• TABLE OF CONTENTS
HIDE
 Historic note
 Front Cover
 Title Page
 Table of Contents
 Objectives of the seminar
 Role of the computer in the livestock...
 Monsanto computerized technology...
 Availability and adequacy of data...
 Availability and adequacy of data...
 Adjustment of nutrient content...
 Availability and adequacy of data...
 Least-cost rations for dairy...
 Availability and adequacy of data...
 Role of the computer in a cattle...
 IFAS participants














Group Title: Bulletin - University of Florida Institute of Food and Agricultural Sciences ; 8
Title: An evaluation of animal nutrition data used in the computer formulations of rations
CITATION PAGE IMAGE ZOOMABLE
Full Citation
STANDARD VIEW MARC VIEW
Permanent Link: http://ufdc.ufl.edu/UF00072555/00001
 Material Information
Title: An evaluation of animal nutrition data used in the computer formulations of rations proceedings of a seminar
Series Title: University of Florida. Institute of Food and Agricultural Sciences IFAS bulletin
Physical Description: 81 p. : ; 28 cm.
Language: English
Publisher: Institute of Food and Agricultural Sciences, University of Florida
Place of Publication: Gainesville
Publication Date: 1970
 Subjects
Subject: Animal nutrition   ( lcsh )
Food of animal origin   ( lcsh )
Animals -- Food   ( lcsh )
Electronic data processing   ( lcsh )
Animaux -- Alimentation -- Congráes   ( rvm )
Animaux -- Alimentation -- Recherche -- Congráes   ( rvm )
Genre: non-fiction   ( marcgt )
 Notes
Bibliography: Includes bibliography.
General Note: Sponsored by the Institute of Food and Agricultural Sciences, University of Florida, Feb. 25-26, 1969.
 Record Information
Bibliographic ID: UF00072555
Volume ID: VID00001
Source Institution: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
Resource Identifier: oclc - 00816532

Table of Contents
    Historic note
        Historic note
    Front Cover
        Front Cover
    Title Page
        Title Page
    Table of Contents
        Table of Contents
    Objectives of the seminar
        Page 1
        Page 2
    Role of the computer in the livestock and poultry industry
        Page 3
        Page 4
        Page 5
        Page 6
    Monsanto computerized technology applied to the livestock industry
        Page 7
        Page 8
        Page 9
        Page 10
        Page 11
        Page 12
        Page 13
    Availability and adequacy of data on the composition of feedstuffs
        Page 14
        Page 15
        Page 16
        Page 17
        Page 18
        Page 19
        Page 20
        Page 21
        Page 22
        Page 23
        Page 24
        Page 25
        Page 26
        Page 27
        Page 28
        Page 29
        Page 30
        Page 31
        Page 32
        Page 33
        Page 34
        Page 35
    Availability and adequacy of data on nutrient requirements of broilers and laying hens
        Page 36
        Page 37
        Page 38
        Page 39
        Page 40
        Page 41
        Page 42
        Page 43
        Page 44
        Page 45
        Page 46
        Page 47
        Page 48
        Page 49
        Page 50
        Page 51
    Adjustment of nutrient content of feed ingredients based on variability
        Page 52
        Page 53
        Page 54
        Page 55
        Page 56
        Page 57
    Availability and adequacy of data on nutrient requirements of dairy cattle
        Page 58
        Page 59
        Page 60
    Least-cost rations for dairy cows
        Page 61
        Page 62
        Page 63
        Page 64
    Availability and adequacy of data on nutrient requirements of beef cattle
        Page 65
        Page 66
        Page 67
        Page 68
        Page 69
        Page 70
        Page 71
        Page 72
    Role of the computer in a cattle feedlot office
        Page 73
        Page 74
        Page 75
        Page 76
        Page 77
        Page 78
        Page 79
        Page 80
    IFAS participants
        Page 81
Full Text





HISTORIC NOTE


The publications in this collection do
not reflect current scientific knowledge
or recommendations. These texts
represent the historic publishing
record of the Institute for Food and
Agricultural Sciences and should be
used only to trace the historic work of
the Institute and its staff. Current IFAS
research may be found on the
Electronic Data Information Source
(EDIS)

site maintained by the Florida
Cooperative Extension Service.






Copyright 2005, Board of Trustees, University
of Florida













August, 1970


An Evaluation of

ANIMAL NUTRITION DATA

used in the

COMPUTER FORMULATIONS OF RATIONS























Proceedings of a Seminar


Sponsored by the
Institute of Food and Agricultural Sciences
University of Florida

February 25-26, 1969


IFAS-8











TABLE OF CONTENTS


Page



OBJECTIVES OF THE SEMINAR

J. W. Sites .................... ................................ 1

THE ROLE OF THE COMPUTER IN THE LIVESTOCK AND POULTRY INDUSTRY

W. K. McPherson ............ .... .... .... ........................ 3

MONSANTO COMPUTERIZED TECHNOLOGY APPLIED TO THE LIVESTOCK INDUSTRY

Reed Taylor .................................................... 7

AVAILABILITY AND ADEQUACY OF DATA ON THE COMPOSITION OF FEEDSTUFFS

Spencer Morrison ...................... ......................... 14

AVAILABILITY AND ADEQUACY OF DATA ON NUTRIENT REQUIREMENTS OF
BROILERS AND LAYING HENS

G. F. Combs ....... .......................................... 36

ADJUSTMENT OF NUTRIENT OF FEED INGREDIENTS BASED ON VARIABILITY

G. F. Combs ....................................*............... 52

AVAILABILITY AND ADEQUACY OF DATA ON NUTRIENT REQUIREMENTS OF DAIRY
CATTLE

W. T. Howard ....................... ............................ 58

LEAST-COST RATIONS FOR DAIRY COWS

W. T. Howard and J. L. Albright ........... .................... 61

AVAILABILITY AND ADEQUACY OF DATA ON NUTRIENT REQUIREMENTS OF BEEF
CATTLE

John Algeo ..................................................... 65

THE ROLE OF THE COMPUTER IN A FEED LOT OFFICE

Tom Remington .......................................... .....* 73

IFAS PARTICIPANTS ................................................... 81












OBJECTIVE OF THE SEMINAR


John W. Sites1


At the University of Florida we have been planning this "Livestock

Nutrition Seminar" with a great deal of anticipation. Our motives have

not been entirely alturistic. We feel we have a considerable future ex-

pansion possibility in several of our animal industry production programs.

It is natural that we be concerned with how we may do this most effectively.

Techniques for improving the formulating of livestock diets from ingredients

of known composition at the lowest available costs constitute a highly im-

portant input toward the success of any animal operation. Thus, it was

our objective to hold a conference where research information and application

of techniques already in use could be evaluated and molded into a useful

program. Further, it was deemed most important from a research viewpoint

that such a seminar address itself to needs for additional research; that

we might better understand where information is lacking and orient future

program to provide these deficiencies. These are the major objectives.

In attempting to accomplish these objectives a concerted effort has

been made to bring together the most experienced and competent people pre-

sently engaged in formulating livestock diets and using computerized techniques.

Together then we hope to review the techniques which the industry leaders are

using and learn something of the problems they are encountering.


1Dean for Research, Institute of Food and Agricultural Sciences









A final objective of this conference rests in the hope that it

will serve as a means of communicating to workers of all interests

and disciplines the possibilities for use, the availability of feed,

crop and commodity information and the present state of the art of

"least-cost" formulation. This is perhaps the most desirable ob-

jective of all for it is important that all of us understand more fully

how we can utilize present techniques and adapt them to solving our own

problems.

We look forward to having you all here and hope that all of the

things we anticipate as developing from this conference will in the

next two days become realities.











THE ROLE OF THE COMPUTER

IN THE LIVESTOCK AND POULTRY INDUSTRIES


W. K. McPherson1


The electronic computer is an ingenious device that compiles in-

formation and solves mathematical problems more rapidly and accurately

than human beings or any of the devices human beings have previously

developed. In only a few seconds or minutes, a computer can do the

work that it would take a person days and sometimes weeks to do by

conventional methods.

The impact of using computers on what people do varies directly

with the amount of time they spend keeping records and making mathe-

matical calculations. Businessmen use these new computational devices

to reduce the cost of production and hopefully increase profits. On

the other hand, the use of computers frees scientists of the tedious

and time-consuming tasks of recording data and making calculations

and enables them to spend more time actually designing and conducting

new experiments. In other words, the use of the computer reduces

the unit costs of processing experimental data in quite the same

manner as its use reduces the unit cost of producing goods and ser-

vices.

The first use that was made of computers in the livestock and poultry

industries was for record keeping. At the outset, only the firms

that could make relatively large capital outlays were in a position to


professor, University of Florida










reduce their unit costs in this manner. Later, it became possible for

smaller firms to buy computer services on a time-sharing basis and thus

achieve almost the same economies as the larger firms. Now, many live-

stock and poultry producers purchase computer services offered by pri-

vate firms or non-profit organizations (land grant colleges, American

Farm Bureau Federation, etc.) for less than the cost of keeping their

own records.

About the same time that industrial firms were computerizing their

record keeping operations, scientists in both private companies and

public agencies began to use the computer to solve complex equations.

They quickly learned that the computer could develop a quantitative

basis for evaluating hypotheses more rapidly than they could develop

hypotheses to test. Whereas in the past it was seldom, if ever, practi-

cal to test hypotheses quantitatively, the computer made it possible to

use very large amounts of data to test very complex hypotheses very rap-

idly. The fact that it is now possible to solve complex equations and

when desirable solve them simultaneously constitutes a challenge to the

scientists to make more comprehensive studies of natural and economic

phenomenon. In response to this challenge, the amount of research in

econometrics and cybernetics is increasing rapidly. To the extent

that the livestock and poultry industries benefit from this research,

they will benefit from the use of the computer.

In recent years, the computer has made it possible to base an in-

creasingly large number of management decisions on quantitative estimates

of the costs of and returns from alternative courses of action. In the

livestock and poultry industries many firms are now basing two types of

management decisions on data generated by computers; i.e., inventory







5


control and diet formulation. Businessmen soon found that the computer

could generate the data needed to accurately estimate the size of the in-

ventories that it was necessary to maintain at various locations to

minimize storage and distribution costs and thus increase profits. It

took a bit longer for these industries to alter the abstract linear

programming model enough to calculate the least cost formulation of

diets for different classes, weights and ages of animals when the avail-

ability and prices of ingredients are known. To date, the poultry in-

dustry has made extensive use of this technique. During the past two

or three years firms mixing ruminant animal diets have found that it

is profitable to formulate diets and purchase feedstuffs on the basis

of data generated by the lenear programming technique. The larger feed

lots and consulting nutritionists are now confident that their computer-

formulated beef cattle diets produce gains at the minimum cost and it

will not be long before they are using computers to select the maximum

profit plan for producing cattle.

Now early in 1969, it is quite clear that the computer can solve

problems more rapidly than livestock and poultry industries can define

the problems they want it to solve and assemble the necessary data.

Mathematicians and computer scientists have already perfected more mod-

els than these industries can fully utilize for many years to come.

Those responsible for managing the firms in these industries can im-

prove the quality of their management decisions by defining the prob-

lems they want answers to more precisely. How rapidly the quality of

the management decisions in the industries is improved depends largely

upon the knowledge those now making the decisions have about what the

computer is capable of doing and the form the data must be placed into to










use it.-i.:;(This knowledgeiso not,easy-. to acquire. Jirn; fact,- :itt,; -,1lsnforc .ino,

someinnterdisciplinary -study.% : In: -sharp, contras tj to:; the t, !it.ei~ratjr~ :wthr m:

in recognized. disc ipl ines therere ris'essentially pno, literature pnhow too ,;

apply -the knowledge in,%anyi one'di's.cipline- to chej osiutionr ofp.roblems, in' :-:

another discipline .-;j ;Large .-firmsr overcome. this;difficultyi by.;hiriing ,spe-.i.-, (,

cialists in (the: several disciplines.- organizing, them'into a team, iand.r ,,,

giving.,the team-a: problem .to solve. -By -learning something-i about each :,;

others-', disciplines;, the members of,~these, teams tare ,able; to -solveprob-, c

lems none: of ,them could ;solve lone. r! -i -* *o :.,,, a j i"i -

Once: animal -nutritionists learn, what computers ,can do> for.:them,- they .a

become concerned with the: quality of the datathey use, in solvingitheir-

problems. This is'-the inevitable,.result of. using estimated;,data in making

calculations that are mathematically accurate. The only way the -es-

timated, data now, being used by animal scientists can be improved is; to ,-i:

conduct -more; research.,. In other :words, the use -of; the.computer in. the :,

livestock and poultry industries has focused.Jattention,'on; the 'urgency

of the need for: more research in :animal, science. -Hopefully,i the :evalu-

ation of animaL nutrition and -.feedstuff composition; ata'Lt hat; is. made,:

here in the next- two days will stimulate, research: of this: type. -,,,o:, ii














-.,, ,J-, -. ," ., ;-










Ivsrs, 2i.:'::: n :., MONSANTO COMPUTERIZED TECHNOLOGY APPLIED:.' :
TO THE LIVESTOCK INDUSTRY


ol .:i i::.-:: :,iReedD.,- Taylbr and Duane'P. Schneider1: : :-


INTRODUCTION


During the past year, the Monsanto Company has been involved in the

development of a time-shared computer system for use in the feed industry.

In this paper we will discuss the Monsanto system and specific ap-

plications as they apply to the formulation of rations and to management

decisions for the beef industry.

A time-sharing system consists of a central computer that can be

accessed by a number of users simultaneously from various locations. To

use a system of this type, an input-output terminal and some type of com-

munications facilities are necessary. The Monsanto system, working as a

time-sharing system, uses a teletype terminal with an acoustical coupler

as an input-output device and the telephone system as the communications

facility.

A time-sharing system of this type gives data processing capabilities

to any number of remotely located users. Time-sharing is also cost-sharing

as a user pays only foi'actual computer time used, thus avoiding much of

the high costs of hardware, system maintenance, and programming. With re-

mote access terminals, the system is convenient and a user has instant access

with little turn-around time. Time-sharing has, in effect, made the computer

a utility as accessible as an electrical outlet and a telephone receiver.





1The authors are Technical Manager and Technical Specialists, respectively,
of the Computerized T6!chnology Department-of the Monsanto Company, St.' Louis,
Missouri.









As computer time became more readily available at low costs, there have

been more applications for using this type of a system in feed manufacturing.

Systems are now available that can be beneficial in providing information for

making purchasing decisions and ingredient selection, as well as formulations.

IMPROVING QUALITY CONTROL BY ADJUSTING
FOR VARIABLE MOISTURE CONTENT OF INGREDIENTS

The difficulty of testing and adjusting for nutrient content of ingre-

dients in linear programming has plagued nutritionists in the blending of feeds.

