Feed evaluation for dairy cows Reference methods for assessing rumen degradation characteristics of nutreints Peter Lund, Maria Chrenková & Martin Weisbjerg Department of Animal Science, AU-Foulum, Aarhus University, PO Box 50, DK-8830 Tjele, Denmark & Animal Production Research Centre Nitra Hlohovská 2, 951 41 Lužianky, Slovak Republic Innovative and practical management approaches to reduce nitrogen excretion by ruminants
Outline New requirements for feed evaluation data Protein NDF Starch Conclusion
Classic feed evaluation The cow is seen as a black box Focus on total tract digestibility Feed values are additive A ration s value can be predicted from table values for components
Feed evaluation trends Change from classic systems to models with a more mechanistic approach to describe truly digested nutrients 1992 Cornell Net Energy and Protein System (Russel et al., 1992) 2005 NORFOR (Volden, 2011)
New feed evaluation systems Have skipped: Digestible Weende nutrients as basis Additivity But require for several nutrients: Potential digestibility Fractional rate of degradation To model rumen digestion
Kinetics in the rumen 1. order kinetics!! Rumen pool k p = 1/MRT k d k d = Fractional rate of degradation (%/h) k p = Fractional rate of passage (%/h) Digestibility = k d /(k d +k p )
Digestibility (%) Kinetics helps us understand real life What happens with digestibility of carbohydrates when feed intake is increased? Digestibility (%) 100 80 60 40 20 0 kd = 3 %/h kd = 25 %/h kd = 120 %/h 0 2 4 6 8 Fractional passage rate (k p, %/h)
K d (per hour) Example: sugar+starch affects fiber digestibility in the rumen how? 0,060 0,055 0,050 0,045 0,040 1, grass silage, starch 1, grass silage, sugar 2, lucerne 2, timothy 3, grass silage 3, grass silage 3, barley whole crop silage 3, Hay 0,035 0,030 0,025 0,020 0,015 0,010 0 1 2 3 4 5 6 7 Kg sugar + starch per day
Passage rate total NDF (per ho Example: Dry matter intake up fiber digestibility down why? 0.025 0.02 0.015 0.01 0.005 0 0 5 10 15 20 25 Dry matter intake (kg/day)
Associative effects - NDF Feeding level: k p Forage:concentrate ratio (constant feeding level): Substrate-preference k d ph k d (k p ) feed intake down Rumen degradable protein level: Ammonia down, k d Fatty acids: Unsaturated fatty acids, k d Medium chain fatty acids (C12 and C14), k d
Feed type Potential digestibility Starch content Feed type Fractional rate of degradation, kd Digestibility of NDF Fractional rate of passage, kp Feed intake
What is required? Main nutrients: Protein NDF Starch Degradation characteristics required: Potential degradability Fractional rate of degradation, k d Solubility (for protein) Further, fractional rate of passage as affected by animal and ration is needed, but is not an intrinsic feed characteristic
Degradation (%) Nedbrydningsgrad (%) How do we obtain data? 100 80 60 40 20 0 0 10 20 30 40 50 Tid (t) Time (h) Nylon bag Tilley+Terry Gasproduction curve Dependent on used model Not an in vivo situation Good for table values
How do we obtain data in practice? Parameterisation of new models mainly based on in situ (nylon bag) or in vitro rumen fluid based methods Future feed evaluation in practise Concentrates mainly table values Forages mainly NIRs measures However, NIRs require reliable reference methods. In situ and rumen fluid based methods are not appropriate (expensive, time and resource demanding, difficult in commercial labs) Therefore laboratory methods are needed
Protein
Protein Nutritive value of crude protein (CP) in feedstuffs for ruminants Rate and extent of degradation in the rumen Intestinal digestibility of rumen undegraded protein (RUP) Currently, in situ and in vivo methods are the main reference methods used in European systems (PDI, DCE/OEB, AAT/PBV) and NRC USA. The Cornel Net Carbohydrate Protein Model uses solubility protein in buffers and detergent solutions to fractionate protein in feeds into five fractions. It is chemical laboratory method and/or in vitro method.
