Movement prediction system and movement prediction method

Abstract

To provide a behavior prediction system capable of predicting the behavior of a herd of livestock, which is a group of livestock, in a form that reflects the differences between individuals. [Solution] The movement prediction system comprises a memory unit 211 that stores livestock herd information including individual information of livestock that make up a livestock herd, which is a flock of livestock, the number of individuals that make up the livestock herd, health condition information of the individuals that make up the livestock herd, and life cycle information of the livestock, and a movement prediction unit 212 that predicts the movement of the livestock herd including changes in the number of individuals based on the livestock herd information stored in the memory unit 211, and at least a part of the individual number and health condition information is managed based on data obtained by sensors around the livestock.

Description

[Technical field]

[0001] The present invention relates to a livestock population dynamics prediction system and a livestock population dynamics prediction method. [Background technology]

[0002] The following Patent Document 1 discloses a simulation device that calculates the fluctuation in the number of animals that make up a herd, including a herd of cows raised on a ranch. This simulation device accepts input of the number of parous animals at the beginning of a specified period (e.g., one year), the number of non-parous animals at the beginning of the period, and the number of primiparous animals at the beginning of the period, as well as the birth interval in the herd, and calculates and outputs the number of parous animals at the end of the period, the number of non-parous animals at the end of the period, and the number of primiparous animals at the end of the period based on the inputs. In addition, by repeating the calculation for the above-mentioned specified period for multiple years, the device also calculates the change in each number for each year up to, for example, 10 years in the future.

[0003] This simulation device calculates the population at the end of a period from the population at the beginning of the period using a rate of change set based on past statistics or a rate of change set arbitrarily by the ranch manager. In other words, the population at the end of the period is calculated uniformly using a specific set rate of change. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent No. 5546825 Summary of the Invention [Problem to be solved by the invention]

[0005] However, the rate of change in the number of individuals in a group varies depending on the state of each individual. Therefore, when predicting the number of individuals at the end of a period from the number of individuals at the beginning of the period, if a uniform prediction is made using a specific rate of change, the accuracy of the predicted value will decrease.

[0006] The present invention has been made to solve the above-mentioned problems, and one of its objectives is to provide a movement prediction system that can predict the movement of a livestock herd, which is a group of livestock, in a manner that reflects differences between individuals. [Means for solving the problem]

[0007] A movement prediction system according to one embodiment of the present invention comprises a memory unit that stores livestock herd information including individual information of livestock that constitute a livestock herd, which is a flock of livestock, the number of individuals that constitute the livestock herd, health condition information of the individuals that constitute the livestock herd, and life cycle information of the livestock, and a movement prediction unit that predicts the movement of the livestock herd including changes in the number of individuals based on the livestock herd information stored in the memory unit, and at least a portion of the number of individuals and the health condition information are managed based on data obtained by sensors around the livestock.

[0008] A method for predicting livestock movements according to another aspect of the present invention is a method executed by a processor and includes the steps of storing livestock herd information including individual information of livestock that constitute a livestock herd, which is a flock of livestock, the number of individuals that constitute the livestock herd, health condition information of the individuals that constitute the livestock herd, and life cycle information of the livestock, and predicting the livestock herd movements including changes in the number of individuals based on the stored livestock herd information, wherein at least a portion of the number of individuals and the health condition information are managed based on data obtained by sensors around the livestock.

[0009] According to these aspects, it is possible to store livestock herd information including individual livestock information, number and health condition information of individuals managed at least in part based on data obtained by sensors around the livestock, and life cycle information of the livestock, and to predict the dynamics of the livestock herd, including changes in the number of individuals, based on the stored livestock herd information.

[0010] In each of the above aspects, the livestock herd information may further include genomic information of individuals.

[0011] According to this embodiment, genomic information can be used to predict the dynamics of livestock herds, making it possible not only to simply predict the dynamics of livestock herds statistically, but also to more precisely predict the dynamics of livestock herds as a collection of individuals, reflecting the differences and diversity of each individual.

[0012] In each of the above aspects, the livestock herd information may further include livestock product information of each individual, and the dynamics of the livestock herd may further include trends in the amount of livestock product of each individual.