It is especially troublesome in ruminant nutrition where the moisture content

of roughages and grains is highly variable. Scientists have often stated that

if the nutrient values of ingredients could be adjusted for moisture content

that quality control in ruminant nutrition would be at least 90 percent achieved.

Monsanto has developed its feed blend system in such a manner that the nu-

tritionist can enter the moisture content value for variable ingredients. The

entry of the moisture value will cause the recomputation of all other nutrient

values. The recomputed values provide the basis for the new solution. The

addition of this facility in the Monsanto system has enabled the cattle feed-

er to achieve a high degree of quality control that was previously difficult

to obtain.

COMPENSATING FOR MOISTURE CONTENT
OF FORAGES IN FEED BLENDING

One of the major problems in using Linear Programming in ruminant feed

blending is the high moisture content of some roughages. Monsanto has developed

its system to where the cattle feeder can specify the nutrients required in a

given amount of dry matter and the computer automatically compensates for the

moisture content of roughages. In this manner we can include ingredients high

in moisture content without penalizing them for the water which contributes to

a good percent of their weight but adds nothing to their nutrient values (1).*


Numbers in parentheses refer to publications in literature cited.









SLIDE WEIGHT "OPTIMUM DENSITY" PRINCIPLE

Research has shown, in monogastic nutrition, that body gain and calorie

intake will remain constant over a broad range of dietary calorie densities,

if correct nutrient to calorie ratios are maintained. Putting it another

way, if each calorie in a diet contains the correct levels of all nutrients

required for optimum performance, the monogastic will consume sufficient

amounts of feed to meet its calorie needs. Thus, the intake of calories will

remain constant but feed intake and hence feed efficiency, will be determined

by the caloric density of the diet. Adjusting the linear program to formu-

late to least-cost of nutrient density, rather than to weight, provides much

of the necessary tie-in between least-cost of formulations and least-cost of

gain (2).

Many nutritionists feel this same principle basically holds true in

ruminant feeding. If a feeder wishes to incorporate this principle in ruminant

feeding, the Monsanto system is designed to handle it.

The most economical nutrient density can be found by using the Monsanto

slide weight option with no restrictions other than requirements for nutrients

and energy.

While formulating for energy and nutrient requirements may be the most

economical, it may not in all cases, be nutritionally sound. The nutritionist

has the option of setting dry matter ranges within which the energy and nu-

trient requirements must be met. This gives the nutritionist considerable

control over the solution.


Pricing of Ingredients

The price of an ingredient as given to the computer should include not

only the cost of the raw ingredient, but also the cost of storage, processing,

and transportation. It is less costly to transport nutrients in higher density









ingredients than in bulky ingredients. The computer chooses ingredients

based on their cost and nutrient content. The cost to nutrient ration will

be different among ingredients if the overhead costs are included. If you

are not presently adding these overhead costs to your ingredients, your least-

cost formulations may not be least-cost. Ideally, each ingredient should have

its own load factor, which accurately reflects the cost of handling that par-

ticular ingredient. While using this procedure may not have the degree of

economic impact as basic least-cost formulation or of using the slide weight

principle, it can mean a lot of extra dollars on the positive side of the

balance sheet. It is also very easy to incorporate in your present operations.

This is especially true where one adjusts for moisture or uses the slide weight

option.


FORMULATION ON A PER-HEAD PER-DAY BASIS

Formulating on a per-head per-day basis has many advantages; the computer

lists the pounds of blend that should be fed to an animal to meet its nutrient

requirements, it lists daily feed cost, and it lists the actual nutrients fed

on a per-animal basis. Actual per-head per-day cost of feeding is found by

merely adding an overhead cost to the daily feed cost. Calculation of costs

on this basis is consistent with current practice.


PARAMETRIC LINEAR PROGRAMMING

Parametric linear programming is a further development of linear pro-

gramming. It adds a degree of flexibility to an otherwise rigid structure.

Its use enables one to parameterize (to analyze at different levels) at least

one, and oftentimes several coefficients simultaneously. The coefficients being

parameterized can be prices, diet requirements, or ingredient factors (3).










Parametric Cost Ranging

A major problem faced by feed manufacturers is determining the value

of an ingredient. Another is to determine the best distribution of a lim-

ited ingredient among different feeds. Parametric cost ranging, an applica-

tion in the Monsanto system, is a perfect tool for this type of analysis.

Parametric cost ranging is a technique whereby the value of an ingredient

can be determined at various usage levels. The feed formulator chooses that

ingredient he wants to study, and then lets its price range through a broad

spectrum to determine effects of change. The prices of competing ingredients

remain fixed.

Parametric cost ranging in its most simple form is that of ranging one

cost of one ingredient in one diet in one location. The technique has also

been used to vary many costs simultaneously; to compare ingredients to vary-

ing qualities; to determine the value of ingredients at different geographical

locations or in different rations; to determine the best methods to improve the

economic values of ingredients; to provide direction to research; to determine

the best distribution of limited ingredients among various diets; and to aid

the purchasing agents in making immediate purchases and in forward planning (3).


Parametric Right-Hand Side (Nutrient) Ranging

In this application of parametric linear programming the dietary re-

quirement of a nutrient or nutrients can be ranged and the effects on the cost

analyzed. It allows one to study in detail the economic implication of nu-

trient requirements on a ration. It can be very effective in determining which

nutrients are most important in a ration economically.

Energy ranging on the Monsanto system will allow a nutritionist to deter-

mine the least cost of gain within a given set of nutrient requirements. This

is accomplished by including in the parametric solution a daily yardage cost.

The information obtained here can be used in predicting gain, feed efficiency









and costs of gain when applying the values suggested by Lofgreen and Garrett

in their work at the University of California (3). Monsanto has developed a

system to automate these conversions.


OPTIMUM RESOURCE ALLOCATION

This is a computer application where several feed formulations can be

solved simultaneously within ingredient inventory constraints. Put another

way, the computer will divide up available inventory in such a manner that

each limited ingredient will be used in those diets and in those amounts that

will yield optimum economic returns. Programs are presently available wherein

the feed formulator can inform the computer which ingredients he has available,

their quantity and price; which other ingredients, or additional amounts of

the same ingredients can be obtained on an emergency basis, their quantity and

price; the maximum and minimums of each feed required; the selling prices or

values of finished feeds; and the maximum and minimum inventories of ingredients

that must be left after the solution. With this information available, the com-

puter determines simultaneously the optimum amount of each feed to produce along

with the least-cost formulation of each. This program can move a feed business

considerably along the road toward total profit optimization of the firm (1).


CONCLUSIONS

In addition to these techniques and procedures there are other scientific

computer applications that will have profound effects upon the beef industry.

Large simulation models will aid in overall organization and coordination of

business. Decisions made at any level will be traced in their complexity.

Large models will improve the organization and use of resources to achieve

greater profits. Statistical models will make greater contributions to re-

search efforts. Applications not thought of will become common usage in the

near future.









LITERATURE CITED



1. Reed D. Taylor, "Use of Computers in the Feed Industry." Proceedings of
the 24th Annual Texas Nutrition Conference. College Station, Texas,
October 1969.

2. Kenneth H. Maddy, "The Use of Linear Programming in the Feed Industry."
Paper presented at the 1964 Feed Show held in London and Belfast -
March 1964.

3. Reed D. Taylor, George O. Kohler, Kenneth H. Maddy, and Robert V. Enochian.
"Alfalfa Meal in Poultry Feeds An Economic Evaluation Using Parametric
Linear Programming." Agricultural Economic Report No. 130, Economic Re-
search Service, U. S. Department of Agriculture, Washington, D. C., Jan-
uary 1968.

4. G. P. Lofgreen, and W. N. Garrett. "A System for Expressing Net Energy Re-
quirements and Feed Values for Growing and Finishing Beef Cattle." Journal
of Animal Science 27: 793, May 1968.









AVAILABILITY AND ADEQUACY OF DATA ON THE COMPOSITION OF FEEDSTUFFS


Spencer H. MorrisonI lead an informal discussion of this subject.

During this discussion he distributed copies of the tables that follow.


Director, AGRICON













ILLUSTRATIONS OF THE EIGHT COMPONENT PARTS OF A FEED NAME
ACCORDING TO THE N.R.C. SYSTEMATIC NOMENCLATURE SYSTEM

NAME COMPONENTS
Kind Part Process Maturity Cut
Grim hay s-c immature cut


Alfalfa a

Animal a
(Meat Meal)

Calcium dibasic
Phosphate

Cattle Jersey


aerial pt

carcass res



a


milk


gluten

a

oil

seed


wilted ensiled

dry-rend
dehy-grnd

a



skim dehy



wet-milled
dehy
a

a

solv-extd
grnd


early blm

a



a



a



a

a

a

a


ting
1


cut 1

a


Grade
a

a

mx 4.4P


a commercial



a mx 8
moisture

a a

a a


a mx 7 fbr


aComponent positions so marked are not used in describing the feed.


Table 1


Origin
Alfalfa


Corn

Dynafac

Fish

Soybean


a

a

tuna

a


Class
1

3

5



6




5

5

8

7

5









Table 2


Classification. Feeds of the same origin or parent material (and the

same species, variety or kind, if designated) have been sub-grouped into

the following feed classes.


Code
(1) Dry forages and/or roughages
Hay
Straw
Hulls
Fodder (aerial pt)
Stover (aerial pt wo ears wo husks or wo heads)

(2) Pasture, range plants and feeds fed in the fresh state

(3) Silages

(4) Energy Feeds
Cereal grains
Low in cellulose
High in cellulose
Mill by-products
Low in cellulose
High in cellulose
Fruits
Nuts
Roots

(5) Protein supplements
Animal
Marine
Avian
Plant

(6) Mineral supplements

(7) Vitamin supplements

(8) Additives
Antibiotics
Coloring material
Flavors
Hormones
Medicants





Preferred maturity
term


term Definition


Comparable term


Germinated


Early leaf


Immature


Pre-bloom


Early-bloom


Mid-bloom


Full bloom

Late bloom


Milk stage


Dough stage


Resumption of growth by the embryo in a seed
after a period of dormancy

Stage at which the plant reaches 1/3 of its
growth before blooming

Period between 1/3 and 2/3 of its growth
before blooming (this may include fall
aftermath

Stage including the last third of growth
before blooming


Period between initiation of bloom up to
stage at which 1/10 of the plants are in bloom

Period during which 1/10 to 2/3 of the plants
are in bloom

When 2/3 or more of the plants are in bloom

When blossoms begin to dry and fall and seeds
begin to form

Seeds well formed, but soft and immature


Stage at which the seeds are soft and
immature

Stage at which the plant would normally
be harvested for seed

Stage after the plant is mature, seeds
are ripe and initial weathering has taken
place (applies mostly to range plants)

Plants cured on the stem, seeds have been
cast and weathering has taken place (applies


Mature


Over ripe


Dormant


Sprouted


Fresh new growth, very immature


Prebud stage, young before boot,
before heading out


Bud, bud stage, budding plants, in bud
preflowering, before bloom, heading to
in bloom, boot, heads just showing

Up to 1/10 bloom, initial bloom, head-
ing out, in head

Bloom, flowering plants, flowering, half
bloom, in bloom


3/4 to full bloom


Seed developing, 15 days after silking,
before milk, early pod

Post bloom to early seed, pod stage,
early seed, in tassel, fruiting

Seeds dough, seed well developed, nearly
mature

Fruiting plants, fruiting, in seed, well
matured, dough to glazing, kernals ripe

Late seed, ripe, very mature, well
matured


Seeds cast, mature and weathered


Definition










Table 4


EXAMPLES OF N.R.C. FEED NAMES


Alfalfa, Medicago sativa


Alfalfa, aerial pt, dehy grnd, mn
Ref no 1-00-023
Alfalfa, aerial pt, dehy grnd, mn
Ref no 1-00-024
Alfalfa, aerial pt, dehy grnd, mn
Ref no 1-07-851
Alfalfa, hay, s-c, immature, (10)
Ref no 1-00-050
Alfalfa, hay, s-c, pre-blm, (1)
Ref no 1-00-054
Alfalfa, hay, s-c, early blm, (1)
Ref no 1-00-059
Alfalfa, hay, s-c, mid-blm, (1)
Ref no 1-00-063
Alfalfa, hay, s-c, full blm, (1)
Ref no 1-00-068
Alfalfa, hay s-c, mature, (1)
Ref no 1-00-071
Alfalfa, hay, s-c, over ripe, (1)
Ref no 1-00-072
Alfalfa, hay, s-c, cut 1, (1)
Ref no 1-00-073
Alfalfa, hay, s-c, cut 2, (1)
Ref no 1-00-075
Alfalfa, hay, s-c, cut 3, (1)
Ref no 1-00-076
Alfalfa,leaves, s-c, (1)
Ref no 1-00-146
Alfalfa, stems, s-c, (1)


Ref no 1-00-164
Alfalfa, aerial pt,
Ref no 2-00-181
Alfalfa, aerial pt,
Ref no 2-00-184
Alfalfa, aerial pt,
Ref no 2-00-191
Alfalfa, aerial pt,
Ref no 3-00-205


17 prot, (1)

20 prot, (1)

22 prot, (1)


fresh, pre-blm, (2)

fresh, early blm, (2)

fresh, cut 1, (2)

ensiled, early blm, (3)


Alfalfa, aerial pt w molasses added, ensiled,
Ref no 3-00-238


Animal Scientific name not used

Animal, carcass res, dry-rend dehy grnd, mx 4.4 P, (5)
Meat Meal (AAFCO)
Meat Scrap
Ref no 5-00-385










Table 4 Continued

Animal,carcass res w blood, dry- or wet-rend dehy
grnd, mx 4.4), (5)
Meat meal tankage (AAFCO)
Digester tankage
Ref no 5-00-386
Animal, carcass res w bone, dry-rend dehy grnd,
mn 4.4P, (5)
Meat and bone meal (AAFCO)
Meat and bone scrap
Ref no 5-00-388

Animal-Poultry. Scientific name not used
Animal- poultry, carcass res mx 35 blood dry- or
wet-rend dehy grnd, mn 50 prot, (5)
Feeding tankage (CFA)
Ref no 5-00-410
Animal-poultry, carcass res w bone mx 35 blood
dry- or wet-rend dehy grnd, mn 40 prot, (5)
Feeding meat and bone tankage (CFA)
Ref no 5-00-413

Cattle. Bos spp
Cattle, lungs, raw, (5)
Ref no 5-07-941
Cattle, milk, dehy, feed gr mx 8 moisture mn 26 fat, (5)
Dried whole milk (AAFCO)
Milk, whole, dried
Ref no 5-01-167
Cattle, milk, fresh (5)
Ref no 5-01-168
Cattle, whey albumin, heat and acid-precipitated
dehy, mn 75 prot, (5)
Dried milk albumin (AAFCO)
Milk, albumin, dried
Ref no 5-01-177

