US20060036419A1 - System and method for animal production optimization - Google Patents

System and method for animal production optimization Download PDF

Info

Publication number
US20060036419A1
US20060036419A1 US10/902,504 US90250404A US2006036419A1 US 20060036419 A1 US20060036419 A1 US 20060036419A1 US 90250404 A US90250404 A US 90250404A US 2006036419 A1 US2006036419 A1 US 2006036419A1
Authority
US
United States
Prior art keywords
animal
variable input
value
environment
performance
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Abandoned
Application number
US10/902,504
Other languages
English (en)
Inventor
David Cook
Daniel Barziza
Steve Burghardi
Gregory Engelke
Donald Giesting
John Hahn
Brian Knudson
Wade Martinson
Bruce McGoogan
Michael Messman
Mark Newcomb
Jennifer Ligt
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Cargill Inc
CAN Technologies Inc
Original Assignee
CAN Technologies Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by CAN Technologies Inc filed Critical CAN Technologies Inc
Priority to US10/902,504 priority Critical patent/US20060036419A1/en
Assigned to CAN TECHNOLOGIES, INC. reassignment CAN TECHNOLOGIES, INC. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: HAHN, JOHN J., MARTINSON, WADE S., MCGOOGAN, BRUCE BRIM, BARZIZA, DANIEL, BURGHARDI, STEVE R., COOK, DAVID A., ENGELKE, GREGORY L., GIESTING, DONALD W., KNUDSON, BRIAN J., MESSMAN, MICHAEL A., NEWCOMB, MARK D., VAN DE LIGT, JENNIFER L.G.
Priority to KR1020077005063A priority patent/KR20070052773A/ko
Priority to MX2007001076A priority patent/MX2007001076A/es
Priority to AU2005269324A priority patent/AU2005269324A1/en
Priority to US11/191,257 priority patent/US20060041413A1/en
Priority to RU2007107396/13A priority patent/RU2399289C2/ru
Priority to AU2005269325A priority patent/AU2005269325A1/en
Priority to JP2007523757A priority patent/JP2008508607A/ja
Priority to KR1020077005061A priority patent/KR20070038173A/ko
Priority to US11/191,236 priority patent/US7904284B2/en
Priority to CA002573899A priority patent/CA2573899A1/fr
Priority to EP05775637A priority patent/EP1776685A1/fr
Priority to JP2007523778A priority patent/JP2008508867A/ja
Priority to CA002573901A priority patent/CA2573901A1/fr
Priority to BRPI0513809-4A priority patent/BRPI0513809A/pt
Priority to ARP050103124A priority patent/AR049853A1/es
Priority to CA002573897A priority patent/CA2573897A1/fr
Priority to US11/191,255 priority patent/US7827015B2/en
Priority to KR1020077005062A priority patent/KR20070055522A/ko
Priority to BRPI0513797-7A priority patent/BRPI0513797A/pt
Priority to AU2005269279A priority patent/AU2005269279A1/en
Priority to PCT/US2005/026681 priority patent/WO2006015061A2/fr
Priority to US11/191,238 priority patent/US20060041419A1/en
Priority to BRPI0513775-6A priority patent/BRPI0513775A/pt
Priority to CNA2005800254134A priority patent/CN101023442A/zh
Priority to TW094125350A priority patent/TW200617708A/zh
Priority to CNA200580025243XA priority patent/CN101014973A/zh
Priority to PCT/US2005/026590 priority patent/WO2006015018A2/fr
Priority to PCT/US2005/026589 priority patent/WO2006015017A2/fr
Priority to ARP050103123A priority patent/AR049852A1/es
Priority to RU2007107395/09A priority patent/RU2007107395A/ru
Priority to CNA2005800252425A priority patent/CN101010685A/zh
Priority to MX2007001080A priority patent/MX2007001080A/es
Priority to ARP050103122A priority patent/AR049851A1/es
Priority to JP2007523756A priority patent/JP2008508606A/ja
Priority to TW094125349A priority patent/TW200617707A/zh
Priority to MX2007001081A priority patent/MX2007001081A/es
Priority to EP05775488A priority patent/EP1776664A2/fr
Priority to TW094125348A priority patent/TW200617706A/zh
Priority to EP05775645A priority patent/EP1776665A1/fr
Priority to RU2007107397/09A priority patent/RU2007107397A/ru
Publication of US20060036419A1 publication Critical patent/US20060036419A1/en
Priority to ZA200701705A priority patent/ZA200701705B/en
Priority to ZA200701704A priority patent/ZA200701704B/en
Priority to ZA200701706A priority patent/ZA200701706B/xx
Assigned to CARGILL, INCORPORATED reassignment CARGILL, INCORPORATED CORRECTIVE ASSIGNMENT TO CORRECT THE ASSIGNORS' HAHN, KNUDSON AND MARTINSON IMPROPERLY ASSIGNING THEIR RIGHTS TO CAN TECHNOLOGIES, INC. PREVIOUSLY RECORDED ON REEL 015876 FRAME 0019. ASSIGNOR(S) HEREBY CONFIRMS THE ASSIGNMENT OF ASSIGNORS' COOK, BARZIZA, ENGELKE, GIESTING, MCGOOGAN, MESSMAN, NEWCOMB, VAN DE LIGT AND BURGHARDI INTEREST TO CAN. Assignors: MARTINSON, WADE S., KNUDSON, BRIAN J., HAHN, JOHN J.
Priority to US11/978,541 priority patent/US20080189085A1/en
Priority to US11/978,552 priority patent/US20080183453A1/en
Priority to US11/978,523 priority patent/US20080154569A1/en
Priority to US11/978,536 priority patent/US20080234995A1/en
Priority to US11/978,376 priority patent/US20080154568A1/en
Priority to US12/859,632 priority patent/US20110010154A1/en
Abandoned legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/02—Agriculture; Fishing; Mining
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • A—HUMAN NECESSITIES
    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01K—ANIMAL HUSBANDRY; CARE OF BIRDS, FISHES, INSECTS; FISHING; REARING OR BREEDING ANIMALS, NOT OTHERWISE PROVIDED FOR; NEW BREEDS OF ANIMALS
    • A01K5/00—Feeding devices for stock or game ; Feeding wagons; Feeding stacks
    • A01K5/02—Automatic devices
    • A—HUMAN NECESSITIES
    • A23—FOODS OR FOODSTUFFS; TREATMENT THEREOF, NOT COVERED BY OTHER CLASSES
    • A23K—FODDER
    • A23K10/00—Animal feeding-stuffs
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0635—Risk analysis of enterprise or organisation activities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/10—Office automation; Time management
    • G06Q10/101—Collaborative creation, e.g. joint development of products or services
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/60—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Definitions