[0013] In each of the above aspects, the livestock may be cattle, and the livestock herd may be a cattle herd. Effect of the Invention

[0014] According to the present invention, it is possible to provide a movement prediction system that can predict the movement of a livestock herd, which is a group of livestock, in a manner that reflects differences between individuals. [Brief description of the drawings]

[0015] [Figure 1] FIG. 1 is a diagram illustrating a configuration of a movement prediction system according to an embodiment. [Diagram 2] FIG. 2 is a diagram illustrating an example of a physical configuration of a server device. [Diagram 3] FIG. 2 is a diagram illustrating an example of a physical configuration of a terminal device. [Figure 4] FIG. 2 is a diagram illustrating an example of a functional configuration of a server device. [Diagram 5] 13 is a flowchart for explaining the processing steps for predicting cattle herd dynamics. [Figure 6] FIG. 13 is a diagram illustrating the prediction results of cattle herd dynamics. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0016] Hereinafter, an embodiment of the present invention will be described in detail with reference to the drawings. Note that the same elements are given the same reference numerals and duplicated explanations will be omitted.

[0017] [Outline of the dynamics prediction system] A movement prediction system according to an embodiment will be described with reference to Fig. 1. The movement prediction system 1 includes, for example, a server device 2, an IoT sensor 3, and a terminal device 4. The IoT sensor 3 is a sensor provided in the vicinity of livestock, and is provided, for example, in a shed for keeping livestock, or attached to the livestock.

[0018] The movement prediction system 1 of the embodiment is a system that predicts the movement of a livestock herd based on livestock herd information including, for example, individual information of livestock that constitute the livestock herd, which is a flock of livestock, the number of individuals that constitute the livestock herd, health status information, livestock product information, genomic information, and life cycle information of the livestock.

[0019] In this embodiment, cows are used as an example of livestock, but the livestock is not limited to cows. For example, the present invention can be applied to other livestock including pigs, birds, fish, etc. in the same manner as cows.

[0020] Here, a cow herd, which is an example of a livestock herd, is a group of cows that are spatially cohesive, for example, a group of dairy cows raised on a farm. Herd dynamics refers to the increase and decrease in the number of cows in a herd and the amount of milk produced over time. Predicting herd dynamics refers to predicting how the number of cows in a herd and the amount of milk produced will change in the future. This prediction makes it possible to estimate future trends, such as, for example, whether in one year there will be a certain number of dairy cows and they will be able to produce a certain number of liters of milk per day.

[0021] The server device 2 is a computer device that executes processes to realize functions such as predicting cow herd dynamics. The terminal device 4 is a computer device used by a user who requests a prediction of cow herd dynamics. The terminal device 4 may be, for example, a PC (personal computer), a notebook PC, a tablet terminal, a smartphone, or other terminal device.

[0022] The server device 2 and the IoT sensor 3, and the server device 2 and the terminal device 4 are configured to be able to communicate with each other via a network N. The network N may be, for example, the Internet, a LAN, a dedicated line, a telephone line, a mobile communication network, WiFi (Wireless Fidelity), Bluetooth (registered trademark), other communication lines, or a combination thereof, and may be either wired or wireless.

[0023] [Server device configuration] As shown in FIG. 2, the server device 2 includes, as its physical components, a processor 21, a communication interface 22, and a storage device 23, for example.

[0024] The processor 21 is, for example, a central processing unit (CPU) or a micro processing unit (MPU), etc. The processor 21 functions as a control unit that realizes various functions of the server device 2 by executing a program 231 stored in the storage device 23.

[0025] The communication interface 22 functions as a communication unit that connects to the network N and communicates with other devices on the network N.

[0026] The storage device 23 is, for example, a computer-readable recording medium such as a disk drive or a semiconductor memory. The storage device 23 functions as a storage unit that stores a program 231 for implementing various functions of the server device 2, various data 232 used by the program 231, and the like. Note that a part of the data 232 may be stored in an external device or system.

[0027] The various data 232 includes, for example, data constituting herd information, which is information about a herd of cows. The herd information will be described in detail later.