Chicken. Gallus domesticus
Chicken, cull hens, whole, raw, (5)
Ref no 5-07-950
Chicken, heads, raw, (5)
Ref no 5-07-949
Chicken, offal w feet, raw, (5)
Ref no 5-07-951

Corn. Zea mays
Corn, aerial pt, s-c, dough stage, (1)
Corn fodder, sun-cured, dough stage
Ref no 1-02-774
Corn aerial pt, s-c, mature, (1)
Corn fodder, sun-cured, mature
Ref no 1-02-772










Table 4 Continued

Corn aerial pt wo ears wo husks, s-c, mature, (1)
Corn stover, sun-cured, mature
Ref no 1-02-776
Corn, cobs, grnd, (1)
Ground corn cob (AAFCO)
Ref no 1-02-782
Corn, aerial pt, fresh, (2)
Corn fodder, fresh
Ref no 2-02-806
Corn, aerial pt, ensiled, milk stage, (3)
Corn fodder silage, milk stage
Ref no 3-02-818
Corn, aerial pt, ensiled, dough stage, (3)
Corn fodder silage, dough stage
Ref no 3-02-819
Corn, aerial pt, ensiled, mature, (3)
Corn fodder silage, mature
Ref no 3-02-820
Corn, ears w husks, ensiled, (3)
Ref no 3-02-839
Corn, ears, grnd, (4)
Corn and cob meal (AAFCO)
Ear corn chop (AAFCO)
Ground ear corn (AAFCO)
Ref no 4-02-849
Corn, ears w husks, grnd (4)
Corn and cob meal with husks (AAFCO)
Ear corn chop with husks (AAFCO)
Ground ear corn with husks (AAFCO)
Ground snapped corn
Ref no 4-02-850
Corn, grain, flaked, (4)
Ref no 4-02-859
Corn, grits by-prod, mn 5 fat, (4)
Hominy feed (AAFCO)
Hominy feed (CFA)
Ref no 4-02-887










Table 5

FEED NOMENCLATURE SYSTEM

EXAMPLE OF FUTURE N.R.C. FEED COMPOSITION TABLES NET ENERGY VALUES
WILL BE ADDED WHEN DATA BECOMES AVAILABLE. VALUES FOR OTHER
MINERALS, VITAMINS AMINO ACIDS WILL ALSO BE LISTED

Mean
Feed name or analysis As fed Dry Coef var No
+ %

Oats. Avena sativa
Oat cereal by-prod, mx 4 fbr, (4)
Feeding oat meal (AAFCO)
Oat middlings (CFA)
Oat meal
Ref no 506-1067 US Region 06


Dry matter
Ash
Crude fiber
Ether extract
Protein, crude (N x 6.25)
Cattle
Sheep
Swine

Cattle
Sheep
Swine


Energy, gross
Cattle
Sheep
Swine


dig
dig
dig


coef
coef
coef


dig prot
dig prot
dig prot


GE kcal/kg2
GE dig coef %
GE dig coef %
GE dig coef %


Cattle
Sheep
Swine


Cattle
Sheep
Swine
Chickens
Turkeys


Cattle
Sheep
Swine
Calcium
Phosphorus
Riboflavin


ME
ME
ME
MEn
MEn


kcal/kg
kcal/kg
kcal/kg

kcal/kg
kcal/kg
kcal/kg
kcal/kg
kcal/kg


TDN %
TDN %
TDN %
%/

mg/kg


91.0
2.3
4.0
5.8
15.8
80.
82.
85.

12.6
13.0
13.4


4503.
84.
83.
85.

3783.
3737.
3828.

3102.
3064.
3139.
3086.
3040.

92.
91.
93.
0.08
0.49
1.8


100.0
2.5
4.4
6.4
17.4
80.
82.
85.

13.9
14.3
14.0

4948.
84.
83.
85.

4156.
4107.
4206.

4079.
3368.
3449.
3391.
3341.


101. .. 4
100. .. 3
103. .. 3
0.09 10 20
0.54 8 20
2.0 17 10







22


Table 5 Continued

Some of the values in this table have been estimated to show format
only. The (4) after the NRC name refers to the class, energy feeds;
AAFCO refers to the Association American Feed Control Officials name;
CFA refers to the Canada Feeds Act name; region 06 in the US refers
to the states of Oklahoma and Texas.

Or in megcal./kg.










Table 6

(Publication 1232 NAS-NRC, 1964)


Maize, husks

Dry Matter % 88.9 100.0 39 2

Ash % 2.7 3.0 39 18

Crude Fiber % 30.7 34.5 39 3

Ether extract % .7 0.8 39 29

Protein (N x 6.25) % 3.1 3.5 39 8

Protein, dig, rum % .3 .3

Energy, dig, rum kcal/kg 2954. 3307.

Energy, metab, rum kcal/kg 2420. 2710.

TDN, rum % 67. 75.

Carotene mg/kg .6 .7 1

Vitamin A equiv IU/gm 1.0 1.2










Table 7


AAFCO COLLABORATIVE PROGRAM RESULTS:


FAT


Type of Feed

Molasses Stock

Dairy Cone.

Dist. Sols. (AOAC)

ditto, (Dist. Feed Res.)

Dairy Cone.

Dairy Conc.

Weighted Avs.


Milk Replacer
(Roese-Gottlieb)

Milk Replacer (RG)

ditto, regular method


ANALYSES

No.
Labs.

100

91

68

65

103

101

528


Result
Av.

1.0

2.3

5.1

8.8

2.3

1.6

3.08


18.5

9.2

5.0


Std.
Dev.

.27

.82

.61

.62

.28

.0014

.40


Coeff.
of var.

27.0

35.7

11.1

7.4

12.2

14.4

18.74
(2.5-3.66)


1.03

.52

.89


5.6

5.7

17.8


Y-ar

1965

1965

1966

1966

1966

1967


1966


1967

1967


--










From Publication 1232 (NAS-NRC)


"The varibility in average composition of feeds is large, and failure
to appreciate its magnitude can lead to unwarranted assumptions as to the
nutrient content of mixtures arrived at by arithmetic from average values.
Since the data in NAS-NRC publications 449 and 585 give the mean, minimum,
maximum and number of analyses, the standard deviations of the numerical
data were estimated from a factor, f, (specific in normal populations for
the number of observations; and the differences between the maximum values
recorded) according to the following relationship4:

S.D. = range : f

Example values of f for certain values of n are:

n f
10 3
30 4
100 5
400 6
2500 7

The computed standard deviations were divided by their means to
obtain the coefficients.

The composition values are shown both on an 'as fed' basis
(col. 1) and on a 'moisture-free' basis (col. 2). Also shown (when known)
is the number of samples represented by each average (col. 3); and, where
5 or more analyses were averaged and the range of individual values given,
the coefficient of variation (standard deviation as a % of the mean) of
the individual values from their mean has been computed (col. 4). Thus
for a feed having a protein content of 12%, and a coefficient of variation
of + 20%, we learn that, while the average of the samples analyzed was
12%, one out of every six would probably carry less than 9.6% and one in
six would be expected to analyze more than 14.4% of protein."



Snedecor, G.W. 1937. Statistical methods, 2nd Ed., p. 84. Iowa State
University press, Ames Iowa.






26


Table 8


COMPOSITION AND DIGESTIBILITY OF SOUTHWESTERN FORAGES


Source Forage Cell ADF Lignin Silica Dig*
wall

Dry Matter Basis

Ark. Bahia grass 72 41.0 7.4K 1.3 49
Switch can 72 42. 7.9K 2.5 41
Prarie grass hay 70 44 7.2K 3.6 33
Fescue March 20 49 24 2.3K 3.1 79
April 10 40 20 1.9K 1.5 83
May 15 64 36 5.1K 1.0 66
June 26 65 43 6.4K 4.5 43
Ace wheat 18-25" ht. 52 30 2.8K 1.4 80
boot to bloom 68 41 5.2K 1.6 64
S "soft dough 62 37 6.7K 1.3 58
Ark. Coastal Bermuda 76 37 6.0K 2.3 44
Ariz. June 10 65 35 4.8K 6.2 47
Aug. 10 65 35 4.9K 4.1 55
La. 67 37 5.5K 6.7 42
Iowa Reed canary 60 33 3.8S 5.1 51
60 33 3.8S 5.6 39
Kan. Buffalograss 73 46 6.7K 6.7 41
Sorghum silage 59 37 7.6K 1.9 58
Wheat straw 77 45 9.5K 3.1 32
Cal. Barley straw 72 45 5.2S --- 54
Texas Rice hulls 81 66 14. S 24.0 10


In vitro apparent digestibility by
VanSoest et al. (20).


the Tilley method as modified by


S Lingnin by the acid detergent--72% sulfuric acid procedure (VanSoest, 12).

K Permanganate lignin (VanSoest & Wine, 19).


1968 Proc. Ariz. Nutr. Conf. 46.









Table 9
(Van Soest, 1968 unpublished)


COMPOSITION OF SOME FEEDSTUFFS


Acid Dry matter
Acid
Description Crude Cell Lignin Crude digestibility
fiber Walls fiber protein Non-
fiber
Ruminant ruminant
Dry matter basis

Corn (demt) grain 2.2 10.1 2.7 0.6 9.8 86
Wheat grain 2.3 12.4 3.1 0.7 11.6 85
Barley Grain 7.0 20 6.3 1.0 12.7 80
Oats grain 12 31 16.4 2.8 13.6 75

Soybean meal 6.4 12.0 9.0 0.3 44 84
Sesame meal 6.9 16.8 9.8 2.0 52 77
Linseed meal 9.0 25 16.4 6.7 38 81
Cottonseed meal 12.4 27 18.0 6.6 44 79
Safflower meal 35 58 41 13.7 24 50

Cooked feathers 1.5 19.5 12.2 96 70
Raw feathers 1.3 9.0 65.0 96 10
Tankage 3.7 25 3.6 67 68

Rice bran 13 24.1 15.9 4.3 15 61
Malt sprouts 12.5 45 16 1.1 26 77
Wheat bran 11 47 12.1 4.0 17.4 73
Dried beet pulp 21 54 33 2.5 10.0 76

Alfalfa hay (early) 23.5 40 25 5.3 20 62 50
Alfalfa hay (late) 39 55 40 9.0 14 53
Orchardgrass (early) 24 52 27 2.7 24 72 38
Orchardgrass (late) 35 70 40 4.7 11 57 20
Timothy 32 65 43 7.0 10 50
Coastal Bermudagrass 32 76 38 5.6 6.4 46

Soybean hulls 28 63 45 2.0 15 68
Soybean straw 43 67 53 12.0 11 44
Barley straw 38 72 45 5.2 2.6 54
Wheat straw 42 82 53 7.6 3.2 36
Corn cobs + husk 32 83 45 5.0 2.3 50
Rice hulls 43 81 66 14 3.0 10









Table 10


IMPORTANT COMPONENTS OF CATTLE DIETS AND THEIR APPARENT
DIGESTIBILITY AS MEASURED IN TRIALS WITH SHEEP


Composition Apparent Digestibility
Crude Gross Crude Gross
Cattle Diet Protein Fiber Energy Protein Fiber Energy


(%) (%) (Kcal/g) (%) (%) (%)

June 15.3 46.4 4.58 69 67 70

July 10.4 49.7 4.55 59 63 63

September 6.3 52.9 4.58 34 52 50

December 4.1 55.9 4.60 2 45 43




Table 11




Trial Number 1 2 3

Horse Number A B A B A B


Average dry matter intake
in kg/day 8.18 8.30 11.42 9.64 12.60 8.61

Relative dry matter intake
in gm/day/kg 3/4 77.8 90.6 106.5 104.3 116.2 92.6

Coefficient of
Ration Constituent Coeiient o
Digestion (%)

Dry Matter 65.7 66.9 63.9 66.0 67.9 66.1

Crude Fiber 30.4 35.5 27.1 31.5 41.3 38.6

Acid Detergent Fiber 37.4 36.2 29.1 32.8 43.5 43.4

Nitrogen 79.6 79.7 78.8 79.7 73.2 74.9

Energy 62.2 65.0 60.7 62.8 64.3 62.1
_____________________________________________________________________









Table 12


REGRESSION OF ESTIMATED NET ENERGY (Y) ON PERCENT TDN (X)

(dry basics)


No. of
Feed Class Samples Equation r# s


Concentrates 86 Y = 4.40 + 0.933 X 0.88 5.4

Roots 8 Y = 1.000 X 1.00

Roughage:
green 38 Y = -6.66 + 0.958 X 0.95 1.8
dry 61 Y = -11.57 + 0.965 X 0.81 3.4
silages 23 Y = 22.43 + 0.442 X 0.59 2.8
straws 9 Y = -18.79 + 0.875 X 0.81 6.9

Combined 225 Y = -31.48 + 1.346 X 0.96 5.9


# all significant at 1% level.

r = sample correlation coefficient.

s = standard error of the estimate.










Other formulas used:

Used in many forage testing labs.:

DP = (CP X 0.946) 3.52

TDN = (DP X 14) + 3240 (CF X 39.1) X 100
3563


From Stallcup and Davis Ark. Bul. 704 (October, 1965):

Two of the regression equations calculated from data
in "Assessing the Feed Value of Forages by Direct and
Indirect Methods" are:

DP = .915 CP 3.51

TDN = 110.5 1.5 CF


See also: Meyer and Jones for extensive tables for the
estimation of DP and TDN of Alfalfa of various moisture
contents from their crude fiber content Calif. Bul. 784
(March, 1962).