  • 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 an animal production system based on one or more optimization criteria.
  • 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.
  • a producer i.e. a farmer, rancher, aquaculture specialist, etc.
  • a producer 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.
  • almost any input may be treated as a variable input.
  • the contribution of almost any input may be increased, decreased, or changed in some other way over time.
  • additional animal feed may be obtained, additional facilities may be constructed, additional labor may be hired, etc.
  • Every variable input may further be associated with one or more effects of variation. For example, for almost every variable input, an increase in the amount of the variable input is associated with an increase in the cost of the variable input.
  • constructing additional facilities may be associated with building costs, financing costs, maintenance costs, etc. Additionally, the increase in the amount of the variable input is associated with an increase in the benefit provided by the variable input.
  • the construction of the additional facilities may be associated with an increase in the number of animals that may be produced at the facility, or a reduction in animal crowding that will increase the production of each animal, etc.
  • What is needed is a system for and method of receiving inputs related to an animal production facility and processing the inputs to determine the effect of modifications to one or more of the inputs. What is further needed is such a system and method where the inputs are related to animal environment, animal type, animal feed ingredients, animal health, animal metabolic status, and/or animal economic data. Still further, what is needed is a system for and method of determining optimized inputs related to an animal production facility based on the minimization or maximization of an objective criteria.
  • 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, wherein at least one of the animal information inputs is designated as a variable input.
  • the system further includes an enterprise supervisor engine that is configured to generate an optimized value for the at least one variable input based on at least one optimization criteria and an animal feed formulation.
  • 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, wherein at least one of the animal information inputs is designated as a variable input.
  • the method further includes 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 an animal feed formulation and at least one optimization criteria.
  • Yet another embodiment of the invention relates to a system for generating an animal feed formulation.
  • the system includes a simulator engine configured to receive a plurality of animal information inputs and generate animal requirements based on the animal information inputs, a formulator engine, the formulator engine configured to receive a plurality of animal feed ingredient inputs and generate at least one animal feed formulation composed of the animal feed ingredients based on the animal requirements, wherein at least one of the animal feed ingredient inputs is designated as a variable input, and an enterprise supervisor engine configured to optimize the at least one animal feed formulation according to at least one optimization criteria, and further configured to generate an optimized value for the at least one variable input based on the at least one optimization criteria.
  • the system includes an optimization engine, having an objective function program therein, configured to receive a feed formulation input provided to the optimization engine.
  • 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 modeling output to the optimization engine.
  • the optimization engine optimizes the objective function to provide an optimized solution for the at least one variable input based on the modeling output.
  • Yet another embodiment of the invention relates to a method for generating optimized values for variable inputs to an animal production optimization system.
  • the method includes the steps of receiving animal information input, including at least one variable input, generating modeling output based on the animal information input, receiving a feed formulation input to the objective function, and generating an objective function based on the modeling output and the feed formulation input.
  • the method further includes optimizing the objective function to provide an optimized value for the at least one variable input.
  • FIG. 1 is a general block diagram illustrating an animal production optimization system, according to an exemplary embodiment
  • FIG. 2 is a general block diagram illustrating an enterprise supervisor for an animal production optimization system, according to an exemplary embodiment
  • FIG. 3 is a general block diagram illustrating a simulator for an animal production system, according to an exemplary embodiment
  • FIG. 4 is a general block diagram illustrating an ingredients engine and a formulator for an animal production system, according to an exemplary embodiment
  • FIG. 5 is a flowchart illustrating a method for animal production optimization, according to an exemplary embodiment.
  • a computer system 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.
  • RAM random access memory
  • ROM read-only memory
  • multiple workstations, databases, processes, or computers can be utilized.
  • hardwired circuitry may be used in place of, or in combination with, software instructions to implement the functions described.
  • the embodiments described herein are not limited to any particular source for the instructions executed by the computer system.
  • System 100 includes an enterprise supervisor 200 , a simulator 300 , an ingredient engine 400 , and a formulator 500 .
  • System 100 may be implemented utilizing a single or multiple computing systems.
  • 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.
  • 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.
  • system 100 may be implemented as a combination of single computing systems implementing multiple processes and distributed systems.
  • 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 input 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.
  • 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.
  • 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.
  • 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.
  • 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).
  • 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.
  • 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
  • Animal age may include an actual age or a physiological state associated with an age.
  • 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.
  • Animal job may include a physiologic state as described above, such as gestation, lactation, growth, 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.
  • 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.
  • Animal morphology includes a body shape exhibited by an animal.
  • 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 like the length of intestinal villi or depth of intestinal crypts.
  • 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.
  • body mass composition may include edible component compositions such as fillet yield, breast meat yield, tail meat yield, etc.
  • 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, etc.
  • Animal genotype may include any representation of all or part of the genetic constitution of an individual or group.
  • an animal genotype may include DNA markers associated with specific traits, sequencing specific segments of DNA, etc.
  • 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.
  • 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.
  • Composition of output may include the composition of a product produced by an animal.
  • 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, etc.
  • 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 species, etc.
  • Enzyme information may include the current content, quantity and/or composition of any enzyme, such as protease, amylase, and/or lipase, produced by the pancreas, produced within the gastrointestinal tract, enzymes produced by a microbial population, etc.
  • Microbial and/or enzyme information may further include information about potential nutritional biomass represented by the 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.
  • 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.
  • Examples of animal information input related to the environment may include ambient temperature, wind speed or draft, photoperiod or the amount of daylight exposure, acclimation, seasonal effects, air quality, water quality, water flow rate, aeration rate, system substrate, filter surface area, filtration loan capacity, 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, hectares, acres, volume, etc.), system preparation such as using liming, discing, etc., aeration rate, system type, etc.
  • system size e.g. the size in square meters, hectares, acres, volume, etc.
  • system preparation such as using liming, discing, etc.
  • aeration rate system type, etc.
  • 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.
  • an aqua producer may modify nutrient input to an aquatic environment by altering a feed design or feeding program for the animals in the environment.
  • 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.
  • EAS environmental appraisal system
  • 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, feathering, lighting intensity, lighting time patterns, 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.
  • 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.
  • 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, etc.), expected and/or targeted growth curves, survival rates, expected harvest dates, etc.
  • an animal expected output e.g., milk yield, product composition, body composition, etc.
  • a user defined requirement e.g., a user defined requirement
  • a risk tolerance e.g., 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, etc.
  • expected and/or targeted growth curves e.
  • 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 .
  • the animal information input or some part of the input may be retrieved from a database or other information source.
  • 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, etc. Each calculated value may include an option enabling a user to manually override the calculated value.
  • each animal information input may include a variety of information associated with that input.
  • 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.
  • 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.
  • 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.
  • 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 .
  • 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.
  • additional information may be stored and retrievable from within system 100 or an associated database.
  • 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.
  • 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.
  • a current size breakdown for shrimp in a pond versus a potential size breakdown 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.
  • 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 1 General Characteristics Impact of the ration on the greater Quantity and/or composition Quantity and/or environment: (e.g. nitrogen, phosporus, composition of urine etc.) of manure 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.
  • 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 .
  • 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.
  • optimization or some portion of the optimization may be performed by a different component of system 100 .
  • optimization described herein with reference to supervisor 200 may alternatively be performed by simulator 300 .
  • optimization of animal feed formulation may be performed by formulator 500 .
  • 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.
  • User interface 210 may be any type of interface configured to allow a user to provide input and receive output from system 100 .
  • user interface 210 may be implemented as a web based application within a web browsing application.
  • 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.
  • user interface 210 may be implemented using a spreadsheet based interface, a custom graphical user interface, etc.
  • 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.
  • 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 .
  • Optimization criteria may include any criteria, target, or combination of targets or balanced goals that are desirable to the current user.
  • 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.
  • 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.
  • 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 may be solved using gradient-based methods, or more preferably simplex methods such as Nelder-Mead or Torczon's algorithm.
  • 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 ).
  • other optimization methods may be applied, including but not limited to, pseudo-gradient based methods, stochastic methods, etc.
  • 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.
  • 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.
  • Enterprise supervisor 200 may further be configured to receive real world feedback based on the application of the optimized values for the variable inputs.
  • the real world feedback may be compared to the performance projections, further discussed below with reference to FIG. 3 .
  • Real world feedback can be provided using any of a variety of methods such as automated monitoring, manual input of data, etc.
  • 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.
  • 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.
  • Simulator 300 includes a requirements engine 310 , an animal performance simulator 320 , an environment performance simulator 330 , and an economic performance simulator 340 .
  • 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 performance projection, such as animal requirements and/or performance projections, including animal performance projections, economic performance projections, environmental performance projections, etc.
  • 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.
  • 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.
  • models developed by the National Research Council (NRC) consisting of algebraic equations that provide nutrient requirements based on empirical correlations.
  • 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.
  • 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.
  • 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.
  • 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.
  • the staggered corrector algorithm is a particularly efficient method for the sensitivities calculation, which is often the bottleneck in the overall optimization calculation.
  • 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.
  • Simulator 300 and the associated models may be configured and structured to facilitate periodic updating.
  • simulator 300 and the associated models may be implemented as a dynamic link library (DLL).
  • DLL dynamic link library
  • a DLL may be easily exported but not viewed or modified in any structural way.
  • 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.
  • 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.
  • each model may be associated with a plurality of models based on an additional categorization, such as developmental stage, stress level, etc.
  • 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.
  • the animal requirements may include a relatively small set of nutrients or a large set of nutrients.
  • the set of animal requirements may include constraints or limits on the amount of any particular nutrient, combination of nutrients, and/or specific ingredients.
  • 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.
  • animal requirements may be generated that define ranges of acceptable nutrient levels.
  • utilizing nutrient ranges allows greater flexibility during animal feed formulation, as will be described further below with reference to FIG. 3 .
  • 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. 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 may be 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.
  • 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. 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.
  • Table 2 below includes an exemplary listing of nutrients that may be included in 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.
  • 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.
  • 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 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 within 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, etc.), water stability of feed, uneaten food, etc.
  • 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.
  • 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.
  • 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.
  • the performance projections may include a wide variety of information related to outputs produced based on the provided set inputs.
  • 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.
  • 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.
  • performance projections may be generated based on a single simulator or a combination of less than all of the simulators.
  • Requirements engine 310 may further include additional simulators as needed to generate performance projections that are customized to satisfy a specific user criteria.
  • requirements engine 310 may include a bulk composition simulator, egg composition simulator, meat fat composition, waste output simulator, etc.
  • 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.
  • 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.
  • 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, zoo plankton, or small fish in an aquaculture pond, etc.
  • 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 in consumption ratios, such as a ratio of water to dry feed matter consumed. 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 as third ingredient listing 430 .
  • third ingredient listing 430 may include an aquatic ecosystem total nutrient content.
  • the ecosystem contribution to total nutrition may be included in several ways.
  • a sample may be drawn and analyzed for total nutrient content and included as third listing 430 .
  • 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 excretion, temperature, sunlight, etc.
  • Third ingredient listing 430 may further include performance projections generated by simulator 300 .
  • 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.
  • Each listing of ingredients may further include additional information associated with the ingredients.
  • a listing of ingredients may include a listing of costs associated with that ingredient.
  • an ingredient at the first location may include a costs associated with producing the ingredient, storing the ingredient, dispensing the ingredient, etc.
  • an ingredient at the second location may include a cost associated with purchasing the ingredient
  • an ingredient at the third location may include a cost associated with maintaining the environment.
  • the additional information may include any type of information that may be relevant to later processing steps.
  • 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.
  • 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.
  • 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 .
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • Method 600 generally includes identifying optimized values for one or more animal information inputs according to at least one optimization criteria.
  • 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.
  • 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.
  • 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.
  • 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.
  • enterprise supervisor 200 is configured to communicate the animal information inputs and optimization criteria to simulator 300 .
  • simulator 300 is configured to generate a set of animal requirements in a step 620 .
  • 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 .
  • 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 .
  • user interface 210 may alternatively be associated with simulator 300 according to an alternative embodiment.
US10/902,504 2004-07-29 2004-07-29 System and method for animal production optimization Abandoned US20060036419A1 (en)

