US20070113791A1 - Computer system for determining a customized animal feed - Google Patents

Computer system for determining a customized animal feed Download PDF

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US20070113791A1
US20070113791A1 US11656608 US65660807A US2007113791A1 US 20070113791 A1 US20070113791 A1 US 20070113791A1 US 11656608 US11656608 US 11656608 US 65660807 A US65660807 A US 65660807A US 2007113791 A1 US2007113791 A1 US 2007113791A1
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data
animal
feed
ration
ingredients
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Steve Burghardi
Brian Knudson
Loren Peterson
David Cook
Mark Oedekoven
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CAN Technologies Inc
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CAN Technologies Inc
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    • AHUMAN NECESSITIES
    • A01AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01KANIMAL HUSBANDRY; CARE OF BIRDS, FISHES, INSECTS; FISHING; REARING OR BREEDING ANIMALS, NOT OTHERWISE PROVIDED FOR; NEW BREEDS OF ANIMALS
    • A01K5/00Feeding devices for stock or game ; Feeding wagons; Feeding stacks
    • A01K5/02Automatic devices
    • AHUMAN NECESSITIES
    • A23FOODS OR FOODSTUFFS; THEIR TREATMENT, NOT COVERED BY OTHER CLASSES
    • A23KFODDER
    • A23K20/00Accessory food factors for animal feeding-stuffs
    • A23K20/10Organic substances
    • A23K20/142Amino acids; Derivatives thereof
    • AHUMAN NECESSITIES
    • A23FOODS OR FOODSTUFFS; THEIR TREATMENT, NOT COVERED BY OTHER CLASSES
    • A23KFODDER
    • A23K40/00Shaping or working-up of animal feeding-stuffs
    • AHUMAN NECESSITIES
    • A23FOODS OR FOODSTUFFS; THEIR TREATMENT, NOT COVERED BY OTHER CLASSES
    • A23KFODDER
    • A23K50/00Feeding-stuffs specially adapted for particular animals
    • A23K50/10Feeding-stuffs specially adapted for particular animals for ruminants
    • AHUMAN NECESSITIES
    • A23FOODS OR FOODSTUFFS; THEIR TREATMENT, NOT COVERED BY OTHER CLASSES
    • A23KFODDER
    • A23K50/00Feeding-stuffs specially adapted for particular animals
    • A23K50/30Feeding-stuffs specially adapted for particular animals for swines
    • AHUMAN NECESSITIES
    • A23FOODS OR FOODSTUFFS; THEIR TREATMENT, NOT COVERED BY OTHER CLASSES
    • A23KFODDER
    • A23K50/00Feeding-stuffs specially adapted for particular animals
    • A23K50/80Feeding-stuffs specially adapted for particular animals for aquatic animals, e.g. fish, crustaceans or molluscs
    • YGENERAL 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
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02ATECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A40/00Adaptation technologies in agriculture, forestry, livestock or agroalimentary production
    • Y02A40/80Adaptation technologies in agriculture, forestry, livestock or agroalimentary production in fisheries management
    • Y02A40/81Aquaculture, i.e. culture of aquatic animals
    • Y02A40/812Aquaculture, i.e. culture of aquatic animals of fish
    • Y02A40/818Alternative feeds for fish aquaculture

Abstract

A method and system for creating a customized animal feed is disclosed. The method and system include having ingredient data from more than one location, animal data, evaluation data, and optimization weighting data. The specifications for a customized feed are generated using ingredient data representative of the mix of ingredients available at one or more locations. A customized feed is generated which is designed to fulfill the nutritional requirements for the animal's diet. The nutritional requirements are derived from the animal data. Furthermore, the feed is optimized based upon the profile data, the feed data, the evaluation data, and the optimization weighting data.

Description

    CROSS REFERENCE TO RELATED PATENT APPLICATIONS
  • This application is a continuation of application Ser. No. 11/348,298, filed Feb. 6, 2006, which is a continuation of application Ser. No. 10/985,365, filed Nov. 10, 2004, which is a continuation of application Ser. No. 10/715,053, filed Nov. 17, 2003, now U.S. Pat. No. 6,863,023, which is a continuation of application Ser. No. 09/739,550, filed Dec. 15, 2000, now U.S. Pat. No. 6,681,717.
  • FIELD OF THE INVENTION
  • The present invention relates to a computerized system for determining a customized feed for animals, such as cattle, swine poultry, fish, crustaceans and the like. In particular, the system determines a feed mix based upon data relating to information such as animal characteristics, available ingredients, speed of product production, and cost of production.
  • BACKGROUND
  • In food production, and specifically producing animal products such as milk, beef, pork, eggs, chicken, fish etc., there is need to improve production efficiency. Production efficiency, i.e. producing the maximum quantity of animal products while minimizing the time and cost of production for those products, is important in maintaining a competitive advantage.
  • A producer (i.e. a farmer, rancher, pork producer, and the like) generally wants to maximize the amount of animal product produced (e.g. gallons of milk, pounds of beef or pork produced) while keeping the costs associated with feed at a low level in order to achieve maximum animal productivity. The maximized amount of animal product should be produced at a minimized cost to the producer. Costs to the producer include the cost of feed needed to produce the animal products, as well as the costs of related equipment and facilities needed in the production of animal products. In order to minimize the effect of fixed costs associated with equipment and facilities, the maximum amount of animal product should preferably be produced in a minimum time period.
  • Producers are constantly trying to increase these production efficiencies. One way of increasing production efficiencies is by altering the feed which animals are fed. For example, a feed with certain amounts of nutrients can cause an animal to grow or produce animal products quickly and/or perform better, whereas a different feed with different amounts of nutrients may cause an animal to grow or produce animal products on a more cost effective basis.
