Method for estimating composition ratio by somatic cell count in cattle group

Abstract

To provide a method for estimating a composition ratio by somatic cell count in a cow group, enabling more accurate monitoring than monitoring individual cows, and thereby ensuring stable supply of raw milk.SOLUTION: A method for estimating a composition ratio by somatic cell count in a cow group, including: obtaining partial regression coefficients using a multiple regression model based on a data group, in which at least one of a cow group health factor derived from milk components of milk produced by the cow group, a cow group milk yield factor indicating a ratio of the cow group's milk yield during a specified period before milking to a representative value reflecting an expected milk yield of the cow group, and a cow group environmental factor during rearing of the cow group, is used as an explanatory variable, and a ratio of the number of cows indicating somatic cell counts within the specified range to the number of cows belonging to the cow group is used as an objective variable; and determining the ratio of cows indicating somatic cell counts within the specified range of the estimated cow group, based on the explanatory variable of the estimated cow group and the partial regression coefficients. The method allows estimating the ratio of cows indicating somatic cell counts within the specified range.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a method for classifying dairy cows in a herd according to their somatic cell counts and estimating their composition ratio. More specifically, the present invention relates to a method for estimating the composition ratio (head ratio) of dairy cows whose somatic cell counts are within a certain range. [Background technology]

[0002] Somatic cell count, formerly known as milk white blood cell count, is a collective term for the white blood cell count and exfoliated epithelial cells in milk. The somatic cell count increases due to stress and illness. The standard value for the effect of somatic cells ingested through milk on the human body has not yet been clearly defined. However, raw milk with a high somatic cell count is likely to be contaminated with pathogens or antibiotics.

[0003] Furthermore, an increase in somatic cell count not only reduces milk quality, but also leads to reduced milk yields, fewer calves, and ultimately expensive medical costs, which can result in significant losses for dairy farming operations.

[0004] One of the causes of an increase in somatic cell count is diseases around the udder. In particular, mastitis is known to be one of the factors that increase the somatic cell count. Since the increase in somatic cell count due to mastitis increases exponentially, the presence of one dairy cow with mastitis increases the total somatic cell count in the milk of a herd. Therefore, DNA markers and the like have been proposed to accurately diagnose mastitis (Patent Document 1). [Prior art documents] [Patent documents]

[0005] [Patent Document 1] JP 2023-141560 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]

[0006] Patent Document 1 attempts to determine the presence or absence of mastitis from the cytological viewpoint of dairy cows. However, an increase in somatic cell count can also occur due to diseases other than mastitis. For example, the increase can also occur due to stress caused by high temperatures in summer. In other words, it is not easy to determine whether the increase in somatic cell count is due to a fatal disease, seasonality, or other causes. In other words, the increase or decrease in somatic cell count for each individual does not necessarily reflect the tendency of the herd. [Means for solving the problem]

[0007] The method of estimating the composition ratio by somatic cell count in a herd of the present invention was devised in consideration of the above-mentioned problems, and does not directly estimate an increase or decrease in somatic cell count, but rather estimates the composition ratio (head ratio) of dairy cows whose somatic cell counts are within a certain range in the entire herd.

[0008] More specifically, the method for estimating the composition ratio by somatic cell count in a cattle herd according to the present invention includes the following steps: Herd condition factors obtained from the milk components of the milk produced by the herd; A herd milk yield factor indicating the ratio of milk yield of the herd in a certain period before milking to a representative value reflecting the expected milk yield of the herd; At least one of the cattle herd environmental factors during the rearing of the cattle herd is an explanatory variable; A step of obtaining partial regression coefficients using a multiple regression model from a data group in which the ratio of the number of dairy cows exhibiting a somatic cell count within a certain range to the number of dairy cows belonging to the herd is used as a response variable; The method is characterized by comprising a step of determining the proportion of dairy cows in the estimated herd that have somatic cell counts within a certain range from the explanatory variables of the estimated herd and the partial regression coefficients. Effect of the Invention

[0009] The method for estimating the composition ratio by somatic cell count in a herd according to the present invention does not directly estimate the increase or decrease in somatic cell count, but estimates the composition ratio (proportion of the number of heads) of dairy cows that have somatic cell counts within a certain range in the herd. This estimate can take into account parameters such as the number of calves throughout the year, seasonal changes, and type of feed, and indicates the proportion of abnormal somatic cell counts that may occur in the herd.

[0010] Therefore, when the composition ratio of dairy cows showing a somatic cell count equal to or higher than a certain value in the herd, as examined by the raw milk actually milked, increases from the estimated value obtained by the method for estimating the composition ratio by somatic cell count in the herd according to the present invention, it can be recognized that there is a sign of abnormality. In other words, more accurate monitoring can be achieved than by monitoring the somatic cell count of each dairy cow, and stable raw milk can be provided.

[0011] In addition, the method for estimating the composition ratio by somatic cell count in a herd according to the present invention can estimate the composition ratio of dairy cows whose somatic cell counts fall within a certain range, and can also estimate the composition ratio of dairy cows that are at risk of becoming abnormal before they clearly have an abnormal somatic cell count. Therefore, measures can be taken to reduce the somatic cell count before it becomes abnormal. [Brief description of the drawings]

