g kg−1 ), solvent extracted rapeseed meal (52 g kg−1 ) and minerals (5 g kg−1 ). Animals received two portions daily, fed as totally mixed rations. Incubations were conducted in duplicate for the 3 h period and in quadruplicate for the 336 h period. Triplicate incubations were performed for all the other periods. All feeds were incubated at the same time and a maximum of 35 bags per cow were introduced in one time. At the end of each incubation period, the bags were collected and washed in a washing machine (AEG, Öko Turnamat 2800, AEG-Electrolux, Stockholm, Sweden) for 40 min with cold tap water and then dried at 70 ◦ C for 24 h. The residues where ground to pass a 1 mm screen and were analysed for OM and CP. The washout fraction (W) was determined by washing with cold tap water. The residue after 336 h incubation was considered to be the undegradable fraction (U). The degradable fraction (D) was calculated as 100-W-U. Residues after different incubation periods were fitted to a first-order degradation model (Robinson et al., 1986) in order to calculate the rate of degradation (kd , fraction per h). The amount of fermentable organic matter (FOM) was calculated from in situ incubations (Cone et al., 1994), assuming a passage rate 0.06 h−1 . Calculations were conducted without a discrete lag-time. 2.3. Gas production incubations Two non-lactating cows, weighing on average 480 (±42 kg) fitted with ruminal canula (Bar Diamond Inc., Parma, ID, USA), were used in this experiment to collect the rumen fluid. Animals were fed on a diet composed of 1 kg of standard compound feed, containing about 150 g kg−1 starch, offered in the morning and ad libitum grass hay in the morning and afternoon. Animals had free access to water and to mineral-vitamin blocks. Rumen fluid samples were withdrawn 2 h after the morning feeding into pre-warmed insulated flasks previously filled with CO2 . Rumen fluid was strained through four layers of cheesecloth and kept at 39 ◦ C under CO2 . Rumen fluid used to measure gas production was mixed (1:2, v/v) with an anaerobic buffer/mineral solution as described by Cone et al. (1996). All laboratory handlings were carried out under continuous flushing with CO2 . Samples (500 mg) were accurately weighed into 250 ml serum bottles (Schott, Mainz, Germany) and incubated in 60 ml buffered rumen fluid. Each sample was incubated in one run in duplicate. The gas production was recorded for 72 h using a fully automated system (Cone et al., 1996). Gas curves were fitted to four mathematical models. An Exponential mono-phasic model (Ørskov and McDonald, 1979): Y = A(1 − e−ct ) where A = estimated asymptotic gas production; c = constant rate of gas production.
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A Logistic mono-phasic model (Schofield et al., 1994): A
Y=
1 + e(2−4SRt)
where A = estimated asymptotic gas production; SR = specific rate of gas production. A Gompertz mono-phasic model (Lavrenˇciˇc et al., 1997):
Y = A e(−C)e
(Dt)
where A = estimated asymptotic gas production; C = relative rate of gas production; D = constant factor of microbial efficiency. Constant rate of gas production (c) was calculated as c=
D C
A mono-phasic Sigmoidal model as described by Groot et al. (1996) Y=
A 1 + (B/t)
C
where A = estimated asymptotic gas production; B = time of incubation at which half of the asymptotic gas production has been formed; C = sharpness of the switching characteristic for the profile. The time at which maximum rate of gas production is reached (TRmax Gi ) was calculated according to Yang et al. (2005): TRmax G = B
C − 1 1/C C +1
Fractional rate of substrate fermentation (R) was calculated using the equation proposed by Groot et al. (1996) R=
CTRmax G(C−1) BC + TRmax GC
2.4. Statistical analysis All models were fitted to data by nonlinear regression using the NLIN procedure of SAS (1999). Multiple regression analyses were used to investigate the relationships between in situ parameters, gas production parameters and chemical composition data using the REG procedure of SAS (1999). The multiple regression equations were evaluated with the determination coefficient adjusted for the degrees of freedom (R2 ) and the residual standard deviation (R.S.D.). Equations were fitted by the least square method following a stepwise procedure with the 0.05 probability level for inclusion or deletion of variables. 3. Results 3.1. Chemical composition The chemical composition of the investigated feedstuffs is presented in Table 1. Crude protein ranged from 66 g kg−1 DM for citrus pulp to 559 g kg−1 DM for soybean meal. Starch ranged from 1 g kg−1 DM for palm kernel expeller to 186 g kg−1 DM for amygold. Sugars content ranged from 2 g kg−1 DM for beet pulp (pressed) and brewery grains to 272 g kg−1 DM for citrus pulp. Crude fat ranged from 13 g kg−1 DM in Beet Pulp (pressed) to 109 g kg−1 DM in brewery grains. Cell wall content (NDFom) ranged from 86 g kg−1 DM for soybean meal to 590 g kg−1 DM for palm kernel expeller.