  • Current systems for creating animal feed are not fully capable of helping producers evaluate and improve production efficiencies. Current systems commonly generate an overall nutrient profile which is related to a set of animal characteristics. Such systems then look at the overall nutrient profile and compare what nutrients may be had from the on-farm ingredients. From this comparison, a “nutritional gap” can be calculated, i.e., the nutritional requirements that the producer needs to fulfill his production goals after accounting for the use of his on-site feed. This nutritional gap is then compared to the nutritional components which may be available from ingredients located at a supplier's mill. Through a comparison of the nutritional gap and the nutritional components available from the mill, current systems allow a supplier to provide a cost effective custom feed which is optimized to permit an animal to produce desired animal products on a cost minimized basis.
  • Currently systems exist that are capable of taking the amounts of on-farm ingredients to be used in the overall diet of the animal into account. This is typically done by accounting for the on-farm component of the animal's diet as a fixed input parameter in the determination. It would be advantageous to be able to modify the amounts of on-farm ingredients to be used in forming the custom feed as part of the optimization process. Moreover, current systems are generally limited to generating the custom feed based on a single evaluation criteria, typically based on the cost of the feed (e.g., on a cost of feed per unit of animal weight gain basis). It would be advantageous to have a system which is capable of utilizing more than one evaluation criteria in generating the custom feed.
  • SUMMARY
  • One embodiment of the present invention provides a system for determining customized feed for animals, such as farm livestock, poultry, fish and crustaceans. The system stores animal data representative of the characteristics of the animal, feed data representative of the feed ingredients located at one or more locations, and evaluation data representative of at least one evaluation criteria. The evaluation criteria are generally related to factors representative of animal productivity. An optimization weighting is used to indicate the weight assigned to the evaluation criteria. Examples of evaluation criteria include (i) animal production rate (e.g., the rate of animal weight gain or the rate of production of a food product such as milk or eggs); (ii) cost of feed per unit animal weight gain; and (iii) feed weight per unit animal weight gain. The system includes a data processing circuit, which may be one or more programmed microprocessors, in communication with a data storage device or devices which store the data. The data processing circuit is configured to generate profile data representative of a nutrient profile for the animals based upon the animal data. In effect, the nutrient profile is a description of the overall diet to be fed to the animals defined in terms of a set of nutritional parameters (“nutrients”). Using the profile data, the data processing circuit generates ration data representative of a combination of ingredients from one or more locations. The ration data is generated by the data processing circuit based upon the profile data, the feed data, the evaluation data, and the optimization weighting data.
  • Another embodiment of the system includes system for determining customized feed for at least one animal. The system includes first memory means for storing animal data representative of the characteristics of the animal, second memory means for storing feed data representative of the feed ingredients located at at least one location, third memory for storing evaluation data representative of at least two evaluation criteria, and processing means for generating profile data representative of a nutrient profile for the animal based upon the animal data, processing means further generating ration data representative of a combination of ingredients from the location, the ration data being generated by the processing means based upon the profile data, the feed data and the evaluation data. The system further includes fourth memory means for storing optimization weighting data representative of the effect a respective evaluation criteria has on the generation of the ration data, the processing means further generating the ration data based upon the optimization weighting data.
  • A further embodiment of the present invention provides a method for determining customized feed for at least one animal. The method includes storing animal data representative of the characteristics of the animal, storing feed data representative of the feed ingredients located at at least one location, storing evaluation data representative of at least two evaluation criteria, storing optimization weighting data representative of the effect a respective evaluation criteria, generating profile data representative of a nutrient profile for the animal based upon the animal data; and generating ration data representative of a combination of ingredients from the location, the ration data being generated based upon the profile data, the feed data, the evaluation data, and the optimization weighting data.
  • As modifications to the embodiments described herein, systems and/or methods may rely on more than one optimizing criteria and/or feed data representative of ingredients located at more than one location. For example, ingredients which could be used to create the ration may be located at the farm associated with the animals as well as at the mill of an ingredient supplier. Depending upon the requirements of the system, processing can be consolidated in one processor or divided between processors in communication via a network such as a LAN or the Internet. Furthermore, the processors may be located in devices such as workstations, portable PC's and/or hand held computers.
  • In other variations of the embodiments described herein, the systems and/or methods may further include a memory portion in communication with the digital processor which stores variation data representative of a range for one or more nutrients of the nutrient profile. The digital processor is capable of generating a set of ration data based upon the variation data. A memory portion of the system may store variation data which corresponds to preselected incremental variations for the values assigned to one or more individual nutrients in the nutritional profile.
  • Throughout this application, the text refers to various embodiments of the system and/or method. The various embodiments described are meant to provide a variety of exemplary examples and should not be construed as descriptions of alternative species. Moreover, it should be noted that the descriptions of the various embodiments provided herein may be of overlapping scope. The embodiments discussed herein are merely illustrative and are not meant to limit the scope of the present invention.
  • BRIEF DESCRIPTION OF THE DRAWINGS
  • FIG. 1 is a general schematic representation of the data flow in one embodiment of the present System.
  • FIG. 2 is a general schematic representation of the data flow in another embodiment of the System which is designed to be used to generate a custom product (“Custom Ration”) and/or feed mix from on-site ingredients (“On-Farm Ration”) optimized for milk production and/or quality.
  • FIG. 3 is a general schematic representation of the data flow in a variation of the System shown in FIG. 1.
  • DETAILED DESCRIPTION
  • An exemplary system, and process which can be used in producing a customized feed for animals, such as livestock, poultry, fish or crustaceans is described herein. How the system and process can increase production efficiencies by customizing feed is also disclosed. It is particularly desirable if the system and methods are capable of determining an optimized feed using one or more evaluation criteria. Examples of suitable evaluation criteria include a feed cost per unit animal weight gain basis, an animal production rate basis (e.g., based upon a rate of animal weight gain or a rate of production of an animal product, such as milk or eggs), and a feed amount per unit of animal weight gain basis.
  • In one embodiment of the present system, a computer system may be used which has a processing unit that executes sequences of instructions contained in memory. More specifically, execution of the sequences of instructions causes the processing unit to perform various operations, which are described herein. The instructions may be loaded into a random access memory (RAM) for execution by the processing unit from a read-only memory (ROM), a mass storage device, or some other persistent storage. In other embodiments, hardwired circuitry may be used in place of, or in combination with, software instructions to implement the present method. Thus, the embodiments described herein are not limited to any specific combination of hardware circuitry and/or software, nor to any particular source for the instructions executed by the computer system.
