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RATIONAL  METHODS  FOR  PREDICTING  QUALITY 
AND  DIGESTIBLE  ENERGY  CONCENTRATION  OF 
WARM-SEASON  FORAGES  FOR  RUMINANTS 


By 


Edward  J.  Golding,  III 


A  DISSERTATION  PRESENTED  TO  THE  GRiVDUATE  COUNCIL  OF 

THE  UNIVERSITY  OF  FLORIDA 

IN  PARTIAL  FULFILLMENT  OF  THE  REQUIREMENTS  FOR  THE 

DEGREE  OF  DOCTOR  OF  PHILOSOPHY 


UNIVERSITY  OF  FLORIDA 
1976 


,  to  the  future  of  my  family, 

Astrid 
Nico3.e 
Christopher; 

and  to  those  who  understood. 


ACKNOWLEDGMENTS 

The  author  wishes  to  express  his  deep  appreciation  to  Dr. 
John  E.  Moore,  Chairman  of  the  Supervisory  Committee,  for  his 
continued  interest,  helpful  ideas  and  professional  guidance  during 
the  investigations  documented  in,  and  during  the  writing  of,  this 
manuscript.   Appreciation  is  also  extended  to  the  Members  of  the 
Supervisory  Committee,  Dr.s  C.  B.  Ammerman,  J.  H.  Conrad,  D.  E. 
Franke  and  G.  0.  Mott,  for  willingly  sharing  their  knowledge  and 
for  reviewing  this  dissertation.   The  statistical  aid  provided  by 
Dr.  R.  C.  Littell  and  Dr.  D.  0.  Dixon,  and  the  mathematical  assistance 
of  Dr.  K.  N.  Sigmon  are  greatly  appreciated.   Thanks  are  also  ex- 
tended to  Miss  Jan  Ferguson  and  Mrs.  Edwina  Williams  for  aid  in 
conducting  chemical  and  Jji  vitro  analyses.   Help  provided  the  author 
by  the  staff  and  personnel  of  the  Nutrition  Laboratory  in  solving 
the  every-day  problems  is  gratefully  acknowledged,  as  is  the  assistance 
and  friendship  of  fellow  graduate  students. 

Financial  assistance  provided  by  the  Department  of  Animal  Science, 
University  of  Florida,  is  gratefully  acknowledged,  as  are  the  efforts 
of  Dr.  Moore  and  Dr.  Conrad  in  obtaining  these  funds. 

The  author  wishes  to  express  his  appreciation  to  Mrs.  Susan 
Weller  and  Mrs.  Pat  Beville  for  typing  this  manuscript. 


TABLE  OF  CONTENTS 


Page 


ACKNOWLEDGMENTS 

LIST  OF  TABLES 

LIST  OF  APPENDIX  TABLES 

LIST  OF  FIGURES 

ABSTRACT 

CHAPTER 

I   INTRODUCTION 

II   REVIEW  OF  LITERATURE 

Expressions  of  Forage  Quality 

Digestible  Energy  Intake,  Digestible  Dry  Matter  Intake 

and  Digestible  Organic  Matter  Intake 
Nutritive  Value  Index 
Retention  Time  of  Organic  Matter  in  the  Rumen 

Control  of  Forage  Intake  by  Ruminants 

The  Distention  Mechanism 

Site  and  mechanism  of  distention  control 

Relative  importance  of  "fill"  and  "retention  time" 

Factors  Affecting  Retention  Time 

Factors  affecting  rates  of  digestion  and  passage 

Intake  vs  Rate  of  Digestion  ^  Vitro 

Prediction  of  Forage  Nutritive  Value 

Applicability  of  Predictors  of  Nutrient  Digestibility 
Chemical  analyses 

In  vitro  organic  matter  digestion 
Summative  equations 

Applicability  of  Predictors  of  DE  Concentration  in 
Forage  DM 

In  vitro  cellulose  digestibility 
Dry  matter  digestibility 
Crude  protein  percentage  of  dry  matter 

Prediction  of  Forage  Quality 

Applicability  of  Various  Techniques  for  Quality  Pre- 
diction 


Ix 


1 

3 

3 
3 

7 
7 

8 

8 

10 
11 
12 
14 
17 

18 
19 
19 
19 
20 

20 

20 
21 
22 

22 

23 


TABLE  OF  CONTENTS-continued 


Page 


Chemical  analyses  and  suiuniative  equations  23 

In  vitro  techniques  23 

The  nutritive  value  index  25 

Retention  time  of  organic  matter  in  the  rumen  25 

Mathematical  Modeling  of  Dynamic  Systems  25 

III   PREDICTION  OF  DIGESTIBLE  ENERGY  CONCENTRATION  IN  FORAGE 

FOR  PURPOSES  OF  MARKETING  SOUTHERN  HAYS  28 

Introduction  28 

Experimental  Procedure  29 

Theory  Related  to  Rational  Prediction  of  DE/DM  29 

Prediction  of  DOM  30 

Prediction  of  DE/DM  32 

Testing  the  Procedure  33 

Forages  and  ^IL  ^i^o  data  33 

Laboratory  analyses  and  prediction  testing  34 

Results  and  Discussion  35 
Chemical  Analyses  35 
In  Vivo  and  ^H  Vitro  Determinations  38 
NDF  digestibility  38 
Digestible  neutral-detergent  fiber  and  neutral- 
detergent  solubles  39 
OM  digestibility  41 
Digestible  organic  matter  41 
Metabolic  fecal  organic  matter  by  calculation  41 
Estimation  of  metabolic  fecal  organic  matter  by 

regression  42 

Prediction  of  NDFD,  DOM  and  DE/DM  43 

Testing  of  DOM  and  DE/DM  Predictions  47 

Acceptability  limits  for  judging  the  predictions  47 

Acceptability  of  the  predictions  48 

General  Discussion  54 

Non-Forage  Factors  Affecting  DE/DM  5  4 

Research  Needs  for  Rapid  and  Accurate  DOM  Prediction  55 

Summary  57 

IV   ELIMINATION  OF  ORGANIC  SOLVENTS  IN  THE  STUDY  OF  IN  VITRO 

NEUTRAL-DETERGENT  FIBER  DIGESTION  59 

Introduction  59 

Experimental  Procedure  59 


TABLE  OF  CONTENTS-continued 


Page 


Results  and  Discussion  61 

Summary  66 

V  A  RATIONAL  METHOD  FOR  PREDICTING  QUALITY  OF  WARM-SEASON 

FORAGES  FOR  RUMINANTS  67 

Introduction  67 

Experimental  Procedure  68 
Development  of  Theory  Related  to  Rational  Method  for 

Prediction  of  Forage  Quality  68 

Estimation  of  RTOM  70 

Establishing  the  value  of  g  71 

Estimation  of  k,  72 

Estimation  of  k  73 

Theory  relative  to  'a',  and  its  estimation  73 

Prediction  of  organic  matter  digestibility  75 

Testing  the  Procedure  76 

Forages  and  ^  vivo  data  76 

Laboratory  analysis  of  forages  77 

Regression  analyses  78 

Generation  of  the  prediction  equation  78 

Testing  the  acceptability  of  quality  predictions  79 

Results  and  Discussion  82 

Laboratory  Characteristics  of  Forages  Utilized  82 

Actual  and  Predicted  2ll  Vivo  Values  of  Forages  Utilized  84 

Testing  of  DOMI  and  OMI  Predictions  90 
Acceptability  limits  for  quality  and  intake  predictions    90 

Acceptability  of  quality  and  intake  predictions  92 
Utility  of  Relationships  Between  Various  Measurements 

and  Analyses  99 

Prediction  of  organic  matter  digestibility  99 
Prediction  of  intake  from  neutral-detergent  fiber 

percentage  100 

Prediction  of  k  101 

Prediction  of  k  103 

General  Discussion  104 

Non-Forage  Factors  Which  Could  Override  or  Modify  RTOM  104 

Theoretical  Methods  for  Prediction  of  k  106 

Summary  109 

APPENDIX  112 

LITERATURE  CITED  131 

BIOGRAPHICAL  SKETCH  149 


LIST  OF  TABLES 
Table  Page 

1.  MEASURES  OF  CENTRAL  TENDENCY  AND  DISPERSION  OF  THE 
CHARACTERISTICS  OF  52  FLORIDA  FORAGES  36 

2.  GROSS  ENERGY,  ENERGY  DIGESTIBILITY  AND  ACTUAL  AND 
PREDICTED  DIGESTIBLE  ENERGY  CONCENTRATIONS  IN  10 
WARM-SEASON  GRASSES  44 

3.  CONSERVATIVE  AND  LIBERAL  ACCEPTABILITY  LLMITS  FOR 
TESTING  PREDICTIONS  OF  DIGESTIBLE  ORGANIC  MATTER  (DOM) 

AND  DIGESTIBLE  ENERGY  (DE)  CONCENTRATION  49 

4.  EFFECT  OF  ACETONE  ON  THE  DETERMINATION  OF  ASH-FREE 
NEUTRAL-DETERGENT  FIBER  (NDFA)  IN  EIGHT  HAYS  62 

5.  EFFECT  OF  HAY  AND  STOP-METHOD  ON  IN  VITRO  RESIDUAL 

ASH-FREE  NEUTRAL-DETERGENT  FIBER  (NT)FA)  64 

6.  EFFECT  OF  STOP-METHOD  AND  REPLICATE  (REP)  ON  IN  VITRO 
RESIDUAL  ASH-FREE  NEUTRAL-DETERGENT:  FIBER  (NDFA)  65 

7.  MEASURES  OF  CENTRAL  TENDENCY  AND   DISPERSION  OF  THE 
LABORATORY  CHARACTERISTICS  OF  31  WARM-SEASON  GRASSES  83 

8.  MEASURES  OF  CENTRAL  TENDENCY  AND  DISPERSION  OF  THE 
ACTUAL  AND  PREDICTED  IN  VIVO  VALUES  OF  31  WARM- 
SEASON  GRASSES  85 

9.  CONSERVATIVE  AND  LIBERAL  ACCEPTABILITY  LIMITS  FOR 
TESTING  PREDICTIONS  OF  DIGESTIBLE  ORGANIC  MATTER 

INTAKE  (DOMI)  AND  ORGANIC  MATTER  INTAKE  (OMI)  93 


LIST  OF  APPENDIX  TABLES 
Table  Page 

10.  LABORATORY  CHARACTERISTICS  OF  31  WARM-SEASON  GRASSES        113 

11.  ACTUAL  AND  PREDICTED  IN  VIVO  VALUES  OF  31  WARM-SEASON 
GRASSES  118 

12.  LABORATORY  CHARACTERISTICS  AND  ACTUAL  AND  PREDICTED 
IN  VIVO  VALUES  OF  21  ADDITIONAL  FORAGES  USED  TO  TEST 
THEORETICAL  EQUATION  FOR  PREDICTION  OF  DIGESTIBLE 

ORGANIC  MATTER  (DOM)  AS  PERCENT  OF  DRY  MATTER  123 

13.  ALGEBRAIC  MANIPULATIONS  OF  EQUATION  9  REQUIRED  TO 

PRODUCE  EQUATION  10  (CHAPTER  V)  126 

14.  DYNAMO  COMPUTER  PROGRAM  FOR  PREDICTION  OF  FORAGE  DOMI, 
DEI  (WITH  AND  WITHOUT  ENERGY  SUPPLEMENT) ,  REPLACEMENT 
RATE,  OMI,  OMD,  DOM,  DE/DM,  DE/OM,  NDFD ,  DNDF,  A,  B 

AND  RTOM  127 


LIST  OF  FIGURES 
Figure  Page 

1.  TEST  OF  THEORETICALLY  RATIONAL  METHOD  FOR  PREDICTION 
OF  DIGESTIBLE  ORGANIC  MATTER  AS  A  PERCENTAGE  OF  DRY 

MATTER.  50 

2.  COMPARISON  OF  IN  VIVO  DIGESTIBLE  ENERGY  (DE)  WITH  THAT 
PREDICTED  FROM  ACTUAL  IN  VIVO  DIGESTIBLE  ORGANIC  MATTER 

(DOM)  AND  CRUDE  PROTEIN  (CP)  BY  THE  EQUATION:  52 

DE  =  4.15  DOM  +  1.299  CP  -  4.59. 
100 

3.  COMPARISON  OF  IN  VIVO  DIGESTIBLE  ENERGY  (DE)  WITH  THAT 
PREDICTED  FROM~PREDICTED  IN  VIVO  DIGESTIBLE  ORGANIC 

MATTER  (DOMp)  AND  CRUDE  PROTEIN  (CP)  BY  THE  EQUATION:       33 

DE  =  4.15  DOMp  +  1.299  CP  -  4.59. 
100 

4.  RELATIONSHIP  BETWEEN  NEUTRAL -DETERGENT  FIBER  DIGESTIBILITY 
IN  VIVO  AND  IN  VITRO  FOR  EACH  OF  THREE  WARM-SEASON  GRASSES 
(Data  taken  from  Velasquez,  1974).  86 

5.  RELATIONSHIP  BETWEEN  DIGESTIBLE  ORGANIC  MATTER  INTAKE 
AND  RETENTION  TIME  OF  ORGANIC  MATTER  IN  THE  RUMEN  FOR 

THREE  SPECIES  OF  WARM-SEASON  GRASSES.  89 

6.  RELATIONSHIP  BETWEEN  ORGANIC  MATTER  INTAKE  AND  ACID- 
DETERGENT  FIBER  PERCENTAGE  FOR  THREE  SPECIES  OF  WARM- 
SEASON  GRASSES.  91 

7.  TEST  OF  RETENTION  TIME  OF  ORGANIC  MATTER  IN  THE  RUMEN  AS 

A  RATIONAL  PREDICTOR  OF  DIGESTIBLE  ORGANIC  MATTER  INTAKE.    94 

8.  TEST  OF  THEORETICALLY  RATIONAL  METHOD  FOR  PREDICTION  OF 
ORGANIC  MATTER  INTAKE.  97 

9.  TEST  OF  EMPIRICAL  PREDICTION  OF  ORGANIC  MATTER  INTAKE 

FROM  ACID-DETERGENT  FIBER  PERCENTAGE  OF  DRY  MATTER.  98 


Abstract  of  Dissertation  Presented  to  the  Graduate  Council 

of  the  University  of  Florida  in  Partial  Fulfillment  of  the 

Requirements  for  the  Degree  of  Doctor  of  Philosophy 

RATIONAL  METHODS  FOR  PREDICTING  QUALITY 

AND  DIGESTIBLE  ENERGY  CONCENTRATION  OF 

WARM-SEASON  FORAGES  FOR  RUMINANTS 

By 

Edward  J.  Golding,  III 

March,  1976 

Chairman:   Dr.  John  E.  Moore 
Major  Department:   Animal  Science 

Three  separate  studies  were  conducted  to   (a)  devise  and  test 
theoretically  rational  and  acceptably  accurate  methods  for  predicting 
digestible  organic  matter  (DOM),  digestible  energy  (DE)  concentration 
and  DOM  intake  (DOMI)  of  forages;  and  (b)  investigate  alternative  lab- 
oratory methods  which  eliminate  organic  solvents  in  the  determination 
of  ash-free  neutral-detergent  fiber  (NDFA)  in  hay  samples  and  resi- 
dues of  in  vitro  fermentation.   These  studies  included  43  hays  from 
three  species  of  warm-season  grasses  (Paspalum  notatum  Flugge,  Cynodon 
dactylon  (L)  Pers. ,  and  Digitaria  decumbens  Stent)  and  nine  hays  of 
Medicago  sativa  L.   Conservative  and  liberal  acceptability  limits, 
which  corresponded  to  the  weighted  average  of  the  95  percent  confi- 
dence interval  for  means,  and  plus  and  minus  two  weighted  average 
population  standard  deviation  estimates,  respectively,  were  used  to 
judge  acceptability  of  DOM,  DE  and  DOMI  predictions. 

Acceptable  predictions  of  DE  for  10  grasses  were  based  on  DOM 
and  digestible  crude  protein  concentrations  in  forage.   Acceptable  pre- 
dictions of  DOM  were  made  for  all  52  forages  by  summing  neutral-detergent 
solubles  (NDS)  and  in  vivo  digestible  NT)FA,  and  subtracting  a  constant 
10.3  for  metabolic  fecal  organic  matter,  all  as  percentages  of  dry  matter 


(DM) .   The  Van  Soest  Summative  Equation  did  not  accurately  predict  NDFA 
digestibility  (NDFD)  for  warm-season  grasses;  and  discrepancies  apparent- 
ly exist  between  alfalfa  and  grasses  with  respect  to  the  in  vivo  NDFD  - 
in  vitro  NDFD  relationship.   The  remaining  challenge  with  respect  to 
DOM  and  DE  prediction,  therefore,  is  development  of  a  rapid  procedure 
for  accurate  prediction  of  digestible  NDFA  over  a  wide  range  of  forages. 
Acetone  washes  for  determinations  of  NDFA  in  hays  appeared  necess- 
ary, but  probably  could  be  excluded  when  analyzing  for  in  vitro  residual 
NDFA.   Terminating  fermentation  by  setting  tubes  in  an  ice-water  bath 
to  the  level  of  their  contents  for  1  hr  was  an  acceptable  alternative 
to  use  of  toluene.   These  studies  were  based  on  eight  hays  having  a 
wide  range  of  NDFA  and  NDFD. 

Ruminal  retention  time  of  organic  matter  (RTOM)  was  a  theoretically 
rational  independent  variable  for  predicting  DOMI.   A  published  equation 
for  plotting  disappearance  of  cellulose  from  the  rumen  through  time 
was  applied  to  total  organic  matter  (OM)  and  used  to  estimate  RTOM 
values  for  31  grasses.   For  each  grass,  the  value  for  the  rate  constant 
(k  )  for  rate  of  digestion  was  estimated  by  in  vitro  procedures,  while 
the  rate  constant  (k^)  for  rate  of  passage  was  calculated  from  known 
lignin  intake.   Estimates  of  the  potentially  digestible  fraction  of 
ruminal  OM  ('a')  were  made  using  the  formula  for  'a'  developed  in  this 
study,  and  the  potentially  indigestible  fraction  of  ruminal  OM  was 
equal  to  one  minus  'a'.   An  equation  for  prediction  of  DOMI  from  RTOM 
was  generated  using  15  forages,  and  the  16  predictions  produced  by 
this  equation  were  acceptable.   Actual  values  of  k^,  though  relatively 
invariant,  cannot  be  assumed  constant  among  forages,  nor  can  these 
values  be  predicted  accurately  at  present  from  predicted  values  of  'a'. 


Rational  prediction  of  OM  intake  (OMI)  was  accomplished  by  divid- 
ing predicted  DOMI  by  predicted  OM  digestibility.   This  procedure  was  more 
acceptable  than  empirical  prediction  of  OMI  from  acid-detergent  fiber 
or  NDFA  percentage. 


CHAPTER  I 
INTRODUCTION 

In  1972,  forages  provided  some  73  percent  of  the  feed  require- 
ments for  beef  cattle,  63  percent  for  dairy  cattle  and  about  89 
percent  for  sheep  and  goats  in  the  United  States  (Wedin  et  al.,  1975). 
These  figures  may  be  higher  today,  since  grain  feeding  is  less  profit- 
able in  many  areas  and  ruminant  production  is  becoming  increasingly 
dependent  upon  grassland  farming.   Thus,  it  is  becoming  imperative 
that  producers  possess  accurate  information  relative  to  the  quantity 
and  quality  of  forage  available  to  them.   Relatively  fast  and  accurate 
estimation  of  forage  production  offers  little  problem  to  the  experienced 
practicioner ,  but  rapid  and  accurate  prediction  of  forage  quality 
cannot  be  achieved  across  a  wide  range  of  forage  species.   Such  pre- 
dictions of  forage  quality  are  required  by  the  objective  producer 
even  if  limited  grain  is  to  be  fed  to  ruminants.   Thus,  an  important 
research  area  today  concerns  the  rapid  and  accurate  prediction  of 
quality  across  a  wide  range  of  forages. 

Accurate  knowledge  of  forage  quality  by  itself  however,  may  be  of 
little  value  to  the  producer.   This  is  because  digestible  nutrient 
intake  may  greatly  diverge  across  a  number  of  given  situations  from 
that  indicated  by  laboratory  predictions  of  forage  quality.   Dynamic 
computer  modeling  is  a  process  which  is  becoming  more  widely  employed 
in  today's  research  in  several  biological  disciplines.   Such  modeling 
may  be  capable  of  accurately  predicting  digestible  nutrient  intake  for 


a  given  situation  if  the  factors  which  affect  this  parameter,  the 
methods  by  which  such  factors  mediate  their  influence,  and/or  the 
magnitudes  of  the  effects  of  these  factors  can  be  elucidated  over  a 
wide  range  of  forages.   Divulgence  and  use  of  such  information, 
however,  requires  cooperation  by  teams  of  researchers  who  integrate 
their  knowledge  in  order  to  achieve  the  rapid,  efficient  and  accurate 
solution  to  a  common  problem.   Such  cooperation  among  researchers  is 
fast  becoming  a  requirement  if  the  persistence  or  betterment  of  man's 
present  standard  of  living  is  to  be  insured. 

In  this  dissertation,  three  separate  studies  were  conducted  to 
(a)  devise  and  test  theoretically  rational  and  acceptably  accurate 
methods  for  predicting  digestible  energy  concentration  and  digestible 
organic  matter  intake  of  forages;  and  (b)  investigate  alternative 
laboratory  methods  which  eliminate  organic  solvents  in  the  determination 
of  ash-free  neutral-detergent  fiber  in  hay  samples  and  residues  of 
in  vitro  fermentation. 


CHAPTER  II 
REVIEW  OF  LITERATURE 
Expressions  of  Forage  Quality 
Moore  and  Mott  (1973)  state  that  when  forage  availability  is  not 
a  limiting  factor,  and  when  animal  potential  is  invariant  between 
treatments,  then  the  best  measure  of  forage  quality  is  output  per  ani- 
mal.  Minson  (1968)  suggested  that  this  output  could  be  in  terms  of 
milk,  meat  or  wool,  and  also  agreed  (1971a)  that  the  grazing  trial  is 
the  most  reliable  method  of  estimating  the  quality  of  different  forages 
or  the  effect  of  a  management  treatment  upon  quality.   Grazing  trials 
to  determine  output  per  animal  must  be  long-term  endeavors  and  they 
become  costly  in  terms  of  resources,  time  and  capital.   Thus,  an 
expression  of  forage  quality  which  is  less  expensive  to  determine, 
but  which  yields  equally  acceptable  results,  becomes  a  necessity  if  a 
large  number  of  forages  are  to  be  evaluated. 

Digestible  Energy  Intake,  Digestible  Dry  Matter  Intake  and  Digestible 
Organic  Matter  Intake 

According  to  Heaney  (1970) ,  an  expression  of  the  feeding  value  of 
forages,  or  of  forage  quality,  must  have  the  following  inherent  charac- 
teristics: (1)   it  must  be  measurable  with  a  high  degree  of  precision; 
(2)  observations  on  a  small  number  of  animals  under  controlled  experi- 
mental conditions  must  be  applicable  to  a  more  general  production 
situation  and  animal  population;  and  (3)  it  must  be  highly  correlated 
with  animal  production  when  the  evaluated  feed  is  fed  to  animals.   This 
author  also  stated  that  researchers  now  accept  the  combination  of  intake 


and  digestibility  into  a  single  expression,  such  as  digestible  energy 
intake  (DEI),  for  evaluating  forage  quality,  and  that  this  is  the  most 
effective  method  ever  used.   Jones  (1972),  Milford  and  Minson  (1965a) 
and  Crampton  et  al.  (1960)  agreed  that  intake  and  digestibility  should 
be  combined  for  determining  quality,  and  Heaney  (1970)  and  Ventura 
(1973)  have  reported  evidence  that  neither  intake  nor  digestibility  by 
themselves  can  be  considered  a  reliable  expression  of  forage  quality. 

Why,  however,  should  intake  and  digestibility  qualify  as  contrib- 
utors to  an  expression  of  forage  quality?   In  a  practical  sense,  it  is 
foolish  to  think  of  any  indicator  of  forage  quality  which  does  not 
include  some  measure  of  the  amount  of  a  forage  that  animals  will 
voluntarily  eat,  for  animals  will  not  produce  without  consuming  the 
energy  and  nutrients  needed  for  conversion  to  livestock  products.   Minson 
et  al.  (1964),  Osbourn  et  al.  (1966)  and  Milford  (1967)  reported  devia- 
tions from  a  general  constant  relationship  between  voluntary  intake 
and  dry  matter  (DM)  digestibility,  both  among  and  within  forage  species. 
Thus,  a  direct  and  separate  estimate  of  voluntary  intake  of  a  forage 
must  be  made  for  inclusion  in  an  expression  of  forage  quality  along  with 
digestibility  (Minson  and  Haydock,  1971).   Some  measure  of  nutritive 
value  or  net  energy  (NE)  (Raymond,  1968;  Moore  and  Mott,  1973)  must 
also  be  included  in  the  expression,  since  consumed  nutrients  and  energy 
will  not  contribute  to  production  unless  they  are  digested  and  utilized. 
Since  these  functions  are  not  solely  under  the  influence  of  intake, 
the  digestibilities  of  either  DM  or  organic  matter  (CM)  may  be  used 
as  expressions  of  nutritive  value  (Moore  and  Mott,  1973)  because  NE 
for  maintenance  and  fattening  have  been  predicted  satisfactorily  from 
energy  digestibility,  which  in  turn  is  well  correlated  with  the 


digestibility  of  either  DM  or  OM  (Armstrong  et^  al • ,  1964).  Thus, 
digestible  dry  matter  intake  (DDMI)  or  digestible  organic  matter 
intake  (DOMI)  can  be  included  with  DEI  as  quality  expressions.  Jones 
(1972),  Ventura  (1973)  and  Marsh  (1974)  concurred  that  DOMI  is  synonomous 
with  forage  quality. 

If  DEI, DDMI  and  DOMI  are  to  be  considered  expressions  of  forage 
quality,  they  should  be  highly  correlated  with  average  daily  gain  of 
animals  consuming  the  forage  in  question.  Since  an  increase  in  digestible 
energy  (DE)  is  associated  closely  with  increases  in  metabolizable  energy 
(ME)  and  NE,  it  should  be  expected  that  increases  in  DEI,  as  well  as  in 
other  quality  expressions,  would  be  well  correlated  with  increases  in 
NE  intake  and  output  per  animal.  This  has  been  found  to  be  true.  Elliott 
e_t  al .  (1961)  and  Holmes  e_^  al^.  (1966),  working  with  tropical  pastures, 
found  that  liveweight  gain  of  cattle  was  linearly  related  to  DDMI.  Heaney 
(1970)  observed  that  forage  DEI  agreed  exceptionally  well  with  growth 
rates  of  lambs  fed  forages  for  seven  to  eight  weeks,  and  Montgomery  and 
Baumgardt  (1965a)  found  no  significant  differences  between  DEI's  or 
between  average  daily  gains  when  ruminants  consumed  high-quality  rations . 
Thus,  as  stated  by  Pfander  (1970),  probably  the  most  practical  measure 
of  forage  quality  would  be  DEI  per  kilogram  of  body  weight  raised  to  the 
.75  power.  However,  measurements  of  DDMI  or  DOMI  are  probably  just  as 
acceptable  where  energy  determinations  cannot  be  or  have  not  b^en  made. 