[0012] [Figure 1] FIG. 1 is a diagram showing the steps of a method for estimating the composition ratio of somatic cell counts in a cattle herd according to the present invention. [Diagram 2] FIG. 1 is a diagram showing the configuration of a somatic cell count composition ratio estimation system that realizes a method for estimating the composition ratio by somatic cell count in a cattle herd. [Diagram 3] FIG. 1 is a flow diagram showing the processing flow of a system (main flow) for estimating the composition ratio by somatic cell count in a cattle herd. [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 multiple regression model, and the multiple regression model. [Figure 6]This figure explains the process of determining the proportion of dairy cows in an estimated herd with a linear score of 3 or higher using a somatic cell count calculation formula based on the explanatory variables of the estimated herd and partial regression coefficients obtained from the multiple regression model. [Figure 7] FIG. 13 is a flow chart showing the processing flow for inputting herd milk yield factors. [Figure 8] FIG. 13 is a flow chart showing the process for calculating the herd standard milk yield SgtY. [Figure 9] This figure explains an example of calculating the average value MYgcs when using the herd standard milk yield SgtY as the average value MYgcs of the milk yield of the estimated herd in a similar reference period in a climate similar to the one in which the estimated herd will be milked. [Figure 10] This is a flow chart showing the process of calculating the herd estimated milk yield GEY as the herd standard milk yield SgtY. [Figure 11] This is a flow chart showing the process of calculating the estimated milk yield NEY of each individual belonging to a herd in order to calculate the herd estimated milk yield GEY. [Figure 12] FIG. 13 is a diagram illustrating the process of creating an interpolation formula from actual milk volume RY. [Figure 13] FIG. 11 is a flowchart showing a process for creating an interpolation formula. [Figure 14] 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 15] FIG. 11 is a flowchart showing a process for creating a regression equation. [Figure 16] FIG. 13 is a diagram conceptually showing the relationship between postpartum days N and estimated milk yield NEY when factor parameters are p1 and p2. [Figure 17] FIG. 11 is a flowchart showing a process flow when performing factor analysis to find an optimal solution for factor parameters. [Figure 18] This is a diagram explaining the comparison of actual milk yield RY for a certain period m with estimated milk yield NEY obtained from the regression equation during factor analysis. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] The method for estimating the composition ratio of somatic cell counts in a herd of cattle 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.

[0014] <Outline of the method for estimating the composition ratio of somatic cell counts in cattle herds> An outline of the method for estimating the composition ratio by somatic cell count in a herd of cattle according to the present invention will be described. The somatic cell counts that divide the composition ratio can be set to any fixed range of values. This fixed range of values ​​is represented as "R[t0~t1]". For example, if the range of somatic cell counts is 20,000 cells / mL to 40,000 cells / mL, the "thousands / mL" will be omitted and it will be represented as R[20~40]". Note that "A~B" means "A or more, B or less" (both A and B values ​​are included in the range).

[0015] The fixed value range may also be expressed as a linear score. The linear score will be explained in detail in the explanation of formula (3). For example, a linear score of 3 indicates that the somatic cell count is in the range of 71 to 141 thousand cells / mL. Therefore, the linear score of 3 can be expressed as R[71 to 141]. It can also be said that the fixed value range is R[71 to 141].

[0016] Furthermore, the lower limit of the constant value range may be 0. In this case, it indicates that the somatic cell count is substantially equal to or less than the upper limit. For example, if the constant value range of the somatic cell count is "0 to 50,000 cells / mL", it is expressed as a constant value range R[0 to 50], which indicates that the count is 50,000 cells / mL or less. Similarly, the constant value range may have an upper limit of infinity. If the upper limit is infinity, it indicates that the somatic cell count is substantially equal to or greater than the lower limit. For example, if the somatic cell count is "50,000 cells / mL to infinity", it is expressed as a constant value range R[50 to ∞], which indicates that the count is 50,000 cells / mL or more.

[0017] Furthermore, since the linear score originally represents the range of somatic cell counts, a "certain value range of linear score 3 or more" represents a somatic cell count of 71,000 cells / mL or more, which is the lower limit of linear score 3. A certain value range of linear score 3 or less represents a somatic cell count of 141,000 cells / mL or less, which is the upper limit of linear score 3. These are represented as R[71~∞] and R[0~141], respectively. The following explanation will be given for a somatic cell count of linear score 3 or more (R[71~∞]).

[0018] In the present invention, the composition ratio RLSt of dairy cows with a linear score of 3 or more in a herd of cows that is the subject of estimation (hereinafter also referred to as an "estimation target herd") is the proportion (number ratio (%)) of dairy cows with a linear score of 3 or more among the dairy cows that make up the estimation target herd. The composition ratio RLSt of dairy cows with a linear score of 3 or more in an estimation target herd is also referred to as the composition ratio by somatic cell count RLSt. The composition ratio by somatic cell count RLSt with a linear score of 3 or more may also be referred to as the composition ratio by somatic cell count RLS3.

[0019] The composition ratio RLSt by somatic cell count is calculated using a multiple regression model from at least one of the following items: herd physical condition factors obtained from the components of milked raw milk, herd milk yield factors that indicate the ratio between the expected milk yield of the estimated herd and the actual milk yield milked from the estimated herd, and herd environmental factors such as temperature and humidity. Here, "calculation of the composition ratio RLSt by somatic cell count" means obtaining a value from the formula (2) described later, but may be called "estimation of the composition ratio RLSt by somatic cell count" to indicate future possibilities. Therefore, the "method for estimating the composition ratio by somatic cell count in a herd" according to the present invention may be called "method for calculating the composition ratio RLSt by somatic cell count" or "method for estimating the composition ratio RLSt by somatic cell count".

[0020] This is shown in more detail in Figure 1. The method for calculating the composition ratio RLSt by somatic cell count according to the present invention is roughly divided into two steps. In the first step, a multiple regression analysis is performed to correlate the composition ratio RLSt by somatic cell count, which is the objective variable, with at least one item belonging to the herd physical condition factors, herd milk yield factors, and herd environmental factors of the herd as explanatory variables from many data groups related to the farm. As mentioned above, the somatic cell counts that are the boundaries of the composition ratios are predetermined. For the multiple regression analysis, a multiple regression model expressed by equation (1) is used.

[0021]

number

[0022] The purpose of the first step is to obtain the partial regression coefficients β0, β1, β2, β3, etc. in equation (1). Therefore, the first step can be said to be a step of determining the partial regression coefficients using a multiple regression model. Also, although three factors, herd physical condition factors, herd milk yield factors, and herd environmental factors, are described here, it is sufficient to use at least one item belonging to these. An item is one of the measurement items belonging to herd physical condition factors, herd milk yield factors, and herd environmental factors.

[0023] In addition, a plurality of items selected from these factors may be used as explanatory variables. For example, the items Tma and Tmb included in the cow herd physical condition factors may be used as explanatory variables.

[0024] In the second step, the composition ratio IDRLSt by somatic cell count of the estimated herd is calculated from the partial regression coefficients obtained in the first step and the explanatory variables of the estimated herd. Specifically, the composition ratio IDRLSt by somatic cell count of the estimated herd is obtained using the formula for calculating the composition ratio RLSt by somatic cell count (formula (2)). Note that for the items related to herd environmental factors among the explanatory variables of the estimated herd, the values ​​of the items of environmental factors predicted on the milking day are used. Note that the herd physical condition factors, herd milk yield factors, and herd environmental factors of the estimated herd are defined as ID herd physical condition factors, ID herd milk yield factors, and ID herd environmental factors.

