Dynamics prediction system and dynamics prediction method

Abstract

Provided is a dynamics prediction system that can predict the dynamics of a livestock group, which is a herd of livestock, in a form reflecting differences between individuals. The dynamics prediction system comprises: a storage unit that stores livestock group information including individual information of livestock constituting the livestock group, which is a herd of livestock, the number of individuals constituting the livestock group, health condition information of the individuals constituting the livestock group, and life-cycle information of the livestock; and a dynamics prediction unit that predicts the dynamics of the livestock group, including a change in the number of individuals, on the basis of the livestock group information stored in the storage unit. At least a portion of the number of individuals and the health condition information is managed on the basis of data acquired via sensors in the environment of the livestock.

Description

Movement prediction system and movement prediction method

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

[0002] Patent Document 1 below discloses a simulation device that calculates fluctuations in the number of animals that make up a herd, including a herd of cattle raised on a ranch. This simulation device accepts inputs of the number of parous animals at the beginning of a predetermined period (e.g., one year), the number of non-parous animals at the beginning, and the number of primiparous animals at the beginning, as well as the birth interval in the herd. Based on these inputs, the simulation device calculates and outputs the number of parous animals, the number of non-parous animals, and the number of primiparous animals at the end of the period. Furthermore, by repeating the calculations for the predetermined period over multiple years, the simulation device also calculates the yearly trends in each number 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.

[0004] Patent No. 5546825

[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 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 objects is to provide a dynamics prediction system that can predict the dynamics of a livestock herd, which is a group of livestock, in a manner that reflects the differences between individuals.

[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 make up a livestock herd, which is a flock of livestock, the number of individuals that make up the livestock herd, health status information of the individuals that make up the livestock herd, and life cycle information of the livestock, and a movement prediction unit that predicts the movement of the livestock herd, including trends 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 health status information is managed based on data obtained by sensors around the livestock.

[0008] A method for predicting livestock dynamics 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 make up a livestock herd, which is a flock of livestock, the number of individuals that make up the livestock herd, health status information of the individuals that make up the livestock herd, and life cycle information of the livestock; and predicting the livestock herd dynamics, including trends in the number of individuals, based on the stored livestock herd information, wherein at least a portion of the number of individuals and health status information is 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 status 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 aspect, it is possible to predict the dynamics of livestock herds using genomic information, which makes it possible not only to predict the dynamics of livestock herds simply statistically, but also to more precisely predict the dynamics of livestock herds as a collection of individuals, reflecting the differences and diversity between individuals.

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

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

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

[0015] It is a diagram illustrating an example of the configuration of a behavior prediction system according to an embodiment. It is a diagram illustrating an example of the physical configuration of a server device. It is a diagram illustrating an example of the physical configuration of a terminal device. It is a diagram illustrating an example of the functional configuration of a server device. It is a flowchart for explaining the processing procedure when predicting cattle herd behavior. It is a diagram illustrating an example of the prediction result of cattle herd behavior.

[0016] Hereinafter, embodiments 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 redundant description will be omitted.

[0017] [Overview of Movement 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 where the livestock are kept, or attached to the livestock.

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

[0019] In this embodiment, cows are used as an example of livestock, but this does not limit the livestock to cows. For example, the present invention can be applied to other livestock including pigs, birds, fish, etc. in the same way 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 over time in the number of cows in the herd and the milk yield. Predicting herd dynamics refers to predicting how the number of cows in the herd, milk yield, etc. will change in the future. This prediction makes it possible to estimate future trends, such as, for example, whether there will be a certain number of dairy cows in one year and how many liters of milk will be produced per day.

[0021] The server device 2 is a computer device that executes processes to realize functions such as predicting herd dynamics. The terminal device 4 is a computer device used by users who request predictions of 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, Wi-Fi (Wireless Fidelity), Bluetooth (registered trademark), other communication lines, or a combination thereof, and may be wired or wireless.

[0023] [Configuration of Server Device] As shown in FIG. 2, the server device 2 includes, as its physical configuration, 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 connects to the network N and functions as a communication unit that communicates with other devices on the network N.

[0026] The storage device 23 is a computer-readable recording medium such as a disk drive or semiconductor memory. The storage device 23 functions as a storage unit that stores programs 231 for implementing various functions of the server device 2, various data 232 used by the programs 231, etc. Note that 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 cattle. The herd information will be described in detail later.

[0028] [Configuration of Terminal Device] 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 programs stored in the storage device 43, thereby functioning as a control unit that realizes various functions of the terminal device 4.

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

[0031] The storage device 43 is a computer-readable recording medium such as a disk drive or a semiconductor memory, and 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. The input device 44 may be, for example, a keyboard, a touch panel, a pen tablet, a mouse, or a microphone.

[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] 4, the server device 2 includes, as functional components, a storage unit 211 and a movement 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, managing, for example, herd information. Herd information includes, for example, individual information on the cattle that make up the herd, the number of individuals that make up the herd, health information on the individuals that make up the herd, livestock product information on the individuals that make up the herd, genomic information on the individuals that make up the herd, life cycle information on the cattle, and information on the herd's movement history. Each piece of information that makes up the herd information is preferably managed by linking it to an individual identification ID that identifies the individual. Each piece of information is explained below.