No  matter  which  expression  is  used  to  indicate  forage  quality,  the 
relative  contributions  of  intake  and  digestibility  to  the  value  of  the 
expression  are  not  the  same.  According  to  Keaney  (1970),  the  range  in 
recorded  values  of  intake,  going  from  low-  to  high-quality  forages,  is 


about  2.5  times  that  of  recorded  digestibilities.   Thus,  intake  is  more 
than  twice  as  important  as  digestibility  in  determining   the  value  of 
forage  quality  over  a  range  of  forages.   Crampton  (1957),  Moore  (1968), 
Osbourn  et  al.  (1970)  and  Ventura  et,  al.  (1975)  agreed  that  intake  is 
the  more  important  factor  in  determining  quality.   Milford  and  Minson 
(1965a),  working  with  tropical  grasses,  found  that  daily  DDMI  was 
more  closely  correlated  with  intake  of  DM  than  with  its  digestibility. 
Crampton  et^   al^.  (1960)  reported  that  variation  in  intake  accounted  for 
70  percent  of  the  variability  in  the  Nutritive  Value  Index.   Intake 
is  more  important  than  digestibility  in  determining  quality  among  forages, 
and  intake  of  a  given  forage  is  more  variable  between  animals  than  is 
digestibility  (Blaxter  et^  al. ,  1961;  Minson  et  al. ,  1964;  Heaney  et  al. , 
1968;  Capote,  1975).   Therefore,  intake  should  be  investigated  using 
six  to  ten  animals  per  determination,  while  three  or  four  will  suffice 
for  digestibility  (Heaney,  1970).   This  variability  in  intake  among 
animals  on  a  given  forage  may  be  due  to  animal  variation  with  respect 
to    (1)  weight  (Heaney,  1970;  Capote,  1975);  (2)  fatness  (Bines  et  al. , 
1969;  Foot,  1972;  Capote,  1975);  (3)  physiological  rumen  volume  (Purser 
and  Moir,  1966);  and/or  (4)  retention  time  of  DM  in  the  rumen  (Campling 
et  al .  ,  1961;  Phillips  £t  al. ,  1960;  Hungate,  1966).   This  discussion 
of  intake  variability  is  not  to  imply,  however,  that  changes  in  digest- 
ibility are  insignificant  in  their  influence  upon  DEI  or  the  other 
expressions  of  quality.   Blaxter  ^  al •  (1961),  under  ad  libitum  feeding 
conditions,  calculated  that  a  change  in  digestibility  of  DM  from  50  to 
55  percent  resulted  in  a  100  percent  increase  in  weight  gain. 


Nutritive  Value  Index 

The  Nutritive  Value  Index  (NVI)  was  proposed  by  Crampton  e_t  al. 
(1960)  as  another  expression  of  forage  quality.   For  temperate  forages, 
this  index  was  highly  correlated  with  both  12-hour  ±n   vitro  cellulose 
digestibility  (IVCD)  (Donef er  et  al.  ,  1960;  Johnson  et  a]^.  ,  1962b)  and 
IVCD  multiplied  by  the  solubility  of  forage  DM  in  1.0  N  sulfuric  acid 
(Johnson  and  Dehority,  1968).   Like  the  other  proposed  quality  ex- 
pressions, it  also  includes  measures  of  (a)  voluntary  intake  (actually 
"relative  intake"  compared  to  that  of  the  standard  forage  proposed  by 
Crampton  et  al.  (I960))  and  (b)  digestibility  (energy  digestibility 
(ED)).   Minson  and  Milford  (1966)  examined  NVI  in  relation  to  DEI  and 
found  that,  though  NVI  was  highly  correlated  with  DEI  for  the  three 
forages  studied,  the  regression  coefficient  for  one  of  the  forages  was 
significantly  different  from  the  others.   Interpretation  of  the  re- 
sults of  Johnson  et   al.  (1962a)  reveals  that  for  forages  consumed  in 
the  fresh  state,  prediction  of  an  NVI  value  from  12-hour  IVCD  would 
require  different  regression  equations  depending  upon  the  DM  percentage 
of  the  in  vitro  sample.  '  This  was  because  of  differences  between  the 
12-hour  IVCD  values  of  undried  versus  artificially  dried  samples.   Due 
to  the  absence  of  a  constant  caloric  value  for  conversion  of  NVI  to 
DEI,  and  due  to  the  lack  of  tables  of  animal  requirements  for  NVI, 
Minson  and  Milford  (1966)  concluded  that  the  more  direct  method  of 
expressing  forage  quality  in  terms  of  DEI  per  unit  of  metabolic  size 
was  superior  to  the  NVI  system. 
Retention  Time  of  Organic  Matter  in  the  Rumen 

Blaxter  (1962)  reported  that  the  qualities  of  different  feeds  were 
proportional  to  the  rates  at  which  they  passed  through  the  gut  of 


ruminants.  Thornton  and  Minson  (1972,  1973)  and  Laredo  and  Minson  (1975) 
concluded  that  the  retention  time  of  OM  in  the  rumen  (RTOM)  was  highly 
and  inversely  correlated  with  DOMI.  Thus,  if  RTOM  could  be  accurately 
predicted  by  means  of  laboratory  analyses,  it  might  prove  a  method 
which  could  greatly  reduce  the  need  for  intake  and  digestion  trials  in 
forage  evaluation  research.  Part  of  the  research  in  this  dissertation 
examines  this  hypothesis. 

Control  of  Forage  Intake  by  Ruminants 
Capote  (1975)  and  Golding  (1973)  have  written  literature  reviews 
covering  many  of  the  proposed  control  mechanisms,  as  well  as  many  of 
the  factors  which  interact  to  control  the  voluntary  intake  of  ruminants. 
Included  in  these  reviews  are  the  effects  of  the  following  factors 
upon  intake:  animal  breed,  weight,  size,  age,  rate  of  production  and 
fatness;  ration  caloric  concentration;  crude  protein  percentage  of 
forage;  blood  and  rumen  metabolites;  environmental  conditions,  such 
as  ambient  temperature,  humidity  and  solar  radiation;  physiological 
condition,  including  lactation  and  pregnancy;  hormones;  frequency  of 
feeding;  water  deprivation;  amino  acids;  minerals;  crude  protein 
supplementation;  and  energy  supplementation.   Thus,  these  factors  will  not 
be  dealt  with,  or  will  be  touched  upon  only  lightly,  in  the  present 
review.   This  review  will  attempt  to  cover  the  physical  or  distention 
mechanism  which  has  been  proposed  for  control  of  forage  intake  by 
mminants,  as  well  as  the  factors  which  relate  to  the  function  of 
this  mechanism. 
The  Distention  Mechanism 

The  theoretical  distention  mechanism  for  controlling  forage  intake, 
formally  proposed  by  Montgomery  and  Baumgardt  (1965a)  and  Conrad  (1966), 


appears  to  be  an  extension  of  the  following  concepts  set  forth  by 
Crampton  et  ^.  (1960) :   (1)  some  specific  degree  of  rumen  load  reduction 
probably  is  the  primary  determinant  of  recurring  hunger  in  ruminants; 
(2)  the  rates  of  forage  cellulose  and  hemicellulose  degradation  are 
correlated  with  the  rate  at  which  the  rumen  load  is  reduced;  and  (3) 
the  time  period  after  which  the  rumen  load  reaches  the  degree  of 
reduction  at  which  hunger  recurs  is  characteristic  of  the  specific 
forage  involved.   Moore  and  Mott  (1973)  state  that  if  nitrogen  is 
not  limiting,  the  mechanism  related  to  the  distention  theory  is  that 
which  most  often  controls  intake  of  forage-fed  animals.   Work  by 
Campling  et  al.  (1962),  Egan  (1965),  Weston  (1967)  and  Minson  and  Milford 
(1968),  however,  indicated  that  the  distention  mechanism  may  also  be 
involved  in  regulating  intake,  at  least  in  part,  when  dietary  crude 
protein  (CP)  is  less  than   7    percent  of  total  DM.   Baile  (1968), 
Waldo  (1970),  Welch  (1967)  and  Weston  (1966)  all  agreed  that  physical 
distention  of  the  rumino-reticulum  was  an  important  feedback  mechanism 
for  the  regulation  of  forage  intake  by  ruminants. 

The  distention  control  mechanism  is  generally  dominant  with  long 
forages  until  the  digestibility  of  DM  reaches  some  upper  point  in  the 
range  of  about  65-70  percent.   The  form  in  which  forage  is  fed  (Blaxter 
et  al.  ,  1961;  Montgomery  and  Baumgardt,  1965a)  and  the  physiological 
condition  of  the  animal  consuming  it  (Waldo,  1970)  have  been  shown  to 
change  this  point.   This  theory  suggests  that  forage  intake  and  digest- 
ibility should  be  highly  correlated.   This  may  be  true  when  forage 
quality  differences  are  due  to  maturity  differences,  but  may  not  be  so 
when  quality  differences  are  due  to  forage  species  (Milford,  1967; 


10 


Weston  and  Hogan,  1967;  Minson  et  al. ,  1964;  Van  Soest,  1964)  or 
cultivars  within  a  species  (Osbourn  ^  al. ,  1966).   This  possibly  is 
because  chemical  and  structural  differences  may  exist  which  cause 
differing  rates  of  digestibility,  though  final  digestibilities  are 
similar,  across  a  given  set  of  forages  (Van  Soest,  1965a;  Demarquilly 
et  al .  ,  1965;  Milford  and  Minson,  1965a).   Thus,  the  relationship 
between  intake  and  digestibility  may  be  sufficiently  accurate  for 
predictive  purposes  only  when  limited  to  maturity  differences  within 
individual  species  and/or  cultivars. 
Site  and  mechanism  of  distention  control 

Earlier  work  by  Blaxter  et  al .  (1956,  1961)  and  Conrad  et  al. 
(1964)  stressed  the  importance  of  distention  of  the  entire  gastro- 
intestinal tract  of  ruminants  in  controlling  forage  intake.   Thus,  the 
rate  of  passage  of  digesta  through  the  entire  tract  was  thought  important 
in  limiting  consumption.   However,  fecal  DM  output  varies  across  the 
various  forms  in  which  forages  are  fed  (chopped,  wafered,  ground, 
pelleted,  etc)  (Waldo,  1970),  and  positive  relationships  have  been 
reported  between  intake  ^and  amounts  of  material  in  sections  of  the 
tract  posterior  to  the  rumino-reticulum  (Ingalls  et  al . ,  1966).   Such 
observations  led  many  workers  (Ingalls  e_t  al.  ,  1966;  Ulyatt  et  al.  , 
1967;  Waldo,  1970;  Ulyatt,  1973)  to  discount  the  importance  of  sections 
of  the  tract  posterior  to  the  rumino-reticulum  (hereafter  referred  to 
as  the  rumen)  in  limiting  forage  consumption.   Thus,  most  researchers 
now  agree  with  the  contention  of  Campling  et_  al^.     (1961)  and  Waldo  (1970) 
that  the  rumen  is  that  portion  of  the  gastro- intestinal  tract  in  which 
distention  control  over  forage  intake  is  exercised.   Further,  they  agreed 
that  distention  control  is  governed  by  two  main  factors:  (a)  fill,  or 


11 


the  amount  of  digesta  in  the  rumen  (Campling  and  Balch,  1961;  Weston, 

1966)  and  (Jj)  retention  time,  or  the  extent  of  delay  of  digesta  in  the 

rumen  (Campling,  1965,  1970;  Thornton  and  Minson,  1972).   Importance  of 

the  rumen  to  this  mechanism  is  indicated  by  receptors  sensitive  to 

ruminal  stretch  or  tension  (Bell,  1961;  Comline  and  Titchen,  1961; 

Kay,  1963;  Leek,  1969),  though  the  nature  and  location  of  the  sensory 

nerve  endings  have  not  yet  been  reported  (Campling,  1970).   Still,  some 

evidence  indicates  that  intake  of  finely  ground  and  pelleted  forage 

diets  may  be  partly  controlled,  either  directly  or  indirectly,  by 

distention  of  the  abomasum  and  intestines  (Campling _et _al. ,  1963; 

Campling  and  Freer,  1966). 

Relative  importance  of  "fill"  and  "retention  time" 

Retention  time  is  probably  more  important  than  fill  in  limiting 

forage  intake  because  rumen  DM  fill  per  kilogram  of  metabolic  weight 

(W,  '   )  has  been  shown  relatively  constant  across  a  range  of  forage 
kg 

quality  (Blaxter  e_t  al .  ,  1961;  Ulyatt  et^  al •  ,  1967;  Thornton  and  Minson, 
1972).   Campling  e^  al.  (1961)  and  Egan  (1970),  feeding  diets  of 
cereal  straws  and  hays,  reported  that  fill  on  such  diets  was  low  re- 
lative to  that  produced  by  higher-quality  forages.   The  straw  and  hay 
diets  contained  less  than   1   percent  nitrogen.   Thus,  nitrogen  de- 
ficiency may  have  led  to  the  low  fill  observed  with  these  diets.   In 
such  cases,  amount  of  rumen  fill  may  be  more  important  in  controlling 
forage  intake  than  when  forages  of  higher  quality  are  considered. 
The  actual  amount  of  DM  fill  in  the  rumen  has  been  reported  to  vary 
from  1.7  percent  (Thomas  e_t  £l.  ,  1961)  to  2.2  percent  (Waldo  et  al.  , 
1965)  or  2.44  percent  (Ingalls  et  al. ,  1966)  of  body  weight.   This 


12 


variation  may  be  due  to  differences  in  DM  intake  across  the  diets 
used  in  these  experiments  (Ingalls  ^  al. ,  1966;  Egan,  1970;  Thomas 
et  al. ,  1961). 

That  intake  depends  to  a  great  extent  upon  average  retention  time 
of  material  in  the  rumen  has  been  shown  by  many  researchers  (Ulyatt,  1^  1; 
Oltjen  ejt  al.  ,  1971;  Elliott  and  Topps,  1960;  Laredo  and  Minson,  1975; 
Thornton  and  Minson,  1972,  1973).   Calculations  made  in  the  present 
study  from  data  presented  by  Laredo  and  Minson  (1975)  and  Thornton 
and  Minson  (1972,  1973)  showed  that  retention  time  of  OM  in  the  rumen 
(RTOM)  was  highly  correlated  with  digestible  OM  intake  per  W   '   .   If 
RTOM  could  be  predicted  accurately  from  parameters  related  to  forage 
composition  and/or  structure,  accurate  predictions  of  forage  quality 
might  be  obtainable  from  RTOM.   This  could  decrease  the  necessity  of 
running  intake  and  digestibility  trials  with  ruminants.   Theory  and 
relationships  between  various  parameters  reported  by  Waldo  e^  al.  (1972) 
may  prove  useful  in  attaining  this  goal. 
Factors  Affecting  Retention  Time 

For  a  given  forage,  RTOM  depends  upon  rate  of  digestion  in  the 
rumen  (Campling,  1965;  Jones  and  Bailey,  1974)  and  upon  the  rate  at 
which  undigested  residues  leave  this  organ  (Campling,  1964,  1965; 
Waldo  et  al. ,  1972).   This  suggests  that  there  are  two  important  rates 
to  consider,  and  that  there  are  two  types  of  material  in  the  rumen: 
(a)  one  which  leaves  the  rumen  due  to  digestion  (i.  e. ,  by  absorption 
and  eructation),  and  (b)  one  which  must  exit  via  passage  to  the  lower 
gut  (Waldo  _et  al.,  1965,  1972).   McLeod  and  Minson  (1974a)  and  de  la 
Torre  (1974)  suggest  that  this  latter  material  contains  all  the  lignified 
fractions  of  the  plant  cell  wall,  and  hence  all  the  lignin  in  the  diet. 


13 


However,  part  of  the  potentially  digestible  material  must  also  evacuate 
the  rumen  via  passage,  since  not  all  of  this  material  is  digested  in 
the  rumen.   This  important  fact  is  included  in  the  model  derived  by 
Waldo  et  al.  (1972)  to  describe  the  manner  in  which  cellulose  disappears 
from  the  rumen.   Also  included  in  this  model  are  the  concepts  that  both 
the  rate  of  digestion  of  digestible  material  (Gill  et  al . ,  1969;  Smith 
et  al.,  1971,  1972)  and  the  rate  of  passage  of  indigestible  residues 
(Meyer  &L  3l.  ,    1967;  Alexander  et  al .  ,  1969;  Brandt  and  Thacker,  1958) 
follow  the  laws  set  forth  for  first-order  dynamic  processes.   That  is, 
these  rates  proceed  in  proportion  to  the  amounts  of  material  undergoing 
digestion  and  passage.   Gill  et  al.  (1969)  reported  that  the  relative 
rate  of  digestible  cellulose  digestion  (K)  in  vitro  was  highly  correlated 
with  the  digestible  DM  intake  of  cows  consuming  high-DM  legume-grass 
silage.   Such  was  not  the  case  when  Lechtenberg  et  al.  (1974)  fed  corn 
stover  silage  from  two  different  corn  genotypes  to  sheep.   These  authors 
postulated  that  rate  of  digestion  of  total  cell  walls  was  more  important 
than  K  in  determining  intake.   They  also  reported  that  rate  of  digestion 
of  total  cell  walls  was  affected  by  lignif ication,  but  that  K  was  not. 

A  negative  relationship  between  intake  and  retention  time  has 
been  noted  by  many  workers  (Thornton  and  Minson,  1972;  Ingalls  et  al. , 
1966;  Waldo  et  al . ,  1965).   The  decrease  in  RTOM  with  increased  level 
of  feeding  may  be  responsible  for  observed  decreases  in  DM  digestibility 
(Laredo  and  Minson,  1975).   However,  Minson  (1966)  reported  that  re- 
tention time  of  DM  in  the  rumen  was  only  slightly  influenced  by  the 
level  of  DM  intake  of  the  same  diet,  since  reducing  intake  by  51  percent 
yielded  only  an  18  percent  increase  in  retention  time.   Thus,  Thornton 


14 


and  Minson  (1972)  reasoned  that  among  forage  diets  which  exhibited 
DM  intakes  of  from  659  to  1355  g/day,  intake  level  could  have  accounted 
for  no  more  than  20  percent  of  the  difference  in  ruminal  retention  time, 
which  varied  from  13.3  to  27.1  hours.   These  investigators  concluded 
that  retention  time  in  the  rumen  was  controlled  largely  by  chemical 
composition  of  forage,  particularly  neutral-detergent  fiber  (NDF) 
and  lignin.   Lignin  was  highly  correlated  with  intake  in  their  study, 
however,  and  such  is  not  always  the  case  (Golding,  1973). 

Non-forage  factors  which  may  exert  important  influences  upon  RTOM 
have  been  reported.   Graham  and  Williams  (1962)  observed  that  retention 
time  of  residues  in  the  gut  increased  as  pregnancy  advanced  in  sheep 
given  a  constant  amount  of  feed.   Studies  by  Warren  et^  al.  (1974) 
and  Wayman  _et  al.  (1962)  indicated  that  RTOM  may  increase  at  high  ambient 
temperatures.   In  this  latter  study,  animals  were  forced-fed  at  high 
ambient  temperatures  to  offset  any  effect  of  decreased  intake  on  RTOM, 
Factors  affecting  rates  of  digestion  and  passage 

Of  the  many  factors  which  interact  to  determine  rates  at  which 
forage  OM  digests  in  and  passes  from  the  rumen,  probably  chemical 
composition  and  organizational  structure  of  cell  walls  of  ingested 
forage  are  the  most  important  (Akin  et  al. ,  1974a;  Van  Soest,  1965a; 
Thornton  and  Minson,  1972;  Laredo  and  Minson,  1973).   That  chemical 
composition  is  important  in  this  respect  was  shown  by  Campling  e_t  al. 
(1962),  Egan  (1965)  and  Weston  (1967).   These  workers  increased  rate 
of  digestion  and  the  intake  of  diets  low  in  CP  percentage  by  supplement- 
ing with  urea.   Such  increases  would  not  have  occurred  unless  CP  was  the 

first  limiting  factor.   The  fact  that  lignin  can  limit  rate  of  digestion 


15 


was  brought  out  by  Lechtenberg  et  al .  (1974)  and  Crampton  (1957). 
Thornton  and  Minson  (1972)  reported  a  high  correlation  between  lignin 
content  of  forage  and  retention  time  of  DM  in  the  rumen.   This,  at 
least  in  part,  was  undoubtedly  a  reflection  of  the  effect  of  lignin  on 
rate  of  digestion  of  forage  cell  walls.   When  forages  are  supplemented 
with  energy,  rates  of  DM  or  cellulose  digestibility  decline,  perhaps 
due  to  mineral  imbalances  (Burroughs  et   al, ,  1948)  or  nitrogen  competi- 
tion (el-Shazley  et  al, ,  1961).   Minimum  requirements  of  some  rumen 
bacteria  for  phosphorus,  magnesium,  calcium,  sodium  and  potassium  have 
been  established,  and  iron,  cobalt,  copper,  manganese  and  zinc  have 
been  shown  to  be  beneficial  to  others  (Hungate,  1966),   Therefore, 
deficiencies  of  these  minerals,  as  well  as  imbalances  concerning  their 
relative  concentrations  in  the  rumen,  could  cause  decreases  in  rate  of 
digestion  of  forage  cell  walls. 

Rate  of  passage  of  indigestible  OM  from  the  rumen  is  limited  by 
the  rate  at  which  large  particles  in  this  organ  are  reduced  to  a  size 
small  enough  to  pass  to  the  omasum.   Rate  of  breakdown,  while  certainly 
under  the  influence  of  composition  of  feed  and  animal  factors  such 
as  efficiency  of  chewing  and  strength  and/or  frequency  of  rumlnal 
contractions,  is  regulated  to  a  great  extent  by  organizational  structure 
of  the  forage  cell  wall  fraction  (Akin  e^  al. ,  1974a;  Ulyatt,  1973), 
Further  histochemical  studies  of  the  nature  of  those  by  de  la  Torre 
(1974),  Akin  £t  al,  (1974a)  and  Monson  ^  al.  (1972)  are  needed  to 
elucidate  the  relationships  between  cell  wall  structure  and  rates  of 
OM  breakdown  in,  and  passage  from,  the  rumen.   Perhaps  grinding  energy, 
as  proposed  by  Chenost  (1965),  deserves  more  experimental  work  as  a 
method  for  predicting  rate  of  breakdown  in  the  rumen. 


16 


Other  factors  which  aid  in  determining  rates  of  digestion  and 
passage  are  those  related  to  animal  breed,  the  form  in  which  forages 
are  fed  and  supplementation  of  forages  with  energy.   Phillips  (1961) 
and  Hungate  et  al.  (1960)  observed  that  Zebu  cattle  demonstrated  higher 
rates  of  digestion  and  passage  of  forage  diets  than  did  steers  of 
European  breeds.   In  the  latter  of  these  studies,  the  higher  rates 
correlated  well  with  lower  retention  times  exhibited  by  Zebu  animals, 
and  it  was  postulated  that  the  advantage  of  Zebus  relative  to  European 
breeds  with  respect  to  rates  of  digestion  and  passage  would  be  increased 
under  conditions  of  stress  and  submaintenance  feeding.   Forages  fed  in 
pelleted  form  have  shown  faster  rates  of  digestion  and  passage  and 
decreased  retention  times  of  digesta  in  the  rumen  relative  to  the  same 
forages  fed  in  the  long  or  chopped  forms  (Johnson  _et^  al- .  1964;  Oltjen 
et^  al. ,  1971;  Laredo  and  Minson,  1975).   The  advantage  demonstrated 
by  pellets  in  this  respect  seems  to  be  greatest  when  low-quality  forages 
are  fed  (Minson  and  Milford,  1968).   Intake  is  not  always  increased  by 
pelleting,  however,  if  nitrogen  is  a  limiting  factor  (Minson,  1967; 
Minson  and  Milford,  1968).   Supplementation  of  forage  with  energy 
causes  the  forage  portion  of  the  diet  to  be  retained  longer  in  the  rumen 
than  when  forage  is  fed  alone  (Montgomery  and  Baumgardt,  1965b;  Campling, 
1966;  Eng  et_  al . ,  1964).   This  appears  to  be  caused  by  decreased 
activity  of  the  cellulolytic  microflora  in  the  rumen  when  forage  is 
supplemented  with  starchy  energy  feeds  (Campling,  1970;  el-Shazley 
et  al .  ,  1961).   A  lower  intake  of  forage  may  also  contribute  to  lengthen- 
ing its  retention  time  under  these  conditions. 


17 


Intake  vs  Rate  of  Digestion  In  Vitro 

Since  rate  of  digestion  in  the  rumen  has  been  shown  to  be  instrumental 
in  determining  retention  time  (Campling,  1965;  Jones  and  Bailey,  1974) 
and,  therefore,  intake,  it  follows  that  in  vitro  rate  of  digestion  should 
also  be  highly  correlated  with  these  parameters.   Minson  and  Milford 
(1967a)  found  that  12-hour  in  vitro  DM  digestion  predicted  the  voluntary 
intake  of  Rhodes  grass  (Chloris  gayana) ,  and  Crampton  et_  al .  (1960) 
suggested  that  rate  of  J:!!  vitro  cellulose  digestion  was  related  to 
voluntary  intake.   Donefer  et  al.  (1960)  and  Johnson  et^  al.     (1962b) 
reported  that  12-hour  IVCD  was  highly  correlated  with  NVI,  and  Jones 
(1972)  found  that  intake  of  four  temperate  grasses  was  highly  correlat- 
ed with  rate  of  isi   vitro  DM  digestion. 

Minson  (1971a)  described  less  promising  results  when  correlating 
in  vitro  digestion  rate  with  intake  of  different  varieties  of  Panicum. 
Karn  et  al.  (1967),  using  temperate  grasses  and  alfalfa,  obtained  low 
correlation  coefficients  (r  values)  between  intake  and  ±n   vitro  rates 
of  cellulose  or  DM  digestion  at  various  times  between  five  and  11  hours 
of  fermentation.   Laredo  and  Minson  (1973)  observed  no  difference  be- 
tween mean  In  vitro  rates  of  digestion  of  leaf  and  stem  fractions  of 
five  different  warm-season  grasses,  though  mean  voluntary  intake  of 
leaf  was  46  percent  higher  than  that  of  stem.   These  authors  stated  that 
this  discrepancy  was  probably  due  to  the  fact  that  all  in  vitro  samples 
had  been  ground  to  pas5  a  one  millimeter  (mm)  screen,  thus  destroying 
the  structural  differences  across  samples  which  had  produced  the  large 
difference  in  in_  vivo  intake.   This  work  suggests  that  high  correlations 
reported  by  other  workers  between  intake  and  rate  of  in_  vitro   digestion 
may  have  been  caused  by  factors  other  than  differences  in  fiber  structure 
across  forages. 


18 


Examination  of  the  grasses  used  by  Crampton  et^  £l.  (1960)  and 
Minson  and  Milford  (1967a)  showed  a  positive  correlation  between  intake 
and  nitrogen  content  of  those  forages  (Laredo  and  Minson,  1973).   These 
results  suggest  that  when  forage  samples  are  ground  to  pass  a  1  mm 
screen  prior  to  determining  in  vitro  rates  of  digestion,  such  determin."- 
tions  will  be  highly  correlated  with  intake  only  when  intake  is  related 
to  chemical  composition.   High  correlations  should  not  be  the  case  when 
cell  wall  structure  plays  the  dominant  role  in  determining  intake, 
since  differences  in  i^  vivo  rates  of  digestion  probably  will  be  masked 
in  vitro . 

Another  discrepancy  between  in  vivo  and  In  vitro  rates  of  digestion, 
which  could  cause  low  correlations  between  intake  and  J^  vitro  rate  of 
digestion,  could  occur  with  forages  of  low  CP  percentage.   Glover  et  al. 
(1960)  showed  that  when  forages  which  contained  less  than    5  percent 
CP  on  a  DM  basis  were  fed  over  prolonged  periods  of  time,  a  sharp 
decrease  in  in  vivo  digestibility  was  likely  to  occur.   This  decrease 
probably  would  not  appear  j^  vitro  due  to  the  relatively  short  fermentation 
times,  and  also  because  rumen  fluid  for  sample  inoculation  is  normally 
drawn  from  donor  animals  whose  diets  are  adequate  in  CP.   Thus,  low 
in  vivo  rate  of  digestion  and  decreased  intake  of  low  CP  forages  which 
had  been  fed  for  some  time  might  not  be  reflected  by  ±n   vitro  rate  of 
digestion  of  that  forage. 