  • Creating a customized feed typically involves processing and manipulating at least four basic data sets (see, e.g., FIG. 1): first feed data representative of the collection of ingredients located at a first location 1, second feed data representative of the collection ingredients located at a second location 2, animal data representative of characteristics of the animal 3 (e.g., parameters related to its genotype, production level, environment and/or feeding regime), and evaluation criteria 4. As will be explained below, very often first and second feed data representative of sets of ingredients located at an on-farm site (first ingredients 1 located at a first location) and ingredients located at a supplier's mill site (second ingredients 2 located at a second location) are used to generate the recommended mix of ingredients to be fed to the animal. In many instances, the ration data define an overall diet for the animal which includes custom rations from more than one location (e.g., a custom ration from a first location 7 and a custom ration from a second location 8 as depicted in FIG. 1). These can be combined to create a customized feed (“ration”) which fulfills the animal data requirements while meeting the evaluation criteria 4.
  • The evaluation criteria may be chosen from such suitable criteria related to animal productivity as (i) animal production rate, (ii) cost of feed per unit animal weight gain, and (iii) feed weight per unit animal weight gain.
  • In some modified embodiments, the present system may include additional memory portions for storing nutrient level constraints 5 and/or ingredient level constraints 6. This may be useful where, for example, it has been established that higher levels of certain nutritional components could pose a risk to the health of an animal being fed the custom feed. For example, if the custom feed includes some trace minerals, such as selenium, present in too great an amount, the custom feed may have adverse health consequences to the animal. Various embodiments of the present invention allow constraints to be placed on the maximum and/or minimum amounts of one or more nutrients in the profile data generated. In some embodiments, this may be used together with the animal data as a basis to calculate the profile data. These constraints may be stored in a memory location as part of the system or the system may permit an individual operator to input one or more constraints on the amount of particular nutrient(s) in the profile data generated by the system. Similarly, it may be desirable to limit the amounts of one or more ingredients in either a custom product mix or in the overall diet to be fed to the animal. For example, for ease of formulation of a custom feed in pellet form it may be desirable to limit the amount of certain ingredients and/or require the inclusion of minimum amounts of specified ingredients.
  • The first data set that is generally input into the system and subsequently stored in a memory portion includes data representative of characteristics of the animal. Examples of types of data representative of animal characteristics (“animal data”) include beginning weight of the animal; a desired weight of the animal; an environment of the animal; a feed form; an actual or desired production level of the animal; and a relationship of animal muscle to fat of the animal. For example, the nutrient profile generated for a particular animal can vary based upon a number of different characteristics of the animal relating to one or more of its genotype, environment, current condition (e.g., defined in terms of health and/or weight), desired production level, feed form (e.g., meal or pellet), current production level, desired final condition (e.g., defined in terms of final weight and/or relationship of animal muscle to fat of the animal) and the like. Tables 1 and 2 below list illustrative sets of animal characteristics which can be used as a basis to generate nutritional profiles to be used in designing custom rations (“custom feeds”) for swine and dairy cattle, respectively.
    TABLE 1
    Animal Characteristics Suitable for Generating
    a Nutritional Profile for a Feed for Swine
    Animal Category Genotype (lean gain)
    Finisher Effective Ambient Temperature
    Gilt Replacement Temperature
    Grow Draft
    Prebred Bedding
    Sow % of pigs that are wet)
    Gestation Pigs per pen
    Lactation Pig density (square feet per pig)
    Artificial Insemination Boar Health
    Begin Weight Flooring Type
    End Weight Total pigs born/litter
    Feed Disappearance (Intake) Litter weight gain
    Feed Wastage Total pigs born/litter
    Feed Form
  • TABLE 2
    Animal Characteristics Suitable for Generating
    a Nutritional Profile for Dairy Cattle
    Target Milk Weight (volume) Body Weight
    Target Milk Butterfat % Body Weight Change
    Target Milk Protein % Body Condition Score (current)
    Current Milk Weight (volume) Body Condition Score (desired)
    Current Milk Butterfat % Actual Dry Matter Intake
    Current Milk Protein % Environmental Temperature
    Percent of group in first lactation Environmental Humidity
    Percent of group in second lactation Genotype
  • The animal data representative of the characteristics of the animal may be inputted into a computer system with a memory portion available and configured to store the data. The animal data representative of the characteristics of the animal may be inputted into the system by a variety of methods known to those skilled in the art including a keyboard, mouse, touchpad, computer, internet or other related device.
  • The system includes a data processing circuit which is configured to generate profile data representative of a nutrient profile for the animals based upon the animal data. In effect, the nutrient profile is a description of the overall diet to be fed to the animals defined in terms of a set of nutritional parameters (“nutrients”). Depending on the desired degree of sophistication of the system, the profile data may include a relatively small set of amounts of nutrients or large number of amounts of nutrients. Table 3 includes an illustrative list of nutrients that may be used delineating profile data for animals such as pigs and dairy cattle. Of course, the list of nutrients used in generating profile data may differ for different types of livestock or other animals. Tables 4 and 5 respectively contain lists of nutrients suitable for use in generating nutritional profiles for swine and dairy cattle, respectively.
  • The data processing circuit in the present system is also configured to generate ration data representative of a combination of ingredients from one or more locations. The ration data is generated by the data processing circuit based upon the profile data, feed data representative of the feed ingredients available at the location(s) and evaluation data representative of one or more evaluation criteria.