[0028] [Terminal device configuration] As shown in FIG. 3, the terminal device 4 includes, as its physical configuration, a processor 41, a communication interface 42, a storage device 43, an input device 44, and a display device 45, for example.

[0029] The processor 41 is, for example, a CPU, an MPU, etc. The processor 41 executes a program stored in the storage device 43 to function as a control unit that realizes various functions of the terminal device 4.

[0030] The communication interface 42 functions as a communication unit that connects to the network N and communicates with other devices on the network N.

[0031] The storage device 43 is, for example, a computer-readable recording medium such as a disk drive or a semiconductor memory. The storage device 43 functions as a storage unit that stores programs for implementing various functions of the terminal device 4 and various data used by the programs.

[0032] The input device 44 functions as an input unit that accepts input from a user. As the input device 44, for example, a keyboard, a touch panel, a pen tablet, a mouse, a microphone, etc. can be used.

[0033] The display device 45 functions as a display unit that displays images, screens, etc. As the display device 45, for example, an organic EL display, a liquid crystal display, etc. can be used.

[0034] [Functions of the dynamics prediction system] 4, the server device 2 includes, as functional components, a storage unit 211 and a behavior prediction unit 212. Each unit will be described below.

[0035] The storage unit 211 is a database that collects various scattered data and manages them in a centralized manner, for example, managing herd information. Herd information includes, for example, individual information of the cows that make up the herd, the number of individuals that make up the herd, health information of the individuals that make up the herd, livestock product information of the individuals that make up the herd, genome information of the individuals that make up the herd, life cycle information of the cows, and movement history information of the herd. Each piece of information that makes up the herd information is preferably managed by linking it to an individual identification ID that identifies an individual. Each piece of information is explained below.

[0036] The individual information of a cow is basic information about an individual cow. The individual information of a cow may include, for example, breed, sex, date of birth, etc.

[0037] The number of individuals constituting the herd can be managed by classification according to the breeding level, such as first-born calves (calves under 2 months old), heifers (cows with no history of parturition and before pregnancy is confirmed), primigravid cows (cows with no history of parturition but whose conception is confirmed), multiparous cows (cows with a history of parturition), and cull cows (cows that are no longer used for dairy or breeding purposes).

[0038] The population is preferably managed for each of the above categories in a predetermined unit of time, such as one year. Specifically, the number of animals kept at the beginning of the period, the increase or decrease in the number of animals kept during the period, and the number of animals kept at the end of the period can be recorded. The increase or decrease during the period can include, for example, the number of animals increased due to birth, the number of animals decreased due to culling or accidents, the number of animals increased due to animals transferred from other categories, the number of animals decreased due to animals transferred to other categories, the number of animals increased due to animals introduced from outside, and the number of animals decreased due to animals sold to outside parties.

[0039] Health condition information is information about the health of cows. Health condition information may include, for example, body temperature, pulse rate, weight, milk yield, conception information, calving information, disease information, blood test information, etc. Conception information may include the number of conceptions, conception intervals, conception history, etc. Calving information may include the number of calvings, calving intervals, calving history, etc. Disease information may include the details of the disease, treatment period, disease history, etc.

[0040] At least a part of the population and health condition information is preferably acquired using the IoT sensor 3. Specifically, the population may be managed by acquiring data from an IC tag attached to each cow using the IoT sensor 3. In addition, the body temperature, pulse rate, weight, milk yield, and other information of the cows included in the health condition information may also be acquired by the IoT sensor 3 and managed.

[0041] Here, the information acquired using the IoT sensor 3 is not limited to these pieces of information, and may include information that can be acquired by the sensor as statistical data on the cow herd, as appropriate. For example, inventory status information including the type of feed and the amount of feed in stock, and climate information including the temperature, humidity, and wind direction in the cowshed, may also be acquired using the IoT sensor 3. Note that the sensor is not limited to the IoT sensor 3, and any sensor capable of measuring physical quantities may be used as appropriate.

