U.S. patent application number 10/985365 was filed with the patent office on 2005-06-16 for computer system for determining a customized animal feed.
This patent application is currently assigned to CAN Technologies, Inc.. Invention is credited to Burghardi, Steve R., Cook, David A., Knudson, Brian J., Oedekoven, Mark A., Peterson, Loren.
Application Number | 20050126500 10/985365 |
Document ID | / |
Family ID | 24972816 |
Filed Date | 2005-06-16 |
United States Patent
Application |
20050126500 |
Kind Code |
A1 |
Burghardi, Steve R. ; et
al. |
June 16, 2005 |
Computer system for determining a customized animal feed
Abstract
A method and system for creating a customized animal feed is
disclosed. The method and system include having ingredient data
from more than one location, animal data, evaluation data, and
optimization weighting data. The specifications for a customized
feed are generated using ingredient data representative of the mix
of ingredients available at one or more locations. A customized
feed is generated which is designed to fulfill the nutritional
requirements for the animal's diet. The nutritional requirements
are derived from the animal data. Furthermore, the feed is
optimized based upon the profile data, the feed data, the
evaluation data, and the optimization weighting data.
Inventors: |
Burghardi, Steve R.; (Eden
Prairie, MN) ; Knudson, Brian J.; (Chanhassen,
MN) ; Peterson, Loren; (Loretto, MN) ; Cook,
David A.; (Coon Rapids, MN) ; Oedekoven, Mark A.;
(Minneapolis, MN) |
Correspondence
Address: |
CARGILL, INCORPORATED
LAW/24
15407 MCGINTY ROAD WEST
WAYZATA
MN
55391
US
|
Assignee: |
CAN Technologies, Inc.
|
Family ID: |
24972816 |
Appl. No.: |
10/985365 |
Filed: |
November 10, 2004 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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10985365 |
Nov 10, 2004 |
|
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09739550 |
Dec 15, 2000 |
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6681717 |
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Current U.S.
Class: |
119/51.01 |
Current CPC
Class: |
A23K 50/10 20160501;
A23K 20/142 20160501; A23K 40/00 20160501; A23K 50/30 20160501;
Y02A 40/818 20180101; A23K 50/80 20160501; A01K 5/02 20130101 |
Class at
Publication: |
119/051.01 |
International
Class: |
A01K 005/00; A01K
039/00 |
Claims
What is claimed is:
1. A system for determining customized feed for at least one
animal, the system comprising: a first memory portion configured to
store animal data representative of the characteristics of the
animal; a second memory portion configured to store feed data
representative of the feed ingredients located at at least one
location; a third memory portion configured to store evaluation
data representative of at least two evaluation criteria; a data
processing circuit in communication with the memory portions and
configured to generate nutrient profile data representative of a
nutrient profile for the animal based upon the animal data, the
data processing circuit being further configured to generate ration
data representative of a combination of ingredients from the first
and second locations, the ration data being generated by the data
processing circuit based upon the profile data, the first and
second feed data and the evaluation data; and a fourth memory
portion in communication with the data processing circuit and
configured to store optimization weighting data representative of
the effect a respective evaluation criteria has on the generation
of the ration data, the data processing circuit further generating
the ration data based upon the optimization weighting data.
2. The system of claim 1, wherein the animal data is representative
of at least one of a beginning weight of the animal; a desired
weight of the animal; an environment of the animal; a feed form; an
actual or desired production level of the animal; and a
relationship of animal muscle to fat of the animal.
3. The system of claim 1, wherein the feed ingredients include at
least one of a grain source, a protein source, a vitamin source, a
mineral source and a fat source.
4. The system of claim 3, wherein the animal data is representative
of at least one of a beginning weight of the animal; a desired
weight of the animal; an environment of the animal; a feed form; an
actual or desired production level of the animal; and a
relationship of animal muscle to fat of the animal.
5. The system of claim 4, wherein the evaluation criteria include
at least two of (i) animal production rate, (ii) the cost of feed
per unit animal weight gain, and (iii) the feed weight per unit
animal weight gain.
6. The system of claim 5, wherein the feed ingredients include at
least one of a grain source, a protein source, a vitamin source, a
mineral source and a fat source.
7. The system of claim 1, wherein the optimization weighting data
may be selected to cause one of the evaluation criteria to have no
effect on the generation of the ration data.
8. The system of claim 1, wherein the memory portions are portions
of a digital memory and a parallel data bus is coupled between the
digital memory and the data processing circuit to facilitate
communication therebetween.
9. The system of claim 1, wherein the memory portions are portions
of a plurality of digital memories and a network couples the
digital memories to the data processing circuit to facilitate
communication therebetween.
10. The system of claim 1, wherein the nutrient profile data is
representative of at least two nutrient components, and the system
further includes a fifth memory portion in communication with the
digital processor, the sixth memory portion storing variation data
representative of a range for the nutrient components of the
nutrient profile and the digital processor generates a set of
ration data based upon the variation data.
11. The system of claim 10, wherein the nutrient components include
at least true digestible lysine and net energy.
12. The system of claim 1, wherein the feed data includes an amount
for each feed ingredient.
13. The system of claim 12, wherein the amount for each feed
ingredient can be constrained according to one or more
criteria.
14. The system of claim 13, wherein the amount of each feed
ingredient can be constrained according to at least two
criteria.
15. A system for determining customized feed for at least one
animal, the system comprising: first memory means for storing
animal data representative of the characteristics of the animal;
second memory means for storing feed data representative of the
feed ingredients located at at least one location; third memory for
storing evaluation data representative of at least two evaluation
criteria; processing means for generating profile data
representative of a nutrient profile for the animal based upon the
animal data, processing means further generating ration data
representative of a combination of ingredients from the location,
the ration data being generated by the processing means based upon
the profile data, the feed data and the evaluation data; and fourth
memory means for storing optimization weighting data representative
of the effect a respective evaluation criteria has on the
generation of the ration data, the processing means further
generating the ration data based upon the optimization weighting
data.