    TABLE 3
    Nutrients Suitable for Generating
    a Nutritional Profile
    Animal Fat Rumres Nfc
    Ascorbic Acid Salt
    Biotin Selenium
    Cal/Phos Simple Sugar
    Chloride Sodium
    Choline Sol Rdp
    Chromium Sulfur
    Cobalt Sw Obs Me
    Copper Thiamine
    Arginine (Total and/or Digestible) Total Rdp
    Cystine (Total and/or Digestible) Verified Adf
    Isoleucine (Total and/or Digestible) Verified Ash
    Leucine (Total and/or Digestible) Verified Calcium
    Lysine (Total and/or Digestible) Verified Dry Matt
    Methionine (Total and/or Digestible) Verified Fat
    Phenylalanine (Total and/or Digestible) Verified Fiber
    Threonine (Total and/or Digestible) Verified Hemi
    Tryptophan (Total and/or Digestible) Verified Moisture
    Valine (Total and/or Digestible) Verified Ndf
    Folic Acid Verified Neg
    Phosphate Verified Nel
    Iodine Verified Nem
    Iron Verified Nfc
    Lactose Verified Phos
    Lasalocid Verified Protein
    Magnesium Verified Rup
    Manganese Vitamin A
    Monensin Vitamin B12
    Niacin Vitamin B6
    Potassium Vitamin D
    Protein Vitamin E
    Pyridoxine Vitamin K
    Rh Index Zinc
    Riboflavin
    Rough Ndf
    Rum Solsug
  • TABLE 4
    Nutrients Suitable for Generating
    a Nutritional Profile for Swine
    Biotin
    Cal/Phos
    Choline
    Coppr Add
    Folic Acid
    Iodine Add
    Iron Add
    Mang Add
    Niacin
    Pantotnc
    Pyridoxine
    Riboflavin
    Salt
    Selenium Add
    Sodium
    Sw Digphos
    Thiamine
    True Swine Digestible isoleucine
    True Swine Digestible lysine
    True Swine Digestible methionine
    True Swine Digestible threonine
    True Swine Digestible tryptophan
    True Swine Digestible valine
    V Calcium
    V Phos
    V Protein
    Vit A
    Vit D
    Vit E
    Vit K
    Vitamin B12
    Zinc
  • TABLE 5
    Nutrients Suitable for Generating
    a Nutritional Profile for Dairy Cattle
    Acid Detergent Fiber
    Biotin
    Calcium
    Chloride
    Cobalt
    Copper
    Dietary Cation Anion Difference
    Digestible Neutral Detergent Fiber
    Dry Matter
    Fat
    Intestinally Digestible Arginine
    Intestinally Digestible Histidine
    Intestinally Digestible Isoleucine
    Intestinally Digestible Leucine
    Intestinally Digestible Lysine
    Intestinally Digestible Methionine
    Intestinally Digestible Phenylalanine
    Intestinally Digestible Threonine
    Intestinally Digestible Tryptophan
    Intestinally Digestible Valine
    Iodine
    Iron
    Magnesium
    Manganese
    Neutral Detergent Fiber
    Neutral Detergent Fiber from
    Roughage
    Niacin
    Non Fiber Carbohydrates
    Non-Protein Nitrogen
    Phosphorus
    Potassium
    Protein
    Rumen Degradable Protein
    Rumen Undegraded Alanine
    Rumen Undegraded Histidine
    Rumen Undegraded Isoleucine
    Rumen Undegraded Leucine
    Rumen Undegraded Lysine
    Rumen Undegraded Methionine
    Rumen Undegraded Phenylalanine
    Rumen Undegraded Protein
    Rumen Undegraded Tryptophan
    Rumen Undegraded Valine
    Salt
    Selenium
    Sodium
    Soluble Protein
    Soluble Sugar
    Starch
    Sulfur
    Verified Net Energy for Lactation
    Vitamin A
    Vitamin D
    Vitamin E
    Zinc
  • Evaluation criteria are typically related to factors representative of animal productivity and reflect an aspect of production a producer would like to optimize. The present system allows a producer to select evaluation criteria (e.g. cost/gain, cost/output, animal production rate, and/or feed/gain) which fits the producer's production goals. For example, a dairy producer may focus on the cost of feed required to produce a unit of output (cost/output), whereas a pork producer may focus on cost/gain or rate of gain.
  • Examples of suitable animal production criteria which may be used as evaluation criteria in the generation of ration data include (i) animal production rate, (ii) the cost of feed per unit animal weight gain, and (iii) the feed weight per unit animal weight gain. The animal production rate may simply be a measure representative of the rate of weight gain of the animal in question (rate of gain). For example, a pork producer may wish to optimize rate of gain by selecting a feed which maximizes the rate at which a pig gains weight. This could be selected if a pig farmer was interested in turning over production as quickly as possible in a fixed asset which has limited space. The evaluation data may include data representative of the cost of feed required to produce a unit of weight gain of the animal (“cost/gain” basis). For example, a pork producer may wish to optimize cost/gain by selecting a feed which minimizes the feed cost required to make a pig gain a unit of weight. The evaluation data can include data representative of the amount of feed required to produce a unit of gain (feed/gain). For example, a producer may wish to optimize the feed/gain by selecting a feed which minimizes the amount of feed required to produce a unit of gain. A producer might select this criterion if they were faced with feed storage space constraints.
  • Examples of other suitable animal production rates which may be used as an evaluation criteria include rates of production of food products, such as milk or eggs, from the animal. Other suitable evaluation criteria include the cost of feed required to produce a unit of output of a particular animal product (“cost/output”). For example, a milk producer may wish to optimize the cost/output by selecting a feed which minimizes the cost of feed required to produce a unit of milk. In addition to utilizing evaluation data representative of only a single evaluation criteria, the present system may be capable of using evaluation data representative of a combination of two or more evaluation criteria in generating the ration data. For example, when considering an appropriate feed, a producer may wish to generate a custom feed based on the rate of production as well as cost of the feed (typically on a cost/gain basis).
  • Furthermore, the producer may choose to weight the relative contributions of two or more evaluation criteria. The system may include a data processing circuit which generates ration data based in part upon a weighted average of more than one evaluation criteria. In one specific embodiment, the system generates ration data based in part upon a 70:30 weighted average of two evaluation criteria (primary and secondary), such as a combination of cost of feed per unit animal weight gain and animal production rate. The system may also allow a user to alter the relative weighting accorded to the various evaluation criteria selected.