[0042] The livestock product information is information about products obtained from cows. The livestock product information may include, for example, historical information about milk yields of dairy cows, historical information about raw milk production, historical information about raw milk shipments, and historical information about beef shipments.

[0043] Genomic information is genetic information that can be used to represent differences in characteristics between individuals, and can include, for example, information about milk yield, such as whether an individual produces a lot of milk, or disease, such as whether the individual is susceptible to a particular disease.

[0044] As the genomic information of dairy cows, it is preferable to use evaluation indexes such as the reproductive ease of each individual, milk yield, etc. By using such genomic information, it becomes possible to predict the dynamics of the herd more precisely, not only simply statistically, but also as a collection of individuals, in a form that reflects the differences and diversity of each individual.

[0045] The life cycle information is information about the life cycle of a cow. The life cycle information can include, for example, information about when calving will occur, the birth probability at that time, when the cow will end its life, and so on.

[0046] In addition, the life cycle information may include information such as: a calf becomes fertile approximately x months after birth, gives birth to a calf approximately x months after conception, becomes milkable approximately x days after giving birth, and becomes fertile again after a milking period of approximately x months and a dry period of approximately x months.

[0047] The change history information is history information about the changes of individuals that make up the cattle herd. The change history information is information that is registered when a change occurs in a cattle due to, for example, culling, death, accident, sale, introduction, etc.

[0048] The behavior prediction unit 212 predicts herd behavior based on the herd information stored in the memory unit 211, and outputs the prediction result. The predicted herd behavior can include, for example, monthly or annual trends in the number of individuals (total or by division) constituting the herd, and monthly or annual trends in the amount of livestock products of the individuals constituting the herd. Each of these trends will be specifically described below.

[0049] The predicted changes in population in the target herd can be, for example, that there will be ○ dairy cows in one year, ○ dairy cows in two years, ○ dairy cows in three years, ○ dairy cows in four years, and ○ dairy cows in the target time of five years.

[0050] The prediction results for the trend in livestock production for the herd being predicted will be, for example, ○ liters of milk per day in one year, ○ liters of milk per day in two years, ○ liters of milk per day in three years, ○ liters of milk per day in four years, and the target date of ○ liters of milk per day in five years.

[0051] An example of a processing procedure for predicting cow herd dynamics will be described below with reference to Fig. 5. This processing procedure illustratively includes the following six processing steps (1) to (6). Note that, although this processing procedure will be described for dairy cows as an example, the processing procedure for beef cattle can also be expressed by a similar procedure.

[0052] (1) Obtain herd information for the farm to be predicted [Step S101]. (2) Calculate the life cycle of existing heifers [Step S102]. (3) Calculate the life cycle of existing mature cows [Step S103]. (4) Calculate the life cycle of dairy cows that will be born as a result of parturition [Step S104]. (5) Calculate the life cycle of cows to be introduced from outside [Step S105]. (6) Display the prediction results for herd dynamics [Step S106]. Below, (1) to (6) will be explained in order.

[0053] (1) Obtain information about the herd of cattle on the farm to be predicted [Step S101]: The birth dates and classifications of the cows currently existing in the farm to be predicted are obtained from the herd information managed in an integrated database. Classifications can be set for each farm. Examples of classifications include suckling cows, growing cows, heifers, mature cows, milking cows, and dry cows.

[0054] (2) Calculate the life cycle of existing heifers [Step S102]: The behavior prediction unit 212 sequentially repeats the following processes (2-1) to (2-5) for each individual cow during the period until the separately estimated date of death or date of culling.

[0055] (2-1) Estimated breeding start date: The behavior prediction unit 212 estimates the breeding start date based on the breeding start date (number of days after birth) of the heifer inputted as parameters by the user and the birth date of the individual cow.

[0056] (2-2) Estimation of the date of conception: The behavior prediction unit 212 estimates the pregnancy date based on the estimated breeding start date, past reproductive performance of cows of the same type (e.g., estrus detection rate, conception rate, pregnancy rate), and genome information of individual cows. The reproductive performance and genome information can be obtained from herd information.