16. The system of claim 15, wherein the animal data is
representative of at least one of a beginning weight of the animal;
a desired weight of the animal; an environment of the animal; a
feed form; an actual or desired production level of the animal; and
a relationship of animal muscle to fat of the animal and the feed
ingredients include at least one of a grain source, a protein
source, a vitamin source, a mineral source and a fat source.
17. The system of claim 15, wherein the evaluation criteria include
at least two of (i) animal production rate, (ii) the cost of feed
per unit animal weight gain, and (iii) the feed weight per unit
animal weight gain.
18. The system of claim 17, wherein the feed ingredients include at
least one of a grain source, a protein source, a vitamin source, a
mineral source and a fat source.
19. A method for determining customized feed for at least one
animal, the method comprising: storing animal data representative
of the characteristics of the animal; storing feed data
representative of the feed ingredients located at at least one
location; storing evaluation data representative of at least two
evaluation criteria; storing optimization weighting data
representative of the effect a respective evaluation criteria;
generating profile data representative of a nutrient profile for
the animal based upon the animal data; and generating ration data
representative of a combination of ingredients from the location,
the ration data being generated based upon the profile data, the
feed data, the evaluation data, and the optimization weighting
data.
20. The method of claim 19, wherein the feed data includes an
amount for each feed ingredient.
Description
CROSS REFERENCE TO RELATED PATENT APPLICATIONS
[0001] This application is a continuation of application Ser. No.
10/715,053, filed Nov. 17, 2003, which is a continuation of
application Ser. No. 09/739,550, filed Dec. 15, 2000, now U.S. Pat.
No. 6,681,717.
FIELD OF THE INVENTION
[0002] The present invention relates to a computerized system for
determining a customized feed for animals, such as cattle, swine,
poultry, fish, crustaceans and the like. In particular, the system
determines a feed mix based upon data relating to information such
as animal characteristics, available ingredients, speed of product
production, and cost of production.
BACKGROUND
[0003] In food production, and specifically producing animal
products such as milk, beef, pork, eggs, chicken, fish etc., there
is need to improve production efficiency. Production efficiency,
i.e. producing the maximum quantity of animal products while
minimizing the time and cost of production for those products, is
important in maintaining a competitive advantage.
[0004] A producer (i.e. a farmer, rancher, pork producer, and the
like) generally wants to maximize the amount of animal product
produced (e.g. gallons of milk, pounds of beef or pork produced)
while keeping the costs associated with feed at a low level in
order to achieve maximum animal productivity. The maximized amount
of animal product should be produced at a minimized cost to the
producer. Costs to the producer include the cost of feed needed to
produce the animal products, as well as the costs of related
equipment and facilities needed in the production of animal
products. In order to minimize the effect of fixed costs associated
with equipment and facilities, the maximum amount of animal product
should preferably be produced in a minimum time period.
[0005] Producers are constantly trying to increase these production
efficiencies. One way of increasing production efficiencies is by
altering the feed which animals are fed. For example, a feed with
certain amounts of nutrients can cause an animal to grow or produce
animal products quickly and/or perform better, whereas a different
feed with different amounts of nutrients may cause an animal to
grow or produce animal products on a more cost effective basis.
[0006] Current systems for creating animal feed are not fully
capable of helping producers evaluate and improve production
efficiencies. Current systems commonly generate an overall nutrient
profile which is related to a set of animal characteristics. Such
systems then look at the overall nutrient profile and compare what
nutrients may be had from the on-farm ingredients. From this
comparison, a "nutritional gap" can be calculated, i.e., the
nutritional requirements that the producer needs to fulfill his
production goals after accounting for the use of his on-site feed.
This nutritional gap is then compared to the nutritional components
which may be available from ingredients located at a supplier's
mill. Through a comparison of the nutritional gap and the
nutritional components available from the mill, current systems
allow a supplier to provide a cost effective custom feed which is
optimized to permit an animal to produce desired animal products on
a cost minimized basis.
[0007] Currently systems exist that are capable of taking the
amounts of on-farm ingredients to be used in the overall diet of
the animal into account. This is typically done by accounting for
the on-farm component of the animal's diet as a fixed input
parameter in the determination. It would be advantageous to be able
to modify the amounts of on-farm ingredients to be used in forming
the custom feed as part of the optimization process. Moreover,
current systems are generally limited to generating the custom feed
based on a single evaluation criteria, typically based on the cost
of the feed (e.g., on a cost of feed per unit of animal weight gain
basis). It would be advantageous to have a system which is capable
of utilizing more than one evaluation criteria in generating the
custom feed.
SUMMARY
[0008] One embodiment of the present invention provides a system
for determining customized feed for animals, such as farm
livestock, poultry, fish and crustaceans. The system stores animal
data representative of the characteristics of the animal, feed data
representative of the feed ingredients located at one or more
locations, and evaluation data representative of at least one
evaluation criteria. The evaluation criteria are generally related
to factors representative of animal productivity. An optimization
weighting is used to indicate the weight assigned to the evaluation
criteria. Examples of evaluation criteria include (i) animal
production rate (e.g., the rate of animal weight gain or the rate
of production of a food product such as milk or eggs); (ii) cost of
feed per unit animal weight gain; and (iii) feed weight per unit
animal weight gain. The system includes a data processing circuit,
which may be one or more programmed microprocessors, in
communication with a data storage device or devices which store the
data. The data processing circuit is configured to generate profile
data representative of a nutrient profile for the animals based
upon the animal data. In effect, the nutrient profile is a
description of the overall diet to be fed to the animals defined in
terms of a set of nutritional parameters ("nutrients"). Using the
profile data, the data processing circuit generates ration data
representative of a combination of ingredients from one or more
locations. The ration data is generated by the data processing
circuit based upon the profile data, the feed data, the evaluation
data, and the optimization weighting data.