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Table 2 The washout (W), undegradable (U) and degradable fractions (D) as well as degradation rates (kd ) of OM determined in situ, and fermentable organic matter (FOM, g kg−1 OM) of feedstuffs. Feedstuff
W
U
D
kd a (h−1 )
FOMb (g kg−1 OM)
Amygold Amygold (extra steep) Beet pulp (molassed) Beet pulp (pressed) Brewery grains Citrus pulp Lin seed hulls Lucerne meal Lupin Maize gluten feed Palm kernel expeller Rapeseed meal SEc Rumiraap Sunflower meal SE Soybean meal
0.21 0.27 0.32 0.00 0.01 0.43 0.02 0.23 0.27 0.42 0.11 0.21 0.17 0.19 0.27
0.04 0.02 0.04 0.03 0.19 0.02 0.11 0.27 0.02 0.03 0.19 0.16 0.16 0.27 0.00
0.075 0.70 0.64 0.97 0.80 0.56 0.88 0.49 0.71 0.55 0.71 0.63 0.67 0.55 0.73
0.0300 0.033 0.068 0.041 0.034 0.068 0.089 0.049 0.073 0.035 0.041 0.089 0.016 0.084 0.092
437 492 627 374 284 691 518 428 627 592 378 557 296 486 676
S.E.M.d
0.008
0.005
0.005
0.002
a b
Calculated without lag-period. FOM was calculated using the mean values of W, D and kd .
c
Solvent extracted. Incubations were conducted in duplicate for the 3 h period and in quadruplicate for the 336 h period. Triplicate incubations were performed for all the other periods. d
3.2. In situ degradation of OM The in situ degradation fractions and the kd values of the feedstuffs are presented in Table 2. The W fraction ranged from 0 for beet pulp (pressed) to 0.43 for citrus pulp. For the majority of feedstuffs the U fraction was very low, not exceeding 0.05. However, brewery grains, lin seed hulls, lucerne meal, palm kernel expeller, rapeseed meal, rumiraap, and sunflower meal presented values that varied between 0.11 and 0.27. The D fraction ranged from 0.49 for lucerne meal and 0.97 for beet pulp (pressed). Values for kd , ranged from 0.03 h−1 for amygold to 0.092 h−1 for soybean meal. The amount of FOM in the feedstuffs ranged from 28.4 g kg−1 for brewery grains to 691 g kg−1 for citrus pulp. 3.3. Gas production profiles fitted with mono-phasic models Gas production profiles showed variations in both rate and extent of fermentation among feedstuffs (Table 3), for all four mathematical models. The estimated asymptotic gas production (A) and the constant rate of gas production (c), obtained from data fitted with the Exponential model (253 ± 11.9 ml/g OM and 0.130 ± 0.0231 h−1 , respectively) were generally higher than the values obtained from data fitted with the Logistic (237 ± 11.2 ml/g OM and 0.066 ± 0.0027 h−1 , respectively) and the Gompertz (242 ± 11.2 ml/g OM and 0.068 ± 0.0031 h−1 , respectively) models, which presented a similar range of values within the two parameters. On the contrary, the asymptotic gas production (A) of Groot’s model was generally higher (279 ± 11.5 ml/g OM) than the values obtained with the Exponential model, with the exception of palm kernel expeller. The majority of data obtained for the fractional rate of substrate fermentation (R) were lower (0.090 ± 0.0043 h−1 ) than those obtained for the constant rate of gas production (c) in the Exponential model. 3.4. Estimation of in situ parameters The in situ degradation constants were estimated from gas production parameters alone or together with chemical composition data.