Prediction  of  Forage  Nutritive  Value 

Moore  and  Mott  (1973)  stated  that  most  forage  researchers  generally 
use  ED  or  apparent  digestibility  of  DM  (DMD)  or  CM  (OMD)  as  expressions 
of  forage  nutritive  value.   They  also  concluded,  based  upon  work  by 


19 


Armstrong  (1964),  Armstrong  et^sl.    (1964)  and  Graham  (1967)  that 

digestible  energy  (DE)  per  g  DM  (DE/DM)  was  a  very  meaningful  and 

useful  criterion  of  forage  nutritive  value  for  both  tropical  and 

temperate  forages. 

Applicability  of  Predictors  of  Nutrient  Digestibility 

Chemcial  analyses 

No  chemical  determination,  whether  based  upon  the  Weende  proxi- 
mate analysis  system,  the  Van  Soest  method  of  fiber  fractionation  or 
one  of  several  solubility  techniques,  will  predict  0>ro  or  DMD  with 
a  high  degree  of  consistent  accuracy  across  a  wide  range  of  forage 
species  (Johnson  and  Dehority,  1968;  Butterworth  and  Diaz,  1970; 
Moore  and  Mott,  1973;  Golding,  1973).   Nor  will  an  empirical  multiple 
regression  equation  based  upon  several  such  chemical  determinations 
allow  accurate  prediction  of  nutritive  value  over  such  a  range  of 
forages  (Butterworth  and  Diaz,  1970;  Moore  and  Mott,  1973;  Golding, 
1973) .   Some  chemical  determinations  will  exhibit  a  high  degree  of 
correlation  with  OMD  or  DMD  over  a  narrow  range  of  forages  if  maturity 
is  the  primary  determinant  of  quality.   However,  when  the  resultant 
regression  equation  is  applied  to  a  different  set  of  forages  than 
that  which  produced  the  equation,  digestibility  predictions  are  gener- 
ally lacking  in  accuracy. 
In  vitro  organic  matter  digestion 

The  in  vitro  CM  digestion  procedure  for  estimation  of  in  vivo  OMD, 
while  more  rational  and  accurate  for  this  purpose  than  chemical  analyses, 
is  akin  to  these  analyses  in  that  discrepancies  exist  in  the  i^  vivo  - 
in  vitro  relationship  across  forage  species  (Moore  and  Mott,  1973; 


20 


McLeod  and  Minson,  1974b).   Thus,  across  species,  different  regression 
equations  must  be  utilized  in  the  prediction  of  O^DD.   Weller  (1973) 
found  that  the  particle-size  distribution  over  12  warm-season  grasses 
from  three  different  species  did  not  contribute  to  this  discrepancy. 
However,  among  forages  which  exhibit  true  differences  in  the  structur.. 
make-up  of  fibrous  OM,  fine  grinding  of  forages  before  subjecting 
them  to  fermentation  may  still  influence  the  degree  of  an  observed 
discrepancy  (Laredo  and  Minson,   1973). 
Summative  equations 

Summative  equations  presented  by  Van  Soest  (1965b)  and  Minson 
(1971b)  (for  prediction  of  DMD  and  OMD,  respectively)  are  also  not 
good  predictors  of  DMD  or  OMD  when  applied  to  a  wide  range  of  forage 
species.   These  equations  are  theoretically  rational  in  that  they  sum 
the  various  apparently  digestible  fractions  of  DM  or  OM.   However,  they 
include  hypothesized  cause  and  effect  relationships  which  are  invalid 
across  forages  for  estimating  digestibilities  of  various  fibrous  frac- 
tions.  If  the  summative  principle  could  be  combined  with  the  Nutritive 
Entity  concept  of  Lucas  and  Smart  (1959) ,  and  with  some  consistent 
cause  and  effect  relationship  for  predicting  digestibility  of  fibrous 
OM,  the  OMD  could  be  more  accurately  predicted  across  forages.   This 
has  been  shown  by  Velasquez  (1974),  who  used  in  vitro  NDF  digestion  to 
predict  in  vivo  digestibility  of  NDF.   This  procedure,  however,  is 
too  time  consuming  to  be  employed  in  routine  forage  evaluation. 
Applicability  of  Predictors  of  DE  Concentration  in  Forage  DM 
In  vitro  cellulose  digestibility 

Hershberger  ^  al.  (1959)  found  a  high  correlation  (r  =  ,92) 
between  kilocalories  (kcal)  DE/DM  and  IVCD  after  24  hr  of  fermentation 


21 


over  four  temperate  grasses  and  two  legumes.   Johnson  e_t  al.  (1962b) 
reported  a  high  r  value  (.99)  for  the  relationship  between  ED  of 
temperate  grasses  and  IVCD  at  24  hr,  but  when  legumes  were  included 
in  the  analysis,  r  dropped  to  .88  after  24  hr,  as  opposed  to  .99 
after  just  12  hr  of  fermentation.   Thus,  it  appeared  to  these  workers 
that  12-hr  IVCD  compared  most  favorably  with  ED  across  forage  species. 
Johnson  and  Dehority  (1968),  working  with  temperate  species,  found 
r  values  of  only  .79,  .54  and  .64  within  groups  of  22  grasses,  25 
legumes  and  30  mixed  forages,  respectively,  for  the  relationship  be- 
tween ED  and  12- hr  IVCD.   Across  all  77  forages,  the  r  value  for  this 
relationship  was  only  .64,  suggesting  that  IVCD,  after  either  12  or 
24  hr  of  fermentation,  would  not  be  an  accurate  predictor  of  DE/DM. 
Also,  none  of  the  Van  Soest  fiber  fractions  or  solubility  techniques 
studied  by  Johnson  and  Dehority  (1968)  would  effectively  fill  this 
role.   Across  all  forages,  cellulose  solubility  in  cupriethylenediamine 
multiplied  by  DM  solubility  in  1.0  N  sulfuric  acid  exhibited  the  highest 
correlation  with  ED,  but  r  was  only  .82. 
Dry  matter  digestibility 

Moir  (1961)  reported  a  highly  significant  relationship  between 
DE/DM  and  DM  digestibility  (DMD)  (r  =  .98),  and  proposed  that  a  gen- 
eral equation  could  be  used  to  predict  DE/DM  from  DMD.   Butterworth 
(1964),  however,  found  an  r  value  of  only  .86  between  these  two 
parameters  for  24  tropical  forages.   Minson  and  Milford  (1966)  stated 
that  when  they  used  the  equation  suggested  by  Moir  (1961)  to  predict 
DE/DM,  estimates  from  pastures  of  low  digestibility  were  in  agreement 
with  actual  DE/DM  values.   With  pastures  of  high  digestibility,  actual 


22 


DE/DM  values  were  7  percent  lower  than  estimated.   Therefore,  use  of 

a  general  equation  to  predict  DE/DM  from  DMD  could  lead  to  inaccurate 

results. 

Crude  protein  percentage  of  dry  matter 

Glover  _et  al.  (1960)  showed  that  over  a  wide  range  of  CP,  as  a 
percentage  of  DM,  CP  was  well  correlated  with  DE  concentration  in  for- 
ages.  Minson  and  Milford  (1966)  also  observed  a  positive  correlation 
(r  =  .84)  between  the  caloric  value  of  OM  and  CP  percentage.   There- 
fore, wide  variation  in  CP  may  explain  part  of  the  discrepancy  found 
in  the  relationship  between  DE/DM  and   DMD.   This  would  occur  because 
digestible  CP  (DCP) ,  which  is  highly  correlated  with  CP  (Holter  and 
Reid,  1959;  Milford  and  Minson,  1965b),  exhibits  a  higher  caloric  value 
than  does  digestible  carbohydrate  (Maynard  and  Loosli,  1969).   It  is 
doubtful,  however,  that  CP  will  be  always  highly  correlated  with  DE/DM 
over  a  wide  range  of  forages,  since  CP  does  not  generally  exhibit  a 
strong  cause  and  effect  relationship  with  DMD.   Still,  some  combination 
of  rational  factors  related  to  DMD  and  CP  may  produce  more  accurate 
predictions  of  DE/DM  over  a  wide  range  of  forages  than  would  either 
of  these  factors  alone. 

Prediction  of  Forage  Quality 

The  best  measure  of  forage  quality  is  output  per  animal  under 
certain  conditions  (Moore  and  Mott,  1973).   Heaney  (1970)  and  Ventura 
(1973)  found  that  neither  intake  nor  digestibility  alone  could  be 
considered  a  reliable  expression  of  forage  quality.   Thus,  most  re- 
searchers now  accept  some  combination  of  intake  and  nutritive  value, 
such  as  DEI,  DOMI  or  DDMI ,  as  meaningful  expressions  of  forage  quality. 


23 


Applicability  of  Various  Techniques  for  Quality  Prediction 
Chemical  analyses  and  summative  equations 

Moore  and  Mott  (1973)  and  Golding  (1973)  have  presented  liter- 
ature reviews  of  research  to  define  methods  for  accurate  prediction 
of  forage  quality.   Conclusions  of  these  authors  will  be  briefly 
summarized  in  this  section,  and  in  those  which  follow.   Quality  over 
a  wide  range  of  forage  species  defied  prediction  by  single  laboratory 
chemical  analyses,  or  by  empirical  multiple  regression  equations  based 
upon  several  such  determinations.   Digestibility  probably  could  be 
accurately  predicted  by  summative  equations  if  these  were  based  upon 
the  Nutritive  Entity  concept  of  Lucas  and  Smart  (1959)  and  included  an 
accurate  predictor  of  cell  wall  digestibility.   Accurate  summative 
equations  for  digestibility  prediction  would  not  insure  accurate  pre- 
diction of  forage  quality,  however,  since  digestibility  and  intake 
are  not  highly  correlated  over  a  wide  range  of  forage  species. 
In  vitro  techniques 

In  vitro  OM  digestion  (IVOMD)  by  rumen  microorganisms  is  the  best 
available  predictor  of  OMD.   Across  forages,  however,  discrepancies 
have  been  observed  in  the  in  vivo  -  in  vitro  relationship,  i.  e. , 
IVOMD  many  times  will  be  different  at  a  given  level  of  in  vivo  OMD 
(Moore  and  Mott,  1973).   Even  if  in  vivo  OMD  could  be  accurately  pre- 
dicted from  IVOMD  with  a  high  degree  of  consistency,  IVOMD  would  not 
be  a  generally  accurate  predictor  of  forage  quality  due  to  the  frequent 
lack  of  relationship  between  intake  and  digestibility.   It  is  possible 
that  microanatomical  studies  (Monson  et  al. ,  1972;  Akin  and  Burdick, 
1973;  de  la  Torre,  1974)  of  differences  in  composition,  organization 


24 


and  rates  of  digestion  of  structural  components  of  OM  may  help  in 
removing  discrepancies  in  the  relationships  between  ±n   vivo  OMD  and 
IVOMD,  and  between  intake  and  digestibility. 

In  vitro  rates  of  DM  or  cellulose  digestion  have  been  studied 
(Cramp ton  et  al, ,  1960;  Minson  and  Milford,  1967a;  Karn  ^  al . ,  1967; 
Minson,  1971a;  Laredo  and  Minson,  1973)  as  possible  predictors  of 
forage  quality.   Such  determinations  may  be  inadequate  for  this  purpose 
when  structure  of  fibrous  OM  fractions  is  important  for  control  of 
forage  intake.   This  is  because  forage  samples  are  generally  ground 
to  pass  a  1  mm  screen  before  being  studied  by  in  vitro  procedures, 
thus  greatly  removing  variation  in  structure  of  fibrous  OM  fractions 
among  forages.   It  is  possible  that  studies  of  ±n   vitro  rates  of  OM 
digestion  which  employ  more  intact  forage  samples  could  produce  rates 
of  digestion  which  would  correlate  highly  with  forage  quality.   Such 
samples  might  also  be  examined  microscopically  before  being  fermented, 
and  some  attribute (s)  might  correlate  highly  with  rate  of  OM  digestion. 
In  this  case,  microscopic  examination  of  a  small  forage  sample  would 
lead  to  rapid  prediction  of  forage  quality.   It  is  quite  probable, 
however,  that  both  chemical  composition  and  the  structural  nature  of 
fibrous  OM  must  be  considered  in  an  attempt  to  produce  a  method  for 
prediction  of  quality  over  a  wide  range  of  forage  species. 

The  rate  constant  for  rate  of  cellulose  digestion  in  vitro  (K) 
was  reported  by  Gill  et   al.  (1969)  to  be  highly  correlated  with  digest- 
ible DM  intake  of  high-DM  legume-grass  silage  by  cows.   This  concept 
was  refuted  by  Lechtenberg  et  al.  (1974),  who  fed  corn  stover  silage 
to  sheep.   Thus,  it  is  doubtful  that  K  is  highly  correlated  with 
forage  quality  in  a  general  sense. 


25 


The  nutritive  value  index 

The  NVI  system  (Crampton  et^  al.  ,  1960)  for  estimation  of  forage 
quality  has  received  wide-spread  attention  from  researchers,  as  well 
as  much  use  in  a  practical  sense.   Discrepancies  revealed  by  Minson 
and  Milford  (1966)  and  Johnson  ejt  al.  (1962a)  between  actual  forage 
quality  and  NVI  values  may,  however,  decrease  the  utility  of  this  system 
as  a  quality  estimator  over  a  wide  range  of  forages.   Also,  the  fact 
that  rapid  NVI  determination  is  based  upon  12-hr  IVCD  might  cause 
errors  in  quality  estimation  when  structure  of  fibrous  OM  is  an  im- 
portant determinant  of  intake. 
Retention  time  of  organic  matter  in  the  rumen 

Thornton  and  Minson  (1972,  1973)  showed  that  RTOM  determined 
in  vivo  was  highly  and  negatively  correlated  with  forage  quality.   There- 
fore, RTOM  may  ultimately  prove  to  be  an  accurate  predictor  of  quality 
across  forages,  since  it  seemingly  should  be  related  to  both  chemical 
composition  and  structural  microanatomy  of  forage  OM.   Across  forages, 
values  of  RTOM  may  reflect  rate  and  extent  of  OM  digestion  in  the 
rumen  which,  in  turn,  should  be  determined  by  the  microbial  and  physical 
degradability  of  forage  OM. 

Mathematical  Modeling  of  Dynamic  Systems 

Forrester  (1968),  Joandet  and  Cartwright  (1975)  and  Blincoe  (1975) 
presented  material  which  outlined  the  steps  to  be  followed,  types  of 
information  required  and  benefits  to  be  reaped  when  mathematical  computer 
models  are  employed  to  simulate  the  action  of  a  given  dynamic  system. 
Forrester  (1968)  developed  a  method  for  modeling  such  systems,  and 
Pugh  (1973)  presented  the  computer  language,  called  Dynamo,  which  makes 


26 


use  of  this  modeling  procedure  possible.   This  procedure  has  been 
employed  successfully  in  such  fields  as  ecology,  sociology,  plant 
sciences,  physical  sciences  and  engineering.   In  the  areas  of  animal 
science  and  agronomy,  models  utilizing  this  procedure,  as  well  as 
others,  have  been  applied  to  simulate  forage  production  (Bravo,  1973; 
Smith  and  Williams,  1973;  Patten,  1972);  forage  production  under 
grazing  (Christian  et  al. ,  1972;  Paltridge,  1972;  Vickery,  1972); 
rumen  fermentation  (Baldwin  et  al. ,  1970);  animal  energetics  (Baldwin 
and  Smith,  1971a);  intermediate  metabolites  (Baldwin  and  Smith,  1971b); 
energy  metabolism  of  the  steer  under  f eedlot  conditions  (Paine  et  al. , 
1972);  sheep  production  (Wright,  1970);  the  efficiency  of  nutrient 
utilization  by  different  cattle  genotypes  (Joandet,  1967);  and  the 
utility  of  different  production  alternatives  from  the  economic  point 
of  view  (Shumway  et  al. ,  1974;  Anderson,  1972;  Trebeck,  1972).   The 
relationship  between  OMI  by  cattle  on  pasture  and  forage  production 
has  been  included  in  the  simulation  of  forage  production  and  pasture 
utilization  by  the  animal  (Vickery  and  Hedges,  1974;  Donnelly  et  al. , 
1970;  Jones,  1969;  Morley  and  Spedding,  1968).   This  relationship 
has  been  based  upon  a  relatively  simple  set  of  assumptions  not  con- 
sistent with  most  practical  situations  (Joandet  and  Cartwright,  1975). 
Thus,  proper  description  of  the  forage  production  -  OMI  relationship 
is  one  problem  which  must  be  overcome  to  achieve  accurate  simulation 
of  pasture  production  and  forage  utilization  by  the  ruminant  animal. 

The  utility  of  mathematical  models  lies  in  the  fact  that  they 
require  accurate  definition  of  the  structure  and  function  of  the  system 
or  process  which  is  being  modeled  (Forrester,  1968).   They  also  indicate 


27 


areas  where  research  is  needed  by  means  of  the  degree  of  sensitivity 
which  they  display  to  changes  in  inputs;  and  they  make  possible  the 
study  of  situations  which  cannot  be  achieved  experimentally,  or  which 
are  too  risky  or  expensive  to  attempt  otherwise  (Blincoe,  1975).   Per- 
haps the  greatest  attribute  of  mathematical  computer  models,  however, 
is  that  their  construction  and  use,  as  well  as  the  interpretation  of 
results  which  they  generate,  generally  require  the  effort  of  a  team  of 
researchers  who  integrate  their  knowledge  in  trying  to  solve  a  common 
problem. 


CHAPTER  III 

PREDICTION  OF  DIGESTIBLE  ENERGY  CONCENTRATION  IN  FORAGE 
FOR  PURPOSES  OF  MARKETING  SOUTHERN  HAYS 

Introduction 
Forages  probably  would  be  marketed  most  fairly  on  the  basis  of 
their  potential  to  support  animal  production,  i.  e. ,  forage  quality. 
Estimation  of  quality  for  marketing  purposes  requires  a  fast,  simple 
and  accurate  method  which  presently  is  not  available  over  a  wide 
range  of  forage  species.   Thus,  marketing  of  forages  must  be  based 
on  some  other  forage  attribute.   Intake  will  not  serve  as  this 
attribute  since  lack  of  a  sufficiently  accurate  method  for  prediction 
of  intake  (Minson,  1971a)  is  the  main  cause  of  problems  encountered 
in  attempting  to  predict  forage  quality.   At  present,  therefore,  for- 
ages must  be  marketed  on  the  basis  of  their  nutritive  value.   This 
nutritive  value  cannot  be  that  which  is  realized  in  a  given  situation, 
but  must  be  that  which  is  a  basic  attribute  of  a  given  forage.   Digest- 
ibility of  dry  matter  (DM)  or  organic  matter  (OM)  as  estimated  from 
in  vitro  digestions  would  satisfy  this  requirement  over  narrow  ranges 
of  forages.   A  rancher  probably  has  little  appreciation  of  such  a 
parameter,  however,  and  animals'  energy  requirements  are  tabulated 
on  the  basis  of  total  digestible  nutrients  (TDN)  or  digestible  energy 
(DE)  concentration,  not  digestibility.   Forages  should  be  marketed, 
therefore,  based  on  their  values  of  kcal  DE/g  DM  (DE/DM) .   This  value 
is  closely  related  to  TDN,  since  the  caloric  value  of  TDN  is  approximately 


28 


29 


4.4  kcal  DE/g  TDN  (Maynard  and  Loosli,  1969).   If  TDN  of  a  forage  is 
known,  DE/DM  can  be  calculated  and  a  bomb  calorimeter  need  not  be 
employed. 

If  a  prediction  method  for  DE/DM  is  to  be  adopted  widely  for 
hay  classification  and  marketing,  it  should  be  equally  applicable 
to  grasses,  legumes  and  mixed  hays,  and  must  be  inexpensive  and  rapid. 
No  laboratory  chemical  analysis,  whether  used  alone  or  in  combination 
with  other  such  analyses,  will  serve  for  accurate  prediction  of  DE/DM 
over  a  wide  range  of  forages  (Johnson  and  Dehority,  1968;  Butterworth 
and  Diaz,  1970;  Moore  and  Mott,  1973),  nor  will  in  vitro  digestion  of 
OM  or  DM  (Minson  and  Milford,  1966;  Moore  and  Mott,  1973).   Summative 
equations  (Van  Soest,  1965b;  Minson,  1971b)  may  be  adequate  for  DE/DM 
prediction  if  they  encompass  cause  and  effect  relationships  which  hold 
true  across  forages  (Moore  and  Mott,  1973),  and  if  they  include  the 
Nutritive  Entity  concept  of  Lucas  and  Smart  (1959). 

The  objective  of  this  study  was  to  define  and  test  a  theoretically 
rational  method,  based  upon  use  of  summative  equations,  for  the  accept- 
ably accurate  prediction  of  DE/DM  over  a  wide  range  of  Southern  hay- 
crop  forages. 

Experimental  Procedure 
Theory  Related  to  Rational  Prediction  of  DE/DM 

Forage  digestible  OM  (DOM),  as  a  percentage  of  DM,  generally  con- 
tains very  little  digestible  ether  extract  (DEE)  (Glover  and  Dougall, 
1960).   Thus,  DOM  is  comprised  mainly  of  digestible  crude  protein  (DCP) 
and  digestible  carbohydrate,  both  as  percentages  of  DM.   The  digestible 
carbohydrate  fraction,  in  terms  of  the  Weende  proximate  analysis,  is 


30 


composed  of  digestible  crude  fiber  (DCF)  and  digestible  nitrogen-free 
extract  (DNFE) .   If  forage  DOM  could  be  predicted  and  the  caloric 
value  of  its  digestible  carbohydrate  fraction  added  to  that  of  DCP  in 
DOM,  then  an  accurate  estimation  of  DE/DM  should  result. 

With  most  hays,  the  numerical  values  of  TDN  and  DOM  are  nearly 
identical.   This  can  be  illustrated  as  follows  (values  as  percentage 
of  DM) : 

TDN  =  DCP  +  DCF  +  DNFE  +2.25  (DEE),  (1) 

and 

DOM  =  DCP  +  DCF  +  DNFE  +  DEE.  (2) 

Therefore , 

TDN  =  DOM  +  1.25  (DEE).  (3) 

Since  DEE  is  negligible  in  most  hays,  values  of  TDN  and  DOM  are 
numerically  equal  for  all  practical  purposes.   This  fact,  in  itself, 
probably  will  not  lead  to  rapid  and  accurate  prediction  of  DOM  across 
a  wide  range  of  forages  since  TDN  is  not  predictable  with  a  high 
degree  of  accuracy  over  such  a  range  (Butterworth  and  Diaz,  1970). 
The  inability  to  predict  TDN  accurately  among  forages  may  be  due  to 
two  facts:  (a)  crude  fiber  (CF)  and  nitrogen-free  extract  (NFE)  are 
not  Nutritive  Entities  (Moore  and  Mott,  1973);  and  (b)  CF  and  NFE 
generally  do  not  conform  to  their  definitions  in  terms  of  composition 
(Harris  et  al. ,  1972). 
Prediction  of  DOM 

The  first  step  in  the  rational  prediction  of  DE/DM  in  the  laboratory 
is  the  prediction  of  DOM,  as  a  percentage  of  DM.   Rational  laboratory 


31 


prediction  of  DOM  is  probably  best  approached  using  the  Nutritive 
Entity  concept  of  Lucas  and  Smart  (1959)  as  expressed  in  terms  of  a 
summative  equation  (Raymond,  1969;  Barnes,  1973;  Moore  and  Mott, 
1973).   Perhaps  the  most  rational  summative  equation  for  DOM  prediction 
would  include  the  fractions  which  constitute  this  parameter  (values  as 
percentages  of  DM) : 

DOM  =  DNDS  +  DNDF  -  MFOM,  (A) 

where  DNDS  =  digestible  ash-free  neutral-detergent  solubles; 
DNDF  =  digestible  ash-free  neutral-detergent  fiber; 
and  MFOM  =  metabolic  fecal  OM  excretion. 
The  DNDS  include  readily  soluble  or  digestible  carbohydrates, 
DCP  and  whatever  digestible  lipids  may  be  present.  Total  ash-free 
neutral-detergent  solubles  (NDS)  are,  in  theory,  readily  and  almost 
completely  degraded  in  the  rumen,  either  by  direct  solution  or  by 
rapid  microbial  digestion.   True  digestibility  of  NDS  may  be  approxi- 
mately 100  percent  (Van  Soest,  1967),  and  NDS  may  be  considered  a 
Nutritive  Entity  which  varies  as  a  percentage  of  DM  among  forages, 
but  not  in  digestibility.   Thus,  it  is  possible  that  replacing  DNDS 
in  equation  (4)  with  NDS  would  not  lead  to  unacceptable  errors  in 
pr-diction  of  DOM.   This  would  produce  the  following  rational  summative 
equation  for  prediction  of  DOM  (values  as  percentages  of  DM): 

DOM  =  NDS  +  DNDF  -  MFOM.  (5) 

Ash-free  neutral-detergent  fiber  (NT)F)  varies  as  a  percentage 
of  DM,  and  in  digestibility,  among  forages.   With  forages  high  in 
NDF,  such  as  warm-season  grasses,  there  is  not  a  close  relationship 
between  percentage  and  digestibility  of  NDF  or  its  constituents 


32 


(Velasquez,  1974;  Minson,  1971b).   Since  NDF  is  not  a  Nutritive  Entity, 
both  NDF  and  NDF  digestibility  (NDFD)  must  be  known  to  estimate  DNDF. 

Both  NDS  and  DNDF  must  be  determined  for  each  forage  for  which 
DOM  is  to  be  predicted,  unless  one  of  them  is  invariant  within  a  narrow 
and  well-defined  group  of  forages.   Since  MFOM  in  concept  is  a  constant 
proportion  of  DM  (Van  Soest,  1967),  there  may  be  little  error  associat- 
ed with  use  of  one  MFOM  value  for  all  forages  when  predicting  DOM. 
If  two  of  the  three  right-hand  members  of  equation  (5)  are  not  highly 
variable  across  a  given  group  of  forages,  then  values  for  the  third 
member  should  be  highly  correlated  with  DOM.   Such  a  situation  may 
occur  within  legumes  (Tilley  et  al. ,  1969;  Johnson  and  Dehority,  1968). 
In  such  a  case,  an  empirical  prediction  equation  based  only  on  the 
third  component  could  predict  i^  vivo  DOM  values  which  were  nearly 
identical  with  those  of  forages  used  to  generate  the  equation.   This 
same  equation  might  not  predict  DOM  accurately  within  a  large  group  of 
forages  in  which  a  different  component  of  the  equation  was  more  highly 
correlated  with  DOM,  or  in  which  two  of  the  three  components  were  highly 
variable.   Therefore,  if  a  summative  equation  for  prediction  of  DOM 
is  to  have  wide  applicability,  independent  estimates  of  NDS  and  DNDF 
will  probably  be  required  for  each  forage. 
Prediction  of  DE/DM 

The  DE  value  of  DOM  (DE/DOM)  varies  among  forages  due  to  variation 
in  DCP  as  a  percentage  of  DM  (Minson  and  Milford,  1966;  Golding,  1973). 
Such  variation  in  DE/DOM  is  due  to  different  average  caloric  values  of 
proteins  and  carbohydrates  (5.65  vs  4.15  kcal/g;  Maynard  and  Loosli, 
1969).   A  correction  can  be  made  for  this  difference,  and  perhaps  should 


33 


be  made  since  DCP  may  vary  widely  among  forages.   A  theoretical  correction 
is  indicated  by  the  following  equation,  under  the  assumption  that  DEE 
as  a  percentage  of  DM  =  0  (DE  values  as  kcal/g  DM;  others  as  percentages 
of  DM) : 

^„  _  4.15  (DCF  +  DNFE)  +  5.65  (DCP) 

°^  ~         Too  •        (6) 

Since  equation  (2),  for  most  forages,  can  be  written 

DOM  =  DCP  +  DCF  +  DNFE,  (7) 


then 


_„  _  4.15  (DOM)  +1.50  (DCP)  . 