[0009] Another embodiment of the system includes system for
determining customized feed for at least one animal. The system
includes first memory means for storing animal data representative
of the characteristics of the animal, second memory means for
storing feed data representative of the feed ingredients located at
at least one location, third memory for storing evaluation data
representative of at least two evaluation criteria, and processing
means for generating profile data representative of a nutrient
profile for the animal based upon the animal data, processing means
further generating ration data representative of a combination of
ingredients from the location, the ration data being generated by
the processing means based upon the profile data, the feed data and
the evaluation data. The system further includes fourth memory
means for storing optimization weighting data representative of the
effect a respective evaluation criteria has on the generation of
the ration data, the processing means further generating the ration
data based upon the optimization weighting data.
[0010] A further embodiment of the present invention provides a
method for determining customized feed for at least one animal. The
method includes storing animal data representative of the
characteristics of the animal, storing feed data representative of
the feed ingredients located at at least one location, storing
evaluation data representative of at least two evaluation criteria,
storing optimization weighting data representative of the effect a
respective evaluation criteria, generating profile data
representative of a nutrient profile for the animal based upon the
animal data; and generating ration data representative of a
combination of ingredients from the location, the ration data being
generated based upon the profile data, the feed data, the
evaluation data, and the optimization weighting data.
[0011] As modifications to the embodiments described herein,
systems and/or methods may rely on more than one optimizing
criteria and/or feed data representative of ingredients located at
more than one location. For example, ingredients which could be
used to create the ration may be located at the farm associated
with the animals as well as at the mill of an ingredient supplier.
Depending upon the requirements of the system, processing can be
consolidated in one processor or divided between processors in
communication via a network such as a LAN or the Internet.
Furthermore, the processors may be located in devices such as
workstations, portable PC's and/or hand held computers.
[0012] In other variations of the embodiments described herein, the
systems and/or methods may further include a memory portion in
communication with the digital processor which stores variation
data representative of a range for one or more nutrients of the
nutrient profile. The digital processor is capable of generating a
set of ration data based upon the variation data. A memory portion
of the system may store variation data which corresponds to
preselected incremental variations for the values assigned to one
or more individual nutrients in the nutritional profile.
[0013] Throughout this application, the text refers to various
embodiments of the system and/or method. The various embodiments
described are meant to provide a variety of exemplary examples and
should not be construed as descriptions of alternative species.
Moreover, it should be noted that the descriptions of the various
embodiments provided herein may be of overlapping scope. The
embodiments discussed herein are merely illustrative and are not
meant to limit the scope of the present invention.
BRIEF DESCRIPTION OF THE DRAWINGS
[0014] FIG. 1 is a general schematic representation of the data
flow in one embodiment of the present System.
[0015] FIG. 2 is a general schematic representation of the data
flow in another embodiment of the System which is designed to be
used to generate a custom product ("Custom Ration") and/or feed mix
from on-site ingredients ("On-Farm Ration") optimized for milk
production and/or quality.
[0016] FIG. 3 is a general schematic representation of the data
flow in a variation of the System shown in FIG. 1.
DETAILED DESCRIPTION
[0017] An exemplary system, and process which can be used in
producing a customized feed for animals, such as livestock,
poultry, fish or crustaceans is described herein. How the system
and process can increase production efficiencies by customizing
feed is also disclosed. It is particularly desirable if the system
and methods are capable of determining an optimized feed using one
or more evaluation criteria. Examples of suitable evaluation
criteria include a feed cost per unit animal weight gain basis, an
animal production rate basis (e.g., based upon a rate of animal
weight gain or a rate of production of an animal product, such as
milk or eggs), and a feed amount per unit of animal weight gain
basis.
[0018] In one embodiment of the present system, a computer system
may be used which has a processing unit that executes sequences of
instructions contained in memory. More specifically, execution of
the sequences of instructions causes the processing unit to perform
various operations, which are described herein. The instructions
may be loaded into a random access memory (RAM) for execution by
the processing unit from a read-only memory (ROM), a mass storage
device, or some other persistent storage. In other embodiments,
hardwired circuitry may be used in place of, or in combination
with, software instructions to, implement the present method. Thus,
the embodiments described herein are not limited to any specific
combination of hardware circuitry and/or software, nor to any
particular source for the instructions executed by the computer
system.
[0019] Creating a customized feed typically involves processing and
manipulating at least four basic data sets (see, e.g., FIG. 1):
first feed data representative of the collection of ingredients
located at a first location 1, second feed data representative of
the collection ingredients located at a second location 2, animal
data representative of characteristics of the animal 3 (e.g.,
parameters related to its genotype, production level, environment
and/or feeding regime), and evaluation criteria 4. As will be
explained below, very often first and second feed data
representative of sets of ingredients located at an on-farm site
(first ingredients 1 located at a first location) and ingredients
located at a supplier's mill site (second ingredients 2 located at
a second location) are used to generate the recommended mix of
ingredients to be fed to the animal. In many instances, the ration
data define an overall diet for the animal which includes custom
rations from more than one location (e.g., a custom ration from a
first location 7 and a custom ration from a second location 8 as
depicted in FIG. 1). These can be combined to create a customized
feed ("ration") which fulfills the animal data requirements while
meeting the evaluation criteria 4.
[0020] The evaluation criteria may be chosen from such suitable
criteria related to animal productivity as (i) animal production
rate, (ii) cost of feed per unit animal weight gain, and (iii) feed
weight per unit animal weight gain.
[0021] In some modified embodiments, the present system may include
additional memory portions for storing nutrient level constraints 5
and/or ingredient level constraints 6. This may be useful where,
for example, it has been established that higher levels of certain
nutritional components could pose a risk to the health of an animal
being fed the custom feed. For example, if the custom feed includes
some trace minerals, such as selenium, present in too great an
amount, the custom feed may have adverse health consequences to the
animal. Various embodiments of the present invention allow
constraints to be placed on the maximum and/or minimum amounts of
one or more nutrients in the profile data generated. In some
embodiments, this may be used together with the animal data as a
basis to calculate the profile data. These constraints may be
stored in a memory location as part of the system or the system may
permit an individual operator to input one or more constraints on
the amount of particular nutrient(s) in the profile data generated
by the system. Similarly, it may be desirable to limit the amounts
of one or more ingredients in either a custom product mix or in the
overall diet to be fed to the animal. For example, for ease of
formulation of a custom feed in pellet form it may be desirable to
limit the amount of certain ingredients and/or require the
inclusion of minimum amounts of specified ingredients.
