U.S. patent application number 11/191238 was filed with the patent office on 2006-02-23 for system and method for optimizing animal production based on a target output characteristic.
This patent application is currently assigned to CAN Technologies, Inc.. Invention is credited to Daniel Barziza, Steve R. Burghardi, David A. Cook, Gregory L. Engelke, Donald W. Giesting, Jennifer L.G. van de Ligt, Bruce Brim McGoogan, Michael A. Messman, Mark D. Newcomb.
Application Number | 20060041419 11/191238 |
Document ID | / |
Family ID | 35107022 |
Filed Date | 2006-02-23 |
United States Patent
Application |
20060041419 |
Kind Code |
A1 |
Newcomb; Mark D. ; et
al. |
February 23, 2006 |
System and method for optimizing animal production based on a
target output characteristic
Abstract
A system for generating optimized values for variable inputs to
an animal production system. The system includes a simulator engine
configured to receive a plurality of animal information inputs and
generate a performance projection. At least one of the animal
information inputs is designated as a variable input. The system
further includes an enterprise supervisor engine configured to
generate an optimized value for the at least one variable input
based on an optimization criteria for at least one target output
characteristic.
Inventors: |
Newcomb; Mark D.;
(Independence, MN) ; Barziza; Daniel; (Belle
Plaine, MN) ; Burghardi; Steve R.; (Eden Prairie,
MN) ; Cook; David A.; (Coon Rapids, MN) ;
Engelke; Gregory L.; (New Brighton, MN) ; Giesting;
Donald W.; (Minnetonka, MN) ; McGoogan; Bruce
Brim; (Plymouth, MN) ; Messman; Michael A.;
(Becker, MN) ; Ligt; Jennifer L.G. van de;
(Brooklyn Park, MN) |
Correspondence
Address: |
CARGILL, INCORPORATED
LAW/24
15407 MCGINTY ROAD WEST
WAYZATA
MN
55391
US
|
Assignee: |
CAN Technologies, Inc.
|
Family ID: |
35107022 |
Appl. No.: |
11/191238 |
Filed: |
July 27, 2005 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
10902504 |
Jul 29, 2004 |
|
|
|
11191238 |
Jul 27, 2005 |
|
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Current U.S.
Class: |
703/22 |
Current CPC
Class: |
A01K 5/02 20130101; A23K
10/00 20160501; G06Q 10/101 20130101; G16H 20/60 20180101; G06Q
10/00 20130101; G06Q 10/0635 20130101; G06Q 10/04 20130101 |
Class at
Publication: |
703/022 |
International
Class: |
G06F 9/45 20060101
G06F009/45 |
Claims
1. A system for generating optimized values for variable inputs to
an animal production system, comprising: a simulator engine
configured to receive a plurality of animal information inputs and
generate a performance projection, wherein at least one of the
animal information inputs is designated as a variable input; and an
enterprise supervisor engine configured to generate an optimized
value for the at least one variable input based on an optimization
criteria for at least one target output characteristic.
2. The system of claim 1, further including a formulator engine,
the formulator engine configured to receive animal feed ingredient
information and generate the animal feed formulation composed of
the animal feed ingredients based on the performance
projection.
3. The system of claim 1, wherein generating an optimized value for
the at least one variable input includes providing a projected
effect for the target output characteristic based on the
modification to the at least one variable input.
4. The system of claim 1, wherein the variable input is one of an
animal factor, an environmental factor, an animal feed formulation,
and an economic factor.
5. The system of claim 1, wherein the simulator engine includes an
animal performance simulator configured to generate an animal
performance profile based upon the target output characteristic and
the animal information input including at least one variable
input.
6. The system of claim 5, wherein the enterprise supervisor engine
is configured to actuate the simulator engine based upon variations
in the variable input to generate a plurality of animal performance
profiles.
7. The system of claim 6, wherein the enterprise supervisor is
further configured to select an optimized value for the at least
one variable input based on application of the at least one
optimization criteria to the plurality of animal performance
profiles.
8. A method for determining optimized values for inputs to an
animal production system, comprising: receiving a plurality of
animal information inputs, wherein at least one of the animal
information inputs is designated as a variable input; receiving a
target output characteristic; generating at least one performance
projection based on the animal information inputs; and generating
an optimized value for the at least one variable input based on the
at least one performance projection, the target output
characteristic and at least one optimization criteria.
9. The method of claim 8, further including generating at least one
animal feed formulation composed of animal feed ingredients based
on the target output characteristic.
10. The method of claim 9, further including optimizing the at
least one animal feed formulation according to at least one
optimization criteria.
11. The method of claim 8, wherein generating an optimized value
for the at least one variable input includes providing an effect of
modification to the at least one variable input.
12. The method of claim 8, wherein the variable input is one of an
animal factor, an environmental factor, an animal feed, and an
economic factor.
13. The method of claim 8, further including generating a plurality
of animal performance profiles based upon the animal feed
formulation information and the animal information input including
at least one variable input.
14. The method of claim 13, further including generating a
plurality of animal performance profiles based on variations in the
at least one variable input.
15. The method of claim 14, further including selecting a preferred
value for the at least one variable input based on application of
the at least one optimization criteria to the plurality of animal
performance profiles.
16. The method of claim 8, further including iteratively generating
a plurality of animal performance profiles based on variation of
the at least one variable input.
17. The method of claim 8, wherein the optimization criteria
includes at least one of productivity per unit of ammonia released,
productivity per unit of phosphorous released, water exchange rate,
and aeration rate.
18. An animal production optimization system, comprising: an
optimization engine, having an objective function program therein,
configured to receive a target output characteristic; and an animal
production modeling system configured to receive animal information
input, including at least one variable input, receive feed
formulation input, and provide at least one modeling output to the
optimization engine, the modeling output including a value for the
target output characteristic, wherein the optimization engine
utilizes the objective function program to provide an optimized
solution for the at least one variable input based on the modeling
output and the value for the target output characteristic.
19. The animal production optimization system of claim 18, further
including a user interface configured to allow the designation, by
a user, of one or more variable inputs and selection of a target
output characteristic.
20. The animal production optimization system of claim 18, further
including a formulator engine configured to generate to the feed
formulation input.
21. The animal production optimization system of claim 18, wherein
optimizing the objective function includes iteratively generating
modeling output based on variations to the one or more variable
input.
22. The animal production optimization system of claim 18, wherein
the variable input is one of an animal factor, an environmental
factor, and an economic factor.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation-in-part of U.S.
application Ser. No. 10/902,504, filed Jul. 29, 2004, the entire
content of which is hereby incorporated by reference.
BACKGROUND
[0002] The present invention relates generally to the field of
systems for and methods of animal production. More particularly,
the present invention relates to systems for and methods of
optimizing animal production to produce output having one or more
nutrient characteristics.
[0003] An animal production system may include any type of system
or operation utilized in producing animals or animal based
products. Examples may include farms, ranches, aquaculture farms,
animal breeding facilities, etc. Animal production facilities may
vary widely in scale, type of animal, location, production purpose,
etc. However, almost all animal production facilities can benefit
from identifying and implementing improvements to production
efficiency. Improvements to production efficiency can include
anything that results in increased production results, improved
proportional output of desired products versus less desirable
products (e.g. lean vs. fat), and/or decreased production
costs.
[0004] A producer (i.e. a farmer, rancher, aquaculture specialist,
etc.) generally benefits from maximizing the amount or quality of
the product produced by an animal (e.g. gallons of milk, pounds of
meat, quality of meat, amount of eggs, nutritional content of eggs
produced, amount of work, hair/coat appearance/health status, etc.)
while reducing the cost for the inputs associated with that
production. Exemplary inputs may include animal feed, animal
facilities, animal production equipment, labor, medicine, etc.
[0005] Animal feeds are compositions of a large variety of raw
materials or ingredients. The ingredients can be selected to
optimize the amount of any given nutrient or combination of
nutrients in an animal feed product based upon the nutrient
composition of the ingredients used.
[0006] The nutritional composition of any one feed ingredient can
be used in combination with the nutritional composition of every
other ingredient in the feed to produce an animal feed that
optimizes with a potential maximum or minimum evaluation criteria.
One example of an evaluation criteria is nutrient characteristics
of output produced by an animal. For example, a particular cow feed
composition can be made that will deliver an improved balance of
essential amino acids post ruminally. This has been shown to have
the effect of increasing the protein content of the cow's milk
production.
[0007] What is needed is methods and systems for formulating an
animal feed to produce output having one or more characteristics.
Further, there is a need for a system and method to recommend
changes to one or more variable inputs to an animal production
system to produce output having one or more characteristics.
SUMMARY
[0008] One embodiment of the invention relates to a system for
generating optimized values for variable inputs to an animal
production system. The system includes a simulator engine
configured to receive a plurality of animal information inputs and
generate a performance projection. At least one of the animal
information inputs is designated as a variable input. The system
further includes an enterprise supervisor engine configured to
generate an optimized value for the at least one variable input
based on an optimization criteria for at least one target output
characteristic.
[0009] Another embodiment of the invention relates to a method for
determining optimized values for inputs to an animal production
system. The method includes receiving a plurality of animal
information inputs. At least one of the animal information inputs
is designated as a variable input. The method further includes
receiving a target output characteristic, generating at least one
performance projection based on the animal information inputs, and
generating an optimized value for the at least one variable input
based on the at least one performance projection and the target
output characteristic and at least one optimization criteria.
[0010] Yet another embodiment of the invention relates to an animal
production optimization system. The system includes an optimization
engine configured to receive a target output characteristic. The
optimization engine has an objective function program. The system
further includes an animal production modeling system configured to
receive animal information input, including at least one variable
input, receive feed formulation input, and provide at least one
modeling output to the optimization engine. The modeling output
includes a value for the target output characteristic. The
optimization engine utilizes the objective function program to
provide an optimized solution for the at least one variable input
based on the modeling output and the value for the target output
characteristic.
[0011] Other features and advantages of the present invention will
become apparent to those skilled in the art from the following
detailed description and accompanying drawings. It should be
understood, however, that the detailed description and specific
examples, while indicating preferred embodiments of the present
invention, are given by way of illustration and not limitation.
Many modifications and changes within the scope of the present
invention may be made without departing from the spirit thereof,
and the invention includes all such modifications.
