Conception rate estimation method

Abstract

To determine a conception rate by conception work, for providing effective information contributing to management of a stock farm.SOLUTION: A conception rate estimation method comprises the steps of: acquiring a partial regression coefficient using a logistic regression model from a data group with one or more explanatory variables being at least one of an individual factor acquired from milk of cows, a milk amount factor indicating the milk amount of the cows to a representative value reflecting a milk amount of a population to which the cows belong, and an environment factor in conception work of the cows, and with an objective variable being success / failure of conception of the cow; and determining the conception rate of the estimated individual, on the basis of the one or more explanatory variables of the estimated individual belonging to the population, and the partial regression coefficient. Thereby, the conception rate estimation method can determine the conception rate, which is useful reference data in determining a period of the conception work and the number of cows for which the conception work is performed all at once.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a conception rate estimation method for estimating the conception rate of dairy cows in relation to the characteristics of the herd to which the dairy cows belong. [Background technology]

[0002] In the dairy industry, the number of dairy cows that can be milked has a large impact on farm management. A dairy cow can be milked for three or four years after giving birth, which is about two years after birth. Therefore, in order to produce milk steadily, it is necessary to always have a certain number of dairy cows available, while allowing for a change of generations.

[0003] Current technology can determine the sex of the fertilized egg, increasing the chances of producing a female dairy cow. However, the conception rate is not 100%, and there are cases where the cow does not become pregnant despite the conception procedure.

[0004] Such unsuccessful conceptions are a setback in the planning of dairy cow population management. Even if conception work is carried out with the unsuccessful conceptions in mind, if there is an estimate of the probability of success (or failure), this information will be useful for dairy farm management.

[0005] Patent Document 1 discloses a livestock fertility estimation model generation device using multiple estimated indexes, the device having a learning actual value acquisition unit that acquires learning actual values ​​for each of multiple estimated indexes including a common index and a learning index, an index structure identification unit that identifies an index structure indicating a parent-child relationship between the multiple estimated indexes, a conditional probability table generation unit that generates a conditional probability table for each of the multiple estimated indexes, an evaluation actual value acquisition unit that acquires evaluation actual values ​​for each of the multiple estimated indexes, an evaluation estimate value calculation unit that calculates a simplified estimate based on the evaluation actual value for each common index and a complete estimate based on the evaluation actual value for each common index and the evaluation actual value for each learning index, and a memory unit that stores the conditional probability table as a livestock fertility estimation model based on the simplified estimates and the complete estimates.

[0006] Here, the reproductive ability of a female cow is estimated based on the assumption that the reproductive organs of the cow, i.e., the ovaries and uterus, are functioning properly, or that the functions of the ovaries and uterus of the cow have been restored after calving. For individuals that are judged to have high reproductive ability, the preparation for artificial insemination and the monitoring of estrus behavior are given higher priority, and veterinary measures such as hormone treatment are carried out. [Prior art documents] [Patent documents]

[0007] [Patent Document 1] JP 2020-91716 A [Patent Document 2] JP 2023-18569 A [Patent Document 3] JP 2023-59706 A Summary of the Invention [Problem to be solved by the invention]

[0008] It can be said that Patent Document 1 judges the fecundity of dairy cows from a veterinary perspective. However, it requires measurement of the reproductive organs of each individual and a veterinarian's assessment, which is time-consuming. In addition, the acquired influences of dairy cows, such as the environment in which they are raised and the type of feed, are not taken into consideration. [Means for solving the problem]

[0009] The conception rate estimation method of the present invention has been devised in consideration of the above-mentioned problems, and provides a method for estimating conception rate by taking into account at least one of easily measurable individual information, milk yield in the population to which the individual belongs, and the rearing environment.

[0010] More specifically, the conception rate estimation method according to the present invention comprises the steps of: Individual factors obtained from the milk components of dairy cows and A milk yield factor indicating the milk yield of the dairy cow relative to a representative value reflecting the milk yield of the population to which the dairy cow belongs; At least one of the environmental factors at the time of the conception work of the dairy cow is an explanatory variable; A step of obtaining partial regression coefficients using a logistic regression model from a data group having the success or failure of conception of the dairy cow as a response variable; The method further comprises a step of determining a conception rate of the subject individual from the explanatory variables of the subject individual belonging to the population and the partial regression coefficient. Effect of the Invention

[0011] The conception rate estimation method of the present invention takes into account the physical condition of the individual as well as various influences from the cattle herd to which the individual belongs and at least one of the rearing environment, so that it is possible to determine not only the conception rate of the individual, but also differences in conception rate due to differences in season, environment, feed, etc.

[0012] In addition, since the physical condition of an individual is judged from the components of the milk that is expressed, it is possible to obtain the effect that testing is easy and quick. [Brief description of the drawings]

[0013] [Figure 1] FIG. 1 is a diagram showing the steps of a conception rate estimation method according to the present invention. [Diagram 2] FIG. 1 is a diagram showing a configuration of a conception rate estimation system for implementing a conception rate estimation method. [Diagram 3] FIG. 1 is a flow chart showing the process flow of a conception rate estimation method (main flow). [Figure 4] FIG. 13 is a flowchart showing the flow of a process for determining explanatory variables. [Diagram 5] FIG. 1 is a diagram illustrating data collected when obtaining partial regression coefficients using a logistic regression model, and the logistic regression model. [Figure 6] FIG. 13 is a diagram explaining the process of calculating the conception rate Pimp using the conception rate calculation formula from the explanatory variables of the estimated individual and the partial regression coefficients calculated from the logistic regression model. [Figure 7] FIG. 13 is a flow chart showing the flow of processing for inputting milk volume factors. [Figure 8]FIG. 13 is a diagram for explaining original data LD1 collected to obtain a reference milk volume necessary for calculating the milk volume factors of an individual to be estimated. [Figure 9] FIG. 11 is a flow chart showing the flow of processing for calculating the standard milk volume StY. [Figure 10] (M1) A diagram illustrating the mean value MYs of the milk yield of the population during the reference period. [Figure 11] (M2) A graph explaining the average milk yield Mcs of the population in a similar reference period in a climate similar to that in which the estimated individual undergoes conception work. [Figure 12] (M3) A diagram illustrating the average milk yield MYrs of each dairy cow belonging to the population during an individual reference period. [Figure 13] (M4) This is a graph explaining the average milk yield MYrcs during the individual reference period for each dairy cow that underwent conception during a similar reference period in a climate similar to that in which the estimated individual underwent conception. [Figure 14] FIG. 11 is a flow chart showing the process of calculating the estimated milk yield NEY as an average value. [Figure 15] FIG. 13 is a diagram illustrating the process of creating an interpolation formula from actual milk volume RY. [Figure 16] FIG. 11 is a flowchart showing a process for creating an interpolation formula. [Figure 17] FIG. 13 is a diagram explaining the process of organizing the milk volume and factor parameters obtained from the interpolation formula to create a regression formula. [Figure 18] FIG. 11 is a flowchart showing a process for creating a regression equation. [Figure 19] FIG. 1 is a diagram conceptually showing the relationship between the number of days since calving N and the estimated milk yield EY when the factor parameters are p1 and p2. [Figure 20] FIG. 11 is a flowchart showing a process flow when performing factor analysis to find an optimal solution for factor parameters. [Figure 21] FIG. 13 is a diagram for explaining a comparison of actual milk yield RY over a certain period m with estimated milk yield EY obtained from a regression equation during factor analysis. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0014] The conception rate estimation method according to the present invention will be described below with reference to the drawings. Note that the following description is an example of one embodiment of the present invention and an example, and the present invention is not limited to the following description. The following description can be modified without departing from the spirit of the present invention.