  • For instance, in the example referred to above, the producer may want to generate ration data using a combination of evaluation criteria that is weighted 70% on a cost/gain basis and 30% on a rate of animal weight gain basis. One method for providing such a weighted optimization analysis is to generate one solution for ration data using cost/gain as the sole evaluation criteria and generating a second for ration data using rate of animal weight gain as the sole evaluation criteria. Ration data which is representative of the weighted combined solution can be achieved by summing 70% of the amounts of ingredients from the cost/gain ration data set and 30% of the amounts of ingredients from the rate of gain ration data set. For example, in the instance where cost/gain ration data (generated solely on a cost/gain basis) includes 10% dehulled corn meal, and rate of gain ration data (generated solely on a rate of gain basis) includes 15% dehulled corn meal, if a producer chose cost/gain as the primary evaluation criteria the ingredient mix in the diet will include roughly 70% of the 10% dehulled corn meal requirement, and 30% of the 15% dehulled corn meal requirement summed to produce the amount of dehulled corn meal in the overall diet (i.e., circa 11.5% dehulled corn meal). This weighted summation is then repeated for all the amounts of ingredients present in the two custom diets generated by the two approaches. As one skilled in the art will recognize, there are other methods of generating ration data based on a weighted combination of evaluation criteria. The present system can also be configured to generate ration data based on other weightings of combinations of two or more evaluation criteria (e.g., two evaluation criteria weighted on either a 60:40 or 80:20 basis). In some embodiments of the present system, the weighting factors assigned to various evaluation criteria can themselves be input parameter(s) chosen by a producer to reflect the needs of his/her particular situation.
  • FIG. 2 depicts the general flow of data in one embodiment of the present system. The system shown in FIG. 2 includes a data processing circuit 30 configured to generate a nutrient profile 32 based on the animal data 31 and optional adjustments which may be provided by a nutritionist. Other data processing circuits generate lists of nutrient amounts associated with individual ingredients available at an on-farm site 33 and manufacturing site 34. A data processing circuit 36, which includes a linear program generates a custom product based on evaluation criteria 35. The linear program typically also generates the custom product solution based on pricing data associated with both the on-farm and manufacturing site ingredients. In one embodiment, retail and wholesale pricing information may be normalized to allow the linear program to facilitate consideration of potential ingredients with different types of associated prices as the basis for a solution to a single multivariable problem. The linear program is a mathematical model capable of solving problems involving a large number of variables limited by constraints using linear math functions. A variety of different linear programs capable of solving problems of this type are known to those of skill in the art. One example of a program of this type is commercially available from Format International as part of computer software system for solving complicated multivariable problems.
  • Memory portions of the systems which store animal data, evaluation data, and feed data representative of on-hand ingredients and/or mill ingredients are in communication with a data processing unit capable of generating ration data. The data processing unit can include a data processing circuit or a digital processing circuit. The memory portions which store the animal data, feed data for on-hand and mill ingredients, and evaluation data may be in communication with the data processing unit by inputted keyboard commands, mouse commands, a network connection with another computer, personal data assistants, via a modem connection, via an internet, or via an intranet.
  • Data processing circuit(s) which include the linear program can take input data (e.g., profile data, feed data, evaluation data and ingredient constraint data) as a basis to compute ration data. Ration data includes data specifying a combination of ingredients solution which is solved to fulfill a desired nutrient profile based on one or more evaluation criteria. Ration data generated by the present system generally includes data representative of the types and amounts of ingredients to be used to provide an overall custom diet for an animal. The ration data provided by the system generally also specifies a solution that is described in terms of a combination of types and amounts of ingredients from a first location (e.g., an on-farm location) and types and amounts of ingredients from at least one additional site (e.g., one or more supplier locations). Where the overall set of potential ingredients includes ingredients located at more than one location, the custom feed specified by the ration data may be made of ingredients located at either a single location or from more than one location. For example, the ration data may define a custom feed made up from ingredients located solely at supplier location or made up from ingredients located at both an on-farm location and a supplier location.
  • The ration data generally include custom feed data representative of a combination of amounts of the feed ingredients. The custom feed data may specify the type and corresponding amounts of the ingredients to be used in formulating the overall diet of an animal. This may be made up from a set of ingredients available at more than one location, e.g., from ingredients available at a producer's site and as well as ingredients available at a supplier location. The present system may also provide custom feed data which specifies the types and amounts of ingredients to be used from individual locations. For example, the custom feed data may include a listing of the types and amounts of ingredients available at a first location (e.g., on-farm ingredients) to be used to form a first feed mix and a listing of the types and amounts of ingredients available at a second location (e.g., ingredients available at a supplier location) to be used to form a second feed mix. In such instances, the custom feed data will typically also specify the amounts of the first and second feed mixes that are to be used to make up the overall custom diet for an animal.
  • The ration data typically includes amounts of a variety of types of ingredients. The actual ingredients available at any particular location can vary over time and will generally vary on a regional basis as well as reflect the type of animal feed that is typically produced and/or stored at the particular site. Commonly, the ration data include feed data representative of amounts of ingredients from a number of different ingredient categories, such as a grain source, a protein source, a vitamin source, a mineral source (e.g., a macromineral source and/or a trace mineral source) and/or a fat source. Table 6 includes a list of exemplary ingredients suitable for use in formulating custom feed mixes for a variety of animals. Tables 7, 8 and 9 include lists of ingredients which may be used in generating custom feed products for swine or dairy cattle.
