Evaluation system and evaluation method

Abstract

To evaluate easily the state of rumen fermentation of a ruminant.SOLUTION: An estimation device (30) includes an estimation part (31) for estimating a methane production amount of a ruminant based on a milk yield of milk produced by a ruminant which is an evaluation object. An evaluation system (100) includes an evaluation device (20) having an estimation part (21) for estimating the state of rumen fermentation of the ruminant based on the methane production amount of the ruminant which is the evaluation object estimated by the estimation device (30).SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to an evaluation system and an evaluation method. [Background technology]

[0002] Ruminant livestock such as cows are herbivores, and produce milk and meat by digesting plant-based feed. Ruminants digest plant feed using four stomachs. Of the four stomachs, the first stomach, called the rumen, is large, with a volume of approximately 100 liters, and is home to a wide variety of rumen microorganisms. Plant feed ingested by ruminants is first broken down by fermentation (rumen fermentation) by rumen microorganisms, and becomes a source of energy and protein for the ruminants, supporting their growth and milk and meat production.

[0003] It is known that feeding ruminants large amounts of grain feed in the hope of increasing milk production and improving milk quality can cause acidification of the rumen fluid (rumen acidosis). Rumen acidosis can cause so-called production diseases such as indigestion, reduced feed intake and milk production, and reduced conception rates, reducing the productivity of ruminants. Therefore, in order to maintain the productivity of ruminants, it is important to provide appropriate feed so that rumen fermentation functions normally. Therefore, it is necessary to manage feeding by taking into account the state of rumen fluid, which reflects the state of rumen fermentation.

[0004] Conventionally, rumen fermentation has been evaluated by measuring the feed intake (DMI: dry matter intake) and the pH of the rumen fluid (rumen pH) of livestock. Non-Patent Document 1 proposes a new index for evaluating rumen fermentation: the theoretical turnover rate of the rumen liquid fraction (TTOR), which is calculated from measurements of rumen pH, the concentration of short-chain fatty acids in the rumen fluid, DMI, and the body weight of the cow.

[0005] It is also known that methane production in ruminants affects rumen fermentation. Non-Patent Document 2 describes calculating methane production from milk yield corrected for 4% fat in order to investigate the state of rumen fermentation. [Prior art documents] [Non-patent literature]

[0006] [Non-Patent Document 1] Mitsumori et. al., Animal Science Journal, http: / / doi.org / 10.1111 / asj.13305, 2019 / 10 / 24, [online] [Non-patent document 2] Kurihara et al., Japan Sci. Soc. Press, pp.199-208, 1997 Summary of the Invention [Problem to be solved by the invention]

[0007] Conventionally, DMI has been calculated by subtracting the residual feed that was not ingested from the fed feed, but weighing the fed and residual feed is extremely difficult for ordinary farms. Furthermore, rumen pH is measured by measuring rumen fluid collected with a gastric catheter or by using a pH meter or a pH sensor installed in the rumen, but it is not easy for ordinary farms to collect rumen fluid or install a pH sensor. Therefore, it would be extremely beneficial to be able to easily evaluate the state of rumen fermentation in a way that is practical for ordinary farms.

[0008] In order to easily evaluate the state of rumen fermentation, it is desirable to be able to easily estimate DMI and rumen pH, but Non-Patent Documents 1 and 2 do not describe any techniques for estimating these.

[0009] An object of one aspect of the present invention is to provide an evaluation system that can easily evaluate the state of rumen fermentation. [Means for solving the problem]

[0010] An estimation device according to one embodiment of the present invention is an estimation device for estimating the methane production amount of a ruminant, and is equipped with an estimation unit that estimates the methane production amount of the ruminant based on the milk yield of the ruminant being evaluated.

[0011] An evaluation system according to one embodiment of the present invention is an evaluation system for evaluating the state of rumen fermentation of a ruminant, and is equipped with an evaluation device having an estimation unit that estimates the state of rumen fermentation of the ruminant based on the methane production amount of the ruminant being evaluated estimated by the estimation device.

[0012] An estimation method according to one embodiment of the present invention is a method for estimating the methane production amount of a ruminant, and includes an estimation step of estimating the methane production amount of the ruminant to be evaluated based on the milk yield of the milk produced by the ruminant.

[0013] An evaluation method according to one embodiment of the present invention is a method for evaluating the state of rumen fermentation in a ruminant, and includes an estimation step of estimating the state of rumen fermentation in the ruminant based on the methane production amount of the ruminant to be evaluated estimated by the estimation method. [Effects of the Invention]

[0014] According to one aspect of the present invention, the state of rumen fermentation can be easily evaluated without weighing the fed feed and the remaining feed or measuring the pH of the rumen fluid. [Brief explanation of the drawings]

[0015] [Figure 1] FIG. 1 is a schematic diagram showing correlations between indices used in an estimation device and an evaluation system according to an embodiment of the present invention. [Figure 2] 1 is a block diagram showing an overview of an estimation device and an evaluation system according to an embodiment of the present invention. [Figure 3] This is a graph showing the correlation between the theoretical turnover rate of the rumen liquid fraction (TTOR) calculated from the rumen short-chain fatty acid concentration and methane production, and the estimated TTOR value estimated from 4% fat-corrected milk yield. [Figure 4] 1 is a graph showing the correlation between the theoretical turnover rate of the rumen liquid fraction (TTOR) calculated from the rumen short-chain fatty acid concentration and methane production, and the estimated TTOR value estimated from milk yield. [Figure 5] 1 is a graph showing the correlation between the product of the estimated TTOR value estimated from milk yield and the C2 / C3 ratio and the measured rumen pH value. [Figure 6] 1 is a graph showing the correlation between the product of the estimated TTOR value estimated from milk yield and the C2 / C3 ratio and the measured rumen pH value. [Figure 7] 1 is a graph showing the correlation between the estimated value of TTOR estimated from milk yield and dry matter intake, and the estimated value of TTOR estimated from milk yield (TTOR(milk)) and milk yield. [Figure 8] 1 is a graph showing the correlation between the estimated value of TTOR estimated from milk yield and dry matter intake, and the estimated value of TTOR estimated from milk yield (TTOR(milk)) and milk yield. [Figure 9] 1 is a graph showing the correlation between the estimated value of TTOR estimated from milk yield and dry matter intake, and the estimated value of TTOR estimated from milk yield (TTOR(milk)) and milk yield. [Figure 10] 1 is a graph showing the correlation between the estimated value of TTOR estimated from milk yield and dry matter intake calculated from feeding standards, and the estimated value of TTOR estimated from milk yield (TTOR(milk)) and milk yield. DETAILED DESCRIPTION OF THE INVENTION

[0016] Hereinafter, one embodiment of the present invention will be described in detail.

[0017] [Rating System] An evaluation system according to one embodiment of the present invention is an evaluation system for evaluating the state of rumen fermentation in a ruminant. The evaluation system evaluates the state of rumen fermentation by rumen microorganisms in the first stomach, called the rumen, of a ruminant. The evaluation system evaluates the state of rumen fermentation in ruminants based on various indicators related to the energy metabolism of ruminants, as shown in Figure 1. The evaluation system estimates the state of rumen fermentation based on the methane production of the ruminant, which is estimated based on the milk yield of the ruminant.