[0015] <Outline of conception rate estimation method> The conception rate estimation method according to the present invention will be outlined below. In the present invention, the conception rate Pimp of an individual to be estimated (hereinafter also referred to as an "estimated individual") is calculated using a logistic regression model from at least one of the following items: individual factors obtained from the components of milk, milk volume factors that represent the milk volume of the estimated individual compared to the milk volume of the population to which the estimated individual belongs, and environmental factors such as temperature and humidity. Here, "calculation of conception rate Pimp" refers to obtaining a value from equation (2) described later, but may be called "estimation of conception rate Pimp" since it indicates future possibility.

[0016] This is shown in more detail in Figure 1. The conception rate estimation method according to the present invention is roughly divided into two steps. In the first step, a multivariate analysis is performed to correlate the conception rate Pimp based on binary data of success or failure of conception as the objective variable, with individual factors, milk yield factors, and at least one item belonging to environmental factors as explanatory variables from a large number of data groups related to the farm. A logistic regression model expressed by equation (1) is used for the multivariate analysis.

[0017]

number

[0018] Here, "Ln" represents logarithm with base e (Napier's constant). The purpose of the first step is to obtain the partial regression coefficients β0, β1, β2, β3, etc. of equation (1). Therefore, the first step can be said to be a step of finding partial regression coefficients using a logistic regression model. Also, although three types of factors are described here: individual factors, milk yield factors, and environmental factors, it is sufficient that at least one item belonging to these is used. An item is one of the measurement items belonging to individual factors, milk yield factors, and environmental factors.

[0019] In addition, multiple items selected from these factors may be used as explanatory variables. For example, items Tma and Tmb included in the individual factors may be used as explanatory variables. The logistic regression model gives partial regression coefficients for each item.

[0020] In the second step, the partial regression coefficients obtained in the first step are used to obtain a conception rate calculation formula (equation (2)) for calculating the conception rate Pimp from each explanatory variable. Note that for the items related to environmental factors among the explanatory variables of the estimated individual, the values ​​of the items of environmental factors predicted at the time of conception are used.

[0021] In formula (2), individual factors and milk yield factors are set to "previous month" and environmental factors to "this month", but this means that the estimation date is in this month, the conception work is carried out on a date in the future from the estimated date this month, and individual factors and milk yield factors are from before the conception work, so data from before the estimation date is used, and environmental factors use values ​​from the future of the estimation date. It is not necessarily in units of "months".

[0022]

number

[0023] <Explanation of period and timing> The terms "time point at which the conception operation is carried out", "estimated time" and "reference period" used in the following explanation will now be explained.

[0024] "The time when the conception work is performed" refers to the moment when the conception work is performed. It may also be specified by the day when the conception work is performed. It may also be abbreviated to "the time of the conception work." The time when the conception work is performed may have a range of values. In other words, it may be treated as the day when the conception work is performed, the week when the conception work is performed, or the month when the conception work is performed. Conception work refers to acts related to conception, such as artificial insemination and embryo transplantation.

[0025] A certain period of time prior to the time of carrying out the conception work is called the "reference period." For the individual factors and milk yield factors of dairy cows, values ​​for only one day cannot exclude cases where the values ​​occur by chance on that day, so it is preferable to use the average values ​​for a certain period of time. Therefore, the reference period is a certain period during which the individual factors and milk yield factors of the estimated individual before the conception work are observed.

[0026] The reference period is at least 60 days, preferably within 30 days, more preferably within 2 weeks, and most preferably 3 days to the day before. The last day of the reference period (the day in the reference period closest to the time when the impregnation work is performed) should be within 2 weeks of the time when the impregnation work is performed, more preferably within 1 week, most preferably within 3 days, and ideally the day before. In other words, there may be a day that is not a reference period between the last day of the reference period and the time when the impregnation work is performed (the day when it is performed).

[0027] The "time of estimation" is the time when the conception rate Pimp is estimated. Usually, the "time of estimation" is a time in the future from the last day of the reference period and a time in the past from the "time when conception work is performed."

[0028] In other words, normally, values ​​from the past are used for individual factors and milk yield factors, and predicted values ​​from the future are used for environmental factors. Note that, when calculating the conception rate Pimp from a past time point, values ​​from the past may be used for environmental factors.

[0029] <Hardware configuration> Figure 2 shows the configuration of a fertility rate estimation system that executes the fertility rate estimation method according to the present invention. The fertility rate estimation system 1 according to the present invention is composed of a terminal 10, a main body 12, and a memory 14. The terminal 10 can be composed of a computer having a CPU (Central Processing Unit), memory, and a display screen, and can include a mobile communication terminal (a so-called smartphone).

[0030] The main body 12 is composed of a CPU. It may be one CPU or multiple CPUs connected together. The main body 12 is a device that provides services, so it may be called a server. The memory 14 is a memory used by the main body 12.

[0031] The main body 12 and the terminal 10 are connected to be able to communicate with each other. The communication may be wired or wireless. The terminal 10 may also be called a client. The terminal 10 is preferably installed in a ranch 16A or the like where dairy cows are actually raised. Here, "installed" may mean that the terminal 10 is physically installed in the ranch 16A, or that a person in charge or manager of the ranch 16A possesses a mobile communication terminal. Of course, the terminal 10 may be installed in a place other than the ranch 16A. Moreover, the ranch 16 may be any ranch where dairy cows are raised.

[0032] A plurality of farms 16 may be connected to the main body 12. This is because the conception rate estimation system 1 calculates the conception rate by collecting and analyzing a large amount of data related to individual factors, milk yield factors, and environmental factors, and therefore it is desirable to collect a large amount of data. In addition, the main body 12 may be connected to external information 18 via the Internet. This is because not only data obtained from the farms 16 but also information related to nationwide weather can be obtained via the Internet and used.

[0033] The main unit 12 may obtain climate data from external information 18. In a closed-type barn, the environment inside the barn is managed, and data related to environmental factors can be obtained reliably. However, in an open-type barn, it is difficult to obtain the wind speed inside the barn on that day. In such cases, external climate data can be used as a reference.

[0034] Various types of weather-related data are provided, and these can also be used effectively. In particular, future predicted values ​​for environmental factors may be used to estimate the conception rate Pimp. In such cases, external weather predictions can be suitably used. Also, the farm data FD may be transmitted to the main unit 12 by a method other than the terminal 10. Of course, the terminal 10 that transmits the farm data FD to the main unit 12 and the terminal 10 that inputs the factor parameters P and calculates the estimated milk yield may be separate.