[0022] The first data set that is generally input into the system
and subsequently stored in a memory portion includes data
representative of characteristics of the animal. Examples of types
of data representative of animal characteristics ("animal data")
include beginning weight of the animal; a desired weight of the
animal; an environment of the animal; a feed form; an actual or
desired production level of the animal; and a relationship of
animal muscle to fat of the animal. For example, the nutrient
profile generated for a particular animal can vary based upon a
number of different characteristics of the animal relating to one
or more of its genotype, environment, current condition (e.g.,
defined in terms of health and/or weight), desired production
level, feed form (e.g., meal or pellet), current production level,
desired final condition (e.g., defined in terms of final weight
and/or relationship of animal muscle to fat of the animal) and the
like. Tables 1 and 2 below list illustrative sets of animal
characteristics which can be used as a basis to generate
nutritional profiles to be used in designing custom rations
("custom feeds") for swine and dairy cattle, respectively.
1TABLE 1 Animal Characteristics Suitable for Generating a
Nutritional Profile for a Feed for Swine Animal Category Genotype
(lean gain) Finisher Effective Ambient Temperature Gilt Replacement
Temperature Grow Draft Prebred Bedding Sow % of pigs that are wet)
Gestation Pigs per pen Lactation Pig density (square feet per pig)
Artificial Insemination Boar Health Begin Weight Flooring Type End
Weight Total pigs born/litter Feed Disappearance (Intake) Litter
weight gain Feed Wastage Total pigs born/litter Feed Form
[0023]
2TABLE 2 Animal Characteristics Suitable for Generating a
Nutritional Profile for Dairy Cattle Target Milk Weight (volume)
Body Weight Target Milk Butterfat % Body Weight Change Target Milk
Protein % Body Condition Score (current) Current Milk Weight
(volume) Body Condition Score (desired) Current Milk Butterfat %
Actual Dry Matter Intake Current Milk Protein % Environmental
Temperature Percent of group in first lactation Environmental
Humidity Percent of group in second lactation Genotype
[0024] The animal data representative of the characteristics of the
animal may be inputted into a computer system with a memory portion
available and configured to store the data. The animal data
representative of the characteristics of the animal may be inputted
into the system by a variety of methods known to those skilled in
the art including a keyboard, mouse, touchpad, computer, internet
or other related device.
[0025] The system includes a data processing circuit which is
configured to generate profile data representative of a nutrient
profile for the animals based upon the animal data. In effect, the
nutrient profile is a description of the overall diet to be fed to
the animals defined in terms of a set of nutritional parameters
("nutrients"). Depending on the desired degree of sophistication of
the system, the profile data may include a relatively small set of
amounts of nutrients or large number of amounts of nutrients. Table
3 includes an illustrative list of nutrients that may be used
delineating profile data for animals such as pigs and dairy cattle.
Of course, the list of nutrients used in generating profile data
may differ for different types of livestock or other animals.
Tables 4 and 5 respectively contain lists of nutrients suitable for
use in generating nutritional profiles for swine and dairy cattle,
respectively.
[0026] The data processing circuit in the present system is also
configured to generate ration data representative of a combination
of ingredients from one or more locations. The ration data is
generated by the data processing circuit based upon the profile
data, feed data representative of the feed ingredients available at
the location(s) and evaluation data representative of one or more
evaluation criteria.
3TABLE 3 Nutrients Suitable for Generating a Nutritional Profile
Animal Fat Rumres Nfc Ascorbic Acid Salt Biotin Selenium Cal/Phos
Simple Sugar Chloride Sodium Choline Sol Rdp Chromium Sulfur Cobalt
Sw Obs Me Copper Thiamine Arginine (Total and/or Digestible) Total
Rdp Cystine (Total and/or Digestible) Verified Adf Isoleucine
(Total and/or Digestible) Verified Ash Leucine (Total and/or
Digestible) Verified Calcium Lysine (Total and/or Digestible)
Verified Dry Matt Methionine (Total and/or Digestible) Verified Fat
Phenylalanine (Total and/or Digestible) Verified Fiber Threonine
(Total and/or Digestible) Verified Hemi Tryptophan (Total and/or
Digestible) Verified Moisture Valine (Total and/or Digestible)
Verified Ndf Folic Acid Verified Neg Phosphate Verified Nel Iodine
Verified Nem Iron Verified Nfc Lactose Verified Phos Lasalocid
Verified Protein Magnesium Verified Rup Manganese Vitamin A
Monensin Vitamin B12 Niacin Vitamin B6 Potassium Vitamin D Protein
Vitamin E Pyridoxine Vitamin K Rh Index Zinc Riboflavin Rough Ndf
Rum Solsug
[0027]
4TABLE 4 Nutrients Suitable for Generating a Nutritional Profile
for Swine Biotin True Swine Digestible isoleucine Cal/Phos True
Swine Digestible lysine Choline True Swine Digestible methionine
Coppr Add True Swine Digestible threonine Folic Acid True Swine
Digestible tryptophan Iodine Add True Swine Digestible valine Iron
Add V Calcium Mang Add V Phos Niacin V Protein Pantotnc Vit A
Pyridoxine Vit D Riboflavin Vit E Salt Vit K Selenium Add Vitamin
B12 Sodium Zinc Sw Digphos Thiamine
[0028]
5TABLE 5 Nutrients Suitable for Generating a Nutritional Profile
for Dairy Cattle Acid Detergent Fiber Non-Protein Nitrogen Biotin
Phosphorus Calcium Potassium Chloride Protein Cobalt Rumen
Degradable Protein Copper Rumen Undegraded Alanine Dietary Cation
Anion Difference Rumen Undegraded Histidine Digestible Neutral
Detergent Fiber Rumen Undegraded Isoleucine Dry Matter Rumen
Undegraded Leucine Fat Rumen Undegraded Lysine Intestinally
Digestible Arginine Rumen Undegraded Methionine Intestinally
Digestible Histidine Rumen Undegraded Phenylalanine Intestinally
Digestible Isoleucine Rumen Undegraded Protein Intestinally
Digestible Leucine Rumen Undegraded Tryptophan Intestinally
Digestible Lysine Rumen Undegraded Valine Intestinally Digestible
Methionine Salt Intestinally Digestible Phenylalanine Selenium
Intestinally Digestible Threonine Sodium Intestinally Digestible
Tryptophan Soluble Protein Intestinally Digestible Valine Soluble
Sugar Iodine Starch Iron Sulfur Magnesium Verified Net Energy for
Lactation Manganese Vitamin A Neutral Detergent Fiber Vitamin D
Neutral Detergent Fiber from Vitamin E Roughage Zinc Niacin Non
Fiber Carbohydrates
[0029] Evaluation criteria are typically related to factors
representative of animal productivity and reflect an aspect of
production a producer would like to optimize. The present system
allows a producer to select evaluation criteria (e.g. cost/gain,
cost/output, animal production rate, and/or feed/gain) which fits
the producer's production goals. For example, a dairy producer may
focus on the cost of feed required to produce a unit of output
(cost/output), whereas a pork producer may focus on cost/gain or
rate of gain.