[0018] [Correlation between indicators showing the state of rumen fermentation] FIG. 1 is a schematic diagram showing the correlations between indices used in the estimation device and evaluation system according to one embodiment of the present invention. As shown in FIG. 1, the interrelationships between various indices related to ruminant energy metabolism are shown in Non-Patent Document 1 and a prior application by the present inventor (Patent Application No. 2019-232023). Non-Patent Document 1 and the prior application are incorporated herein by reference in their entirety. Hereinafter, Non-Patent Document 1 will be referred to as Reference Document 1. Reference Document 1 shows that there is a close relationship between the rumen pH (rumen pH), rumen short-chain fatty acid concentration (SCFA concentration: rumen short-chain fatty acid concentration), dry matter intake (DMI), and theoretical turnover rate (TTOR) of the rumen liquid fraction calculated from cow body weight, and DMI, milk yield, and rumen pH. In FIG. 1, dashed arrows indicate the correlations between the indices shown in Reference Document 1, solid arrows indicate the correlations between the indices revealed in the prior application, and dashed arrows indicate the correlations between the indices revealed by the present invention.

[0019] TTOR is an index that indicates the state of rumen fermentation. Here, the state of rumen fermentation is expressed by various indexes related to the fermentation of feed in the rumen. In addition to the above-mentioned TTOR, indexes that indicate the state of rumen fermentation include rumen pH, DMI, SCFA concentration, SCFA amount, methane concentration in the liquid phase fraction in the rumen, and methane production rate.

[0020] As shown by the dashed arrow in Figure 1, TTOR can be calculated from the methane production and SCFA concentration of ruminants. Specifically, the estimated rumen volume (PRV) is calculated from the methane production and the methane concentration in the liquid phase fraction in the rumen calculated from the SCFA concentration, and TTOR is calculated using this PRV and the metabolic body weight (MBW) of the ruminant.

[0021] Methane production can be calculated by known methods based on DMI. DMI is calculated by weighing the feed and residual feed and subtracting the residual feed from the feed. The methane concentration in the liquid phase fraction in the rumen can be calculated from the flow of metabolic hydrogen in ruminal fermentation based on the SCFA concentration measured in ruminal fluid collected from the rumen. Estimated rumen volume represents an estimate of the total volume of the liquid phase in the rumen. Metabolic body weight is calculated by multiplying the body weight of the ruminant by 0.75.

[0022] Rumen pH is an important indicator for assessing ruminal acidosis, but it has traditionally been measured using a pH meter to collect ruminal fluid using a gastric catheter or a pH sensor installed in the rumen. Reference 1 shows that it is possible to estimate indicators of the state of ruminal fermentation, such as rumen pH, from TTOR calculated from methane production and SCFA concentration, without using such measurement methods.

[0023] As shown in Reference 1, if rumen pH and other parameters could be estimated from TTOR, it would be extremely advantageous because it would eliminate the need to sample rumen fluid or install a pH sensor in the rumen each time the state of rumen fermentation is evaluated. However, it is not easy to calculate methane production or SCFA concentration, especially on average farms, and as a result, it is not easy to calculate TTOR. Therefore, it would be even more advantageous if the state of rumen fermentation could be estimated more easily without calculating TTOR.

[0024] The present inventors have discovered for the first time that methane production can be estimated based on the milk yield of ruminant animals (Non-Patent Document 2), and that the state of rumen fermentation can be estimated from the methane production estimated based on the milk yield. Milk produced by ruminant animals is obtained by milking the ruminant animals, and since milking is required every day, several times a day, during the lactation period, it can be easily obtained even by ordinary farms. In addition, the milk yield of milk produced by ruminant animals is measured on a daily basis by ordinary farms to evaluate the milk produced.

[0025] Therefore, the present invention, which makes it possible to estimate the state of rumen fermentation based on the milk yield produced by ruminants without using actual measurements of methane production or SCFA concentration, is extremely advantageous as a means of easily evaluating the state of rumen fermentation.

[0026] [Ruminants] The evaluation system targets ruminant livestock. Ruminants have four stomachs that digest feed mainly composed of plant-derived ingredients and chew the cud. Ruminant livestock evaluated by the evaluation system include cows, goats, sheep, etc., with cows being the most representative. The evaluation system evaluates the state of rumen fermentation of ruminants based on the milk they produce. Therefore, the evaluation system is particularly suitable for evaluating dairy cows, whose milk is collected daily and whose production volume and composition are monitored.

[0027] The evaluation system evaluates the state of rumen fermentation based on estimated methane production based on the milk yield of ruminants. In other words, the evaluation system can be said to evaluate the state of rumen fermentation based on milk yield. Milk yield refers to the weight of milk produced by ruminants per day.

[0028] The evaluation system may utilize milk production results that indicate the milk components of milk produced by ruminants. Milk components may include at least one of milk fat percentage, milk protein percentage, solids-non-fat percentage, lactose percentage, ammonia nitrogen percentage, and composition ratios such as the milk protein / milk fat ratio. These milk production indicators are routinely measured to manage milk production on dairy farms. Milk fat percentage means the weight ratio of fat to milk volume. Milk protein percentage means the weight ratio of protein to milk volume. Solids-non-fat percentage means the weight ratio of components excluding water and fat to milk volume. Lactose percentage means the weight ratio of lactose to milk volume. Ammonia nitrogen percentage means the ratio of nitrogen contained in urea to milk volume. Milk protein / milk fat ratio means the weight ratio of protein to fat to milk volume. The method for measuring milk volume and milk components is not particularly limited, and can be measured using conventionally known methods.

[0029] The milk production performance of ruminants is measured by combining the milk produced by multiple ruminants, which can make it difficult to measure the performance of each individual animal. Milk yield, on the other hand, can be easily measured for each individual ruminant. Therefore, the evaluation system makes it easy to evaluate the state of rumen fermentation for each individual animal based on methane production estimated from easily measurable milk yield.

[0030] The configuration of the evaluation system will be described with reference to Fig. 2. Fig. 2 is a block diagram showing an outline of an estimation device and an evaluation system according to one embodiment of the present invention. As shown in Fig. 2, the evaluation system 100 includes an estimation device 30 and an evaluation device 20. The estimation device 30 includes an estimation unit 31. The evaluation device 20 includes an estimation unit 21. The evaluation device 20 may further include a management information generation unit 22, a diagnosis unit 23, a storage unit 24, and a calculation unit 25. The evaluation system 100 may further include a measurement device 10.

[0031] [Estimation device] An estimation device 30 according to one embodiment of the present invention estimates the methane production of a ruminant animal. The estimation device 30 includes an estimation unit 31 that estimates the methane production of a ruminant animal based on the milk yield of the ruminant animal to be evaluated.