^^ 100  (8) 


Values  of  DCP  can  be  predicted  from  CP  for  most  forages  (except  those 
that  are  heat  damaged)  by  the  following  equation  (N.R.C.,  1971)  (values 
as  percentages  of  DM) : 

DCP  =  .866(CP)-3.06.  (9) 

The  combined  theoretical  equation  for  predicting  DE/DM  from  predicted 
DOM  and  determined  CP  is  (DE  as  kcal/g  DM;  others  as  percentages  of 

DM) 

_  4.15  (DOM)  +  1.50  (.866  CP  -  3.06). 
°^  100    '  (10) 


DE 


4.15  (DOM)  +  (1. 50-. 866)  (CP)  -  (1.50-3.06), 

100  (11) 


_  4.15  (DOM)  +  1.299  (CP)  -  4.59. 

^^  -  Too  (12) 


Testing  the  Procedure 
Forages  and  in  vivo  data 

Fifty- two  forages,  including  43  warm-season  grasses  and  nine 
cuts  of  'Florida  66'  alfalfa  (Medicago  sativa  L.)  were  used  in  this 


34 


study.   The  43  grasses  were  comprised  of  31  which  will  be  described 

in  Chapter  V,  and  eight  additional  cuts  of  Pensacola  bahiagrass 

(Paspalum  no ta turn  Flugge)  ;  one  of  Suwannee  bermudagrass  (Cynodon 

dactylon  (L)  Pers.);  and  three  of  Pangola  digitgrass  (Digitaria 

decumbens  Stent).   The  latter  12  grasses  were  fed  at  less  than  ad 

libitum  levels  $:estricted-f ed)  to  sheep  when  studied  i^  vivo ,  while 

alfalfas  were  fed  ad^  libitum.   All  other  details  of  in  vivo  trials 

involving  alfalfas  or  restricted-fed  grasses  were  as  will  be  described 

for  31  grasses  in  Chapter  V. 

Laboratory  analyses  and  prediction  testing 

Procedures  relative  to  (a)  laboratory  analyses;  (b)  determinations 

of  correlation  coefficients  (r  values)  and  residual  standard  deviations 

(s    values)  between  laboratory  analyses  and/or  in  vivo  values;  (c)  pre- 
y.x  

diction  of  in  vivo  parameters;  (d)  setting  of  conservative  and  liberal 
acceptability  limits  (±t(s//n)  and  ±  2s,  respectively);  and  (e)  test- 
ing of  predictions,  were  the  same  as  will  be  described  in  Chapter  V. 
In  addition,  in  vivo  digestible  energies  (kcal/g  DM)  were  determined 
for  10  grasses.   Values  of  DE/DM  (kcal/g)  were  predicted  for  these  10 
grasses  using  equation  (12) .   Predicted  values  of  DOM,  as  a  percentage 
of  DM,  were  obtained  for  all  52  forages  from  equation  (5).   Values 
of  NDS  inserted  into  equation  (5)  were  calculated  as  CM  minus  ash-free 
NDF  (both  as  percentages  of  DM) ,  while  DNDF  values  were  those  determin- 
ed in  vivo.   Values  of  MFOM  for  each  hay  were  calculated  as  (NDS  +  DNDF) 
minus  in  vivo  DOM,  all  as  percentages  of  DM. 


35 


Results  and  Discussion 
Chemical  Analyses 

Ranges,  means  and  coefficients  of  variation  (CV)  resulting  from 
various  laboratory  chemical  analyses  conducted  on  the  nine  alfalfas 
and  43  grasses  are  presented  in  table  1.   Values  of  these  parameters 
for  individual  forages  can  be  found  in  Appendix  tables  10  and  12. 

Values  of  CP  ranged  from  16.8  to  30.6  for  alfalfa,  with  a  mean 
of  22.5  (table  1),  and  from  3.8  to  19.5  for  grasses,  with  a  mean  of 
9.3.   Though  mean  alfalfa  maturity  was  less  than  that  of  grasses  (4.7 
vs  7.3  wk,  respectively),  these  data  reflect  the  generally  higher  CP 
percentages  of  legumes.   For  all  forages,  mean  CP  was  11.6  percent. 

Values  of  ash-free  NDS  ranged  from  39.9  to  50.3  for  alfalfa, 
and  the  mean  was  46.0  percent.   The  NDS  content  of  grasses  ranged 
from  16.1  to  30.5  percent  with  a  mean  of  20.7  percent.   Mean  NDS  for 
all  forages  was  25.0  percent.   Though  differences  in  average  maturity 
between  alfalfa  and  gragses  may  have  somewhat  influenced  these  results, 
NDS  was  much  higher  in  legumes  than  in  grasses  (Van  Soest,  1965a; 
Smith  et  al.,  1972).   There  was  much  less  ash-free  NDF  present  in 
legumes  than  in  grasses.   Alfalfa  contained  an  average  of  only  44.0 
percent  of  NDF,  while  this  figure  for  grasses  was  75.1  percent.   Again, 
differences  in  average  maturity  between  alfalfa  and  grasses  may  have 
influenced  these  results,  but  grasses  are  generally  considered  to 
contain  more  NDF  than  legumes.   For  all  forages,  NDF  ranged  from  33.2 
to  81.3  percent,  and  averaged  69.7  percent. 


36 


TABLE  1.   MEASURES  OF  CENTRAL  TENDENCY  AND  DISPERSION  OF  THE 
CHARACTERISTICS  OF  52  FLORIDA  FORAGES 


Item 


Alfalfa 


Number  of  forages 
Number  of  species 
Crude  protein  (CP)^ 

Organic  matter  (OM) 

Neutral-detergent  solubles 
(ash-free)  (N-DS)^ 

Neutral -detergent  fiber 
(ash-free) (NDF)^ 

NDF  digestibility  (ash-free) 
(NDFD)^ 

Predicted  NDFD  (ash-free)^ 

In  vitro  NDF  digestion  (ash-free) 
(IVNDFD)  72  hr^ 

Digestible  NDF  (ash-free) (DNDF)^ 
OM  digestibility  (OMD) 
Digestible  OM  (DOM)^ 
Predicted  DOM^ 
Metabolic  fecal  OM  (MFOM)^ 


9 

1 

Mean  (CV)^ 

22.5  (19.9) 

Range 

16.8-30.6 

Mean  (CV) 

89.9   (3.2) 

Range 

83.5-92.1 

Mean  (CV) 

46.0   (7.7) 

Range 

39.9-50.3 

Mean  (CV) 

44.0  (13.5) 

Range 

33.2-52.1 

Mean  (CV) 

54.9  (11.2) 

Range 

47.4-64.4 

Mean  (CV) 

- 

Range 

- 

Mean  (CV) 

43.5  (13.5) 

Range 

38.5-55.2 

Mean  (CV) 

23.9   (8.8) 

Range 

20.8-26.9 

Mean  (CV) 

67.1   (5.6) 

Range 

62.5-72.2 

Mean  (CV) 

60.3   (3.5) 

Range 

57.6-64.2 

Mean  (CV) 

59.5   (4.6) 

Range 

56.5-64.6 

Mean  (CV) 

9.5  (11.2) 

Range 

7.5-10.9 

^As  %  of  dry  m.atter.   In  %.   Coefficient  of  variation,  in  %, 


°Restricted-fed.   Ad  libitum-fed, 


37 


Table   1  -   extended. 


d 
Grasses 

Grasses 

Grasses 

All 

12 

31 

43 

52 

3 

3 

3 

4 

9.3  (18.8) 
5.5-11.9 

9.3  (47.1) 
3.8-19.5 

9.3  (40.9) 
3.8-19.5 

11.6  (54.8) 
3.8-30.6 

95.1   (.8) 
94.2-96.4 

96.0   (1.3) 
93.3-97.5 

95.7   (1.2) 
93.3-97.5 

94.7   (2.9) 
83.5-97.5 

19.5   (6.9) 
17.6-21.5 

21.1  (21.9) 
16.1-30.5 

20.7  (19.5) 
16.1-30.5 

25.0  (41.7) 
16.1-50.3 

75.6   (2.2) 
73.4-78.1 

74.9   (7.4) 
63.6-81.3 

75.1   (6.4) 
63.6-81.3 

69.7  (18.5) 
33.2-81.3 

60.4  (12.7) 
45.8-68.9 

55.3  (17.5) 
42.0-76.1 

56.7  (16.5) 
42.0-76.1 

56.4  (15.7) 
42.0-76.1 

57.6  (16.2) 
38.4-68.6 

53.3  (18.2) 
39.4-71.8 

54.5  (17.8) 
38.4-71.8 

- 

54.3  (13.4) 
41.3-63.4 

52.2  (23.9) 
33.9-74.8 

52.8  (21.2) 
33.9-74.8 

51.2  (21.5) 
33.9-74.8 

45.7  (12.9) 
35.3-52.8 

40.9  (11.4) 
33.1-50.0 

42.3  (12.8) 
33.1-52.8 

39.1  (22.1) 
20.8-52.8 

11.8  (12.2) 
10.3-14.2 

9.9  (21.1) 
6.9-14.1 

10.5  (20.1) 
6.9-14.2 

10.3  (19.3) 
6.9-14.2 

56.1  (10.9) 
44.9-63.3 

54.3  (14.6) 
42.8-69.7 

54.8  (13.6) 
42.8-69.7 

57.0  (14.7) 
42.8-72.2 

53.3  (10.5) 
43.3-60.0 

52.1  (13.7) 
41.7-65.0 

52.4  (12.8) 
41.7-65.0 

53.8  (12.7) 
41.7-65.0 

54.9   (9.7) 
44.4-60.8 

51.7  (15.7) 
40.2-67.8 

52.6  (14.3) 
40.2-67.8 

53.8  (13.8) 
40.2-67.8 

38 


In  Vivo  and  In  Vitro  Determinations 

Ranges,  means  and  CV's  from  in  vivo  and  in  vitro  determinations 
conducted  using  all  52  forages  are  also  presented  in  table  1.   Values 
of  these  parameters  for  individual  forages  can  be  found  in  Appendix 
tables  11  and  12. 
NDF  digestibility 

Mean  in  vivo  NDFD  percents  were  similar  for  alfalfa  and  grasses, 
though  grasses  exhibited  a  wider  range  in  NDFD.   The  fact  that  NDFD 
percents  were  similar,  though  alfalfa  was  less  mature  than  grasses  and 
contained  considerably  less  NDF,  suggests  that  NDFD  probably  is  not  con- 
trolled by  NDF  concentrations.   The  similar  NDFD  percents  may  have 
been  due  to  higher  lignin  concentrations  in  alfalfa  NDF  (Smith  et^  al.  , 
1972).   The  wider  NDFD  range  exhibited  by  grasses  was  due  probably  to 
a  wider  range  in  grass  maturity  relative  to  alfalfa  maturity  (Appendix 
tables  11  and  12).   Within  grasses,  NDFD  was  higher  in  those  which 
were  restricted-fed  (table  1).   This  effect  may  have  been  due  to  in- 
creases in  retention  time  when  forages  were  restricted-fed  (Blaxter 
et  al. ,  1956)  ,  but  Minson  (1966)  reported  that  intake  level  had  a 
relatively  small  effect  upon  rumen  retention  time  of  a  given  forage. 
In  the  present  study,  grasses  which  had  been  restricted-fed  were  per- 
haps naturally  higher  in  NDFD  than  grasses  which  were  fed  ad  libitum. 
Support  for  this  hypothesis  is  drawn  from  the  fact  that  IVNDFD  after 
72  hr  of  fermentation  was  higher  for  restricted-fed  grasses  than  for 
other  grasses. 

Mean  values  of  IVNDFD  after  72  hr  were  also  lower  for  alfalfa 
than  for  grasses  (43.5  and  52.8  percent,  respectively).   Since  Smith 


39 


et  al.  (1972)  reported  that  lignin  concentration  was  higher  in  legume 
NDF  than  in  that  of  grasses,  the  lower  I VNDFD  exhibited  by  alfalfa 
after  72  hr  may  have  been  due  to  the  fact  that  mechanical  breakdown  of 
NDF  is  not  simulated  by  the  in  vitro  system.  Since  mechanical  break- 
down does  take  place  in  vivo,  lack  of  this  process  in  vitro  could  exp!'  '.n 
the  discrepancy  shown  here  between  alfalfa  and  grasses  in  the  in  vivo  - 
in  vitro  relationship.   Whatever  the  cause  of  this  discrepancy  among 
forages,  different  equations  would  have  to  be  employed  for  alfalfa  and 
grasses  in  predicting  in  vivo  NDFD  from  IVNDFD  after  72  hr  of  fer- 
mentation.  Also,  the  relationship  between  in  vivo  NDFD  and  IVNDFD  after 
72  hr  may  not  be  quite  as  strong  for  alfalfa  as  for  grasses.   For  31 
grasses,  this  relationship  was  characterized  by  an  r  value  of  .96 

(r^=.92)  and  an  s    of  2.71,  while  for  the  nine  alfalfas  these  values 
y.x 

were  .91  (.83)  and  2.73,  respectively. 

Digestible  neutral-detergent  fiber  and  neutral-detergent  solubles 

Mean  ash-free  DNDF  was  lower  for  alfalfa  than  for  grasses  (23.9 
and  42.3  percent,  respectively;  table  1),  since  alfalfa  contained  a 
lower  percent  of  ash-free  NDF  than  did  grasses.   Within  alfalfa,  the 
facts  that  MFOM  is  theoretically  constant  (Van  Soest,  1967)  and  that 
DNDF  was  smaller  and  less  variable  in  an  absolute  sense  than  NDS 
raise  the  possibility  that  NDS  alone  would  be  a  good  predictor  of 
alfalfa  DOM.   Simple  linear  regression  revealed  that  while  the  relation- 
ship between  DOM  and  DNDF  for  alfalfa  was  represented  by  an  r  value  of 

-.10  (r  =.01)  and  an  s    of  2.30,  the  relationship  between  DOM  and 
y.x 

NDS  exhibited  such  values  of  .77  (.60)  and  1.46,  respectively.   The 


40 


relationship  between  DOM  and  (NDS  +  DNDF)  for  alfalfa  was  represented 

2 
by  an  r  value  of  .93  (r  =.87)  and  an  s    of  .85.   Thus,  (NDS  +  DNDF) 

may  be  a  better  predictor  of  alfalfa  DOM  than  NDS  would  be  alone.   This 

hypothesis  should  be  investigated  in  terms  of  acceptability  of  alfalfa 

DOM  predictions.   Within  grasses,  though  DNDF  was  higher  and  more 

variable  in  absolute  terms  than  was  NDS,  it  also  appears  that  (NDS  + 

DNDF)  would  be  a  better  predictor  of  DOM  than  DNDF  would  be  alone. 

Restricted-fed  grasses  exhibited  higher  DNDF  than  did  grasses  fed 

ad  libitum  (table  1) ,  since  restricted-fed  grasses  showed  higher  NDFD 

percents.   As  pointed  out  earlier,  these  higher  NDFD  percents  may  not 

have  been  strictly  the  direct  result  of  lower  intake. 

It  is  possible  that  one  reason  for  failure  of  the  Van  Soest 

Summative  Equation  (Van  Soest, 1965b)  to  accurately  predict  digestibility 

of  warm-season  grasses  (Velasquez,  1974)  may  be  that  in  such  grasses 

ash-free  DNDF  is  larger  and  more  variable  than  digestible  ash-free 

NDS  (DNDS),  or  (ash-free  NDS  -  MFOM) .   The  Van  Soest  Summative  Equation 

was  developed  on  temperate  forages,  and  in  these  forages  DNDF  appears 

less  variable  than  DNDS' (Tilley  e^  al • ,  1969).   In  the  present  study, 

ash-free  DNDF  for  31  grasses  averaged  40.9  percent  (table  1),  and  the 

2 
variance  (s  )  associated  with  this  mean  was  21.7  units.   Ash-free  DNDS 

2 
averaged  11.1  percent  and  s  was  15.2  units.   Therefore,  the  high  amount 

and  variability  of  DNDF  in  warm-season  grasses  may  limit  the  utility 
of  the  Van  Soest  Summative  Equation,  per  se,  for  predicting  diges- 
tibility of  such  grasses. 


41 


OM  digestibility 

Mean  OMD  was  higher  for  alfalfa  than  for  grasses  (67.1  and  54.8 
percent,  respectively;  table  1).   This  was  due  to  NDS  being  higher  in 
alfalfa,  and  to  NDFD  being  similar  between  alfalfa  and  grasses.   The 
CV  for  OMD  was  less  for  alfalfa  (5.6  percent)  than  for  grasses  (13.6 
percent).   This  was  caused  by  the  higher  mean  OMD  for  alfalfa,  and 
probably  also  by  the  lower  maturity  range  among  alfalfas  than  among 
grasses.   Within  grasses,  mean  OMD  was  higher  for  those  which  had 
been  restricted-fed.   This,  however,  may  not  have  been  directly  related 
to  lower  intake  of  these  grasses. 
Digestible  organic  matter 

Mean  DOM,  as  a  percentage  of  DM,  was  also  higher  for  alfalfa 
than  for  grasses  (60.3  and  52.4  percent,  respectively).   This  was  due 
to  the  much  higher  NDS  in  alfalfa  more  than  compensating  for  higher 
DNDF  in  grasses.   Values  of  CV  for  DOM  were  lower  for  alfalfa  than 
for  grasses,  probably  due  to  the  same  factors  which  caused  this  same 
response  with  respect  to  OMD.   Values  of  DOM  for  restricted-fed  grasses 
were  similar  to  those  for  grasses  fed  ad  libitum  because  NDS  in  re- 
stricted-fed grasses  was  slightly  lower  than  in  ad  libitum- fed  grasses 
and  MFOM  was  slightly  higher. 
Metabolic  fecal  organic  matter  by  calculation 

Mean  values  of  MFOM,  as  a  percentage  of  DM,  were  9.5  for  alfalfa, 
10.5  for  grasses  and  10.3  for  all  forages.   Thus,  a  constant  value  of 
10.3  was  inserted  for  MFOM  into  equation  (5)  when  making  DOM  predictions. 
Within  grasses,  MFOM  means  were  (as  percentages  of  DM)   bahiagrass,  12.2; 


42 


bermudagrass,  8.3;  and  Pangola  digitgrass,  11.4.   The  reason  that  MFOM 
for  the  12  restricted-fed  grasses  was  higher  than  the  average  for  all 
forages  was  that  eight  restricted-fed  grasses  were  bahiagrasses  and 
three  were  Pangola  digitgrasses.   Mean  values  of  MFOM  in  this  study 
compare  favorably  with  the  9.5  percent  reported  by  Minson  (1971b) 
for  Panicum  species,  and  the  9.8  and  12.9  percent  metabolic  fecal  DM 
for  temperate  forages  found  by  Colburn  et^  al.  (1968)  and  Van  Soest 
(1967),  respectively.   Capote  (1975)  reported  that  10  percent  could  be 
used  for  bermudagrass  pellet  MFOM.   Still,  accurate  prediction  of 
MFOM  for  a  given  forage,  rather  than  use  of  a  constant  value  for  this 
parameter  among  forages,  might  produce  more  accurate  DOM  predictions 
when  using  equation  (5). 
Estimation  of  metabolic  fecal  organic  matter  by  regression 

Velasquez  (1974)  reported  that  MFOM  for  warm-season  grasses  was 
5.4  percent.   Actual  mean  MFOM,  as  a  percentage  of  DM,  for  the  40 
forages  utilized  by  this  worker,  however,  was  10.4.   This  discrepancy 
possibly  arose  because  the  value  of  5.4  for  MFOM  was  the  ordinate 
intercept  which  resulted  when  a  test  for  nutritional  uniformity  (Lucas 
and  Smart,  1959)  was  applied  to  NDS,  as  a  percentage  of  DM,  over  a 
small  range  of  NDS,  i.  e. ,  15.9  to  30.5  percent.   It  is  true  that  in 
a  test  for  nutritional  uniformity  of  NDS,  the  ordinate  intercept  should 
approximate  MFOM  if  the  slope  of  the  resultant  regression  line  is  close 
to  1.0,  as  it  should  have  been  in  the  work  by  Velasquez  (1974)  (Van 
Soest,  1967;  Minson,  1971b).   However,  the  slope  of  the  regression  line 
was  only  .75.   This  low  slope  probably  resulted  because  Velasquez  (1974) 
worked  with  a  narrow  range  of  NDS  values  which,  in  effect,  reduced  the 


43 


sample  size  from  the  true  population  of  NDS  values  for  all  existing 
forages.   This  small  sample  size  probably  did  not  allow  the  true 
slope  of  the  regression  line  for  all  existing  forages  to  express  itself, 
and  resulted  in  an  inaccurate  estimation  of  this  slope,  which  should 
have  been  close  to  1.0.   Thus,  incorrect  estimation  of  the  slope  for 
the  true  relationship  between  DNDS  and  NDS  probably  caused  the  ordinate 
intercept  of  5.4  to  inaccurately  estimate  the  true  average  MFOM  value 
of  10.4  percent.   This  hypothesis  was  tested  in  the  present  study. 
Using  31  grasses  which  ranged  in  NDS  from  16.1  to  30.5  percent,  the 
regression  of  DNDS  on  NDS  produced  a  line  with  a  slope  of  .75,  and 
an  MFOM  estimate  of  4.7  percent.   Actual  mean  MFOM  for  these  31  grasses 
was  9.9  percent.   Repeating  this  analysis  using  all  52  forages  re- 
sulted in  an  NDS  range  of  from  16.1  to  50.3  percent,  or  a  range  which 
was  238  percent  of  that  used  previously.   In  this  case,  regression  of 
DNDS  on  NDS  produced  a  line  with  a  slope  of  1.00,  and  an  MFOM  estimate 
of  10.4  percent.   Actual  mean  MFOM  for  these  52  forages  was  10.3  per- 
cent (table  1).   Thus,  if  a  test  of  nutritional  uniformity  is  used  to 
estimate  the  actual  mean  value  of  MFOM,  a  range  of  NDS  large  enough  to 
allow  expression  of  the  true  slope  of  the  relationship  between  DNDS  and 
NDS  must  be  used. 
Prediction  of  NDFD ,  DOM  and  DE/DM 

Ranges,  means  and  CV s  for  predicted  values  of  NDFD  and  DOM  are 
presented  in  table  1.   Predicted  values  of  these  parameters  for  indi- 
vidual forages  appear  in  Appendix  tables  11  and  12.   Individual  values 
for  actual  and  predicted  DE/DM  for  10  grasses  are  presented  in  table  2. 


44 


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45 


For  grasses,  predicted  values  of  ash-free  NDFD  ranged  from  38.4 
to  71.8,  and  the  mean  was  54.5  percent  (table  1).   Predictions  of  NDFD 
were  not  made  for  alfalfas  since  no  previously  generated  prediction 
equation  was  available,  and  the  nine  alfalfas  used  in  this  study- 
were  not  enough  to  effectively  generate  and  test  such  an  equation.   For 
all  43  grasses,  simple  linear  regression  of  actual  NDFD  on  predicted 


NDFD  produced  an  r  value  of  .98  (r  =.96)  and  an  s    of  2.0  percentage 
units.   These  values  were  high  even  within  grasses  which  had  been  re- 


stricted-fed, i.  e.,  r=.98  (r  =.96)  and  s  ^=1-5  percentage  units.   Thus 


even  when  warm-season  grasses  are  restricted-fed,  IVNDFD  after  72  hr 
of  fermentation  appears  to  be  an  excellent  independent  variable  from 
which  to  accurately  predict  NDFD.   Determination  of  this  IVNDFD  parameter 
in  the  laboratory,  however,  requires  considerable  time  and  effort,  and 
development  of  a  more  rapid  procedure  for  predicting  in  vivo  NDFD  would 
accelerate  and  facilitate  forage  evaluation.   Micro-anatomical  studies 
of  forages  (Akin  ^  al . ,  1974a;  Monson  et  al. ,  1972;  de  la  Torre,  1974) 
may  aid  in  developing  such  a  procedure. 

Another  possible  reason  why  the  Van  Soest  Summative  Equation  (Van 
Soest,  1965b)  does  not  apply  to  warm-season  grasses  (Velasquez,  1974) 
may  be  that  the  equation  used  to  predict  NDFD  is  not  truly  applicable 
to  such  forages.   The  equation  presented  by  Van  Soest  (1965b)  for  NDFD 

prediction  is 

NDFD  =  147.8  -  78. 9L,  (13) 

where  L  represents  the  common  log  of  lignin  percentage  in  acid -detergent 
fiber.   For  the  43  warm-season  grasses  utilized  in  the  present  study, 


46 


simple  linear  regression  of  actual  NDFD  on  NDFD  predicted  using  equation 

2 
(13)  produced  an  r  value  of  only  .73  (r  =.54)  and  an  s    of  6.4  per- 
•^  y.x 

centage  units.   Thus,  it  appears  that  equation  (13),  which  was  develop- 
ed on  temperate  forages,  does  not  possess  a  high  degree  of  utility  for 
accurate  prediction  of  NDFD  in  warm-season  grasses. 

For  alfalfa,  predictions  of  DOM,  as  a  percentage  of  DM,  generated 
by  equation  (5)  ranged  from  56.5  to  64.6  (table  1),  with  a  mean  of  59.5; 
and  for  grasses,  predictions  ranged  from  40.2  to  67.8,  with  a  mean  of 
52.6.   For  all  forages,  DOM  predictions  ranged  from  40.2  to  67.8,  and 

the  mean  was  53.8  percent.   Using  all  52  forages,  regression  of  actual 

2 
in  vivo  DOM  on  predicted  DOM  produced  an  r  value  of  .96  (r  =.93)  and 

an  s    of  1.8  percentage  units.   Within  either  grasses  or  alfalfa 
y.x 

this  relationship  was  just  as  strong,  with  r  and  s  ^  values  being 

9  2 

.96  (r  =.93)  and  1.8  for  grasses,  and  .94  (r  =.88)  and  .8  for  alfalfa, 

respectively.   These  results  suggest  that  equation  (5)  can  be  used  for 

accurate  prediction  of  DOM  in  alfalfa  and  warm-season  grasses,  and 

that  MFOM  can  be  considered  constant  at  10.3  percent  for  these  forages. 

Before  either  of  these  hypotheses  can  be  considered  true,  however,  DOM 

predictions  must  be  acceptable  when  judged  by  statistically  defined 

acceptability  limits. 

Predictions  of  DE/DM  (kcal/g)  were  generated  for  10  warm-season 

grasses  using  equation  (12).   Two  predictions  of  DE/DM  were  produced 

for  each  grass:  (a)  by  inserting  actual  in  vivo  DOM  into  equation  (12); 

and  (b)  by  using  predicted  values  of  DOM  in  this  equation.   Actual 

values  of  DE/DM  ranged  from  1.82  to  2.68,  with  a  mean  of  2.18  kcal/g 

(table  2).   Predictions  generated  by  method  (a)  ranged  from  1.78  to 


47 


2.57,  with  a  mean  of  2.11;  and  for  method  (b) ,  from  1.72  to  2.50,  with 

a  mean  of  2.15.   Regression  of  actual  DE/DM  on  that  predicted  by  method 

2 

(a)  produced  an  r  value  of  .99  (r  =.99)  and  an  s    of  .03  kcal/g.   This 

procedure  with  respect  to  method  (b)  resulted  in  an  r  value  of  .93 

2 
(r  =.87)  and  an  s    of  .10  kcal/g.   These  results  suggest  that  equation 
y.x 

(12)  is  rational  for  prediction  of  DE/DM,  and  that  equation  (5)  can 
be  employed  in  conjunction  with  equation  (12)  for  accurately  predicting 
this  parameter.   As  was  the  case  relative  to  such  conjecture  concern- 
ing DOM  prediction,  predictions  of  DE/DM  by  both  method  (a)  and  method 

(b)  must  be  acceptable  when  judged  by  statistically  defined  limits 
before  the  above  hypotheses  can  be  considered  true. 