[0057] An example of a procedure for estimating the date of conception using the pregnancy rate is described below in (a) to (f). (a) The breeding period for a single cow is fixed, for example, at a 21-day cycle. (b) During the prediction period of the herd dynamics, the cows that correspond to the breeding start date on the life cycle are extracted on a daily basis. The number of extracted cows is multiplied by the pregnancy rate to determine the number of cows that will conceive. The pregnancy rate is calculated by multiplying the estrus detection rate by the conception rate. (c) Pregnant cows will enter the next stage of their life cycle, the gestation period, the day after breeding begins. (d) For non-pregnant cows, the next breeding start date will be the date that is 21 days after the breeding start date.

[0058] (e) Repeat steps (b) to (d) above to identify cows that are pregnant on a daily basis. (f) As a result of the above (e), cows that fall under the category of long-term infertile are culled. Here, cows that have passed a long-term infertile period that can be arbitrarily set by the user are determined to be long-term infertile.

[0059] (2-3) Estimation of delivery date: The behavior prediction unit 212 estimates the delivery date based on the estimated pregnancy date, past reproductive performance (e.g., pregnancy period, birth accident rate, delivery accident rate) of the same type of cow, and genome information of the individual. The reproductive performance and genome information can be obtained from the herd information.

[0060] Specifically, the system estimates the calving date by adding the gestation period input by the user as a life cycle parameter to the estimated pregnancy date. In addition, the system extracts cows that correspond to the calving date on their life cycle on a daily basis during the prediction period of the cow herd dynamics. The system multiplies the number of extracted cows by the birth accident rate and the calving accident rate to determine the number of calves that will be born and the number of mother cows that will be culled due to calving accidents.

[0061] (2-4) Estimated date of start of milking: The behavior prediction unit 212 estimates the milking start date based on the estimated parturition date and the period from parturition to milking for cows of the same type in the past. The period from parturition to milking can be acquired from the cow herd information.

[0062] (2-5) Estimation of the start date of dry off: The behavior prediction unit 212 estimates the start date of the dry milking period based on the estimated milking start date and the past milking period of the same type of cow. The milking period can be acquired from the herd information.

[0063] (3) Calculate the life cycle of existing cows [Step S103]: The behavior prediction unit 212 sequentially repeats the processes from (3-1) to (3-5) below for each individual cow during the period until the separately estimated date of death or date of culling.

[0064] (3-1) Estimated breeding start date: The behavior prediction unit 212 estimates the breeding start date based on the previous calving date and the period from calving to the start of breeding for the same type of cow in the past. The period from calving to the start of breeding can be acquired from the herd information.

[0065] (3-2) Estimation of the date of conception: The behavior prediction unit 212 estimates the pregnancy date based on the estimated breeding start date, past reproductive performance (e.g., conception rate, pregnancy rate) of cows of the same type, and individual genome information. The reproductive performance and genome information can be obtained from the herd information.

[0066] (3-3) Estimation of delivery date: The behavior prediction unit 212 estimates the delivery date based on the estimated pregnancy date, past reproductive performance (e.g., pregnancy period, birth accident rate, delivery accident rate) of the same type of cow, and genome information of the individual. The reproductive performance and genome information can be obtained from the herd information.

[0067] (3-4) Estimated date of start of milking: The behavior prediction unit 212 estimates the milking start date based on the estimated parturition date and the period from parturition to milking for cows of the same type in the past. The period from parturition to milking can be acquired from the cow herd information.

[0068] (3-5) Estimation of the start of dry off date: The behavior prediction unit 212 estimates the start date of the dry milking period based on the estimated milking start date and the past milking period of the same type of cow. The milking period can be acquired from the herd information.

[0069] (4) Calculate the life cycle of the dairy cow born as a result of parturition [Step S104]: The behavior prediction unit 212 sequentially repeats the processes from (4-1) to (4-6) below for each individual cow during the period until the separately estimated date of death or date of culling.

[0070] (4-1) Estimated date of birth: The movement prediction unit 212 sets the delivery date estimated in (2-3) or (3-3) above as the date of birth of the calf to be born.

[0071] (4-2) Estimated breeding start date: The behavior prediction unit 212 uses the breeding start date (number of days after birth) input by the user as a life cycle parameter as a starting point.