BRIEF DESCRIPTION OF THE DRAWINGS
[0012] The exemplary embodiments will hereafter be described with
reference to the accompanying drawings, wherein like numerals
depict like elements, and:
[0013] FIG. 1 is a general block diagram illustrating an animal
production optimization system, according to an exemplary
embodiment;
[0014] FIG. 2 is a general block diagram illustrating an enterprise
supervisor for an animal production optimization system, according
to an exemplary embodiment;
[0015] FIG. 3 is a general block diagram illustrating a simulator
for an animal production system, according to an exemplary
embodiment;
[0016] FIG. 4 is a general block diagram illustrating an
ingredients engine and a formulator for an animal production
system, according to an exemplary embodiment; and
[0017] FIG. 5 is a flowchart illustrating a method for animal
production optimization, according to an exemplary embodiment.
DETAILED DESCRIPTION
[0018] In the following description, for the purposes of
explanation, numerous specific details are set forth in order to
provide a thorough understanding of the present invention. It will
be evident to one skilled in the art, however, that the exemplary
embodiments may be practiced without these specific details. In
other instances, structures and devices are shown in diagram form
in order to facilitate description of the exemplary
embodiments.
[0019] In at least one exemplary embodiment illustrated below, a
computer system is described which has a central processing unit
(CPU) that executes sequences of instructions contained in a
memory. More specifically, execution of the sequences of
instructions causes the CPU to perform steps, which are described
below. The instructions may be loaded into a random access memory
(RAM) for execution by the CPU from a read-only memory (ROM), a
mass storage device, or some other persistent storage. In other
embodiments, multiple workstations, databases, processes, or
computers can be utilized. In yet other embodiments, hardwired
circuitry may be used in place of, or in combination with, software
instructions to implement the functions described. Thus, the
embodiments described herein are not limited to any particular
source for the instructions executed by the computer system.
[0020] Referring now to FIG. 1, a general block diagram is shown
illustrating an animal production optimization system 100,
according to an exemplary embodiment. System 100 includes an
enterprise supervisor 200, a simulator 300, an ingredient engine
400, and a formulator 500.
[0021] System 100 may be implemented utilizing a single or multiple
computing systems. For example, where system 100 is implemented
using a single computing system, each of enterprise supervisor 200,
simulator 300, ingredient engine 400, and formulator 500 may be
implemented on the computing system as computer programs, discrete
processors, subsystems, etc. Alternatively, where system 100 is
implemented using multiple computers, each of enterprise supervisor
200, simulator 300, ingredient engine 400, and formulator 500 may
be implemented using a separate computing system. Each separate
computing system may further include hardware configured for
communicating with the other components of system 100 over a
network. According to yet another embodiment, system 100 may be
implemented as a combination of single computing systems
implementing multiple processes and distributed systems.
[0022] System 100 is configured to receive animal information input
including at least one variable input and analyze the received
information to determine whether variation in one or more of the
variable inputs will increase animal productivity or satisfy some
other optimization criteria. Animal productivity may be a relative
measure of the amount, type, or quality of output an animal
produces relative to the expense associated with that production.
Animal information input can include any type of information
associated with an animal production system. For example, animal
information input may be associated with a specific animal or group
of animals or type of animals, an animal's environment, an economy
related to the animal production, etc. Animal productivity may
further be configured to include positive and negative outputs
associated with the production. For example, animal productivity
may be configured to represent harmful gaseous emissions as an
expense (based on either financial costs associated with clean up
or the negative impact on the environment), reducing the overall
productivity.
[0023] Information associated with a specific animal or a group or
type of animals may include, but is not limited to, a species, a
state, an age, a production level, a job, a size (e.g. current,
target, variability around, etc.), a morphology (e.g. intestinal),
a body mass composition, an appearance, a genotype, a composition
of output, a collection of microbial information, health status, a
color, etc. The information associated with a specific animal may
be any type of information relevant for determining the
productivity of the animal.
[0024] Species information can include a designation of any type or
class of animals such as domestic livestock, wild game, pets,
aquatic species, humans, or any other type of biological organism.
Livestock may include, but is not limited to, swine, dairy, beef,
equine, sheep, goats, and poultry. Wild game may include, but is
not limited to, ruminants, such as deer, elk, bison, etc., game
birds, zoo animals, etc. Pets may include, but are not limited to,
dogs, cats, birds, rodents, fish, lizards, etc. Aquatic species may
include, but are not limited to, shrimp, fish (production), frogs,
alligators, turtles, crabs, eels, crayfish, etc. and include those
species grown for productive purposes (e.g., food products).
[0025] Animal state may include any reference or classification of
animals that may affect the input requirement or production outputs
for an animal. Examples may include, but are not limited to, a
reproductive state, including gestation and egg laying, a lactation
state, a health state or stress level, a maintenance state, an
obese state, an underfed or restricted-fed state, a molting state,
a seasonal-based state, a compensatory growth, repair or recovery
state, a nutritional state, a working or athletic or competitive
state, etc. Animal health states or stress level may further
include multiple sub-states such as normal, compromised,
post-traumatic (e.g. wean, mixing with new pen mates, sale, injury,
transition to lactation, etc.), chronic illness, acute illness,
immune response, an environmental stress, etc.
[0026] Animal age may include an actual age or a physiological
state associated with an age. Examples of physiologic states may
include a developmental state, a reproductive state including
cycles, such as stage and number of pregnancies, a lactation state,
a growth state, a maintenance state, an adolescent state, a
geriatric state, etc.
[0027] Animal job may include a physiologic state as described
above, such as gestation, lactation, growth, egg production, etc.
Animal job may further include the animal's daily routine or actual
job, especially with reference to canine and equines. Animal job
may also include an animal movement allowance, such as whether the
animal is generally confined versus allowed free movement in a
pasture, or, for an aquatic animal, the different water flows the
aquatic animal experiences, etc.
[0028] Animal size may include the actual weight, height, length,
circumference, body mass index, mouth gape, etc. of the animal. The
animal size may further include recent changes in animal size, such
as whether the animal is experiencing weight loss, weight gain,
growth in height or length, changes in circumference, etc.
[0029] Animal morphology includes a body shape exhibited by an
animal. For example, a body shape may include a long body, a short
body, a roundish body, etc. Animal morphology may further include
distinct measurement of internal organ tissue changes such as the
length of intestinal villi, depth of intestinal crypts, and/or
other organ sizes or shapes.
[0030] Animal body mass composition may include a variety of
composition information such as a fatty acid profile, a vitamin E
status, a degree of pigmentation, a predicted body mass
composition, etc. The body mass composition generally is a
representation of the percentage or amount of any particular
component of body mass, such as lean muscle, water, fat, etc. The
body mass composition may further include separate representations
composition for individual body parts/sections. For example, body
mass composition may include edible component compositions such as
fillet yield, breast meat yield, tail meat yield, etc.
[0031] Animal appearance may include any measure or representation
of an animal appearance. Examples can include the glossiness of an
animal's coat, an animal's pigmentation, muscle tone, feather
quality, feather cover, etc.
[0032] Animal genotype may include any representation of all or
part of the genetic constitution of an individual or group. For
example, an animal genotype may include DNA markers associated with
specific traits, sequencing specific segments of DNA, etc. For
example, the genotype may define the genetic capability to grow
lean tissue at a specific rate or to deposit intramuscular fat for
enhanced leanness or marbling, respectively. Additionally, genotype
may be defined by phenotypic expression of traits linked to
genotypic capacity such as the innate capacity for milk production,
protein accretion, work, etc.
[0033] Composition of output may include the composition of a
product produced by an animal. For example, the composition of
output may include the nutrient levels found in eggs produced by
poultry or milk produced by dairy cows, the amount, distribution,
and/or composition of fat in meat products, a flavor and texture
profile for a meat product, interrelationship between compositional
part ratios, etc.
[0034] Microbial and/or enzyme information may include current
microbial populations within an animal or within an animal's
environment. The microbial and/or enzyme information may include
measures of the quantity or proportion of gram positive or negative
species or other classifications such as aerobes, anaerobes,
salmonella species, E. coli strains, etc. Enzyme information may
include the current content, quantity and/or composition of any
enzyme sub-type or activation state, such as protease, amylase,
and/or lipase, produced by the pancreas, produced within the
gastrointestinal tract, enzymes produced by a microbial population,
a microbial community relationship at various ages, etc. Microbial
and/or enzyme information may further include information about
potential nutritional biomass represented by the current and/or a
suggested microbial community that may be used as a feed source for
some species (e.g., ruminants, aquatic species, etc.). The
microbial and/or enzymatic environment may be monitored using any
of a variety of techniques that are known in the art, such as
cpn60, other molecular microbiological methods, and in vitro
simulation of animal systems or sub-systems.
[0035] Animal information input associated with an animal or group
of animals' environment may include, but is not limited to, factors
related specifically to the environment, factors related to the
animal production facility, etc. Animal environment may include any
factors not associated with the animal that have an effect on the
productivity of the animal or group of animals.
[0036] Examples of animal information input related to the
environment may include ambient temperature, wind speed or draft,
photoperiod or the amount of daylight exposure, light intensity,
light wave length, light cycle, acclimation, seasonal effects,
humidity, air quality, water quality, water flow rate, water
salinity, water hardness, water alkalinity, water acidity, aeration
rate, system substrate, filter surface area, filtration load
capacity, ammonia levels, geographic location, mud score, etc. The
environmental information may further include detailed information
regarding the system containing the animal or animals, such as
system size (e.g. the size in square meters, size in square
centimeters, hectares, acres, volume, etc.), system type (pens,
cages, etc.), system preparation such as using liming, discing,
etc., aeration rate, system type, etc. Although some environmental
factors are beyond the control of a producer, the factors can
usually be modified or regulated by the producer. For example, the
producer may reduce draft by closing vents, raise ambient
temperature by including heaters or even relocating or moving
certain animal production operations to a better climate for
increasing productivity. According to another example, an aqua
producer may modify nutrient inputs to an aquatic environment by
altering a feed design or feeding program for the animals in the
environment. According to an exemplary embodiment, animal
information input related to the environment may be generated
automatically using an environmental appraisal system (EAS) to
calculate a thermal impact estimate for an animal and to provide
measurements for the animal's current environment.