[0030] Examples of suitable animal production criteria which may be
used as evaluation criteria in the generation of ration data
include (i) animal production rate, (ii) the cost of feed per unit
animal weight gain, and (iii) the feed weight per unit animal
weight gain. The animal production rate may simply be a measure
representative of the rate of weight gain of the animal in question
(rate of gain). For example, a pork producer may wish to optimize
rate of gain by selecting a feed which maximizes the rate at which
a pig gains weight. This could be selected if a pig farmer was
interested in turning over production as quickly as possible in a
fixed asset which has limited space. The evaluation data may
include data representative of the cost of feed required to produce
a unit of weight gain of the animal ("cost/gain" basis). For
example, a pork producer may wish to optimize cost/gain by
selecting a feed which minimizes the feed cost required to make a
pig gain a unit of weight. The evaluation data can include data
representative of the amount of feed required to produce a unit of
gain (feed/gain). For example, a producer may wish to optimize the
feed/gain by selecting a feed which minimizes the amount of feed
required to produce a unit of gain. A producer might select this
criterion if they were faced with feed storage space
constraints.
[0031] Examples of other suitable animal production rates which may
be used as an evaluation criteria include rates of production of
food products, such as milk or eggs, from the animal. Other
suitable evaluation criteria include the cost of feed required to
produce a unit of output of a particular animal product
("cost/output"). For example, a milk producer may wish to optimize
the cost/output by selecting a feed which minimizes the cost of
feed required to produce a unit of milk. In addition to utilizing
evaluation data representative of only a single evaluation
criteria, the present system may be capable of using evaluation
data representative of a combination of two or more evaluation
criteria in generating the ration data. For example, when
considering an appropriate feed, a producer may wish to generate a
custom feed based on the rate of production as well as cost of the
feed (typically on a cost/gain basis).
[0032] Furthermore, the producer may choose to weight the relative
contributions of two or more evaluation criteria. The system may
include a data processing circuit which generates ration data based
in part upon a weighted average of more than one evaluation
criteria. In one specific embodiment, the system generates ration
data based in part upon a 70:30 weighted average of two evaluation
criteria (primary and secondary), such as a combination of cost of
feed per unit animal weight gain and animal production rate. The
system may also allow a user to alter the relative weighting
accorded to the various evaluation criteria selected.
[0033] For instance, in the example referred to above, the producer
may want to generate ration data using a combination of evaluation
criteria that is weighted 70% on a cost/gain basis and 30% on a
rate of animal weight gain basis. One method for providing such a
weighted optimization analysis is to generate one solution for
ration data using cost/gain as the sole evaluation criteria and
generating a second for ration data using rate of animal weight
gain as the sole evaluation criteria. Ration data which is
representative of the weighted combined solution can be achieved by
summing 70% of the amounts of ingredients from the cost/gain ration
data set and 30% of the amounts of ingredients from the rate of
gain ration data set. For example, in the instance where cost/gain
ration data (generated solely on a cost/gain basis) includes 10%
dehulled corn meal, and rate of gain ration data (generated solely
on a rate of gain basis) includes 15% dehulled corn meal, if a
producer chose cost/gain as the primary evaluation criteria the
ingredient mix in the diet will include roughly 70% of the 10%
dehulled corn meal requirement, and 30% of the 15% dehulled corn
meal requirement summed to produce the amount of dehulled corn meal
in the overall diet (i.e., circa 11.5% dehulled corn meal). This
weighted summation is then repeated for all the amounts of
ingredients present in the two custom diets generated by the two
approaches. As one skilled in the art will recognize, there are
other methods of generating ration data based on a weighted
combination of evaluation criteria. The present system can also be
configured to generate ration data based on other weightings of
combinations of two or more evaluation criteria (e.g., two
evaluation criteria weighted on either a 60:40 or 80:20 basis). In
some embodiments of the present system, the weighting factors
assigned to various evaluation criteria can themselves be input
parameter(s) chosen by a producer to reflect the needs of his/her
particular situation.