[0035] In this way, the conception rate estimation system 1 collects a large amount of data from various places and calculates the conception rate Pimp by analyzing the collected data, so a preferred embodiment is one in which the service is provided via a network. In other words, the conception rate estimation system 1 may be configured as a cloud.

[0036] The cloud form for implementing the conception rate estimation system 1 is preferably the typical SaaS (Software as a Service), but it may also be in the form of PaaS (Platform as a Service), HaaS (Hardware as a Service), or IaaS (Infrastructure as a Service).

[0037] <Data used> The conception rate estimation system 1 of the present invention can calculate the conception rate Pimp by inputting items related to at least one of the individual factors and milk yield factors of the estimated individual before the conception operation, and the environmental factors at the time of the desired estimation.

[0038] Individual factors include vital data obtained from the components of milk extracted from each individual dairy cow. By analyzing the components in the milk, vital data can be obtained without taking blood samples.

[0039] Specifically, these items include milk urea nitrogen (MUN: Milk Urea Nitrogen), protein / fat ratio (P / F: Protein / Fat), degraded intake protein (DIP: Degraded Intake Protein), and undegraded intake protein (UIP: Undegraded Intake Protein). There is an appropriate range for the concentrations of these items in milk for healthy dairy cows. These vital data are items included in individual factors, and multiple items may be used in one estimation.

[0040] It is known that MUN and P / F, among others, are difficult to conceive if they are out of the appropriate range. Therefore, it is preferable to use items such as MUN and P / F as individual factors.

[0041] Data relating to each individual dairy cow, such as registration number, pedigree, parity, days since calving, milk yield, weight, medical history, owner, rearing place, type and ratio of feed, etc., are managed and collected as individual data. Of these data, data that changes over time, such as parity, days since calving, milk yield, weight, and medical history, may be updated daily or at regular intervals, such as every three days or every week.

[0042] Additionally, the composition of feed that was given to the cow in the past, the number of feedings, etc. may be stored as feed data. These data may also be used as items included in the individual factors. Although milk yield is an individual factor, it may be excluded from the individual factors because it is used in the milk yield factor described later.

[0043] Items belonging to the milk volume factor include milk volume capacity, which is expressed as the ratio of the milk volume of the individual to be estimated to the reference milk volume (milk volume / reference milk volume). It may also be said to be only milk volume capacity. The reference milk volume is the milk volume of the population to which the individual to be estimated belongs. The milk volume of the population is not particularly limited as long as it is a value that can represent the milk volume of the entire population. Therefore, there may be multiple milk volume capacities depending on how the reference milk volume is determined. Note that in the logistic regression model, multiple milk volume capacities calculated using different reference milk volumes may be used as items for calculating partial regression coefficients.

[0044] The reference milk yield may be, for example, the maximum amount, the median, the minimum amount, or the average amount of the population in a certain period of time. The average milk yield per cow in the population in the month before the conception operation is easy to calculate and can be easily used as the reference milk yield.

[0045] Furthermore, the "milk yield of a population" taking into consideration the conditions at the time of milking, such as parity and days after calving, and environmental factors, such as temperature and humidity, may be used as the standard milk yield. N EY (referred to herein as "estimated milk yield") N EY".) can be used as a "population milk yield" that takes into account the state of the cow at the time of milking and environmental factors. Note that N is used to limit the number of days after parturition. Therefore, the estimated milk yield is expressed as N It can be written as "EY" or simply "EY."

[0046] There are various methods for calculating the standard milk yield. N The main approaches considered, including those of EY, are described in more detail below.

[0047] The environmental factors are items related to the environment in which dairy cows are raised. Among these, the climate data includes items such as temperature (air temperature), humidity, hours of sunlight, wind direction, and wind speed, and are known to have an effect on dairy cows. It may also include the temperature (air temperature) inside the cowshed and the humidity inside the cowshed. It may also include processed data of the climate data, such as THI (Temperature Humidity Index). Note that THI is an index expressed by equation (3), as an example. The air temperature in equation (3) is the same as the temperature, the temperature inside the cowshed, etc. in this specification.

[0048]

number

[0049] Furthermore, the climate data may not only be the climate inside and outside the barn, but may also be the climate within a 1 km square area of ​​the farm, the regional climate, or climate data from a national weather forecast service.

[0050] <Processing flow> Fig. 3 shows the process flow (main flow) of the conception rate estimation system 1. Note that the conception rate estimation system 1, which implements the conception rate estimation method according to the present invention, is mostly performed by software operations. Therefore, a "unit" corresponding to each process may be provided in the computer.

[0051] When the conception rate estimation system 1 is started (step S100), an end determination is made (step S102). The end determination may be made based on the user's intention, such as to stop estimating the conception rate. If it is to be ended (Y branch of step S102), the conception rate estimation system 1 is stopped (step S104). If not (N branch of step S102), the process proceeds to step S106.

[0052] The next process is to determine the population (step S106). Dairy cows are raised in groups. In particular, in large-scale farms, multiple dairy cows are often grouped together in each barn or breeding block. In these groups, the degree of stress experienced by each dairy cow varies depending on the characteristics of the dairy cows living together.

[0053] Therefore, even for the same individual, the feeding habits of the individual may differ depending on the group to which it belongs, which may affect conception. Also, by collecting data on only dairy cows with specific behavioral patterns from different groups, changes in conception rates may be observed. In addition, feed ingredients may be intentionally changed for each group. Therefore, the population is specified so that the evaluation of the individual to be estimated is within the group to which the individual belongs.

[0054] A group of dairy cows that are actually raised as one unit is called a cow herd. A population is a group of dairy cows that is used as data for the logistic regression analysis performed in the present invention. Usually, a population is often used as the same as a cow herd, but a population can be formed by including different cow herds or by selecting only specific dairy cows from different cow herds.

[0055] In other words, the method of determining the population may be arbitrary. Different herds of cattle may be grouped together as a population to calculate the conception rate Pimp. A population may be formed by collecting only individuals with the same parity. A population may also be formed by only individuals with similar timing (season or climate) for carrying out the conception work. The process of determining the population (step S106) may be performed by user input.

[0056] For a specified population, information on past births of dairy cows belonging to the population, milk yield, milk components, and environmental data when these were measured are collected and processed. Such data can be collected from memory 14 or the like where it has been stored in advance.

[0057] Next, explanatory variables are determined (step S108). The explanatory variables are selected from data on whether or not conception has been confirmed for each dairy cow in the determined population. This is because whether or not conception has been confirmed is the objective variable in logistic regression analysis. At least one of individual factors, milk yield factors, and environmental factors is selected as the explanatory variable that explains this objective variable (whether or not conception has been confirmed).

[0058] The process of determining explanatory variables (step S108) is explained in Fig. 4. When the process moves to the flow of determining explanatory variables (step S108) with reference to Fig. 4, the user is asked to select individual factors (step S130), milk yield factors (step S134), and environmental factors (step S138). If the user wishes to input each factor, the user selects the Y branch for each decision.