[0037] Examples of animal information input related to a production
facility may include animal density, animal population interaction,
feeder type, feeder system, feeder timing and distribution,
pathogen loads, bedding type, type of confinement, facility type,
feathering, lighting intensity, lighting time patterns, time in
holding pen, time away from feed, etc. Animal information input for
a production facility may be modified by a producer to increase
productivity or address other production goals. For example, a
producer may build additional facilities to reduce population
density, obtain additional or different types of feeding systems,
modify the type of confinement, etc.
[0038] Animal information input associated with economic factors
may include, but is not limited to, animal market information.
Animal market information may include, but is not limited to,
historical, current and/or projected prices for outputs, market
timing information, geographic market information, product market
type (e.g., live or carcass-based), etc.
[0039] Animal information inputs may further include any of a
variety of inputs that are not easily classifiable into a discrete
group. Examples may include an animal expected output (e.g., milk
yield, product composition, body composition, etc.), a user defined
requirement, a risk tolerance, an animal mixing (e.g., mixing
different animals), variations with an animal grouping, etc., buyer
or market requirements (e.g. Angus beef, Parma hams, milk for
particular cheeses, a grade for tuna, etc.), expected and/or
targeted growth curves, survival rates, expected harvest dates,
etc.
[0040] The above described animal information input may include
information that is directly received from a user or operator
through a user interface, as will be described below with reference
to FIG. 2. Alternatively, the animal information input or some part
of the input may be retrieved from a database or other information
source.
[0041] Further, some of the inputs may be dependent inputs that are
calculated based on one or more other inputs or values. For
example, an animal's stress level may be determined or estimated
based on population density, recent weight loss, ambient
temperature, metabolic indicators such as glucose or cortisol
levels, etc. Each calculated value may include an option enabling a
user to manually override the calculated value. Similarly, immune
states may vary according to age, nutrient types and input level,
microbial challenges, maternal passive immunity provision, etc.
[0042] Yet further, each animal information input may include a
variety of information associated with that input. For example,
each animal information input may include one or more subfields
based on the content of the animal information input. For example,
where an indication is provided that an animal is in a stressed
state, subfields may be received indicating the nature and severity
of the stress.
[0043] According to an exemplary embodiment, the animal information
input includes a capability to designate any of the animal
information inputs as a variable input. A variable input may be any
input that a user has the ability to modify or control. For
example, a user may designate ambient temperature as a variable
input based on the ability to modify the ambient temperature
through a variety of methods such as heating, cooling, venting,
etc. According to an alternative embodiment, system 100 may be
configured to automatically recommend specific animal information
inputs as variable inputs based on their effect on productivity or
satisfying the optimization criteria, as will be further discussed
below with reference to FIG. 2.
[0044] Designation of a variable input may require submission of
additional information, such as a cost and/or benefit of variation
of the variable input, recommended degrees of variation for
optimization testing, etc. Alternatively, the additional
information may be stored and retrievable from within system 100 or
an associated database.
[0045] The animal information inputs may further include target
values as well as current values. A target value may include a
desirable level for animal productivity or some aspect of animal
productivity. For example, a producer may wish to target a specific
nutrient level for eggs produced by poultry. Therefore, the
producer may enter current nutrient levels for eggs currently being
produced as well as target nutrient values for the eggs. According
to another example, a current size breakdown for shrimp in a pond
versus a potential size breakdown. The target values and current
values may be utilized by system 100 to make changes in an animal
feed formulation or to make changes to variable inputs as will be
described further below. Further, the target values may be viewed
as equality constraints and/or inequality constraints for the
optimization problem.
[0046] Table 1 below lists exemplary animal information inputs that
may be provided as inputs to animal production optimization system
100. This listing of potential animal information inputs is
exemplary and not exclusive. According to an exemplary embodiment,
any one or more of the listed animal information inputs can be
designated as a variable input. TABLE-US-00001 TABLE 1 General
Characteristics Impact of the ration on the greater Quantity and/or
composition Quantity and/or environment: (e.g. nitrogen,
phosphorus, composition of urine etc.) of manure or litter per
animal Quantity and/or quality of odor from facility Swine
Characteristics Sow reproductive performance No. of pigs born No.
of pigs born alive No. of pigs weaned Piglets birth weight
Uniformity of baby pigs Mortality of baby pigs Piglets weaning
weight Sow body condition score Sow lactation back fat loss Sow
lactation weight loss Interval weaning to estrus Sow longevity
Working boar Body condition score Working frequency Semen quality
Finisher Average daily gain Average daily lean gain Average daily
feed intake per weight gain Average daily feed intake per Feed
wastage Feed form lean gain Mortality Days to market Feed cost per
kg gain Feed cost per kg lean gain Medication usage per pig
Dressing percentage Lean percentage Back fat thickness Fatty acid
composition Evaluation Criteria for Environment Thermal environment
(Draft, Air quality (Dust, Humidity, Pig/pen Floor type, Bedding,
Insulation) Ammonia, Carbon dioxide, etc) Pig density Health
condition Feeder type Pigs/feeder hole Water quality and quantity
Immune status Evaluation Criteria for appearance Hair coat
condition Skin color Ham shape Body shape and length Evaluation
Criteria for meat/fat quality Meat and fat color Iodine value Fatty
acid profile PSE Juiciness Flavor Tenderness Marbling score Water
holding capacity Evaluation Criteria for Health Suckling piglets
Eye condition (dry and dirty or Skin condition (elastic or Hair
condition (dense or bright and vital eyes) dry) and color (pink or
coarse) pale) Dirtiness of around anus Breathe with open mouth
Belly condition Finisher Respiratory disease Body temperature
Cannibalism (tail, ear, belly biting) Skin and hair condition
(mange Stool condition Swollen knee and ankle and parasites) joint
Dirtiness of around eyes Nose condition Respiratory sound
(Difficulties in breathing) Activity Microbial profile or levels
Sows MMA (Mastitis, Metritis, Stool condition Abortion and
stillbirth Agaclactia) (constipation) Wet belly Body shaking
Vaginal and uterine prolapse Body condition score Interval weaning
to estrus Feed intake (sick sows eat less) Leg problem Body
temperature Dairy Characteristics Cow reproductive performance
Breeding per conception Live birth Days to first estrous Calf birth
weight Days open Days to cleaning Calf weaning weight Cow body
condition score MUN and BUN Cow body reserve change Calving
interval Blood hormones progesterone and estrogen Lactation Milk
per day Body fatty acid loss or gain Average daily feed intake per
kg milk Feed wastage Feed form Mortality Lactation length Feed cost
per kg milk Milk per year and lifetime milk Morbidity Body amino
acid loss or gain Fatty acid composition of milk (CLA, EPA and DHA,
18:2 to 18:3 ratio of milk) Evaluation Criteria for Environment
Thermal environment (Draft, Air quality (Dust, Humidity, Blood
cortisol, NEFA Floor type, Bedding, Insulation) Ammonia, Carbon
dioxide, etc) Animal density Health condition Feed presentation
method Cows per bunk or waterer Water quality and quantity Cow care
and comfort space score card Evaluation Criteria for appearance
Hair coat condition Skin color Body condition score Body shape and
length Color of mucus membrane Appearance of eyes and ears
Evaluation Criteria for milk quality Milk color Milk protein
composition Milk fat yield Milk flavor Milk lactose Milk protein
yield Milk fatty acid composition Total milk solids Evaluation
Criteria for Health Calves Eye condition (dry and dirty or Skin
condition (elastic or Hair condition (dense or bright and vital
eyes) dry) and color (pink or coarse) pale) Dirtiness of around
anus Breathe with open mouth Belly condition Body Temperature
Heifers Respiratory disease Body temperature Skin and hair
condition (mange Stool condition Swollen knee and ankle and
parasites) joint Dirtiness of around eyes Nose condition
Respiratory sound (Difficulties in breathing) Activity Cows
Mastitis, Metritis Stool condition Abortion and stillbirth
(constipation) manure screener Blood measures EX: cortisol, Body
shaking Vaginal and uterine NEFA, BHBA, alkaline prolapse
phosphitase, progesterone estrogen bun Body condition score Calving
interval Feed intake (sick cows eat less) Leg problem Body
temperature Milk urea nitrogen Companion Animal and Equine
Characteristics Hair coat shine Hair coat-fullness Skin scale/flake
level Fecal consistency Gas production Breath Immune status
Antioxidant status Body condition (thin, normal, obese) skeletal
growth rate Endurance Digestive health status Circulatory health
status Hoof quality Hair quality Body fluid status Workload (NRC
specifies light, medium and heavy workloads) Characteristics to
optimize for athlete animals: Speed Sprint Muscular glycogen spare
Muscular glycogen recovery Decrease recovery time Endurance after
exercise Body condition Health and Welfare of the Animal: Welfare
and behavior (calmer or Relationship between Dry matter intake
energetic diet): NDF/starch or forage/grain Long fiber intake
Electrolytes General health status: Low allergenicity Digestive
health Improving immunologic Increasing antioxidant status status
Minimize digestive upset Immunologic status Beef Characteristics
Cow reproductive performance Conception rate Weaning rate Calf
birth weight Calf mortality Calf weaning weight Cow body condition
score Interval weaning to estrus Calving interval Bulls Body
condition score Breeding Soundness Growing and Finishing Average
daily lean gain Average daily feed intake Feed cost per unit gain
per gain Feed cost per unit lean gain Stocking Rate Evaluation
Criteria for Environment Air quality Nutrient excretion Evaluation
Criteria for appearance Hair coat condition Height Height/weight
ratio Evaluation Criteria for meat/fat quality Meat and fat color
Fatty acid profile Juiciness Flavor Tenderness Marbling score
Dressing percentage Red meat yield Muscle pH Intra muscular fat
Antioxidant status Evaluation Criteria for Health Mortality
Medication cost Morbidity Poultry Characteristics Egg and
reproduction Egg number Fertility Hatchability Egg weight Egg mass
Egg internal quality (Haugh Units) Egg yolk color Eggshell quality
Egg bacteriological content (Salmonella-fee) Fertile eggs breakout
analysis Performance Average daily gain Average daily feed intake
Feed conversion Mortality Occurrences of Leg Feed cost per kg gain
live problem weight Feed cost per dozen eggs Yield of Eviscerated
Yield of body parts carcass (breast, thigh, back etc.) Flock
Uniformity Feed consumption Environment Temperature Air quality
(Dust, Bird density Humidity, Ammonia, Carbon dioxide, etc) Feeder
space Lighting program Water quality and quantity Litter quality
(Wet droppings) Biosecurity Immune Status Microbial profile or
levels Evaluation Criteria for appearance Feathering score Skin
color Skin scratching score Feed appearance (color, texture, etc.)