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

[0037] The number of individuals constituting a herd can be managed by classification according to the level of rearing, such as day-old calves (calves under two months old), heifers (cows with no history of parturition and before conception has been confirmed), primigravid cows (cows with confirmed conception but with no history of parturition), multiparous cows (cows with a history of parturition), and cull cows (cows no longer used for dairy or breeding purposes).

[0038] The population is preferably managed for each of the above categories in units of a predetermined period, 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 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 introduction from outside, and the number of animals decreased due to sale to outside parties.

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

[0040] It is preferable to acquire at least part of the population and health condition information 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 included in the health condition information may also be acquired and managed using the IoT sensor 3.

[0041] Here, the information acquired using the IoT sensor 3 is not limited to these pieces of information, and can appropriately include information that can be acquired by the sensor as statistical data related to the herd. 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, etc. may also be acquired using the IoT sensor 3. Note that the sensor is not limited to the IoT sensor 3, and any sensor that can measure physical quantities can be used as appropriate.

[0042] Livestock product information is information about products obtained from cows. Livestock product information can 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 describe differences in the characteristics of individuals, and can include, for example, information about milk yield, such as whether an individual produces a lot of milk, or disease-related characteristics, such as whether an individual is susceptible to certain diseases.

[0044] As genomic information for dairy cows, it is preferable to use evaluation indices such as the reproductive ease of individuals and milk yield. By using such genomic information, it becomes possible to not only predict herd dynamics statistically but also to more precisely predict herd dynamics as a collection of individuals, reflecting the differences and diversity between individuals.

[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, and when the cow will end its life.

[0046] Additionally, the life cycle information may include information such as the fact that 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 in individuals that make up the cattle herd. The change history information is information that is registered when a change in a cattle occurs 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 results. 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 explained in detail below.

[0049] The predicted population trends for the herd being predicted are, 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 in the herd being predicted are, for example, that in one year, ○ liters of milk will be produced per day, in two years, ○ liters of milk will be produced per day, in three years, ○ liters of milk will be produced per day, in four years, and finally, ○ liters of milk will be produced per day in the target year of five years.

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

[0052] (1) Obtain herd information for the ranch 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 born as a result of calving [Step S104]. (5) Calculate the life cycle of cows to be introduced from outside [Step S105]. (6) Display the predicted results of herd dynamics [Step S106]. Steps (1) to (6) are explained below in order.

[0053] (1) Obtain herd information for 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 herd information managed in an integrated database. Classifications can be set for each farm. Examples of classifications include suckler cows, growing cows, heifers, multiparous cows, milking cows, and dry cows.

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

[0055] (2-1) Estimation of 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 input as parameters by the user and the date of birth of the individual cow.

[0056] (2-2) Estimation of pregnancy date: 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 genomic information of the individual cow. Reproductive performance and genomic information can be obtained from herd information.

[0057] An example of a procedure for estimating the conception date using the pregnancy rate is described below in (a) to (f). (a) The insemination period for one cow is fixed, for example, as a 21-day cycle. (b) During the prediction period for cattle herd dynamics, cows that correspond to the insemination start date in their life cycle are extracted on a daily basis. The number of extracted cows is multiplied by the pregnancy rate to identify the number of cows that will conceive. The pregnancy rate is calculated by multiplying the estrus detection rate by the conception rate. (c) Cows that conceive move on to the next life cycle, the pregnancy period, from the day after the insemination start date. (d) For cows that do not conceive, the next insemination start date is set to the date 21 days after the insemination start date.

[0058] (e) Repeating the above steps (b) to (d) to identify cows that are pregnant on a daily basis. (f) As a result of the above step (e), cows that are classified as long-term infertile are culled. Here, cows that have passed a user-settable number of days of long-term infertile are determined to be long-term infertile.

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

[0060] Specifically, the system estimates the calving date by adding the gestation period entered by the user as a life cycle parameter to the estimated pregnancy date. Additionally, during the prediction period for herd dynamics, the system extracts cows whose calving dates in their life cycles correspond to the estimated pregnancy date. The system then multiplies the number of extracted cows by the birth accident rate and the calving accident rate to determine the number of calves to be born and the number of mother cows to be culled due to calving accidents.

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

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

[0063] (3) Calculating the life cycle of existing multiparous cows [Step S103]: The behavior prediction unit 212 sequentially executes the following processes (3-1) to (3-5) for each individual cow for the period until the separately estimated date of death or retirement date.

[0064] (3-1) Estimation of breeding start date: The behavior prediction unit 212 estimates the breeding start date based on the previous calving date and the period until the start of breeding after calving of cows of the same type in the past. The period until the start of breeding after calving can be obtained from the herd information.

[0065] (3-2) Estimation of pregnancy date: 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 the individual's genomic information. The reproductive performance and genomic information can be obtained from herd information.