[0059] If an individual factor is to be input (Y branch at step S130), the individual factor is input (step S132); if a milk yield factor is to be selected (Y branch at step S134), the milk yield factor is input (step S136); if an environmental factor is to be input (Y branch at step S138), the environmental factor is input (step S140). If no factor is to be selected, the N branch may be selected at steps S130, S134, and S138. The input of milk yield factors (step S136) will be described in more detail later.

[0060] Then, when the input of each factor is completed, the process returns to the main routine (step S142). Note that the explanatory variables selected in step S108 are always associated with the result of conception success or failure (objective variable), and the process returns to the main routine. Referring again to FIG. 3, when step S108 is executed, a data set of explanatory variables and objective variables for obtaining partial regression coefficients from a logistic regression model is obtained.

[0061] Next, a logistic regression model (referred to as "L regression model" in Figure 3) is created from the explanatory variables and objective variables obtained in step S108 (step S110). A specific example is shown in Figure 5. Referring to Figure 5, a case is shown in which the explanatory variables are MUN and P / F as individual factors, milk yield capacity with the average milk yield of the entire herd one month prior to conception as the standard milk yield as milk yield factor, and THI as environmental factor.

[0062] Population C AAssume that there are k dairy cows, C1 to Ck, in the data set. These dairy cows have given birth and have the results of conception or not. Note that data on different parity numbers for the same dairy cow may also be available. In that case, they may be treated as separate data.

[0063] Data on MUN, P / F, milk yield potential before conception, THI at the time of conception (here, the day of conception), and conception success or failure are collected for each dairy cow. These data can be measured during the reference period for each dairy cow. Although it is preferable to use values ​​at the time of conception for environmental factors, average values ​​at a certain point in the reference period or for a certain period during the reference period may also be used.

[0064] The objective variable is the success or failure of conception. Success is usually represented by "1" and failure by "0". As it is a binary value, the opposite is also possible. Data is collected for each dairy cow, with the items MUN, P / F, milk yield potential, THI, and success or failure of conception forming one set. This becomes the original data LD1 for the logistic regression analysis. In other words, the original data LD1 is a collection of explanatory variables and objective variables. The result of step S108 can be said to be the original data LD1 for the logistic regression analysis.

[0065] Based on these original data LD1, the partial regression coefficients β1 to β4 and the intercept β0 can be obtained for each of the items MUN, P / F, milk yield potential, and THI from the logistic regression model of equation (4). The partial regression coefficients can be obtained by a known method. In this specification, the partial regression coefficients include the intercept β0.

[0066] 3 again, once the partial regression coefficients have been found in step S110, explanatory variables for the individual to be estimated are input (step S112). For example, these are individual factors (MUN, P / F) and milk yield factors (milk yield capacity) for the individual to be estimated in the reference period, and environmental factors (THI) at the time when the individual to be estimated is scheduled to carry out the conception work. The time when the conception work is scheduled to be carried out can be input based on forecast information such as a weather forecast.

[0067] Next, the conception rate Pimp is calculated from the conception rate calculation formula shown in (2) (step S114). A specific example is shown in FIG. 6. The logistic regression model (formula (4)) is shown again. The individual to be estimated is now Cs. The explanatory variables of the individual Cs to be estimated, namely MUN(Cs), P / F(Cs), milk yield capacity (Cs), and the predicted THI (referred to as "THI(Dday)") on the day when the individual Cs to be estimated undergoes conception work (referred to as "Dday") are input (FIG. 3: step S112). The conception rate calculation formula (5) is calculated from these explanatory variables and the partial regression coefficient, and the conception rate Pimp is obtained (FIG. 3: step S114).

[0068] Referring again to FIG. 3, next, a question is asked as to whether there are any other individuals to be estimated (step S116). This is a question as to whether data on the individuals will be re-input. If re-input is to be performed (Y branch at step S116), the process returns to step S112. If not (N branch at step S116), the process returns to step S102. This transition in the process allows the user to select whether to end the process (step S104) or to reselect the population (step S106).

[0069] As described above, the conception rate estimation method according to the present invention is implemented by the conception rate estimation system 1 executing the process of Fig. 3. For example, if a certain number of dairy cows are to be impregnated each month, the conception rate Pimp can be calculated for each dairy cow. This result also gives the probability of failure to conceive (1-Pimp), which can be used as a guide to determine how many cows will ultimately be impregnated this month.

[0070] <Milk yield potential in milk yield factors> In determining explanatory variables (step S108) in Fig. 4, for the milk yield factor (step S136), milk yield potential is an item of the original data LD1 for creating the logistic regression analysis. Milk yield potential is calculated by the milk yield of the individual to be estimated / the reference milk yield.

[0071] Figure 7 shows more detailed processing of step S136 in Figure 4. When the milk yield factor is selected, first, the average milk yield ΣY / r of the individual to be estimated during the reference period is calculated (step S150). Here, ΣY is the total milk yield of the individual to be estimated during the reference period, and r is the number of days in the reference period. In other words, the average milk yield per day of the individual to be estimated is calculated.

[0072] Next, the standard milk volume StY is calculated (step S152). The standard milk volume StY may be a representative value that reflects the milk volume of the population. As described above, the maximum, minimum, median, etc. of the milk volume of the population can be used. However, among them, the so-called average value can be used as a suitable method. The average milk volume of the population that can be preferably used will be described in detail later.

[0073] Since the standard milk volume StY is calculated in step S152, the milk volume capacity is calculated by milk volume / standard milk volume (ΣY / r / StY) (step S154). Since the milk volume capacity is calculated in this way, the process returns to step S138 (FIG. 4) (step S156).

[0074] Next, the calculation of the standard milk volume StY (step S152) in FIG. 7 will be described in detail. FIG. 8 conceptually shows the original data LD1 collected when calculating the conception rate Pimp of the estimated individual. The time axis progresses from left to right on the page. The white triangle represents the time of the conception work of the estimated individual. Whether the estimated individual will be conceived or not has not yet been determined, as the conception work has not yet occurred. A dashed line with a cross in a circle is shown to the right of the white triangle, indicating that the success or failure of conception has not yet been determined. The time of the conception work is referred to as "Dday". This may usually be expressed as "day".

[0075] The line to the left of the time of conception (white triangle) represents the reference period. The estimation date is the day immediately prior to the time of conception. Of the explanatory variables for the individual to be estimated, individual factors and milk yield factors will be data from this reference period. It is preferable to find the average value during the reference period as the average value per cow, or the average value per day, or the average value per cow per day. However, data on individual factors and milk yield factors may be obtained from the reference period by other methods. A suitable reference period is around two weeks to one month. It is even more preferable if data can be collected for the three days immediately prior to the time of conception.

[0076] The data enclosed in the dashed box is the original data LD1 collected to calculate the conception rate Pimp of the estimated individual. Data on k dairy cows, C1 to Ck, is collected here. These are dairy cows that belong to the population. Each dairy cow has had experience with conception work in the past, and there is also data on whether they were successful or not. They are arranged in chronological order from C1 to Ck in the conception work.