Table 13


COLLABORATIVE AMINO ACID STUDY ON 50% SOYBEAN MEAL
Soy Bean
Amino acid Laboratory Number Meal
% 1 2 3 4 5 6 7 8 9 10 11 12 Ave 49.3%


Arginine

Histidine

Isoleucine

Leucine

Lysine

Methionine

Phenylalanine

Threonine

Tryptophan

Alanine

Aspartic

Cystine

Glutamic

Glycine

Proline

Serine

Tyrosine


4.12

1.37

2.59

3.75

3.10

0.50

2.38

2.04

0.58


4.05

1.55

2.50

4.16

3.66

0.53

2.83

1.94


. 2.16

. 5.27

. 1.22

. 8.44

. 2.22

. 2.88

. 2.66

. 1.94


3.76

1.27

2.19

3.63

3.12

0.46

2.07

1.79



1.93

5.43

0.59

8.65

1.94

2.46

2.34

1.44


3.70

1.40

2.30

3.90

4.10

0.70

2.60

2.40



2.2

5.6

1.1

8.7

2.1

2.5

2.5

1.8


3.60

1.32

2.29

3.88

3.16

0.62

2.54

1.99



2.18

5.76

0.49

9.34

2.11

2.59

2.59

1.65


3.71

1.35

2.36

4.15

3.20

0.32

1.06

2.03


3.62

1.28

2.22

3.56

3.14

0.63

2.34

1.73


... 1.91

... 5.39



8.89 8.75

2.42 1.90

... 2.54

... 2.15

... 1.75


3.44

1.29

2.50

4.14

2.23

trace

2.62

2.06



1.37

6.23

0.44

9.10

2.30

2.34

2.89

1.93


3.75

1.38

2.47

3.95

3.20

0.72

2.63

2.00



2.17

5.92

1.18

9.53

2.20

2.60

2.55

1.85


3.68

1.34

2.46

4.01

3.14

0.34

2.)56

1.96



2.21

5.97

n /Q9


3.47

1.20

2.39

4.12

3.10

0.50

2.65

2.03



2.29

6.26

1 1 c


3.83

1.33

2.19

3.96

3.12

0.68

2.44

2.00



2.16

5.87


9.96 9.87 9.60

2.13 2.33 2.11

2.65 2.67 2.55

2.43 2.67 2.68

1.36 1.61 1.65


Phoenix Beckman Tedinico Beckman


3.728

1.340

2.372

3.934

3.189

.545(11)

2.393

1.998



2.058(10)

5.770(10)

.859( 8)

9.257(11)

2.160(11)

2.578(10)

2.546(10)

1.698(10)


3.72

(1.19)

(2.64)

(3.76)

3.14

.72

(2.45)

(2.00)

.73


.74



2.63






(1.55)

Combs


Beckman


11icro. Technico Analyzer


Moore echnico Technico Analyzer







Table 14
COMPARATIVE AMINO ACID ANALYSIS ON FEEDSTUFFS


Amino Acid (percent)

Cystine Methionine_ Tryptophan

Material F.S. Micro. A.A.A. F.S. Micro. A.A.A. F.S. Micro. A.A.A.


Alfalfa

Fish meal

Mink feed

Mink feed +

Animal protein

Soy meal 50%

Animal protein

Meat product

Meat product

Meat product

Protein Isolate

Protein Isolate

Fish meal

Fish meal


0.4

1.1


0.11 ...

0.42 0.57

0.46 .

0.53 .

0.94 ...

0.66 0.94

0.98 0.98


1.69

2.23

0.75

0.79


1.68

1.83

0.65

0.61


0.7


1.90

1.40

1.70

0.98

.1.25

1.8 1.97

1.8 1.95


F.S. Feed Stuff
Micro Microbiological Assay
A.A.A. Pheonix Amino Acid Analyzer


0.61

0.61


0.50


0.17



0.67

0.64



0.56



1.50

1.00

1.33

1.29

1.24

2.07

2.20


0.40



0.32

0.34

0.72

0.81

0.79

0.62

0.44

0.54

0.79

1.15

0.91

1.09


0.26



0.37

0.46

1.00

0.62

0.11

0.73

0.51

0.82

1.09

1.42

0.97

1.09


0.6


0.6

0.6










ADDENDA:


From "Effect of Forage Maturity on Digestibility, Intake, and
Nutritive Value of Alfalfa, Timothy, and Orchardgrass by
Equine" J. M. Darlington and T. V. Hershberger, Pa. State
University J. Animal Sci., 27, 6, 9572-1576, Nov, 1968.


Table 1


STAGE OF MATURITY OF FORAGES AT HARVEST


Harvesta

Forage Composition Firstb Secondc Thirdd

Alfalfa 80% Alfalfa Prebloom Early bloom Mid-bloom
20% Orchardgrass Prebloom Early bloom Mid-bloom

Timothy 100% Timothy Prebloom Early bloom Mid-bloom

Orchardgrass 80% Orchardgrass Prebloom Early bloom Mid-bloom
15% Trefoil Early bloom Mid-bloom Mid-bloom
5% Red clover Prebloom Mid-bloom Mid-bloom


aEach harvest was a "first-cutting"
bHarvested June 3, 1966
Harvested June 13, 1966
Harvested June 23, 1966


Table 2
PROXIMATE COMPOSITION OF FORAGES

Percent of dry matter Energy
Dry Crude Crude Ether kcal./
Forage Harvest Matter Protein fiber extract NFE Ash gm. D.M.
%
Alfalfa First 93.00 15.05 28.32 3.46 42.68 10.43 4309.7
Alfalfa Second 91.19 14.40 32.66 2.67 40.67 9.60 4291.7
Alfalfa Third 93.90 9.05 38.74 1.70 42.29 8.21 4335.1

Timothy First 93.86 9.99 32.47 3.26 46.69 7.59 4423.1
Timothy Second 94.47 8.32 35.12 3.01 47.27 6.28 4476.7
Timothy Third 90.55 6.47 36.38 2.80 48.89 5.46 4491.2

Orchardgrass First 92.85 14.00 30.86 2.90 44.14 8.10 4374.5
Orchardgrass Second 93.15 10.87 32.26 3.37 45.94 7.56 4453.5
Orchardgrass Third 90.92 6.53 37.98 2.73 46.91 5.85 4363.4









Table 3


APPARENT DIGESTION COEFFICIENTS OF FORAGESa'b


Dry matter Crude protein Crude fiber
Forage/Harvest First Second Third First Second Third First Second Third

Alfalfa 69.11a 61.72d 57.33ef 74.51a 72.11e 55.22f 58.21a 54.82c 53.23f

Timothy 65.94b 60.65d 59.35e 65.23b 62.13d 55.34f 65.44b 59.75d 57.96g

Orchardgrass 63.16c 60.16d 54.77f 68.35b 66.95e 52.47a 58.57a 58.57a 49.89h



Ether extract NFE
Forage/Harvest First Second Third First Second Third

Alfalfa 46.11a 6.02e -6.4ef 75.1la 67.22c 62.92d

Timothy 31.64b 1765d 16.05g 71.04b 64.65c 63.95d

Orchardgrass 26.367b 33.06e 17.37 67.86b 65.86c 61.07d


aWithin each category, means on the same line having different
superscript numbers differ significantly (P<05).

Within each category, means in the same column having different
superscript letters differ significantly (P<05).












Table 4


TOTAL DIGESTIBLE NUTRIENTS, DIGESTIBLE ENERGY, VOLUNTARY INTAKE AND
NUTRITIVE VALUE INDICES OF FORAGESa,b


Forage/Harvest First

Alfalfa 63.61a

Timothy 63.24a

Orchardgrass 59.26b


TDN
Second
%
56.02c

57.95c

56.67c


Third

52.03d

56.95e

52.08d


Digestible energy
First Second Third
%
64.8la 55.92d 51.63f

62.04b 56.55d 55.15g

58.06c 54.87e 49.18h


Voluntary intake
First Second Third
(gm. D M./day/WU./5kg.)
95.11a 87.4Lc 65.03e

82.34b 87.05c 82.24f

80.96b 79.86d 82.56f


Nutritive Value
First Second

77.11a 61.02d

63.84b 61.34d

58.66c 54.67e


aWithin each category means on the same line having different superscript numbers differ significantly (P--05).

bMeans in the same column having different superscript letters differ significantly (P-<05).


Index
Third

42.03f

56.65g

50.68h


--









AVAILABILITY AND ADEQUACY OF DATA ON NUTRIENT REQUIREMENTS
OF BROILERS AND LAYING HENS

Gerald F. Combs1

Minimum Energy Levels for Broilers

Although there is a considerable range of energy levels which can be

used when the level of potentially limiting nutrients is satisfactorily

maintained in relation to the energy content, there is a minimum nutrient

potency, even for pelleted or crumbled feeds, below which optimum growth of

fast growing broilers will not be obtained. Several experiments, with both

male and.female broilers reared in combination and separately in floor pens,

indicate that starting diets used in four weeks of age should contain at least

1450 metabolizable kilocalories per pound, in order to support optimum rate

of growth. Similartly, studies have shown that the metabolizable energy con-

tent of finisher diets should be at least 1475 kilocalories per pound to

support best growth rate. These results are graphically shown in Figure 1.

The adverse effect on body weight was only very slight, however, when starting

and finisher diets containing 1425 and 1450 kilocalories of metabolizable

energy per pound, respectievly, were fed. Feed consumption per unit weight of

broiler showed a straight line relationship with energy content of the feed

through the range of energy levels studies. There was no measurable differ-

ence between kilocalories consumed per pound of body weight with broilers fed

the low-energy diets and those fed the high-energy diets.

However, where growth rate is impaired, a longer period would be required

for broilers to reach the same body weights. This, of course, would result in

relatively poorer feed efficiency at the lower energy levels. The dotted line

in Figure 1 indicates the approximate amount of feed which would have been

required per unit gain at the time when comparable body weights were obtained

with diets of different energy contents.



Assistant Chief, International Programs Staff Nutrition Program, Public
Health Service, Department of Health, Education, and Welfare.









Table 1


RECOMMENDED AMINO ACID-ENERGY RATIOS FOR BROILER
RATIONS BY PERIODS (UNIVERSITY OF MARYLAND)1


% Amino Acid 7 Metabolizable Megacalories per Pound
Amino Acid 0-4 weeks 5-8 weeks After 8 weeks

Methionine2 0.31 0.28 0.23

Meth. + Cystine2 0.59 0.52 0.44

Lysine2 0.81 0.72 0.62

Tryptophane 0.16 0.14 0.12

Threonine 0.56 0.49 0.42

Arginine 0.88 0.77 0.66

Glycine 0.70 0.60 0.50

Phenylalanine 0.56 0.49 0.42

Phenyl. + Tyrosine 1.04 0.92 0.78

Valine 0.68 0.60 0.51

Isoleucine 0.6 0.53 0.45

Leucine 1.12 0.99 0.84

Histidine 0.32 0.28 0.24

Desirable C/P ratio 60-63 67-70 72-75


1Metabolizable energy values used. These ratios
environmental temperature conditions and showed
for summer feeding conditions where hot weather


pertain to moderate
increase by 5 to 10%
is expected.


The lysine, methinoine and total sulfur amino acid-energy ratios are based
on University of Maryland requirement data for relative available amino
content of feedstuffs. Other requirement data except for arginine are
based on NRC requirement for poultry (Revised, 1966). The arginine-energy
ratio for starting chicks is slightly less than that of NRC, based on data of
Lewis, et.al., University of Nottingham, England.










From such adjusted feed conversion values, one can determine the feed most

economical for use by simply multiplying the amount of feed required per

pound of live weight, as nutrient potency is increased, times the respective

cost of each of the different feeds, Figure 2. The calculations, of course,

should be based on the current prices in the area and not average prices, if

such an approach is to be used to maximize the efficiency of broiler production

at a minimum cost.


Amino Acid Energy Ratios for Broilers

The amino acid-energy ratios, suggested by studies at the University of

Maryland, are listed in Table 1. Of the several amino acids listed, the values

for methionine plus cystine, methionine, and lysine are of considerable prac-

tical importance. These values are based on direct requirement studies with

broiler chickens. The equations for total sulfur amino acids and lysine upon

which these levels are based are given to Table 2. Even these values are

subject to further evaluation as environmental conditions and growth rates of

broilers continue to change.

In addition to floor pen studies, battery tests have been conducted

recently with diets, shown to respond only to added lysine and which have been

assayed for availabel lysine, with broiler strain chicks to determine the lysine

requirements during the early period of growth. The calculated lysine require-

ment during the first few weeks, based on these trials, is given in Table 3.

The glycine level, as recommended by the National Research Council, appears

to be higher than required. In one trial, diets containing only 72% of the

level suggested by the NRC, related to energy content, performed satisfactorily.


Testing Amino Acid Specifications

In order to test the suitability of the overall amino acid specifications

for broiler starter rations as presented in Table 1, a field trial was conducted









Table 2


SUMMARY OF UNIVERSITY OF MARYLAND AMINO ACID REQUIREMENT EQUATIONS
FOR BROILER CHICKENS


Period Amino Acid Regression Equation1 2


For Relative Body Weight Gain

Starting (0-4 weeks) .................... TSAA3 Y = 118.7 log x + 129.8

Starting (0-4 weeks) .................... Lysine4 Y = 36.2 log x + 110.3

Finishing (4-8 weeks) .................... TSAA Y = 166.8 log x + 116.9

Finishing (4-8 weeks) .................... Lysine Y = 67.9 log x + 109.9


For Relative Caloric Uptake/Unit Gain (Y)

Starting (0-4 weeks) .................... TSAA Y = 81.4 80.84 log x

Starting (0-42 weeks) .................... Lysine Y = 85.1 50.2 log x

Finishing (4-8 weeks) .................... TSAA Y = 89.9 112.6 log x

Finishing (4k-8 weeks) .................... Lysine Y = 84.8 97.0 log x


1Where x, is the percent amino acid in diet-+- metabolizable megacalories per lb.

Y equals the percent relative gain in body weight or percent relative con-
sumption of metabolizable energy per unit gain as compared with controls.
3
Total sulfur amino acids include available methionine (based on chick assay)
plus available cystine as estimated.

4Based on biologically available lysine as measured by chick bioassay.










in which diets contained 90, 100 or 110% of the total sulfur amino acid levels

recommended and 95, 100 and 110% of the recommended lysine levels. Significantly

poorer growth was obtained when the total sulfur amino acid levels were dropped

to 90% of the recommended minimum levels. Dropping the lysine levels from 100

to 95% of the recommended levels resulted in numerically, but not significantly

poorer growth. Proper fed diets which contained 100% of the specified levels

performed as well as those fed diets containing 110% of the Maryland recommended

levels. The specifications, together with other specifications used for

calculation of broiler starting and finisher rations as suggested by Combs and

Nott, 1967, are given in Table 4.

In another trial, the Maryland specifications for broiler finisher rations

were also tested. Rations were also tested. Rations formulated to supply 92,

99, 106 and 113% of the Maryland amino acid specifications were fed. The diet

which supplied 99% of the estimated amino acid needs, supported body weight

gains comparable to that obtained with the higher levels. This rate of gain

was significantly higher than that obtained with the diet which contained only

92% of the Maryland suggested amino acid needs. These data are interpreted to

mean that the present Maryland finisher specifications are adequate to support

the level of growth observed in these tests.


Temperature and Feed Consumption (Broilers)

Work at the University of Connecticut (Prince et.al., 1961) showed that

feed efficiency in broilers between 4 and 8 weeks of age followed the equation

Y = 0.00148x + 0.2842, where y is feed efficiency and x is degrees Fahrenheit.