[0032] The estimation unit 31 estimates the methane production of ruminants based on the measured value of the milk yield of milk produced by the ruminants. The estimation unit 31 estimates the methane production based on the milk yield measured by the measuring device 10, which will be described later. The estimation unit 31 estimates the methane production based on the measured value of milk yield, using a relational equation that represents the correlation between milk yield and methane production. The relational equation that represents the correlation between milk yield (kg / day) and methane production (MY) is as follows: MY(milk)(mol / day)=(8.19+(300 / milk yield))×milk yield / 22.4...Formula (A)

[0033] Equation (A) was obtained by reference to the equation for estimating methane production from 4% fat-corrected milk yield (fcm) described in Non-Patent Document 2. Non-Patent Document 2 is incorporated herein in its entirety by reference. Hereinafter, Non-Patent Document 2 will be referred to as Reference Document A. Reference Document A describes estimating methane production from a 4% fat-corrected milk yield, which is obtained by correcting milk yield to a milk yield with 4% fat content. Estimation unit 31 estimates the methane production of a ruminant based on the measured value of the milk yield of milk produced by the ruminant, rather than the 4% fat-corrected milk yield.

[0034] [Measuring equipment] The measuring device 10 measures the milk yield of milk produced by the ruminant animal to be evaluated. A conventionally known device for measuring the milk yield and milk components of milk milked from ruminants can be used as the measuring device 10. The measuring device 10 is preferably an automatic analyzer that automatically analyzes the milk. The measuring device 10 sends the measured milk yield results to the estimation device 30 and the evaluation device 20.

[0035] Furthermore, the measuring device 10 may have a function of measuring the pH of rumen fluid collected from a ruminant, the SCFA concentration in the rumen fluid, and the like.

[0036] 〔Evaluation Device〕 The evaluation device 20 evaluates the rumen fermentation state of the ruminant animal to be evaluated. The evaluation device 20 evaluates the rumen fermentation state based on the methane production amount estimated by the estimation device 30 based on the milk yield. That is, it can be said that the evaluation device 20 evaluates the rumen fermentation state based on the milk yield.

[0037] (Estimation Unit) The estimation unit 21 estimates the rumen fermentation state of the ruminant animal based on the milk yield. The rumen fermentation states estimated by the estimation unit 21 are, for example, TTOR, rumen pH, and DMI. Further, the estimation unit 21 can also estimate indices representing the rumen fermentation state such as SCFA concentration, SCFA amount, methane concentration in the liquid phase fraction in the rumen, and methane production amount based on the TTOR estimated based on the milk yield.

[0038] As will be described later, the estimation unit 21 estimates the rumen fermentation state using a relational expression representing the correlation between each index representing the rumen fermentation state. Such a relational expression may be obtained within the evaluation system 100 or may be acquired from the outside and stored in the evaluation system 100. Therefore, for example, in general farms, there is no need to perform measurements and calculations for obtaining such a relational expression. By obtaining the relational expression from the outside and only substituting the milk yield into the relational expression, the rumen fermentation state can be estimated.

[0039] <Estimation of TTOR> The estimation unit 21 estimates TTOR based on the milk yield as shown below, for example. The estimation unit 21 estimates TTOR from the milk yield of the ruminant animal to be evaluated based on the correlation between the TTOR calculated based on the methane production amount and SCFA concentration released by the ruminant animal and the milk yield of the milk produced by the ruminant animal.

[0040] The correlation between the calculated TTOR and the milk yield of the ruminant animal used by the estimation unit 21 is represented by a regression equation obtained by regression analysis. The regression equation can be obtained directly from a plot on a scatter plot created between the TTOR calculated based on the methane production rate and SCFA concentration of the ruminant animal and the milk yield of the ruminant animal. For example, a regression line is obtained from the scatter plot using the least squares method, and the regression equation is obtained from the obtained regression line.

[0041] The estimation unit 21 then calculates TTOR by substituting the value of the milk yield of the milk produced by the ruminant to be evaluated into the obtained regression equation. In this way, the estimation unit 21 can calculate TTOR simply by substituting the value of the milk yield of the milk produced by the ruminant to be evaluated into the above-mentioned regression equation. The regression equation used by the estimation unit 21 may be one that has been determined in advance by the calculation unit 25, which will be described later, or one that has been determined in advance and obtained externally.

[0042] <Rumen pH Estimation> The estimation unit 21 can also estimate the pH in the rumen of the ruminant from the measurement results of the milk yield of the ruminant to be evaluated based on TTOR (theoretical turnover rate of the rumen liquid phase fraction) estimated from the milk yield as described above. The estimation unit 21 estimates the rumen pH from the measurement results of the milk yield of the milk produced by the ruminant to be evaluated, for example, based on the correlation between the estimated TTOR and the actual measured value of rumen pH.

[0043] The correlation between the estimated TTOR and the measured rumen pH used by the estimation unit 21 is represented by a regression equation obtained by regression analysis. The regression equation can be obtained, for example, by creating a scatter plot of the estimated TTOR and measured milk yield against the measured rumen pH and then directly from the plot on the scatter plot. For example, a regression line can be obtained from the scatter plot using the least squares method, and the regression equation can be obtained from the obtained regression line.

[0044] Then, the estimation unit 21 calculates the rumen pH by substituting the value of the milk yield of the milk produced by the ruminant to be evaluated into the regression equation obtained. In this way, the estimation unit 21 can calculate the rumen pH only by substituting the value of the milk yield of the milk produced by the ruminant to be evaluated into the above-mentioned regression equation. The regression equation used by the estimation unit 21 may be obtained in advance by the calculation unit 25 described later, or the regression equation obtained in advance may be acquired from the outside.

[0045] <Estimation of DMI> The estimation unit 21 can also estimate the DMI of the ruminant to be evaluated from the measurement result of the milk yield of the ruminant based on the TTOR estimated from the milk yield as described above. For example, the estimation unit 21 estimates the DMI from the measurement result of the milk yield of the milk produced by the ruminant to be evaluated based on the correlation between the estimated TTOR and the measured value of DMI.

[0046] The correlation between the estimated TTOR and the measured value of DMI used by the estimation unit 21 is represented by a regression equation obtained by regression analysis. The regression equation can be obtained directly from the plots on the scatter diagram, for example, by creating a scatter diagram of the values related to the estimated TTOR and the measured value of milk yield and the values related to the estimated TTOR and the measured value of DMI. For example, in the scatter diagram, a regression line is obtained by the least squares method, and the regression equation is obtained from the obtained regression line.

[0047] Here, instead of the measured value of DMI, the estimation unit 21 may obtain a regression equation using the DMI(FS) calculated from the feeding standard of dairy cows by the methods described in Reference 9 and NRC(2001) etc. described later. The calculation method of DMI(FS) based on the feeding standard of dairy cows will be described later in the examples. [[ID=​The estimation unit 21 then calculates the DMI by substituting the value of the milk yield of the milk produced by the ruminant to be evaluated into the obtained regression equation. In this way, the estimation unit 21 can calculate the DMI simply by substituting the value of the milk yield of the milk produced by the ruminant to be evaluated into the above-mentioned regression equation. The regression equation used by the estimation unit 21 may be one that has been calculated in advance by the calculation unit 25, which will be described later, or one that has been calculated in advance and obtained externally.