Testing  of  DOM  and  DE/DM  Predictions 
Acceptability  limits  for  judging  the  predictions 

Theory  underlying  the  definition  of  acceptability  limits  used  in 
this  study  is  presented  in  Chapter  V.   Differences  between  actual  and 
predicted  values  of  DOM  or  DE/DM  were  deemed  acceptable  in  a  conservative 
sense  if  the  absolute  values  of  these  differences  were  less  than  or  equal 
to  the  weighted  average  of  t(s/i/n)  when  this  expression  was  evaluated  at 
the  .95  confidence  level  for  all  52  or  10  forages,  respectively.   Such 
differences  were  termed  acceptable  in  a  liberal  sense  when  their  absolute 
values  were  less  than  or  equal  to  the  weighted  average  of  2s  for  the  52 
or  10  forages.   Though  in  this  study  acceptability  limits  were  calculat- 
ed for  each  forage  species,  only  limits  applicable  to  all  forages  were 
used  to  test  predictions.   This  policy  negates  the  necessity  of  referring 
to  such  limits  for  each  forage  species  when  qualifying  predictions  of 


48 


DOM  or  DE/DM.   Conservative  and  liberal  acceptability  limits  determined 
by  applying  their  respective  expressions  to  the  52  or  10  forages,  as 
well  as  to  alfalfa,  bahiagrass,  bermudagrass  or  Pangola  digitgrass 
alone,  are  shown  in  table  3. 
Acceptability  of  the  predictions 

Figure  1  shows  the  test  of  acceptability  of  DOM  predictions.   The 
points  plotted  in  this  figure  are  coordinates  of  actual  DOM  and  pre- 
dicted DOM  values,  and  the  continuous  middle  line  represents  the  set  of 
points  where  actual  DOM  equals  predicted  DOM.   The  vertical  deviation 
of  any  plotted  point  from  the  continuous  middle  line  represents  the 
error  in  predicting  that  value  of  DOM.   The  inner  set  of  broken  lines 
represent  conservative  acceptability  limits,  and  the  outer  set  of 
broken,  dotted  lines  mark  the  liberal  acceptability  limits.   In  this 
study,  all  52  DOM  predictions  were  acceptable  when  judged  by  liberal 
limits,  and  38  predictions  were  acceptable  by  conservative  standards. 
These  results  confirm  the  hypotheses  that  equation  (5)  can  be  used  to 
produce  acceptable  predictions  of  DOM  for  alfalfa  and  warm-season  grasses, 
and  that  MFOM  can  be  considered  constant  at  10.3  percent  among  these  for- 
ages.  Forages  for  which  DOM  predictions  were  not  acceptable  when  judged 
by  conservative  limits  included  one  alfalfa,  six  bahiagrasses ,  six 
bermudagrasses  and  one  Pangola  digitgrass.   Margins  by  which  DOM  pre- 
dictions for  these  forages  were  unacceptable  according  to  conservative 
limits  ranged  from  .1  to  1.3  percentage  units,  and  only  five  such  margins 
were  greater  than  .5  percentage  units.   Since  rn  vivo  DNDF  values  were 
inserted  into  equation  (5)  to  produce  DOM  predictions,  the  absolute 
value  of  a  given  difference  between  actual  and  predicted  DOM  was  equal 


49 


TABLE  3.  CONSERVATIVE  AND  LIBERAL  ACCEPTABILITY  LIMITS  FOR  TESTING 
PREDICTIONS  OF  DIGESTIBLE  ORGANIC  MATTER  (DOM)  AND  DIGESTIBLE  ENERGY 
(DE)  CONCENTRATION 


FORAGE 

SPECIES 

Item 

Alfalfa 

Bahia 

Bermuda 

Pangola 

All 

DOM,  %  of  dry  matter 

Conservative  [±t(s/v^)] 

±2.6 

±2.2 

±2.5 

±3.1 

±2.6 

Liberal  (±2s) 

±3.3 

±4.3 

±5.2 

±5.0 

±4.7 

DE,  kcal/g  dry  matter 

Conservative 

- 

±.12 

±.10 

±.17 

±.13 

Liberal 

- 

±.24 

±.20 

±.32 

±.25 

50 


40    43    46    49    52    55    58  ^  61    64    67    70 
PREDICTED  DIGESTIBLE  ORGANIC  MATTER  (Y) ,  %  OF  DRY  MATTER 


Figure  1.   Test  of  theoretically  rational  method  for  prediction 
of  digestible  organic  matter  as  a  percentage  of  dry 
matter. 


51 


to  the  absolute  value  of  the  difference  between  MFOM  for  a  given 
forage  and  the  assumed  constant  MFOM  value  of  10.3  percent.  Absolute 
differences  between  this  constant  MFOM  value  and  actual  mean  MFOM 
for  bahiagrass  and  bermudagrass  were  1.9  and  2.0  percentage  units, 
respectively.  More  accurate  estimation  of  actual  mean  MFOM  for 
bahiagrass  and  bermudagrass  would  result  in  a  greater  number  of 
conservatively  acceptable  DOM  predictions  for  these  grasses  when 
using  equation  (5).   For  all  forages  in  this  study,  however,  use  of 
a  constant  MFOM  value  of  10.3  percent  produced  acceptable  predictions 
of  DOM. 

Figure  2  shows  that  prediction  of  DE/DM  (kcal/g)  by  method  (a) 
slightly  underestimated  actual  DE/DM  for  all  10  grasses.   This  may 
have  been  due  to  assuming  DEE,  as  a  percentage  of  DM,  to  be  non- 
existent in  these  forages.   However,  all  10  DE/DM  predictions  were 
acceptable  when  judged  by  conservative  limits.   This  result  confirms 
the  hypothesis  that  equation  (12)  is  rational  for  prediction  of  DE/DM 
for  warm-season  grasses.   Figure  3  shows  that  prediction  of  DE/DM  by 
method  (b)  produced  predictions  which  both  overestimated  and  under- 
estimated actual  DE/DM.  Inspection  of  Appendix  tables  11  and  12  and 
table  2  shows  that  actual  DE/DM  was  over-predicted  when  actual  DOM  was 
over-predicted  by  equation  (5),  and  vice-versa.   The  one  exception  to 
this  generality  may  have  been  due  to  the  possibility  that  10-wk  Pangola 
digitgrass  (table  2)  contained  a  small  but  significant  amount  of  DEE. 
Still,  figure  3  indicates  that  all  DE/DM  predictions  resulting  from 
method  (b)  were  acceptable  when  judged  by  liberal  limits,  and  that 


52 


iVt   178   1.9   2.0   271   272   271   O   273   276   2.7 
PREDICTED  DE  (Y),  kcal/g  DM 


Figure  2.   Comparison  of  in  vivo  digestible  energy  (DE)  with 

that  predicted  from  actual  In  vivo  digestible  organic 
matter  (DOM)  and  crude  protein  (CP)  by  the  equation: 

DE  =  4.15  DOM  +  1.299  CP  -  4.59. 
100 


53 


2.7  r 


2.6  - 


2.5  - 


2.4  - 


2.3  - 


2.2 


2.1  - 


1.7   1.8   1.9   2.0   2.1   2.2   2.3   2.4   2.5   2.6   2.7 
PREDICTED  DE   (Y),kcal/g  DM 


Figure  3.   Comparison  of  in  vivo  digestible  energy  (DE)  with 
that  predicted  from  predicted  iji  vivo  digestible 
organic  matter  (DOM  )  and  crude  protein  (CP)  by 
the  equation: 

DE  =  4.15  DOM  +  1.299  CP  -  4.59. 

P 

100 


54 


nine  predictions  were  acceptable  by  conservative  limits.   That  pre- 
dicted DE/DM  for  4-wk  bermudagrass  was  too  low  to  be  acceptable  by- 
conservative  limits,  though  its  actual  DOM  value  was  underpredicted 
to  a  lesser  extent  than  that  of  8-wk  bermudagrass  (table  2),  may 
have  been  due  to  a  higher  amount  of  DEE  in  4-wk  bermudagrass.   The 
results  shown  in  figure  3  confirm  the  supposition  that  equation  (5) 
can  be  employed  effectively  together  with  equation  (12)  to  produce 
acceptable  predictions  of  DE/DM. 

General  Discussion 
Non-Forage  Factors  Affecting  DE/DM 

When  predicted  DOM,  as  a  percentage  of  DM,  from  equation  (5)  is 
inserted  into  equation  (12)  ,  acceptably  accurate  predictions  of  DE/DM 
(kcal/g)  can  be  obtained  for  warm-season  grasses.   This  procedure, 
however,  predicts  DE/DM  as  an  attribute  of  a  given  forage,  and  not  as 
that  which  might  be  realized  in  a  given  production  situation.   This 
is  because  there  are  many  factors  influencing  forage  nutrient  digest- 
ibility in  a  given  situation  which  are  not  taken  into  account  in 
existing  laboratory  procedures  used  to  estimate  digestibility.   Such 
factors  generally  are  related  to  the  animals  which  consume  a  given 
forage,  to  environmental  conditions  or  to  the  management  policy  under 
which  forages  are  fed  to  ruminants.   These  factors  are  termed  non- 
forage  factors,  since  they  are  not  related  directly  to  chemical  compo- 
sition  or  structural  organization  of  forages,  per  se.   Non-forage 
factors  which  have  been  documented  in  the  literature,  and  which  may 
affect  DE/DM  in  a  given  situation,  include 

(1)  type  of  ruminant  to  which  forages  are  fed,  i.  e. ,  cattle 
or  sheep  (Cipolloni  ejL  al .  ,  1951;  Alexander  e_t  al.  ,  1962; 
Blaxter  et  al. ,  1966) ; 


55 


(2)  breed  of  ruminant  within  type  (Ashton,  1962;  Ledger  et  al. , 
1970;  Riewe  and  Lippke,  1970;  Essig  e^  al .  ,  1975); 

(3)  the  form  in  which  forages  are  fed,  i.  e. ,  long,  chopped, 
ground  or  ground  and  pelleted  (Rodrigue  and  Allen,  1956; 
Minson,  1967;  Church,  1969;  Terry  ejt  al.  ,  1972); 

(4)  energy  supplementation  of  forages  (Burroughs  e_t  al .  ,  1949; 
el-Shazly  et  al . ,  1961;  Clanton  and  Rittenhouse,  1970; 
Gelding,  1973); 

(5)  CP  supplementation  of  low-CP  forages  (Smith,  1962;  Campling 
_et  al .  ,  1952;  Chapman  and  Kretschraer,  1964;  Ventura,  1973); 

(6)  level  of  feeding  (Blaxter  et  al. ,  1956;  Moe  et  al. ,  1965; 
Brown,  1966;  Terry  e^  al. ,  1972); 

(7)  animal  level  of  internal  parasites  (Spedding,  1954;  cited 
by  Raymond,  1969) ; 

(8)  ambient  temperature  (Blaxter  and  Wainman,  1961;  Bailey, 
1964);  and 

(9)  water  deprivation  (French,  1956;  Phillips,  1961). 

These  non-forage  factors,  if  present  at  functional  levels  in  a  given 

production  situation,  could  cause  discrepancies  between  actual  and 

predicted  DE/DM.   If  effects  of  these  factors  upon  DE/DM  could  be 

quantified  over  a  wide  range  of  forage  quality,  then  dynamic  computer 

modeling  might  allow  accurate  prediction  of  DE/DM  for  given  production 

situations. 

Research  Needs  for  Rapid  and  Accurate  DOM  Prediction 

Results  of  this  study  showed  that  acceptable  predictions  of  DOM 
were  obtained  when  a  constant  MFOM  value  of  10.3  percent  of  DM  was 
inserted  into  equation  (5)  along  with  actual  in  vivo  DNDF.   Thus, 
further  work  on  MFOM  appears  unnecessary  in  achieving  the  rapid  and 
acceptable  prediction  of  DOM  for  alfalfa  and  warm-season  grasses. 

Attention  now  must  be  turned  toward  the  rapid,  acceptable  pre- 
diction of  DNDF  percentage.   Percentage  of  NDF  can  be  determined 


56 


rapidly  in  the  laboratory  (Van  Soest  and  Wine,  1967),  but  in  pre- 
dicting DNDF  it  is  not  sufficient  to  measure  only  NDF,  since  this 
fiber  fraction  is  not  a  Nutritive  Entity  (Moore  and  Mott,  1973). 
Digestibility  of  NDF  is  predicted  best  by  IVNDFD  after  a  72-hr 
fermentation.   Thus,  NDFD  prediction  is  laborious  and  time  consuming, 
and  it  is  possible  that  discrepancies  exist  among  forages  in  this 
in  vivo-in  vitro  relationship.   In  this  study,  the  only  chemical 
component  which  correlated  highly  with  NDFD  or  DNDF  in  31  grasses 
was  lignin  (r=-.  89  and  -.81,  respectively),  but  these  correlations 
may  not  be  this  high  over  a  wider  range  of  forages  (Moore  and  Mott, 
1973).   Correlation  coefficients  between  (NDS  +  DNDF)  and  lignin  or 
NDF  were  -.91  or  -.90,  respectively,  for  the  31  grasses,  but  again 
these  r  values  probably  will  decline  over  a  wider  range  of  forages. 
Moir  (1972)  suggested  that  DNDF  be  considered  constant  among  temperate 
and  tropical  grasses  at  40  percent  of  CM,  and  among  legumes  at  19.8 
percent.   In  the  present  study,  DNDF  ranged  from  33.1  to  52.8  percent 
of  DM  for  43  grasses  (table  1),  and  from  20.8  to  26.9  percent  for 
nine  alfalfas.   Thus,  the  suggestion  of  Moir  (1972)  appears  inaccurate. 
Research  now  must  be  directed  toward  development  of  a  rapid,  acceptable 
procedure  for  predicting  DNDF  percentage,  or  NTDFD  percent,  as  a  forage 
attribute  over  a  wide  range.   Results  of  recent  microscopic  studies 
of  NDF  degradation  by  rumen  microorganisms  suggest  that  NDF  in  some 
forage  tissues  is  indigestible,  whereas  NDF  in  other  tissues  is  digest- 
ible (Moore  and  Mott,  1973;  Barnes,  1973;  de  la  Torre  e^  al • ,  1974). 
Therefore,  the  NT)F  fraction  may  contain  two  Nutritive  Entities,  one 


57 


being  potentially  digestible  NDF  with  a  digestibility  near  IOC  percent, 
and  the  other  being  potentially  indigestible  NDF  with  a  digestibility 
near  0  percent.   This  concept  has  been  suggested  as  the  basis  for  a 
model  of  cellulose  digestion  (Waldo  et  al . ,  1972),  and  could  be  valid 
for  NDF  digestion  as  well.   Thus,  microscopic  studies  to  examine 
patterns  of  NDF  organization  and  degradation  over  a  wide  range  of 
forages  may  aid  in  development  of  a  procedure  for  rapid,  acceptable 
prediction  of  DNDF  among  forages. 

Summary 
Summative  equation  and  Nutritive  Entity  concepts  should  serve  as 
the  basis  for  rational  prediction  of  the  digestible  energy  (DE)/DM 
(kcal/g)  values  of  forages.   Acceptably  accurate  predictions  of  DE/DM 
can  be  obtained  by  summing  the  caloric  values  of  the  major  portions 
of  forage  digestible  OM  (DOM),  i.  e. ,  digestible  carbohydrate  and 
digestible  CP.   Acceptable  predictions  of  DOM  can  be  obtained  by 
summing  digestible  neutral-detergent  solubles  (DNDS)  and  digestible 
neutral-detergent  fiber  (DNDF),  and  subtracting  metabolic  fecal  OM 
(MFOM),  all  as  percentages  of  DM.   Use  of  a  constant  10.3  percent  for 
MFOM  and  determination  of  neutral-detergent  solubles  (NDS)  to  estimate 
DNDS  both  lend  themselves  to  a  rapid  and  inexpensive  procedure  for 
prediction  of  DOM.   If  MFOM  is  to  be  predicted  for  a  given  group  of 
forages  using  a  test  for  nutritional  uniformity,  care  must  be  taken 
to  include  forages  which  exhibit  a  wide  range  of  NDS  in  the  analysis. 
Though  DNDF  is  smaller  and  less  variable  than  NT)S  within  alfalfa, 
this  latter  parameter  by  itself  may  not  produce  acceptable  predictions 


58 


of  alfalfa  DOM.   Values  for  neutral-detergent  fiber  digestibility  (NDFD) 
can  be  predicted  accurately  for  grasses  from  in  vitro  NDFD  after  72  hr 
of  fermentation.   This  in  vitro  procedure  is  time  consuming  and  laborious, 
and  discrepancies  apparently  exist  in  the  iii  vivo  NDFD  -  In  vitro  NDFD 
relationship  among  alfalfas  and  grasses.   Thus,  the  remaining  challer  ' 
with  reference  to  DOM,  and  ultimately  DE/DM,  prediction  is  development 
of  a  rapid  procedure  for  acceptable  prediction  of  DNDF  over  a  wide 
range  of  forages. 

The  reasons  that  Van  Soest's  Summative  Equation,  which  was  develop- 
ed on  temperate  forages,  does  not  predict  digestibility  of  warm-season 
grasses  accurately  may  be  that   (a)  in  warm-season  grasses,  DNDF  appears 
larger  and  more  variable  than  DNDS;  and  (b)  the  equation  used  to  pre- 
dict NDFD  does  not  predict  this  parameter  accurately  for  warm-season 
grasses. 


CHAPTER  IV 

ELIMINATION  OF  ORGANIC  SOLVENTS  IN  THE  STUDY  OF  IN  VITRO 
NEUTRAL-DETERGENT  FIBER  DIGESTION 

Introduction 

In  vitro  studies  of  forage  neutral-detergent  fiber  (NDF)  digestion 
will  be  increasingly  important  in  the  future  (Chapter  III) .   Two  organic 
solvents  which  may  be  hazardous  to  laboratory  technicians  have  been  used 
in  determination  of  in  vitro  NDF  digestion,  e.  g.,  toluene  (Goering  and 
Van  Soest,  1970)  when  contents  of  fermentation  tubes  were  held  for 
later  analysis;  and  acetone  in  the  NDF  determinations  (Van  Soest  and 
Wine,  1967).   The  effectiveness  of  toluene  may  be  questionable,  since 
Meites  et  al.  (1951)  reported  that  cellulolytic  activity  of  rumen  micro- 
organisms continued  for  up  to  48  hr  after  addition  of  toluene. 

The  objectives  of  this  experiment  were   (a)  to  establish  whether 
acetone  washes  were  required  for  accurate  determination  of  ash-free 
NDF  (NDFA)  in  either  hay  samples  or  in  residues  after  ^il  vitro  fermenta- 
tion; and  (b)  to  develop  a  procedure  for  terminating  in  vitro  fermentation 
which  did  not  require  use  of  toluene. 

Experimental  Procedure 

Artificially  dried  hays  of  different  quality  made  from  two  cuts  of 
'Florida  66'  alfalfa  (Medicago  sativa  L.)  (hays  73-B  and  73-D,  Appendix 
table  12),  two  cuts  of  Pensacola  bahiagrass  (Paspalum  notatum  Flugge) 
(47-2A  and  47-4C,  Appendix  tables  10  and  11),  two  cuts  of  Suwannee 
bermudagrass  (Cynodon  dactylon  (L)  Pers.)  (65-lA  and  65-lF,  Appendix 


59 


60 


tables  10  and  11) ,  and  two  cuts  of  Pangola  digitgrass  (Digitarla  decum- 
bens  Stent)  (55- 2A,  Appendix  tables  10  and  11;  75-2C,  Appendix  table  12) 
were  used  to  examine  the  necessity  of  acetone  washes  in  determinations 
of  NDFA,  as  a  percentage  of  DM,  in  hay  samples.   Treatments  used  were 
(a)  the  control,  in  which  residues  were  washed  with  both  boiling  water 
and  acetone  (Van  Soest  and  Wine,  1967);  and  (b)  the  modification,  in 
which  residues  were  washed  with  boiling  water  alone.   Dried  residue  was 
ashed  and  the  difference  in  weight  between  dry  and  ashed  residues  re- 
presented weight  of  NDFA.   Three  separate  runs,  each  including  all  eight 
hays,  were  made  for  a  total  of  three  NDFA  observations  per  treatment. 
The  experimental  design  and  analysis  of  variance  were  those  for  complete 
randomized  blocks  (Snedecor  and  Cochran,  1967) ,  with  hays  representing 
blocks. 

The  same  eight  hays  were  used  to  investigate  the  necessity  of  wash- 
ing ^  vitro  residual  NDF  with  acetone  when  determining  iii  vitro  NDFA 
digestion,  and  to  determine  an  alternate  stop-method  to  toluene  for 
terminating  fermentation.   Iji  vitro  fermentations  followed  the  procedure 
of  Moore  and  Mott  (1974)  except  that  fermentation  lasted  28  hr  and  res- 
idual NDFA  was  determined.   Toluene  was  not  used  in  any  stop-method. 
The  four  stop-methods  were   (a)  immediate  analysis  for  residual  NDFA 
(Goering  and  Van  Soest,  1970);  (b)  addition  to  tubes  of  25  ml  neutral- 
detergent  (ND)  reagent,  followed  by  40  hr  of  refrigeration;  (c't  immersion 
of  tubes  to  the  level  of  their  contents  in  an  ice-water  bath  for  1  hr, 
followed  by  40  hr  of  refrigeration;  and  (d)  addition  to  tubes  of  6  ml 
of  20  percent  (v/v)  hydrochloric  acid  (HCl)  in  1,  1,  2  and  2  ml  increments. 


61 


followed  by  40  hr  of  refrigeration.   Acetone  treatments  were  as  describ- 
ed earlier  for  determination  of  NDFA  in  hay  samples.   Treatments  were 
analyzed  in  duplicate  within  run,  and  two  separate  runs  were  made,  for 
a  total  of  four  observations  per  treatment.   The  experimental  design 
consisted  of  complete  randomized  blocks,  with  blocking  by  hays.   Analysis 
of  variance  was  done  using  least-squares  procedures  described  by  Harvey 
(1960),  and  interaction  means  were  compared  using  Duncan's  Multiple  Range 
Test  (Duncan,  1955).   Due  to  high  precision  of  in  vitro  and  chemical 
determinations,  statistical  testing  was  done  at  the  .01  level  to  reduce 
the  possibility  of  claiming  differences  significant  when  such  differences 
were  not  large  in  a  biological  sense. 

Results  and  Discussion 

Table  4  shows  results  of  determinations  of  hay  NDFA  for  the  eight 
individual  hays  when  the  two  acetone  treatments  (+  or  -)  were  applied. 
Treatment  means  for  +  or  -  acetone  were  68.4  and  69.2  percent,  respec- 
tively.  There  was  no  difference  (.01  <  P  <  .05)  between  these  means. 
However,  the  rankings  of  4-wk  bahiagrass  and  13-wk  Pangola  digitgrass 
were  reversed  between  treatments.   The  largest  difference  between  treat- 
ments for  an  individual  hay  (2.2  percentage  units  with  2-wk  Pangola 
digitgrass)  was  a  positive  3.3  percent  of  the  +  acetone  value,  or 
greater  than  the  3  percent  generally  accepted  for  laboratory  error.   Thus, 
though  there  was  no  difference  (.01  <  P  <  .05)  between  acetone  treatments, 
results  of  this  experiment  suggest  that  unacceptable  errors  in  deter- 
mination of  hay  NDFA  could  result  if  an  acetone  wash  is  not  employed. 

In  the  in  vitro  experiment,  acetone  was  not  involved  in  any  two- 
way  interactions  (P>.05).   Since  the  main  effect  of  acetone  was  not 


62 


TABLE  4.   EFFECT  OF  ACETONE  ON  THE  DETERMINATION  OF  ASH-FREE  NEUTRAL- 
DETERGENT  FIBER  (NDFA)  IN  EIGHT  HAYS 


Hay 


TREATMENT 

+Acetone 

-Acetone 

41.3^ 

41.5 

47.2 

47.3 

67.6 

69.8 

73.0 

74.8 

77.3 

78.7 

78.2 

78.5 

78.8 

79.3 

83.6 

83.5 

73-B  (3-wk  alfalfa) 

73-D  (4.5-wk  alfalfa) 

55-2A  (2-wk  Pangola  digitgrass) 

65-lA  (2-wk  bermudagrass) 

47-2A  (4-wk  bahiagrass) 

75-2C  (13-wk  Pangola  digitgrass) 

47-4C  (mature  bahiagrass) 

65-lF  (12-wk  bermudagrass) 


Mean 


68.4 


69.2 


Number,  weeks  of  maturity  and  species.   NDFA,  %  of  dry  matter. 


63 


significant  (P>.05),  results  of  this  experiment  suggest  that  washing 

in  vitro  residues  with  only  boiling  water  should  not  produce  errors 

in  determinations  of  i^  vitro  residual  NDFA  or  _in  vitro  NDFA  digestion. 

Results  differed  (P<.01)  among  in  vitro  stop-methods.   However, 
there  were  interactions  (P<.01)  between  stop-method  and  hays,  and  between 
stop-method  and   replications  (reps).   Table  5  shows  the  statistical 
comparison  of  stop-method  x  hays  means,  and  table  6  presents  these 
comparisons  for  stop-method  x  reps  means.   For  all  hays,  terminating 
fermentation  with  either  25  ml  of  ND  reagent  or  an  ice-water  bath, 
both  followed  by  40  hr  of  refrigeration,  produced  values  for  in  vitro 
residual  NDFA  which  were  not  different  (P>.01)  from  those  produced 
by  control.   Terminating  fermentation  with  6  ml  of  HCl,  followed  by 
refrigeration,  yielded  values  for  in  vitro  residual  NDFA  which  were 
higher  (P<.01)  than  control  values  for  all  hays.   These  results  suggest 
that  stopping  fermentation  with  either  ND  reagent  or  an  ice-water  bath 
could  replace  toluene  in  this  respect.   Table  6  reveals  that  only  the 
ice-water  stop-method  produced  results  equal  (P>.01)  to  those  of  the 
control  for  all  reps.   The  ND  reagent  yielded  lower  (P<.01)  in  vitro 
residual  NDFA  than  the  control  in  rep  3,  and  the  6  ml  HCl  produced  re- 
sults higher  (P<.01)  than  the  control  for  all  reps.   Thus,  results  of 
this  experiment  suggest  that  only  the  ice-water  method  could  stop 
fermentation  effectively  in  tubes  which  were  to  be  held  for  subsequent 
in  vitro  residual  NDFA  determinations.   The  ice-water  method  is  also  the 
easiest  of  the  four  methods  when  large  numbers  of  tubes  are  involved. 
Tubes  could  be  stored  in  a  refrigerator  for  at  least  40  hr  between  ice- 
bath  treatment  and  analysis. 


64 


TABLE  5.   EFFECT  OF  HAY  AND  STOP-METHOD  ON  IN  VITRO  RESIDUAL  ASH- FREE 
NEUTRAI.-DETERGENT  FIBER  (NDFA)     


STOP-METHOD 


25  ml 


6  ml 


Hay 


Control   ND  Reagent   Ice-water   HCl 


73-B  (3-wk  alfalfa) 

73-D  (4.5-wk  alfalfa) 

55-2A  (2-wk  Pangola  digitgrass) 

65-lA  (2-wk  bermudagrass) 

47-2A  (4-wk  bahiagrass) 

75-2C  (13-wk  Pangola  digitgrass) 

65-lF  (12-wk  bermudagrass) 

47-40  (mature  bahiagrass) 


26.3 

34.3^ 

36.3^ 

46.0^ 

57.8^ 

59.1^ 

63.8^ 

64.6* 


d,e 


25. r 

34.0^ 
35.1^ 
44.3^ 
56.8^ 
58.5* 
62.8* 
63.8* 


26.3 

35.1^ 

37.1^ 

44.9^ 

56.8^ 

58.7^ 

63.1^ 

65.0^ 


28.6 


36.7 


40.1 


49.1 


63.6 


61.4 


67.0 


70.6 


Mean 


48.5 


47.6 


48.4 


52.2 


^Number,  weeks  of  maturity  and  species.   In  vitro  residual  NDFA  run  im- 
mediately. *^Neutral-detergent  reagent  (Goering  and  Van  Soest,  1970). 