[0034] FIG. 2 depicts the general flow of data in one embodiment of
the present system. The system shown in FIG. 2 includes a data
processing circuit 30 configured to generate a nutrient profile 32
based on the animal data 31 and optional adjustments which may be
provided by a nutritionist. Other data processing circuits generate
lists of nutrient amounts associated with individual ingredients
available at an on-farm site 33 and manufacturing site 34. A data
processing circuit 36, which includes a linear program generates a
custom product based on evaluation criteria 35. The linear program
typically also generates the custom product solution based on
pricing data associated with both the on-farm and manufacturing
site ingredients. In one embodiment, retail and wholesale pricing
information may be normalized to allow the linear program to
facilitate consideration of potential ingredients with different
types of associated prices as the basis for a solution to a single
multivariable problem. The linear program is a mathematical model
capable of solving problems involving a large number of variables
limited by constraints using linear math functions. A variety of
different linear programs capable of solving problems of this type
are known to those of skill in the art. One example of a program of
this type is commercially available from Format International as
part of computer software system for solving complicated
multivariable problems.
[0035] Memory portions of the systems which store animal data,
evaluation data, and feed data representative of on-hand
ingredients and/or mill ingredients are in communication with a
data processing unit capable of generating ration data. The data
processing unit can include a data processing circuit or a digital
processing circuit. The memory portions which store the animal
data, feed data for on-hand and mill ingredients, and evaluation
data may be in communication with the data processing unit by
inputted keyboard commands, mouse commands, a network connection
with another computer, personal data assistants, via a modem
connection, via an internet, or via an intranet.
[0036] Data processing circuit(s) which include the linear program
can take input data (e.g., profile data, feed data, evaluation data
and ingredient constraint data) as a basis to compute ration data.
Ration data includes data specifying a combination of ingredients
solution which is solved to fulfill a desired nutrient profile
based on one or more evaluation criteria. Ration data generated by
the present system generally includes data representative of the
types and amounts of ingredients to be used to provide an overall
custom diet for an animal. The ration data provided by the system
generally also specifies a solution that is described in terms of a
combination of types and amounts of ingredients from a first
location (e.g., an on-farm location) and types and amounts of
ingredients from at least one additional site (e.g., one or more
supplier locations). Where the overall set of potential ingredients
includes ingredients located at more than one location, the custom
feed specified by the ration data may be made of ingredients
located at either a single location or from more than one location.
For example, the ration data may define a custom feed made up from
ingredients located solely at supplier location or made up from
ingredients located at both an on-farm location and a supplier
location.
[0037] The ration data generally include custom feed data
representative of a combination of amounts of the feed ingredients.
The custom feed data may specify the type and corresponding amounts
of the ingredients to be used in formulating the overall diet of an
animal. This may be made up from a set of ingredients available at
more than one location, e.g., from ingredients available at a
producer's site and as well as ingredients available at a supplier
location. The present system may also provide custom feed data
which specifies the types and amounts of ingredients to be used
from individual locations. For example, the custom feed data may
include a listing of the types and amounts of ingredients available
at a first location (e.g., on-farm ingredients) to be used to form
a first feed mix and a listing of the types and amounts of
ingredients available at a second location (e.g., ingredients
available at a supplier location) to be used to form a second feed
mix. In such instances, the custom feed data will typically also
specify the amounts of the first and second feed mixes that are to
be used to make up the overall custom diet for an animal.
[0038] The ration data typically includes amounts of a variety of
types of ingredients. The actual ingredients available at any
particular location can vary over time and will generally vary on a
regional basis as well as reflect the type of animal feed that is
typically produced and/or stored at the particular site. Commonly,
the ration data include feed data representative of amounts of
ingredients from a number of different ingredient categories, such
as a grain source, a protein source, a vitamin source, a mineral
source (e.g., a macromineral source and/or a trace mineral source)
and/or a fat source. Table 6 includes a list of exemplary
ingredients suitable for use in formulating custom feed mixes for a
variety of animals. Tables 7, 8 and 9 include lists of ingredients
which may be used in generating custom feed products for swine or
dairy cattle.
6TABLE 7 Ingredients Suitable for Use in Producing a Custom Feed
for a Finishing Diet for Swine Alimet Linseed Meal Bakery Product
L-Lysine HCl Beet Pulp Lt. Barley Brewers Rice L-Threonine Brown
Sugar Malt Sprouts Calcium Carb Meat And Bone Meal Cane Sugar
Menhaden Fish Canola Meal Molasses Cereal Fines Mono-Dical Phos Cg
Feed Monosod Phos Choline Oat Mill Byproducts Copper Sulfate Oat
Mill Byproducts Corn - Ground Fine Oats - Ground Corn Gluten Meal
Oats - Rolled Corn Oil Pork Bloodmeal Corn Starch Safflower Meal
Dehydrated Alfalfa Salt Distillers Grains With Soil Selenium Dried
Potato Waste Soybean Hulls Dynasol Soybean Meal Fat Soybean Oil Fat
Sprayed Sunflower Feather Meal Tryptosin Feeding Rate Wheat Midds
Fish Meal
[0039]
7TABLE 8 Ingredients Suitable for Use in Producing a Custom Feed
for Breeding Swine Alimet Methionine Animal Fat Mineral Oil Ascorb
Acid Molasses-Cane Bakery Product Mono-Dicalcium Phosphate
Bentonite Oat Hulls Blood Meal - Beef/Pork Red Flavor Calcium
Carbonate Rice Bran Cereal Fines Salt Choline Chloride Selenium
Copper Sulfate Soybean Hulls Corn Germ Meal Threonine Corn Gluten
Feed Tryptophan Distillers Grains With Solubles Vitamin E Dry
Methionine Hydroxy Analog Wheat Midds Fish Meal Wheat Starch Malt
Sprouts Zinc Oxide Meat And Bone Meal; Pork Carcass Zinc
Sulfate
[0040]
8TABLE 9 Ingredients Suitable for Producing a Custom Feed for Dairy
Cattle Calcium Carbonate Salt Copper Sulfate Selenium Corn Gluten
Meal Sodium Sesquicarbonate Fat Soybean Hulls Magnesium Oxide
Soybean Meal Meat And Bone Meal, Pork Trace Minerals Mono-Dical
Phos Urea Niacin Vitamin-E Pork Blood Meal Wheat Midds K/Mg/Sulfate
Zin-Pro Yeast
[0041] When feeding animals, producers may not be able to satisfy
nutritional requirements of the animals solely using on-hand
ingredients (e.g., on-farm ingredients). To satisfy the animal's
nutritional requirements, producers may desire to use on-hand
ingredients in conjunction with a custom feed product made up of
feed ingredients available from an outside supplier, such as a
mill, feed mixer, and the like. The outside supplier will commonly
have a range of ingredients available or on hand in their inventory
(e.g., corn in various forms, soybean meal, wheat mids, barley,
oats, animal fat, various vitamin supplements).