Aquaculture Animal Characteristics Initial weight Size variability
Developmental stage Target weight Stocking density Body composition
(or meat composition) Body condition Animal or meat color Survival
rate Feedings per day Feeding activity Swimming Speed Feed water
stability Desired shelf-life Specific growth rate Meat yield (e.g.,
fillet, tail meat, Mouth gape Cost per unit gain etc.) FCR Days to
market Genotype Pigmentation Feed Consumption Harvest Biomass
Number of days to "X" animal $ cost/unit weight gain % of yield of
target size product (shrimp tails, fillet, etc.) $ profit/unit
production biomass Return on investment Cycles per year $ of
feed/unit weight of $ of feed/$ of biomass Total harvest biomass
production % of animals in target size range Mortality rate Product
shelf life Average animal size $ of profit/unit of culture Average
weight gain/week area or volume Weight of production/unit of
Species Days of culture (stocking aeration date) Aquaculture
Environmental Characteristics System type and size Ammonia, pH,
dissolved Water flow rate oxygen, alkalinity, temp., hardness, etc.
Water exchange rate Nutrient load Natural productivity biomass
(species specific forage base) Population health Environmental
pathogen Temperature, oxygen, etc. load variability System
Substrate Water Filtration Rate Feed on feeding tray Total
Filtration Capacity Photoperiod Processing form for feed
(Mechanical and Chemical)
Medicine application Aeration rate Nitrogen level Aeration pattern
Feeding tray # and Feed distribution pattern positioning Secchi
disc reading Immune status Microbial profile or levels Phosphorus
level
[0047] Referring now to the components of system 100, supervisor
200 may be any type of system configured to manage the data
processing function within system 100 to generate optimization
information, as will be further discussed below with reference to
FIG. 2. Simulator 300 may be any type of system configured to
receive animal information or animal formulation data, apply one or
more models to the received information, and generate performance
projections such as animal requirements, animal performance
projections, environmental performance projections, and/or economic
performance projections as will be further discussed below with
reference to FIG. 3. Ingredient engine 400 may be any kind of
system configured to receive a list of ingredients and generate
ingredient profile information for each of the ingredients
including nutrient and other information. Formulator 500 may be any
type of system configured to receive an animal requirements
projection and ingredient profile information and generate animal
formulation data, as will be further discussed below with reference
to FIG. 4.
[0048] Referring now to FIG. 2, a general block diagram
illustrating an enterprise supervisor 200 for an animal production
optimization system 100 is shown, according to an exemplary
embodiment. Enterprise supervisor 200 includes a user interface 210
and an optimization engine 230. Enterprise supervisor 200 may be
any type of system configured to receive animal information input
through user interface 210, submit the information to simulator 300
to generate at least one animal requirement, submit the at least
one animal requirement to formulator 500 to generate least cost
animal feed formulation given the animal requirement, submit the
optimized formulation to simulator 300 to generate a performance
projection and to utilize optimization engine 230 to generate
optimized values for one or more variable inputs.
[0049] According to an alternative embodiment, optimization or some
portion of the optimization may be performed by a different
component of system 100. For example, optimization described herein
with reference to supervisor 200 may alternatively be performed by
simulator 300. Further, optimization of animal feed formulation may
be performed by formulator 500.
[0050] Enterprise supervisor 200 may include or be linked to one or
more databases configured to automatically provide animal
information inputs or to provide additional information based upon
the animal information inputs. For example, where a user has
requested optimization information for a dairy production
operation, enterprise supervisor 200 may be configured to
automatically retrieve stored information regarding the user's
dairy operation that was previously recorded to an internal
database and also to download all relevant market prices or other
relevant information from an external database or source.
[0051] User interface 210 may be any type of interface configured
to allow a user to provide input and receive output from system
100. According to an exemplary embodiment, user interface 210 may
be implemented as a web based application within a web browsing
application. For example, user interface 210 may be implemented as
a web page including a plurality of input fields configured to
receive animal information input from a user. The input fields may
be implemented using a variety of standard input field types, such
as drop-down menus, text entry fields, selectable links, etc. User
interface 210 may be implemented as a single interface or a
plurality of interfaces that are navigable based upon inputs
provided by the user. Alternatively, user interface 210 may be
implemented using a spreadsheet based interface, a custom graphical
user interface, etc.
[0052] User interface 210 may be customized based upon the animal
information inputs and database information. For example, where a
user defines a specific species of animal, enterprise supervisor
200 may be configured to customize user interface 210 such that
only input fields that are relevant to that specific species of
animal are displayed. Further, enterprise supervisor 200 may be
configured to automatically populate some of the input fields with
information retrieved from a database. The information may include
internal information, such as stored population information for the
particular user, or external information, such as current market
prices that are relevant for the particular species as described
above.
[0053] Optimization engine 230 may be a process or system within
enterprise supervisor 200 configured to receive data inputs and
generate optimization information based on the data inputs and at
least one of the optimization criteria. According to an exemplary
embodiment, optimization engine 230 may be configured to operate in
conjunction with simulator 300 to solve one or more performance
projections and calculate sensitivities in the performance
projection. Calculating sensitivities in the performance
projections may include identifying animal information input or
variable inputs that have the greatest effect on overall
productivity or other satisfaction of the optimization criteria.
Optimization engine 230 may further be configured to provide
optimized values for the animal information inputs or variable
inputs based on the sensitivity analysis. Optimization may include
any improvement to productivity or some other measure according to
the optimization criteria. The process and steps in producing the
optimized values are further discussed below with reference to FIG.
5.
[0054] Optimization criteria may include any criteria, target, or
combination of targets or balanced goals that are desirable to the
current user. In a preferred embodiment, the optimization criteria
is maximizing productivity. Maximizing productivity may include
maximizing a single or multiple factors associated with
productivity such as total output, output quality, output speed,
animal survival rates, etc. Maximizing productivity may further
include minimizing negative values associated with the
productivity, such as costs, harmful waste, etc. Alternative
optimization criteria may include profitability, product quality,
product characteristics, feed conversion rate, survival rate,
growth rate, biomass/unit space, biomass/feed cost, cost/production
day, cycles/year, etc. Alternatively, the optimization criteria may
include minimizing according to an optimization criteria. For
example, it may be desirable to minimize the nitrogen or phosphorus
content of animal excretion.
[0055] Where the optimization criteria is used to optimize a target
output characteristic, the target value may be a desired value for
a characteristic of some output produced by the animal production
system. For example, a dairy producer may desire a milk output
product having enhanced milk protein. A milk output product having
increased protein concentration can increase cheese yield, making
the output product more valuable for a cheese producer. To capture
this value, the animal producer may, for example, utilize system
100 to obtain a recommendation for modifications to one or more of
the variable inputs to generate a diet using amino acid metabolism
concepts that will lead to a 0.3% increase in milk protein in
animals fed the diet. Another producer may seek milk production
that is especially low in fat content to create yogurt. Similar to
the milk with increased protein content that diet may be tailored
to produce the output having the low fat characteristic. Another
desirable characteristic may be a high level of polyunsaturated
fat, represented by the amount of linolenic acid C18:3 in milk or
animal meat to make the output product healthier for the eventual
consumer. Other animal information inputs may also be varied to
produce the output having the desired characteristics.
[0056] The target output characteristics may also be used to
generate recommendations to configure the animal production system
to produce output that has reduced or minimized characteristics.
The minimized characteristics may be advantageous in reducing
harmful or detrimental characteristics of the output. For example,
dairy production waste generally has high levels of nitrogen and
phosphorus that are regulated by stringent environmental standards.
Animal producers often face high costs ensuring compliance with
these standards. Accordingly, system 100 may be configured such
that the total output product, the amount of waste, or a
characteristic of the output product, the nitrogen and phosphorus
levels in the waste, is reduced. Producing the optimized waste may
include analyzing the nutrients being fed to an animal to avoid
overfeeding digestible phosphorus and balancing rumen and cow
metabolism to maximize nitrogen retention. Although the analysis
may yield clear recommendations, producing optimized waste may
require analyzing or presenting opposing recommendations and their
projected effects to facilitate the balancing of mutually exclusive
advantages between an increase in animal performance and reduced
waste management costs.
[0057] Managing phosphorus characteristics in output may
additionally provide advantages in an aquaculture production
system. Phosphorus is an important macromineral for the skeletal
development of fish species and key metabolic nutrient for growth
and proper metabolism for all aqua species. Insufficient dietary
phosphorus in aquafeeds can lead to depression of growth and
skeletal formation for aqua species. However, phosphorus is also a
key limiting nutrient in freshwater aquaculture systems and excess
dietary phosphorus can quickly lead to overproduction of algae
causing instability to the health of the system. Excess phosphorus
is also undesirable because it is an unnecessary cost.
[0058] A formulation system can use available phosphorus nutrient
in an aquatic environment in conjunction with a phosphorus nutrient
in the animal feed formulation generated by system 100 to meet the
needed animal requirement with highly available sources and
optimize the excess phosphorus entering the aquatic environment.
Empirical data from animal digestibility or environmental samples
may be used to increase the precision by which this nutrient is
managed in the formulation process.
[0059] According to another exemplary embodiment, the targeted
characteristic may be the nutrient composition of an aquatic meat
product. For example, the targeted characteristic may be the fatty
acid profile of the meat product. Aquatic meat products have
received considerable recognition for generally containing a
healthier profile of fatty acids for human diet than many
terrestrial meat sources. The composition of fatty acids in these
aquatic meats have largely been based on normal deposition that
occurs from consumption of natural foods or artificial feeds, which
often contain these fatty acids to meet the animal's requirements.
Accordingly, system 100 may be configure to generate and animal
feed formulation having an array of fatty acids that, when fed to a
target culture species, results in an improved fatty acid profile,
i.e., more beneficial to human health. A similar example would
involve the use of higher levels of vitamin E and selenium to
impart an increased shelf-life to the fillet.