H  e  f 

In  vitro  residual  NDFA,  %  of  dry  matter.   '  Means  in  same  row  bearing 


le  superscript  are  not  different  (P  >  .01) 


65 


TABLE  6.   EFFECT  OF  STOP-METHOD  AND  REPLICATE  (REP)  ON  IN  VITRO  RESIDUAL 


ASH- 

-FREE 

NEUTRAL -DETERGENT  FIBER  (NDFA) 

STOP-METHOD 

25  ml  ^ 

Reagent         Ice-water 

6  ml 

Rep  # 

Contro! 

ND 

HCl 

1 

48. 8*^' 

d 

48.5^            48.8'^ 

53.5^ 

2 

49.0^ 

48.1^            50.3^ 

54. 1^ 

3 

48.1'^ 

46.3^            47.1*^ 

50.6^ 

4 

48.2*^ 

47.3^            47. 2^^ 

50.3^ 

Mean 

48.5 

47.6             48.4 

52.1 

In  vitro 

residual  NDFA 

run 

inimediately.   Neutral-detergent 

reagent 

(Goering  and  Van  Soest,  1970).   In  vitro  residual  NDFA,  %  of  dry  matter, 

d  e  f 
'  '  Means  in  same  row  bearing  same  superscript  are  not  different 

(P  >  .01). 


66 


Summary 
An  experiment  which  included  two  hays  from  each  of  'Florida  66' 
alfalfa,  Pensacola  bahiagrass,  Suwannee  bermudagrass  and  Pangola 
digitgrass  was  conducted  to  investigate  the  necessity  of  acetone  washes 
for  determinations  of  ash-free  neutral-detergent  fiber  (NDFA)  in  hays 
or  in  vitro  residues;  and  to  establish  an  alternate  method  to  toluene 
for  terminating  in  vitro  fermentation.   Acetone  washes  for  determina- 
tions of  NDFA  in  hays  appeared  necessary,  but  probably  could  be  ex- 
cluded when  analyzing  for  in  vitro  residual  NDFA.   Fermentation  could 
be  terminated  by  setting  tubes  in  an  ice-water  bath  for  1  hr,  and 
tubes  can  then  be  stored  under  refrigeration. 


CHAPTER  V 

A  RATIONAL  METHOD  FOR  PREDICTING  QUALITY  OF 
WARM-SEASON  FORAGES  FOR  RUMINANTS 

Introduction 

Forage  quality  must  be  known  if  intensive  high-forage  systems 
of  ruminant  production  are  to  be  based  soundly  upon  principles  of 
nutrition  and  economics.   Since  determining  quality  of  a  large  number 
of  forages  by  means  of  grazing  trials  is  an  almost  impossible  task, 
forage  researchers  now  generally  accept  the  intake  of  digestible 
energy  (DE) ,  digestible  dry  matter  (DDM)  or  digestible  organic  matter 
(DOM)  by  ruminants  fed  ad_  libitum  in  confinement  as  expressions  of 
forage  quality  (Heaney,  1970;  Holmes  et  al . ,  1966;  Jones,  1972).   For 
prediction  of  any  of  these  measures  of  digestible  nutrient  intake, 
digestibility  generally  can  be  predicted  with  acceptable  accuracy  by 
one  of  several  methods,  the  best  of  which  is  probably  the  two-stage 
in  vitro  fermentation  system  (Moore  and  Mott,  1973).   Intake  is  not 
always  highly  correlated  with  digestibility,  especially  among  forage 
species  (Minson  et  al. ,  1964;  Van  Soest,  1964;  Milford,  1967).   At 
present,  there  appears  to  be  no  method  which  accurately  predicts 
intake  among  forages.   Therefore,  a  fast,  simple,  accurate  procedure 
for  prediction  of  intake  and/or  quality  is  of  utmost  necessity  for 
forage  evaluation  and  efficient  ruminant  production. 

The  objective  of  the  present  investigation  was  to  devise  and  test 
a  theoretically  rational  and  acceptably  accurate  method  based  upon 
laboratory  forage  analyses  for  prediction  of  forage  quality  over  a 
wide  range  of  forage  species. 


67 


68 


Experimental  Procedure 

Development  of  Theory  Related  to  Rational  Method  for  Prediction  of 
Forage  Quality 

Forage  intake  by  ruminants  is  regulated  by  distention  or  fill 
of  some  part  of  their  gastro- intestinal  tract  (Crampton  e^  al. ,  1960; 
Montgomery  and  Baumgardt,  1965a;  Conrad,  1966;  Hungate,  1966).   The 
level  of  fill  at  which  distention  limits  intake  apparently  fluctuates 
with  such  factors  as  nitrogen  status  of  the  animal  (Egan,  1970)  and 
the  animal's  physiological  state  (Campling,  1970).   Any  theory  derived 
to  predict  forage  quality  must  center  around  distention  or  fill  as  the 
limiting  mechanism  when  forage  dry  matter  (DM)  digestibility  is  less 
than  approximately  65  to  70  percent.   Pelleting  of  forage  may  lower 
this  digestibility  figure  (Montgomery  and  Baumgardt,  1965a).   Campling 
(1965,  1970)  concluded  that  voluntary  intake  was  limited  by  capacity 
of  the  rumen  (including  reticulum)  and  by  extent  of  delay  of  food  in 
this  organ.   Extent  of  delay  of  digesta  is  equivalent  to  retention 
time  of  digesta  in  the  rumen,  so  that  this  latter  parameter  can  be 
used  with  equal  success  in  theoretical  considerations. 

Thornton  and  Mlnson  (1972)  presented  an  equation  for  predicting 
voluntary  DM  intake  from  retention  time.   Converting  this  equation 
to  the  organic  matter  (CM)  basis  gives 

OMI  =  24(Q/RTOM),  (D 

where  OMI  =  voluntary  OM  intake,  g/day; 

Q  =  quantity  of  OM  in  the  rumen,  g;        \ 
and  RTOM  =  retention  time  of  OM  in  the  rumen,  hr. 
Since  (Q/RTOM)  is  the  rate  at  which  OM  leaves  the  rumen,  in  g/hr, 
equation  (1)  is  consistent  with  the  theory  developed  by  Hungate  (1966) 


69 


that  rates  at.  which  material  flows  into  and  out  of  the  rumen  must  be 
equal  when  the  distention  mechanism  is  limiting  intake. 

If  both  sides  of  equation  (1)  are  divided  by  animal  metabolic 
weight  (W   '   ),  the  form  of  the  equation  is  unchanged,  but  OMI  has 

Kg 

units  of  g/W   '   /day  while  Q  is  in  g/W  "   .   Since  Q  is  constant 

Kg  Kg 

among  forages  (Blaxter  eX   al.  ,    1961;  Ulyatt  e_t  al .  ,  1967;  Thornton  and 

Minson,  1972),  OMI  (g/W   *   /day)  can  be  considered  a  function  of  RTOM 
kg 

(hr),  such  that: 

OMI  =  f(RTOM).  (2) 

Multiplying  both  sides  of  equation  (2)  by  apparent  digestibility  of 
OM  (OMD)  gives  the  following  equation: 

DOMI  =  f  (RTOM -OMD),  (3) 

where  DOMI  represents  digestible  OM  intake,  or  forage  quality,  in 
g/Wj^'   /day. 

The  right-hand  members  of  equation  (3),  i.  e. ,  RTOM  and  OMD, 
are  highly  correlated  among  forages  (Thornton  and  Minson,  1973), 
Therefore,  (RTOM- OMD)  should  correlate  highly  with  RTOM,  and  should 
be  obtained  accurately  by  knowing  RTOM  and  the  relationship  between 
RTOM  and  OMD.   Based  upon  this  theoretical  consideration,  the  follow- 
ing equation  can  be  written; 

DOMI  =  f  (RTOM)  .  (4) 

This  equation  is  theoretically  rational  and  reflects  a  high  degree 
of  functional  integrity  since  RTOM  should  be  indicative  of  both 
chemical  and  structural  composition  of  forage  OM.   Thus,  a  procedure 
for  estimating  RTOM,  or  a  value  highly  correlated  with  it,  from 
laboratory  analyses  was  developed  and  tested  for  predicting  forage 
quality. 


70 


Estimation  of  RTOM 

An  equation  developed  by  Waldo  et  a^.  (1972)  for  plotting 
disappearance  of  cellulose  from  the  rumen  through  time  was  used  for 
estimating  RTOM,  in  hr.   This  equation  is 

-(k.  +  k„)t  ,  ,  -k  t  , 

g  =  ae   1    2   +  be  2  ,  (5) 

where  g  =  decimal  fraction  representing  remaining  labeled 
cellulose  in  the  rumen  as  a  function  of  time  per 
unit  of  labeled  cellulose  intake; 

a  =  decimal  fraction  of  total  ruminal  cellulose  which 
is  potentially  digestible; 

b  =  one  minus  'a',  or  decimal  fraction  of  total  ruminal 
cellulose  which  is  potentially  indigestible; 

k^  =  grams  of  cellulose  digested  per  hour  per  gram  of 
digestible  cellulose  present  in  the  rumen; 

k„  =  grams  of  indigestible  cellulose  passing  from  the 
rumen  per  hour  per  gram  of  indigestible  cellulose 
present  in  this  organ; 

and  t  =  time,  in  hr. 

This  equation  was  applied  ^o  the  total  OM  present  in  the  rumen.   Assuming 

that  'a',  b,  k,  and  k^  are  known,  if  some  rational  value  for  g  can  be 

developed  for  the  time  when  t  is  equal  to  RTOM,  then  t,  and  therefore 

RTOM,  can  be  found  by  sequential  approximation  using  Newton's  method 

for  approximating  roots  of  equations  (Thomas,  1972).   This  iterative 

procedure  must  be  used  in  estimating  t  since  equation  (5)  cannot  be 

solved  in  a  general  way  for  t. 

The  equation  used  to  approximate  t,  or  RTOM,  in  equation  (5)  by 

Newton's  sequential  method  was 

-(k,  +  k„)t   ,  ,  -k„t  (r^ 
ae   1     2   n  +  be   2  n  -  g     ,    (b) 

^'^^  "     n   ^  ,  ^   -(k.  +  k^)t    ,  ,  -k„t 

-(k  +  k  )ae   1     2   n  -  k  be   2  n 


71 


where  t   =  zero  initially,  and  t  , ,  for  all  subsequent 
n         .    .  n+1  ^ 

approximations; 

t  ,,  =  value  of  each  approximation  in  hr,  and  RTOM  in 
n+1   ,    ^     ,   ^ .  ^ , 

hr  after  the  final  approximation; 

and  'a',  b,  k  and  k„  are  as  defined  for  equation  (5).  Starting  with 

t   equal  to  zero,  four  approximations  should  be  sufficient  to  estimate 

the  final  value  of  t  ,,,  or  RTOM,  to  within  .1  hr  of  its  actual  value. 
n+1 

Establishing  the  value  of  g 

Hungate  (1966)  assumed  that  rumen  digesta  was  homogenous,  and 
that  rumen  evacuation  proceeded  according  to  a  first-order  exponential 
decay  function.  Calculations  made  in  the  present  study  from  data 
presented  by  Thornton  and  Minson  (1972,  1973),  who  worked  under  the 
same  assumptions,  showed  a  high  correlation  (-.94  and  -.92  in  1972 
and  1973,  respectively)  between  DOMI  and  RTOM.   Per  unit  of  digesta 
undergoing  partial  and  continuous  removal  from  the  rumen  via  a  first- 
order  exponential  decay  function,  the  fraction  still  in  the  rumen 

— xt 
at  time  t  is  equal  to  e    .In  this  expression,  the  rate  constant 

'x'  equals  the  constant  rate  at  which  a  unit  of  digesta  is  removed 
from  the  rumen. 

Equation  (5)  assumes  that  there  are  two  types  of  digesta  under- 
going removal  from  the  rumen,  i.  e.,  one  which  is  potentially  totally 
digestible,  and  which  leaves  the  rumen  by  digestion  and  passage;  and 
one  which  evacuates  the  rumen  only  by  passage,  since  it  is  totally 
indigestible  (Waldo  et_  al^.  ,  1972).  Therefore,  equation  (5)  does  not 
fit  exactly  the  general  form  of  the  first-order  exponential  decay 
function.   However,  the  semilog  plot  of  equation  (5),  which  would  be 
linear  if  digesta  disappeared  from  the  rumen  via  a  true  first-order 
function,  appears  only  slightly  curvilinear  (Waldo  et  al^.  ,  1972). 


72 


This  fact,  plus  the  success  of  Thornton  and  Minson  (1972,  1973) 
in  correlating  DOMI  with  RTOM  under  the  first-order  assumption, 
suggests  that  taking  this  assumption  as  true  might  not  lead  to  errors 
of  appreciable  importance  in  determining  an  acceptable  value  of  RTOM 
from  equation  (5) . 

If  the  suggestion  that  rumen  evacuation  adheres  to  a  first- 
order  process  is  acceptable,  then  .37  can  be  inserted  into  equation 
(6)  for  g  (Hungate,  1966).   Mathematically,  this  is  because,  with 
reference  to  a  "container"  which  empties  itself  via  first-order 
dynamics,  RTOM  is  equal  to  1/x  (Waldo  et  ad . ,  1965;  Hungate,  1966). 

If  t,  at  the  time  it  equals  RTOM,  is  set  equal  to  1/x  in  the  expression 

-xt  -1 

e    ,  then  the  value  of  this  expression  is  e   ,  or  .37,   Thus,  if 

assuming  that  rumen  evacuation  adheres  to  first-order  dynamics  does 

not  produce  major  errors,  inserting  a  constant  value  for  g  of  .37 

into  equation  (6)  means  that  t  will  equal  RTOM  when  the  equation  is 

solved  by  sequential  approximation  with  'a',  b,  k  and  k„  known. 

Estimation  of  k. 

Values  of  k,  (g/hr/g)  for  forages  in  this  study  were  determined 
according  to  the  procedure  described  by  Gill  e_t  a^.  (1969)  and 
Lechtenberg  e^  al .  (1974).   The  mathematical  basis  for  estimating  k 
by  this  procedure  can  be  appreciated  by  studying  the  initial  pages  of 
Chapter  10  in  Fruton  and  Simmonds  (1958).   In  vitro  OM  digestion 
(IVOMD)  was  measured  after  3,  6,  15,  30,  48,  60  and  72  hr  of  fermenta- 
tion, and  was  calculated  for  each  forage  at  each  time  as  the  mean  of 
three  separate  determinations.   The  percent  of  OM  digested  after  72  hr 
of  ^  vitro  fermentation  was  assumed  to  represent  complete  digestion 
of  a  given  forage  (Akin  et  al. ,  1973,  1974b). 


73 


Estimation  of  k 

Since  no  procedure  could  be  found  for  estimation  of  k^  from 
laboratory  analyses,  this  parameter  was  calculated  from  known  lignin 
intakes   of  the  31  forages  by  the  following  equation  presented  by 
Waldo  et  al.  (1972)  : 

amount  of  lignin  fed/hr 
2  ~  average  amount  of  rumen  lignin  (7) 

The  numerator  of  this  equation  was  calculated  by  dividing  known 

lignin  intake  (g/W  "'^/day)  by  24.   The  denominator  was  estimated 
kg 

using  a  simple  linear  regression  equation  generated  from  data  re- 
ported by  Ingalls  £t  al .  (1966): 

Rumen  lignin  (g/W,   "  )  = 
.35  +  1.54  lignin  intake  (g/\g  '  /day).        (8) 

As  described  by  equation  (7),  dividing  lignin  intake  (s/\g  '  /hr) 
by  rumen  lignin  (g/W  ^''^)   yielded  an  estimate  of  k^  (g/hr/g)  .   In 
generating  the  regression  equation  from  data  provided  by  Ingalls  et  al. 
(1966),  values  for  rumen  lignin  at  6  hr  postprandial  were  used,  this 
being  the  theoretically  closest  approximation  to  rumen  lignin  under 
steady-state  conditions  available  from  these  data. 
Theory  relative  to  'a',  and  its   estimation 

Waldo  et  al.  (1972)  reported  that  for  cellulose,  'a'  was  equal 

to   A   ,  where  A  was  amount  of  potentially  digestible  cellulose 

A  +  B  ,.     -ui 

present  initially  in  the  rumen,  and  B  was  amount  of  indigestible 

cellulose  initially  present  in  this  organ.   This  concept  was  extend- 
ed to  cover  total  ruminal  OM  instead  of  just  cellulose  when  estimating 


74 


'a'  for  grasses  utilized  in  the  present  investigation.   Amount  of 
ruminal  OM  depends  upon  intake  (Thomas  et  al . ,  1961;  Egan,  1970), 
and  would  be  difficult  to  predict  when  intake  was  not  known.   This, 
of  course,  would  be  the  case  in  most  instances  where  forage  quality 
predictions  were  required.   Thus,  it  was  necessary  to  develop  an 
equation  for  estimation  of  'a'  in  which  intake  was  not  included. 

The  rationale  behind  development  of  such  an  equation  was  based 
upon  the  manner  in  which  amounts  of  OM  designated  A  and  B  would  occur 
in  the  rumen  under  steady-state  conditions.   Given  such  conditions, 
and  knowledge  that  digestible  material  evacuates  the  rumen  via  first- 
order  kinetics  (Waldo  etal.,  1972),  A  (g/W^^g"^^)  «°^ld  equal  DOMI 

(g/W   -^^/day)  multiplied  by  the  average  number  of  days  which  DOMI 

kg 
would  be  delayed  in  the  rumen,  or  retention  time  of  this  intake. 

,,     ^^    ]: »   since  digestible  OM 

This  retention  time  would  equal  24 (k  +  k  ) 

leaves  the  rumen  via  both  digestion  and  passage.   The  value  of  B 
(g/W   -^^  would  equal  amount  of  indigestible  OM  intake  (g/W^^'   /day) 
multiplied  by  days  of  retention  time  of  this  indigestible  material. 
In  this  case,  retention  time  would  be  -^  '   since  indigestible  OM 
evacuates  the  rumen  via  only  passage,  which  is  also  a  first-order 
process.   Thus,  if  forage  quality  were  to  be  predicted,  'a'  would 

A , 

be  equal  to  ^  _|_  g  or: 

^  ,  ,  (OMD  )  (OMI) 


24(k^  +  ^)     P^  ^      (,) 


i (OMD  )  (OMI)  +  T^  (100-OMD  )  (OMI) 

24  (k,  +  kj     p'         24k         P 


where  OMI  is  in  g/W^  "^^/day.   By  algebraic  manipulation  (Appendix 

K.g 


75 


table  13),  equation  (9)  can  be  reduced  to  the  following  equation: 


(k„)  (OMD  )  ,--, 

1^.  =  2 2 ,       (10) 

^     [(kj  (OMD  )]  +  [(k  +  k„)  (100-OMD  )] 
2      p        12  p 

where  'a'  =  decimal  fraction  of  total  ruminal  OM  which  is 
potentially  digestible; 

k^  =  rate  of  digestion  rate  constant,  g/hr/g; 

k„  =  rate  of  passage  rate  constant,  g/hr/g; 

and  OMD   =  predicted  in  vivo  OMD,  in  percent. 

Since  k„  was  calculated  from  previously  known  J_n  vivo  data  in  this 
investigation,  estimation  of  'a'  provided  no  subsequent  problems. 
Values  of  b   were  calculated  as  one  minus  'a'. 

Prediction  of  organic  matter  digestibility 

In  equation  (10),  predicted  OMD  (OMD  ),  in  percent,  was  determined 
using  the  following  formula: 

OMD   =  NDS  -10.3  +  (NDF-NDFD),  ^^^^ 

p  OM 

where  NDS  =  ash-free  neutral-detergent  solubles  (OM 
minus  NDF) ,  as  percent  of  DM; 

NDF  =  ash-free  neutral-detergent  fiber,  as  per- 
cent of  DM; 

NDFD  =  decimal  fraction  representing  NDF 
digestibility; 

OM  =  organic  matter,  as  decimal  fraction  o^  DM; 

and   10.3  =  average  value  of  metabolic  fecal  OM,  as 
percent  of  DM  (Chapter  III) . 


Predictions  of  digestible  OM  (DOM),  as  a  percentage  of  DM,  were  made 
using  the  numerator  of  equation  (11) . 


76 


Values  for  NDFD  in  equation  (11)  were  estimated  as  suggested 
by  Velasquez  (1974),  and  simple  linear  regression  equations  for 
this  purpose  were  generated  from  data  reported  by  this  author. 
These  equations  were, 


for  Pensacola  bahiagrass,   NDFD  =  12.69  +  .8Sz;   (12) 

100 


for  Coastal  or  Suwannee  bermudagrass ,  NDFD  =  15.27  +  . 71z;   (13) 

100 


and  for  Pangola  digitgrass,   NDFD  =  -2.69  +  l.Oz;   (14) 

100 


where  z  =  in  vitro  NDF  digestion  (IVtTDFD)  after  72  hr  of  fermentation, 
in  percent.   In  the  present  investigation,  values  of  z  inserted  into 
equations  (12) ,  (13)  and  (14)  were  means  of  three  individual  observa- 
tions done  in  separate  runs. 
Testing  the  Procedure 
Forages  and  in  vivo  data 

Thirty-one  warm-season  grasses  of  known  in  vivo  OliD  and  OMI  by 
sheep  were  utilized  for  testing  the  theoretically  rational  procedure 
for  prediction  of  DOMI ,  or  forage  quality,  from  RTOM.   These  31  for- 
ages included  five  cuts  of  Pensacola  bahiagrass  (Paspalura  notatum 
Flugge) ,  15  cuts  of  Coastal  or  Suwannee  bermudagrass  (Cynodon  dactylon 
(L)  Pers.)  and  11  cuts  of  Pangola  digitgrass  (Digitaria  decumbens 
Stent).   These  forages  had  been  studied  previously  in  seven  different 
in  vivo  trials  comprising  187  individual  animal  observations  of  both 
intake  and  digestibility.   In  these  trials,  all  grasses  were  fed 


77 


ad  libitum  as  chopped,  artificially  dried  hays  to  yearling  or  mature 
wethers  in  individual  metabolism  crates  with  slatted  wooden  floors. 
Water,  salt  and  def luorinated  phosphate  were  provided  ad^  libitum.   Each 
experimental  period  consisted  of  a  14-day  preliminary  period  to  allow 
sheep  to  attain  voluntary  intake,  followed  by  a  seven-day  total- 
collection  period  for  determination  of  voluntary  intake  and  apparent 
nutrient  digestibility.   Voluntary  OMI  was  calculated  on  the  basis  of 
grams  per  kilogram  of  body  weight  raised  to  the  .75  power  per  day 

(g/W   ■   ),  and  nutrient  digestibility  was  calculated  in  percent, 
kg 

Forage  quality,  or  DOMI  in  g/W   "   /day,  was  determined  for  each  of 

kg 

the  31  forages  by  multiplying  OMI  in  g/w   *   /day  by  apparent  OMD. 

kg 

Laboratory  analysis  of  forages 

Analyses  previously  determined  on  all  31  forages  were  DM,  CM 
and  crude  protein  (CP)  ,  the  latter  two  being  as  percentage  of  DM 
(A.O.A.C,  1970);  acid-detergent  fiber  (ADF)  and  acid-insoluble  lignin, 
both  on  the  DM  basis  (Van  Soest,  1963);  and  NDF  as  a  percentage  of  DM 
(Van  Soest  and  Wine,  1967).   For  this  latter  determination,  dry  residue 
was  ashed  and  the  difference  between  dry  and  ashed  weights  was  calculat- 
ed as  weight  of  ash-free  NDF. 

In  the  present  study,  ash-free  NDS  as  a  percentage  of  DM  was 
calculated  by  subtracting  ash-free  NDF  from  OM.   Determination  of 
IVOMD  after  various  periods  of  fermentation  was  done  according  to  the 
two-stage  procedure  outlined  by  Moore  and  Mott  (1974). 

Velasquez  (1974)  determined  IVNDFD  by  employing  the  procedure  of 
Moore  and  Mott  (1974)  during  72  hr  of  fermentation.   One  ml  of  toluene 
per  tube  followed  by  refrigeration  was  used  to  stop  fermentation  and 
hold  residual  NTDF  for  subsequent  determination  (Goering  and  Van  Soest, 


78 


1970).   Residual  ash-free  NDF  was  determined  by  the  method  of  Van  Soest 
et^  al .  (1966).   This  procedure  was  modified  slightly  for  determining 
IVNDFD  in  the  present  investigation.   The  modification  consisted  of 
using  an  ice-water  bath  for  1  hr  instead  of  1  ml  of  toluene  to  stop 
fermentation  after  72  hr  of  incubation.   Tubes  and  their  contents 
were  refrigerated  for  40  hr  before  ash-free  residual  NDF  was  determined 
(Chapter  IV). 
Regression  analyses 

Correlations  among  laboratory  analyses  and/or  In  vivo  parameters 
were  determined  using  the  UFSPL020  linear  regression  and  correlation 
program  on  the  University  of  Florida's  IBM  370  digital  computer. 
Generation  of  the  prediction  equation 

With  both  forage  quality,  in  terms  of  DOMI,  and  estimated  RTOM 
known  for  each  of  the  31  grasses,  15  grasses  were  selected  as  re- 
presentative of  the  entire  group  in  order  to  generate  a  simple  linear 
regression  equation  for  prediction  of  forage  quality  from  estimated 
RTOM,  i.  e. ,  (DOMI)  =  b  +  b   (RTOM).   The  15  forages  selected  covered 
the  entire  maturity  range  of  the  31  grasses  (2  to  14  wk)  and  met  the 
restriction  of  containing  at  least  6  percent  CP  on  a  DM  basis.   This 
restriction  was  imposed  so  that  the  relationship  between  DOMI  and 
RTOM  exhibited  by  the  prediction  equation  would  not  be  confounded  with 
other  possible  effects  of  low  CP  upon  measurements  of  forage  quality. 
For  generation  of  the  prediction  equation,  DOMI  values  for  the  15 
selected  forages  were  regressed  upon  these  forages'  estimated  RTOM 
values  using  a  simple  linear  regression  program.   Values  of  DOMI  for 
the  remaining  16  forages  were  predicted  using  this  equation. 


79 


Prediction  of  DOMI  for  a  given  forage  can  be  obtained  with  the 
Dynamo  computer  program  shown  in  Appendix  table  14  when  values  of 
constants  (C  statements)  are  known. 
Testing  the  acceptability  of  quality  predictions 

In  testing  the  utility  of  the  resultant  prediction  equation,  the 
question  now  arises:   How  much  can  predicted  DOMI  deviate  from  actual 
DOMI  and  still  be  considered  an  acceptable  prediction?   The  answer 
to  this  question  cannot  be  supplied  by  r  values  or  residual  standard 
deviation  (s  ^)    values  unless  some  criterion  of  acceptability  relative 
to  the  absolute  value  of  prediction  error  is  attached  to  them.   Perhaps 
the  answer  varies  depending  upon  the  context  in  which  one  is  working. 
Researchers  must  work  within  a  policy  of  error  acceptance  which  is 
based  upon  sound  statistical  considerations.   One  such  policy  would 
accept  predictions  from  a  given  equation  if  the  equation  could  predict 
means  within  their  95  percent  confidence  interval  95  percent  of  the 
time.   Under  this  policy,  the  width  of  the  confidence  interval  en- 
compassing a  given  mean  would  be  ±t(s//n)  ,  where  t  is  the  tabulated 
approximation  to  the  normal  when  the  population  variance  is  unknown; 
s  is  the  estimate  of  the  population  standard  deviation;  and  n  is  the 
number  of  observations  upon  which  the  mean  is  based.   If  the  value  of 
t  at  the  95  percent  level  of  confidence  is  used  in  calculating  the 
value  of  the  above  expression,  then  a  prediction  which  falls  within 
the  resultant  range  must  be  acceptable.   This  is  because  if  the 
experiment  were  repeated  it  could  be  stated  with  95  percent  confidence 
that  the  mean  would  fall  within  the  calculated  range.   Thus,  if  a 
prediction  falls  within  this  range,  it  must  be  acceptable  since  it 
is  of  the  same  worth  as  repeating  the  experiment.   This  policy,  it 
seems,  would  be  the  ultimate  in  judging  prediction  acceptability. 