[0049] The estimation unit 21 sends an index representing the estimated state of rumen fermentation to the management information generation unit 22, the diagnosis unit 23, the storage unit 24, and the calculation unit 25, which will be described later.

[0050] (Management information generation department) The management information generation unit 22 generates feeding management information for the ruminant based on the state of rumen fermentation of the ruminant estimated by the estimation unit 21. The feeding management information includes information on the amount of feed (DMI, etc.), number of feedings, feeding time, type of feed, feed ingredients, feed ingredient ratios, amount of water consumed, etc. The management information generation unit 22 generates the feeding management information by taking into consideration the state of rumen fermentation of the ruminant as well as the breed, age in months (age), weight, calving date, number of days since calving, estrus, pregnancy, lactation period, number of milkings, dry period, etc. of the ruminant, the temperature, humidity, wind speed, rainfall, etc. of the feeding management location, the location (address) of the feeding management location, the management type, etc.

[0051] The management information generation unit 22 generates, for example, based on predetermined data associating the state of rumen fermentation with husbandry management information, husbandry management information corresponding to the state of rumen fermentation estimated by the estimation unit 21 as husbandry management information for the ruminant being evaluated.

[0052] The management information generating unit 22 may send the generated breeding management information to the storage unit 24, or may display it on a display unit (not shown) to notify the user.

[0053] (Diagnostics Department) The diagnosing unit 23 diagnoses at least one of the feeding and management state and production disease of the ruminant based on the state of rumen fermentation of the ruminant estimated by the estimating unit 21 .

[0054] Production diseases of ruminants include rumen acidosis, indigestion, loose stools, reduced feed intake, increases or decreases in milk components such as reduced milk fat percentage, reduced milk yield, onset of laminitis, reduced conception rate, etc. The diagnosis unit 23 diagnoses the production disease incidence probability of the ruminant being evaluated as the production disease incidence probability of the ruminant being evaluated, based on, for example, predetermined data correlating the state of rumen fermentation with the probability of production disease incidence.

[0055] The ruminant feeding and management conditions include various items related to feeding and management, such as feed intake, body weight, body condition score, rumen fill score, feces score, lameness score (locomotion score), blood components (glucose, non-esterified fatty acids (NEFA), beta-hydroxybutyric acid (BHBA), calcium, total protein, albumin, aspartate aminotransferase (AST), gamma-glutamyl transpeptidase (GGT), ammonia nitrogen, glucose, triglycerides, total cholesterol (T-Cho), insulin, luteinizing hormone, etc.), urinalysis results (uric acid, pH, creatinine, etc.), milk production results indicating milk yield and milk components, and number of milkings per day. The diagnosing unit 23 diagnoses whether the feeding and management conditions corresponding to the state of rumen fermentation estimated by the estimating unit 21 are appropriate, for example, based on predetermined data correlating the state of rumen fermentation with appropriate feeding and management conditions.

[0056] The diagnosing unit 23 may send information relating to the diagnosed feeding management conditions and the diagnosis results of production diseases to the storage unit 24, or may display the information on a display unit (not shown) to notify the user.

[0057] (Storage part) The memory unit 24 stores information representing the correlation between milk yield and the state of rumen fermentation of ruminants. The memory unit 24 stores, for example, a regression equation representing the correlation between TTOR and milk yield, a regression equation representing the correlation between TTOR and rumen pH, a regression equation representing the correlation between TTOR and DMI, etc. The memory unit 24 may also store an index representing the state of rumen fermentation estimated by the estimation unit 21. The memory unit 24 may also store feeding management information generated by the management information generation unit 22 and information regarding production diseases diagnosed by the diagnosis unit 23. The memory unit may be, for example, a conventionally known computer memory.

[0058] (calculation section) The calculation unit 25 calculates a relational equation that represents the correlation between milk yield and the state of rumen fermentation of ruminants. For example, the calculation unit 25 calculates a regression equation that represents the correlation between TTOR and milk yield, a regression equation that represents the correlation between TTOR and rumen pH, and a regression equation that represents the correlation between TTOR and DMI.

[0059] For example, the calculation unit 25 first calculates TTOR from the measured values ​​of methane production and SCFA concentration emitted by ruminants in a population sample. Then, the calculation unit 25 calculates a regression equation representing the correlation between TTOR and milk yield by regression analysis of the correlation between the calculated TTOR and the measured value of milk yield in the population sample. The calculation unit 25 may also calculate methane production based on DMI using a known method. Furthermore, the calculation unit 25 may calculate the methane concentration in the liquid phase fraction in the rumen from the flow of metabolic hydrogen during ruminal fermentation, based on the SCFA concentration measured in rumen fluid collected from the rumen.

[0060] Here, the population may be a population of ruminants raised on a specific farm, a population of ruminants raised under specific rearing conditions, or a population of livestock ruminants consisting of a plurality of ruminants. The population is preferably a population of ruminants raised under similar rearing conditions as the ruminants to be evaluated, and more preferably a population of ruminants raised on the same farm as the ruminants to be evaluated. Using a relational expression calculated from such a population allows for more accurate estimation of the state of rumen fermentation. Furthermore, the population may be a population whose individual conditions, such as breed, age, and parity, and whose feed conditions, such as feed type, components, and feeding amount, are similar to those of the ruminants to be evaluated. Furthermore, the state of rumen fermentation of an individual ruminant may be estimated using a relational expression calculated from a population of data obtained at different times for that individual ruminant. This allows for analysis of each individual.

[0061] [Estimation method] An estimation method according to one embodiment of the present invention is a method for estimating the methane production of a ruminant. The estimation method includes an estimation step of estimating the methane production of a ruminant based on the milk yield of the milk produced by the ruminant to be evaluated. In other words, the estimation method is one aspect of the estimation process in the estimation device according to one embodiment of the present invention described above. Therefore, details of the estimation method are as described above for the estimation device according to one embodiment of the present invention.

[0062] [Evaluation method] An evaluation method according to one embodiment of the present invention is a method for evaluating the state of rumen fermentation in a ruminant. The evaluation method includes an estimation step of estimating the state of rumen fermentation in a ruminant based on the methane production of the ruminant to be evaluated estimated by an estimation method according to one embodiment of the present invention. In other words, the evaluation method is one aspect of the evaluation process in the evaluation system according to one embodiment of the present invention described above. Therefore, details of the evaluation method are as described in the evaluation system according to one embodiment of the present invention described above.

[0063] [Software implementation example] The estimation device 30 and evaluation device 20 of the present invention may be realized by a computer. In this case, the control programs for the estimation device 30 and evaluation device 20 that cause the computer to operate as each part (software element) of the estimation device 30 and evaluation device 20, and the computer-readable recording medium on which the control programs are recorded, also fall within the scope of the present invention.

[0064] The control blocks of the estimation device 30 and the evaluation device 20 (particularly the estimation unit 31, the estimation unit 21, and the calculation unit 25) may be realized by a logic circuit (hardware) formed on an integrated circuit (IC chip) or the like, or may be realized by software.