The methods for determination a potential CP degradability and intestinal digestibility Ruminal degradability (rumen undegraded protein (RDP) & rumen undegraded protein (RUP)) in situ in vitro (CNCPS) Intestinal digestibility mobile bag enzymatic in vitro
Feed protein fractions - in situ vs. CNCPS Soluble (bags) In situ Feed Potentially degradable Insoluble Undegradable Buf. sol. NPN Sol.protein A B1 CNCPS Feed Buf. insol.-ndin B2 Buf. insol NDIN - ADIN B3 ADIN Indigestible C
Chrenková et al. unpublished Conclusion in situ vs. CNCPS comparisons Universal prediction of in situ RDP and RUP according to CNCPS seems problematic work in progress Preliminary results show high correlation (R 2 =0.80) between in situ parameter b (insoluble but degradable CP fraction) and CNCNS protein subfraction B 2 (% of CP) There were negative correlations between in situ parameters a and c and buffer insoluble CP (r = - 0.892 ** and r = -0.854 **, resp.).
in vitro RUP digestibility (%) Chrenková et al. unpublished Comparison in vitro to mobile bag - intestinal digestibility of rumen undegraded feed protein (RUP) r=0.803 for all tested feeds; r=0.846 for cereals; r=0.917 for legumes; r=0.702 oilseeds meal ; r= 0.954 for maize silages and r=0.548 for lucerne silages 100 90 80 70 60 50 40 30 20 10 0 0 10 20 30 40 50 60 70 80 90 100 Mobile bag RUP digestibility (%)
Conclusion for intestinal digestibility An in vitro method for assessing digestibility of rumen undegraded protein in the intestine has shown high correlations (r > 0.75) to in situ digestibility.
NDF
Potential digestibility or indigestibility (indf) NorFor reference for indf is 288 h in situ CNCPS use 2.4 x ADL
INDF-indigestible NDF NorFor standard for INDF NDF in 10-15 µm Dacron bags 288 h incubation
INDF (g/kg DM) 160 indf vs. ADL 140 120 100 80 60 40 20 Primary growth, 1. Harvest Primary growth, 2. Harvest Primary growth, 3. Harvest 1. Regrowth 2. Regrowth 3. Regrowth Lineær Linear (primary growth) Lineær Linear (regrowths) 5 15 25 35 45 ADLom (g/kg DM) Krämer et al. 2012. Animal Feed Sci. Technol.
indf (% DM) indf vs. ADL effect of forage type 25 CNCPS 20 Festulolium y = 3.53x P. ryegrass 15 y = 2.4x H. ryegrass Festulolium 10 5 0 White clover 0 1 2 3 4 5 6 7 ADL (% DM) Cocksfoot Lucerne y = 1.66x W. clover R. clover Weisbjerg et al. 2010
indf vs. in vitro OM indigestibility INDF (g/kg DM) Perennial ryegrass only effect of growth and harvest time 160 140 120 100 80 60 40 Primary growth, 1. Harvest Primary growth, 2. Harvest Primary growth, 3. Harvest 1. Regrowth 2. Regrowth 3. Regrowth Ekspon. Expon. (primary growth) Lineær Linear (regrowths) 20 150 200 250 300 350 400 IOMD (g/kg OM) Krämer et al. 2012. Animal Feed Sci. Technol. 177, 40 51
Conclusions indf Universal prediction equations not accurate Within some feed types simple or multiple regression equations have potential for indf estimation Examples: All: indf(g/kg/dm) = 30.56 + 2.48ADL(g/kg DM) 0.44ADL(g/kg NDF); RMSE 35.8 Grass/clover: indf(g/kg/dm) = 41 93.58 + 0.30NDF 1.04ADL(DM)+ 0.909ADL(NDF); RMSE 16.3
Particle loss risk for particle loss during in situ incubations Late cut grass silage Area mm 2 Cumulative distribution functions of particle area in Dacron bag residues Alfalfa silage - machine-washing ( ) - 24 h in situ rumen incubation (-----) - 288 h in situ rumen incubation ( ) Vertical line is set at 0.005 mm 2 particle area. Krämer et al. 2013. J Dairy Sci. Area mm 2