80 


This  method  of  judging  prediction  acceptability  may  be  too  con- 
servative for  present  use.   There  exists  a  paucity  of  literature 
relative  to   (1)  manners  in  which  all  factors  which  affect  forage 
quality  mediate  their  effects;  and  (2)  quantification  of  effects  of 
these  factors  and  their  interactions  upon  forage  quality  over  a  wide 
range  of  forages.   Thus,  it  is  practically  impossible  to  generate 
a  prediction  equation  which  will  yield  acceptable  predictions  of 
forage  quality  in  any  given  situation  for  any  given  forage.   This 
means  that  either  we  will  do  without  acceptable  prediction  equations, 
or  that  we  must  relax  the  standards  by  which  we  judge  such  equations. 
The  standards  still  must  be  based,  however,  upon  sound  statistical 
considerations.   Such  a  set  of  standards  probably  v.'ould  be  produced 
by  considering  mean  predictions  acceptable  if  they  fall  within  plus 
and  minus  two  population  standard  deviation  estimates  (±2s)  from 
the  actual  mean.   The  rationale  for  postulating  this  acceptability 
range  is  that  the  range  is  still  limited  by  the  population  standard 
deviation,  and  that  according  to  the  statistical  Empirical  Rule 
(Mendenhall,  1971),  approximately  95  percent  of  the  individual  obser- 
vations taken  from  a  normal  population  will  fall  into  this  range.   A 
prediction  in  this  range  may  differ  at  the  95  percent  confidence 
level  from  the  actual  mean,  but  it  should  approximate  closely  at  least 
some  of  the  individual  observations  which  constitute  the  mean.   There- 
fore, acceptability  limits  at  ±2s  from  the  mean  would  allow  use  of 
a  "good"  prediction  equation,  though  the  equation  was  not  "perfect" 
according  to  limits  at±t(s//n)   from  the  mean.   The  acceptability 
range  afforded  by  ±2s  would  be  wider  than  that  provided  by   ±t(s//ri) 


81 


but  the  multiple  would  not  be  necessarily  great  since  the  number 
of  observations  per  treatment  in  animal  experiments  is  usually  small. 
Also,  if  the  experiments  were  conducted  well,  s  may  be  small  and 
the  difference  between  the  two  limits  may  be  minor  in  absolute  term-S. 
In  comparing  two  or  more  prediction  equations  which  appear  "good" 
when  judged  by  the  wider  acceptability  limits,  the  number  of  pre- 
dictions from  each  which  fall  into  the  range  defined  by  ±t(s/>/n) 
from  the  mean  when  t  is  taken  at  the  95  percent  confidence  level 
might  be  taken  into  account  in  choosing  the  equation  for  predicting 
means . 

In  this  study,  DOMI  predictions  were  judged  with  reference  to 
statistically  defined  acceptability  lim.its  around  the  actual  m.ean 
DOMI  values  being  predicted.   All  predictions  which  fell  within  the 
range  defined  by  acceptability  limits  were  judged  acceptable.   Since 
quality  values  being  predicted  were  in  terms  of  mean  DOMI,  a  con- 
servative set  of  acceptability  limits  was  defi.ied  by  a  weighted 
average  of  the  expression  ±t(s/v^)  for  all  31  forages.   This  weighted 
average  was  calculated  by  determining  the  weighted  average  of  s  for 

the  31  forages.   The  weighted  average  for  s  was  calculated  as  the 

2 
square  root  of  the  value  of  the  expression  Z(n.  -  l)s.   .   in  this 


Z(n.  -  1) 
1 

expression,  i  defines  each  individual  among  the  31  forages.   The 
value  of  n  in  the  expression  for  the  weighted  95  percent  confidence 
interval  was  taken  as  the  arithmetic  mean  of  the  n.  for  all  31  for- 
ages.  This  average  value  of  n  also  was  used  to  determine  the  tabulated 
t  at  the  95  percent  confidence  level.   A  liberal  set  of  acceptability 
limits  also  was  determined,  these  being  defined  by  the  expression  ±2s. 


82 


Results  and  Discussion 
Laboratory  Characteristics  of  Forages  Utilized 

Ranges,  means  and  coefficients  of  variation  (CV)  for  laboratory 
characteristics  of  the  31  wami-season  grasses  used  in  this  investi- 
gation are  reported  in  table  7.   Individual  values  are  shown  in 
Appendix  table  10.   As  percentages  of  DM,  mean  CP  for  all  forages  was 
9.3,  while  mean  ash-free  NDF  and  NDS  were  74.9  and  21.1,  respectively. 
The  CV  for  NDS  was  about  three  times  that  for  NDF  because  the  esti- 
mated standard  deviations  (s)  for  NDS  and  NDF  were  similar  (4.6  and 
5.5  percentage  units,  respectively).   Mean  IVOMD  increased  from 
14.6  to  50.9  percent  as  fermentation  time  increased  from  3  to  72  hr. 
As  fermentation  time  became  longer,  the  CV  for  IVOMD  decreased  because 
the  mean  increased  faster  than  did  s.   Thus,  it  appears  that  IVOMD 
is  less  variable  in  a  relative  sense,  though  more  variable  in  absolute 
terms,  after  longer  periods  of  fermentation.   This  may  explain  results 
reported  by  Velasquez  (1974) ,  who  found  that  IVOMD  after  72  hr  of 
fermentation  correlated  slightly  higher  with  in  vivo  OMD  than  did 
IVOMD  after  48  hr.   This  slightly  higher  correlation  probably  would 
not  offset  problems  encountered  in  substituting  fermentation  times  of 
72  hr  for  those  of  48  hr  now  used  as  standard  procedure  in  routine, 
large-scale  programs  of  forage  analysis. 

Values  of  'a'  averaged  .2518  for  all  forages  (table  7),  and  the 
mean  for  b  was  .7482.   These  results  indicate  that  among  forages,  only 
about  25  percent  of  total  ruminal  OM  would  be  potentially  digestible 
at  a  given  point  in  time. 

Estimated  kj^  averaged  .0557  g/hr/g  (table  7),  or  about  2,5  tim.es 
average  calculated  k2,  which  was  .0227  g/hr/g.   The  CV  for  k2  for 


83 


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31  forages  was  only  5.3  percent,  and  the  range  for  these  values  was 
only  .0047  units.   Since  at  present  no  laboratory  procedure  exists 
for  estimation  of  k2 ,  these  results  suggest  that  assuming  k2  to  be 
constant  among  forages  at  .0227  g/hr/g  might  not  lead  to  errors  of 
appreciable  importance  in  estimation  of  RTOM.   Estimated  RTOM  ranged 
from  28.35  to  36.37  hr  in  this  study,  with  a  mean  of  33.45  hr  and 
a  CV  of  only  7  percent  for  all  forages. 
Actual  and  Predicted  In  Vivo  Values  of  Forages  Utilized 

Ranges,  means  and  CV's  for  actual  and  predicted  mean  values  of 
in  vivo  parameters  are  presented  in  table  8.   Means  for  individual 
grasses  are  reported  in  Appendix  table  11.   Calculated  IIFOM  (ash- 
free  NDS  minus  ash-free  DNDS)  averaged  9.9  percent  of  DM.   This  is 
analogous  to  the  9.5  percent  of  DM  reported  by  Minson  (1971b),  who 
worked  with  three  cultivars  of  each  of  two  Paul cum  species,  but  higher 
than  the  5.4  percent  found  by  Velasquez  (1974)  for  warm-season  grasses. 
For  temperate  forages,  metabolic  fecal  DM  has  been  reported  at  12.9 
percent  (Van  Soest,  1967)  and  9.8  percent  (Colburn  e_t  al.  ,  1968; 
Deinum  and  Van  Soest,  1969). 

Actual  in  vivo  NDFD  ranged  from  42.0  to  76.1  percent,  with  a 
mean  of  55.3  and  a  CV  of  17.5  percent  (table  8).   Predictions  of 
this  parameter  ranged  from  39.4  to  71.8  percent,  with  a  mean  of  53.3 
and  a  CV  of  18.2  percent.   The  separate  regression  equations  used  to 
predict  NDFD  are  shown  in  figure  4.   Simple  linear  regression  of 

in  vivo  NDFD  on  predicted  NDFD  for  all  31  grasses  produced  an  r 

2 
value  of  .98  (r'~=.96)  and  an  s    of  1.90  percentage  units.   Thus, 

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Y  =  12.69  +  .88X 
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60 


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70    75    80 

IN  VITRO  NEUTRAL-DETERGENT  FIBER  DIGESTION  (ASH-FREE),  % 

AT  72  hr 


Figure  4.   Relationship  between  neutral-detergent  fiber 
digestibility  in  vivo  and  in  vitro  for  each 


of  three  warm-season  grasses. 
from  Velasquez,  1974). 


(Data  taken 


87 


ash-free  in  vitro  NDFD  after  72  hr  of  fermentation  appears  an  excellent 
predictor  of  in  vivo  NDFD.   Determinations  of  in  vitro  NDFD,  however, 
require  seven  working  days,  which  is  too  slow  for  rapid  estimation 
of  forage  quality.   Actual  in  vivo  DNDF  ranged  from  33.1  to  50.0 
percent  of  DM,  with  a  mean  of  40.9  and  a  CV  of  11.4  percent  (table  8). 
This  mean  is  in  accord  with  the  40  percent  of  CM  found  by  Moir  (1972) 
for  both  temperate  and  tropical  forages,  but  seems  too  variable  to 
be  considered  constant  or  to  depend  upon  physiology  of  ruminant  digestion 
rather  than  forage  quality,  as  suggested  by  that  author. 

Actual  in  vivo  DOM  and  predictions  of  this  parameter  were  quite 

similar  in  range,  mean  and  CV  (table  8).   Simple  linear  regression 

2 
of  in  vivo  DOM  on  predicted  DOM  yielded  an  r  value  of  .97  (r  =.94) 

and  an  s  ^^  of  1.7  percentage  units.   Actual  in  vivo  OMD  and  predicted 

OMD  V7ere  also  quite  similar,  and  regression  analysis  produced  an  r 

2 
value  of  .95  (r  =.90)  and  an  s    of  2.6  percentage  units.   Since 

y.x        ^       * 

DOM  prediction  is  based  upon  estimation  of  NDFD,  which  in  turn  depends 
upon  in  vitro  ITOFD  after  72  hr  of  fermentation,  IVOMD  after  48  hr 
would  not  have  to  be  determined  in  order  to  obtain  accurate  predictions 
of  in  vivo  OMD  for  warm-season  grasses.   The  future  challenge  is  to 
develop  an  accurate  predictor  of  in  vivo  NDFD  which  is  determined 
more  quickly  and  easily  in  the  laboratory  than  is  in  vitro  'TOFD  after 
72  hr  of  fermentation. 

That  RTOM  might  be  utilized  to  obtain  accurate  predictions  of 
forage  quality  over  a  wide  range  of  forage  species  has  been  suggested 
by  Thornton  and  Minson  (1972,  1973)  and  Laredo  and  Minson  (1975).   In 


their  studies,  RTOM  was  determined  by  means  of  hourly  ad  libitum 
feeding  of  sheep  and  complete  removal  of  digesta  from  the  rumen  via 
a  fistula  (Minson,  1966).   Measuring  RTOM  by  this  method  does  little 
to  expedite  forage  quality  determinations,  since  DOMI  itself  can  be 
measured  in  vivo  during  the  same  amount  of  time,  and  probably  at 
lower  cost.   In  the  present  study,  actual  DOMI  ranged  from  19.3  to 
50.7  g/W   "   /day  for  the  31  grasses,  with  a  mean  of  31.2  and  a  CV 
of  30.8  percent  (table  8).   Fifteen  of  these  grasses  were  used  to 
generate  an  equation  for  prediction  of  DOMI from  estimated  RTOM.   The 
equation,  shown  in  figure  5,  was 

Y  =  169.8  -  4.14  (RTOM),  (15) 

where  Y  represents  predicted  DOMI  (g/W   *   /day) .   Use  of  this  equation 

Kg 

to  predict  quality  for  the  remaining  16  grasses  resulted  in  DOKI  pre- 
dictions which  ranged  from  20.1  to  47.8  g/W   *   /day,  with  a  mean  of 

kg 

27.9  and  a  CV  of  28.8  percent.   Simple  linear  regression  of  actual 

in  vivo  DOMI  for  these  16  grasses  on  their  respective  predicted  DOMI 

2 
values  produced  an  r  value  of  .95(r  =.90)  and  an  s    of  2.4  units. 

y.x 

Thus,  this  rational  method  for  predicting  forage  quality  appeared 

promising,  but  the  acceptability  of  predictions  generated  by  equation 

(15)  remained  to  be  tested  by  use  of  appropriate  acceptability  limits. 

Actual  (MI  by  sheep  ranged  from  39.6  to  84.7  g/W,     /day  for 

kg 

all  31  grasses,  with  a  mean  of  56.9  and  a  CV  of  21.6  percent  ('-able  8). 

Predictions  of  this  parameter  generated  for  16  grasses  (predicted 

DOMI  V  predicted  OMD)  ranged  from  38.7  to  76.7  g/W   '^^/day,  with  a 

kg 

mean  of  56.4  and   a  CV  of  20.7  percent.   Simple  linear  regression  of 


89 


15 


O  Bahia 

•  Bermuda 

^  Pangola 

Y  =  169.8  -  4.14X 

r  =  -.95;  P<.01 

=  3.63 
y.x 


29 


30 


31 


32 


33 


34 


35    36 


37 


RETENTION  TIME  OF  ORGANIC  MATTER  IN  THE  RUMEN,  hr 


Figure  5.   Relationship  between  digestible  organic  matter 
intake  and  retention  time  of  organic  matter  in 
the  rumen  for  three  species  of  warm-season 
grasses. 


90 


actual  on  predicted  in  vivo  OMI  for  these  grasses  produced  an  r  of 

2 
.95  (r  =.91)  and  an  s    of  3.2  units.   Empirical  predictions  of 
y.x 

OMI  from  ADF  as  a  percentage  of  DM  were  made  as  suggested  by  Weller 
(1973).   Figure  6  shows  the  empirical  equation  used  to  predict  OMI 
from  ADF: 

Y  =  169.0  -  2.82  (ADF),  (16) 

.75 
where  Y  represents  predicted  OMI  (g/W     /day) .   This  equation  was 

Kg 

generated  with  15  of  the  31  grasses  and  used  to  predict  OMI  for  the 
other  16.   Measures  of  central  tendency  and  dispersion  are  shown  in 

table  8,   Regression  of  ±r\   vivo  OMI  on  OMI  predicted  from  ADF  yield- 

2 
ed  an  r  value  of  only  .77  (r  =.59)  and  an  s    of  6.6  units.   Thus, 

y.x 

empirical  use  of  ADF  to  predict  OMI  appears  less  promising  than  use 

of  (DOMI  -^  OMD)  when  both  of  these  latter  parameters  are  predicted 

rationally. 

Testing  of  DOMI  and  OMI  Predictions 

Acceptability  limits  for  quality  and  intake  predictions 

Differences  between  actual  and  predicted  values  of  DOMI  (forage 
quality)  or  OMI  were  deemed  acceptable  in  a  conservative  sense  if 
the  absolute  values  of  these  differences  were  less  than  or  equal  to 
the  weighted  average  of  the  expression  t(s//n)  at  the  .95  confidence 
level  for  all  31  grasses  utilized  in  this  investigation.   Such  differ- 
ences were  termed  acceptable  in  a  liberal  sense  when  their  absolute 
values  were  less  than  or  equal  to  the  weighted  average  of  2s  for  all 
31  grasses.   Though  in  this  study  acceptability  limits  were  calculated 
for  each  grass  species,  only  limits  applicable  to  all  grasses  were 
used  to  test  predictions.   This  would  negate  the  necessity  of  referring 


85  t- 


80 


91 


75 


3 

00 


70  - 


65  - 


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55  - 


50  - 


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40 


- 

\ 

• 

\^ 

O 

o 

• 

Bahia 
Bermuda 

•  \ 

& 

Pangola 

\  O 

Y 

=  169.0  -  2.82X 

\ 

r 

=  -.88;  P<.01 

N. 

s  • 

=  6.87 

.  1 

32    34    36    38    40    42    44    46 
ACID-DETERGENT  FIBER,  %  OF  DRY  MATTER 


48 


Figure  6.   Relationship  between  organic  matter  intake 

and  acid-detergent  fiber  percentage  for  three 
species  of  warm-season  grasses. 


92 


to  limits  for  individual  grass  species  when  qualifying  predictions  of 
quality  in  practice.   Conservative  and  liberal  acceptability  limits 
determined  by  applying  their  respective  expressions  across  all  forages, 
as  well  as  to  bahiagrass,  bermudagrass  or  Pangola  digitgrass  alone, 
are  shown  in  table  9. 
Acceptability  of  quality  and  intake  predictions 

Figure  7  shows  the  test  of  acceptability  for  forage  quality  (DOMI) 
predictions.   The  points  plotted  in  this  figure  are  coordinates  of 
actual  DOMI  and  predicted  DOMI  values,  and  the  continuous  middle  line 
represents  the  set  of  points  where  actual  DOMI  equals  predicted  DOMI. 
Thus,  the  vertical  deviation  of  any  plotted  point  from  the  continuous 
middle  line  represents  error  in  predicting  that  value  of  DOMI.   The 
inner  set  of  broken  lines  represent  conservative  acceptability  limits, 
and  the  outer  set  of  broken,  dotted  lines  mark  the  liberal  acceptability 
limits.   In  this  study,  all  DOMI  predictions  were  acceptable  when  judg- 
ed by  liberal  limits,  and  14  of  the  16  predictions  were  acceptable 
when  judged  by  conservative  limits.   Thus,  the  theoretically  rational 
prediction  of  DOMI  from  RTOM  may  provide  an  acceptable  method  for 
prediction  of  quality  for  warm-season  grasses.   Unacceptability  in  a 
conservative  sense  of  quality  predictions  for  two  Pangola  digitgrass 
hays  may  have  been  caused  by  effects  of  factors  not  related  to  RTOM 
upon  quality  measurements  for  these  two  hays.   These  hays  exhibited 
RTOM  values  of  29.46  and  30.95  hr,  and  in  vivo  OMD ' s  of  65.9  and  65.3 
percent,  respectively.   Therefore,  it  is  possible  that  some  chemostatic 
regulator- influenced  OMI,  at  least  in  part,  for  these  two  hays  as 
suggested  by  Montgomery  and  Baumgardt  (1965a)  and  Conrad  (1966). 


93 


TABLE  9.   CONSERVATIVE  AND  LIBERAL  ACCEPTABILITY  LIMITS  FOR  TESTING 
PREDICTIONS  OF  DIGESTIBLE  ORGANIC  MATTER  INTAKE  (DOMI)  AND  ORGANIC 
MATTER  INTAKE  (OMI) 


FORAGE  SPECIES 


Item 


Bahia 


Bermuda     Paneola 


All 


DOMI,  g/W  '^^/day 

Kg 

Conservative  [±t(s//n)]  ±2.4 

Liberal  (±2s)  ±5.1 
OMI,  g/Wj^  '^^/day 

Conservative  ±4.9 

Liberal  ±10.3 


±3.7        ±5.6 
±7.8        ±8.2 


±7.0 
±14.7 


±9.0 
±13.2 


±3.9 

±7.5 

±7.1 
±13.6 


94 


PREDICTED  DIGESTIBLE  ORGANIC  MATTER  INTAKE  (Y),  g/W   '   /day 

kg 


Figure  7.   Test  of  retention  time  of  organic  matter  in  the  rumen 
as  a  rational  predictor  of  digestible  organic  matter 
intake. 


95 


Perhaps  quality  predictions  for  forages  which  exhibit  such  low  RTOM 
and  high  OMD  values  could  be  improved  by  including  some  rational 
chemostatic  regulating  factor  along  with  RTOM  in  an  equation  for 
predicting  quality. 

Of  the  16  grasses  for  which  quality  predictions  were  made,  10 
contained  less  than  7  percent  CP  on  a  DM  basis.   Egan  (1965)  and 
Weston  (1967)  indicated  that  OMI  of  such  forages  may  be  influenced 
more  by  nitrogen  status  of  the  animal  than  by  RTOM.   If  this  were 
true,  it  would  be  expected  that  quality  predictions  for  such  forages 
would  be  too  high  when  determined  from  RTOM.   Such  was  not  the  case 
in  the  present  study,  since  quality  predictions  for  all  10  grasses 
of  low  CP  percentage  were  acceptable  when  judged  by  conservative 
acceptability  limits.   These  results,  however,  may  not  invalidate 
totally  the  assumptions  of  Egan  (1965)  and  Weston  (1967),  but  may  be 
indicative  of  the  different  lengths  of  in  vivo  experimental  periods 
used  by  these  authors  compared  to  those  employed  with  hays  used  in 
the  present  investigation.   Clark  and  Quin  (1951)  stated  that  it 
frequently  had  been  found  that  sheep  kept  exclusively  on  a  diet  of 
poor-quality  grass  hay  showed  a  gradual  decline  in  intake  from 
about  the  third  week  on  such  a  treatment.   Data  presented  by  these 
workers  showed  that  intake  declined  after  the  third,  fourth  or  fifth 
week  when  low-quality  forages  were  fed.   Hays  employed  in  the  present 
study  were  fed  to  sheep  for  three  weeks,  while  Egan  (1965)  used 
7.5  to  11  weeks,  and  Weston  (1967)  used  7  to  13  weeks.   Thus,  hays 
studied  in  the  present  investigation  may  have  been  fed  to  sheep  for 


96 


periods  of  time  too  short  to  allow  animals  to  achieve  a  nitrogen  status 
low  enough  to  override  RTOM  as  the  primary  determinant  of  DOMI.   In 
this  case,  RTOM  still  would  determine  quality  of  forages  low  in  CP 
percentages,  as  indicated  by  results  reported  here. 

Figures  3  and  9  show  tests  of  acceptability  for  OMI  predictions 
made  by  rational  and  empirical  methods,  respectively.   Figure  8 
shows  that  rational  prediction  of  OMI  for  16  grasses  from  predicted 
DOMI  divided  by  predicted  OMD  produced  predictions  which  were  all 
acceptable  when  judged  by  liberal  acceptability  limits,  and  15  which 
were  acceptable  by  conservative  limits.   The  one  bermudagrass  for 
which  predicted  OMI  was  conservatively  unacceptable  exhibited  a  CP 
percentage  of  5.6  on  a  DM  basis.   However,  OMI  predictions  for  the 
nine  other  grasses  with  less  than  7  percent  CP  on  a  DM  basis  all  fell 
within  conservative  acceptability  limits.   These  results  strengthen 
the  conclusion  that  in  this  investigation,  nitrogen  status  of  the 
animal  generally  was  less  important  than  RTOM  in  determining  in  vivo 
OMI  of  grasses  with  low  CP  percentages.   Figure  9  presents  results 
of  empirical  prediction  of  OMI  from  ADF  as  a  percentage  of  DM,  as 
suggested  by  Weller  (1973).   All  predictions  for  the  same  16  grasses 
used  above  were  acceptable  by  liberal  standards,  but  five  predictions 
were  unacceptable  when  judged  by  conservative  limits.   Thus, the 
rational  method  for  predicting  OMI  was  more  promising  among  grass 
species  than  the  empirical  prediction  method.   Also,  the  high  negative 
r  value  (-.88)  found  in  this  study  for  the  relationship  between  OMI 
and  ADF  for  15  forages  would  not  be  expected  in  all  cases.   Johnson 
and  Dehority  (1968)  reported  an  r  value  of  only  -.46  for  the  relation- 
ship between  relative  intake  of  22  temperate  grasses  and  ADF  percentage 


97 


90  K 


35   40 


80 


75 


PREDICTED  ORGANIC  MATTER  INTAKE  (Y),  g/W,  "   /day 

kg 

Figure  8.   Test  of  theoretically  rational  method  for  prediction 
of  organic  matter  intake. 


98 


PREDICTED  ORGANIC  MATTER  INTAKE  (Y) ,  g/W   "'"/day 


Figure  9.   Test  of  empirical  prediction  of  organic  matter 
intake  from  acid-detergent  fiber  percentage 
of  dry  matter. 


99 


of  these  grasses,  and  -.31  when  legumes  and  mixed  forages  were  in- 
cluded in  the  analysis.   Van  Soest  (1965a)  found  r  values  ranging  from 
-.88  to  .20  between  voluntary  intake  and  ADF  within  seven  species  of 
temperate  forages.   The  value  of  this  statistic  was  -.53  for  all  83 
individual  forages.   This  author  also  reported  that  the  relationship 
between  voluntary  intake  and  ADF  declined  as  that  between  voluntary 
intake  and  digestibility  decreased.   Since  this  latter  relationship 
is  not  always  strong,  especially  over  a  wide  range  of  forage  species, 
it  is  doubtful  that  the  r  value  between  OMI  and  ADF  always  would  be 
as  high  as  found  in  the  present  study.   Therefore,  prediction  of  OMI 
should  be  undertaken  on  a  rational  rather  than  on  an  empirical  basis. 
Utility  of  Relationships  Between  Various  Measurements  and  Analyses 
Prediction  of  organic  matter  digestibility 

In  this  study,  OMD  was  predicted  with  equation  (LI),  in  which 
NDFD  was  predicted  from  IVNDFD  after  72  hr  of  fermentation.   Since 
DOM  as  a  percentage  of  DM,  or  the  numerator  of  equation  (Ll)  ,  must 
be  estimated  in  order  to  predict  DE/g  DM  (Chapter  III)  ,  prediction  of 
OMD  can  be  achieved  simply  by  dividing  DOM  by  OM  as  a  percentage  of 
DM.   Thus,  determination  of  IVOMD  after  48  hr  of  fermentation  is 
avoided  in  predicting  0^ro.   This  determination  apparently  can  be 
avoided  in  other  situations  where  IVNDFD  is  measured  at  72  hr.   For 
all  31  grasses,  the  r  value  for  the  relationship  between  in  vivo  OMD 

and  IVNDFD  at  72  hr  was  .96,  and  s    was  2.2  percentage  units.   The 

y.x 

relationship  between  jiTi  vivo  OMD  and  IVOMD  at  48  hr  was  characterized 
by  r  and  s    values  of  .96  and  2.4  percentage  units,  respectively. 
Weller  (1973)  reported  that  in  vivo  OMD  and  IVNDFD  at  72  hr  were  highly 


100 


and  positively  correlated  for  12  warm-season  grasses  (r  =  .96; 
s    =2.0  percentage  units).   Thus,  OMD  probably  could  be  predicted 
directly,  with  a  high  degree  of  accuracy,  from  IVNDFD  after  72  hr  of 
fermentation.  Measurement  of  this  in  vitro  parameter,  however,  re- 
quires more  time  than  does  determination  of  IVOMD  at  48  hr.   There- 
fore, changing  existing  systems  of  analysis  from  IVOMD  at  48  hr 
to  IVNDFD  at  72  hr  of  fermentation  probably  would  be  unwarranted. 
Prediction  of  intake  from  neutral-detergent  fiber  percentage 

Van  Soest  (1965a)  suggested  that  NDF  limited  intake  when  this 
constituent  comprised  more  than  55  to  60  percent  of  DM.   In  the  present 
investigation,  NDF  percentage  ranged  from  63.6  to  81.3  percent  on  the 
DM  basis.   However,  for  all  31  grasses,  the  relationship  between  OMI 

and  NDF  was  characterized  by  r  =  -.12  and  s    =12.4.   For  bahiagrass 

y.x 

and  bermudagrass  individually,  these  values  were  only  slightly  higher , 
i.  e. ,  -.23  and  9.2,  or  -.47  and  9.5,  respectively.   With  Pangola 
digitgrass,  r  =  .91  and  s    =  5.3.   Such  a  strong  relationship,  however, 
may  be  more  associative  than  cause  and  effect  in  nature.   Johnson  and 
Dehority  (1968)  reported  an  r  value  of  only  -.21  for  the  relation- 
ship between  relative  intake  and  NDF  percentage  of  temperate  grasses, 
and  -.56  for  grasses,  legumes  and  mixed  forages  combined.   Van  Soest 
(1965a)  found  this  value  to  be  -.65  for  83  temperate  forages,  while 
it  ranged  from  -.95  to  +.57  within  species.   Thus,  results  of  these 
studies  and  those  of  the  present  investigation  strongly  indicate  that 
NDF  as  a  percentage  of  DM  would  not  be  an  accurate  predictor  of  OMI 
over  a  wide  range  of  forage  species. 