[0065] In the latter case, the estimation device 30 and the evaluation device 20 each include a computer that executes instructions from a program, which is software that realizes each function. This computer includes, for example, one or more processors and a computer-readable recording medium that stores the program. The object of the present invention is achieved when the processor in the computer reads and executes the program from the recording medium. The processor may be, for example, a central processing unit (CPU). The recording medium may be a "non-transitory tangible medium," such as a read-only memory (ROM), tape, disk, card, semiconductor memory, or programmable logic circuit. The computer may also include a random access memory (RAM) for loading the program. The program may be supplied to the computer via any transmission medium (such as a communication network or broadcast waves) capable of transmitting the program. One aspect of the present invention may also be realized in the form of a data signal embedded in a carrier wave, in which the program is embodied by electronic transmission.

[0066] The present invention is not limited to the above-described embodiments, and various modifications are possible within the scope of the claims. Embodiments obtained by appropriately combining the technical means disclosed in different embodiments are also included in the technical scope of the present invention. [Example]

[0067] An embodiment of the present invention will now be described.

[0068] 1. Calculation of TTOR based on measured values ​​of DMI and rumen fluid components The experiments from 1-1 to 1-6 are based on the description in Reference 1. The data measured in Reference 1 were obtained at different times for each individual cow.

[0069] (1-1. Animal Management and Sampling) Eleven multiparous Hostein dairy cows raised at research facilities in Chiba, Ibaraki, Ishikawa, Kanagawa, and Toyama prefectures were used in the experiment. These cows were housed in tie-stall barns at the facilities and were fed a commercially available dry mixed feed twice daily (9:00 and 16:00) for 3 weeks before calving, at a concentration that met 120% of their energy requirements according to the Japanese Feed Standards (NARO, 2006).

[0070] During the lactation period, the cows were fed a diet containing timothy hay twice a day (9:00 and 16:00) at a concentration that satisfied 100% of their energy requirements according to the Japanese Feed Standards (NARO, 2006). The difference between the fed and remaining diets was taken as DMI and was measured daily for each individual cow throughout the experiment.

[0071] During the study period, from 3 weeks before calving to 12 weeks after calving, the rumen pH of each cow was measured using a wireless pH sensor placed in its stomach. Rumen pH values ​​were continuously recorded every 10 minutes during the measurement period. The pH measured at 1:00 PM was used as the representative value of rumen pH for the day.

[0072] In this study, experimental procedures were used in accordance with the Japanese Standards for the Care of Laboratory Animals and were approved by the Animal Care Committee of the Institute of Livestock and Grassland Science, NARO, Japan.

[0073] Milk yield from each cow was measured daily, and milk composition was analyzed weekly. Ruminal fluid was collected via a tube placed in the cow's stomach 4 hours after morning feeding. Ruminal fluid collection occurred 3 weeks before calving and 4, 8, and 12 weeks after calving. Ruminal fluid was strained through four layers of cheesecloth and stored at -20°C until further analysis.

[0074] (1-2.Component analysis) Blood samples were collected from 11 dairy cows 3 weeks before calving and 4, 8, and 12 weeks after calving. Blood samples were collected from the coccygeal vein using a suction tube containing anticoagulant and analyzed as described by Hasunuma et al., 2016.

[0075] Plasma concentrations of total protein, albumin, aspartate aminotransferase (AST), gamma-glutamyl transpeptidase (GGT), ammonia nitrogen, glucose, triglycerides, total cholesterol (T-Cho), and nonesterified fatty acids were analyzed using a Model 7020 automated analyzer (Hitachi, Ltd.). The concentrations of organic acids in ruminal fluid were measured using high-performance liquid chromatography (Alliance HPLC system; Waters, Milford). The concentrations of milk fat, milk protein, solids-non-fat, somatic cells, and ammonia nitrogen were determined using automated analyzers at each laboratory.

[0076] (1-3. Statistical analysis) Statistical analysis was performed by two-way analysis of variance (ANOVA) followed by Tukey's multiple comparison post-hoc test, and significance was determined by the least significant difference method at 5% (P < 0.05) using Excel 2011 software (Microsoft) with the add-in software Statcel3 (OMS Publishing). Simple regression analysis was performed.

[0077] (1-4. How to calculate TTOR) The correlation of data used for the theoretical analysis of rumen fluid turnover is shown in Figure 1. Methane production (MY) was estimated from DMI using the following equation (1-1):

[0078] MY(mol / day)=[19.14×DMI(kg / day)+2.54] / 16.042...Equation (1-1) The methane concentration in the liquid phase fraction in the rumen (RM) was calculated from the metabolic hydrogen flow during ruminal fermentation, i.e., the methane concentration in the liquid phase fraction in the rumen (RM) was calculated based on the metabolic hydrogen used (HU) and metabolic hydrogen produced (HP) during ruminal fermentation.

[0079] Here, the hydrogen in the fermentation intermediate HP and the short-chain fatty acids (HUS) in the liquid phase fraction in the rumen were estimated from the concentrations of acetate (C2), propionate (C3), and butyrate (C4) in the rumen fluid using the following equations (1-2) and (1-3). Short-chain fatty acids are the main energy source for the host and ruminant functions.

[0080] HP(mM)=2×C2+C3+4×C4...Equation (1-2) HUS(mM)=2×C3+2×C4...Formula (1-3) If the metabolic hydrogen used in ruminal fermentation (HU) is the sum of the metabolic hydrogen (HUS) used in the production of short-chain fatty acids and the metabolic hydrogen (HUM) used in methane production in ruminal fermentation, the recovery rate of metabolic hydrogen (HP) produced in ruminal fermentation to metabolic hydrogen (HU) used in ruminal fermentation is estimated to be 0.9 (Demeyer, 1991...). Therefore, the metabolic hydrogen (HU) used in ruminal fermentation can be calculated using the following equation (1-4):

[0081] HU=0.9×HP=HUS+HUM...Equation (1-4) Therefore, the metabolic hydrogen HU used in the rumen was calculated using the following equation (1-5) (Goel, Makkar and Becker (2009)).

[0082] HU = HUS + HUM = (2 × C3 + 2 × C4) + (4 × methane) Equation (1-5) Therefore, the formula for calculating the methane concentration (RM) in the liquid phase fraction in the rumen is the following formula (1-6):

[0083] RM(mM)=(HU-HUS) / 4=[(0.9×HP)-HUS] / 4...Equation (1-6) The present inventors calculated the predicted lumen volume (PRV) using the following formula (1-7).

[0084] PRV(L / day)=MY(mol / day) / [RM(mM) / 1000]...Equation (1-7) The short-chain fatty acid production amount was calculated using the ruminal concentration of short-chain fatty acids (SCFA) and estimated ruminal volume (PRV) according to the following formula (1-8). The inventors calculated the short-chain fatty acid yield using the MY and RM concentrations estimated from the DMI and ruminal short-chain fatty acid concentrations, respectively.