[0042] In addition to data specifying the types and amounts of
ingredients to be used to provide the overall custom diet for an
animal, the ration data generated by the present system can also
include other data associated with the overall custom diet.
Examples of such other data include cost data representative of a
cost associated with the custom feed data, feed weight data
representative of a feed weight associated with the custom feed
data, and performance data representative of projected animal
performance associated with the custom feed data. For example,
Table 10 below lists a number of categories of ration data that may
be useful in assisting a producer and/or supplier in evaluating a
custom feed with respect to productivity, animal performance and
cost effectiveness. The availability of these types of information
can provide a producer and/or supplier with additional information
concerning the effects of variations in dietary composition on
factors such as cost, volume of feed, wastage and animal
performance. As with the listing(s) of the types and amounts of
ingredients, the cost data and feed weight data can be
representative of costs and feed weights associated with the
overall custom diet and/or with feed mix(es) to be provided from
individual locations.
9TABLE 10 Illustrative Categories of Ration Data Associated with a
Custom Feed for Swine End Weight Lean Gain Days in Phase Lean % Avg
Daily Gain Effective Ambient Temp Avg Daily Feed Intake Cost of
Gain Total Feed Consumed Total Cost per phase Feed/Gain
[0043] In other variations of the embodiments described herein, the
systems and/or methods may also include a memory portion in
communication with the digital processor which stores variation
data representative of a range for one or more nutrient components
of the nutrient profile. The digital processor is capable of
generating a set of ration data based upon the variation data. The
memory portion may store variation data which correspond to
preselected incremental variations for the values assigned to one
or more individual nutrients in the nutritional profile. For
example, memory portion may store variation data which correspond
to preselected incremental positive and negative variations of the
values assigned to two individual nutrients, such as true
digestible lysine and net energy. The digital processor would
generate ration data corresponding to each of the eight possible
additional combinations of values for the two specified nutrients.
Together with the ration data associated with the original
nutritional profile, the resulting set of nine ration data
corresponding to the various combinations of values for each
specified nutrient (original value, original value plus an
increment; original value minus an increment) would make up a three
by three matrix of ration data. One example of this approach is
illustrated in Table 11 below. A general approach to generating a
set of ration data based upon variation data is depicted
schematically in FIG. 3. The determination of ration data for the
center point in the matrix ("Ration Data 5") corresponds to the
solution generated by the data processing circuit based on the
nutrient profile. In the example shown in Table 11, the nutrient
profile has values of 0.90% for true digestible lysine and 2150
kcal/kg for net energy. Each of the eight other ration data in the
set depicted in Table 11 corresponds to a ration data generated for
a modified nutrient profile in which the value for at least one
nutrient has been varied by a specified increment. For example,
Ration Data 1 represents ration data associated with a modified
nutrient profile has values of 0.95% for true digestible lysine and
2100 kcal/kg for net energy. Ration Data 6 represents ration data
associated with a modified nutrient profile in which only the value
for true digestible lysine (0.85%) has been varied from the values
in the nutrient profile. The generation of such a matrix can
facilitate an evaluation of the effect of incremental variations in
amounts of specified nutrient(s) on the assessment of optimum
ration data for a given evaluation criteria.
10TABLE 11 True Digestible Lysine 0.95% 0.90% 0.85% Net 2100 Ration
Data 1 Ration Data 2 Ration Data 3 Energy 2150 Ration Data 4 Ration
Data 5 Ration Data 6 (kcal/kg) 2200 Ration Data 7 Ration Data 8
Ration Data 9
[0044] The invention has been described with reference to various
specific and illustrative embodiments and techniques. However, it
should be understood that many variations and modifications may be
made while remaining within the spirit and scope of the
invention.
11TABLE 6 Exemplary Ingredients Suitable for Use in Formulating
Custom Feed Mixes Acidulated Soap Stocks Active Dry Yeast Alfalfa
Meal Alfalfa-Dehydrated Alimet Alka Culture Alkaten Almond Hulls
Ammonium Chloride Ammonium Lignin Ammonium Polyphosphate Ammonium
Sulfate Amprol Amprol Ethopaba Anhydrous Ammonia Appetein Apramycin
Arsanilic Acid Ascorb Acid Aspen Bedding Availa Avizyme Bacitracin
Zinc Bakery Product Barley Barley-Crimped Barley-Ground