92
Table 3 Parameters of gas production profiles fitted with mono-phasic models and residual standard deviation (R.S.D.).
Amygold Amygold (extra steep) Beet pulp (molassed) Beet pulp (pressed) Brewery grains Citrus pulp Lin seed hulls Lucerne meal Lupin Maize gluten feed Palm kernel expeller Rapeseed meal SEe Rumiraap Sunflower meal SEe Soybean meal S.E.M.f a b
Exponentiala
Logisticb
Gompertzc
Sigmoidald
A
c
R.S.D.
A
SR
R.S.D.
A
C
D
c
R.S.D.
A
B
C
R
R.S.D.
333 301 333 345 178 335 202 175 294 271 250 198 184 170 224
0.060 0.068 0.116 0.091 0.072 0.124 0.127 0.108 0.099 0.082 0.060 0.108 0.067 0.142 0.099
8.1 5.1 8.2 16.3 4.0 7.9 3.5 2.8 7.6 2.4 17.6 4.7 4.7 4.1 4.8
305 279 316 324 165 318 193 166 278 254 227 188 171 162 211
0.045 0.050 0.081 0.066 0.054 0.087 0.087 0.074 0.070 0.058 0.046 0.076 0.049 0.081 0.070
12.8 12.7 17.2 16.2 11.4 18.3 10.8 9.8 12.9 14.3 12.4 12.3 11.5 10.8 11.3
310 284 322 326 170 326 196 170 282 261 229 193 179 167 216
2.76 2.61 2.49 3.42 2.31 2.22 2.36 2.16 2.77 2.30 3.99 2.14 2.06 2.01 2.47
0.127 0.134 0.211 0.214 0.128 0.204 0.220 0.172 0.198 0.140 0.163 0.172 0.104 0.173 0.180
0.046 0.051 0.085 0.063 0.054 0.092 0.093 0.080 0.072 0.061 0.040 0.080 0.050 0.086 0.073
6.7 7.6 12.5 7.7 8.4 14.6 7.9 6.6 8.0 9.5 5.0 8.9 7.7 7.5 8.1
347 319 349 341 202 361 214 192 302 299 238 220 237 191 238
11.8 10.7 6.2 7.4 11.3 5.9 5.7 7.1 7.1 9.5 10.8 7.2 15.9 6.7 7.4
1.53 1.45 1.49 2.00 1.21 1.30 1.40 1.24 1.64 1.27 2.35 1.2 1.10 1.14 1.42
0.103 0.068 0.118 0.117 0.065 0.121 0.176 0.103 0.108 0.076 0.092 0.102 0.050 0.114 0.098
4.8 3.5 5.6 5.8 2.6 4.1 1.9 1.6 2.4 2.5 8.4 2.2 2.2 1.7 3.3
0.032
0.0022
0.0006
0.014
0.0012
4.2
0.0064
3.9
0.0007
4.0
4.8
0.17
A: estimated asymptotic gas production (ml/g OM); c: constant rate of gas production (h−1 ). A: estimated asymptotic gas production (ml/g OM); SR: specific rate of gas production (h−1 ).
A: estimated asymptotic gas production (ml/g OM); C: relative rate of gas production (h−1 ); D: constant factor of microbial efficiency; c: constant rate of gas production (h−1 ). A: estimated asymptotic gas production (ml/g OM); B: time of incubation at which half of the asymptotic gas production has been formed (h); C: sharpness of the switching characteristic for the profile; R: fractional rate of substrate fermentation (h−1 ). e Solvent extracted. c
d
f
All samples analysed in duplicate in single samples.
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Feedstuff
Table 4 Estimation of in situ parameters with gas production and chemical composition using mono-phasic models. Predicted variablea
Model
Fermentation parameters and chemical compositionb variables
Fermentation parameters Equation
R2
– – – – –
– – – – –
– – – – –
– – −3.6 + 138.9 × SR 15.1 + 523.4 × SR 46.4 − 462.9 × SR
– – 0.65 0.31 0.51
– – 0.0002 0.0177 0.0016
– – 1.5 10.0 6.5
– – −2.5 + 118.5 × c −5.9 + 483.9 × c + 0.09 × A 49.8 − 200.9 × D
– – 0.62 0.50 0.58
– – 0.0003 0.0063 0.0006
– – 1.6 9.3 6.1
– – 11.9 − 0.72 × B 76.6 − 3.1 × B −10.8 + 3.1 × B
– – 0.64 0.42 0.86
– – 0.0002 0.0055 <0.0001
– – 1.5 10.0 5.7
P
R.S.D.