From this relationship, an equation reflecting relative feed consumption in

relation to the ambient temperature has been derived. This equation is as

follows: y = 1.18 0.003x where y is defined as the relative feed consump-

tion of broilers during the 4 to 8 week period and x is degrees Fahrenheit at









bird level. This equation gives a relative factor of 1.0 at 600F, 1.06

at 400F, and .95 at 800F. In essence it amounts to a 3% change in feed

consumption as the temperature changes each 100 from 600F.

The practical application of this is found in hot weather broiler

production where the amino acid-energy ratios of the feed are narrowed

sufficiently to insure the same intake of essential amino acids needed

for growth when higher temperatures reduce feed consumption. For example,

the methionine-cystine value for broilers during the finishing period would

be expressed as .44, .46, .47, and .49% per thousand kilocalories of

metabolized energy per pound for temperatures of 60, 70, 80, and 900F,

respectively. These are the inside temperatures at bird level.


Protein Level and Feed Efficiency

Because higher protein levels per se have been observed to reduce

voluntary food intake in broiler chickens, a number of tests have been con-

ducted with broilers to determine the effect of increasing the protein level

in the diet with and without increasing the levels of the limiting amino acids

(Methionine + cystine and lysine). The data from four such trials have been

combined and are presented graphically in Figure 3. In these trials, improve-

ment in feed efficiency has been as great when the protein level was raised

without increasing the levels of the first limiting amino acids as when all

amino acids were increased in balance.

This reduction in energy intake from additional protein is believed

to be due to an effect on physiological appetite. It is possible, of course,

that some amino acid, other than lysine or total sulfur amino acid, is

suboptimal and that this is increased as the protein level is raised. This

possibility does not appear to be the explanation, however, since addition

of all amino acids in combination with added glutamic acid did not improve










efficiency as was obtained from additional protein. Other studies on glycine

requirements have eliminated the possibility that amino acid might be involved.

Body composition studies reveal that the body fat content is slightly lower when

higher protein levels are fed. This slightly improved feed conversion obtained

with higher protein levels is considered most likely to be the result of a

general effect on appetite, rather than due to the addition of the limiting

amino acid or acids.

Studies conducted with higher protein levels during the warm weather,

however, fail to show an improvement in results. This is as one might predict

since the increased heat production resulting from metabolism of extra protein

would materially restrict food consumption and might impair rate of growth.


Body Composition Data of Broilers by Ages

Since it is important to determine the body composition of broilers of

different ages, in order to estimate amino acid requirements form other Maryland

prediction equations, an effort has been made to develop suitable equations for

this purpose from whole body composition data previously determined on male

broilers from 1 to 63 days of age. (Combs, 1968)

Ranges of composition data by ages, together with the regression equations

from which they were derived, are given in Table 5. Protein and fat content

increases materially as a broiler grows older while the water content decreased.

The water:nitrogen ration also narrows, although it is quite characteristic for

any particular age bird for any given breed, despite marked changes in diet

quality or feeding level.


Equations for Predicting Amino Acid Requirements of Growing Broilers

Prediction equations have been devised for calculation of total sulfur

amino acids (Combs, 1961), available methionine (Potter, 1968), and available

lysine (Thomas, 1967). These equations were obtained with young growing chicks









during a period from approximately 10 to 28 days of age. These prediction

equations relate requirements for these amino acids to needs for maintenance

and nitrogen gain at specified body composition states. The Maryland equations

previously derived are listed as follows:

For Total Sulfur Amino Acids:

RTSAA = (0.05C2 + 0.28)NM + (0.4417C2 + 3.392C + 273.26)NG

For Available Methionine:

RMet = 3.482NM + (4.09C2 48.13C + 250.75)NG
For Available Lysine:

RLys = 7.7NM + (6C2 52.594C + 488.77)NG


(Note: For amino acid equations, average R is the amino acid requirement in

milligrams per day; NM is the average amount of nitrogen maintained in grams

for the period; NG is the average daily nitrogen gained in grams; and C is %

carcass nitrogen on a dry basis).

By using the equations given in Table 5, the body composition of normal

male broilers may be estimated and the approximate needs for lysine, methionine

or total sulfur amino acids based on the above. By knowing the approximate

feed consumption, these can be expressed as percent of diet.


Nutritive Requirements of Laying Hens

During the past several years, research has been conducted at the University

of Maryland to measure the minimum needs of laying hens for protein and the

essential amino acids, methionine and lysine. From these results, prediction

equations have been devised for methionine (Shank, 1968) and lysine (Thomas, 1967).

Earlier studies at Maryland provide use with a similar equation for predicting

the feed requirements (Byerlyk 1941) from which we can calculate the energy needs.

The feed which Byerly (1941) fed contained approximately 1260 kilocalories of

metabolizable energy per pound. Then from data obtained in California studies









on the effect of temperatures on feed consumption of laying (Wilson, 1967),

a temperature correction factor was calculated (Combs, 1968). All of this has

been incorporated in a computer program written to calculate the nutritional

needs of laying hens as temperature, body size, level of egg production, egg

size, weight gain and energy content of the diet changes. The levels of

essential amino acids, other than lysine and methionine, are calculated from

the methionine level based on the proportion found in egg protein. Similarly,

a minimum level of protein per se is calculated from the methionine requirement,

assuming that minimum protein level is met if the protein contains 2% methionine.

This approach provides for a safety margin of approximately 10-15%, as 15 grams

of protein seems enough to provide the daily minimum amino nitrogen needs of the

high producing hens.

Table 6 has been included to illustrate the effect of ambient temperature

and percent production on the minimal requirements of the laying hen. For

purposes of this calculation, hens averaging 4 Ibs. in body weight, gaining

one gram per hen per day in body weight, producing eggs averaging 57 grams each

and consuming a diet containing 1325 kilocalories of metabolizable energy per

pound were used. All of the equations referred to above are included in the

footnotes of Table 6. For the calcium level needed, it is assumed that the

dietary calcium retained in the egg varies from approximately 55% to 45% as egg

production level increases.

The left hand column of the table gives the average production for the

flock while the second column gives the level of production to which one can

afford to feed the flock in order for those better hens in the flock, to lay

more nearly optimally. This level of production, for which one should design

the feed, is determined by the computer from the flock average based on the

linear equation shown in footnote 2 of Table 6. The equations for energy,

lysine and methionine requirements are used to determine the quantitative needs











of the better hens daily. The feed consumption given in the Table is that

for the average hen in the flock. The calculations, however, use the feed

consumption for the better hens in determining the levels of nutrients, which

are required to support the appropriate level of production. The lysine and

methionine levels are determined directly whereas other amino acids are

estimated based on their proportion in egg protein to the methionine and/or

lysine level. (See Tryptophane, Table 6).

The wide differences in nutrient levels obtained indicate that more

attention is needed if laying hens are to be adequately but economically

nourished. These relationships appear to be well suited to computer approaches,

which might be used in determining more nearly exact specifications for flocks

or groups of flocks of laying hens under defined environmental conditions. The

potential savings which could be realized from such an approach appear to be

considerable.










LITERATURE CITED


Byerly, T.C., 1941. Md. Agr. Exp. Sta. Bull. Al.

Combs, G.F., 1940. Proc. U. of Md. Nutr. Conf., p. 28.

Combs, G.F., 1964. Fed. Proc. 23:46.

Combs, G.F., 1967. Proc. U. of Md. Nutr. Conf., p. 88.

Combs, G.F., 1968. Proc. U. of Md. Nutr. Conf., p. 86.

Combs, G.F., and H. Nott, 1967. Feedstuffs, Vol. 39, No. 42, p. 36.

Potter, J., 1968. Masters thesis, U. of Maryland.

Prince, R.P., L.M. Potter and W.W. Irish, 1961. Poultry Science 40:102.

Shank, F., 1968. Ph.D. thesis, U. of Maryland.

Shank, F., G.F. Combs and O.P. Thomas, 1968. Poultry Science Abs.,
57th Annual Meeting.

Thomas, O.P., 1967. Feedstuffs, Vol. 39, No. 1, p. 19.

Wilson, W.O., 1957. Poultry Science 36:1254.









Table 3



LYSINE REQUIREMENT OF CHICKS DURING STARTING PERIOD

Mg. lysine required/kcal.
Period, For Growth For Feed
Days Efficiency

7-14 3.56 (.79) 3.63 (.80)

7-21 3.52 (.78) 3.64 (.80)

7-28 3.49 (.77) 3.59 (.79)


26% Protein Series (3314 kcal./kg.)

7-14 3.56 (.79) 3.72 (.82)

7-21 3.47 (.77) 3.66 (.81)

7-28 3.45 (.76) 3.62 (.80)


Shank et al. (1968)
() values in parenthesis are % divided megacalories per lb.









Table 4


UNIVERSITY OF MARYLAND


REVISED NUTRIENT RESTRICTIONS
OF BROTLER RATIONS1


FOR L.P. FORMULATION


Starters Finishers
Min. Max. Exact Min. Max. Exact


Weight ...................
Metabolizable megacalories
Protein ..................
Lysine, Ib................
Methionine, Ib............
Methionine + cystine, lb .
Trytophane, Ib ...........
Arginine, Ib ..............
Histidine, lb.............
Leucine, lb...............
Isoleucine, lb............
Phenylalanine, lb.........
Phenyl. + tyrosine, Ib....
Threonine, lb ............
Valine, lb................
Glycine, Ib ........ .......
Calcium, Ib................
Phosphorus (available, lb.
Thiamin, gm ...............
Niacin, gm ...............
Riboflavin, gm ...........
Pantothenic acid, gm......
Vitamin B12, mg...........
Choline, gm...............
Pyridozine, gm ...........
Folacin, gm ..............
Vitamin A, stab. equiv.,
1,000 I.U.'s ..........
Biotin, gm ................
Sodium, Ib................
Potassium, lb.............
Xanthophyll (Biol. avail.),
Mg.


226
11.7
4.50
8.55
2.32
12.8
4.6
16.2
8.7
8.1
15.1
8.1
9.8
10.15
9.5
4.7
1.25
28
3.5
8.0
5.0
7.50
2.6
0.32


3,000.
.05 ..
2.0


8. ...


1,000 .
1,450
280 ...
















10.0 ..


205
10.6
4.1
7.67
2.07
11.4
4.1
14.6
7.8
7.2
13.6
7.2
8.8
8.85
9.0
4.5
1.20
24
3
7
4
700
2.25
0.26


1,00

26
*.

*. *
*- *


*** *
*** *

*** *


3,000
.045...
2.0


2.2 .
8.0 ...


0 .
1,475
0
0 *. *
















9.5














2.2 ...
7.5 **


iThese specifications are intended for rations to be fed under moderate
temperature conditions. Under hot weather conditions, amino acid levels
should be increased from 5 to 10% to compensate for reduced intake per
unit gain.










Table 5



BODY COMPOSITION DATA BASED ON
ANAYLSIS OF TOTAL FASTED CARCASS (INCLUDING FEATHERS)
OF MALE BROILER CHICKENS FED COMPLETE DIETS TO VARIOUS AGES

Whole Body Content, %
Age, Protein Ether Carcass Water:
Days (n x 6.25) extract Water nitrogen, % nitrogen
(1) (2) (3) dry basis ratio
(4) (5)


1 17.1 6.00 72.8 10.0 28.1

7 17.6 6.6 71.7 9.9 26.5

14 18.2 7.2 70.4 9.9 24.7

21 18.8 7.9 69.2 9.8 23.2

28 19.4 8.5 67.9 9.7 21.8

35 20.0 9.2 66.6 9.6 20.6

42 20.6 9.8 65.4 9.6 19.5

49 21.2 10.5 64.1 9.5 18.7

56 21.8 11.1 62.9 9.4 18.0

63 22.4 11.8 61.1 9.3 17.6


1Values in the table were obtained from the following equations, derived
from 35 observations, each of which were based on from 4 to 9 analysis
on composite samples.

1. % Protein, y = 0.0854x r- 17.02
2. % Ether Extract, y = 0.0937x 5.90
3. % Water, y = -0.1801x 72.94
4. % Nitrogen, dry basis, y = -0.0106x +210.01
5. Water: Nitrogen ratio, y = 0.001851x 0.2891x +28.41


Where x = age of broilers in days









Footnotes Continued (Table 6)

4Based on lysine requirement equation of Thomas (1967): milligrams lysine per
hen per day = 0.05W + 6.2dW + 5.0E, where W,AW and E are as defined in
footnote 3 above.

Based on methionine requirement equation of Shank (1968): milligram methionine
per hen per day = 0.037W + 4.5dW + 5.39E, where W,A W and E are as defined
above.

6Feed requirement experssed as flock average, rather than that of hens laying
at highest level for which diet is designed.

7Levels needed to support highest production for best hens as shown in column 2
in table.

This represents the minimum level of crude protein needed for amino nitrogen
needs, even if all requirements of essential amino acids are met.
9
Trytophane requirement calculated from methionine needs, based on proportion
in egg protein (this can be done satisfactorily for all other essential amino
acids).

1Calcium needs are calculated to support production of best hens, assuming that
retention of dietary, excreted calcium in egg shell varies inversely with the
level excreted as follows: y = 0.69 0.1x, where 9 = retention of dietary
calcium in the egg, expressed as a factor, and x represents the grams of
calcium excreted in the egg shell daily.









Table 6

AMINO ACID, PROTEIN, ENERGY AND CALCIUM NEEDS OF LAYING HENS AS INFLUENCED
BY EGG PRODUCTION AND AMBIENT TEMPERATURE


Egg Prod., % Req./hen/day Gms. Min. dietary % Req 7
Flock Best Metab Mgms. Mgms. Feed/Prot. Lys. Met. Try.9 Ca.0
Av. Hens2 kcal.3 lys.4 met5 hen/
day6


550F

60 80 365 650 318 113 12.7 .52 .254 .125 2.88
70 85 374 686 333 119 13.0 .54 .260 .128 3.05
80 90 383 722 349 125 13.3 .55 .266 .130 3.23
90 95 392 758 364 131 13.6 .57 .271 .133 3.41

650F

60 80 342 650 318 105 13.6 .56 .272 .133 3.07
70 85 351 686 333 111 13.0 .57 .278 .136 3.25
80 90 360 722 349 117 14.2 .59 .283 .139 3.44
90 95 368 758 364 123 14.2 .60 .288 .141 3.63

750F

60 80 318 650 318 97 14.6 .60 .292 .143 3.30
70 85 327 686 333 103 14.9 .61 .297 .146 3.49
80 90 336 722 349 109 15.1 .63 .303 .148 3.68
90 95 345 758 364 115 15.4 .64 .308 .151 3.87

850F

60 80 650 650 318 89 15.8 .64 .315 .154 3.56
70 85 686 686 333 95 16.0 .66 .320 .157 3.76
80 90 722 722 349 101 16.3 .67 .326 .160 3.95
90 95 758 758 364 107 16.5 .69 .330 .162 4.15


iCalculations made for 4-1b. hens,


gaining 1 gm. body weight daily, producing 57


gram eggs and fed a ration containing 1325 metabolizable kilocalories per pound.