    TABLE 7
    Ingredients Suitable for Use in Producing
    a Custom Feed for a Finishing Diet for Swine
    Alimet
    Bakery Product
    Beet Pulp
    Brewers Rice
    Brown Sugar
    Calcium Carb
    Cane Sugar
    Canola Meal
    Cereal Fines
    Cg Feed
    Choline
    Copper Sulfate
    Corn - Ground Fine
    Corn Gluten Meal
    Corn Oil
    Corn Starch
    Dehydrated Alfalfa
    Distillers Grains With Soil
    Dried Potato Waste
    Dynasol
    Fat
    Fat Sprayed
    Feather Meal
    Feeding Rate
    Fish Meal
    Linseed Meal
    L-Lysine HCl
    Lt. Barley
    L-Threonine
    Malt Sprouts
    Meat And Bone Meal
    Menhaden Fish
    Molasses
    Mono-Dical Phos
    Monosod Phos
    Oat Mill Byproducts
    Oat Mill Byproducts
    Oats - Ground
    Oats - Rolled
    Pork Bloodmeal
    Safflower Meal
    Salt
    Selenium
    Soybean Hulls
    Soybean Meal
    Soybean Oil
    Sunflower
    Tryptosin
    Wheat Midds
  • TABLE 8
    Ingredients Suitable for Use in Producing
    a Custom Feed for Breeding Swine
    Alimet
    Animal Fat
    Ascorb Acid
    Bakery Product
    Bentonite
    Blood Meal - Beef/Pork
    Calcium Carbonate
    Cereal Fines
    Choline Chloride
    Copper Sulfate
    Corn Germ Meal
    Corn Gluten Feed
    Distillers Grains With Solubles
    Dry Methionine Hydroxy Analog
    Fish Meal
    Malt Sprouts
    Meat And Bone Meal; Pork Carcass
    Methionine
    Mineral Oil
    Molasses-Cane
    Mono-Dicalcium Phosphate
    Oat Hulls
    Red Flavor
    Rice Bran
    Salt
    Selenium
    Soybean Hulls
    Threonine
    Tryptophan
    Vitamin E
    Wheat Midds
    Wheat Starch
    Zinc Oxide
    Zinc Sulfate
  • TABLE 9
    Ingredients Suitable for Producing
    a Custom Feed for Dairy Cattle
    Calcium Carbonate Salt
    Copper Sulfate Selenium
    Corn Gluten Meal Sodium Sesquicarbonate
    Fat Soybean Hulls
    Magnesium Oxide Soybean Meal
    Meat And Bone Meal, Pork Trace Minerals
    Mono-Dical Phos Urea
    Niacin Vitamin-E
    Pork Blood Meal Wheat Midds
    K/Mg/Sulfate Zin-Pro
    Yeast
  • When feeding animals, producers may not be able to satisfy nutritional requirements of the animals solely using on-hand ingredients (e.g., on-farm ingredients). To satisfy the animal's nutritional requirements, producers may desire to use on-hand ingredients in conjunction with a custom feed product made up of feed ingredients available from an outside supplier, such as a mill, feed mixer, and the like. The outside supplier will commonly have a range of ingredients available or on hand in their inventory (e.g., corn in various forms, soybean meal, wheat mids, barley, oats, animal fat, various vitamin supplements).
  • In addition to data specifying the types and amounts of ingredients to be used to provide the overall custom diet for an animal, the ration data generated by the present system can also include other data associated with the overall custom diet. Examples of such other data include cost data representative of a cost associated with the custom feed data, feed weight data representative of a feed weight associated with the custom feed data, and performance data representative of projected animal performance associated with the custom feed data. For example, Table 10 below lists a number of categories of ration data that may be useful in assisting a producer and/or supplier in evaluating a custom feed with respect to productivity, animal performance and cost effectiveness. The availability of these types of information can provide a producer and/or supplier with additional information concerning the effects of variations in dietary composition on factors such as cost, volume of feed, wastage and animal performance. As with the listing(s) of the types and amounts of ingredients, the cost data and feed weight data can be representative of costs and feed weights associated with the overall custom diet and/or with feed mix(es) to be provided from individual locations.
    TABLE 10
    Illustrative Categories of Ration Data
    Associated with a Custom Feed for Swine
    End Weight Lean Gain
    Days in Phase Lean %
    Avg Daily Gain Effective Ambient Temp
    Avg Daily Feed Intake Cost of Gain
    Total Feed Consumed Total Cost per phase
    Feed/Gain
  • In other variations of the embodiments described herein, the systems and/or methods may also include a memory portion in communication with the digital processor which stores variation data representative of a range for one or more nutrient components of the nutrient profile. The digital processor is capable of generating a set of ration data based upon the variation data. The memory portion may store variation data which correspond to preselected incremental variations for the values assigned to one or more individual nutrients in the nutritional profile. For example, memory portion may store variation data which correspond to preselected incremental positive and negative variations of the values assigned to two individual nutrients, such as true digestible lysine and net energy. The digital processor would generate ration data corresponding to each of the eight possible additional combinations of values for the two specified nutrients. Together with the ration data associated with the original nutritional profile, the resulting set of nine ration data corresponding to the various combinations of values for each specified nutrient (original value, original value plus an increment; original value minus an increment) would make up a three by three matrix of ration data. One example of this approach is illustrated in Table 11 below. A general approach to generating a set of ration data based upon variation data is depicted schematically in FIG. 3. The determination of ration data for the center point in the matrix (“Ration Data 5”) corresponds to the solution generated by the data processing circuit based on the nutrient profile. In the example shown in Table 11, the nutrient profile has values of 0.90% for true digestible lysine and 2150 kcal/kg for net energy. Each of the eight other ration data in the set depicted in Table 11 corresponds to a ration data generated for a modified nutrient profile in which the value for at least one nutrient has been varied by a specified increment. For example, Ration Data 1 represents ration data associated with a modified nutrient profile has values of 0.95% for true digestible lysine and 2100 kcal/kg for net energy. Ration Data 6 represents ration data associated with a modified nutrient profile in which only the value for true digestible lysine (0.85%) has been varied from the values in the nutrient profile. The generation of such a matrix can facilitate an evaluation of the effect of incremental variations in amounts of specified nutrient(s) on the assessment of optimum ration data for a given evaluation criteria.
    TABLE 11
    True Digestible Lysine
    0.95% 0.90% 0.85%
    Net 2100 Ration Data 1 Ration Data 2 Ration Data 3
    Energy 2150 Ration Data 4 Ration Data 5 Ration Data 6
    (kcal/kg) 2200 Ration Data 7 Ration Data 8 Ration Data 9
  • The invention has been described with reference to various specific and illustrative embodiments and techniques. However, it should be understood that many variations and modifications may be made while remaining within the spirit and scope of the invention.