[0025]

number

[0026] In the explanation of formula (2) in Figure 1, the ID herd physical condition factors, ID herd milk yield factors, and ID herd environmental factors are data corresponding to the "current month," and the composition ratio IDRLSt by somatic cell count of the estimated herd corresponding to the "next month" is obtained. This means that the composition ratio IDRLSt by somatic cell count for the "next month" is predicted and calculated using the herd physical condition factors and herd milk yield factors that can be understood through the milking work of the "current month," and the herd environmental factors that represent the environment up until the time of the milking work of the "current month." Note that the herd environmental factors may use values ​​that assume the environment of the "next month" (for example, average values). Also, the unit is not necessarily "month."

[0027] <Explanation of period and timing> The terms "date to perform milking operation", "estimated date", and "reference period" used in the following explanation will be explained.

[0028] "Days when milking is performed" literally means days when milking is performed. Milking is performed almost every day, and sometimes milking is performed twice a day. In such cases, the milked raw milk may be considered to come from the same day when milking is performed. "Days when milking is performed" may be abbreviated to "milking days". Milking days may have a range. In other words, a specific day of the week or a specific day of the month may be treated as a milking day for that week or month. When there is a range for milking days, the average amount of milk pumped during that range may be treated as the milk volume on the milking day. For example, Wednesday of each week may be the milking day for that week, and the average amount of milk pumped for that week may be considered as the milk volume on that milking day. The milked raw milk is subjected to measurement of somatic cell count.

[0029] A certain period of time in the past, including the milking day, is called the "reference period." For dairy cow herd physical condition factors and herd milk yield factors, it is not possible to eliminate cases where the values ​​for only one day coincidentally become the same on that day, so it is preferable to use the average values ​​for a certain period. Therefore, the reference period is a certain period during which the herd physical condition factors and herd milk yield factors of the estimated herd before the milking day are observed.

[0030] The reference period is at least 60 days, preferably 30 days, more preferably 2 weeks, and most preferably 3 to 0. The last day of the reference period (the day closest to the milking day in the reference period) is preferably within 2 weeks of the milking day, more preferably within 1 week, and most preferably within 3 days, or may be the same day.

[0031] The "estimated date" is the date on which the RLS3 somatic cell count composition ratio is estimated. The "estimated date" is usually a date in the future from the end of the reference period.

[0032] Normally, the herd condition factors, herd milk yield factors, and herd environmental factors are used based on values ​​from the past day before the estimation date. For the herd environmental factors, values ​​assuming the environment on the estimation date (for example, average values) may be used.

[0033] <Linear score> The linear score LS is expressed by equation (3) and is calculated by adding 3 to the integer part obtained by rounding off the first decimal place of the logarithm of the somatic cell count. LCell is the number of somatic cells per ml counted in units of thousands. For example, if the number of somatic cells in raw milk is 100,000 / ml, LCell on the right hand side of equation (3) is 100,000 / 1000=100, the log part is 0, and the linear score LS is 3. In equation (3), SubRound() is a function that rounds off the number in parentheses to the first decimal place.

[0034]

number

[0035] The Linear Score LS is given an evaluation as shown in Table 1. That is, a Linear Score LS of 0 to 2 indicates a healthy cow, 3 to 4 indicates a cow requiring caution, and 5 to 9 indicates a high possibility of mastitis.

[0036] [Table 1]

[0037] <Hardware configuration> 2 shows the configuration of a system for estimating composition ratio by somatic cell count that executes a method for estimating composition ratio by somatic cell count in a cattle herd according to the present invention. The system for estimating composition ratio by somatic cell count 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).

[0038] The main body 12 is composed of a CPU. It may be a single 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.

[0039] 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 16 or the like where dairy cows are actually raised. Here, "installed" may mean that the terminal 10 is physically installed in the ranch 16, or that a person in charge or manager of the ranch 16 possesses a mobile communication terminal. Of course, the terminal 10 may be installed in a place other than the ranch 16. Furthermore, the ranch 16 may be any ranch where dairy cows are raised.

[0040] A plurality of farms 16 may be connected to the main body 12. The system 1 for estimating the composition ratio by somatic cell count collects a large amount of data related to factors of physical condition of the cow herd, factors of milk yield of the cow herd, and factors of the cow herd environment, and calculates the composition ratio by somatic cell count RLSt by analyzing the collected data, so it is desirable to collect a large amount of data. The main body 12 may also be connected to external information 18 via the Internet. This is because not only data obtained from the farms 16 but also information on nationwide weather can be obtained via the Internet and used.

[0041] The main unit 12 may obtain climate data from external information 18. In a closed-type barn, the environment inside the barn is controlled, and data related to cow herd 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.

[0042] Various types of weather-related data are provided, and these can also be used effectively. In particular, future predicted values ​​for herd environmental factors may be used to estimate the somatic cell count composition ratio RLSt. In such cases, external weather predictions can be suitably used. Furthermore, 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.

[0043] In this way, the somatic cell count-specific composition ratio estimation system 1 collects a large amount of data from various places and calculates the somatic cell count-specific composition ratio RLSt by analyzing the collected data, so a preferred embodiment is one in which the service is provided via a network. In other words, the somatic cell count-specific composition ratio estimation system 1 may be configured as a cloud.

[0044] The cloud form for implementing the somatic cell count composition ratio 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).

[0045] <Data used> The somatic cell count composition ratio estimation system 1 of the present invention can calculate the somatic cell count composition ratio RLSt, which is the composition ratio in a herd of dairy cows whose somatic cell counts fall within a predetermined range, by inputting items related to at least one of the herd physical condition factors and herd milk yield factors on the milking day of the herd to be estimated, and the herd environmental factors on the day to be estimated.

[0046] [Cattle herd] A herd is a group of multiple dairy cows. An abnormal increase in somatic cell count is often caused by environmental factors such as viral infection. Therefore, it is preferable for a herd to be made up of dairy cows that are raised in close proximity to each other. For example, it is possible to group cows in the same barn, in the same pen, or in groups that are close to each other in terms of milking order. On the other hand, it is not excluded to form a herd with dairy cows that are raised in different locations. Also, the entire farm can be treated as one herd.