2Level of production which diet will support on individual hen basis, calculated
from: = 0.5x + 50, where x = average production for the flock and y = percent
production of best hens deemed economical to support.

3Calculated from modified equation of Byerly (1941): (y = T(1.45W 0.653) +
3.134W + 3.15E, where W = body weighL in grams, W = weight change in grams
per day, E = grams of egg yield per hen per day and T is a temperature correc-
tion factor based on work of Wilson (1957): Temperature correction factor (T) =
1.78 0.012x, where x = OF at hen level.











ADJUSTMENT OF NUTRIENT CONTENT OF FEED
INGREDIENTS BASED ON VARIABILITY


G. F. Combs1


It is important to determine the magnitude of nutrient variability

which occurs in a feedstuffs. For the past 2 years, we have collected

laboratory data obtained by local commercial feed organizations and our

state chemist. A special data processing computer program was prepared

(Nott and Combs, 1967) to calculate the mean, standard deviation, and

coefficient of variability. In addition, we have calculated an "adjusted"

mean which is the true mean adjusted by standard deviation. In the case

of most nutrients, as protein, standard deviation is subtracted, but

for certain others, including crude fiber, ash and moisture, standard

deviation is added to the mean to obtain the "adjusted" mean. Such

an approach insures one that 69% of the time a feed ingredient should

contain as much or more of a specific nutrient as the "adjusted" mean

indicates. This correction was selected on the basis that the minimum level of

a nutrient would be met in a mixed feed approximately 95% of the time, if an

ingredient was supplied equally in the diet by three different feedstuffs.

Such an approach permits adjustments related to the variability of the feed-

stuffs and eliminates the need for safety margins in requirement data. Table

1, lists the protein content for a number of commonly used feedstuffs, to-

gether with the adjusted mean and coefficient of variation. Note the marked

difference between the coefficients of variation for different ingredients.




1Assistant Chief, International Programs Staff Nutrition Program, Public
Health Service, Department of Health, Education, and Welfare.









Table 1 Mean and Adjusted Mean Protein Content of Feed Ingredients, Together
with Their Coefficients of Variation

Number of Mean Crude "Adjusted" Coefficient of
Ingredient Samples Protein(%) Crude Protein Variation
(%) (%)


Corn Meal (52) 9.4 9.1 6.0
Wheat middlings (164) 16.5 15.8 7.7
Wheat shorts (105) 16.6 16.1 6.5
Wheat bran (90) 15.4 14.8 7.5
Cob meal (66) 4.3 3.9 17.6
Gluten feed (66) 22.5 21.4 9.4
Hominy feed (114) 10.8 10.4 8.5
60% Corn gluten meal (374) 62.1 60.1 6.6
50% Peanut meal (20) 54.0 53.0 3.8
Dist. dr. grains (157) 28.6 27.6 6.7
17% Dhy. alfalfa meal (70) 17.8 17.2 6.4
20% Dhy. alfalfa meal (239) 21.1 20.6 5.2
22% Dhy. alfalfa meal (70) 22.6 22.0 5.6
Dr. bakery prod. (61) 10.6 9.9 13.9
44% Soybean meal (309) 45.6 44.8 3.4
50% Soybean meal (1972) 49.9 49.3 2.3
Meat and Bone scraps (996) 50.3 49.0 5.2
Poultry by-prod. meal (601) 63.4 62.2 3.9
Hyd. feather meal (129) 83.9 82.5 3.4
Menhaden fishmeal (77) 63.1 62.2 2.8
Anchovy fishmeal (444) 64.8 63.8 3.2
Herring fishmeal (18) 67.9 66.1 5.4


Table 2 illustrates seasonal variation
gulten mean and dehydrated alfalfa meal.


in xanthophyll content of corn


TABLE 2


Seasonal trend
of corn gluten meal
content of


in xanthophyll content (mg/lb)
(A) and the vitamin A ('000 IU/lb)
22% alfalfa leaf meal (B)


January March
April June
July September
October December
Combined Data


1Mean + standard deviation
2Sample size


176 + 211
158 + 22
117 +- 15
127 + 38
149 -- 32


(18)2
(24)
(18)
(8)
(101)


197
172
152
203
180


(12)
(27)
(7)
(9)
(55)









Much effort also has been given to direct determination of available

lysine and methionine in feedstuffs using improved chick bioassay procedure

developed at the University of Maryland. Results obtained in this work are

shown in Table 3. By applying information of this type to the adjusted

protein content of ingredients, one can obtain the safe value for use in

routine formulation.


Table 3 Available Lysine and Methionine Content of Feed Ingredients as
Determined by Chick Bicassay

Ingredient Available Available
Lysine Methionine


Soybean meal (50%) 6.3+ .16 (14) 1.4+ .07 (10)
Corn gluten meal (60%) 1.4 ( 6) 2.8+ .09 ( 4)
Poultry by-prod. meal 4.2+ .38 ( 5) 1.8+ .20 ( 8)
Meat Scraps (50%) 5.0+ .40 ( 4) 1.2+ .15 ( 4)
Corn meal 3.0+ .28 ( 8) 2.3+ .12 ( 6)
C. Dist. dr. grains w/sol 2.1+ .14 ( 5) 2.18 ( 5)
Menhaden fishmeal 7.3+ .50 ( 7) 3.4+ .34 (25)
Anchovy, Peru 6.8+ .102 (21) 3.1+ .30 (17)
Anchovy, Chile 7.4+ .22 (12) 3.2+ .21 ( 7)
Herring, Norway 7.1+ .64 (12) 3.3+ .13 (13)
Herring, Canada 7.9+ .26 ( 8) 3.4+ .09 ( 8)
Tuna meal 6.2+ .76 ( 6) 3.3+ .29 ( 6)
Blue crab 4.3 ( 1) 2.2 ( 1)


In some instances, calculations can be made to arrive at better estimates

of the lysine content of cereals than is obtained by proportional adjustments

for protein level. Regression equations have been developed for corn, wheat,

barley and milo involving protein level (x) and chemical lysine values (y)

(Table 4). These permit a slightly better estimate of the lysine content, as

the lysine content of the protein changes as the level of protein in the cereal

goes up. Except for milo, the equations reflect highly significant relation-

ships.









Table 4 Equations for Calculating the Lysine Content of Various
Cereal Grains Based on Differences in Crude Protein Content

Ingredient Equation Correlation
Coefficient


Corn Y = 0.016x + 0.135 0.75

Wheat Y = 0.0172x + 0.133 0.81

Barley Y = 0.0153x + 0.251 0.79

Milo Y = 0.009x + 0.120 0.45


Where Y = % lysine in cereal and x = crude protein content (N x 6.26


Similarly, one can calculate the calcium and phosphorus content of

different fishmeals based on the ash content. Highly significant regression

equations for this are given in Table 5. These were developed by Ambrose,

Payne and Kifer, Bureau of Commercial Fisheries (unpublished data). In

addition, equations are given for Menhaden fish meal which permit the

calculation of phosphorus content from calcium content and vice versa.


Table 5 Calculation of Calcium and Phosphorus Content of Fish Meals
Based on Ash Content1

Fish Meal Regression Equation Correlation
Coefficient


Menhaden % Ca. = 0.2796 (% ash) + 0.13 0.84
(60)2 % Phos. = 0.2002 (% ash) 0.421 0.78
% Ca. = 1.758 (% phos) 0.476 0.74
% Phos. = 0.569 (% Ca.) + 0.271 0.74

Cnd. Herring % Ca. = 0.374 (% ash) 1886 0.82
(30) % Phos. = 0.2136 (% ash) 0.718 0.86

Nor. Herring % Ca. = 0.4124 (% ash) 2.271 0.87
(35) % Phos. = 0.2149 (% ash) 0.718 0.86

Anchovy, (Peru) % Ca. = 0.2453 (% ash) + 0.187 0.83
(72) % Phos. = 0.1843 (% ash) 0.218 0.89

1Data of Ambrose, Payne and Kifer, Bureau of Commercial Fisheries (Unpublished).

2Numbers in parentheses refer to number of samples.









During the past year, some progress has been made in determining

sodium content of feedstuffs, using improved atomic absorption techniques.

These sodium values are considerably lower in many cases, than previous

values obtained by earlier methods. Data relating to sodium requirements

of broilers and sodium content of certain feed ingredients has been

summarized by Nott (1968). (See Table 6).


Table 6 Sodium content4 of feedstuffs analysed by atomic absorption
spectroscopy


Corn, yellow
Corn gluten meal (60% protein)
Fish meal, Anchovy (Peruvian)
Fish meal, Anchovy (Chilean, full)
Fish meal, Herring (Canadian)
Fish meal, Herring (Norwegian)
Fish meal, Menhaden
Fish meal, Tuna
Meat scraps (55% protein)
Poultry by products meal
Soybean oil meal, (50% protein)
Defluorinated rock phosphate (CDP)
Dicalcium phosphate (Dynafos)
Dicalcium phosphate (Belgian)
Limestone (Campbell)
Trace mineral mix (Delamix w/2% Zn)


0.005
0.013
0.87 + .34
0.80
0.59
0.42 + .19
0.34 + .05
0.72
0.73
0.44
0.007
4.5
0.073
0.008
0.003
0.0125


Footnotes:

1 Numbers in parenthesis indicate number of samples assayed.

2 Analysis conducted on two composite samples. Kindly performed by
Internation Minerals and Chemicals, Skokie, Illinois.

3 Data provided by Bureau of Commercial Fisheries, College Pard, Md.

SAnalysed by State Inspection Service, College Park, Md.


(8)1
(10)1
(12)2
(5)2
(8)2
(13)2
(12)2
(7)2
(4)
(8)1
(16)1
(2)3
(2)3
(1)3
(1)3
(1)3























CITED LITERATURE

Ambrose, M.E., W.L. Payne and R.R. Kifer, 1968
(unpublished, Bureau of Comm. Fisheries).

Combs, G.F., 1968. Md. Nutrition Conference Proc. P. 86.

Combs, G.F. and H. Nott, 1967. Feedstuffs vol. 39 no. 42,
p. 36.

Nott, H., 1968. Md. Nutrition Conderence Proc. p. 30.

Nott, H. and G.F. Combs, 1967. Feedstuffs, vol. 39,
no. 41, p. 21.









AVAILABILITY AND ADEQUACY OF DATA ON NUTRIENT
REQUIREMENTS OF DAIRY CATTLE


W. T. Howard1

Energy

The current NRC energy allowances are satisfactory for maintenance, growth,

reporduction, and mild production up to 60-70 Ibs. of FCM daily. Cows cannot,

or will not, consume adequate energy to meet their energy needs above 60-70

Ibs. of daily FCM.

Increasing the low fiber or grain-concentrate portion of the ration to

more than 60% of the total dry ration has not been beneficial. Little gain

in energy intake occurs when cows are full-fed a ration containing less than

13-18% crude fiber. A TDN ration specification of 65-70% TDN will be adequate

under most conditions. Mild fat test depression occurs when rations are fed

that contain less than 13-18% fiber.

Adequate levels of easily fermented fiber of the correct physical form

must be included in the ration in order to maintain mild fat percentage and

normal rumen functions. Finely ground or pelleted forages do not maintain

normal fat percentage even when adequate levels of fiber are percent.

Recent Wisconsin data indicates that the addition of bentonite to fat

depressant diet may aid in correcting fat depression.

Protein

The protein allowance for mild production was increased by NRC in 1966

to coincide with the energy requirements. Recently, Cornell workers indicated

that a higher digestible protein allowance for mild production was in order.

These workers suggest increases in digestible protein ranging from 13 to 24%

over the current requirements, but do not suggest a further increase in

energy requirements.


Assistant Professor, University of Wisconsin









It has been clearly demonstrated that the cow early in lactation and pro-

ducing at high levels cannot consume adequate energy to meet her needs. We can

supply adequate protein for the high level of production.

However, the rumen is limited in its capacity to synthesize protein, but

can degrade a very high proportion of the protein entering the rumen. It may

be desirable to treat or process natural proteins to facilitate its passage

through the rumen to the abomasum intact. Research to determine the relative

protein breakdown rate for different protein sources may reveal that certain

feeds have a low rate of degradation in the rumen.


Minerals

Present NRC requirements for trace minerals and major minerals are adequate

for formulation of rations for growth, maintenance, and reproduction. The

involvement of calcium and phosphorus in the milk fever syndrome has not been

clarified. Further research in this area is urgently needed.

The optimum level of sulfur to obtain maximum level of production when

feeding high levels of non-protein nitrogen has not been ascertained.

Special problems in mineral nutrition may be found in particular areas of

the country, but few problems are encountered when trace mineral iodized salt

is fed along with adequate calcium and phosphorus.


Vitamins

Vitamins A and D are the only vitamins that we can justify adding to the

mature dairy cow ration. The calf must be treated as a monogastric animal until

rumen function has been established.

There is very little evidence indicating the need to add vitamins A and D to

the usual dairy ration. However, the addition of minimum amounts of A and D as

an insurance factor can be justified. The added cost per year may be less than










$2.00 per cow. The added production to pay for this cost would be difficult,

if not impossible, to measure.


Ration Specifications for Least-Cost Rations

for Hiuh-Producing Dairy Cows


Crude Protein

TDN

Crude Fiber

Calcium

Phosphorus

Ca:P Ratio

Urea

Trace Mineral Salt


Air-Dry Ration

14%

65-70%

13-18%

.5-.7%

.35-.5%

1.4 to 2.0:1

.75 to 1%

.5%


There may be other non-nutritive specifications necessary for feed

handling or manufacture, but these appear to be the primary nutrient specifica-

tions needed for high-producing cows. It must be assumed that the fiber is

present in a physical form adequate to sustain normal rumen function.

It may be desirable to add sufficient vitamin A and D supplements to

the ration to insure the daily intake of 30,000 to 40,000 units of A and

8,000 to 10,000 units of D per cow.








LEAST-COST RATIONS FOR DAIRY COWS


W. T. Howard and J. L. Albright1


Recently, computers have been used to formulate least-cost rations for

most types of livestock. In computer formulation of least-cost rations,

individual feeds are utilized not just as corn, for example, but as an

ingredient containing a specified amount of protein, T.D.N., calcium, and

phosphorus. The computer is able to formulate rations conforming to pre-

scribed levels of protein, T.D.N., calcium, phosphorus, etc. No individual

feedstuff is given preference except that a minimum or maximum amount can be

specified to meet special requirements or to insure safety. For example,

urea must be limited to safe levels.