[0085] Short-chain fatty acid production (mol / day) = SCFA (mM) × PRV Equation (1-8) TTOR, (weight) 0.75The turnover rate of the rumen liquid phase fraction was calculated using the metabolic body weight (MBW) expressed as follows: TTOR = 1 / (1-9) where 1 / (1-9) is the metabolic weight of the cow and 2 / (1-9) is the metabolic weight of the cow. The present inventors use the term TTOR to mean the turnover rate of the rumen liquid phase fraction per unit metabolic body weight per day. Note that TTOR may also be the turnover rate of the rumen liquid phase fraction per unit of length, weight, volume, etc. related to the cow's body and its excretion, such as body weight, the weight of rumen contents derived from body weight, rumen volume derived from body weight, blood volume derived from body weight, organ weight derived from body weight, withers height, feces volume, urine volume, expired air volume, etc.

[0086] TTOR((L / day) / MBW)=PRV / MBW···Formula (1-9) (1-5.Measurement results) Parameters related to DMI, body weight, rumen fermentation, blood components, and milk components are shown in Table 1.

[0087] [Table 1]

[0088] (1-6. Calculation of TTOR) Based on the measurement results shown in Table 1, various parameters representing the properties of rumen fermentation were calculated using the above-mentioned formulas (1-1) to (1-9), and are shown in Table 2.

[0089] [Table 2]

[0090] 2. Calculating TTOR based on milk yield and 4% fat corrected milk yield The following data were calculated based on the measurement data (body weight, dry matter intake (DMI), concentrations of acetate, propionate, and butyrate in the liquid phase fraction in the rumen, rumen pH, milk yield, and milk components) described in References 2 to 8. References 2 to 8 are hereby incorporated by reference in their entirety.

[0091] Reference 2: Khafipour, E., Krause, D. O., & Plaizier, J. C. (2009). Alfalfa pellet-induced subacute ruminal acidosis in dairy cows increases bacterial endotoxin in the rumen without causing inflammation. Journal of Dairy Science, 92(4), 1712-1724. Reference 3: Gao, X., & Oba, M. (2016). Characteristics of dairy cows with a greater or lower risk of subacute ruminal acidosis: Volatile fatty acid absorption, rumen digestion, and expression of genes in rumen epithelial cells. Journal of dairy science, 99(11), 8733-8745. Reference 4: Hagg, F. M., Erasmus, L. J., Henning, P. H., & Coertze, R. J. (2010). The effect of a direct fed microbial (Megasphaera elsdenii) on the productivity and health of Holstein cows. South African Journal of Animal Science, 40(2) 101-112. Reference 5: Tager, L. R., & Krause, K. M. (2011). Effects of essential oils on rumen fermentation, milk production, and feeding behavior in lactating dairy cows. Journal of dairy science, 94(5), 2455-2464. Reference 6: Nasrollahi, SM, Zali, A., Ghorbani, GR, Shahrbabak, MM, & Abadi, MHS (2017). Variability in susceptibility to acidosis among high producing mid-lactation dairy cows is associated with rumen pH, fermentation, feed intake, sorting activity, and milk fat percentage. Animal feed science and technology, 228, 72-82. Reference 7: Colman, E., Fokkink, WB, Craninx, M., Newbold, JR, De Baets, B., & Fievez, V. (2010). Effect of induction of subacute ruminal acidosis on milk fat profile and rumen parameters. Journal of dairy science, 93(10), 4759-4773. Reference 8: Macmillan, K., Gao, X., & Oba, M. (2017). Increased feeding frequency increased milk fat yield and may reduce the severity of subacute ruminal acidosis in higher-risk cows. Journal of dairy science, 100(2), 1045-1054.

[0092] An estimation equation for estimating TTOR from milk yield and 4% fat-corrected milk yield was developed. First, using the above-mentioned equation (A), methane production was calculated from milk yield (milk) as described in References 2-8. Similarly, methane production was calculated from 4% fat-corrected milk yield (fcm) instead of milk yield in equation (A).

[0093] Next, using the same method as in 1 above, TTOR calculated from the estimated methane production and the measurement data described in References 2 to 8, TTOR calculated from milk (TTOR(milk)) and TTOR calculated from fcm (TTOR(fcm)) were calculated. A scatter plot of TTOR(fcm) versus TTOR is shown in Figure 3, and a scatter plot of TTOR(milk) versus TTOR is shown in Figure 4.

[0094] The multiple correlation coefficient (R 2 ) were 0.5875 and 0.7237, respectively. TTOR(milk) using milk volume had a higher R 2 was high, and no significant difference was observed in the t-test. In other words, there was no significant difference between using methane production estimated from 4% fat-corrected milk yield and using methane production estimated from milk yield, and in fact, it is possible that using methane production estimated from milk yield may result in a more accurate estimate. Therefore, it was shown that milk yield can be used as a substitute for 4% fat-corrected milk yield. For ordinary farms, it is difficult to determine the milk fat percentage of each individual cow, but since it is relatively easy to determine milk yield, it is practical and desirable to apply milk yield to the estimation formula.

[0095] [3. Estimation of rumen pH from milk yield TTOR] (Estimation formula 1) An estimation equation was created to estimate rumen pH from milk yield, SCFA concentration, SCFA amount, and TTOR (milk).Although multiple calculation equations can be created to estimate pH from milk yield, SCFA concentration, SCFA amount, and TTOR (milk), as an example, an estimation equation was created here to estimate rumen pH from TTOR (milk) and the ratio of acetate concentration (C2) to propionate concentration (C3) (C2 / C3 ratio).

[0096] First, TTOR(milk) × C2 / C3 was calculated based on the measurement data described in References 2 to 8.

[0097] Then, the correlation between the calculated TTOR(milk)×C2 / C3 and the actual rumen pH measured in References 2 to 8 was determined by regression analysis. Rumen pH = 0.0413 × (TTOR(milk) × C2 / C3) + 5.2089 Equation (3-1) Multiple correlation coefficient (R 2 ) was 0.2274.

[0098] When the estimated rumen pH was calculated by substituting TTOR(milk) × C2 / C3 into formula (3-1), the average estimated rumen pH was 5.96. This value was very close to the average measured rumen pH of 5.96, and no significant difference was observed by the t-test.

[0099] However, the multiple correlation coefficient (R 2 ) was low at 0.227, and the data suggested that there was a large difference between estimated rumen pH and measured rumen pH.

[0100] As a method to correct this drawback, the following study was conducted. Equation (3-1) is an estimation formula using the measurement data described in References 2 to 8 as samples. However, we considered calculating the estimated rumen pH more accurately by using each of the measurement data described in each reference as samples and deriving an estimation formula equivalent to Equation (3-1) for each. References 2 to 8 each measured data on dairy cows in different facilities (farms) under different rearing conditions. Therefore, deriving an estimation formula equivalent to Equation (3-1) for each reference means deriving an estimation formula for each farm.