[0077] The time of conception for each cow in the original data LD1 is shown as a black triangle. The confirmed success or failure is shown as a solid line with a cross inside a circle. Each cow also has a reference period before conception.

[0078] The data collection period for the original data LD1 is approximately 3 to 5 years prior to the time of carrying out the conception work for the estimated individual. This is because dairy cows can become pregnant and give birth for approximately 3 to 5 years, and prior to that time the members of the herd will have changed. However, there are cases where it is desired to investigate dairy cows of the same lineage, so data going back more than 3 to 5 years is not excluded.

[0079] In addition, the data on milk yield capacity of dairy cows in the original data LD1 collected when calculating the standard milk yield StY also requires the standard milk yield StY. Therefore, when calculating the standard milk yield StY for each dairy cow, data equivalent to the original data LD1 shown in FIG. 8 is also required.

[0080] That is, each dairy cow in the population selected for the original data LD1 also requires data of the dairy cow prior to the time of its own conception work. More specifically, to obtain the milk yield capacity of dairy cow Ck in Fig. 8, data from 3 to 5 years prior to the time of the conception work of dairy cow Ck is required. In addition to the data related to the conception work shown in Fig. 8, data on the daily milk yield of each dairy cow during the data collection period is also recorded and may be included in the original data LD1.

[0081] The main average values ​​that can be adopted as standard milk yield (StY) are as follows: (M1) The average milk yield of the population during the reference period, which is called the average MYs. (M2) The average milk yield of the population in a climate similar to that in which the estimated individual is impregnated. This is called the average MYcs. (M3) The average milk yield of each individual cow in the population during the reference period, which we will call MYrs. (M4) The average milk yield during the reference period for cows impregnated in a climate similar to that in which the subject was impregnated. This is called MYrcs. (M5) Estimated milk yield N EY. (M6)Others.

[0082] The above average values ​​are collectively referred to as the "average milk volume of the population." Figure 9 shows a flow of the process of calculating the standard milk volume StY (step S152 in Figure 7). When the process of calculating the standard milk volume StY starts (step S152 in Figure 9), the average value MYs (step S170), the average value MYcs (step S174), the average value MYrs (step S178), the average value MYrcs (step S182), the estimated milk volume N A selection step of EY (step S186) is provided. When selecting these average values, the Y branch of each step is selected, and each average value is set as StY (steps S172, S176, S180, S184, and S188).

[0083] Note that there may be an average milk volume other than the above five average milk volumes (steps S190 and S192). Also, multiple standard milk volumes StY may be selected, and milk volume capacity may be calculated for each standard milk volume StY. In this case, the standard milk volumes StY in steps S172, S176, S180, S184, S188, and S192 are differentiated from standard milk volumes StY1, standard milk volumes StY2, etc. In other words, when multiple standard milk volumes StY are selected, the standard milk volumes StY are not overwritten. Each average value is described in detail below.

[0084] <Average MYs> First, Fig. 10 shows the average milk yield MYs of the population during the reference period of (M1) (step S172 in Fig. 9). This is the average milk yield of the dairy cows (C1 to Ck) belonging to the original data LD1 during the reference period of the estimated individual. Data related to the conception work of each dairy cow shown in Fig. 8 is not used.

[0085] The year in which the conception work will be carried out is assumed to be a certain month in 2023. The symbol YCi represents the milk yield of the i-th dairy cow Ci during the reference period (reference period of the individual to be estimated). Therefore, the total milk yield of the population is ΣYCi. Note that Σ represents the sum from i=1st to i=kth. This also applies to the following explanations. In Figure 10, the data on milk yields of each dairy cow during the reference period is collectively represented by the symbol YCBR.

[0086] To calculate the average, the total milk yield can be calculated as the milk yield per cow, or the data can be calculated as the data per day and per cow. In other words, the "average milk yield of the population" is the average total milk yield per cow during a certain period of time, or the average per day and per cow. It can also be simply the average total milk yield per day of the cows in the population, or the average over several days, weeks, or months.

[0087] In the following explanation, the reference period is r days, and the average value per day per cow (there are k dairy cows) is calculated. That is, the average value MYs (= reference milk yield) in the case of Figure 10 is given by equation (6). This average value MYs is easy to calculate and reflects the characteristics of the estimated individual relative to the population, but does not take into account the climate before conception or the number of days after calving. The "2023" above the summation formula Σ represents the year of the total milk yield. The same applies below.

[0088]

number

[0089] <Average value MYcs> Next, the average milk yield MYcs (step S176 in FIG. 9) of a population in a climate similar to that in which the estimated individual (M2) undergoes conception work will be explained with reference to FIG.

[0090] In this method, the average value MYcs is the average value of milk yield of all dairy cows in the population during a similar reference period. The "similar reference period" refers to a period equivalent to the reference period having a climate similar to that of the reference period of the estimated individual, including past data in the original data LD1. The "similar period" is preferably a period that is 0.5 to 1.5 times the reference period. The "similar reference period" may also include the reference period.

[0091] A "similar climate" is one in which the average temperature, average humidity, or average THI during that period falls within ±20% of the difference between the annual maximum and minimum values. The starting date of the "similar reference period" can be any period in which a similar climate exists and the length of the similar reference period can be secured. Note that the "similar reference period" does not exclude the same past calendar as the reference period of the estimated individual.

[0092] In Figure 11, the average value includes not only the milk yield at the time of conception in 2023, when conception will be performed, but also the milk yield of past populations with similar climates, such as 2022 and 2021. This is because if the calendar is the same, the climate is assumed to be similar. This average value MYcs can be said to be a value that takes into account the differences in the climate in which conception will be performed. The average value MYcs (= reference milk yield) in the case of Figure 11 is given by formula (7). The reason for dividing by "3" before the square brackets is because the average of the total milk yield for the three years from 2021 to 2023 is to be calculated. Therefore, it can be said that the total milk yield is divided by the number of years calculated.

[0093]

number

[0094] <Average MYrs> Next, the average milk yield MYrs (step S180 in FIG. 9) during the reference period (called the "individual reference period") of each dairy cow belonging to the population (M3) will be explained using FIG. 12. This method is the average milk yield during the individual reference period, which is a certain period prior to the conception work performed by each dairy cow that became the original data LD1 and is approximately the same period as the reference period. Here, the "similar period" may be interpreted in the same way as the average milk yield MYcs.

[0095] Each dairy cow, C1-Ck, has had conception work done in the past, and the milk volume during the individual reference period prior to the conception work was recorded. If the milk volume during the individual reference period r (days) of the i-th dairy cow is CirY, then the total milk volume is expressed as ΣCirY. The average value (=standard milk volume) is calculated based on this total milk volume. Considering that conception work is often carried out 40-60 days after calving, this method can be said to be an average value that takes into account the number of days after calving. The average value MYrs in the case of Figure 12 is expressed by equation (8).

[0096]

number

[0097] <Average value MYrcs> Next, the average milk yield MYrcs (step S184 in FIG. 9) during the reference period of dairy cows that were impregnated in a climate similar to that in which the estimated individual (M4) was impregnated will be explained using FIG.