Several of the large feed manufacturers are using computers and least-

cost ration formulations to compound commercial feeds. Substantial savings

in formula cost have been reported by companies using computers to formulate

feeds.

An experiment was conducted at Purdue University during the winter of

1965-66, comparing two least-cost rations to a conventionally formulated ration.

The grain ration was mixed with the forage for all three rations to form a com-

plete ration that was bunk fed free choice. Corn silage was used as the sole

forage. On many Indiana farms corn silage would be the least-cost forage. One

least-cost ration (Ration I) was a single least-cost ration based on the pre-

vious year's prices from August to March. A second least-cost ration (Ration II)

was changed each two weeks over the 20-week experiment utilizing price changes.

The thire ration (Ration III) was a conventionally formulated ration, which in-

cluded corn silage, corn soybean meal, molasses, 20 Ibs. each of trace mineral

salt and dicalcium phosphate per ton of grain. No urea was added to Ration III.


1Assistant Professor, University of Wisconsin and Professor, Perdue
University, respectively.









All rations were calculated to contain a minimum of 14% crude protein;

70% T.D.N.; 30% calcium; .25% phosphorus; .5% trace mineral iodized salt;

2% molasses; and 30% corn silage, the forage source. Vitamin A and D

supplement was added to insure at least 1000 I.U. of A and 200 I.C.U. of D

per pound of air-dry ration. Urea (45%N) was restricted to 1% of the total

air-dry ration. The corn silage was converted to a hay basis on 90% dry

matter for all ration calculations.

The three rations were fed to three groups of 16 cows (11 Holstein and

5 Red Danish for 20 weeks. The experiment involved approximately the 60th to

200th day of lactation on the cows in the experiment. All cows were fed a com-

plete ration consisting of corn silage and about 15-18 lbs. of grain per day

for two weeks prior to calving. After calving until the experiment started, the

cows were fed rations conforming to the nutrient specifications previously

mentioned. No grain was fed in the milking parlor. No cases of ketosis of

offfeed conditions were observed during the time the complete feed rations

were fed. Services per conception did not differ between treatments.



Table 1. Experimental Results

Dry Matter Intake Milk Prod. Milk Fat Solids-
Per 100 lb. body weight lb./day % not-fat-%

Ration I Least-Cost 3.57 lb. 50.5 3.8 8.8

Ration II Least-Cost 3.43 48.3 3.8 8.9

Ration III Conventional 3.45 51.1 3.9 8.8


Feed intakes (shown in Table 1) were very similar between rations. On the

basis of intake of dry feed per 100 Ibs. of body weight feed intakes were es-

sentially equal between treatments. Also shown in Table 1 are milk production

and composition information. Although there is a range in production between

this was essentially due to differences in production present at the start of the









experiment and not due to the rations fed. The decline in production per

week was normal and nearly identical between the groups. One cow in each

of the groups peaked at 100 lbs. of milk daily and five other cows in

the experiment milked over 90 Ibs. daily on their high days. Thus, it

was concluded that the rations were for all practical purposes equal.

Feed costs were reduced by about 10% for the least-cost rations

compared to the conventionally formulated ration. This cost reduction

was realized without a decrease in nutritive value of the ration. The

most drastic ration compositions change for cows in group III was ob-

served when their rations were changed from one in which the grain con-

tained 80% corn to one that contained 87% wheat without harmful effects.

Feeding 1% urea in the total air-dry ration in the least-cost rations

did not depress feed intake or milk production. The main savings in

feed cost came from the addition of urea to the rations. Rapid drastic

ration compositions changes under conditions of the experiment where

the grain was blended with the silage and feed free choice, did not adver-

sely affect animal performance.


Summary

Least-cost rations formulated to meet a set of nutrient specifications

maintained milk production as well as a conventionally formulated ration.

Corn silage was apparently satisfactory as the sole forage for high producing

dairy cows.

Urea added to make up 1% of the air-dry ration did not lower nutritive

value of the ration but did substantially reduce feed costs.

Under conditions of the experiment it was demonstrated that changing

ration composition to take advantage of feed price changes was not detrimental

as long as the nutrient composition was maintained at the specified levels.







FEEDING GUIDES FOR COMPLETE POWER-FED RATIONS


Daily Production of 4% Milk


(lbs.)


80 and above


Percentage of the Total Ration from Grain-Concentrate
Excellent Forage Average Forage Fair Forage


- % of Ration from Grain


60-70*


10

25

50

70*


30

50

70*

80*


Daily Production
of 4% Milk


Crude Protein
Excellent
Forage


in Air-Dry
Average
Forage


Ration
Fair
Forage


T.D.N.
Excellent
Forage


in Air-Dry
Average
Forage


(Ibs.) % C.P. in Ration % T.D.N. in Ration -

20 12 12 12 55 55 55

40 13 13 13 55-60* 55-60* 55-60*

60 13 13 13 60-65 60-65 60-65

80 14 14 14 65-70 65-70 65-70


* Digestive disturbances and low fat test may be a problem with some cows when
grain-concentrate make up more than 65-70% of the total diet on a dry basis.

** Use higher values when feeding large amounts of corn or sorghum silage.


Ration
Fair
Forage









AVAILABILITY AND ADEQUACY OF DATA ON
NUTRIENT REQUIREMENTS OF BEEF CATTLE


John W. Algeol


Regarding the "availability" of data on nutrient requirements there is

much information in the scientific literature and in such publications as

Morrison's "Feeds and Feeding", the NRC's "Nutrient Requirements of Domestic

Animals," and the ARC's "Nutrient Requirements of Farm Livestock" (U. K.).

The accuracy of such data is generally good, however, updating is constantly

necessary in order to meet the needs for precision required for computer

formulation.

In our experience it appears that the agreement in accuracy between

various researchers is greatest at or near the maintenance level. Thus, cow

and bull ration requirements and slow growing and conditioning requirements

are quite accurate and probably need little attention. In high efficiency

diets, however, there are discrepancies between systems and availability as

well as accuracy suffer from the fact that we do not know enough about the

optimal needs for growth and fattening. We do note differences, however, in

the total recommendations for the various nutrients required for production.

For example, in energy recommendations Blaxter has a system based on metabo-

lizable energy, some Arizona workers prefer TDN, Morrison recommends estimated

net energy (ENEm+p) at times and TDN in other situations such as cold climates

Lofgreen uses net energy for maintenance (NEm) and net energy for production

(NEp) and at Santa Ynez Research Farm we use and ENEm+p system modified to our

needs through our research and experience. I feel that the NEm + NEp system

is theoretically and potentially the most accurate. However, there are two

drawbacks to its use now, one is that we feel our present knowledge of ENEm+p


IPresident and Animal Nutritionist, Santa Ynez Research Farm










is stronger in that we have more information on more commodities in this system

and secondly, it is less cumbersome and facilitates computer programing if we

run on a single energy value.

We have not used Blaxter's system in our work since it is our feeling

that metabolizable energy falls a bit short of the net energy system by not

considering the heat increment which is very important in ruminants. At this

point our experience indicates that the energy input in a high efficiency

diet should provide a net to the animal of about twenty-five percent more mega-

calories daily than the Morrison tables recommend. For example, on 600 pound

yearlings Morrison recommends 9.7 11.2 megcal NEm+p and we feel that animals

at this weight should have at least 14.00 megcal NEm+p daily. To attain this

intake at 20 pounds an animal will need a 70 megcal/cwt diet. Based on our work

this animal will require about 5.36 megcal/lb. of gain thus giving a potential

ADG of 2.61 Ibs. with a theoretical feed conversion ratio of 7.66 : 1. At

an 11.2 megcal intake the steer has a gain potential of 2.09 Ibs/day with a

feed conversion potential of 9.57 : 1 or if the steer gains 2.61 Ibs/day he must

consume 25 pounds and the diet would have about 56 megcal/cwt.

Economically speaking in the above example if the feed cost is $50.00/ton

on the 70 megcal diet the 56 megcal diet must cost $10.00/ton less to produce

an equal gain cost.

NRC standards are written in Digestible Energy (DE) and TDN and appear to

be similar to the Morrison standards in the amount of energy intake if one con-

verts DE to TDN using the standard factor of 2.

Protein requirements as expressed by either NRC or Morrison have proved

to be entirely adequate in our experience. It is interesting to note that NRC

is slightly lower on digestible crude protein (DCP) than the Morrison standard

on fattening calves and is slightly higher on the yearling cattle. In our









practice we use crude protein (C) rather than DCP since we have found that

the ingredients used in high efficiency finishing diets have an adequate CP

digestibility, thus insuring sufficient DCP. In our linear program we do not

make a DCP restriction but have a row in which this value is given so that we

can double check its adequacy.

In studies we conducted a few years ago we did not find it necessary to use

over 12% CP in small calves on high energy diets. In fact, when CP levels were

raised to 16% the animals appeared to utilize their diets somewhat less efficiently

probably due to an excessive deamination need which may have affected the net

amount energy available.

The use of NPN may have an effect on the animals protein requirement when

low energy diets are fed. In our growing studies urea was not utilized as effec-

tively as cottonseed protein when energy levels were in the 50-55 megcal/cwt

range. However, when the grain level was increased and the energy level was in

the 60-65 megcal/cwt range the animals grew as efficiently as did their mates fed

supplemental cottonseed protein. Thus, NPN restrictions must be programed as a

variable amount of the total CP intake based upon the energy level of the diet.

In high energy finishing diets we have routinely used NPN sources to

meet the total supplemental protein needs of our animals since the late 1950's.

As indicated above it is our opinion that NPN in the form of urea, diammonium

and monoammonium phosphates will not change the protein requirement of the

animals in high energy feeding regimes. The ammonium phosphates are used

only at levels high enough to meet the phosphorus requirements when these

sources provide phosphorus at a lower cost per unit of P than other sources.

If additional protein is required to meet the requirement we use urea for

this purpose. If the phosphorus source is non-nitrogenous then urea is

utilized for the total protein supplement except as indicated above where











lower energy diets are used such as in growing and conditioning operations.

Mineral requirements can be affected by a number of interactions and

provide an area where more adequate data may be needed. The calcium level

for example in both NRC and Morrison is adequate for medium grain, low fat

diets. However, fat additions increase calcium requirements and most prac-

ticing ruminant nutritionists will increase the element to 0.5 or 0.6% of the

diet.

The Oklahoma workers recommend higher phosphorus levels than those of

either the NRC or Morrison standards. According to ARC, and our experience

is confirmator, the need is for 0.28 0.35% of the diet or 30 to 32 gm in

high efficiency diets. We have seen recommendations in the industry as high

as 0.44% of the diet in fattening steers; however, in our experience this is

excessive and is conducive to the information of phosphatic urinary calculi.

Potassium has been shown in work by Pfander and by Roberts to be critical

at levels below 0.40% and can be limiting factor especially in high corn

and milo diets. Our own research has confirmed that of both the above workers

and accordingly we include this element in our linear programs. Feeding

standards should certainly include this element perhaps in the range of 0.5

to 0.7% of the diet for beef cattle. At the present time ARC, NRC and

Morrison standards do not recognize a need for this element and changes are

definitely in order.

Sodium and chlorine requirements appear to be met by 0.5% NaCl according

to NRC while Morrison recommends free choice usage rather than inclusion in

mixed rations. ARC recommends 8.2 gm Na and 11.3 gm Cl for rapid growth and

this appears low based upon our experience. We feel that for optimal gains on

high energy diets the requirement lies in the range of 0.5% to 0.75% of the

diet.










With regard to magnesium, NRC and Morrison say only that dairy calves

need 0.6 gm per 100 lbs. body weight and ARC recommends an increase from

5.0 gm/day at 440 lbs. (200 kg) to 9.5 gm at 1100 Ibs. (500 kg) and this

translates to about 0.08% of the diet. We have not conducted research on

this element and our diets normally contain 0.12 to 0.18% of the element

from natural sources.

While the recommendations for the trace elements, iodine, iron, copper,

cobalt and maganese, appear to be somewhat different in the NRC, ARC and

Morrison standards any of these recommendations appear adequate under prac-

tical useage. ARC recommendations in this area are more specific than are

either NRC or Morrison. Zinc is treated rather lightly in NRC and Morrison

and these recommendations were adequate for the higher roughage diets of

the past. ARC suggests 50 mg/kg of diet dry matter and from the Purdue work

it is apparent that the need is probably in the range of 50 to 100 mg/kg

of diet.

Regarding vitamin requirements, we have found, as have many others,

that vitamin A is of critical importance especially in high energy finishing

diets. NRC, ARC and Morrison give their requirements in terms of carotene

whereas the practicing ruminant nutritionist has long since discontinued

consideration of this compound for high energy diets. Carotene may be used

as a standard in mature breeding cattle but it is apparently poorly used in

young fast growing cattle as evidenced by the fact that on diets with ample

carotene liver vitamin A stores will decline to the point of depletion in

80 to 100 days on such a regime unless vitamin A is given orally or

parenterally. In our studies we have even seen liver vitamin A decline in

calves on total diet of dehydrated alfalfa pellets which analize over

100,000 IU of theoretical vitamin A.










Most practicing nutritionists today recommend 20,000 to 30,000 IU of

vitamin A as the daily intake for optimal production in finishing animals.

Our data based on liver biopsy and growth studies indicates a minimum safe

level for high energy diets and fast growing calves to be about 16,000 IU

of vitamin A.

NRC, ARC and Morrison discuss vitamin E primarily in terms of muscular

dystrophy and its prevention. Apparently the required level for this pur-

pose is quite low since ARC indicates about 5 mga tocopherol per 100 Ibs.

and NRC indicates less than 40 mg. Much has been claimed for the use of

vitamin E we could see no difference in liver A level or the time required

to deplete the liver post injection.

Vitamin D recommendations vary somewhat between NRC, Morrison and ARC

but in any case they appear to be adequate since the need is apparently

not critical except in certain areas of the nation where animals are deprived

of sunlight or under indoor confined feeding conditions.

The synthesis of B vitamins in the rumen appears to obviate the need

for recommendations for these compounds. The enzyme systems and bodily

functions of the ruminant undoubtedly require these compounds but only in

special cases of the very young and in disease or toxic compound blockages

do they become important. We have periodically added B complex vitamins to

our experimental diets as energy and other components have been changed

over the years, however, we have not found an advantage for any of these

factors. According to Rerat the very high grain diets now in use provide

an excellent media for B vitamin synthesis in the rumen.

While we are using least cost linear programming to a large degree in

our practice we fully recognize the limitations we impose on the system

due to our need for a more accurate data input. The more restrictions one







71


imposes on the linear program the farther from least cost the output will

be. It is obvious that while there are large areas of agreement in nutrient

requirements there are also a good many areas which need attention in order

to precisely and accurately utilize computer programming in optimizing live-

stock diet.














LITERATURE CITED


ARC, 1965.