[0060] The targeted characteristic may also be non-nutrient
related. For example, changing the free amino acid content of meat
to change its flavor, limiting the concentrations of or choosing
improved bioavailability of nutrients that become toxic when they
accumulate in zero water exchange systems, targeting specific
levels of beta-carotene, astaxanthin or other pigments that can be
used metabolically as an anti-oxidant, Vitamin A precursor, or to
impart coloration to the meat or skin, etc.
[0061] Target output characteristics may include, but are not
limited to, end product composition or characteristics including
meat yield as a percentage of body weight, saleable product yield,
yield of specific body parts, fatty acid profile, amino acid
content, vitamin content, marbling, iodine value, water holding
capacity, tenderness, body or product color, pigment level, body or
product shelf life, etc. The target output characteristic may also
include, but is not limited to, a waste composition or
environmental effect, including uneaten food amounts, leaching or
loss of nutrients such as nitrogen, ammonia, phosphorus, vitamins,
attractants, etc., fecal consistency, fecal/urinary output,
including total output, ammonia or nitrogen load in system,
phosphorus load in system, organic matter bypass, etc., biological
oxygen demand, bypass energy, gaseous emissions, C/N ratio of waste
stream etc. Although the above examples are provided, a person of
ordinary skill in the art can recognize that the target output
characteristic may be any output generated in a production
system.
[0062] Advantageously, system 100 may optimize across all variable
animal information inputs to generate recommendations for producing
the output having specified target characteristics at the lowest
cost. The recommendation may include a single optimal
recommendation or a plurality of recommendations yielding
equivalent benefits.
[0063] Optimization engine 230 may be configured to implement its
own optimization code for applications where feed ingredient
information from formulator 500 is combined with other information
and/or projections calculated in simulator 300. Optimization
problems that coordinate several independent calculation engines,
referred to as multidisciplinary optimizations, may be solved using
gradient-based methods, or more preferably simplex methods such as
Nelder-Mead or Torczon's algorithm. Preferably, optimization engine
230 may be configured to implement a custom combination of a
gradient-based method for variables on which the optimization
criteria depends smoothly (decision variables fed to simulator 300)
and a simplex method for variables on which the objective function
has a noisy or discontinuous dependence (diet requirements fed to
formulator 500). Alternatively, other optimization methods may be
applied, including but not limited to, pseudo-gradient based
methods, stochastic methods, etc.
[0064] Enterprise supervisor 200 may be further configured to
format the optimization results and provide the results as output
through user interface 210. The results may be provided as
recommended optimized values for the variable inputs. The results
may further include recommended values for additional animal
information inputs, independent of whether the animal information
input was designated as a variable input. The results may further
include a projection of the effects of implementation of the
optimized values for the variable inputs.
[0065] Enterprise supervisor 200 may be configured to implement a
Monte Carlo method where a specific set of values is drawn from a
set of distributions of model parameters to solve for optimized
values for the variable inputs. This process may be repeated many
times, creating a distribution of optimized solutions. Based on the
type of optimization, enterprise supervisor 200 maybe used to
select either the value most likely to provide the optimal solution
or the value that gives confidence that is sufficient to meet a
target. For example, a simple optimization might be selected which
provides a net energy level that maximizes the average daily gain
for a particular animal. A Monte Carlo simulation may provide a
distribution of requirements including various net energy levels
and the producer may select the net energy level that is most
likely to maximize the average daily gain.
[0066] Enterprise supervisor 200 may further be configured to
receive real world empirical feedback based on the application of
the optimized values for the variable inputs. The empirical
feedback may be used to adjust the variable inputs to further
optimize the animal production system. The empirical feedback may
further be compared to the performance projections to track the
accuracy of the projections. Empirical feedback can be provided
using any of a variety of methods such as automated monitoring,
manual input of data, etc.
[0067] Empirical feedback may be any type of data that is gathered
or generated based on observations. The data may be gathered by an
automated system or entered manually based on a users observations
or testing. The data may be gathered in real-time or on any
periodic basis depending on the type of data that is being
gathered. This data may also already be represented in the animal
information inputs and be updated based on any changing values. The
empirical feedback to be monitored will generally include animal
information inputs that impact an animal production system product,
herd health, etc. on a daily basis. The empirical feedback may
include, but is not limited to, environment information, animal
comfort-information, animal feed information, production system
management information, animal information, market conditions or
other economic information, etc. For example, in a beef production
system, the empirical feedback may include carcass data, linear
measurements, ultrasound measurements, daily intakes, etc.
[0068] Environment information may include information regarding
the animal's environment that may affect animal productivity. For
example, temperatures above the thermo-neutral zone may decrease an
animal's feed intake. Temperature may also affect a rate of
passage, which in turn may have an effect on nutrient
digestibility, bypass of protein/amino acids, nutrients in
excretion, etc. Temperature may also increase intake of animal
feed. For example, wind in cold temperatures will increase
maintenance energy for warmth (shivering).
[0069] The environmental information may also include
non-temperature information. For example, in warm temperatures,
wind can assist in cooling requiring less loss of dry matter
intake, less energy wasted in cooling attempts (panting).
Similarly, increasing relative humidity may decrease cow comfort
based on an increased heat load when the temperature is
warm/hot.
[0070] The empirical feedback may further be dependent on the cow's
environment. For example, weather events (sun, snow, rain, mud,
etc.) are important for cows housed outside. Weather events can
impact the body temperature of the cow and the animal's need for
shivering or panting further impacting intakes, digestibility, etc.
If cows travel from pasture to parlor, mud or stormy/snowy weather
can impact the amount of energy required to get to the parlor and
back, raising maintenance requirements.
[0071] Other environmental information may be related to the
general quality of the animal's environment and the level of stress
placed on the animal. For example, animal crowding can have a
strong impact on an animal's productivity. In overcrowding
conditions, dominant cows will get feed first and remaining cows
will get a sorted feed which contains different nutrients than
formulated feed. Further, cows also need to spend a certain amount
of time lying down in order to maximize production. Yet further,
overcrowding may cause cows to lie in alleys resulting in increased
potential of stepped on teats and mastitis or stand too long. Other
exemplary environmental information may include the amount of
light, access to water and feed, proper bedding and stalls to
encourage cows to lie down, milking protocol such that cows are not
held in a holding pen longer than one hour at a time, etc.
[0072] Although the above examples are provide in reference to a
cow, it should be understood that the described system and method
can be similarly applied to any animal. For example, poultry
animals may similarly face stress and/or less than optimal growth
based on increased temperature. This additional stress can be
reduce by, for example, increasing fan use to cause a direct wind,
using intermittent misting, etc.
[0073] Other empirical feedback may include analysis of the actual
animal feed being consumed by animals. For example, a sample may be
taken from the animal feed as it is being fed to animals to analyze
the nutrient content and assure that the diet being fed is the diet
that was formulated to optimize production. The analysis may
include an analysis of ingredients as the arrive at the animal
production system. To reduce excessive deviation from a formulated
animal feed, more variable ingredients can be used at lower
inclusion rates. Similarly, empirical testing may include analysis
of the ingredients found naturally at the animal production
facility, such as the quality of the water ingested by the animals.
Water may deliver some minerals in various amounts or have a
specific pH level that should be accounted for in diet
formulations
[0074] Empirical testing may further include monitoring the
management practices of the animal production system. Management
practice may include feed timing, personnel, production gathering
practices, etc. For example, an animal production systems personnel
may have an affect on production by having an effect on cow comfort
level. The number of people, their experience level, the time it
takes to complete tasks, etc. can all impact cow comfort.
[0075] Animal management practices also may be monitored. Animal
management practices may include any practices that may have an
effect on the animals. For example, animal production may be
affected be feeding time practices. Feeding timing can impact that
quality of feed provided, especially in hot weather. The system may
be further configured to monitor the frequency and duration of time
during which feed is provided to the animal such that the animal is
able to eat.
[0076] Animal production gathering practices may also have an
effect. Animal production gathering may include any process to
obtain the results of the animal production, such as the number of
milkings per day, egg gathering frequency, etc. that will influence
production potential. More milkings may increase production in
well-managed herds. It may also be beneficial to increase milkings
in cows just starting their lactations to facilitate
production.
[0077] Empirical testing may further include monitoring the animals
within the animal production system. For example, an animal may be
monitored for metabolic indicators; Metabolic indicators may be
indicative of metabolic problems such as milk fever, ketosis,
imbalances in dietary protein, overheating, etc. Other monitored
characteristics may include characteristics that must be tested
within a laboratory such as non-esterified fatty acids (NEFA), beta
hydroxyl butyrate (BHBA), urine pH, milk urea nitrogen (MUN), blood
urea nitrogen (BUN), body temperature, blood AA, manure
characteristics, carbon dioxide levels, minerals, fat pad probes
for pesticide residue testing, etc. Other characteristics may be
monitored through observation, such as animals in heat, limping
animals, sick animal, pregnancy, etc. that may not eat and produce
as well as normal. Yet other characteristics may be a combination
of these categories. Other physiological measurements may include
microbial profile or hut histological measurements.
[0078] Empirical testing provides the advantage of verifying the
accuracy of predictive models generated by simulator 300.
Optimization results generated from imperfect models may different
from real world results obtained through empirical testing. System
100 may be configured to provide dynamic control based on the
empirical testing feedback, adjusting animal information inputs or
generate values, such as an animal's feed formulation, to achieve
specified targets based on the difference between model results and
empirical testing feedback. Further, simulator 300 may be
configured to adjust how models are generated based on the data
obtained through the empirical testing to increase the accuracy of
future models.
[0079] Further, enterprise supervisor 200 may be configured to
enable dynamic control of models. After setting an initial control
action, for example the feed formulation, as will be discussed
below with reference to FIG. 5, the animal response may be
monitored and compared with the prediction. If the animal response
deviates too far from the prediction, a new control action, e.g.,
feed formulation, may be provided. For example, if the performance
begins to exceed prediction, some value may be recovered by
switching to a less costly feed formulation, different water flow
rate, etc. If performance lags prediction, switching to higher
value feed formulation, may help to ensure that the final product
targets are met. Although the control action is described above
with reference to a feed formulation, the control action may be for
any control variable, such as water flow rate, feeding rate, etc.
Similarly, the adjustments may be made to that control variable,
such as by increasing or decreasing the flow rate, etc.
[0080] Referring now to FIG. 3, a general block diagram
illustrating a simulator 300 is shown according to an exemplary
embodiment. Simulator 300 includes a requirements engine 310, an
animal performance simulator 320, an environment performance
simulator 330, and an economic performance simulator 340.