[0098] This average value is the average value of milk yields during the individual reference period of dairy cows in the population that have individual reference periods in the similar reference period in which the climate is similar to that of the reference period of the individual to be estimated in the original data LD1. In other words, it is the average value of milk yields during the individual reference period of each dairy cow that has an individual reference period in the similar reference period.

[0099] In Figure 13, if the estimated individual undergoes conception at some point in 2023, the total milk volume C5rY+CkrY is calculated as the milk volume of a dairy cow in the original data LD1 (here, C5) that underwent conception at the same time (similar climate) in 2022 and 2021, and a dairy cow in the original data LD1 (here, Ck) that underwent conception at the same time in 2021, and the average value is calculated from this.

[0100] In addition, Figure 13 shows formula (9) for calculating the average value MYrcs using the general formula, assuming that there were kb dairy cows in 2021 and ka dairy cows in 2022 during the same period. In addition, in formula (9), Σ means the corresponding ka dairy cows in the population. This method can be said to be an average that takes into account the number of days since calving and the climate in which the conception work is performed. In addition, here, dairy cows that were conception work performed in a similar climate in the original data LD1 are selected, but the original data LD1 may be created by collecting only dairy cows that were conception work performed in a climate similar to that in which the estimated individual will be conception work.

[0101]

number

[0102] <Calculating estimated milk yield> Next, the estimated milk yield of (M5) N The process of EY (step S188 in FIG. 9) will now be described. NBy carrying out the EY calculation process, the factor parameter P related to milk yield and the estimated milk yield that can be milked from a dairy cow on the Nth day after parturition in the population to which the estimated individual belongs can be calculated. N The relationship between milk yield and EY (kg) can be obtained. Here, the factor parameter P is selected from among farm data including individual factors such as climate data (environmental factors), individual data such as the weight of the dairy cow itself, feed intake, and feed data such as the type of feed consumed, which are thought to explain milk yield well.

[0103] The factor parameter P may be directly measurable items or may be data obtained by processing data of directly measurable items (for example, THI). N You can also search for it during the process of calculating EY. For example, if you specify a value for THI, you can obtain the relationship between the number of days since calving N and the milk yield when THI is at that value (which may have a range). Estimated milk yield N Once EY is obtained, the estimated milk yield of the factor parameter P corresponding to the conception operation at the same postpartum day N as the estimated individual's postpartum day N is calculated. N EY is the standard milk yield StY.

[0104] Figure 14 shows a flow when the estimated milk volume EY is selected as the standard milk volume StY. When the process of taking the estimated milk volume EY as the average value is selected (step S188 in Figure 9), the calculation conditions for the estimated milk volume EY are input (step S202). The calculation conditions include the interval for creating the interpolation formula, the factor parameter P for creating the regression formula, etc.

[0105] Next, an interpolation formula is created (step S204). The interpolation formula is explained in FIG. 15. In FIG. 15, the horizontal axis is the number of days since calving N (days), and the vertical axis is the actual milk yield RY (kg). Note that, for the estimated milk yield EY, an interpolated milk yield (interpolated milk yield) and a milk yield calculated by a regression formula (estimated milk yield) are defined, so the milk yield actually obtained by milking is called the "actual milk yield RY" to distinguish them.

[0106] For example, this is data for a certain period (for example, milk volume on August 1, 2022) of dairy cows belonging to the population. Each point plotted here is the actual milk volume RY for each dairy cow, so it is a scatter plot. However, because it is the actual milk volume RY, there are also ranges that cannot be plotted. This is the case when there is no dairy cow with the corresponding number of days since calving N. In Figure 15, this is represented by the no-data area VR.

[0107] In this way, if there are gaps in the farm data, there will be a lack of data when creating the regression equation later, and the accuracy of the regression analysis will decrease. Therefore, this scatter diagram is approximated with an appropriate function. A suitable function to be used is the WOOD curve. The WOOD curve is a curve expressed by equation (10), and is well known as a curve that represents milk yield Y against number of days since calving N. Note that, in order to interpolate an actual scatter diagram, it is not necessary to be limited to the WOOD curve, and other functions may be used.

[0108]

number

[0109] Here, A, B, and C are constants, Y is the interpolated milk yield, and N is the number of days since calving. The constants A, B, and C can be determined so as to fit this Wood curve to the scatter plot using the least squares method. In this way, fitting the relationship between the number of days since calving N and the interpolated milk yield Y with a continuous function is called "creating an interpolation equation." The equation used to interpolate the scatter plot can be more generalized and expressed as equation (11).

[0110]

number

[0111] That is, in the interpolation formula (11), the interpolated milk yield Y is expressed as a function of the number of days since calving N. As the form of the function, a WOOD curve can be suitably used, but it is not limited to this. The interpolation formula creation process (step S204) in FIG. 14 is a process for determining the interpolation formula (11) from the scatter diagram of the actual milk yield RY as described above.

[0112] [Create Interpolation Formula] FIG. 16 shows the process of creating an interpolation formula (step S204 in FIG. 14). When the process of creating an interpolation formula starts (step S204), data on the number of days since calving N and the actual milk yield RY are extracted (step S230) from the population determined in step S106 in FIG. 3 (processing flow of the present invention). These can be called actual data. Specifically, a scatter plot may be drawn. Here, actual data is a set of the number of days since calving N and the actual milk yield RY for each dairy cow.

[0113] Next, an interpolation formula that best reflects the actual data of the number of days since calving N and the actual milk yield RY is obtained. For example, the least squares method using the WOOD curve shown above is applied to determine the constants A, B, and C (step S232) (see also Figure 15). This can be said to be a process of finding a function to fit the actual milk yield RY. Then, an interpolation formula of equation (11) is obtained (step S234). This interpolation formula gives the interpolated milk yield Y. After that, the process returns to the EY calculation flow (Figure 14) (step S236).

[0114] An interpolation formula can be created for each day of the dairy cows in the population for the period of the original data LD1, because milking occurs almost every day. However, if there are no major changes in the environment or in each individual dairy cow, the average of the actual data for every three days or every week can be considered as the actual data to create the interpolation formula (11).

[0115] Also, taking a broader view of the time axis, an interpolation formula may be created by regarding the average value of one month's actual data as the actual data. More specifically, the average monthly milk yield of a certain dairy cow is taken as the actual milk yield RY of that dairy cow. Also, the number of days since calving N of that dairy cow is taken as the average for that month (i.e., if N=10 days at the beginning of the month, the number of days since calving N for that month is taken as 25 days). These may be determined in "Input calculation conditions" (step S202) in FIG. 14.

[0116] Continuing to refer to Figure 14. Once the interpolation formula has been created, it is determined whether or not to perform factor analysis (step S206). This determination can be made by the user of the conception rate estimation system 1 via the terminal 10. Factor analysis is a determination as to whether or not to investigate the factor parameter P that can best explain the interpolated milk yield Y obtained from the interpolation formula (11) from within the farm data. For example, it is used when the conception rate estimation system 1 is used for the first time, or when the population is changed significantly.