101 


Prediction  of  k 

In  this  study,  values  of  k  for  all  31  grasses  were  calculated 
from  their  actual  in  vivo  lignin  intakes.   Estimates  of  k  resulting 
from  this  procedure  averaged  .0227  g/hr/g,  which  agrees  with  the  .02 
g/hr/g  assumed  by  Waldo  ^  al.  (1972).   This  fact  and  the  high  degree 
of  acceptability  of  forage  quality  predictions  in  the  present  investiga- 
tion suggest  that  the  procedure  used  here  for  estimation  of  k   is 
acceptable.   However,  for  prediction  of  forage  quality  in  a  practical 
situation,  this  procedure  is  inadequate  because  intake  usually  would 
not  be  known.   Simple  linear  regression  analysis  involving  several 
in  vivo  measurements  and  laboratory  determinations  revealed  that  the 
relationships  between  k„  and  lignin  percentage  of  either  DM  or  NDF 

exhibited  r  and  s    values  of  .82  and  .001,  or  .83  and  .001  units, 
y.x 

respectively.   Since  only  a  maximum  of  68  percent  of  the  variation  in 
k„  was  explained  by  these  parameters,  alternate  methods  for  prediction 
of  k  were  sought. 

Calculated  values  for  k„  were  of  low  magnitude  and  were  relatively 
invariant  in  this  study  (table  7).   Thus,  it  was  possible  that  assum- 
ing k  to  be  constant  among  grasses  at  its  mean  value,  i.  e. ,  .0227 
g/hr/g,  might  not  lead  to  errors  in  prediction  of  DOMI.   Testing  this 
assumption  with  the  16  grasses  used  throughout  this  study  for  predic- 
tive purposes,  however,  resulted  in  only  six  quality  predictions  which 
were  acceptable  when  judged  by  liberal  acceptability  limits  (table  9), 
and  one  which  was  acceptable  by  conservative  standards.   For  two  for- 
ages whose  DOMI  predictions  were  in  error  by  only  0.1  g/W   '   /day 

kg 

when  predictions  were  generated  using  calculated  k„  values,  prediction 


102 


errors  rose  to  6.4  and  7.5  g/W   *   /day  when  the  mean  value  of  k„  was 

Kg  2. 

used.   The  mean  underestimated  the  calculated  values  by  only  .0010 
and  .0011  glhxlg,   respectively.   For  two  other  forages,  DOMI  predictions 
generated  with  calculated  k  values  were  in  error  by  .8  and  2.8  units, 
but  prediction  errors  were  8.5  and  8.3  units,  respectively,  using 
the  mean  value  of  k  .   Calculated  k  values  were  underestimated  by 
X)011  and  IX)09  g/hr/g,  respectively ,  by  the  mean.   Therefore,  assuming 
k   to  be  constant  over  a  wide  range  of  forages  does  not  provide  con- 
sistently acceptable  predictions  of  forage  quality.   Since  estimates 
of  k  obviously  must  be  quite  accurate,  it  is  not  surprising  that 
using  the  value  of  .02  g/hr/g  (Waldo  ^  al • .  1972)  produced  DOMI 
predictions  which  were  too  low.   Predictions  generated  under  the 
assumption  that  k  was  constant  at  .025  g/hr/g  were  too  high. 

A  second  method  for  prediction  of  k„  was  suggested  when  simple 
linear  regression  analysis  revealed  that  the  relationship  between 

'a'  and  (NDS  +  DNDF) ,  as  a  percentage  of  DM,  exhibited  an  r  value  of 

2 
. 88  (r  =  .78)  and  s    of  .034.   This  relationship,  it  seems,  should 

be  more  than  simply  associative.   The  utility  of  this  relationship 

would  be  that,  with  the  value  of  'a'  known,  equation  (10)  could  be 

solved  for  k„  when  predicted  OMD  (OMD  )  was  expressed  as  a  decimal 
2  P 

fraction  rather  than  in  percent.   The  resultant  formula  for  determin- 


ing k  would  be  as  follows: 

a(k  ) (1-OMD  ) 

k„  =  }—■ 2_  .  (17) 

2      OMD   -a  ^ 

P 

To  test  this  method  for  prediction  of  k  ,  values  of  'a'  were 
first  regressed  on  (NDS  +  DNDF) ,  as  a  percentage  of  DM,  using  the  same 
15  grasses  used  throughout  this  study  for  generation  of  prediction 
equations.   The  resultant  equation  was  the  following: 


103 


'a'=  -.1878  +  .0071  (NDS  +  DNDF) .  (18) 

2 
Values  of  r  and  s    for  equation  (18)  were  .88  (r   =  .77)  and  .033, 
y.x 

respectively.   Actual  values  of  'a'  for  the  16  remaining  grasses, 
when  regressed  upon  their  respective  predicted  values  from  equation 

(18),  produced  a  relationship  which  was  characterized  by  an  r  value 

2 
of  .87  (r  =  .77)  and  s    of  .036.   Predicted  values  of  'a'  were 
y.x 

inserted  into  equation  (17) ,  and  this  equation  was  solved  to  predict 
values  for  k  .   Regressing  calculated  values  of  k  for  the  16  grasses 

on  their  respective  k  predictions  produced  an  r  value  of  only  .58 

2 
(r  =  .33)  and  s    of  .001  g/hr/g.   Therefore,  at  present,  this 
y.x 

method  for  prediction  of  k„  does  not  appear  promising.   However,  if 
some  rational  method  could  be  developed  which  would  allow  more  accurate 
prediction  of  'a',  then  equation  (17)  might  be  useful  for  accurate 
prediction  of  k  . 
Prediction  of  k. 


Simple  linear  regression  analysis  revealed  no  laboratory  chemical 
determination  or  in  vivo  measurement  which  could  be  used  for  more 
rapid  determination  of  k  than  is  allowed  by  the  in  vitro  procedures 
followed  in  this  study.   For  all  31  grasses,  the  relationship  between 

k  and  lignin  as  a  percentage  of  DM  exhibited  an  r  value  of  only 

2 
.16  (r  =  .02).   This  finding  agrees  with  results  published  by 

Lechtenberg  et_  al.  (1974),  who  reported  that  although  rate  of  digestion 

of  cell  walls  depended  upon  lignin  as  a  percentage  of  DM,  k  was  not 

affected  by  the  magnitude  of  lignin  concentration  in  DM. 


104 


General  Discussion 

In  this  study,  quality  of  warm-season  grasses  was  predictable 
with  a  high  degree  of  accuracy  from  RTOM  when  this  time  period  was 
longer  than  about  31  hr.   Therefore,  RTOM  apparently  reflects 
both  chemical  composition  and  structural  organization  of  OM.   When 
RTOM  was  below  31  hr,  as  it  was  for  two  Pangola  digitgrasses,  DOMI 
predictions  were  acceptable  when  judged  by  liberal  acceptability 
limits,  but  were  unacceptable  by  conservative  standards.   Therefore, 
forage  quality  may  not  be  always  predictable  from  RTOM  alone,  since 
the  distention  mechanism  and  RTOM  may  be  partially  or  totally  over- 
ridden by  factors  related  to  other  DOMI  control  mechanisms.   This 
would  occur  when  such  factors  reach  levels  at  which  they  become 
important  as  determinants  of  forage  DOMI.   In  this  study,  such 
factors  may  have  been  related  to  some  attribute  of  the  two  Pangola 
digitgrasses,  and  may  have  been  chemostatic  in  nature. 
Non-Forage  Factors  Which  Could  Override  or  Modify  RTOM 

It  also  is  possible  that  in  a  given  situation  where  RTOM  is 
the  main  determinant  of 'forage  quality,  the  j^  vivo  value  of  RTOM 
may  be  modified  by  non-forage  factors  (factors  which  are  not  related 
directly  to  forage  chemical  composition  or  physical  structure, 
per  se)  which  were  not  included  in  the  RTOM  prediction  process.   Non- 
forage  factors  also  might  override  RTOM  as  the  main  determinant  of 
forage  quality.   If  RTOM  is  overridden,  or  is  modified  with  respect 
to  its  magnitude,  the  factors  responsible  need  to  be  identified  and 
their  effects  require  accurate  quantification.   If  these  effects  are 
not  related  to  forage  quality,  then  the  manner (s)  in  which  they  are 


105 


mediated  must  be  elucidated.   If  such  discoveries  could  be  made,  forage 
DOMI  probably  could  be  predicted  accurately  by  dynamic  computer  model- 
ing in  situations  where  RTOM  was  overridden  as  the  main  determinant 
of  DOMI,  or  was  modified  with  respect  to  magnitude  in  some  way  that 
could  not  be  taken  into  account  in  a  laboratory  procedure  for  RTOM 
prediction. 

The  number  of  factors  which  could  override  RTOM,  or  which  could 
influence  its  magnitude,  is  undoubtedly  great.   Several  factors  which, 
in  one  of  these  ways,  may  affect  DOMI  of  a^  libitum  fed  animals  are 

(1)  nitrogen  status  of  the  animal  (Egan,  1965,  1970;  Weston, 
1967); 

(2)  environmental  conditions,  i.  e. ,  ambient  temperature,  humidity 
and  solar  radiation  (Ragsdale  e_t  a]^.  ,  1953;  Brobeck,  1960; 
Wayman  et   al. ,  1962;  Warren  at  al. ,  1974;  Bhattacharya 

and  Uwayjan,  1975;  Koes  and  Pfander,  1975); 

(3)  the  physical  form  in  which  forages  are  fed,  i.  e. ,  long, 
chopped,  ground  or  ground  and  pelleted  (Rodrigue  and  Allen, 
1956;  Blaxter  and  Graham,  1956;  Johnson  et^  al. ,  1964;  Minson 
and  Milford,  1968;  Greenhalgh  and  Reid,  1973); 

(4)  previous  plane  of  nutrition  (Tayler  et^  al . ,  1957;  Tayler, 
1959;  Heaney,  1970;   0 ' Donovan  et  al. ,  1972;  Asplund  et  al. , 
1975); 

(5)  the  method  by  which  hay  is  processed,  i.  e. ,  artificially 
dried,  barn-dried,  rack-dried  or  swath-dried  in  the  field 
(Shepperson,  1960;  Milford  and  Minson,  1968;  Demarquilly 
and  Jarrige,  1970) ; 

(6)  physiological  state  of  the  animal,  i.  e. ,  fatness,  stage 
of  pregnancy  and  stage  of  lactation  (Reid  and  Hinks ,  1962; 
Graham  and  Williams,  1962;  Hutton,  1963;  Arnold  and  Dudzinski, 
1966;  Ulyatt,  1973;  Capote,  1975); 

(7)  animal  type,  i.  e. ,  cattle  or  sheep  (Buchman  and  Hemken, 
1964;  Blaxter  et  al.  ,  1966;  Jones  et^  al.  ,  1972); 

(8)  anim.al  breed  within  type  (Hungate  et  al.  ,  1960;  Phillips, 
1961;  Kappel  et  al. ,  1972); 


106 


(9)  animal  levels  of  macro-  and  micro-  mineral  elements  (Under- 
wood, 1962;  Blaxter,  1962;  Preston  and  Pfander,  1964; 
Telle  et  al. ,  1964;  Miller  et  al. ,  1966;  Weston,  1966; 
Patil  and  Jones,  1970;  Chicco  et^  al,  ,  1973;  Seoane  et  al.  , 
1975); 

(10)  CP  supplementation  of  forages  which  exhibit  CP  levels  of 
less  than  7  percent  on  a  DM  basis  (Campling  et  al. , 
1961;  Coombe  and  Tribe,  1963;  Egan,  1965,  1970;  Elliott, 
1967;  Minson  and  Milford,  1967b;  Lourens,  1968;  Moore  et  al. , 
1970;  Houser,  1970;  Ammerman  et  al. ,  1972;  Siebert  and 
Kennedy,  1972;  Fick  et  al.  ,  1973;  Ventura  et^  ail,  ,  1975); 

(11)  energy  supplementation  (Blaxter  and  Wilson,  1963;  Bisschoff 
et  al . ,  1967;  Clanton  and  Rittenhouse,  1970;  Tayler  and 
Wilkinson,  1972;  Fick  et  al . ,  1973;  Golding,  1973);  and 

(12)  animal  levels  of  certain  hormones  (Blaxter  Bt^  al.  ,    1949; 
Dewar,  1962;  Hervey  and  Hervey,  1964;  Wade  and  Zucker, 
1970). 

It  is  important  that  research  to  quantify  effects  upon  DOMI  of 

factors  listed  above  be  carried  out  over  a  wide  range  of  forage 

quality,  and  that  data  generated  for  this  purpose  be  analyzed  by 

regression  procedures.   Research  must  be  carried  out  in  this  fashion 

so  that   (a)  results  are  applicable  to  a  wide  range  of  forage  species; 

(b)  effects  can  be  predicted  continuously  over  this  range  instead  of 

only  at  a  few  widely  scattered,  intermittent  points;  and  (c)  efficient 

and  accurate  dynamic  modeling  can  be  practiced.   Experimental  designs 

of  the  central-composite  or  San  Cristobal  type  require  a  smaller 

number  of  experimental  animals  than  full-scale  factoral  designs,  and 

could  be  utilized  in  attaining  these  goals. 

Theoretical  Methods  for  Prediction  of  k^ 


Two  theoretically  possible  methods  for  prediction  of  k„  remain 

to  be  tested.   First,  the  amount  of  CM  in  the  rumen  per   W   *    has 

kg 

been  found  constant  among  forages  when  forage  CP  is  greater  than 


107 


7  percent  on  a  DM  basis  (Blaxter  ^  al. ,  1961;  Ulyatt  et  al . ,  1967; 

Thornton  and  Minson,  1972).   If  this  quantity  of  OM  could  be  ascertained 

for  a  given  set  of  non-forage  factors,  then  multiplying  this  quantity 

by  .63  theoretically  should  approximate  the  amount  of  ruminal  OM  per 

W   "    which  is  partially  digestible.   The  other  37  percent  would 
kg 

estimate  OM  which  had  been  in  the  rumen  for  longer  than  a  period  of 
time  equal  to  RTOM,  and  this  should  be  mostly  indigestible.   Multiply- 
ing partially  digestible  ruminal  OM  per  W  *    by  predicted  OMD  would 

kg 

approximate  ruminal  OM  which  is  totally  digestible  per  W   *   ,  and 

kg 

dividing  this  quantity  by  the  originally  assumed  amount  of  ruminal 

OM  per  W   *    should  produce  an  accurate  estimate  of  'a'.   Inserting 
kg 

this  'a'  value  into  equation  (17)  should  produce  an  accurate  prediction 

of  k„.   Potential  problems  to  be  encountered  with  this  method  are, 

first,  though  ruminal  OM  per  W   '    has  been  found  statistically  constant 

kg 

among  forages  this  amount  of  OM  does  vary  numerically.   Assuming  this 

quantity  to  be  constant,  therefore,  could  produce  inaccurate  estimates 

of  'a'  over  a  wide  range  of  forages,  and  this  could  result  in  inaccurate 

k  predictions.   Also,  Assuming  the  37  percent  of  total  ruminal  OM  per 

W,  *    which  has  been  in  the  rumen  for  longer  than  RTOM  to  be  totally 
kg 

indigestible  may  not  always  be  correct,  and  this  could  result  in  under- 
estimation of  'a'.   A  further  difficulty  would  arise  when  forage 
contains  less  than  7  percent  CP  on  a  DM  basis.   In  such  cases,  ruminal 

OM  per  W   *    apparently  is  not  constant  (Campling  _et  al . ,  1961;  Egan, 
kg 

1970).   Thus,  separate  estimates  of  ruminal  OM  per  W   '    would  have 

kg 

to  be  made  for  such  forages.   This  method  for  prediction  of  k  deserves 
to  be  tested,  however,  to  see  if  it  would  produce  acceptable  predictions 
for  this  parameter. 


108 


The  second  possible  method  for  k  prediction  is  based  upon  the 
fact  that  rate  of  passage  of  OM  from  the  rumen  is  equal  to  k  multiplied 
by  the  amount  of  OM  undergoing  passage.   This  method  would  require 
measurement  of  the  electrical  energy  required  to  grind  a  known  amount 
of  forage  OM  in  a  Wiley  mill.   Such  determinations  have  been  made  by 
Chenost  (1966)  and  Laredo  and  Minson  (1973). 

It  is  possible  that  the  electrical  energy  required  to  grind  a 
known  amount  of  forage  OM  may  be  related  to  rate  of  OM  breakdown  in 
(Laredo  and  Minson,  1973),  and  rate  of  OM  passage  from,  the  rumen. 
If  this  is  true  among  forages  and  if  the  OM  undergoing  grinding  is  a 
constant  percentage  among  forages  of  the  amount  of  OM  which  passes 
from  the  rumen  under  steady-state  conditions,  then  dividing  the  energy 
required  to  grind  OM  by  the  amount  of  OM  being  ground  should  produce 
a  value  not  equal  to,  but  highly  correlated  with,  k  .   The  value  of 
k^  then  could  be  predicted  from  a  regression  equation  generated  using 
a  wide  range  of  forages  for  which  k  and  grinding  energy  for  an  amount 
of  OM  highly  correlated  with  rate  of  OM  passage  from  the  rumen  divided 
by  k„  were  known. 

The  difficult  part  of  this  procedure  would  be  determination  of 
the  amount  of  OM  which  should  undergo  grinding.   This  quantity  should 
be  a  constant  percentage  among  forages  of  the  amount  of  OM  which  under- 
goes passage  from  the  rumen  under  steady-state  conditions.   At  present, 
quantification  of  OM  undergoing  passage  requires  knowledge  of  OMI. 
Thus,  a  method  which  does  not  require  such  knowledge  must  be  devised 
for  quantifying  OM  which  passes  from  the  rumen.   It  may  be  that  a 
constant  relationship  exists  between  grinding  energy  and  amount  of  OM 
being  ground.   If  this  were  true,  then  any  quantity  of  OM  could  be 


109 


ground.   If  such  a  relationship  does  not  exist,  however,  a  mathematician 
might  be  able  to  aid  in  quantifying  OM  which  passes  from  the  rumen 
under  steady-state  conditions. 

Summary 
Thirty-one  warm-season  grasses  of  known  in  vivo  intake  and  digest- 
ibility were  used  to  develop  and  test  a  theoretically  rational  method 
for  prediction  of  forage  quality  in  terms  of  digestible  organic  matter 
(OM)  intake  (DOMI).   These  grasses  included  bahiagrasses,  bermudagrasses 
and  Pangola  digitgrasses  which  ranged  in  maturity  from  2  to  14  weeks 
of  regrowth.   Retention  time  of  OM  in  the  rumen  (RTOM)  was  hypothesized 
to  be  a  theoretically  rational  independent  variable  for  prediction  of 
forage  quality.   Values  of  RTOM  for  all  grasses  were  estimated  from 
an  equation  presented  by  Waldo  et^  al.  (1972)  for  plotting  disappear- 
ance of  cellulose  from  the  rumen  through  time.   This  equation  was 
used  in  reference  to  OM  instead  of  cellulose  for  each  forage.   The 
value  of  the  rate  constant  (k  )  for  rate  of  digestion  was  estimated 
using  in  vitro  procedures,  while  the  rate  constant  (k  )  for  rate  of 
passage  was  calculated  from  known  lignin  intake.   Estimates  of  the 
potentially  digestible  fraction  of  ruminal  OM  ('a')  were  made  using  the 
formula  for  'a'  developed  in  this  study,  and  the  potentially  indigest- 
ible fraction  of  ruminal  OM  was  equal  to  one  minus  'a'.   Fifteen  grass- 
es selected  as  representative  of  the  total  of  31  were  used  to  generate 
an  equation  to  produce  acceptably  accurate  predictions  of  forage  quality. 
Predictions  of  DOMI  were  judged  acceptable  in  a  conservative  sense 
if  their  absolute  errors  in  relation  to  actual  DOMI  values  were  less 
than  or  equal  to  the  value  of  one  side  of  the  95  percent  confidence 
interval  for  the  mean  weighted  across  all  31  grasses.   Predictions 


no 


were  deemed  liberally  acceptable  if  the  prediction  errors  were  less 
than  or  equal  to  the  value  of  two  standard  deviation  estimates 
weighted  across  all  grasses. 

Fourteen  of  the  16  DOMI  predictions  were  acceptable  when  judged 
by  conservative  acceptability  limits,  while  all  predictions  were 
acceptable  by  liberal  standards.   Ten  of  the  16  grasses  exhibited 
crude  protein  percentages  of  less  than  7  percent  of  DM. 
Factors  not  related  to  RTOM  may  have  influenced  the  actual  quality 
of  the  two  Pangola  digitgrasses  for  which  DOMI  predictions  were 
conservatively  unacceptable.   Inclusion  in  the  prediction  equation 
of  some  rational  factor  related  to  the  chemostatic  mechanism  for 
controlling  forage  intake  possibly  may  increase  accuracy  of  DOMI 
predictions  in  such  cases. 

Rational  prediction  of  OM  intake  (OMI)  from  predicted  DOMI 
divided  by  predicted  OM  digestibility  (OMD)  appeared  a  better  method 
than  empirical  prediction  of  OMI  from  acid-detergent  fiber  percentage. 
Though  all  predictions  generated  by  this  latter  method  were  liberally 
acceptable,  such  success  might  not  be  expected  over  a  wider  range 
of  forages  which  included  more  genetic  diversity.   Empirical  predic- 
tions of  OMI  from  neutral-detergent  fiber  (NDF)  percentage  were  in- 
accurate. 

Values  of  OMD  were  predicted  accurately  by  dividing  predicted 
digestible  OM  (DOM)  by  OM.   Fast  and  accurate  DOM  predictions,  how- 
ever, await  development  of  a  rapid,  simple  laboratory  determination 
for  accurate  estimation  of  digestible  NDF  or  NDF  digestibility. 
Actual  OMD  could  be  predicted  as  accurately  from  in  vitro  NDF  digestion 


Ill 


after  72  hr  of  fermentation  as  from  in  vitro  OM  digestion  after  48  hr. 
The  former  determination,  however,  requires  more  time. 

Values  of  k^ ,  though  relatively  invariant,  cannot  be  assumed 
constant  among  forages,  nor  can  these  values  be  predicted  accurately 
at  present  from  predicted  values  of  'a'.   Estimation  of  k  must  be 
achieved  at  present  by  use  of  in  vitro  methods.   Lignin  as  a  percentage 
of  DM  was  not  correlated  highly  with  k  . 


APPENDIX 


113 


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126 


TABLE   13.      ALGEBRAIC  MANIPULATIONS  OF   EQUATION  9  REQUIRED  TO 
PRODUCE   EQUATION   10    (CHAPTER  V) 


(OMD    )(OMI) 
P 

=      (9) 


24 (k^  +  k2) 


1  (OMD    ) (OMI)    +  1_   (100-OMD    ) (OMI) 

24 (k,   +  k^)  P  24k.  ^ 


12'  2 


OMD 


24 (k^  +  k^) 


OMD                    (100-OMD    ) 
P ^  E_ 


24 (k^  +  k^)  24k2 


OMD 
2_ 


24 (k^  +  k^) 


24k^(OMD   )   +  24 (k,    +  k„) (100-OMD    ) 

2  p 1  2 2_ 

(24)(24)(k     +  k   )(k   ) 


OMD    (24)(24)(k,    +  k„)(k„) 


[24(k,    +  k^)][24(k^)(0tn)   )   +  24  (k,    +  k.)  (lOO-OMD   )] 
12  z  p  1  z  p 


OMD    (k,    +  k„)(k„) 
pi  II 


(k     +  kJ[(k„)(OMD    )    +    (k.    +  kJ(100-OMD    )] 
1  2  z  p  1  z  p 


(k„)(OMD    ) 
2  P 


[(k„)(OMD    )]    +    [(k^    +  k„) (100-OMD    )] 
2  p  12  p 


(10) 


127 


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■^  Akin,  D.  E. ,  D.  Burdick  and  H.  E.  Amos.   1974a.   Comparative  degrada- 
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BIOGRAPHICAL  SKETCH 

Edward  John  Golding,  III,  was  born  April  11,  1944,  in  Toledo, 
Ohio.   In  June,  1962,  he  graduated  from  DeVilbiss  High  School  in 
Toledo.   From  September,  1962,  to  June,  1963,  he  attended  North- 
western University  at  Evanston,  Illinois.   In  September,  1963,  he 
transferred  to  the  University  of  Idaho,  Moscow,  Idaho,  and  received 
a  Bachelor  of  Science  degree  in  Forestry  (Range  Management)  in 
June,  1967.   From  September,  1967,  to  June,  1970,  he  served  as  a 
Peace  Corps  Volunteer  in  Cauquenes ,  Chile.   After  his  return  to 
Toledo  in  June,  1970,  he  worked  for  one  year  at  Girkins  Welders. 
In  September,  1971,  he  enrolled  in  the  Department  of  Animal  Science 
at  the  University  of  Florida,  and  received  the  Master  of  Science  in 
Agriculture  degree  in  August,  1973.   At  present  he  is  a  candidate 
for  the  degree  of  Doctor  of  Philosophy  in  the  Department  of  Animal 
Science,  University  of  Florida. 

He  is  married  to  the  former  Astrid  Semler  Chipoco,  and  they  have 
a  daughter,  Nicole,  and  a  son,  Christopher.   The  author  is  a  member 
of  Gamma  Sigma  Delta,  Phi  Kappa  Phi  and  Xi  Sigma  Pi. 


149 


I  certify  that  I  have  read  this  study  and  that  in  my  opinion 
it  conforms  to  acceptable  standards  of  scholarly  presentation  and 
is  fully  adequate,  in  scope  and  quality,  as  a  dissertation  for  the 
degree  of  Doctor  of  Philosophy. 


tlf 


H(r/l 


Q-^ 


Moore,  Chairman 
or  of  Animal  Science 


I  certify  that  I  have  read  this  study  and  that  in  my  opinion 
it  conforms  to  acceptable  standards  of  scholarly  presentation  and 
is  fully  adequate,  in  scope  and  quality,  as  a  dissertation  for  the 
degree  of  Doctor  of  Philosophy. 


C.  B.  Ammerman 

Professor  of  Animal  Science 


I  certify  that  I  have  read  this  study  and  that  in  my  opinion 
it  conforms  to  acceptable  standards  of  scholarly  presentation  and 
is  fully  adequate,  in  scope  and  quality, as  a  dissertation  for  the 
degree  of  Doctor  of  Philosophy. 


H.  Conrad 
'Professor  of  Animal  Science 


I  certify  that  I  have  read  this  study  and  that  in  my  opinion 
it  conforms  to  acceptable  standards  of  scholarly  presentation  and 
is  fully  adequate,  in  scope  and  quality, as  a  dissertation  for  the 
degree  of  Doctor  of  Philosophy. 


/(. 


/V  <- 


D.  E.  Franke 

Associate  Professor  of  Animal  Science 


I  certify  that  I  have  read  this  study  and  that  in  my  opinion 
it  conforms  to  acceptable  standards  of  scholarly  presentation  and 
is  fully  adequate,  in  scope  and  quality, as  a  dissertation  for  the 
degree  of  Doctor  of  Philosophy. 


G.  0.  Mott 

Professor  of  Agronomy 


This  dissertation  was  submitted  to  the  Graduate  Faculty  of  the 
College  of  Agriculture  and  to  the  Graduate  Council,  and  was  accepted 
as  partial  fulfillment  of  the  requirements  for  the  degree  of  Doctor 
of  Philosophy. 

March,  1976 


Dean,  Graduate  School 


UNIVERSITY  OF  FLORIDA 


3  1262  08666  929  7