[0047] It is preferable that the herds used as learning data when forming the multiple regression model contain a certain number of dairy cows. If the number of dairy cows in a herd is small, the composition ratio by somatic cell count RLSt will be high even if there is only one dairy cow with a linear score of 3 or more. It is preferable that the herds used as learning data consist of 10 or more cows, and more preferably 20 or more cows. It is also preferable that the number of herds (number of groups) used as learning data is as large as possible. This is because the accuracy of the multiple regression model will increase. The number of herds should be at least 10 or more, and preferably 20 or more.

[0048] [Factors of cow herd condition] A herd condition factor is the average value of vital data per cow obtained from the components of raw milk milked from each individual dairy cow in a herd. Therefore, a herd condition factor is assigned one value per item (vital data).

[0049] Specific items of herd condition factors include milk urea nitrogen (MUN), protein / fat ratio (P / F), degraded intake protein (DIP), and undegraded intake protein (UIP). There is an optimum range for the concentrations of these in milk for healthy dairy cows. These vital data are items included in herd condition factors, and multiple items may be used in one estimation.

[0050] In particular, MUN and P / F values ​​outside the optimum range are indicators of nutritional deficiencies in feed and poor physical condition, which are known to lead to a decline in the physical strength of cows and a tendency for somatic cell counts to increase. Therefore, it can be said that it is appropriate to use items such as MUN and P / F as factors of herd physical condition. Although somatic cell count is vital data, it is used as a target variable, so it is preferable to exclude it as an explanatory variable. Also, a "g" is sometimes added to the right end of the abbreviations, such as "MUNg" and "P / Fg," to indicate that they are herd physical condition factors.

[0051] In addition, data relating to each individual dairy cow belonging to the herd, 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.

[0052] Additionally, the composition of feed that was given to the cows in the past and the number of feedings may be stored as feed data. These data may also be used as items included in the herd physical condition factors. Although milk yield is a herd physical condition factor, it may be excluded from the herd physical condition factors because it is used in the herd milk yield factor described later.

[0053] [Herd milk yield factor] Items belonging to herd milk yield factors include herd milk yield capacity, which is expressed as the ratio of the milk yield of the herd to be estimated to the herd standard milk yield (milk yield / herd standard milk yield). It may be said that the herd milk yield factor is only herd milk yield capacity. The milk yield of the herd to be estimated is the total milk yield produced by the herd to be estimated or the average value per dairy cow in the herd. Since dairy cows are milked every day, it can be a daily value, but it can also be the total milk yield over a certain period of time or the average value during that period.

[0054] The herd standard milk yield is the amount of milk that the herd to be estimated is thought to originally produce (is expected to produce). There are no particular limitations on the herd standard milk yield, so long as it is a value that can represent the milk yield of the herd to be estimated. Therefore, there may be multiple herd milk yield capacities depending on how the herd standard milk yield is determined. In the multiple regression model, multiple herd milk yield capacities determined using different herd standard milk yields may be used as items for determining partial regression coefficients.

[0055] The herd standard milk yield may be, for example, the maximum, median, minimum, or average milk yield of the herd over a certain period of time. The average milk yield per cow of the estimated herd over the month prior to the reference period set before the milking day is easy to calculate and can be easily used as the herd standard milk yield.

[0056] Furthermore, the "milk yield of a herd" taking into consideration the conditions at the time of milking, such as the number of births and the number of days after calving, and environmental factors, such as temperature and humidity, may be used as the herd standard milk yield. N EY (referred to herein as "estimated milk yield") N EY) can be used as an estimate of the milk yield that can be expected from each individual cow, taking into account the condition at the time of milking and environmental factors. N EY is calculated and the average can be used as the standard milk yield for the herd. N The average of EY is called the herd estimated milk yield GEY.

[0057] There are various possible methods for calculating herd standard milk yield, but the main methods, including herd estimated milk yield GEY, will be explained in detail later.

[0058] [Herd environmental factors] The herd environmental factors are items related to the environment in which the herd is raised. Among these, the climatic 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 climatic data, such as THI (Temperature Humidity Index). Note that THI is an index expressed by equation (4), as an example. The air temperature in equation (4) is the same as the temperature, the temperature inside the cowshed, etc. in this specification.

[0059]

number

[0060] 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.

[0061] In addition, the somatic cell count may be affected by the cleanliness of the rearing environment, so the frequency of cleaning of the cow bedding can be included as a herd environment factor. Note that data on the frequency of cleaning of the cow bedding before the milking day may be used.

[0062] <Processing flow> 3 shows the process flow (main flow) of the somatic cell count composition ratio estimation system 1. The somatic cell count composition ratio estimation system 1, which implements the method for estimating the composition ratio by somatic cell count in a herd according to the present invention, is mostly performed by software operations. Therefore, a corresponding "unit" for each process may be provided in the computer.

[0063] When the somatic cell count-based composition ratio 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 somatic cell count-based composition ratio. If it is to be ended (Y branch of step S102), the somatic cell count-based composition ratio estimation system 1 is stopped (step S104). If not (N branch of step S102), the process proceeds to step S106.

[0064] [Determination of estimated herd: S106] The next process is to determine the herd to be estimated (step S106). Although the term "herd" has already been explained, there may be multiple herds on one farm. Of course, the entire farm may be considered as one herd. The determination of the herd to be estimated (step S106) may be performed by input from the user. This step involves specifically specifying the individual dairy cows that make up the herd. A certain range of values ​​required to estimate the composition ratio by somatic cell count may also be determined in this step.

[0065] For a designated cattle herd, information on past births of the dairy cows, 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.

[0066] [Determining explanatory variables: S108] Next, explanatory variables are determined (step S108). Explanatory variables are selected from data in which the composition ratio RLSt by somatic cell count of the herd is determined. In other words, explanatory variables are selected that can be selected from herds in which the composition ratio (composition ratio RLSt by somatic cell count) of dairy cows whose somatic cell counts are within a certain range is determined or can be determined. This is because the composition ratio RLSt by somatic cell count is the objective variable in multiple regression analysis. At least one of herd physical condition factors, herd milk yield factors, and herd environmental factors is selected as an explanatory variable that explains this objective variable (composition ratio RLSt by somatic cell count).