[0101] As an example, Figures 5 and 6 show scatter plots of TTOR(milk) x C2 / C3 calculated using the measurement data described in References 2 and 8 as samples and rumen pH (actual measured value). Figure 5 is a scatter plot using the measurement data described in Reference 2 as samples, and Figure 6 is a scatter plot using the measurement data described in Reference 8 as samples. Based on the scatter plots in Figures 5 and 6, regression lines were calculated using the least squares method, and regression equations were obtained.

[0102] As a result, when the measurement data from Reference 2 was used, the average estimated rumen pH was 6.05 (standard deviation 0.249), which was very close to the average measured rumen pH of 6.05 (standard deviation 0.253).The same was true when the measurement data from Reference 8 was used, where the average estimated rumen pH was 6.18 (standard deviation 0.144), which was very close to the average measured rumen pH of 6.18 (standard deviation 0.149).

[0103] The estimated rumen pH based on the measurement data described in References 2 and 8, and the actual measured values ​​of rumen pH described in References 2 and 8 are summarized in Table 3 below. In Table 3, the estimated rumen pH obtained from equation (3-1) is designated as estimated rumen pH (A). Also in Table 3, the estimated rumen pH obtained from the equations obtained from the measurement data described in References 2 and 8 is designated as estimated rumen pH (B), respectively.

[0104] [Table 3]

[0105] In this way, it was shown that by using an estimation equation obtained by regression analysis of samples from multiple farms or samples from each farm, it is possible to estimate rumen pH with values ​​close to the actually measured value. In other words, by deriving an estimation equation for rumen pH from milk yield and TTOR, it is possible to estimate rumen pH without collecting rumen fluid, and in particular, it was shown that rumen pH can be estimated more accurately by deriving an estimation equation for rumen pH for each farm. Furthermore, as shown in 1 above, it is also useful to use an estimation equation for pH obtained from samples of data groups obtained at different times for each individual cow in the analysis of each individual cow.

[0106] (Estimation formula 2) As another example, we averaged the C2 / C3 ratio to a fixed value and derived an estimation equation for rumen pH from TTOR(milk) alone. The regression equation for TTOR(milk) × C2 / C3 and rumen pH (measured value) based on the measurement data described in Reference 2 was Equation (3-2). Rumen pH = 0.0593 × (TTOR(milk) × C2 / C3) + 5.0139 Equation (3-2)

[0107] Substituting the average C2 / C3 ratio of 2.12 (standard deviation 0.304) from Reference 2 into equation (3-2) yields equation (3-3). Rumen pH = 0.125716 × (TTOR(milk)) + 5.0139 Equation (3-3)

[0108] Similarly, for Reference 8, equation (3-4) was obtained. Rumen pH = 0.167076 × (TTOR(milk)) + 4.8742 Equation (3-4)

[0109] For References 2 and 8, the estimated rumen pH obtained using Equation (3-3) and Equation (3-4) was designated as the estimated rumen pH (C), and is summarized in Table 4 below along with the actually measured rumen pH.

[0110] [Table 4]

[0111] In this way, it was shown that even when the C2 / C3 ratio was fixed and the estimated rumen pH was calculated in the regression equation obtained for each farm, a value close to the rumen pH (actual measured value) could be obtained.

[0112] (Estimation formula 3) As another example, we developed a formula to estimate rumen pH (D) from the product of milk yield and TTOR(milk).

[0113] The regression equation for milk yield × TTOR (milk) and rumen pH (measured) in Reference 2 is given by equation (3-5), where x = milk yield × TTOR (milk). The multiple correlation coefficient (R 2 ) was 0.8083. Rumen pH = 0.0051x + 4.6119··· Equation (3-5)

[0114] Similarly, the regression equation for milk yield × TTOR (milk) and rumen pH (measured) in Reference 8 was Equation (3-6). 2 ) was 0.9525. Rumen pH = 0.0064x + 4.2531··· Equation (3-6)

[0115] For References 2 and 8, the estimated rumen pH obtained using Equation (3-5) and Equation (3-6) was designated as the estimated rumen pH (D), and is summarized in Table 5 below along with the actually measured rumen pH.

[0116] [Table 5]

[0117] In this way, it was shown that the method of calculating estimated rumen pH from the product of milk yield and TTOR (milk) in the regression equation obtained for each farm also yields values ​​close to the rumen pH (actual measured value).

[0118] 4. Estimation of DMI from milk yield and TTOR(milk) Reference 1 shows that there is a strong correlation between milk yield / TTOR and DMI / TTOR. Here, we utilized this correlation to estimate DMI from milk yield using the following method.

[0119] A scatter plot of milk yield / TTOR(milk) and DMI / TTOR(milk) was created for References 2 to 8. The resulting scatter plot is shown in Figure 7.

[0120] If the estimated DMI calculated from TTOR(milk) is DMI(TTOR(milk)), x = DMI(TTOR(milk)), z = TTOR(milk), and milk yield = MILK, then: MILK / z=0.717×(x / z)+2.7411 x=(MILK-2.7411×TTOR(milk)) / 0.717 This becomes:

[0121] Therefore, DMI(TTOR(milk)) is given by equation (4-1). DMI(TTOR(milk))=(MILK-2.7411×TTOR(milk)) / 0.717...Equation (4-1)

[0122] DMI (TTOR (milk)) (estimated DMI) was calculated by substituting milk yield and TTOR (milk) into formula (4-1). The average estimated DMI was 24.27 kg, which was very close to the average DMI (measured value) of 24.12 kg, and no significant difference was observed in the T-test. On the other hand, the multiple correlation coefficient (R 2 ) was not high at 0.3546, suggesting that the data suggest a large difference between the estimated DMI and the DMI (measured value).

[0123] However, the total of the 33 estimated DMI points was 800.9 kg, and the total of the 33 measured DMI points was 795.9 kg. In other words, when the data used here is viewed as a single herd, the calculated total of the estimated DMI is very close to the actual DMI, demonstrating its usefulness in determining the amount of feed to be fed to a single herd.

[0124] The multiple correlation coefficient (R 2 We investigated a method to correct for the low DMI. Since equation (4-1) is an estimation formula obtained using data from references 2 to 8, creating an estimation formula equivalent to equation (4-1) for each reference will enable more accurate calculation of the estimated DMI.

[0125] Therefore, a regression equation between milk yield / TTOR and DMI / TTOR(milk) was determined for each farm (examples shown in Figures 8 and 9), and an estimated DMI was calculated from milk yield and TTOR(milk).

[0126] As an example, the estimated DMI obtained using equation (4-1) using the measurement data described in References 2 and 8 is referred to as estimated DMI (A), and the estimated DMI obtained using a regression equation created using the measurement data described in each reference is referred to as estimated DMI (B). These are shown in Table 6 together with the actually measured DMI.

[0127] [Table 6]

[0128] In this way, the estimated DMI (estimated DMI (B)) estimated using the regression equation created using data obtained from each farm was close to the DMI (actual measured value).

[0129] In this way, it was shown that by using an estimation equation obtained by regression analysis of samples from multiple farms or samples from each farm, it is possible to estimate DMI with a value close to the actually measured DMI value. In other words, by deriving an estimation equation for DMI from milk yield and TTOR, it is possible to estimate DMI without weighing the fed feed and residual feed, and in particular, it was shown that by deriving an estimation equation for DMI for each farm, it is possible to estimate DMI with greater accuracy. Furthermore, as shown in 1 above, it is also useful to use an estimation equation for DMI obtained from samples of data groups obtained for each individual cow at different times in the analysis of each individual cow.