    TABLE 6
    Exemplary Ingredients Suitable for
    Use in Formulating Custom Feed Mixes
    Acidulated Soap Stocks
    Active Dry Yeast
    Alfalfa Meal
    Alfalfa-Dehydrated
    Alimet
    Alka Culture
    Alkaten
    Almond Hulls
    Ammonium Chloride
    Ammonium Lignin
    Ammonium
    Polyphosphate
    Ammonium Sulfate
    Amprol
    Amprol Ethopaba
    Anhydrous Ammonia
    Appetein
    Apramycin
    Arsanilic Acid
    Ascorb Acid
    Aspen Bedding
    Availa
    Avizyme
    Bacitracin Zinc
    Bakery Product
    Barley
    Barley-Crimped
    Barley-Ground
    Barley-Hulless
    Barley-Hulls
    Barley-Midds
    Barley-Needles
    Barley-Rolled
    Barley-St. Bon.
    Barley-Whole
    Barley-With Enzyme
    Baymag
    Beef Peanut Hulls
    Beef Peanut Meal
    Beet
    Beet Pulp
    Biotin
    Biscuit By Product
    Black Beans
    Blood-Flash Dry
    Blueprint Rx
    Bone Meal
    Brewers Rice
    Brix Cane
    Buckwheat
    Bugs
    Cage Calcium
    Calcium Cake
    Calcium Chloride
    Calcium Formate
    Calcium Iodate
    Calcium Sulfate
    Calciun Prop
    Calf Manna
    Canadian Peas
    Cane-Whey
    Canola Cake
    Canola Fines
    Canola Meal
    Canola Oil
    Canola Oil Blender
    Canola Oil Mix
    Canola Screenings
    Canola-Whole
    Carbadox
    Carob Germ
    Carob Meal
    Cashew Nut By Product
    Catfish Offal Meal
    Choline Chloride
    Chromium Tripicolinate
    Citrus Pulp
    Clopidol
    Cobalt
    Cobalt Carbonate
    Cobalt Sulfate
    Cocoa Cake
    Cocoa Hulls
    Copper Oxide
    Copper Sulfate
    Corn Chips
    Corn Chops
    Corn Coarse Cracked
    Corn-Coarse Ground
    Corn Cob-Ground
    Corn Distillers
    Corn Flint
    Corn Flour
    Corn Germ Bran
    Corn Germ Meal
    Corn Gluten
    Corn-High Oil
    Corn Kiblets
    Corn Meal Dehulled
    Corn Oil
    Corn Residue
    Corn Starch
    Corn/Sugar Blend
    Corn-Cracked
    Corn-Crimped
    Corn-Ground Fine
    Corn-Ground Roasted
    Corn-Steam Flaked
    Corn-Steamed
    Corn-Whole
    Cottonseed Culled
    Cottonseed Hull
    Cottonseed Meal
    Cottonseed Oil
    Cottonseed Whole
    Coumaphos
    Culled Beans
    Danish Fishmeal
    Decoquinate
    Dextrose
    Diamond V Yeast
    Disodium Phosphate
    Distillers Grains
    Dried Apple Pomace
    Dried Brewers Yeast
    Dried Distillers Milo
    Dried Porcine
    Dried Whole Milk
    Powder
    Duralass
    Enzyme Booster
    Epsom Salts
    Erythromycin
    Extruded Grain
    Extruded Soy Flour
    Fat
    Feather Meal
    Feeding Oatmeal
    Fenbendazole
    Fermacto
    Ferric Chloride
    Ferrou Cabonate
    Ferrous Carbonate
    Ferrous Sulfate
    Fine Job's Tear Bran
    Fish Meal
    Fish
    Flavoring
    Folic Acid
    French Fry Rejects
    Fresh Arome
    Fried Wheat Noodles
    Gold Dye
    Gold Flavor
    Grain Dust
    Grain Screening
    Granite Grit
    Grape Pomace
    Green Dye
    Green Flavor
    Guar Gum
    Hard Shell
    Hemicellulose Extract
    Hemp
    Herring Meal
    Hominy
    Hygromycin
    Indian Soybean Meal
    Iron Oxide-Red
    Iron-Oxide Yellow
    Job's Tear Broken Seeds
    Kapok Seed Meal
    Kelp Meal
    Kem Wet
    Lactose
    Larvadex
    Lasalocid
    Levams Hcl
    Limestone
    Linco
    Lincomix
    Lincomycin
    Linseed Meal
    Liquid Fish Solubles
    Lupins
    Lysine
    Magnesium
    Magnesium Sulfate
    Malt Plant By-Products
    Manganous Ox
    Maple Flavor
    Masonex
    Meat And Bone Meal
    Meat And Bone Meal
    Meat Meal
    Mepron
    Methionine
    Millet Screenings
    Millet White
    Millet-Ground
    Milo Binder
    Milo-Coarse Ground
    Milo-Cracked
    Milo-Whole
    Mineral Flavor
    Mineral Oil
    Mixed Blood Meal
    Molasses
    Molasses Blend
    Molasses Dried
    Molasses Standard Beet
    Molasses Standard Cane
    Molasses-Pellet
    Mold
    Monensin
    Monoamonum Phos
    Monosodium Glutamate
    Monosodium Phosphate
    Mung Bean Hulls
    Mustard Meal High Fat
    Mustard Oil
    Mustard Shorts
    Narasin
    Natuphos
    Niacin
    Nicarbazin
    Nitarsone
    Oat Cullets
    Oat Flour
    Oat Groats
    Oat Hulls
    Oat Mill Byproducts
    Oat Screenings
    Oat Whole Cereal
    Oatmill Feed
    Oats Flaked
    Oats-Ground
    Oats-Hulless
    Oats-Premium
    Oats-Rolled
    Oats-Whole
    Oyster Shell
    Paddy Rice
    Palm Kernel
    Papain
    Papain Enzyme
    Paprika Spent Meal
    Parboiled Broken Rice
    Pea By-Product
    Pea Flour
    Peanut Meal
    Peanut Skins
    Pelcote Dusting
    Phosphate
    Phosphoric Acid
    Phosphorus
    Phosphorus
    Defluorinated
    Pig Nectar
    Plant Waste
    Poloxalene
    Popcorn
    Popcorn Screenings
    Porcine Plasma; Dried
    Pork Bloodmeal
    Porzyme
    Posistac
    Potassium Bicarbonate
    Potassium Carbonate
    Potassium Magnesium
    Sulfate
    Potassium Sulfate
    Potato Chips
    Poultry Blood/Feather
    Meal
    Poultry Blood Meal
    Poultry Byproduct
    Predispersed Clay
    Probios
    Procain Penicillen
    Propionic Acid
    Propylene Glycol
    Pyran Tart
    Pyridoxine
    Quest Anise
    Rabon
    Rapeseed Meal
    Red Flavor
    Red Millet
    Riboflavin
    Rice Bran
    Rice By-Products
    Fractions
    Rice Dust
    Rice Ground
    Rice Hulls