The nutrient requirement of farm livestock.
No. 2. Ruminants technical reviews and summaries.
Agricultural Research Council, London, England.


Lofgreen, G. P. and W. N. Garrett. 1968. A system for
expressing net energy requirements and feed values
for growing and finishing beef cattle. J. Animal
Sci. 27:793.

Morrison, F. B. 1956. Feeds and Feeding (22nd Ed.). The
Morrison Publishing Co., Ithica, New York.

NRC. 1964. Nutrient requirements of domestic animals. Iv.
Nutrient requirements of beef cattle. National
Research Council, Washington, D.C.

Perry, T. W., W. M. Beeson, W. H. Smith and M.T. Mohler. 1968.
Value of zinc supplementation of natural rations for
fattening beef cattle. J. Animal Sci. 27:1674.

Rerat, A. 1969. Place and conditions of biosynthesis and
absorption of B vitamins in the ruminant. Fifth
International Congress on Nutrition, Washington, D.C.

Roberts, W. K. and V.V.E. St. Omer. 1965. Dietary potassium
requirements of fattening steers. J. Animal Sci.
24:902.

Telle, P.P., R. L. Creston, L. D. Kinter, and W. H. Pfander.
1964. Definition of the ovine potassium requirement.
J. Animal Sci. 18:249.










THE ROLE OF THE COMPUTER IN A CATTLE FEEDLOT OFFICE


Tom RemingtonI


To many people the use of computers to do jobs in science and industry

seems to have become the irodern day magic which makes anything possible. To-

day's computers come in many sizes and many models, with large and with small

capabilities, and may be programmed or operated according to their capabilities

by personnel of much or little training, ability or understanding. In short,

the computer field is tremendously complex and certainly I am not qualified to

discuss computers in general or the general application of computers as bus-

iness aids. Therefore, it seems appropriate that my discussion should be based

on our experience with an IBM 1130 computer in our own feedlot office.

A brief description of Hartman & Williams feedlot operation may serve as a

good background for my discussion. We are commercial cattle feedlot feeding

regularly between 15,000 and 23,000 head of our own and customers' cattle. In

addition, we have developed sales for feed to outside customers of from 50 to

150 tons per day. We use transit weigh trucks to distribute the feed to cattle

within our yard and bill for these feed sales monthly. We produce a lot per-

formance summary when each lot closes for distribution to the customer and

retention in our own records. We confer.with a professional nutritionist but

prefer to make our own feed purchase decisions based on the best information

available to us. In August 1967 we installed an IBM 1130 computer in a near

minimum configuration, that is, a central processing unit with an 8-K core, a

single disk storage with memory capacity of 512,000 words, and a 1442 card-read

punch. All printed output is via the console printer which is actually an IBM

electric typewriter. An 029 key punch was also installed for creating input


General Manager, Hartman & Williams, Inc.









records. This insta'riatiorirequires about 75' of the 'timeS'of "lie 'previously

untrained operator.

We are convinced that in our operation, with our personnel, this installa-

tion-isi'a'money maker. '.Additionally, we believe that 'this basic installaion

can be expanded by the 'addition of other hardware -already availablee eito meet any

of our requirements i ti he 'freseeable future .;

With 'this information aa 'background,' I shall now try to explain *my-con-

ceptiof the-role of 6the' computer iii'an installation such as 'ours based on' my.

experience. 'It 'appears-'tbme that this 'role'canibe divided' into five broad'

areas of service: Cattle- anhdFeed Records, Commodity:Inventory Records;,

'Business Accounting Records, Linear Program'Feed FormulaSolutions,'and Analysis

of these four in order to make enlightened decisions for the future.

In our estimation, the real decision as to whether to install a bookkeeping

mach'ihe or to go into the additional expense' and involvement of a full-fledged

computer installation lay between the' 3rd and 4th 'items xhich I, just listed.

The various types of record keeping'could'have been done on a bookkeeping{ machine,

but least-cost feed formulations and the record'analysis work:.which we' have only

just now begun to implement, require computer power beyond that contained in

bookkeeping machines or even the smallest computers. Therefore, we decided on

the 1130 installation


Cattle and Feed Records

The kinds of cattle and feed records which we accumulate are as follows:

Our machine collects and stores feed sales to each lot within the yard and to

each purchaser without the yard on a daily basis. This information is retained

and fed into the monthly billing, becomes a part of the permanent lot record,

produces a feed production record by feed type, and also feeds into the inventory

program. All this is effected by a single series of entries in the computer.









-Wq-also, store, within them, machine, all: of our .cattle,-activity records ,,or

a month, at a time. These records include the, number,-of head and weights, of all

in-shipments and out-shipments, and, deaths. *-These entries, appear on the monthly

lot invoices for the customer's information.

At the end of each month, we produce an individual invoice for each lot, a

statement of account for each customer showing charges for each lot, all payments

received and interest charges for overdue accounts. Our billing is on a calendar

month basis, with no early cut-off date, and we always mail our feed bills by the

second of the month. This rapid billing produces a secondary advantage in that

we believe that it entitles us to more prompt payment of our charges than we might

otherwise expect; and because of this, we feel justified in charging interest at

an earlier date on over-due feed bills.

We have also designed into the programing various checks as to the accuracy

of our input and thereby reduced the risk of error and misbillings.

Because the above information concerning feed consumption and cattle activity

is stored within the computer we are able to make a close-out summary immediately

after shipping the last cattle from any lot. We need only to enter a feed credit

covering the residual feed left in the feed trough at the time of shipment to

complete the information required in this close-out summary. With this machine

storage feature, we provide the customer, our nutritionist and our own records

with copies of the performance of each lot on the same day the lot closes.

As with any record keeping system, handwritten or machine,,a healthh of,

information;is available .if we can but get ,at it.. With a computer .installation,

some,,concerted efforts at the outset in arranging file records and programing

can make ,much of-this random information-accessible for:use at some future date

and we now find that we are able to make various kinds:of listings and accumu-

lations of data which we had not precisely planned on at the outset. These









include listings and totals of all inputs, weekly and monthly feed output

records, and weekly and monthly cattle receipts and shipments by weight

categories if desired. The potential for this kind of thing is unlimited.


Commodity Inventory Records

In this area, we find it desirable to keep records on receipts of all

commodities. These products come from many sources and from suppliers of

varying degrees of accounting sophistication. One of our largest sources of

feed is, of course, our local farmers and many of these are involved only in

shirt-pocket bookkeeping. Therefore, we must produce an accounting record

which is intelligible both to us and to them, showing what they delivered to

us and what charges are due, in some cases, corrected for high dockage, high

moisture and low bushel weight. Therefore we are programming to produce this

kind of record.

All receipts are automatically added to a continuing inventory.

From the output of mixed feed recording program we obtain data to receive

our continuing inventory by commodity on a daily basis. This gives us a current

inventory status and includes a correct contract status. We are able to obtain

a complete current cost of inventory for use in least-cost feed formulations.

The current inventory status, of course, aids in purchasing and eliminates the

cost of financing excessive inventory.


Business Accounting

We have not implemented this particular computer use in our operation as

yet. Certainly the machine would easily lend itself to do payrolls, payables,

receivables, etc., and to the development of a cost accounting system of most

any degree of sophistication.










Linear Programming

In July 1966 I attended a seminar in which Mr. Wendell Clithero, an IBM

Vice President speaking at a mid-west data processing seminar, stated that in

his opinion any cattle feedlot which did not operate its own computer and its

own analytical chemical laboratory within five years would be out of business.

Time seems to be showing that Mr. Clithero's judgements were, perhaps, too

sweeping, but I have certainly come to appreciate the validity of his beliefs

concerning the necessity, or at least the desirability, of an on-site computer

for ration formulations.

The Linear Programming capability of a computer is the money-maker in a

feedlot operation. This is the capability which makes it more desirable than

a bookkeeping machine. This is the capability which pays for the computer

many times over.

Linear Programmed least-cost feed formulations have been available for many

years, and more recently from an increasing number of sources, including, partic-

ularly, most all reputable consulting nutritionist. However, those who provide

these services are not always immediately available and in the relatively fast

action of today commodity markets, prompt, enlightened decisions are essential.

If we are offered a given commodity, it is often possible to run a least-cost

solution and reply to the vendor within 30 minutes. Least-cost solutions have

eliminated "seat of the pants" buying of commodities which "appear" to be good

buys. The only feed stuffs we buy today are those which will fit into our rations,

after having considered all the nutrient requirements which we choose to formulate

for, and all of the commodities which are today available to us. This means that

a commodity which may have been a good buy during one season is not necessarily

a good buy today even at the same or lesser price.

Our experience shows that we are able to feed least-costed rations without

particular attention to the abruptness with which we change feed ingredients.










This is probably because all rations which are designed for the same purpose

are formulated to constant nutritional levels. Because of this flexibility, we

rely completely on the computer to make purchasing judgement for us, and in the

California area the feed brokers have even been brought to the point where they

no longer argue with the wisdom of the machine's decisions. (The machine often

times provides a useful crutch or leaning post to obtain additional time in making

judgements or to otherwise manipulate the feed vendor.)

Because we operate our own machine, we are able to run as many different

solutions for a given ration as our imagination may dictate. Consequently we

never fudge on a formulation because we may have temporarily run out of a com-

modity, but rather we merely reformulate on the basis of what is now available,

even if this solution may be used for only part of a day.

Another benefit from our on-site computer seems to be that since we do not

fudge on formulations any more, but rather send a least-costed formulation to

the mill, we are also always sending a nutritionally balanced ration to the mill.

This may account for an apparent improved performance in our conversions and

costs since installing our own computer.

As with most sophisticated L P programs, the capability for a series of

parametric solutions is included in our software. This allows us to fix certain

ingredient costs and to run a series of solutions in which parameters such as

cost, ingredient level, or nutrient level are changed by fixed increments in each

of the succeeding parametric solutions. This tool is useful in two ways. First

it allows us to determine precisely at what price level we can use a commodity,

or more of a commodity, and thereby enables us to be very mean buyers at various

times. A second major use which we make of this parametric capability is in

planning future purchases. For example, we arenow in the process of deciding

what prices we. will be able to pay for wheat and barley during our local harvest

season which will begin in late April.









A seventh use that we make of the L P is to formulate for maximum energy

rations. Actually the L P program is an optimization routine. Therefore,

instead of asking for least-cost we ask the machine to fix the cost and give

us the solution which maximizes the energy value of the ration. This allows

us to give the customer the fullest value for his dollar.

An additional use of the Linear Program which we may be able to implement

in the future, would be to input all of the various capabilities of our company

such as feeding capacity, milling capacity, personnel, etc., and have the

machine tell us how to make the optimum use of these various capabilities

including capital. We don's really know whether we are wisest to use our

available cash to purchase cattle for our own account or to buy large feed

inventories and make our money through feed appreciation. But the Linear

Program should be able to give us the answer to this question.


Analysis of Records

As I mentioned before, the computer allows us to store and record large

quantities of information. The analysis of this information to study trends,

patterns of performance, and correlation between performance and various

parameters is another largely unused capability of the computer. We are

presently beginning to code each lot of cattle as to type, class, grade, sea-

son of feeding, weather, feed background, source, supplier, and as many other

characteristics as we believe may have some bearing on performance. From this

we expect that the proper application of methods of statistical analysis through

the computer may allow us to find positive or negative correlations between

performance and some of the above characteristics. Positive or negative

correlation would, of course, lead us to buy more or less of the cattle ex-

hibiting these characteristics.









A computer study of past performance records should allow us to develop a

system for projecting performance of future lots of cattle and thereby aid in

making buying decisions based on whether past performance fed under today's

conditions is likely to produce profitable results in the future.

Studies of past performance should also enable us to develop methods of

estimating current performance of similar cattle and to make close estimations

of current body weight, rate of gain and cost of gain. These factors can then

help us to sell cattle at the optimum selling time, i.e. when the cost of gain

begins to exceed the current market.

Since the computer is a workhorse which is able to produce large quantities

of additional output with very little additional input, it is also possible to

produce daily or weekly reports which show "to-date" information on each lot in

the yard. This information might include Days on Feed, Estimated Rate of Gain,

Estimated Cost of Gain, Cost per Animal to Date, Cost per Pound to Date, and

possibly some information concerning current performance trends. This kind of

information would be almost impossible to keep by hand, but the computer can

store the information, prepare complete reports, and extract from the complete

report by exception reporting those particular factors or trends which we have

indicated to it that we are interested in seeing when they do occur.

Thus the computer is an intriguing tool whose capabilities we have only

begun to challenge. We expect to make ever broader use of it in our future

decision making and along the way to glean information about our business

which, perhaps, we did not even imagine existed. We truly believe that the

role of the computer in our feedlot operation will be limited only by our

ability to conceive and implement new and productive uses for it.
















IFAS PARTICIPANTS


Ammerman, Clarence B., Dr.
Arrington, Lewis R., Mr.
Baker, Frank S., Mr.
Beardsley, Daniel W., Dr.
Bertrand, Joseph E., Dr.
Browning, Charles B., Dr.
Burns, W.C., Dr.

Chapman, Herbert L., Dr.
Combs, George E., Dr.
Cunha, Tony J., Dr.
Damron, Bobby L., Dr.
Davis, George K., Dr.
Durrance, Kenneth L., Mr.
Harris Jr., Barney, Dr.
Harms, Robert H., Dr.
Hayes, Ben W., Dr.
Hentges, J.F., Dr.
Hollis, Gilbert R., Dr.
Jackson, William G., Dr.
Marshall, Sidney P., Dr.
McPherson, W.K., Mr.
Moore, John E., Dr.
Pace, James E., Mr.
Shirely, Ray L., Dr.
Sites, John W., Dr.
Tefertiller, K.R., Dr.
Wallace, Harold D., Dr.
Wing, James M., Dr.


Associate Professor
Professor
Professor
Animal Nutrition
Professor
Professor & Chairman
Assistant Professor & Head


Professor & Head
Professor
Professor & Chairman
Assistant Professor
Professor & Director
Associate Professor
Assistant Professor
Professor & Chairman
Assistant Professor
Professor
Assistant Professor
Assistant Professor
Professor
Professor
Associate Professor
Professor
Professor
Dean for Research
Professor & Chairman
Professor
Professor


Animal Science
Animal Science
North Florida Station
Everglades Station
West Florida Station
Dairy Science
Animal Husbandry
W.C. Florida Station
Range Cattle Station
Animal Science
Animal Science
Poultry Science
Bilogical Science
Animal Science
Dairy Science
Poultry Science
Everglades Station
Animal Science
Suwannee Station
Animal Science
Dairy Science
Agricultural Economics
Animal Science
Animal Science
Animal Science
Experiment Station
Agricultural Economics
Animal Science
Dairy Science




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