[0130] [5. Correction of the DMI estimation formula using regression analysis] The above formula (4-1) for calculating the estimated DMI is generated using the measured DMI value, but it is not easy to measure the measured DMI value on the farm. Therefore, it is preferable to generate a formula for calculating the estimated DMI without using the measured DMI value. In this example, a regression formula for calculating the estimated DMI was obtained using the DMI (FS) calculated from the dairy cow feeding standard shown below instead of the measured DMI value.

[0131] DMI(FS) is calculated by the method described in the following reference 9, NRC (2001), etc. Reference 9 is hereby incorporated by reference into this specification: Reference 9: National Agriculture and Food Research Organization, 2017, Japanese Standard Dairy Cattle Feeding (2017 edition), Central Livestock Association, Tokyo.

[0132] Based on Reference 9, DMI (FS) is calculated by the following formula (5-1), where W (kg) is body weight and FCM (kg) is 4% fat-corrected milk yield: DMI(FS)=1.3922+0.05839×W 0.75 +0.40497×FCM...(Formula 5-1)

[0133] The FCM is expressed as the following equation (5-2), also based on Reference 9: FCM = milk yield (kg) × (0.15 × milk fat percentage (%) + 0.4) (Equation 5-2)

[0134] Using the DMI(FS) calculated by the above formulas (5-1) and (5-2), a scatter plot of milk yield / TTOR(milk) and DMI(FS) / TTOR(milk) was created based on the measurement data in Reference 7. The created scatter plot is shown in Figure 10. Figure 10 is a graph showing the correlation between the estimated value of TTOR estimated from milk yield and dry matter intake (DMI(FS)) calculated from the feeding standard, and the estimated value of TTOR (TTOR(milk)) estimated from milk yield and milk yield.

[0135] If the estimated DMI calculated from TTOR(milk) is DMI(TTOR(milk)), x = DMI(TTOR(milk)), z = TTOR(milk), and milk yield = MILK, then: MILK / z=1.5814×(x / z)-0.7105 x=(MILK+0.7105×TTOR(milk)) / 1.5814 This becomes:

[0136] Therefore, DMI(TTOR(milk)) is given by equation (5-3). DMI(TTOR(milk))=(MILK+0.7105×TTOR(milk)) / 1.5814...Equation (5-3)

[0137] DMI (TTOR(milk)) (estimated DMI) was calculated by substituting milk yield and TTOR(milk) into formula (5-3). The average estimated DMI was 24.16 kg, which was very close to the average DMI (measured value) of 24.19 kg, and no significant difference was observed by t-test.

[0138] Thus, it was shown that DMI can be estimated using the DMI calculated based on the feeding standards for dairy cows using the methods described in Reference 9 and NRC (2001), just as it can be estimated using the actual measured DMI value. In other words, by using the DMI (FS) calculated based on the feeding standards for dairy cows instead of the actual measured DMI value, it is possible to generate an estimation formula for DMI from the regression line. [Industrial Applicability]

[0139] The present invention can be used in the agricultural sector, especially in relation to dairy farming. [Explanation of symbols]

[0140] 10. Measuring equipment 20 Evaluation equipment 21 Estimation part 22 Management information generation section 23 Diagnostic Department 24 Memory section 25 Arithmetic section 30 Estimation device 31 Estimation part 100 rating system

Claims

1. An estimation device for estimating methane production of a ruminant, comprising: The apparatus includes an estimation unit that estimates the methane production amount of a ruminant animal by substituting the milk yield into the following formula (A) based on the milk yield of the milk produced by the ruminant animal to be evaluated. MY (milk) (mol / day) = (8.19+(300 / milk amount)) x milk amount / 22.4...Formula (A) (In the formula, MY (milk) represents methane production, and the unit of milk yield is kg / day.) Estimation device.

2. An evaluation system for evaluating the state of rumen fermentation in ruminants, comprising: An evaluation system comprising an evaluation device having an estimation unit that estimates a state of rumen fermentation of a ruminant animal to be evaluated based on the methane production amount of the ruminant animal estimated by the estimation device according to claim 1.

3. The evaluation system according to claim 2 , wherein the estimation unit estimates a theoretical turnover rate of the liquid phase fraction of the rumen, which represents a state of ruminal fermentation of the ruminant animal, based on the methane production amount of the ruminant animal to be evaluated.

4. 4. The evaluation system according to claim 3, wherein the estimation unit estimates the theoretical turnover rate of the liquid phase fraction of the rumen of the ruminant to be evaluated from the measurement results of the milk yield of the ruminant to be evaluated, based on a correlation between the theoretical turnover rate of the liquid phase fraction of the rumen calculated based on the amount of methane production emitted by the ruminant and the concentration of short-chain fatty acids in the first stomach of the ruminant and the milk yield.

5. 5. The evaluation system according to claim 3, wherein the estimation unit estimates the pH in the rumen of the ruminant from the measurement results of the milk yield of the ruminant being evaluated, based on the theoretical turnover rate of the liquid phase fraction in the rumen estimated from the milk yield.

6. The evaluation system according to claim 3 or 4, wherein the estimation unit estimates the dry matter intake from the measurement results of the milk yield of the ruminant being evaluated, based on the theoretical turnover rate of the liquid phase fraction of the rumen estimated from the milk yield.

7. The evaluation device The evaluation system according to claim 2 , further comprising a management information generation unit that generates feeding management information for a ruminant based on the state of ruminal fermentation of the ruminant estimated by the estimation unit.

8. The evaluation device The evaluation system according to any one of claims 2 to 7, further comprising a diagnosis unit that diagnoses at least one of the feeding and management status and production diseases of the ruminant based on the state of ruminal fermentation of the ruminant estimated by the estimation unit.

9. The evaluation device The evaluation system according to claim 2 , further comprising a storage unit that stores information representing a correlation between the milk yield and a state of ruminal fermentation of the ruminant.

10. The evaluation device The evaluation system according to claim 2 , further comprising a calculation unit that calculates a relational expression that represents a correlation between the milk yield and the state of ruminal fermentation of the ruminant.

11. A method for estimating methane production in ruminants, comprising: The method includes a step of estimating the methane production amount of a ruminant animal to be evaluated by substituting the milk yield into the following formula (A) based on the milk yield of the milk produced by the ruminant animal to be evaluated. MY (milk) (mol / day) = (8.19+(300 / milk amount)) x milk amount / 22.4...Formula (A) (In the formula, MY (milk) represents methane production, and the unit of milk yield is kg / day.) Estimation method.

12. A method for evaluating the state of rumen fermentation in a ruminant, comprising: An evaluation method comprising an estimation step of estimating the state of ruminal fermentation of a ruminant animal to be evaluated based on the methane production amount of the ruminant animal estimated by the estimation method according to claim 11.

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