Equation
R2
P
R.S.D.
5.1 + 0.14 × Sugars + 0.12 × Starch – 9.5 − 0.01 × NDFom 50.9 + 0.02 × Sugars − 0.06 × NDFom + 0.08 × c –
0.62 – 0.32 0.64 –
0.0012 – 0.0164 0.0025 –
8.2 – 2.1 7.9 –
5.1 + 0.12 × Sugars + 0.14 × Starch – −5.2 + 135.1 × SR + 0.007 × CP 30.5 + 255.3 × SR − 0.05 × NDFom + 0.09 × A –
0.62 – 0.79 0.71 –
0.0012 – <0.0001 0.0007 –
8.2 – 1.2 7.0 –
5.1 + 0.12 × Sugars + 0.14 × Starch – −12.3 + 146.5 × c + 0.009 × CP + 2.2 × C 29.2 + 236.2 × c + 0.09 × A − 0.05 × NDFom –
0.62 – 0.88 0.71 –
0.0012 – <0.0001 0.0007 –
8.2 – 0.9 7.0 –
5.1 + 0.12 × Sugars + 0.14 × Starch – 9.9 − 0.76 × B + 0.009 × CP 103.9 − 0.05 × NDFom − 3.8 × B + 0.06 × A − 184.5 × R –
0.62 – 0.90 0.90 –
0.0012 – <0.0001 <0.0001 –
8.2 – 0.8 4.1 –
c
Exponential
– – – – –
Logisticd W D kd FOM (ln 2/kd )100 Gompertze W D kd FOM (ln 2/kd )100 Sigmoidalf W D kd FOM (ln 2/kd )100
a W: washout fraction of organic matter determined wit the nylon bag technique; D: digestible organic matter fraction (100-W-U); kd : degradation rate of organic matter determined with the nylon bag technique; FOM: fermentable organic matter calculated from nylon bag data; (ln 2/kd )100: transformation of kd to a half-time value. b CP: crude protein; NDFom: NDF not assayed with stable amylase expressed exclusive of residual ash; CF: crude fat. c d
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W D kd FOM (ln 2/kd )100
c: constant rate of gas production (h−1 ). A: estimated asymptotic gas production (ml/g OM); SR: specific rate of gas production (h−1 ).
A: estimated asymptotic gas production (ml/g OM); C: relative rate of gas production (h−1 ); D: constant factor of microbial efficiency; c: constant rate of gas production (h−1 ). A: estimated asymptotic gas production (ml/g OM); B: time of incubation at which half of the asymptotic gas production has been formed (h); R: fractional rate of substrate fermentation (h−1 ). e f
93
94
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The stepwise linear regression indicated that gas production alone was a poor predictor of in situ degradation (Table 4). The Exponential model did not show any estimation capability and the in situ degradable fraction (D) was not estimated by any of the models. As to the degradation rate of OM (kd ) and FOM all estimations improved upon inclusion of chemical composition characteristics, crude protein and neutral detergent fibre. It should also be noticed that the estimation of the washout fraction (W) estimation was obtained with only sugar and starch contents (R2 = 0.62; R.S.D. = 8.2). 4. Discussion Results presented in this study showed that the utilization of different mathematical models to adjust data from in vitro gas production influenced the overall prediction capability of in situ parameters, showing that for these feedstuffs the Exponential model was not suitable to estimate these parameters. Furthermore, differences occurred between the models with kd being better predicted by the Logistic model while FOM was better estimated by the Gompertz model. However highest correlations were obtained with the Sigmoidal model (R2 = 0.86) when kd was transformed to its half-life value. Similar results were described by Cone et al. (2002) where an increase from R2 = 0.42 to R2 = 0.53 was obtained when this transformation was applied. These relationships are not in accordance to the data reported by Khazaal et al. (1993) working with hays, Blümmel and Ørskov (1993) working with straws and Sileshi et al. (1996) working with tropical forages, who observed poor relationships between the rate of DM degradation and the rate of gas production. Nevertheless, Cone et al. (2002) working with feedstuffs similar to the ones used in our study reported moderate estimations of kd and FOM. Other studies in grass (Cone et al., 1998), grass silage (Cone et al., 1999) and hay (López et al., 1999) have also showed good relationships between in situ degradation characteristics and in vitro gas production. Differences between reported data can be attributed to several factors that according to Valentin et al. (1999) might be due to methodology, substrates as well as to the mathematical model used. In this way, it seems that while statistical comparisons between models are necessary to evaluate the robustness of fitting it should be noticed that when these models are used to evaluate the degradation characteristics in the rumen of several feedstuffs, special attention should be given to the possible dissimilarity between the obtained parameters. According