[0067] The process of determining the explanatory variables (step S108) is explained in Fig. 4. When the process moves to the flow of determining the explanatory variables (step S108) with reference to Fig. 4, the selection of herd physical condition factors (step S130), herd milk yield factors (step S134), and herd environmental factors (step S138) is asked. If input is desired for each factor, the Y branch is selected for each decision.

[0068] If herd physical condition factors are to be input (Y branch at step S130), the herd physical condition factors are input (step S132); if herd milk yield factors are to be selected (Y branch at step S134), the herd milk yield factors are input (step S136); if herd environmental factors are to be input (Y branch at step S138), the herd environmental factors are input (step S140). If no factors are to be selected, the N branch is selected at steps S130, S134, and S138. The input of herd milk yield factors (step S136) will be described in more detail later.

[0069] Then, when the input of each factor is completed, the process returns to the main routine (FIG. 3) (step S142). Note that the composition ratio by somatic cell count RLSt (objective variable) is always associated with the explanatory variable selected in step S108, and the process returns to the main routine. That is, it can be said that the fixed value range that is the boundary of the composition ratio by somatic cell count is determined in step S108 in FIG. 4 or step S108 in FIG. 3. 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 multiple regression model is obtained.

[0070] [Formation of multiple regression model: S110] Next, a multiple regression model is created from the explanatory variables and the response variables obtained in step S108 (step S110). A more specific example is shown in FIG.

[0071] An example of learning data for creating a multiple regression model is shown in Figure 5. Here, there are k cow herds, CG1, CG2, ..., CGk, and the number of dairy cows in each herd is ch1, ch2, ..., chk.

[0072] These herds may be those that are formed within the same farm, or may be composed of dairy cows from different farms. They may also be herds that existed in the past but do not exist now. Next, assume that the explanatory variables selected are MUNg as the herd physical condition factor, GYA as the herd milk yield factor, and THIg as the herd environmental factor. The composition ratios by somatic cell count of dairy cows with a linear score of 3 or more (fixed value range R[71~∞]) for these items are calculated as GS1, GS2, ...GSk, respectively.

[0073] Each item is a value that represents each factor at a certain time for each herd. The time when each factor was observed is within a "reference period" that indicates a certain period in the past including the milking day. The reference period is at least 60 days, preferably within 30 days, more preferably within 2 weeks, and most preferably 3 to 0 days. Within the same herd, the interval between the day when the factor was observed and the day when the milking was performed should be the same or approximately the same for each dairy cow. However, between herds, the interval between the day when the factor was observed and the day when the milking was performed may differ. Also, each herd may be the same herd as long as the time when the milking was performed is different. The composition ratio by somatic cell count RLSt is the objective variable, and is the percentage (%) of dairy cows in each herd whose somatic cell count is within a certain range.

[0074] Here, we will show an example of calculating the multiple regression model (5) to obtain the composition ratio RLS3 of linear scores of 3 or more, assuming that the number of dairy cows with a "linear score of 3 or more" is the proportion of the total number of cows in the herd. Note that in this case, the "3" in "composition ratio RLS3" indicates that the constant value t is a linear score of 3.

[0075] By calculating the multiple regression model (5), the partial regression coefficients β0 to β3 for each item are calculated. Note that (5) is an example of a formula obtained by making each factor of (1) more specific.

[0076]

number

[0077] Referring again to Figure 3, the explanatory variables of the herd to be estimated are input into the multiple regression model (step S112), and the composition ratio by somatic cell count IDRLS3 is calculated from the multiple regression equation (step S114). In Figure 3, the composition ratio by somatic cell count of the herd to be estimated is generally represented as IDRLSt. This is shown in Figure 6. The multiple regression model in equation (5) is shown again. The explanatory variables of the herd to be estimated are S-MUNg, S-herd milk production capacity, and S-THIg, and these are substituted into the multiple regression model (equation (6)) to calculate the composition ratio by somatic cell count IDRLS3 of the herd to be estimated.

[0078]

number

[0079] [Re-enter: S116] Referring again to Figure 3, next, the user is asked whether there are any other herds of cattle that are to be estimated (step S116). This is a question as to whether data on the herd to be estimated will be re-entered. If data on the herd to be estimated will be re-entered (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 choose whether to end the process (step S104) or to reselect the herd to be estimated (step S106).

[0080] As described above, the method for estimating the composition ratio by somatic cell count in a herd according to the present invention is implemented by the system 1 for estimating the composition ratio by somatic cell count executing the process in Fig. 3. For example, it is possible to estimate the composition ratio of dairy cows whose linear score will be 3 or more by the next milking. This composition ratio by somatic cell count makes it possible to grasp the trend of change in the somatic cell count of the herd to be estimated, and to respond to an increase in the somatic cell count of the herd to be estimated.

[0081] <Horde milk yield potential in relation to milk yield factors> In the explanatory variable determination (step S108) in Fig. 4, the herd milk yield capability is the data item for creating the multiple regression model for the herd milk yield factor input (step S136). The herd milk yield capability is calculated by the estimated herd milk yield / herd standard milk yield.

[0082] Figure 7 shows more detailed processing of step S136 in Figure 4. When the herd milk yield factor is selected, first, the average milk yield ΣGY / r / k of the herd to be estimated during the reference period is calculated (step S150). Here, ΣGY is the total milk yield during the reference period by the individuals constituting the herd to be estimated, r is the number of days in the reference period, and k is the number of dairy cows constituting the herd to be estimated. In other words, the average milk yield per cow per day of the herd to be estimated is calculated.

[0083] Next, the herd standard milk yield SgtY is calculated (step S152). The herd standard milk yield SgtY may be a representative value that reflects the milk yield expected from the herd based on past performance, etc. As described above, the maximum, minimum, median, etc. of the herd's milk yield can be used. However, among these, the so-called average value can be used as a suitable method. The average milk yield of a herd that can be suitably used will be described in detail later.

[0084] Since the herd standard milk volume SgtY is calculated in step S152, the herd milk yield capacity is calculated by milk volume / herd standard milk volume (=ΣGY / r / k / SgtY) (step S154). Since the herd milk yield capacity has now been calculated, the process returns to step S138 (FIG. 4) (step S156).