[0117] If the factor analysis is to be performed (Y branch at step S206), the factor analysis process is performed (step S208). If the factor analysis is not to be performed (N branch at step S206), the process proceeds to the next step. The case where the factor analysis is to be performed (Y branch at step S206) will be described in detail with reference to FIG. 20.

[0118] Next, a regression equation is created (step S210 in FIG. 14). The creation of the regression equation is explained in FIG. 17. For example, the interpolated milk yield Y of a dairy cow with N days since calving for each month can be calculated by the interpolation equation (11) (FIG. 17(a)) shown in FIG. 15. Here, the interpolation equation is expressed in the general form Fw(), and the interpolated milk yield Y is the average milk yield for each month. Therefore, Y 1月 represents the interpolated milk yield in January, and the formula for calculating the interpolation formula (11) for dairy cows with N days after calving is Fw 1月 This is represented as (N).

[0119] Here, the number of days after delivery is 50 days. 50 The interpolated milk yield of dairy cows 50 days after parturition in each month from January to December is Fw 1月 (N 50 ), Fw 2月 (N 50 ), ..., Fw 12月 (N 50 ) is calculated. Of course, Fw m For (N), an interpolation formula is created for each month ("m" represents "month"). Note that here, an example of an interpolation formula for each month for the year 2022 where the number of days since delivery N is 50 days is shown, but the interpolation formula (11) can be calculated in this manner for each month and for each number of days since delivery N for all years used as the original data LD1.

[0120] The factor that can best explain the interpolated milk yield Y for each month is determined as the factor parameter P. For dairy cows belonging to the population, many measurement items are recorded in the original data LD1, so the best factor parameter P can be found by performing the factor analysis process in step S208 of FIG. 14. If the factor parameter P to be used for calculating the estimated milk yield EY has already been determined, use that parameter. There does not have to be just one factor parameter P.

[0121] The creation of the regression equation in step S210 in Fig. 14 involves obtaining a regression equation by the least squares method using the interpolated milk yield Y for each month and one or more factors (factor parameters) (Fig. 17(c)). As is well known, the regression equation is expressed as in equation (12).

[0122]

number

[0123] where EY is the estimated milk yield and x1, x2, . . . x k is a factor (factor parameter P), and a1, a2, . . ., a k , where c is a constant.

[0124] Figure 17(c) shows an example of a regression equation when there is one factor. From the interpolation equation (11) shown in Figure 17(a), the interpolated milk yield Y m (See Figure 17(b)). Here, m represents the month. The interpolated milk yield Y m may be the average of the first three days of the month, or the data for a specific day may represent the amount of interpolated milk for that month.

[0125] Next, by sorting these monthly interpolated milk yields Ym by factor parameter P (here, for example, temperature at noon), the graph in Figure 17(c) can be obtained. Note that here, we will show an example of the results assuming that the estimated milk yield EY of dairy cows 50 days after calving in each month can be well explained by the temperature at noon. The regression equation is expressed by equation (13).

[0126]

number

[0127] Here, x1 is the temperature at noon, and EY is the estimated milk yield. The estimated milk yield EY can be said to be found by regression analysis with the explanatory variables as factor parameters and the interpolated milk yield Y as the objective variable. Note that such a regression equation is created for each N number of days after calving. In other words, if the final day of N days after calving is 300 days, then a regression equation from 1st to 300 days later can be obtained (see Figure 17(e)).

[0128] When there are multiple factors, it is not possible to describe it two-dimensionally as in Figure 17(c), but if a multiple regression equation is obtained using multiple factor parameters, it may be possible to closely approximate the relationship between the number of days since calving N and the interpolated milk yield Y. The regression equation obtained in this way is expressed as equation (14) (see Figure 17(d)).

[0129]

number

[0130] Where: N EY is called the estimated milk yield EY for a dairy cow on postpartum day N with factor parameters P.

[0131] [Create regression equation] Figure 18 shows a flow of the process of creating a regression equation. When the process of creating a regression equation (step S210) in the EY calculation flow shown in Figure 14 is started, the process jumps to Figure 18, where the factor parameter P is input (step S250). The factor parameter P can be input from the terminal 10. In other words, it is input by the user of the conception rate estimation system 1. Here, it is assumed that the factor parameter P is p1 (temperature).

[0132] Next, for each fixed period m (defined in step S202) specified for the factor parameter p1, the interpolated milk yield Ym for each postpartum day N is calculated using the interpolation formula (11) (step S252). That is, the interpolated milk yield Ym in FIG. 17(b) is calculated. This allows the scatter diagram in FIG. 17(c) to be plotted.

[0133] Next, a regression equation is obtained by using the factor parameter p1 as an explanatory variable and the interpolated milk yield Ym as a target variable (step S254). That is, for the factor parameter p1 at this time, the relationship (14) (FIG. 17(d)) ​​of the milk yield when the number of days since parturition N is the same can be obtained. The regression equation can be obtained by any known method.

[0134] Using equation (14), it is possible to create a number for postpartum days N from 1 to the final day (the final day here means the longest day since parturition) (see FIG. 17(e)). Regression equation (14) is a summary of FIG. 17(e). Once the regression equation has been found, the process returns to the routine of finding the estimated milk yield EY in FIG. 14 as the average value (step S256).

[0135] 14 again, once the regression equation (14) for calculating the estimated milk yield EY is obtained, the estimated milk yield EY that serves as the reference milk yield can be obtained by inputting the factor parameter P predicted at the time of conception into the regression equation (14) for the same number of days since parturition as the individual to be estimated (step S212). For example, if the number of days since parturition of the individual to be estimated is Ns and the factor parameter at the time of conception is PD, the estimated milk yield of the individual to be estimated is expressed by equation (15).

[0136]

number

[0137] Next, it is determined whether or not the factor parameter P is to be re-input (step S214). If re-input is to be performed (Y branch at step S214), the case will be described later. If re-input is not to be performed (N branch at step S214), the estimated milk volume is set to the standard milk volume StY. Ns EY is substituted (step S216) to obtain the estimated milk yield. NThe process returns to the routine (step S190 in FIG. 9) in which EY is set as the average value (step S218).

[0138] Figure 19 shows an example of an enlarged diagram of the estimated milk yield lactation curve M on which the estimated milk yield EY is plotted. The estimated milk yield EY is plotted for each number of days since parturition N. The factor parameter P shows a case where a multiple regression equation is calculated using two factors, p1 and p2. The number of days since parturition N was calculated using discrete values ​​(10 days, 20 days, 30 days, 50 days, 80 days, 100 days, 150 days, 200 days, 250 days), but it can also be calculated in daily units. Each estimated milk yield EY is calculated from a different regression equation (different number of days since parturition) represented by equation (14). See Figure 17(e)). In other words, the constants in these equations are different.

[0139] 14 again, if the factor parameter P is to be re-input (Y branch in step S214), step S206 is performed again. If the factor parameter P is to be re-input, the N branch in step S206 is selected, and the regression equation creation process (step S210) is performed. If the process is to be started from the factor analysis (Y branch in step S206), the process proceeds to step S208.