Generally, simulator 300 may be any process or system configured to
apply one or more models to input data to produce output data. The
output data may include any type of projection or determined value,
such as animal requirements and/or performance projections,
including animal performance projections, economic performance
projections, environmental performance projections, etc.
[0081] Specifically, simulator 300 is configured to receive animal
information input from enterprise supervisor 200, process the
information using requirements engine 310 and an animal
requirements model to produce a set of animal requirements.
Further, simulator 300 may be configured to receive feed
formulation data from enterprise supervisor 200 and process the
feed formulation data using any combination of animal performance
simulator 320, environment performance simulator 330, and economic
performance simulator 340 to produce at least one performance
projection.
[0082] An animal requirements model, used by simulator 300 to
convert input values into one or more outputs, may consist of a
system of equations that, when solved, relate inputs like animal
size to an animal requirement like protein requirement or a system
requirement like space allotment or feed distribution. A specific
mathematical form for the model is not required, the most
appropriate type of model may be selected for each application. One
example is models developed by the National Research Council (NRC),
consisting of algebraic equations that provide nutrient
requirements based on empirical correlations. Another example is
MOLLY, a variable metabolism-based model of lactating cow
performance developed by Prof. R. L. Baldwin, University of
California-Davis. A model may consist of a set of explicit ordinary
differential equations and a set of algebraic equations that depend
on the differential variables. A very general model may consist of
a fully implicit, coupled set of partial differential, ordinary
differential, and algebraic equations, to be solved in a hybrid
discrete-continuous simulation.
[0083] A model may be configured to be independent of the
functionality associated with simulator 300. Independence allows
the model and the numerical solution algorithms to be improved
independently and by different groups.
[0084] Preferably, simulator 300 may be implemented as an
equation-based process simulation package in order to solve a wide
variety of models within system 100. Equation-based simulators
abstract the numerical solution algorithms from the model. This
abstraction allows model development independent from numerical
algorithms development. The abstraction further allows a single
model to be used in a variety of different calculations
(steady-state simulation, dynamic simulation, optimization,
parameter estimation, etc.). Simulators may be configured to take
advantage of the form and structure of the equations for tasks such
as the sensitivity calculations. This configuration allows some
calculations to be performed more robustly and/or efficiently than
is possible when the model is developed as a block of custom
computer code. An equation-based process simulation package is
software configured to interact directly with the equations that
make up a model. Such a simulator typically parses model equations
and builds a representation of the system of equations in memory.
The simulator uses this representation to efficiently perform the
calculations requested, whether steady-state simulations, dynamic
simulations, optimization, etc. An equation-based process
simulation package also allows incorporation of calculations that
are more easily written as combination of procedures and
mathematical equations. Examples may include interpolation within a
large data table, calling proprietary calculation routines
distributed as compiled code for which equations are not available,
etc. As newer and better solution algorithms are developed, these
algorithms may be incorporated into simulator 300 without requiring
any changes to the models simulator 300 is configured to solve.
[0085] According to an exemplary embodiment, simulator 300 may be a
process simulator. Process simulators generally include a variety
of solution algorithms such as reverse mode automatic
differentiation, the staggered corrector method for variable
sensitivities, automatic model index reduction, robust Newton
iteration for solving nonlinear systems from poor initial values,
error-free scaling of variable systems, and the interval arithmetic
method for locating state events. Process simulators utilize sparse
linear algebra routines for direct solution of linear systems. The
sparse linear algebra routines can efficiently solve very large
systems (hundreds of thousands of equations) without iteration.
Process simulators further provide a particularly strong set of
optimization capabilities, including non-convex mixed integer
non-linear problems (MINLPs) and global variable optimization.
These capabilities allow simulator 300 to solve optimization
problems using the model directly. In particular, the staggered
corrector algorithm is a particularly efficient method for the
sensitivities calculation, which is often the bottleneck in the
overall optimization calculation.
[0086] Variable inputs for optimization to be solved by simulator
300 may include both fixed and time-varying parameters. Time
varying parameters are typically represented as profiles given by a
set of values at particular times using a specific interpolation
method, such as piecewise constant, piecewise linear, Bezier
spline, etc.
[0087] Simulator 300 and the associated models may be configured
and structured to facilitate periodic updating. According to an
exemplary embodiment, simulator 300 and the associated models may
be implemented as a dynamic link library (DLL). Advantageously, a
DLL may be easily exported but not viewed or modified in any
structural way.
[0088] Requirements engine 310 may be any system or process
configured to receive animal information input and generate animal
requirements by applying one or more requirements models to the set
of animal information input. A requirements model may be any
projection of potential outputs based upon any of a variety of set
of inputs. The model may be as simple as a correlation relating
milk production to net energy in an animal feed or as complex as a
variable model computing the nutrient requirement to maximize the
productivity of a shrimp aquaculture pond ecosystem. Requirements
engine 310 may be configured to select from a plurality of models
based on the animal information inputs. For example, requirements
engine 310 may include models for swine requirements, dairy
requirements, companion animal requirements, equine requirements,
beef requirements, general requirements, poultry requirements,
aquaculture animal requirements, etc. Further, each model may be
associated with a plurality of models based on an additional
categorization, such as developmental stage, stress level, etc.
[0089] Animal requirements generated by requirements engine 310 may
include a listing of nutrient requirements for a specific animal or
group of animals. Animal requirements may be a description of the
overall diet to be fed to the animal or group of animals. Animal
requirements further may be defined in terms of a set of
nutritional parameters ("nutrients"). Nutrients and/or nutritional
parameters may include those terms commonly referred to as
nutrients as well as groups of ingredients, microbial measurements,
indices of health, relationships between multiple ingredients, etc.
Depending on the degree of sophistication of system 100, the animal
requirements may include a relatively small set of nutrients or a
large set of nutrients. Further, the set of animal requirements may
include constraints or limits on the amount of any particular
nutrient, combination of nutrients, and/or specific ingredients.
Advantageously, constraints or limits are useful where, for
example, it has been established at higher levels of certain
nutrients or combination of nutrients could pose a risk to the
health of an animal being fed. Further, constraints may be imposed
based on additional criteria such as moisture content,
palatability, etc. The constraints may be minimums or maximums and
may be placed on the animal requirement as a whole, any single
ingredient, or any combination ingredients. Although described in
the context of nutrients, animal requirements may include any
requirements associated with an animal, such as space requirements,
heating requirements, etc.
[0090] Additionally, animal requirements may be generated that
define ranges of acceptable nutrient levels. Advantageously,
utilizing nutrient ranges allows greater flexibility during animal
feed formulation, as will be described further below with reference
to FIG. 3.
[0091] Requirements engine 310 may be further configured to account
for varying digestibility of nutrients. For example, digestibility
of some nutrients depends on the amount ingested. For example,
wherein an animal ingests a quantity of phosphorous in a diet, the
percentage that is utilized by the animal may decrease in relation
to the quantity ingested. An animal's digestive tract may only be
able utilize a certain amount of phosphorous and the remainder will
be passed through the animal. Accordingly, phosphorous utilization
may have an inverse relationship with the amount of phosphorous in
an animal feed after a certain level is reached. Digestibility may
further depend on the presence or absence of other nutrients,
microbes and/or enzymes, processing effects (e.g. gelatinization,
coating for delayed absorption, etc.), animal production or life
stage, previous nutrition level, etc. Simulator 300 maybe
configured to account for these effects. For example, simulator 300
may be configured to adjust a requirement for a particular nutrient
based on another particular nutrient additive.
[0092] Requirements engine 310 may also be configured to account
for varying digestion by an animal. Animal information inputs may
include information indicating the health of an animal, stress
level of an animal, reproductive state of an animal, methods of
feeding the animal, etc. as it affects ingestion and digestion by
an animal. Shifts based on immune status may cause an increased
maintenance cost to engage protective systems, while reducing
voluntary nutrient intake. For example, the stress level of an
animal may decrease the overall feed intake by the animal, while
gut health may increase or decrease a rate of passage. According to
another example, changes in a microbial profile for an animal may
indicate a shift in digestion of nutrients from enzymatic digestion
to bacterial fermentation.
[0093] Table 2 below includes an exemplary listing of nutrients
that may be included in the animal requirements. According to an
exemplary embodiment, within the animal requirements, each listed
nutrient may be associated with a value, percentage, range, or
other measure of amount. The listing of nutrients may be customized
to include more, fewer, or different nutrients based on any of a
variety of factors, such as animal type, animal health, nutrient
availability, etc. TABLE-US-00002 TABLE 2 Nutrients Suitable for
Generating Animal Requirements ADF Animal Fat Ascorbic Acid
Arginine (Total and/or Ash Biotin Digestible) Calcium Calcium/Phos
ratio Chloride Choline Chromium Cobalt Copper Cystine (Total and/or
Dry Matter Digestible) Fat Fiber Folic Acid Hemicellulose Iodine
Iron Isoleucine (Total and/or Lactose Lasalocid Digestible) Leucine
(Total and/or Lysine (Total and/or Magnesium Digestible)
Digestible) Manganese Margin Methionine (Total and/or Digestible)
Moisture Monensin NDF NEg (Net Energy for NEl (Net Energy NEm (Net
Energy for Gain) Lactation) Maintenance) NFC (Non-Fiber Niacin
Phenylalanine (Total Carbohydrate) and/or Digestible) Phosphorus
Phosphate Potassium Protein Pyridoxine Rh Index (Rumen Health
Index) Riboflavin Rough NDF Rum Solsug (Rumen Soluble Sugars)
Rumres NFC (Ruminant RUP (Rumen Salt Residual Undegradable Protein)
Non-Fiber Carbohydrate) Selenium Simple Sugar Sodium Sol RDP
(Soluble Rumen Sulfur ME (Metabolizable Degradable Protein) Energy)
Thiamine Threonine (Total Total RDP and/or Digestible) Tryptophan
(Total and/or Valine (Total and/or Vitamin A Digestible)
Digestible) Vitamin B12 Vitamin B6 Vitamin D Vitamin E Vitamin K
Zinc Gut Health Index Fatty Acids (EPA, Cholesterol DHA, Linolenic,
etc.) Phospholipids UFC
[0094] Requirements engine 310 may be configured to generate the
animal requirements based on one or more requirement criteria.