Priority Applications (50)

Application Number Priority Date Filing Date Title
US10/902,504 US20060036419A1 (en) 2004-07-29 2004-07-29 System and method for animal production optimization
KR1020077005063A KR20070052773A (ko) 2004-07-29 2005-07-27 환경적 영양소 입력에 기초하여 동물 생산을 최적화하기위한 시스템 및 방법
MX2007001076A MX2007001076A (es) 2004-07-29 2005-07-27 Sistema y metodo para optimizar la produccion de animales con base en entradas de nutrientes ambientales.
AU2005269324A AU2005269324A1 (en) 2004-07-29 2005-07-27 System and method for optimizing animal production based on dynamic nutrient information
US11/191,257 US20060041413A1 (en) 2004-07-29 2005-07-27 System and method for optimizing animal production based on dynamic nutrient information
RU2007107396/13A RU2399289C2 (ru) 2004-07-29 2005-07-27 Система и способ оптимизации животноводческого производства
AU2005269325A AU2005269325A1 (en) 2004-07-29 2005-07-27 System and method for optimizing animal production
JP2007523757A JP2008508607A (ja) 2004-07-29 2005-07-27 動物生産を最適化するシステムおよび方法
KR1020077005061A KR20070038173A (ko) 2004-07-29 2005-07-27 동물 생산을 최적화하기 위한 시스템 및 방법
US11/191,236 US7904284B2 (en) 2004-07-29 2005-07-27 System and method for optimizing animal production based on empirical feedback
CA002573899A CA2573899A1 (fr) 2004-07-29 2005-07-27 Systeme et procede permettant d'optimiser une production animale
EP05775637A EP1776685A1 (fr) 2004-07-29 2005-07-27 Systeme et procede permettant d'optimiser l'elevage d'animaux sur la base des apports de substances nutritives provenant de l'environnement
JP2007523778A JP2008508867A (ja) 2004-07-29 2005-07-27 環境的栄養素入力に基づき動物生産を最適化するシステムおよび方法
CA002573901A CA2573901A1 (fr) 2004-07-29 2005-07-27 Systeme et procede permettant d'optimiser l'elevage d'animaux sur la base des apports de substances nutritives provenant de l'environnement
BRPI0513809-4A BRPI0513809A (pt) 2004-07-29 2005-07-27 sistema e método para otimização da produção animal baseados em informação dinámica de nutriente
ARP050103124A AR049853A1 (es) 2004-07-29 2005-07-27 Sistema y metodo para optimizar la produccion animal en base a la incorporacion de nutrientes ambientales
CA002573897A CA2573897A1 (fr) 2004-07-29 2005-07-27 Systeme et procede pour optimiser la production animale sur la base d'informations dynamiques sur les elements nutritifs
US11/191,255 US7827015B2 (en) 2004-07-29 2005-07-27 System and method for optimizing animal production based on environmental nutrient inputs
KR1020077005062A KR20070055522A (ko) 2004-07-29 2005-07-27 동적 영양소 정보에 기초하여 동물 생산을 최적화하기 위한시스템 및 방법
BRPI0513797-7A BRPI0513797A (pt) 2004-07-29 2005-07-27 sistema e método para otimização de produção animal
AU2005269279A AU2005269279A1 (en) 2004-07-29 2005-07-27 System and method for optimizing animal production based on environmental nutrient inputs
PCT/US2005/026681 WO2006015061A2 (fr) 2004-07-29 2005-07-27 Systeme et procede permettant d'optimiser l'elevage d'animaux sur la base des apports de substances nutritives provenant de l'environnement
US11/191,238 US20060041419A1 (en) 2004-07-29 2005-07-27 System and method for optimizing animal production based on a target output characteristic
BRPI0513775-6A BRPI0513775A (pt) 2004-07-29 2005-07-27 sistema e método para otimização de produção animal baseado em entradas de nutrientes ambientais
CNA2005800254134A CN101023442A (zh) 2004-07-29 2005-07-27 用于基于动态营养信息优化动物生产的系统和方法
TW094125350A TW200617708A (en) 2004-07-29 2005-07-27 System and method for optimizing animal production
CNA200580025243XA CN101014973A (zh) 2004-07-29 2005-07-27 用于优化动物生产的系统和方法
PCT/US2005/026590 WO2006015018A2 (fr) 2004-07-29 2005-07-27 Systeme et procede permettant d'optimiser une production animale
PCT/US2005/026589 WO2006015017A2 (fr) 2004-07-29 2005-07-27 Systeme et procede pour optimiser la production animale sur la base d'informations dynamiques sur les elements nutritifs
ARP050103123A AR049852A1 (es) 2004-07-29 2005-07-27 Sistema y metodo para optimizar la produccion de animales en base a informacion dinamica sobre nutrientes
RU2007107395/09A RU2007107395A (ru) 2004-07-29 2005-07-27 Система и способ оптимизации производства продуктов животноводства на основе входных данных о природных питательных веществах
CNA2005800252425A CN101010685A (zh) 2004-07-29 2005-07-27 用于基于环境营养输入优化动物生产的系统和方法
MX2007001080A MX2007001080A (es) 2004-07-29 2005-07-27 Sistema y metodo para optimizar la produccion de animales con base en informacion dinamica de nutrientes.
ARP050103122A AR049851A1 (es) 2004-07-29 2005-07-27 Sistema y metodo para la optimizacion de la produccion animal
JP2007523756A JP2008508606A (ja) 2004-07-29 2005-07-27 動的な栄養素情報に基づき動物生産を最適化するシステムおよび方法
TW094125349A TW200617707A (en) 2004-07-29 2005-07-27 System and method for optimizing animal production based on dynamic nutrient informaiton
MX2007001081A MX2007001081A (es) 2004-07-29 2005-07-27 Sistema y metodo para optimizar la produccion de animales.
EP05775488A EP1776664A2 (fr) 2004-07-29 2005-07-27 Systeme et procede pour optimiser la production animale sur la base d'informations dynamiques sur les elements nutritifs
TW094125348A TW200617706A (en) 2004-07-29 2005-07-27 System and method for optimizing animal production based on environmental nutrient inputs
EP05775645A EP1776665A1 (fr) 2004-07-29 2005-07-27 Systeme et procede permettant d'optimiser une production animale
RU2007107397/09A RU2007107397A (ru) 2004-07-29 2005-07-27 Система и способ оптимизации производства продуктов животноводства на основе динамической информации о питательных веществах
ZA200701705A ZA200701705B (en) 2004-07-29 2007-02-27 System and method for optimizing animal production based on dynamic nutrient information
ZA200701704A ZA200701704B (en) 2004-07-29 2007-02-27 System and method for optimizing animal production
ZA200701706A ZA200701706B (en) 2004-07-29 2007-02-27 System and method for optimizing animal production based on environmental nutrient inputs
US11/978,541 US20080189085A1 (en) 2004-07-29 2007-10-29 System and method for optimizing animal production
US11/978,552 US20080183453A1 (en) 2004-07-29 2007-10-29 System and method for optimizing animal production based on empirical feedback
US11/978,523 US20080154569A1 (en) 2004-07-29 2007-10-29 System and method for optimizing animal production based on environmental nutrient inputs
US11/978,536 US20080234995A1 (en) 2004-07-29 2007-10-29 System and method for optimizing animal production based on a target output characteristic
US11/978,376 US20080154568A1 (en) 2004-07-29 2007-10-29 System and method for optimizing animal production based on dynamic nutrient information
US12/859,632 US20110010154A1 (en) 2004-07-29 2010-08-19 System and method for optimizing animal production