Accumulated density Accumulated density Particle loss from nylon bags Cumulative distribution functions of particle area in Dacron bag residues after machine-washing ( ), 24 h in situ rumen incubation (-----), and 288 h in situ rumen incubation ( ) for forages and concentrates. Vertical line is set at 0.005 mm 2 particle area. Accumulated density Accumulated density Early cut grass silage Area (mm 2 ) Corn silage Late cut grass silage Area (mm 2 ) Alfalfa silage Area (mm 2 ) Area (mm 2 ) Dried distillers grains Accumulated density Accumulated density Rapeseed meal 2 2
NDF rate of degradation
In vivo NDF digestibility in vivo NDF digestibility In vitro gas vs. in situ The in vitro gas production method was more accurate and biologically the relationship was more correct, but the in situ method was more precise 1 0.8 1 0.8 0.6 0.6 0.4 Y = 0.092 + 0.901 X (R 2 0.93) 0.2 0.2 0.4 0.6 0.8 1 Predicted NDF digestibility - in situ kd 0.4 Y = 0.005 + 1.005 X (R 2 0.90) 0.2 0.2 0.4 0.6 0.8 1 Predicted NDF digestibility - gas kd Weisbjerg, M.R., Rinne, M. & Huhtanen, P. (2009)
NDF rate of degradation Biological methods (in situ, in vitro, in vitro gas) are all very resource demanding All 3 methods useful and pros and cons - however they differ in estimated degradation parameters Backwards calculation at present used in NorFor system might be the future! Beside indf, only common feed information is needed
Backwards calculation Information needed OM digestibility Ash concentration NDF concentration indf concentration All except indf are classical feed analysis Idea: NDS digestibility estimated using Lucas principle NDF digestibility calculated by difference Kd NDF backwards calculated assuming 2 pool rumen model
Estimation of k d for DNDF from in vivo NDF digestibility The theory DNDF digestibility (D) = (k d /(k d +k r ) [1 + k r /(k d + k p )] This equation can be solved according to k d k d =[-(k p + k r )+[(k p + k r ) 2 +4Dk r k p /(1 - D)] 0.5 ]/2 Necessary information and assumptions: In vivo NDF digestibility INDF concentration Mean retention time and distribution of MRT on compartment 1 and 2 Allen & Mertens (1988), Huhtanen et al. (2004)
Assumptions on rumen NDF passage behaviour Two compartment model, with selective retention of young material in the rumen Hypothetical example for sheep fed at maintenance level k d k d CMRT 20 h CMRT 30 h k r 0.05 h -1 k p 0.033 h -1
NDS digestibility (%) Lucas equation. Apparent NDS digestibility plotted against NDS concentration 100 80 60 40 20 0 0 20 40 60 80 100 NDS (% of feed DM) Weisbjerg et al., 2004
Dig. NDS (% of feed DM) Lucas equation. Apparently digested NDS in % of feed DM plotted against NDS concentration 90 80 70 60 50 40 30 20 10 0 y = 1.013x - 9.02 R2 = 0.97 root MSE = 2.3 0 20 40 60 80 100 NDS (% of feed DM) Weisbjerg et al., 2004
Conclusions rate of NDF degradation Biological methods (in situ, in vitro, in vitro gas) useful, but not appropriate Backwards calculation might be the future
Starch
kd in vitro (per h) Rumen starch degradability Rumen starch degradability Weisbjerg et al. 2011 1 0.9 In vitro in situ vs in vivo - starch 0.16 0.14 0.12 0.1 0.08 0.06 0.04 0.02 0 y = 0.0236x + 0.0797 R² = 0.2073 0 0.2 0.4 0.6 0.8 1 1.2 1.4 kd in situ (per h) 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0 y = 0.385x + 0.518 r = 0.84 0 0.2 0.4 0.6 0.8 1 In situ ESD y = 1.39x - 0.08 r = 0.76 0 0.2 0.4 0.6 0.8 1 Conclusion In vitro ESD Weak correlations between in situ and in vitro kd Effective degradability ok correlations for both methods IS k d values unrealistic, however ESD values correlated well