Requirement criteria can be used to define a goal for which the
requirement should be generated. For example, exemplary requirement
criteria can include economic constraints, such as maximizing
production, slowing growth to hit the market, or producing an
animal at the lowest input cost. The animal requirements may be
used to generate an animal feed formulation for an animal.
Accordingly, the animal requirements may be used as animal feed
formulation inputs.
[0095] The requirements engine 310 may further be configured to
generate the animal requirements based on one or more dynamic
nutrient utilization models. Dynamic nutrient utilization may
include a model of the amount of nutrients ingested by an animal
feed that are utilized by an animal based on information received
in the animal information inputs, such as animal health, feeding
method, feed form (mash, pellets, extruded, particle size, etc.),
water stability of feed, uneaten food, water temperature and its
impact on enzyme levels, etc. Nutrient utilization may further
depend on the presence or absence of other nutrient additives,
microbes and/or enzymes, processing effects (e.g. gelatinization,
coating for delayed absorption, etc.), animal production or life
stage, previous nutrition level, etc.
[0096] Simulator 300 may be configured to account for these
effects. For example, simulator 300 may be configured to adjust the
level of a particular nutrient, defined in an animal feed
formulation input, from the level determined based on the animal
requirement to a different level based on the presence or absence
of another particular nutrient. Using the above example for
phosphorous, the amount of phosphorous that is utilized by an
animal may also be affected by other nutrients in the animal's
diet. For example, the presence of a particular microbe in an
animal's digestive track, whether naturally present or added as a
nutrient, may actually increase the phosphorous utilization beyond
the levels that would normally occur and reduce the amount that
enters an animal's waste stream.
[0097] Accordingly, an animal feed formulation input may be
modified based on the nutrient utilization model. However, this
change in the animal feed formulation may have an effect on the
animal feed formulation, including the animal feed formulation that
was just modified. Accordingly, compensating for a nutrient
utilization model may require an iterative calculation, constantly
updating values, to arrival at a final value that is within a
predefined tolerance.
[0098] Requirements engine 310 may also be configured to account
for variations in digestion and utilization of nutrients by an
animal. Animal information inputs may include information
indicating the health of an animal, stress level of an animal,
reproductive state of an animal, methods of feeding the animal,
etc. as it affects ingestion and digestion by an animal. For
example, the stress level of an animal may decrease the overall
feed intake by the animal, while gut health may increase or
decrease a rate of passage. Alternatively, a stress level may alter
the actual metabolism for an animal. For example, an animal's
metabolism may be altered by a stress-induced release of cortisone.
Other exemplary metabolic modifiers may include immune system
cascades of prostaglandins and other pro-inflammatory cytokines,
leukocytes, antibodies, and other immune cells and substances,
growth promoting implants, and adrenergic feed additives. These
reactions shift site and extent of digestion, change nutrient
intake, and force digested nutrients towards a more catabolic
state.
[0099] Animal performance simulator 320 may be a process or system
including a plurality of models similar to the models described
above with reference to requirements engine 310. The models
utilized in animal performance simulator 320 receive an animal feed
formulation from formulator 300 through enterprise supervisor 200
and the animal information inputs and apply the models to the feed
formulation to produce one or more animal performance projections.
The animal performance projection may be any predictor of animal
productivity that will be produced given the animal feed
formulation input and other input variables.
[0100] Environment performance simulator 330 may be a process or
system including a plurality of models similar to the models
described above with reference to requirements engine 310. The
models utilized in environment performance simulator 330 receive
animal feed formulation from formulator 300 through enterprise
supervisor 200 and apply the models to the feed formulation and
animal information inputs to produce a performance projection based
on environmental factors. The environmental performance projection
may be any prediction of performance that will be produced given
the animal feed formulation input, animal information inputs, and
environmental factors.
[0101] Economic performance simulator 340 may be a process or
system including a plurality of models similar to the models
described above with reference to requirements engine 310. The
models utilized in economic performance simulator 340 receive
animal feed formulation from formulator 300 through enterprise
supervisor 200 and apply the models to the feed formulation and
animal information inputs to produce a performance projection based
on economic factors. The economic performance projection may be any
prediction of performance that will be produced given the animal
feed formulation input, animal information inputs, and the economic
factors.
[0102] The performance projections may include a wide variety of
information related to outputs produced based on the provided set
inputs. For example, performance projections may include
information related to the performance of a specific animal such as
the output produced by an animal. The output may include, for
example, the nutrient content of eggs produced by the animal,
qualities associated with meat produced by the animal, the contents
of waste produced by the animal, the effect of the animal on an
environment, etc.
[0103] According to exemplary embodiment, simulators 320, 330, and
340 may be run in parallel or in series to produce multiple
performance projections. The multiple animal performance
projections may remain separated or be combined into a single
comprehensive performance projection. Alternatively, performance
projections may be generated based on a single simulator or a
combination of less than all of the simulators.
[0104] Requirements engine 310 may further include additional
simulators as needed to generate performance projections that are
customized to satisfy a specific user criteria. For example,
requirements engine 310 may include a bulk composition simulator,
egg composition simulator, meat fat composition, waste output
simulator, maintenance energy calculator, etc.
[0105] Referring now to FIG. 4, a general block diagram
illustrating an ingredients engine 400 and a formulator 500 is
shown, according to an exemplary embodiment. Ingredients engine 400
is configured to exchange information with formulator 500.
Ingredients engine 400 and formulator 500 are generally configured
to generate an animal feed formulation based on available
ingredients and received animal requirements.
[0106] Ingredients engine 400 includes one or more listings of
available ingredients at one or more locations. The listing further
includes additional information associated with the ingredients,
such as the location of the ingredient, nutrients associated with
the ingredient, costs associated with the ingredient, etc.
[0107] Ingredients engine 400 may include a first location listing
410, a second ingredient location listing 420, and a third
ingredient location listing 430. First ingredient listing 410 may
include a listing of ingredients available at a first location,
such as ingredients at a user's farm. The second ingredient listing
420 may include a listing of ingredients that are available for
purchase from an ingredient producer. Third ingredient listing 430
may include a listing of ingredients that are found in a target
animal's environment such as forage in a pasture, plankton
(zooplankton, phytoplankton, etc.), or small fish in an aquaculture
pond, etc. The listing of ingredients may further include
environmental nutrient inputs. Environmental nutrient inputs may be
any nutrient or nutrients that are received and/or utilized by an
animal that is not fed to the animal.
[0108] Referring now to third ingredient listing 430, an example of
a listing of ingredients that are found in a target animal's
environment may include a listing of the mineral content of water.
An animal's total water consumption can be estimated based on known
consumption ratios, such as a ratio of water to dry feed matter
consumed. Consumption of an ingredient or nutrient may include
actual consumption as well as receipt by an animal through
absorption, generation through body processes, etc. This ratio may
be either assigned an average value or, more preferably, calculated
from known feed and animal properties: The mineral content of the
water provided by producer may be measured on-site. This water,
with measured mineral content and calculated intake level, may be
incorporated in third ingredient listing 430. Although mineral
content is provided as an example, it should be understood that the
listing of ingredient may include any nutrient level or
characteristic of the water such as the water pH level.
[0109] Alternatively, third ingredient listing 430 may include an
aquatic ecosystem total nutrient content. The ecosystem
contribution to total nutrition may be included in several ways.
For example, a sample may be drawn and analyzed for total nutrient
content and included as third listing 430. Preferably, the models
solved in simulator 300 may be expanded to include not only that
species being produced but other species that live in the ecosystem
as well. The model may include one or more of the following
effects: other species competition for feed, produced species
consumption of other species in ecosystem, and other species growth
over time in response to nutrient or toxin excretion, temperature,
sunlight, etc. The models may further account for
consumption/utilization of the environmental nutrient inputs based
on the life stage of the animal, knowledge of growing conditions,
analysis of ingredients, etc.
[0110] Further, third ingredient listing 430 may be representative
of a closed nutrient system, wherein outputs generated from an
animal feed being fed to an animal are treated as inputs to
generate third ingredient listing 430. For example, an animal may
be initially fed a diet composed of nutrients from first ingredient
listing 410 and/or second ingredient listing 420. The animal's
utilization of the nutrient composition may be determined within
simulator 300, described in further detail below, and provided to
formulator 500 for optimization versus established animal
requirements. Simulator 300 may further be configured to generate a
projection of the quantity and quality of nutrients that are not
utilized by the animal and/or nutrients in the animal's waste that
are provided to the animal's environment.
[0111] The output of un-utilized nutrient or waste stream nutrients
may then be used for projecting changes to the animal's environment
and the composition of third ingredient listing 430. For example,
where the animal is an aquatic animal, such as a shellfish, the
output from the shellfish may be used in calculating projected
changes in the algae standing stock. This modified algae standing
stock is then considered an ingredient in third ingredient listing
430 to the extent that the animals consume the algae standing stock
as part of its diet. The additional ingredient may reduce or
otherwise modify the animal's calculated requirements. It can be
appreciated how the above described interaction may be used to
create a number of cyclical feedback loops to optimize the animal
production. Further, an optimized animal feed may be optimized
based on the requirements of the entire ecosystem biomass in
addition to the animal.
[0112] According to yet another exemplary embodiment, the
performance projections generated by simulator 300 may be used to
estimate the biomass and nutrient content of a first species, that
is a food source for a second species. The first species may be
algal, bacterial, invertebrate, or vertebrate. Accordingly, the
output of simulator 300 may be used to define the ingredients in
third ingredient listing 430, including bioavailability and total
nutrient provision. For example, wherein the first species is brine
shrimp and the second species is an aquarium salt water fish,
simulator 300 may be utilized to generate a recommendation for
optimizing the growth rate and/or nutrient content of the brine
shrimp. The brine shrimp population may also be calculated in view
of feeding projections for the salt water aquarium fish. These
brine shrimp may then be components within third ingredient listing
430 and may be used as components in formulating an optimized
animal feed for the salt water aquarium fish. Specifically, the
ingredients in third ingredient listing 430 may be provided to
variable nutrient engine 450, discussed below, and formulator 500.
Further, the performance projections associated with the first
animal may be used to project future components within third
ingredient listing 430 and their characteristics.