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
US10/902,504 US20060036419A1 (en) 2004-07-29 2004-07-29 System and method for animal production optimization

Related Child Applications (4)

Application Number Title Priority Date Filing Date
US11/191,257 Continuation-In-Part US20060041413A1 (en) 2004-07-29 2005-07-27 System and method for optimizing animal production based on dynamic nutrient information
US11/191,236 Continuation-In-Part US7904284B2 (en) 2004-07-29 2005-07-27 System and method for optimizing animal production based on empirical feedback
US11/191,255 Continuation-In-Part US7827015B2 (en) 2004-07-29 2005-07-27 System and method for optimizing animal production based on environmental nutrient inputs
US11/191,238 Continuation-In-Part US20060041419A1 (en) 2004-07-29 2005-07-27 System and method for optimizing animal production based on a target output characteristic

Publications (1)

Publication Number Publication Date
US20060036419A1 true US20060036419A1 (en) 2006-02-16

Family

ID=35107022

Family Applications (9)

Application Number Title Priority Date Filing Date
US10/902,504 Abandoned US20060036419A1 (en) 2004-07-29 2004-07-29 System and method for animal production optimization
US11/191,236 Expired - Fee Related US7904284B2 (en) 2004-07-29 2005-07-27 System and method for optimizing animal production based on empirical feedback
US11/191,255 Expired - Fee Related US7827015B2 (en) 2004-07-29 2005-07-27 System and method for optimizing animal production based on environmental nutrient inputs
US11/191,257 Abandoned US20060041413A1 (en) 2004-07-29 2005-07-27 System and method for optimizing animal production based on dynamic nutrient information
US11/191,238 Abandoned US20060041419A1 (en) 2004-07-29 2005-07-27 System and method for optimizing animal production based on a target output characteristic
US11/978,523 Abandoned US20080154569A1 (en) 2004-07-29 2007-10-29 System and method for optimizing animal production based on environmental nutrient inputs
US11/978,536 Abandoned US20080234995A1 (en) 2004-07-29 2007-10-29 System and method for optimizing animal production based on a target output characteristic
US11/978,376 Abandoned US20080154568A1 (en) 2004-07-29 2007-10-29 System and method for optimizing animal production based on dynamic nutrient information
US11/978,552 Abandoned US20080183453A1 (en) 2004-07-29 2007-10-29 System and method for optimizing animal production based on empirical feedback

Family Applications After (8)

Application Number Title Priority Date Filing Date
US11/191,236 Expired - Fee Related US7904284B2 (en) 2004-07-29 2005-07-27 System and method for optimizing animal production based on empirical feedback
US11/191,255 Expired - Fee Related US7827015B2 (en) 2004-07-29 2005-07-27 System and method for optimizing animal production based on environmental nutrient inputs
US11/191,257 Abandoned US20060041413A1 (en) 2004-07-29 2005-07-27 System and method for optimizing animal production based on dynamic nutrient information
US11/191,238 Abandoned US20060041419A1 (en) 2004-07-29 2005-07-27 System and method for optimizing animal production based on a target output characteristic
US11/978,523 Abandoned US20080154569A1 (en) 2004-07-29 2007-10-29 System and method for optimizing animal production based on environmental nutrient inputs
US11/978,536 Abandoned US20080234995A1 (en) 2004-07-29 2007-10-29 System and method for optimizing animal production based on a target output characteristic
US11/978,376 Abandoned US20080154568A1 (en) 2004-07-29 2007-10-29 System and method for optimizing animal production based on dynamic nutrient information
US11/978,552 Abandoned US20080183453A1 (en) 2004-07-29 2007-10-29 System and method for optimizing animal production based on empirical feedback

Country Status (14)

Country Link
US (9) US20060036419A1 (fr)
EP (3) EP1776685A1 (fr)
JP (3) JP2008508606A (fr)
KR (3) KR20070038173A (fr)
CN (3) CN101023442A (fr)
AR (3) AR049851A1 (fr)
AU (3) AU2005269325A1 (fr)
BR (3) BRPI0513809A (fr)
CA (3) CA2573897A1 (fr)
MX (3) MX2007001081A (fr)
RU (3) RU2007107397A (fr)
TW (3) TW200617707A (fr)
WO (3) WO2006015018A2 (fr)
ZA (3) ZA200701705B (fr)

Cited By (17)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20060041412A1 (en) * 2004-07-29 2006-02-23 Can Technologies, Inc. System and method for optimizing animal production based on empirical feedback
US20080189085A1 (en) * 2004-07-29 2008-08-07 Can Technologies, Inc. System and method for optimizing animal production
US8170908B1 (en) * 2006-08-10 2012-05-01 Vaughan Jr John Thomas Apparatus and method for processing agricultural materials and changing the proportions of output materials
US20140272028A1 (en) * 2013-03-15 2014-09-18 Nestec Sa Systems and methods for ordering and manufacturing custom pet food
TWI498842B (zh) * 2013-05-22 2015-09-01 Univ Nat Pingtung Sci & Tech 家禽飼養管理方法
US20150245595A1 (en) * 2012-09-12 2015-09-03 Big Dutchman International Gmbh Method and system for monitoring and/or controlling the resource consumption of an agricultural plant
CN104915338A (zh) * 2014-03-12 2015-09-16 北京智慧农业有限公司 基于双胞支持向量回归的动态营养配比方法和系统
US20170013862A1 (en) * 2014-04-08 2017-01-19 Sevecom S.P.A. Method for producing an animal feed and use thereof
US20170215457A1 (en) 2014-07-21 2017-08-03 Sevecom S.P.A. Powdered emulsion for animal feed
US10245296B2 (en) * 2009-12-29 2019-04-02 Colgate-Palmolive Company Compositions including ginger for the amelioration or prevention of inflammatory conditions
WO2019143714A1 (fr) * 2018-01-16 2019-07-25 Habi, Inc. Procédés et systèmes pour une plate-forme de bien-être animal
WO2019216647A1 (fr) * 2018-05-08 2019-11-14 (주)씽크포비엘 Procédé de prédiction de rendements laitiers, composition nutritive de ration complète pour obtenir des rendements laitiers cibles, et composition nutritive de ration complète pour obtenir un coût cible, sur la base de modèles de prédiction basés sur un apprentissage profond
US20210183494A1 (en) * 2017-12-06 2021-06-17 Koninklijke Philips N.V. An apparatus and method for personalized meal plan generation
CN113435967A (zh) * 2021-06-22 2021-09-24 布瑞克农业大数据科技集团有限公司 一种自动确定农产品的营销内容的方法和系统
US11213052B2 (en) 2011-06-10 2022-01-04 Sevecom S.P.A. Use of emulsifiers in association with vegetable oleins in an animal feed
US11347829B1 (en) * 2013-09-26 2022-05-31 ClearHealthBill, LLC Method and system for calculating expected healthcare costs from insurance policy parameters
WO2023039663A1 (fr) * 2021-09-15 2023-03-23 Xpertsea Solutions Inc. Système de détermination de rendement d'étang et procédé de détermination d'un rendement d'un étang contenant des organismes marins