[0072] (4-3) Estimation of the date of conception: The behavior prediction unit 212 estimates the pregnancy date based on the input breeding start date, past reproductive performance (e.g., conception rate, pregnancy rate) of cows of the same type, and genome information of the parent individual. The reproductive performance and genome information can be obtained from the herd information.

[0073] (4-4) Estimation of delivery date: The behavior prediction unit 212 estimates the delivery date based on the estimated pregnancy date, past reproductive performance (e.g., pregnancy period, birth accident rate, delivery accident rate) of the same type of cow, and genome information of the parent individual. The reproductive performance and genome information can be obtained from the herd information.

[0074] (4-5) Estimated date of start of milking: The behavior prediction unit 212 estimates the milking start date based on the estimated parturition date and the period from parturition to milking for cows of the same type in the past. The period from parturition to milking can be acquired from the cow herd information.

[0075] (4-6) Estimation of the start of dry off date: The behavior prediction unit 212 estimates the start date of the dry milking period based on the estimated milking start date and the past milking period of the same type of cow. The milking period can be acquired from the herd information.

[0076] (5) Calculate the life cycle of the cow introduced from outside [Step S105]: The number of cattle to be introduced from outside and the date of introduction are calculated based on the number of cattle that the user wants to maintain and the period until the desired number of cattle is reached (starting from the creation date), which are input as parameters, and the remaining number of cattle per day calculated when predicting the dynamics of the cattle herd. The user can also input the age of the cattle in months as a parameter.

[0077] A more specific explanation will be given. First, the difference between the remaining number of cattle and the desired cattle population scale is calculated as the number of cattle to be increased or decreased. Next, the calculated number of cattle to be increased or decreased is divided by the period until the desired cattle population scale is reached to calculate the number of cattle to be increased or decreased per month. Next, the number of cattle to be introduced from outside per month is calculated by adding or subtracting the number of cattle born or dying in a separately managed ranch to the calculated number. The timing for introducing cattle from outside can be set to, for example, the end of each month.

[0078] The behavior prediction unit 212 sequentially repeats the processes from (5-1) to (5-5) below for each individual cow, starting from the age at the time of introduction, until the separately estimated date of death or retirement date.

[0079] (5-1) Estimated breeding start date: The behavior prediction unit 212 estimates the breeding start date based on the previous calving date and the period from calving to the start of breeding for the same type of cow in the past. The period from calving to the start of breeding can be acquired from the herd information.

[0080] (5-2) Estimation of the date of conception: The behavior prediction unit 212 estimates the pregnancy date based on the estimated breeding start date and the past reproductive performance (e.g., conception rate, pregnancy rate) of cows of the same type. The reproductive performance can be obtained from the herd information.

[0081] (5-3) Estimation of delivery date: The behavior prediction unit 212 estimates the delivery date based on the estimated pregnancy date and the past reproductive performance of the same type of cow (for example, pregnancy period, birth accident rate, delivery accident rate). The reproductive performance can be obtained from the herd information.

[0082] (5-4) Estimated date of start of milking: The behavior prediction unit 212 estimates the milking start date based on the estimated parturition date and the period from parturition to milking for cows of the same type in the past. The period from parturition to milking can be acquired from the cow herd information.

[0083] (5-5) Estimation of the start of dry off date: The behavior prediction unit 212 estimates the start date of the dry milking period based on the estimated milking start date and the past milking period of the same type of cow. The milking period can be acquired from the herd information.

[0084] (6) Display the prediction results of the herd dynamics [Step S106]: The behavior prediction unit 212 displays the results of sorting each cow by category on a daily basis based on each piece of information estimated in (2) to (5) above as the cow herd behavior. This makes it possible to grasp the future existence status and condition of each individual cow. The categories of each cow include, for example, dairy cows, beef cows, etc. The existence status of each individual cow includes, for example, nursing cows, growing cows, milking cows, dry cows, introduced cows, newborn cows, shipped cows, culled cows, accident / dead cows, etc. The status of each individual cow includes, for example, pregnancy, milking, dry milk, etc.