[0113] As shown in the above example, simulator 300, in combination
with third ingredient listing 430, may be used to model an entire
interaction between an animal, the organisms in its environment,
and the environment itself. The interaction may be used to satisfy
current animal requirements and to generate projections for the
animal, other organisms, and the environment.
[0114] For example, the environment of third ingredient listing 430
may include ingredients and associated nutrients within a wheat
grass pasture. The pasture may be fertilized with nitrogen,
potassium, and phosphorus. The fertilizer may be naturally
occurring, such as from cow manure or poultry litter, or man-made,
such as a chemical fertilizer.
[0115] The pasture may be managed by an animal producer such that
the wheat grass does not get more mature than an early boot stage,
an optimum maturity for nutrient quality. Upon maturity, the
pasture may be grazed by 400 pound stocker calves for about two
months. It is recognized that the animal, during grazing will
generally fertilize the wheat grass naturally. As the calves graze
they will continuously gain weight, which is made up primarily of
minerals, water, and protein. Accordingly, the nitrogen, potassium,
and phosphorus that is used to fertilize the wheat grass become a
nutritional component of the calves.
[0116] After the cattle are removed from the pasture, the animal
producer may choose to allow the wheat grass to grow to maturity
for harvesting. The harvested wheat grass may be turned directly
into another food source, such as flour for bread, or it may be
used as bedding in a feedlot. Wheat grass used for bedding may
eventually be collected from the feedlot, along with manure from
the cattle in the feedlot and put back in the pasture. The
nutrients in the straw and manure may be disked down into the field
and are taken up by the roots of the next crop of wheat grass.
[0117] Accordingly, system 100, using simulator 300, maybe
configured to iteratively analyze variable inputs that effect not
only the animals, but also the environment of the animal, which may
in turn affect the animals. Each projection by simulator 300 may be
iteratively performed to determine the effects on related inputs
based on the current projections.
[0118] Third ingredient listing 430 may further include performance
projections generated by simulator 300. For example, the nutrient
content of milk may be modeled for the particular animals for an
individual producer. This milk nutrient content model may be used
as a third ingredient listing 430 for consumption by a nursing
animal.
[0119] Each listing of ingredients may further include additional
information associated with the ingredients. For example, a listing
of ingredients may include a listing of costs associated with that
ingredient. Alternatively, an ingredient at the first location may
include a costs associated with producing the ingredient, storing
the ingredient, dispensing the ingredient, etc., while an
ingredient at the second location may include a cost associated
with purchasing the ingredient, and an ingredient at the third
location may include a cost associated with increasing the biomass,
changing the nutrient profile, altering nutrient availability, etc.
The additional information may include any type of information that
may be relevant to later processing steps.
[0120] Table 3 below includes an exemplary list of ingredients
which may be used in generating the animal feed formulation. The
listing of ingredients may include more, fewer, or different
ingredients depending on a variety of factors, such as ingredient
availability, entry price, animal type, etc. TABLE-US-00003 TABLE 3
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 Ascorbic Acid Aspen Bedding Avizyme Bacitracin Zinc
Bakery Product Barley Barley-Crimped Barley-Ground Barley-Hulless
Barley-Hulls Barley-Midds Barley-Needles Barley-Rolled Barley-Whole
Barley-With Enzyme Baymag Beet Beet Pulp Biotin Biscuit By Product
Black Beans Blood-Flash Dry Bone Meal Brewers Rice Brix Cane
Buckwheat Cage Calcium Calcium Cake Calcium Chloride Calcium
Formate Calcium Iodate Calcium Sulfate Calcium Prop 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 Byproduct 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
Extruded Grain Extruded Soy Flour Fat Feather Meal Feeding Oatmeal
Fenbendazole Fermacto Ferric Chloride Ferrous Carbonate Ferrous
Carbonate Ferrous Sulfate Fine Job's Tear Bran Fish Meal Fish
Flavoring Folic Acid 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 Herring
Meal Hominy Hygromycin Indian Soybean Meal Iron Oxide-Red
Iron-Oxide Yellow Job's Tear Broken Seeds 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 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 Poloxalene Popcorn
Popcorn Screenings Porcine Plasma; Dried Pork Bloodmeal Porzyme
Posistac Potassium Bicarbonate Potassium Carbonate Potassium
Magnesium Potassium Sulfate Sulfate Potato Chips Poultry
Blood/Feather Poultry Blood Meal 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 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
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
[0121] Ingredient engine 400 may further include an ingredient
information database 440. Ingredient information database 440 may
include any kind of information related to ingredients to be used
in generating the feed formulation, such as nutrient information,
cost information, user information, etc. The information stored in
database 440 may include any of a variety of types of information
such as generic information, information specifically related to
the user, real-time information, historic information,
geographically based information, etc. Ingredient information
database 440 may be utilized by ingredient engine 400 to supply
information necessary for generating an optimized feed formulation
in conjunction with information supplied by the user.
[0122] Ingredient information database 440 may further be
configured to access external databases to acquire additional
relevant information, such as feed market information. Feed market
information may similarly include current prices for ingredient,
historical prices for output, ingredient producer information,
nutrient content of ingredient information, market timing
information, geographic market information, delivery cost
information, etc. Ingredient information database 440 may further
be associated with a Monte Carlo type simulator configured to
provide historical distributions of ingredient pricing and other
information that can be used as inputs to other components of
system 100.
[0123] Ingredient engine 400 may further include a variable
nutrient engine 450 configured to provide tracking and projection
functions for factors that may affect the nutrient content of an
ingredient. For example, variable nutrient engine 450 may be
configured to project the nutrient content for ingredients over
time. The nutrient content for some ingredients may change over
time based on method of storage, method of transportation, natural
leaching, processing methods, etc. Further, variable nutrient
engine 450 may be configured to track variability in nutrient
content for the ingredients received from specific ingredient
producers to project a probable nutrient content for the
ingredients received from those specific ingredient producers.
[0124] Variable nutrient engine 450 may be further configured to
account for variability in nutrient content of ingredients. The
estimation of variability of an ingredient may be calculated based
on information related to the particular ingredient, the supplier
of the ingredient, testing of samples of ingredient, etc. According
to exemplary embodiment, recorded and/or estimated variability and
covariance may be used to create distributions that are sampled in
a Monte Carlo approach. In this approach, the actual nutrient
content of ingredients in an optimized feed formulation are sampled
repeatedly from these distributions, producing a distribution of
nutrient contents. Nutrient requirements may then be revised for
any nutrients for which the nutrient content is not sufficient. The
process may be repeated until the desired confidence is achieved
for all nutrients. The actual nutrient content for the ingredients
may be used to generate an animal feed formulation for an animal.
Accordingly, the nutrient content for the ingredients may also be
used as animal feed formulation inputs.
[0125] Referring now to formulator 500, formulator 500 is
configured to receive animal requirements from simulator 300
through enterprise supervisor 200 and nutrient information from
ingredients engine 400 based on available ingredients and generate
an animal feed formulation. Formulator 500 calculates a least-cost
feed formulation that meets the set of nutrient levels defined in
the animal requirements.
[0126] The least-cost animal feed formulation may be generated
using linear programming optimization, as is well-known in the
industry. The least-cost formulation is generally configured to
utilize a users available ingredients in combination with purchased
ingredients to create an optimized feed formulation. More
specifically, the linear programming will incorporate nutrient
sources provided by a user such as grains, forages, silages, fats,
oils, micronutrients, or protein supplements, as ingredients with a
fixed contribution to the total feed formulation. These
contributions are then subtracted from the optimal formulation; the
difference between the overall recipe and these user-supplied
ingredients constitute the ingredient combinations that would be
produced and sold to the customer.
[0127] Alternatively, the formulation process may be performed as a
Monte Carlo simulation with variability in ingredient pricing
included as either historical or projected ranges to created
distribution which are subsequently optimized as described
above.
[0128] Referring now to FIG. 5, a flowchart illustrating a method
600 for animal production optimization is shown, according to an
exemplary embodiment. Method 600 generally includes identifying
optimized values for one or more animal information inputs
according to at least one optimization criteria. Although the
description of method 600 includes specific steps and a specific
ordering of steps, it is important to note that more, fewer, and/or
different orderings of the steps may be performed to implement the
functions described herein. Further, implementation of a step may
require reimplementation of an earlier step. Accordingly, although
the steps are shown in a linear fashion for clarity, several loop
back conditions may exist.
[0129] In a step 605, enterprise supervisor 200 is configured to
receive the animal information inputs. The animal information
inputs can be received from a user through user interface 210,
populated automatically based on related data, populated based on
stored data related to the user, or received in a batch upload from
the user. The received animal information inputs include a
designation of one or more of the animal information inputs as a
variable input. The designation as a variable input may be received
for single, multiple, or all of the animal information inputs.
[0130] In a step 610, enterprise supervisor 200 is configured to
receive an optimization criteria through user interface 210 or,
alternatively, receive a preprogrammed optimization criteria. The
optimization criteria may include maximizing productivity, reducing
expenses, maximizing quality of output, achieving productivity
targets, etc. In an exemplary embodiment, the optimization criteria
may be an objective function requiring minimization or
maximization. The objective function may have constraints
incorporated therein or may be subject to independent constraints.
The objective function may be a function of any combination of
variables of the animal production system.
[0131] In a step 615, enterprise supervisor 200 is configured to
communicate the animal information inputs and optimization criteria
to simulator 300. Upon receiving the animal information inputs and
optimization criteria, simulator 300 is configured to generate a
set of animal requirements in a step 620.
[0132] In a step 625, the set of animal requirements are
communicated from simulator 300 through enterprise supervisor 200
to formulator 500. Formulator 500 is configured to generate a least
cost animal feed formulation based upon the animal requirements and
nutrient information received from nutrient engine 450 in a step
630. The least cost animal feed formulation may be determined based
at least in part on the components within the animals environment,
represented by third ingredient listing 430.
[0133] In a step 635, enterprise supervisor 200 is configured to
generate optimized values for the one or more variable inputs
received in step 605, as discussed in detail above with reference
to FIG. 2.
[0134] Although specific functions are described herein as being
associated with specific components of system 100, functions may
alternatively be associated with any other component of system 100.
For example, user interface 210 may alternatively be associated
with simulator 300 according to an alternative embodiment.
[0135] Many other changes and modifications may be made to the
present invention without departing from the spirit thereof. The
scope of these and other changes will become apparent from the
appended claims.
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