Families Citing this family (75)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5673647A (en) * 1994-10-31 1997-10-07 Micro Chemical, Inc. Cattle management method and system
SE527130C2 (sv) * 2004-01-13 2005-12-27 Delaval Holding Ab Anordning och förfarande för utfodring av djur
EP1887938A4 (fr) 2004-11-30 2010-06-09 Micro Beef Technologies Ltd Detection de troubles de sante au niveau respiratoire et circulatoire chez des animaux, pour une meilleure gestion
US7681527B2 (en) 2005-01-19 2010-03-23 Micro Beef Technologies, Ltd. Method and system for tracking and managing animals and/or food products
US9159126B2 (en) 2006-04-03 2015-10-13 Jbs Usa, Llc System and method for analyzing and processing food product
US7613330B2 (en) * 2006-04-03 2009-11-03 Jbs Swift & Company Methods and systems for tracking and managing livestock through the production process
US20090092715A1 (en) * 2006-07-27 2009-04-09 Beck James F System for real-time characterization of ruminant feed rations
US20080145476A1 (en) * 2006-07-27 2008-06-19 Beck James F Method and feed for enhancing ruminant animal nutrition
CA2666065A1 (fr) * 2006-07-27 2008-01-31 Nutri-Innovations Llc Procede et aliment destines a ameliorer l'alimentation de ruminants
EP1886581B1 (fr) * 2006-08-11 2009-03-11 Maple Leaf Foods Inc. Aliment pour ruminant et méthode de formulation
US8745557B1 (en) 2006-09-11 2014-06-03 The Mathworks, Inc. Hardware definition language generation for data serialization from executable graphical models
US7882462B2 (en) 2006-09-11 2011-02-01 The Mathworks, Inc. Hardware definition language generation for frame-based processing
NL1032611C2 (nl) * 2006-10-03 2008-04-04 Maasland Nv Werkwijze, software en computer voor het managen van een groep melkdieren.
US9420807B2 (en) 2007-04-02 2016-08-23 Purina Animal Nutrition Llc Method of feeding young monogastric mammals and composition fed to young monogastric mammals
NL1033926C2 (nl) 2007-06-03 2008-12-08 Maasland Nv Werkwijze en inrichting voor het beheren van een groep melkdieren, alsmede een computerprogrammaproduct daarvan.
US8234139B2 (en) * 2007-10-15 2012-07-31 Oracle International Corporation Process manufacturing with least cost formulation
GB0813782D0 (en) * 2008-07-28 2008-09-03 Delaval Holding Ab Animal installation with height measurement device
GB0813778D0 (en) * 2008-07-28 2008-09-03 Delaval Holding Ab Monitoring animal condition
GB2473188A (en) * 2009-09-02 2011-03-09 Biosauce Ltd Apparatus for dispensing tailored nutritional products
JP5546825B2 (ja) * 2009-10-06 2014-07-09 明治飼糧株式会社 シミュレーション装置、シミュレーション方法、及びシミュレーションシステム
BR112012013037A2 (pt) * 2009-12-02 2016-11-22 Shell Int Research método e sistema para coordenar controle de processo avançado e otimização em tempo real de um processo, e, artigo de fabricação
US9436441B1 (en) 2010-12-08 2016-09-06 The Mathworks, Inc. Systems and methods for hardware resource sharing
US8850092B2 (en) * 2011-04-11 2014-09-30 Rockwell Automation Technologies, Inc. Input module for an industrial controller
US9355000B1 (en) 2011-08-23 2016-05-31 The Mathworks, Inc. Model level power consumption optimization in hardware description generation
US8655751B2 (en) * 2012-07-11 2014-02-18 Animal Health International, Inc. System and method for control of commodities inventory for animal feed rations
RU2537555C2 (ru) * 2012-10-09 2015-01-10 Федеральное государственное бюджетное учреждение науки Институт биохимии им. А.Н. Баха Российской академии наук Корм сухой полнорационный для собак крупных пород сбалансированного аминокислотного состава с доказанными био-функциональными свойствами (варианты)
WO2014066895A2 (fr) * 2012-10-26 2014-05-01 Farmeron, Inc. Procédé de gestion de production de produit laitier
US20140172678A1 (en) * 2012-12-14 2014-06-19 Craig Alan Stephens Institution simulation
KR102361152B1 (ko) * 2013-03-01 2022-02-09 클레버펫 엘엘씨 동물 인터랙션 장치, 시스템 및 방법
US20140261196A1 (en) * 2013-03-15 2014-09-18 Prairie Systems, LLC System for Managing Livestock Flow
US8660888B2 (en) 2013-04-13 2014-02-25 Leachman Cattle of Colorado, LLC System, computer-implemented method, and non-transitory, computer-readable medium to determine relative market value of a sale group of livestock based on genetic merit and other non-genetic factors
US9817931B1 (en) 2013-12-05 2017-11-14 The Mathworks, Inc. Systems and methods for generating optimized hardware descriptions for models
US10078717B1 (en) 2013-12-05 2018-09-18 The Mathworks, Inc. Systems and methods for estimating performance characteristics of hardware implementations of executable models
CA2873333A1 (fr) * 2013-12-05 2015-06-05 Canarm Ltd. Systeme d'alimentation de truie
CA2839027A1 (fr) * 2014-01-02 2015-07-02 Alltech, Inc. Systemes et procedes d'estimation de l'indice de consommation et de l'empreinte carbonique des animaux producteurs de lait
CN106797892B (zh) * 2015-11-26 2019-10-18 范石军 一种日粮浓度实时智能控制与反馈饲喂系统
US10423733B1 (en) 2015-12-03 2019-09-24 The Mathworks, Inc. Systems and methods for sharing resources having different data types
RU2627564C2 (ru) * 2016-01-20 2017-08-08 Федеральное государственное бюджетное научное учреждение "Всероссийский научно-исследовательский институт пантового оленеводства" (ФГБНУ ВНИИПО) Устройство для отлучения молодняка от матери
CN109313784A (zh) * 2016-06-15 2019-02-05 索尼公司 信息处理设备、方法及其程序
CN106069923B (zh) * 2016-06-21 2018-10-12 马鞍山市徽农农业科技发展有限公司 一种用于河蟹养殖的底栖饵料生物培养基的使用方法
CN106305577A (zh) * 2016-08-23 2017-01-11 山东商业职业技术学院 一种用于水产品无水保活的智能储存箱
CN110198629A (zh) * 2016-12-02 2019-09-03 吉格概念公司 具有仔猪出生检测的产仔系统及其操作方法
CN107027682B (zh) * 2017-06-19 2022-06-24 福州高科新技术开发有限公司 一种鱼塘自动投喂装置及投喂方法和增氧方法
WO2019023611A1 (fr) * 2017-07-27 2019-01-31 WAGZ, Inc. Procédés et systèmes de contrôle et d'alimentation d'un animal domestique
US10806130B2 (en) * 2017-12-19 2020-10-20 Premex, Inc. Method and system for real-time livestock management
KR102077526B1 (ko) * 2017-12-21 2020-02-14 건국대학교 산학협력단 힐링말산업에 필요한 말 선발과 조련 시스템 및 방법
CA3096350A1 (fr) 2018-04-10 2019-10-17 Ajinomoto Co., Inc. Procede, dispositif et programme d'evaluation de l'etat de sante et/ou de l'etat de croissance de betail
US20210251259A1 (en) * 2018-05-09 2021-08-19 Mars, Incorporated Net energy model for companion animals and methods
US11266125B1 (en) * 2018-08-06 2022-03-08 American Institute of Nutrition and Management Inc. System and method for animal growth regulation for improved profitability and mitigated risk
RU2727530C1 (ru) * 2019-02-12 2020-07-22 Федеральное государственное казённое военное образовательное учреждение высшего образования "Военная академия материально-технического обеспечения имени генерала армии А.В. Хрулева" Министерства обороны Российской Федерации Система для оптимизации инвестиционных потоков
JP7416565B2 (ja) 2019-03-26 2024-01-17 株式会社カネカ 情報処理システム、情報管理装置、情報処理方法及びプログラム
CN112005887A (zh) * 2019-05-31 2020-12-01 湖南晶天科技实业有限公司 一种获得高产和隔断毁灭性传染源病原微生物的养殖场以及养殖方法
NL2023357B1 (en) 2019-06-21 2021-02-22 Forfarmers Corp Services B V Method for optimizing animal farming by calculating of intermediate and/or final weights of the animals during their growth period based on various inputs.
KR102188050B1 (ko) * 2019-06-24 2020-12-07 손혜정 영양 정보 제공 시스템 및 방법
WO2021011991A1 (fr) * 2019-07-24 2021-01-28 Smarta Industrial Pty Ltd Système de prédiction et de gestion de productivité locale
CN110543972A (zh) * 2019-08-05 2019-12-06 广州影子科技有限公司 饲料配方的确定方法和系统以及计算机可读存储介质
CN110476969A (zh) * 2019-08-29 2019-11-22 段青文 加速农药降解的组合物及其制备方法和应用
CN111091537B (zh) * 2019-11-28 2022-06-10 攀钢集团攀枝花钢钒有限公司 一种全轧程仿真孔型充满度的评价方法
CN110956404A (zh) * 2019-12-05 2020-04-03 中国农业科学院草原研究所 一种牧场管理优化方法、系统和计算机可读存储介质
CN111109105A (zh) * 2019-12-17 2020-05-08 广东顺德雷舜信息科技有限公司 宠物喂食方法、装置、计算机设备及存储介质
KR102289640B1 (ko) * 2020-01-28 2021-08-17 전북대학교 산학협력단 비대칭 스테레오 카메라를 이용한 비접촉식 모바일 가축 체중 측정시스템
EP3912477A1 (fr) * 2020-05-18 2021-11-24 Dairy Information Technology Services S.L Procédé de détermination d'une composition d'alimentation de groupe optimisée, programme informatique correspondant, systèmes locaux et fédérés
CN111466305A (zh) * 2020-05-27 2020-07-31 电子科技大学中山学院 一种基于宠物项圈的宠物喂食系统
CN111374225B (zh) * 2020-05-29 2020-09-22 双胞胎（集团）股份有限公司 通过生长数据优化猪饲料配比的方法
NL2026185B1 (en) * 2020-07-31 2022-04-04 Forfarmers Corp Services B V Method for enhanced formulation of feed production
EP4018823A1 (fr) 2020-12-23 2022-06-29 Forfarmers Corporate Services B.V. Procédé d'optimisation de l'élevage d'animaux par calcul des poids intermédiaires et/ou finaux des animaux pendant leur période de croissance basé sur différents intrants
KR102527364B1 (ko) * 2021-02-23 2023-04-28 주식회사 라이노박스 반려동물 개별 맞춤형 큐레이션 시스템
CN113016727A (zh) * 2021-03-02 2021-06-25 浙江理工大学 一种基于知识库的蚕桑生产要素推荐系统及方法
CA3174265A1 (fr) 2021-03-07 2022-09-15 William Stone Procede et systeme d'alimentation bases sur l'ia pour fermes piscicoles terrestres
CN113197116A (zh) * 2021-05-07 2021-08-03 青岛不愁网信息科技有限公司 生猪健康监测及出栏时间评估方法、终端设备及存储介质
KR102542763B1 (ko) * 2021-06-08 2023-06-14 건국대학교 산학협력단 양돈의 사양 상태 진단 방법
KR102592533B1 (ko) * 2021-06-16 2023-10-23 전북대학교산학협력단 도축 이력 정보를 이용한 저탄소 제품 인증 방법 및 시스템
TWI792388B (zh) * 2021-07-05 2023-02-11 國立中興大學 泌乳牛產乳效益的預測系統及方法
CN114287649B (zh) * 2021-12-07 2023-04-07 江苏丰尚智能科技有限公司 裂缝压差式饲料生产装置、方法、计算机设备和存储介质
EP4295675A1 (fr) * 2022-06-22 2023-12-27 Adisseo France S.A.S. Procédé pour déterminer des données de commande pour une unité de mélange d'un appareil de mélange d'aliments