to Calabrò et al. (2005) the examination of possible discrepancies among models in values of the main degradation parameters derived is essential as it will determine the possibility to conduct comparative ranking studies of feeds. Lopez et al. (1998) also mentioned that the choice of the mathematical model could result in different parameter estimates affecting the comparison of feeds. There was a lack of predictive capability of all models as to the W and D fractions of in situ incubations. While it is accepted that in spite of methodological differences both methods (nylon bag technique and gas production) give information on the degradation rate of substrates (Cone et al., 1998), it is also true that the problems inherent to these methods are related to the difficulties in comparing the early stages of the degradation process. In the gas production, gas is originated from the degradation of both soluble and insoluble substrates whereas in the in situ method, the disappearance of only the insoluble material is measured. In addition, the W fraction is assumed to be composed by the completely and rapidly degraded fraction; however, this assumption is not totally correct (Dhanoa et al., 1999). The relationship between the W fraction and the sugar and starch contents (R2 = 0.62) are in accordance to what was mentioned before. In fact, this relationship indicates that the W fraction assumes that both these constituents are soluble and immediately degraded, but this is not entirely true. Furthermore, while all potential fermentable components are measured in the gas production method, part of them might be lost in the nylon bag technique and thus are not included in the D fraction. Cone et al. (2006) refer to this subject highlighting that this might be the case of starch. In addition, these results also point out that the inclusion of starch into an immediately fermentable fraction is incorrect. Results presented by Chai et al. (2004) indicate that the majority of starch degradation normally occurs between 6 and 24 h, thus concluding that fermentation of starch and other non-soluble components occurred simultaneously. For all models, the inclusion of chemical characteristics improved the estimation of in situ parameters. For all models CP and NDFom were the main constants included in the regression equations. In the gas production method CP fermentation has a negative influence on gas production (Cone and Van
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Gelder, 1999) as the amount of gas produced, because of a shift in the equilibrium in the carbonate buffer upon production of fatty acids, is reduced by the formation of NH4 when NH3 is released from protein fermentation (Menke and Steingass, 1988). The amount of CP (varied from 66 g kg−1 DM on citrus pulp to 559 g kg−1 DM on soybean meal) as well as the expected differences between its degradation within the feedstuffs used in this experiment point out that the inclusion of CP in the estimation of kd might be the result of correcting for differences between the degraded in situ CP and the rate of gas produced due to protein fermentation in the gas production method. The inclusion of NDFom in FOM estimation is the result of the proportion of gas being produced by the insoluble but degradable fraction that in the case of these feedstuffs is mainly represented by NDFom. In our study NDF was negatively related to FOM and this is consistent to what was mentioned before. 5. Conclusions The results show moderate relationships between in situ and gas production indicating that the methods do not describe the degradation of these feedstuffs in a similar way. As several advantages are generally pointed out to the gas production one possible research area that can be explored would be the application of this technique to directly estimate the fermentable metabolizable energy (FME) and rumen undegraded protein (RUP) through the use on in vivo and in vitro trials. Some of these experiments are now being conducted by this research team. References Beuvink, J.M.W., De Visser, H., Klop, A., 1993. In vitro gas production kinetics of different maize products: a comparison to nylon-bag degradation kinetics. In: Beuvink, J.M.W. (Ed.), Measuring and Modelling In Vitro Gas Production Kinetics to Evaluate Ruminal Fermentations of Feedstuffs. DLO-Research Institute for Livestock Feeding and Nutrition (IVVO-DLO) Report. Lelystad, The Netherlands, pp. 31–41. 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