[0085] Next, the calculation of the herd standard milk yield SgtY (step S152 in FIG. 7) will be described in detail. The herd standard milk yield is the amount of milk expected to be milked from the herd (in this case, the herd to be estimated). There is no particular limitation on the calculation method. However, the calculation method may be based on the milk yield in the same period in the past or the estimated milk yield disclosed in Patent Documents 2 or 3. N The herd estimated milk yield using EY (which may simply be referred to as "estimated milk yield EY") can be suitably used as the herd standard milk yield.

[0086] Figure 8 shows a flow chart for the process of calculating the herd standard milk yield SgtY (step S152 in Figure 7). When the process of calculating the herd standard milk yield SgtY starts (step S152 in Figure 8), a process of selecting the average value MYgcs (step S170) and the herd estimated milk yield GEY (step S174) is provided. When selecting these average values, the Y branch of each step is selected, and each average value is set as SgtY (steps S172, S176).

[0087] In addition, there may be an average milk yield other than the above two average milk yields (steps S178 and S180). In other words, when multiple herd standard milk yields SgtY are selected, they are distinguished by herd standard milk yield SgtY. Each average value will be described in detail below.

[0088] <Average value MYgcs> The average milk yield MYgcs of the estimated herd in a climate similar to the climate when the estimated herd is milked (step S170 in FIG. 8) will be described with reference to FIG.

[0089] In this method, the average MYgcs is the average milk yield of all dairy cows in the herd during a similar reference period. The "similar reference period" refers to a period of the same magnitude as the reference period in the past that had a similar climate to the reference period. The "similar period" is preferably 0.5 to 1.5 times the reference period.

[0090] 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 date as long as the 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 for the estimated cattle herd.

[0091] In Figure 9, time passes from left to right. Similar climates to the reference period set immediately before the milking day (Mday) in 2023, when milking work is performed, also exist in 2022 and 2021, and these are set as similar reference periods in 2021 and 2022, respectively.

[0092] The total milk yield of the estimated herd during the reference period is represented as S-YGC. If the estimated herd consists of k dairy cows and the number of milking days during the reference period is r, the average milk yield of the estimated herd during the reference period is represented as "S-YGC / rk." This is nothing other than the "(average) milk yield of the estimated herd before milking."

[0093] Meanwhile, the average milk yield of the estimated herd in the similar reference periods of 2021 and 2022 is also expressed as "S-YGC / rk". These average milk yields are expressed as in equation (7). Equation (7) is the average milk yield of the estimated herd in the similar reference period. Since this average value is the amount of milk produced by this estimated herd in the past, it can be said to be the amount of milk that can be expected in the reference period of 2023. Therefore, the average value MYgcs can be adopted as the herd standard milk yield.

[0094]

number

[0095] The similar reference period may be within the same year. For example, if there is a reference period in autumn, it may be possible to find a similar reference period in spring. In the above example, the average MYgcs is the average of similar reference periods over the past two years, but it is also possible to find the average milk yield over a similar reference period going back more than two years or any one year. This average MYgcs is a value that takes into account the weather on the day of milking.

[0096] <Calculation of estimated milk yield of cows> FIG. 10 shows a process flow when the herd estimated milk yield GEY is set as the herd standard milk yield SgtY (step S176 in FIG. 8). The herd estimated milk yield GEY is the estimated milk yield for each dairy cow belonging to the herd. N EY and then calculate the average.

[0097] Referring to FIG. 10, when the herd milk yield estimation (step S176) is started, initial settings are made (step S202). In the initial settings, the number of dairy cows (here, k) belonging to the herd to be estimated is input, and a parameter "i" for identifying an individual is initialized. The initialization is a process of setting "i" to 1. The estimated milk yield is also N The total estimated milk yield, ΣEY, which is the integrated value of EY, is set to zero. Also, the estimated milk yield of each dairy cow, which will be shown later, N The calculation conditions for calculating EY may be input.

[0098] Next, the estimated milk yield of the i-th individual Ci N EY is calculated (step S204). This process will be described in detail later. N EY is an estimate of milk yield taking into account the number of days since birth N of individual Ci. Next, the estimated milk yield is calculated by multiplying the total estimated milk yield ΣEY by the estimated milk yield. N EY is incremented (step S206).

[0099] And the estimated milk yield added to the total estimated milk yield ΣEY N It is confirmed whether EY belongs to the last individual Ck of the herd (step S208). Here, this is determined by whether the parameter i is k or not.

[0100] If it is not yet the last individual Ck (N branch in step S208), the parameter i is incremented (step S210), and the process returns to step S204 to estimate the milk yield of the next individual Ci. N Calculate EY. Estimated milk yield of the last individual Ck N If EY has been integrated (Y branch in step S208), the sum of the estimated milk yields ΣEY is divided by k heads to obtain the herd estimated milk yield GEY, and the herd estimated milk yield GEY is further set as the herd standard milk yield SgtY (step S212). Then, the process returns to step S178 in FIG. 8 (step S214).

[0101] Next, the estimated milk yield for each dairy cow N The process of EY (step S204 in FIG. 10) will now be described. NBy carrying out the EY calculation process, the factor parameter P related to the milk yield of an individual and the estimated milk yield that can be estimated from each individual dairy cow belonging to the estimated herd on the Nth day after calving are obtained. N The relationship between EY (kg) and the milk yield of each individual can be obtained. Here, the factor parameter P is selected from the 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 be the item that best explains the milk yield of each individual.

[0102] 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).

[0103] Estimated milk yield N Once EY is obtained, the estimated milk yield of the factor parameter P corresponding to the milking operation at the same postpartum days N as the postpartum days N of each individual belonging to the estimated herd can be calculated. N You can ask for EY.

[0104] Figure 11 shows the estimated milk yield of an individual Ci (i means the i-th dairy cow) belonging to the estimated herd. N The flow when selecting EY is shown below. Estimated milk yield N When EY starts, an interpolation formula is created (step S232).

[0105] The interpolation formula is explained in Figure 12. In Figure 12, the horizontal axis is the number of days after parturition N (days) and the vertical axis is the actual milk yield RY (kg). N Regarding EY, the milk volume of an individual that is interpolated (interpolated milk volume) and the milk volume of an individual that is calculated using a regression equation (estimated milk volume) are defined, so to distinguish them from each other, the milk volume of an individual that is actually obtained by milking is called the "actual milk volume RY".