Citations (35)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4463706A (en) * 1982-05-19 1984-08-07 J.B.M. Associates Method and system for monitoring the dispensing of drugs or nutrients to animals
US4493290A (en) * 1982-05-27 1985-01-15 Dataface Limited Milk yield recording and concentrate feed ration calculation for dairy cows
US4498424A (en) * 1982-05-15 1985-02-12 Holscher & Leuschner Gmbh & Co. Animal feeding method and apparatus
US4517923A (en) * 1983-07-07 1985-05-21 Key Systems, Inc. Animal feeding system
US4532892A (en) * 1981-08-17 1985-08-06 Ralston Purina Company Animal feeding and monitoring system
US4589372A (en) * 1983-04-11 1986-05-20 The University Of Toledo Dispensing system having weight dependent control means
US4712511A (en) * 1985-08-15 1987-12-15 Zamzow Donald D Programmed mix and delivery system
US4729894A (en) * 1984-01-03 1988-03-08 Oklahoma State University Poultry drinking water additive
US5105767A (en) * 1989-01-03 1992-04-21 168189 Canada Limited Computerized monitoring system for animal feed ration processing mills
US5174244A (en) * 1992-03-12 1992-12-29 Gaalswyk Mark K Livestock feed batching system
US5309864A (en) * 1990-11-12 1994-05-10 N.V. Nederlandsche Apparatenfabriek Nedap Method and apparatus for feeding animals
US5355833A (en) * 1992-04-14 1994-10-18 Michel Legrain Computer aided feed distribution equipment for young animals
US5374524A (en) * 1988-05-10 1994-12-20 E. I. Du Pont De Nemours And Company Solution sandwich hybridization, capture and detection of amplified nucleic acids
US5474085A (en) * 1994-02-24 1995-12-12 University Of Prince Edward Island Remote thermographic sensing of livestock
US5478989A (en) * 1989-09-27 1995-12-26 Shepley; Kenneth J. Nutritional information system for shoppers
US5579719A (en) * 1995-04-06 1996-12-03 Iowa State University Research Foundation, Inc. Method and means for quasi ad-libitum feeding for gestation sows in loose housing
US5595444A (en) * 1993-07-02 1997-01-21 Her Majesty The Queen In Right Of Canada, As Represented By The Department Of Agriculture And Agri-Food Canada Method for detecting poor meat quality in groups of live animals
US5636118A (en) * 1992-11-09 1997-06-03 Lextron, Inc. Method and apparatus for uniform delivery of feed ration along a feedbunk
US5668718A (en) * 1993-09-22 1997-09-16 American Institute Of Nutrition And Management Generating growth alternatives
US5673647A (en) * 1994-10-31 1997-10-07 Micro Chemical, Inc. Cattle management method and system
US5700590A (en) * 1994-01-10 1997-12-23 Abbott Laboratories Nutritional formula with ribo-nucleotides
US5816191A (en) * 1995-05-24 1998-10-06 Beaudoin; Gaetan Stall manager system, module and feeding device for management of mealtimes and food distribution in animal stalls
US5843498A (en) * 1994-06-02 1998-12-01 Snow Brand Seed Co., Ltd. Method for depressing methanogenesis in the rumen of a ruminant
US5901660A (en) * 1996-09-12 1999-05-11 Bio-Enhancement Systems, Corp. Communications system for monitoring the presence of animals in a natural feeding environment
US6076043A (en) * 1993-09-22 2000-06-13 American Institute Of Nutrition And Management Utilization effectiveness of nutrients in a population
US6082304A (en) * 1998-07-08 2000-07-04 Crain; Scott L. Feedyard information system and associated method
US6314909B1 (en) * 1997-06-11 2001-11-13 F&M Horwood Nominees Pty Limited Poultry feeding system
US6394963B1 (en) * 2000-06-20 2002-05-28 Eastman Kodak Company Technique for diagnosing attention deficit disorder
US20020082486A1 (en) * 2000-05-25 2002-06-27 Lavery Lawrence A. Foot temperature and health monitoring system
US20020120402A1 (en) * 2000-12-15 2002-08-29 Burghardi Steve R. Computer system for determining a customized animal feed
US6556948B1 (en) * 2000-08-07 2003-04-29 Mckenna Paul A. Apparatus for logging data associated with feed ingredients and rations for delivery to feedlots
US6658308B1 (en) * 2002-06-19 2003-12-02 Can Technologies, Inc. System for and method of customizing an animal feed based on nucleic acid material content
US6805074B2 (en) * 2002-12-23 2004-10-19 Cargill, Incorporated System and method of customizing an animal feed based on heat increment
US6868804B1 (en) * 2003-11-20 2005-03-22 Growsafe Systems Ltd. Animal management system
US6895893B2 (en) * 1999-09-02 2005-05-24 Kristopher Larsen Innovation A/S Breeding control system for free-range animals