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

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

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

[0069] (4) Calculating the life cycle of the dairy cow born as a result of delivery [Step S104]: The behavior prediction unit 212 sequentially executes the following processes (4-1) to (4-6) for each individual cow for the period until the separately estimated date of death or retirement date.

[0070] (4-1) Estimation of Date of Birth: The behavior prediction unit 212 sets the delivery date estimated in (2-3) or (3-3) above as the date of birth of the newborn calf.

[0071] (4-2) Estimation of 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 the starting point.

[0072] (4-3) Estimation of pregnancy date: 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 genomic information of the parent individuals. Reproductive performance and genomic information can be obtained from herd information.

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

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

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

[0076] (5) Calculating the life cycle of cattle introduced from outside [Step S105]: The number of cattle to be introduced from outside and the date of introduction are calculated based on the desired number of cattle to be maintained, which are input as parameters by the user, the period until the desired number of cattle is reached (starting from the month when the data was created), and the daily remaining number of cattle calculated when predicting the dynamics of the cattle herd. The user can also input the age of the cattle to be introduced 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 or from the calculated number of cattle. The timing for introducing cattle from outside can be set, for example, at the end of each month.

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

[0079] (5-1) Estimation of breeding start date: The behavior prediction unit 212 estimates the breeding start date based on the previous calving date and the period until the start of breeding after calving of cows of the same type in the past. The period until the start of breeding after calving can be obtained from the herd information.

[0080] (5-2) Estimation of pregnancy date: 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 herd information.

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

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

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

[0084] (6) Displaying the predicted results of herd dynamics [Step S106]: The dynamics prediction unit 212 displays the results of organizing each cow by category on a daily basis based on the information estimated in (2) to (5) above as herd dynamics. This makes it possible to grasp the future existence status and condition of each individual cow. Examples of cow categories include dairy cows and beef cows. Examples of the existence status of each individual cow include nursing cows, heifers, milking cows, dry cows, introduced cows, newborn cows, shipped cows, culled cows, and cows that have died or been killed in accidents. Examples of the status of each individual cow include pregnancy, milking, dry cows, etc.

[0085] An example of the predicted results of herd dynamics is shown in Figure 6. Figure 6 is a monthly prediction of herd dynamics, including trends in the number of individuals that make up the herd of a certain ranch. The figure displays, for example, monthly trends in the number of calving cows, heifers, milking cows, dry cows, suckling cows, heifers, individual sales (calves, breeding cows), cows scheduled to calve, cows scheduled to complete VWP, cows moving to dry milk this month, cows that will begin shipping raw milk this month, newly introduced cows, newly born cows, culled cows, and cows that have died or been culled due to accidents or deaths.

[0086] As described above, according to the dynamics prediction system 1 of this embodiment, herd information including individual information of the herd, the number and health status 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 trends in the number of individuals constituting the herd and trends in the amount of livestock products of the individuals constituting the herd.

[0087] Therefore, according to the movement prediction system 1, it is possible to predict the movement of a herd of cattle, 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, as a collection of individuals, reflecting the differences and diversity of each individual.

[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 spirit of the present invention. Therefore, the above-described embodiment is merely illustrative in all respects and should not be interpreted as being limiting.

[0090] For example, future operating cash flow for a farm may be predicted based on the herd dynamics predicted in the above-described embodiment. Operating cash flow can be predicted, for example, by multiplying the predicted herd dynamics by feed costs, sales prices, milk yields, carcass weights, and other factors calculated based on market data and the farm's past performance. By allowing the user to specify various parameters used in the prediction as variables, the user can try out simulations under various conditions.

[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 livestock movement prediction system comprising: a memory unit 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 status information of the individuals that make up the livestock herd, and life cycle information of the livestock; and a livestock movement prediction unit that predicts the livestock herd movement including changes in the number of individuals based on the livestock herd information stored in the memory unit, wherein at least a portion of the number of individuals and the health status information are managed based on data obtained by sensors around the livestock.

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

3. A dynamics prediction system as described in claim 1, wherein the livestock herd information further includes livestock product information of the individual, and the livestock herd dynamics further includes trends in the livestock product amount of the individual.

4. The movement prediction system according to claim 1, wherein the livestock is cattle, the livestock herd is a cattle herd, and the livestock herd information is cattle herd information.

5. A dynamics prediction system as described in claim 4, wherein the dynamics prediction unit predicts the dynamics of the herd by estimating the breeding start date, pregnancy date, calving date, milking start date, and dry off start date for each individual cow based on parameters input by the user and the herd information.

6. The dynamics prediction system according to claim 5, wherein the dynamics prediction unit uses genomic information of the individual when estimating the date of conception and the date of delivery.

7. A method executed by a processor, comprising the steps of: storing livestock herd information including individual information of livestock that make up a livestock herd, which is a herd of livestock, the number of individuals that make up the livestock herd, health status information of the individuals that make up the livestock herd, and life cycle information of the livestock; and predicting the dynamics of the livestock herd, including trends 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 status information are managed based on data obtained by sensors around the livestock.

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