Mobile bag disappearance (g/kg) Mobile bag assessment of in vivo starch digestibility 1100 1000 y = 0.88x + 92 R² = 0.19 900 800 700 600 y = 0.58x + 369 R² = 0.06 850 900 950 1000 1050 In vivo starch digestibility (g/kg) Without RP With RP Conclusion Weak correlations between in vivo and mobile bag digestibilities Taghi et al. 2012
Starch approach based on meta analysis on in vivo data Data base on dairy cow starch digestibility, number observations Total tract 284 Rumen 178 Small intestine 55 Hind gut 58 Multiple regression approach
Main results Total tract digestibility info on Source (name) significant not intake Rumen digestibility info on Source (name) and intake significant Small intestinal digestibility Source (name) significant Hind gut digestibility - positively correlated to rumen degradability and small intestinal digestibility Source (name) not important
Starch digestibilities (g/kg) Estimated using GLM obs. dig = source digestibility x source prop. of ration total starch Starch source Total tract Rumen Small intestine Hind gut Wheat Starch 1021 1062 722 704 Corn starch 1003 859 652 826 Wheat 1002 944 675 636 Oat 989 869 701 704 Corn silage1 962 909 819 660 Barley 959 868 759 609 Faba beans 952 773 437 633 Wheat NaOH 933 704 708 61 Corn 916 574 510 469 Peas 909 747 341 561 Sorghum 905 618 na na Barley NaOH 839 670 203 389 1 corn silage > 60% of ration starch Green = highest, blue=high, purple=low, red =lowest
Rumen digestibility Only one starch pool, no soluble and no indigestible fractions Estimate kd backwards, based on estimated rumen starch digestibility (RSD) Kd= kp x (RSD/(1-RSD))
Calculated kd (%/h) Rate of passage 6.09 %/h calculated according to Norfor concentrate equation, using dry matter intake, live weight concentrate proportion from meta analysis Corn starch 37 Wheat 103 Oat 40 Corn silage 1 61 Barley 40 Faba beans 21 Wheat NaOH 15 Corn 8 Peas 18 Sorghum 10 Barley NaOH 12
Post-rumen Small intestine: Tabulated estimated digestibilitites Hind gut: Function of rumen escape (RE) proportion HGSD (g/kg) = 739 0.83RE (g/kg intake) Starch approach is intended to be included in the NorFor model
Conclusion starch Both nylon bag and in vitro methods problematic Best approach for rate of degradation backwards estimation based on in vivo digestibilities However, problematic in evaluation of new and unknown (concentrate mixtures) samples
General conclusions Appropriate methods are needed for practice NIRs probably not the direct solution for kd Protein: Degradation parameters (in situ) were not satisfactorily predicted using N fractions of CNCPS Large variability between and within feed Need for large data sets for proper estimations, work in progress Backwards approach for degradation rate might be future solution
General conclusion, cont. NDF: Kd backwards calculation based on NIRs estimated indf, DOM, NDF and ash indf NIRs, but lab reference method needed, further work required Starch: Lab methods not very promising Best solution probably table values estimated from in vivo digestibilities by backwards calculation
This presentation has been carried out with financial support from the Commission of the European Communities, FP7, KBB-2007-1. It does not necessarily reflect its view and in no way anticipates the Commission s future policy in this area. Innovative and practical management approaches to reduce nitrogen excretion by ruminants