[0106] For example, Figure 12 shows data for a certain period (such as the milk volume on August 1, 2020) for dairy cows belonging to a herd. Each point plotted here is the actual milk volume RY for each dairy cow, so it is a scatter plot. 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 12, 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 (8) 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 that this Wood curve fits 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 (9).

[0110]

number

[0111] That is, in the interpolation formula (9), 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 S232) in FIG. 11 is a process for determining the interpolation formula (9) from the scatter diagram of the actual milk yield RY as described above.

[0112] [Create Interpolation Formula] FIG 13 shows the process of creating an interpolation formula (step S232 in FIG 11). When the process of creating an interpolation formula starts (step S232), data on the number of days since calving N and actual milk yield RY are extracted from the data on the herd (step S250). 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 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 S252) (see also FIG. 12). 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 (9) is obtained (step S254). This interpolation formula gives the interpolated milk yield Y. After that, the process returns to the EY calculation flow (FIG. 11) (step S256).

[0114] An interpolation formula can be created for each day of a dairy cow in a herd, because milking occurs almost every day. However, if there are no major changes in the environment or in the individual dairy cows, the interpolation formula (9) can be created by treating the average of the actual data every three days or every week as the actual data.

[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 particular dairy cow is taken as the actual milk yield RY of that dairy cow. Also, the number of days since parturition 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, then the number of days since parturition N for that month is taken as 25 days). These may be determined in the "Initial Settings" (step S202) in FIG. 10.

[0116] Continuing to refer to Figure 11. Once the interpolation formula has been created, it is determined whether or not to perform factor analysis (step S234). This determination can be made by the user of the somatic cell count composition ratio estimation system 1 via the terminal 10. Factor analysis is a determination as to whether or not to investigate the factor parameter P that best explains the interpolated milk volume Y obtained from the interpolation formula (9) from within the farm data. For example, this is used when the somatic cell count composition ratio estimation system 1 is being used for the first time, or when the cow herd is being significantly changed. Note that a significant change to the cow herd refers to a case in which all of the dairy cows that make up the cow herd are changed.

[0117] If the factor analysis is to be performed (Y branch at step S234), the factor analysis process is performed (step S236). If the factor analysis is not to be performed (N branch at step S234), the process proceeds to the next step. The case where the factor analysis is to be performed (Y branch at step S234) will be described in detail with reference to FIG. 17.

[0118] Next, a regression equation is created (step S238 in FIG. 11). The creation of the regression equation is explained in FIG. 14. 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 (9) shown in FIG. 12 (FIG. 14(a)). 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 (9) 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 mFor (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 for all other numbers of days since delivery N, the interpolation formula (9) can be calculated in this manner on a monthly basis and for each number of days since delivery N.

[0120] The factor that can best explain the interpolated milk yield Y for each month is determined as the factor parameter P. For the dairy cows belonging to the population, many measurement items are recorded in the memory 14, so the best factor parameter P can be found by carrying out the factor analysis process in step S236 of FIG. 11. N If the factor parameter P to be used for finding EY is determined, use it. The factor parameter P does not have to be one.

[0121] The creation of the regression equation in step S238 in Fig. 11 means 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. 14(c)). As is well known, the regression equation is expressed as in equation (10).

[0122]

number

[0123] Where: N EY is the estimated milk yield, x 1 , x 2 , , x k is the factor (factor parameter P), and a 1 , a 2 , ,a k , where c is a constant.

[0124] Figure 14(c) shows an example of a regression equation when there is one factor. From the interpolation equation (9) shown in Figure 14(a), the interpolated milk yield Y m (See Figure 14(b)). Here, m represents the month. The interpolated milk yield Y mmay 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, the temperature at noon), we can obtain the graph in Figure 14(c). Here, the estimated milk yield of dairy cows 50 days after calving for each month is N Here is an example of the results when EY can be well explained by the noon temperature. The regression equation is expressed by equation (11).

[0126]

number

[0127] where x 1 is the temperature at noon and EY is the estimated milk yield. N EY can be said to be calculated by regression analysis with explanatory variables as factor parameters and interpolated milk yield Y as the objective variable. Such regression equations are created for N days after calving. In other words, if the final day of N days after calving is 300 days, a regression equation from 1 to 300 days can be obtained (see Figure 14(e)).

[0128] When there are multiple factors, it is not possible to describe it two-dimensionally as in Figure 14(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 (12) (see Figure 14(d)).

[0129]

number

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

[0131] [Create regression equation] FIG. 15 shows a flow of the process of creating a regression equation. When the process of creating a regression equation (step S238) in the EY calculation flow shown in FIG. 11 is started, the flow jumps to FIG. 15, where the factor parameter P is input (step S260). The factor parameter P can be input from the terminal 10. In other words, it is input by the user of the somatic cell count composition ratio estimation system 1. Here, it is assumed that the factor parameter P is p 1 (Temperature).

[0132] Next, the factor parameter p 1 For each specified period m (which may be determined in step S202 in FIG. 10), the interpolated milk yield Ym for each postpartum day N is calculated using the interpolation formula (9) (step S262). That is, the interpolated milk yield Ym in FIG. 14(b) is calculated. This makes it possible to plot the scatter diagram in FIG. 14(c).

[0133] Next, the factor parameter p 1 A regression equation is obtained by using the factor parameters p as explanatory variables and the interpolated milk yield Ym as a target variable (step S264). 1 For the same number of days since parturition, N, the relationship between milk yield and the formula (12) (FIG. 14(d)) can be obtained. The regression formula can be obtained by any known method.

[0134] Using equation (12), we can create a number for the number of days since parturition N from 1 to the final day (the final day here means the longest day since parturition) (see Figure 14(e)). Regression equation (12) summarizes Figure 14(e). Once the regression equation is found, the estimated milk yield in Figure 11 can be calculated using the regression equation (12). N The flow returns to step S240 of the routine for determining EY (step S266).

[0135] Referring again to Figure 11, the estimated milk yield N Once the regression equation (10) for calculating EY has been obtained, the estimated milk yield at postpartum day N can be calculated by inputting the factor parameter P expected during milking into the regression equation (12) for the same postpartum day as the individual Ci (step S240). NEY can be calculated (step S242). Then, the process returns to step S206 in Fig. 10. For example, if the number of days since parturition of an individual Ci is N and the factor parameter during milking work is MD, the estimated milk yield of the individual Ci is expressed by equation (13).