[0085] An example of a predicted result of herd dynamics is shown in Figure 6. Figure 6 is a monthly prediction of herd dynamics, including the trend in the number of individuals that make up the herd of a certain farm. For example, the figure shows the monthly trends in the number of calving cows, heifers, milking cows, dry cows, suckling cows, growing cows, individual sales (calves, adult cows), cows scheduled to calve, cows scheduled to finish VWP, cows moving to dry milk this month, cows that will start shipping raw milk this month, newly introduced cows, newly born cows, culled cows, and cows that have died or been discarded due to accidents.

[0086] As described above, according to the dynamics prediction system 1 of this embodiment, herd information including individual information of the cows, the number and health condition information of individuals managed at least in part based on data acquired by IoT sensors 3 around the cows, livestock product information, genomic information, and life cycle information is stored, and based on the stored herd information, it is possible to predict herd dynamics including changes in the number of individuals constituting the herd and changes in the amount of livestock products of the individuals constituting the herd.

[0087] Therefore, according to the dynamics prediction system 1, it is possible to predict the dynamics of a cattle herd, which is a group of cattle, in a manner that reflects the differences between individuals.

[0088] In particular, by using genomic information to predict herd dynamics, it becomes possible to predict herd dynamics not simply statistically, but more precisely, by reflecting the differences and diversity of each individual as a collection of individuals.

[0089] It should be noted that the present invention is not limited to the above-described embodiment, and can be embodied in various other forms without departing from the scope of the present invention. Therefore, the above-described embodiment is merely illustrative in all respects and should not be interpreted as being restrictive.

[0090] For example, future operating cash flows of the farm may be predicted based on the herd dynamics predicted in the above-mentioned embodiment. Operating cash flows can be predicted, for example, based on the predicted herd dynamics, by multiplying feed costs, sales prices, milk yields, carcass weights, and the like, which are calculated based on market data and the past performance of the farm. By allowing the user to specify various parameters used in the prediction as variables, the user can try out simulations under various conditions. [Explanation of symbols]

[0091] 1...activity prediction system, 2...server device, 3...IoT sensor, 4...terminal device, 21...processor, 22...communication interface, 23...storage device, 41...processor, 42...communication interface, 43...storage device, 44...input device, 45...display device, 211...storage unit, 212...activity prediction unit, 231...program, 232...data

Claims

1. A storage unit for storing livestock herd information including individual information of livestock constituting a livestock herd, the number of individuals constituting the livestock herd, health condition information of the individuals constituting the livestock herd, and life cycle information of the livestock; A behavior prediction unit that predicts the behavior of the livestock group, including the transition of the number of individuals, based on the livestock group information stored in the memory unit; Equipped with At least a part of the number of individuals and the health condition information is managed based on data acquired by sensors around the livestock. Dynamics prediction system.

2. The livestock herd information further includes genomic information of the individual. The dynamics prediction system according to claim 1.

3. The livestock herd information further includes livestock product information of the individual, The dynamics of the livestock herd further include a change in the amount of livestock product of the individual. The dynamics prediction system according to claim 1.

4. The livestock is a cow, the livestock herd is a cow herd, and the livestock herd information is cow herd information. The dynamics prediction system according to claim 1.

5. The behavior prediction unit predicts the behavior of the herd by estimating the breeding start date, pregnancy date, calving date, milking start date, and dry milking start date for each individual cow based on the parameters input by a user and the herd information. The dynamics prediction system according to claim 4.

6. The behavior prediction unit uses genome information of the individual when estimating the pregnancy date and the delivery date. The dynamics prediction system according to claim 5.

7. 1. A processor-implemented method, comprising: A step of storing livestock herd information including individual information of livestock constituting a livestock herd, which is a flock of livestock, the number of individuals constituting the livestock herd, health status information of the individuals constituting the livestock herd, and life cycle information of the livestock; predicting the dynamics of the livestock herd, including the trends in the number of individuals, based on the stored livestock herd information; Including, At least a part of the number of individuals and the health condition information is managed based on data acquired by sensors around the livestock. Dynamic prediction methods.

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