Barley-Hulless Barley-Hulls Barley-Midds Barley-Needles
Barley-Rolled Barley-St. Bon. Barley-Whole Barley-With Enzyme
Baymag Beef Peanut Hulls Beef Peanut Meal Beet Beet Pulp Biotin
Biscuit By Product Black Beans Blood-Flash Dry Blueprint Rx Bone
Meal Brewers Rice Brix Cane Buckwheat Bugs Cage Calcium Calcium
Cake Calcium Chloride Calcium Formate Calcium Iodate Calcium
Sulfate Calciun Prop Calf Manna Canadian Peas Cane-Whey Canola Cake
Canola Fines Canola Meal Canola Oil Canola Oil Blender Canola Oil
Mix Canola Screenings Canola-Whole Carbadox Carob Germ Carob Meal
Cashew Nut By Product Catfish Offal Meal Choline Chloride Chromium
Tripicolinate Citrus Pulp Clopidol Cobalt Cobalt Carbonate Cobalt
Sulfate Cocoa Cake Cocoa Hulls Copper Oxide Copper Sulfate Corn
Chips Corn Chops Corn Coarse Cracked Corn-Coarse Ground Corn
Cob-Ground Corn Distillers Corn Flint Corn Flour Corn Germ Bran
Corn Germ Meal Corn Gluten Corn-High Oil Corn Kiblets Corn Meal
Dehulled Corn Oil Corn Residue Corn Starch Corn/Sugar Blend
Corn-Cracked Corn-Crimped Corn-Ground Fine Corn-Ground Roasted
Corn-Steam Flaked Corn-Steamed Corn-Whole Cottonseed Culled
Cottonseed Hull Cottonseed Meal Cottonseed Oil Cottonseed Whole
Coumaphos Culled Beans Danish Fishmeal Decoquinate Dextrose Diamond
V Yeast Disodium Phosphate Distillers Grains Dried Apple Pomace
Dried Brewers Yeast Dried Distillers Milo Dried Porcine Dried Whole
Milk Powder Duralass Enzyme Booster Epsom Salts Erythromycin
Extruded Grain Extruded Soy Flour Fat Feather Meal Feeding Oatmeal
Fenbendazole Fermacto Ferric Chloride Ferrou Cabonate Ferrous
Carbonate Ferrous Sulfate Fine Job's Tear Bran Fish Meal Fish
Flavoring Folic Acid French Fry Rejects Fresh Arome Fried Wheat
Noodles Gold Dye Gold Flavor Grain Dust Grain Screening Granite
Grit Grape Pomace Green Dye Green Flavor Guar Gum Hard Shell
Hemicellulose Extract Hemp Herring Meal Hominy Hygromycin Indian
Soybean Meal Iron Oxide-Red Iron-Oxide Yellow Job's Tear Broken
Seeds Kapok Seed Meal Kelp Meal Kem Wet Lactose Larvadex Lasalocid
Levams Hcl Limestone Linco Lincomix Lincomycin Linseed Meal Liquid
Fish Solubles Lupins Lysine Magnesium Magnesium Sulfate Malt Plant
By-Products Manganous Ox Maple Flavor Masonex Meat And Bone Meal
Meat And Bone Meal Meat Meal Mepron Methionine Millet Screenings
Millet White Millet-Ground Milo Binder Milo-Coarse Ground
Milo-Cracked Milo-Whole Mineral Flavor Mineral Oil Mixed Blood Meal
Molasses Molasses Blend Molasses Dried Molasses Standard Beet
Molasses Standard Cane Molasses-Pellet Mold Monensin Monoamonum
Phos Monosodium Glutamate Monosodium Phosphate Mung Bean Hulls
Mustard Meal High Fat Mustard Oil Mustard Shorts Narasin Natuphos
Niacin Nicarbazin Nitarsone Oat Cullets Oat Flour Oat Groats Oat
Hulls Oat Mill Byproducts Oat Screenings Oat Whole Cereal Oatmill
Feed Oats Flaked Oats-Ground Oats-Hulless Oats-Premium Oats-Rolled
Oats-Whole Oyster Shell Paddy Rice Palm Kernel Papain Papain Enzyme
Paprika Spent Meal Parboiled Broken Rice Pea By-Product Pea Flour
Peanut Meal Peanut Skins Pelcote Dusting Phosphate Phosphoric Acid
Phosphorus Phosphorus Defluorinated Pig Nectar Plant Waste
Poloxalene Popcorn Popcorn Screenings Porcine Plasma; Dried Pork
Bloodmeal Porzyme Posistac Potassium Bicarbonate Potassium
Carbonate Potassium Magnesium Sulfate Potassium Sulfate Potato
Chips Poultry Blood/Feather Meal Poultry Blood Meal Poultry
Byproduct Predispersed Clay Probios Procain Penicillen Propionic
Acid Propylene Glycol Pyran Tart Pyridoxine Quest Anise Rabon
Rapeseed Meal Red Flavor Red Millet Riboflavin Rice Bran Rice
By-Products Fractions Rice Dust Rice Ground Rice Hulls Rice Mill
By-Product Rice Rejects Ground Roxarsone Rumen Paunch Rumensin Rye
Rye Distillers Rye With Enzymes Safflower Meal Safflower Oil
Safflower Seed Sago Meal Salinomycin Salt Scallop Meal Seaweed Meal
Selenium Shell Aid Shrimp Byproduct Silkworms Sipernate Sodium
Acetate Sodium Benzoate Sodium Bicarbonate Sodium Molybdate Sodium
Sesquicarbonate Sodium Sulfate Solulac Soweena Soy Flour Soy Pass
Soy Protein Concentrate Soybean Cake Soybean Curd By- Product
Soybean Dehulled Milk By-Product Soybean Hulls Soybean Mill Run
Soybean Oil Soybean Residue Soybeans Extruded Soybeans-Roasted
Soycorn Extruded Spray Dried Egg Standard Micro Premix Starch
Molasses Steam Flaked Corn Steam Flaked Wheat Sugar (Cane)
Sulfamex-Ormeto Sulfur Sulfur Sunflower Meal Sunflower Seed Tallow
Fancy Tallow-Die Tallow-Mixer Tapioca Meal Tapioca Promeance
Taurine Terramycin Thiabenzol Thiamine Mono Threonine Tiamulin
Tilmicosin Tomato Pomace Trace Min Tricalcium Phosphate Triticale
Tryptophan Tryptosine Tuna Offal Meal Tylan Tylosin Urea Vegetable
Oil Blend Virginiamycin Vitamin A Vitamin B Complex Vitamin B12
Vitamin D3 Vitamin E Walnut Meal Wheat Bran Wheat Coarse Ground
Wheat Germ Meal Wheat Gluten Wheat Meal Shredded Wheat Millrun
Wheat Mix Wheat Noodles Low Fat Wheat Red Dog Wheat Starch Wheat
Straw Wheat With Enzyme Wheat-Ground Wheat-Rolled Wheat-Whole Whey
Dried Whey Permeate Whey Protein Concentrate Whey-Product Dried
Yeast Brewer Dried Yeast Sugar Cane Zinc Zinc Oxide Zoalene
* * * * *