[0136]

number

[0137] Figure 16 shows the estimated milk yield. N The figure shows an enlarged view of the estimated milk yield lactation curve M plotted against EY. N The factor parameter P is p 1 andp 2 The multiple regression equation is shown below for the two factors. 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. N EY is calculated from different regression equations (12) (different days after parturition, see Figure 14(e)). In other words, the constants in these equations are different.

[0138] [Factor analysis] Figure 17 shows the process of factor analysis in step S236 in Figure 11. The factor analysis process is the same as the creation of the regression equation (step S238 in Figure 15) up to a certain point. Specifically, steps S260, S262, and S264 shown in Figure 17 are the same as those in Figure 15. Therefore, the same step numbers are also used.

[0139] Referring to FIG. 17, the factor parameter P is input (step S260), and the milk yield Ym for each number of days after calving N for each fixed period m specified for the factor parameter P is calculated using the interpolation formula (9) (step S262). A regression equation is then calculated using the factor parameter P as the explanatory variable and the interpolated milk yield Ym as the target variable (step S264).

[0140] 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 S270). Figure 18 shows this process. The factor parameters are p 1 andp 2 For example, temperature and wind speed. The fixed period is four different days. For example, it may be a representative day in spring, summer, autumn, or winter. Specifically, the days are m1 month n1, m2 month n2, m3 month n3, and m4 month n4. The specific factor parameters p 1 , p 2 The factor parameters can be selected by a separate main factor analysis or by trial and error. 1 , p 2 Enter the above and the estimated milk yield will be calculated from the regression equation (12). N EY can be calculated.

[0141] 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. These values ​​are taken as the absolute value ERR of the difference with 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 calving N is 30. This ERR is also calculated when the number of days since calving N is 50, 100, and 150 days.

[0142] The sum of the absolute values ​​ERR of the differences between the estimated milk yield EY and the actual milk yield RY for four days is represented as the total error TΣ. In this manner, in step S270, the estimated milk yield EY and the actual milk yield RY are compared.

[0143] Referring again to Figure 17, the estimated milk yield N After comparing EY with the actual milk volume RY, a termination determination is made (step S272). NIt is determined whether the absolute value ERR of the difference between EY and the actual milk volume RY is appropriate. Whether ERR is appropriate may be determined in advance or may be determined by the user.

[0144] If the process is to be terminated (Y branch of step S272), N The process returns to the EY calculation flow (FIG. 11: step S240) (step S274). If the process continues (N branch of step S272), the process returns to step S260 again, and the factor parameter P is re-input.

[0145] Referring again to FIG. 11, after the factor analysis (step S236) is completed, the process proceeds to the step of calculating the estimated milk yield (step S240). After the factor analysis step (step S236) is completed, it is considered that a suitable factor parameter P has been found, so the estimated milk yield is calculated. N This is because it is considered acceptable to perform the step of calculating EY (step S240). The steps thereafter are as described above.

[0146] Referring again to Figure 8, if "Other" (step S180) is selected in the process of calculating the herd standard milk yield SgtY (Y branch of step S178), a method of calculating the average value of the herd other than steps S172 and S176 is performed. Note that steps S178 and S180 may be omitted. Then, the process returns to step S154 in Figure 7 (step S182).

[0147] By executing step S152 (Figure 8) for calculating the standard milk volume SgtY as described above, the average value related to the herd's milk volume is set as the standard milk volume SgtY, and processing is returned to the herd milk volume factor input (step S136) in Figure 7, and milk volume capacity is calculated (step S154 in Figure 7). [Industrial Applicability]

[0148] The present invention can be suitably used for estimating somatic cell count at the time of milking based on herd physical condition factors, herd milk yield factors, and herd environmental factors. [Explanation of symbols]

[0149] 1. Somatic cell count composition ratio estimation system 10 Terminal 12 Main unit 14. Memory 16. Ranch 18 External Information

Claims

1. Herd condition factors obtained from the milk components of the milk produced by the herd; A herd milk yield factor indicating the ratio of milk yield of the herd in a certain period before milking to a representative value reflecting the expected milk yield of the herd; At least one of the cattle herd environmental factors during the rearing of the cattle herd is an explanatory variable; A step of obtaining partial regression coefficients using a multiple regression model from a data group in which the ratio of the number of dairy cows exhibiting a somatic cell count within a certain range to the number of dairy cows belonging to the herd is used as a response variable; A method for estimating the composition ratio by somatic cell count in a herd, comprising a step of calculating the proportion of dairy cows in the herd that have somatic cell counts within a certain range from the explanatory variables of the herd and the partial regression coefficients.

2. A method for estimating the composition ratio by somatic cell count in a herd as described in claim 1, wherein the herd milk volume factor is the ratio between the herd standard milk volume, which is a representative value reflecting the expected milk volume of the herd, and the milk volume of the herd to be estimated.

3. The milk yield of the estimated herd is an average value of the milk yield during a reference period, which is a certain period prior to the milking date; The herd standard milk yield is: A method for estimating the composition ratio of somatic cell counts in a herd as described in claim 2, which is the average milk volume of the herd during a similar reference period of approximately the same length as the reference period, in a climate similar to the climate on the day the estimated herd undergoes milking work.

4. The milk yield of the estimated herd is the average value of the milk yield during a reference period, which is a certain period prior to the conception date; The herd standard milk yield is obtained by a step of obtaining an interpolation formula for the relationship between the milk yield of dairy cows belonging to the herd and the number of days since parturition; A step of calculating an interpolated milk yield corresponding to a factor parameter considered to be related to the milk yield of the dairy cows belonging to the herd 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 step of calculating an estimated milk yield corresponding to the factor parameters when the conception work is carried out for each of the dairy cows belonging to the estimated herd from the regression equation that coincides with the number of days since birth of the individual; 3. A method for estimating the composition ratio by somatic cell count in a herd as described in claim 2, wherein an average value of the estimated milk yields of the individual dairy cows is calculated and the average value is set as the standard milk yield of the herd.

5. 5. A method for estimating the composition ratio of somatic cell counts in a herd of cattle according to any one of claims 1 to 4, wherein the herd physical condition factor is at least one of MUN and P / N.

6. A method for estimating the composition ratio of somatic cell counts in a herd of cattle according to any one of claims 1 to 4, wherein the herd environmental factors include THI.

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