[0140] [Factor analysis] Figure 20 shows the process of factor analysis in step S208 in Figure 14. The factor analysis process is the same as the creation of the regression equation (step S210) up to a certain point. Specifically, steps S250, S252, and S254 shown in Figure 20 are the same as those in Figure 18. Therefore, the same step numbers are also used.

[0141] Therefore, the factor parameter P is input (step S250), the milk yield Ym for each number of days after calving N for each specified period m (which is determined in step S202 of FIG. 14) for the factor parameter P is calculated using the interpolation formula (11) (step S252), and a regression equation is calculated using the factor parameter P as the explanatory variable and the interpolated milk yield Ym as the target variable (step S254).

[0142] In the factor analysis, the actual milk yield RY for a certain period m is compared with the estimated milk yield EY obtained from the regression equation (step S260). Figure 21 illustrates this. The case where two factor parameters, p1 and p2, are selected is shown. For example, temperature and wind speed. The case where the certain period is four different days is shown. For example, it may be a representative day in spring, summer, autumn, and winter. Specifically, it is m1 month n1, m2 month n2, m3 month n3, and m4 month n4. The specific factor parameters p1 and p2 for these days are recorded. The factor parameters may be selected by performing a separate main factor analysis or by trial and error. If the factor parameters p1 and p2 are input, the estimated milk yield EY can be calculated from the regression equation (14).

[0143] The number of days since parturition N can be calculated from 1 to the maximum day, but here, the results are shown for four types: 30 days, 50 days, 100 days, and 150 days. The absolute value ERR of the difference between these values ​​and the actual milk yield RY at each date and time. 30 RY is the actual milk yield of a dairy cow on day N 30 after calving. 30 ERR indicates the absolute value of the difference between the estimated milk yield EY and the actual milk yield RY when the number of days since parturition N is 30 days. This ERR is also calculated when the number of days since parturition N is 50 days, 100 days, and 150 days. In the example described later, an example is shown in which the estimated milk yield EY and the actual milk yield RY are compared when the number of days since parturition N is 50 days.

[0144] The sum of the absolute values ​​ERR of the differences between the estimated milk yield EY and the actual milk yield RY for the four days is represented as the total error TΣ. In this manner, in step 260, the estimated milk yield EY and the actual milk yield RY are compared.

[0145] Referring again to Fig. 20, after comparing the estimated milk volume EY and the actual milk volume RY, a termination determination is made (step S262). The termination determination is made based on whether the factor parameter P input in step S250 is appropriate for the absolute value ERR of the difference between the estimated milk volume EY and the actual milk volume RY. Whether ERR is appropriate may be determined in advance or may be determined by the user.

[0146] If it is to be ended (Y branch at step S262), the process returns to the EY calculation flow (FIG. 14: step S212). If it is to be continued (N branch at step S262), the process returns to step S250 again, and the factor parameter P is re-input.

[0147] Referring again to FIG. 14, after the factor analysis (step S208), the process proceeds to the milk yield calculation step (step S212). After the factor analysis step (step S208), it is considered that a suitable factor parameter P has been found, and therefore it is considered that the step of calculating the estimated milk yield EY (step S212) may be performed. The steps thereafter are as described above.

[0148] Referring again to Fig. 9, in the process of calculating the reference milk volume, if "Other" (step S190) is selected (Y branch of step S190), a method of calculating the average value of the population other than steps S172, S176, S180, S184, and S188 is performed. Note that steps S190 and S192 may be omitted.

[0149] By executing step S152 for calculating the standard milk volume StY as described above, the average value related to the milk volume of the population is set as the standard milk volume, and processing is returned to the milk volume factor input in Figure 7 (step S136), and milk volume capacity is calculated (step S154 in Figure 7). [Industrial Applicability]

[0150] The present invention can be suitably used when determining conception rate based on individual factors of dairy cows, milk yield factors, and environmental factors. [Explanation of symbols]

[0151] 1. Fertility Estimation System 10 Terminal 12 Main unit 14. Memory 16. Ranch 18 External Information

Claims

1. Individual factors obtained from the milk components of dairy cows and A milk yield factor indicating the milk yield of the dairy cow relative to a representative value reflecting the milk yield of the population to which the dairy cow belongs; At least one of the environmental factors at the time of the conception work of the dairy cow is an explanatory variable; A step of obtaining partial regression coefficients using a logistic regression model from a data group having the success or failure of conception of the dairy cow as a response variable; A method for estimating a fertility rate comprising the step of determining the fertility rate of an estimated individual from the explanatory variables of the estimated individual belonging to the population and the partial regression coefficients.

2. 2. The method for estimating a conception rate according to claim 1, wherein the milk yield factor is a ratio of a reference milk yield, which is an average milk yield of the population, to the milk yield of the individual to be estimated.

3. The milk yield of the estimated individual is an average value of the milk yield during a reference period, which is a certain period prior to the conception operation; 3. A method for estimating fertility as claimed in claim 2, wherein said reference milk yield is the average milk yield of said population during said reference period.

4. The milk yield of the estimated individual is an average value of the milk yield during a reference period, which is a certain period prior to the conception operation; The reference milk yield is A method for estimating conception rate as described in claim 2, which is the average milk yield of the population during a similar reference period which is in a climate similar to that in which the estimated dairy cow undergoes the conception operation and which has a length similar to that of the reference period.

5. The milk yield of the estimated individual is an average value of the milk yield during a reference period, which is a certain period prior to the conception operation; The reference milk yield is 3. A method for estimating conception rate according to claim 2, which is an average value of milk yields during an individual reference period, which is a certain period prior to the conception operation, received by each dairy cow in the population.

6. The milk yield of the estimated individual is an average value of the milk yield during a reference period, which is a certain period prior to the conception operation; The reference milk yield is During a similar reference period having a length comparable to that of the reference period in a climate similar to that in which the estimated individual will undergo the conception operation, A method for estimating conception rate as described in claim 2, wherein the conception rate is the average milk yield of each dairy cow during an individual reference period which is a certain period prior to the conception operation that each dairy cow in the population underwent.

7. The milk yield of the estimated individual is an average value of the milk yield during a reference period, which is a certain period prior to the conception operation; The reference milk yield is obtained by a step of obtaining an interpolation formula for the relationship between the milk yield and the number of days since parturition of dairy cows belonging to the population; A step of calculating an interpolated milk yield corresponding to a factor parameter considered to be related to the milk yield of dairy cows belonging to the population from the interpolation formula; A step of obtaining a regression equation for each postpartum day with the factor parameters as explanatory variables and the amount of interpolated milk as a response variable; A conception rate estimation method as described in claim 2, in which the standard milk volume is determined as the estimated milk volume obtained from a process of calculating an estimated milk volume corresponding to the factor parameters when carrying out the conception work from the regression equation that coincides with the number of days since birth of the individual to be estimated.

8. 8. The method for estimating conception rate according to claim 1, wherein the individual factor is at least one of MUN and P / N.

9. 8. A method for estimating conception rate according to any one of claims 1 to 7, wherein the environmental factors include THI.

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