Family Cites Families (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
HU209360B (en) 1985-07-12 1994-05-30 Iparszerue Hustermelest Szerve Method for rearing piglets and fattening pigs
US6216053B1 (en) * 1992-11-09 2001-04-10 Lextron, Inc. Apparatus and method for uniformly delivering feed rations along a feedbunk using global positioning system
DE4441992A1 (de) 1994-11-25 1996-05-30 Meyer Lohne Maschf Gmbh Verfahren und Vorrichtung zum Füttern von Tieren, insbesondere Masttieren
US6358546B1 (en) 1999-01-15 2002-03-19 Ralston Purina Company Methods for customizing pet food
KR100418434B1 (ko) 2000-01-21 2004-02-11 김현 스트레스 자동인식 컴퓨터 주변장치 및 이를 이용한스트레스 측정 시스템
WO2001089285A2 (fr) 2000-05-19 2001-11-29 Cargill, Incorporated Procedes et appareil permettant de developper un plan de productions animales optimise afin d'executer automatiquement des transactions commerciales qui le soutiennent et d'analyser les facteurs economiques qui s'y rapportent
CA2415119C (fr) 2000-07-05 2010-03-16 Jeremy Francis Taylor Procede de gestion et de commercialisation de betail base sur des profils genetiques
US7269516B2 (en) * 2001-05-15 2007-09-11 Psychogenics, Inc. Systems and methods for monitoring behavior informatics
CN1415195A (zh) 2002-12-16 2003-05-07 吉林延边林业集团有限公司 森林禽畜的饲养方法
US20060036419A1 (en) * 2004-07-29 2006-02-16 Can Technologies, Inc. System and method for animal production optimization
US20080189085A1 (en) * 2004-07-29 2008-08-07 Can Technologies, Inc. System and method for optimizing animal production
EA200800442A2 (ru) * 2005-07-27 2008-10-30 Каргилл, Инкорпорейтед Система и способ оптимизации животноводческого производства с использованием данных о генотипе

Patent Citations (40)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4532892A (en) * 1981-08-17 1985-08-06 Ralston Purina Company Animal feeding and monitoring system
US4498424A (en) * 1982-05-15 1985-02-12 Holscher & Leuschner Gmbh & Co. Animal feeding method and apparatus
US4463706A (en) * 1982-05-19 1984-08-07 J.B.M. Associates Method and system for monitoring the dispensing of drugs or nutrients to animals
US4493290A (en) * 1982-05-27 1985-01-15 Dataface Limited Milk yield recording and concentrate feed ration calculation for dairy cows
US4589372A (en) * 1983-04-11 1986-05-20 The University Of Toledo Dispensing system having weight dependent control means
US4517923A (en) * 1983-07-07 1985-05-21 Key Systems, Inc. Animal feeding system
US4729894A (en) * 1984-01-03 1988-03-08 Oklahoma State University Poultry drinking water additive
US4712511A (en) * 1985-08-15 1987-12-15 Zamzow Donald D Programmed mix and delivery system
US5374524A (en) * 1988-05-10 1994-12-20 E. I. Du Pont De Nemours And Company Solution sandwich hybridization, capture and detection of amplified nucleic acids
US5105767A (en) * 1989-01-03 1992-04-21 168189 Canada Limited Computerized monitoring system for animal feed ration processing mills
US5478989A (en) * 1989-09-27 1995-12-26 Shepley; Kenneth J. Nutritional information system for shoppers
US5309864A (en) * 1990-11-12 1994-05-10 N.V. Nederlandsche Apparatenfabriek Nedap Method and apparatus for feeding animals
US5174244A (en) * 1992-03-12 1992-12-29 Gaalswyk Mark K Livestock feed batching system
US5355833A (en) * 1992-04-14 1994-10-18 Michel Legrain Computer aided feed distribution equipment for young animals
US5636118A (en) * 1992-11-09 1997-06-03 Lextron, Inc. Method and apparatus for uniform delivery of feed ration along a feedbunk
US5878402A (en) * 1992-11-09 1999-03-02 Lextron, Inc. System and method for uniformly delivering feed rations to the feedbunks of animal pens in a feedlot
US5595444A (en) * 1993-07-02 1997-01-21 Her Majesty The Queen In Right Of Canada, As Represented By The Department Of Agriculture And Agri-Food Canada Method for detecting poor meat quality in groups of live animals
US6115692A (en) * 1993-09-22 2000-09-05 Liu; Jinkui Generating growth alternatives
US6076043A (en) * 1993-09-22 2000-06-13 American Institute Of Nutrition And Management Utilization effectiveness of nutrients in a population
US5668718A (en) * 1993-09-22 1997-09-16 American Institute Of Nutrition And Management Generating growth alternatives
US5700590A (en) * 1994-01-10 1997-12-23 Abbott Laboratories Nutritional formula with ribo-nucleotides
US5474085A (en) * 1994-02-24 1995-12-12 University Of Prince Edward Island Remote thermographic sensing of livestock
US5843498A (en) * 1994-06-02 1998-12-01 Snow Brand Seed Co., Ltd. Method for depressing methanogenesis in the rumen of a ruminant
US6135055A (en) * 1994-10-31 2000-10-24 Micro Beef Technologies, Inc. Cattle management method and system
US5673647A (en) * 1994-10-31 1997-10-07 Micro Chemical, Inc. Cattle management method and system
US5579719A (en) * 1995-04-06 1996-12-03 Iowa State University Research Foundation, Inc. Method and means for quasi ad-libitum feeding for gestation sows in loose housing
US5816191A (en) * 1995-05-24 1998-10-06 Beaudoin; Gaetan Stall manager system, module and feeding device for management of mealtimes and food distribution in animal stalls
US5901660A (en) * 1996-09-12 1999-05-11 Bio-Enhancement Systems, Corp. Communications system for monitoring the presence of animals in a natural feeding environment
US6314909B1 (en) * 1997-06-11 2001-11-13 F&M Horwood Nominees Pty Limited Poultry feeding system
US6082304A (en) * 1998-07-08 2000-07-04 Crain; Scott L. Feedyard information system and associated method
US6895893B2 (en) * 1999-09-02 2005-05-24 Kristopher Larsen Innovation A/S Breeding control system for free-range animals
US20020082486A1 (en) * 2000-05-25 2002-06-27 Lavery Lawrence A. Foot temperature and health monitoring system
US6394963B1 (en) * 2000-06-20 2002-05-28 Eastman Kodak Company Technique for diagnosing attention deficit disorder
US6556948B1 (en) * 2000-08-07 2003-04-29 Mckenna Paul A. Apparatus for logging data associated with feed ingredients and rations for delivery to feedlots
US20020120402A1 (en) * 2000-12-15 2002-08-29 Burghardi Steve R. Computer system for determining a customized animal feed
US6681717B2 (en) * 2000-12-15 2004-01-27 Can Technologies, Inc. Computer system for determining a customized animal feed
US20040098209A1 (en) * 2000-12-15 2004-05-20 Can Technologies, Inc. Computer system for determining a customized animal feed
US6658308B1 (en) * 2002-06-19 2003-12-02 Can Technologies, Inc. System for and method of customizing an animal feed based on nucleic acid material content
US6805074B2 (en) * 2002-12-23 2004-10-19 Cargill, Incorporated System and method of customizing an animal feed based on heat increment
US6868804B1 (en) * 2003-11-20 2005-03-22 Growsafe Systems Ltd. Animal management system