    Rice Mill By-Product
    Rice Rejects Ground
    Roxarsone
    Rumen Paunch
    Rumensin
    Rye
    Rye Distillers
    Rye With Enzymes
    Safflower Meal
    Safflower Oil
    Safflower Seed
    Sago Meal
    Salinomycin
    Salt
    Scallop Meal
    Seaweed Meal
    Selenium
    Shell Aid
    Shrimp Byproduct
    Silkworms
    Sipernate
    Sodium Acetate
    Sodium Benzoate
    Sodium Bicarbonate
    Sodium Molybdate
    Sodium Sesquicarbonate
    Sodium Sulfate
    Solulac
    Soweena
    Soy Flour
    Soy Pass
    Soy Protein Concentrate
    Soybean Cake
    Soybean Curd By-
    Product
    Soybean Dehulled Milk
    By-Product
    Soybean Hulls
    Soybean Mill Run
    Soybean Oil
    Soybean Residue
    Soybeans Extruded
    Soybeans-Roasted
    Soycorn Extruded
    Spray Dried Egg
    Standard Micro Premix
    Starch Molasses
    Steam Flaked Corn
    Steam Flaked Wheat
    Sugar (Cane)
    Sulfamex-Ormeto
    Sulfur
    Sulfur
    Sunflower Meal
    Sunflower Seed
    Tallow Fancy
    Tallow-Die
    Tallow-Mixer
    Tapioca Meal
    Tapioca Promeance
    Taurine
    Terramycin
    Thiabenzol
    Thiamine Mono
    Threonine
    Tiamulin
    Tilmicosin
    Tomato Pomace
    Trace Min
    Tricalcium Phosphate
    Triticale
    Tryptophan
    Tryptosine
    Tuna Offal Meal
    Tylan
    Tylosin
    Urea
    Vegetable Oil Blend
    Virginiamycin
    Vitamin A
    Vitamin B Complex
    Vitamin B12
    Vitamin D3
    Vitamin E
    Walnut Meal
    Wheat Bran
    Wheat Coarse Ground
    Wheat Germ Meal
    Wheat Gluten
    Wheat Meal Shredded
    Wheat Millrun
    Wheat Mix
    Wheat Noodles Low Fat
    Wheat Red Dog
    Wheat Starch
    Wheat Straw
    Wheat With Enzyme
    Wheat-Ground
    Wheat-Rolled
    Wheat-Whole
    Whey Dried
    Whey Permeate
    Whey Protein
    Concentrate
    Whey-Product Dried
    Yeast Brewer Dried
    Yeast Sugar Cane
    Zinc
    Zinc Oxide
    Zoalene

Claims (3)

  1. 1. A system for determining customized feed for at least one animal, the system comprising:
    a first memory portion configured to store animal data representative of the characteristics of the animal;
    a second memory portion configured to store feed data representative of the feed ingredients located at at least one location;
    a third memory portion configured to store evaluation data representative of at least two evaluation criteria;
    a data processing circuit in communication with the memory portions and configured to generate nutrient profile data representative of a nutrient profile for the animal based upon the animal data, the data processing circuit being further configured to generate ration data representative of a combination of ingredients from the first and second locations, the ration data being generated by the data processing circuit based upon the profile data, the first and second feed data and the evaluation data; and
    a fourth memory portion in communication with the data processing circuit and configured to store optimization weighting data representative of the effect a respective evaluation criteria has on the generation of the ration data, the data processing circuit further generating the ration data based upon the optimization weighting data.
  2. 2. A method for determining customized feed for at least one animal, the method comprising:
    storing animal data representative of the characteristics of the animal;
    storing feed data representative of the feed ingredients located at at least one location;
    storing evaluation data representative of at least two evaluation criteria;
    storing optimization weighting data representative of the effect of a respective evaluation criteria;
    generating profile data representative of a nutrient profile for the animal based upon the animal data; and
    generating ration data representative of a combination of ingredients from the location, the ration data being generated based upon the profile data, the feed data, the evaluation data, and the optimization weighting data.
  3. 3. A system for determining customized feed for at least one animal, the system comprising:
    a first memory portion configured to store animal data representative of the characteristics of the animal;
    a second memory portion configured to store feed data representative of feed ingredients;
    a third memory portion configured to store evaluation data of at least one evaluation criteria;
    a data processing circuit in communication with the memory portions and configured to generate nutrient profile data representative of a nutrient profile for the animal based upon the animal data, the data processing circuit being further configured to generate ration data representative of a combination of ingredients, the ration data being generated by the data processing circuit based upon the nutrient profile data, the feed data and the evaluation data; and
    a fourth memory portion in communication with the data processing circuit and configured to store optimization weighting data representative of an effect a respective evaluation criteria has on the generation of the ration data, the data processing circuit further generating the ration data based upon the optimization weighting data.
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