Cited By (28)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7904284B2 (en) 2004-07-29 2011-03-08 Can Technologies, Inc. System and method for optimizing animal production based on empirical feedback
US20080154569A1 (en) * 2004-07-29 2008-06-26 Can Technologies, Inc. System and method for optimizing animal production based on environmental nutrient inputs
US20080154568A1 (en) * 2004-07-29 2008-06-26 Can Technologies, Inc System and method for optimizing animal production based on dynamic nutrient information
US20080183453A1 (en) * 2004-07-29 2008-07-31 Can Technologies, Inc. System and method for optimizing animal production based on empirical feedback
US20080189085A1 (en) * 2004-07-29 2008-08-07 Can Technologies, Inc. System and method for optimizing animal production
US20080234995A1 (en) * 2004-07-29 2008-09-25 Can Technologies, Inc. System and method for optimizing animal production based on a target output characteristic
US20060041412A1 (en) * 2004-07-29 2006-02-23 Can Technologies, Inc. System and method for optimizing animal production based on empirical feedback
US8170908B1 (en) * 2006-08-10 2012-05-01 Vaughan Jr John Thomas Apparatus and method for processing agricultural materials and changing the proportions of output materials
US9043221B1 (en) * 2006-08-10 2015-05-26 John Thomas Vaughan, Jr. Method and apparatus for processing agricultural materials and changing the proportions of output materials
US10583164B2 (en) 2009-12-29 2020-03-10 Colgate-Palmolive Company Compositions including ginger for the amelioration or prevention of inflammatory conditions
US10245296B2 (en) * 2009-12-29 2019-04-02 Colgate-Palmolive Company Compositions including ginger for the amelioration or prevention of inflammatory conditions
US11213052B2 (en) 2011-06-10 2022-01-04 Sevecom S.P.A. Use of emulsifiers in association with vegetable oleins in an animal feed
US20150245595A1 (en) * 2012-09-12 2015-09-03 Big Dutchman International Gmbh Method and system for monitoring and/or controlling the resource consumption of an agricultural plant
US20140272028A1 (en) * 2013-03-15 2014-09-18 Nestec Sa Systems and methods for ordering and manufacturing custom pet food
US9420809B2 (en) * 2013-03-15 2016-08-23 Nestec Sa Systems and methods for ordering and manufacturing custom pet food
TWI498842B (zh) * 2013-05-22 2015-09-01 Univ Nat Pingtung Sci & Tech 家禽飼養管理方法
US11347829B1 (en) * 2013-09-26 2022-05-31 ClearHealthBill, LLC Method and system for calculating expected healthcare costs from insurance policy parameters
CN104915338A (zh) * 2014-03-12 2015-09-16 北京智慧农业有限公司 基于双胞支持向量回归的动态营养配比方法和系统
US20170013862A1 (en) * 2014-04-08 2017-01-19 Sevecom S.P.A. Method for producing an animal feed and use thereof
US11185092B2 (en) 2014-07-21 2021-11-30 Sevecom S.P.A. Powdered emulsion for animal feed
US20170215457A1 (en) 2014-07-21 2017-08-03 Sevecom S.P.A. Powdered emulsion for animal feed
US20210183494A1 (en) * 2017-12-06 2021-06-17 Koninklijke Philips N.V. An apparatus and method for personalized meal plan generation
CN111758113A (zh) * 2018-01-16 2020-10-09 哈比有限公司 用于宠物健康平台的方法和系统
WO2019143714A1 (fr) * 2018-01-16 2019-07-25 Habi, Inc. Procédés et systèmes pour une plate-forme de bien-être animal
US11361368B2 (en) 2018-01-16 2022-06-14 Habi, Inc. Methods for pet wellness platform
WO2019216647A1 (fr) * 2018-05-08 2019-11-14 (주)씽크포비엘 Procédé de prédiction de rendements laitiers, composition nutritive de ration complète pour obtenir des rendements laitiers cibles, et composition nutritive de ration complète pour obtenir un coût cible, sur la base de modèles de prédiction basés sur un apprentissage profond
CN113435967A (zh) * 2021-06-22 2021-09-24 布瑞克农业大数据科技集团有限公司 一种自动确定农产品的营销内容的方法和系统
WO2023039663A1 (fr) * 2021-09-15 2023-03-23 Xpertsea Solutions Inc. Système de détermination de rendement d'étang et procédé de détermination d'un rendement d'un étang contenant des organismes marins

Also Published As

Publication number Publication date
WO2006015061A2 (fr) 2006-02-09
US20080154569A1 (en) 2008-06-26
BRPI0513809A (pt) 2008-05-13
EP1776685A1 (fr) 2007-04-25
ZA200701706B (en) 2008-09-25
AU2005269324A1 (en) 2006-02-09
JP2008508607A (ja) 2008-03-21
BRPI0513775A (pt) 2008-05-13
US20080183453A1 (en) 2008-07-31
CA2573899A1 (fr) 2006-02-09
TW200617707A (en) 2006-06-01
US20080234995A1 (en) 2008-09-25
MX2007001081A (es) 2007-04-18
JP2008508606A (ja) 2008-03-21
CN101014973A (zh) 2007-08-08
AR049852A1 (es) 2006-09-06
MX2007001080A (es) 2007-04-19
US20060041412A1 (en) 2006-02-23
CA2573897A1 (fr) 2006-02-09
MX2007001076A (es) 2008-10-27
WO2006015018A2 (fr) 2006-02-09
AU2005269325A1 (en) 2006-02-09
WO2006015017A2 (fr) 2006-02-09
US20060041413A1 (en) 2006-02-23
AU2005269279A1 (en) 2006-02-09
AR049851A1 (es) 2006-09-06
ZA200701704B (en) 2008-07-30
JP2008508867A (ja) 2008-03-27
US20080154568A1 (en) 2008-06-26
ZA200701705B (en) 2008-07-30
BRPI0513797A (pt) 2008-05-13
US20060041408A1 (en) 2006-02-23
RU2007107397A (ru) 2008-09-10
RU2007107396A (ru) 2008-09-10
EP1776664A2 (fr) 2007-04-25
KR20070052773A (ko) 2007-05-22
US20060041419A1 (en) 2006-02-23
RU2007107395A (ru) 2008-09-10
RU2399289C2 (ru) 2010-09-20
EP1776665A1 (fr) 2007-04-25
CN101023442A (zh) 2007-08-22
CA2573901A1 (fr) 2006-02-09
TW200617706A (en) 2006-06-01
TW200617708A (en) 2006-06-01
KR20070055522A (ko) 2007-05-30
AR049853A1 (es) 2006-09-06
US7904284B2 (en) 2011-03-08
CN101010685A (zh) 2007-08-01
US7827015B2 (en) 2010-11-02
KR20070038173A (ko) 2007-04-09

Similar Documents

Publication Publication Date Title
US7904284B2 (en) System and method for optimizing animal production based on empirical feedback
US20110010154A1 (en) System and method for optimizing animal production
US20070026493A1 (en) System and method for optimizing animal production using genotype information
US6681717B2 (en) Computer system for determining a customized animal feed

Legal Events

Date Code Title Description
AS Assignment

Owner name: CAN TECHNOLOGIES, INC., MINNESOTA

Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:COOK, DAVID A.;BARZIZA, DANIEL;ENGELKE, GREGORY L.;AND OTHERS;REEL/FRAME:015876/0019;SIGNING DATES FROM 20050404 TO 20050407

AS Assignment

Owner name: CARGILL, INCORPORATED, MINNESOTA

Free format text: CORRECTIVE ASSIGNMENT TO CORRECT THE ASSIGNORS' HAHN, KNUDSON AND MARTINSON IMPROPERLY ASSIGNING THEIR RIGHTS TO CAN TECHNOLOGIES, INC. PREVIOUSLY RECORDED ON REEL 015876 FRAME 0019;ASSIGNORS:HAHN, JOHN J.;KNUDSON, BRIAN J.;MARTINSON, WADE S.;REEL/FRAME:019267/0713;SIGNING DATES FROM 20070328 TO 20070425

STCB Information on status: application discontinuation

Free format text: ABANDONED -- FAILURE TO RESPOND TO AN OFFICE ACTION