CN111506881B - System for predicting Chinese Holstein cow mastitis onset risk - Google Patents
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Abstract
The invention discloses a system for predicting the incidence risk of Chinese Holstein cow mastitis. The invention discloses a system for predicting Chinese Holstein cow mastitis onset risk, which comprises a parameter acquisition substance and a risk prediction module, wherein the acquired parameters comprise the field scale and season of a cow to be detected, the delivery and fetal number, the lactation stage and SCC or SCS, and the risk prediction module can be used for predicting the mastitis onset risk. Experiments prove that the system for predicting the incidence risk of mastitis has high accuracy, high sensitivity, high specificity and simple and quick prediction, can realize the early discovery of cows with high incidence risk of mastitis, further prevent the cows from suffering from mastitis in production, has important guiding significance for the early detection and prevention of mastitis of cows, and is suitable for popularization and application.
Description
Technical Field
The invention relates to a system for predicting the incidence risk of Chinese Holstein cow mastitis in the field of animal medicine.
Background
The mastitis of the dairy cow not only obviously reduces the milk yield of the dairy cow to cause serious economic loss, but also can change the components of the milk to obviously reduce the nutritional value and the edible value of the milk. According to the existence of macroscopic changes in breasts or milk, cow mastitis is classified into clinical mastitis and subclinical mastitis. The current indicator for determining the mastitis response is the number of breast milk somatic cells (SCC) in the DHI assay record. DHI is a complete dairy cow production performance recording system aiming at dairy cow lactation performance and milk components. Currently, the standard for judging SCC of milk is 10 ten thousand mL -1 About 50 thousand mL -1 Are not equal. To overcome the shortcomings of SCC in statistical analysis, it is usually converted into a form of body cell score (SCS) that follows a normal distribution.
The early discovery of the mastitis of the dairy cows is beneficial to the treatment of the sick dairy cows, and further can reduce the economic loss related to the mastitis. A plurality of models exist for predicting the mastitis risk of the dairy cattle, wherein the accuracy and prediction value of the models such as Logistic regression, deep learning and random forest are not obviously different. Regression analysis predicts one or more response variables through a set of predictor variables. Such statistical analysis methods can be used to evaluate the expected effect of predictive variables on response variables.
Disclosure of Invention
The invention aims to solve the technical problem of predicting the incidence risk of the mastitis of the dairy cows, particularly Chinese Holstein cows.
The invention firstly provides a system for predicting or assisting in predicting the incidence risk of mastitis in a dairy cow, which comprises a parameter acquisition substance;
the parameter collecting substance is used for collecting the field scale and season, the birth times, the lactation stage and the milk somatic cell number (SCC) or Somatic Cell Score (SCS) of the dairy cow to be detected.
In the above system, the parameter collecting substance may be a device and/or a reagent.
In the above system, the SCS value may be 0 to 9.9.SCS value = log2 (SCC/100,000) +3.
In the above system, the cow mastitis may be clinical mastitis or recessive mastitis of the cow.
The clinical mastitis can satisfy milk SCC of dairy cows>50 ten thousand mL -1 。
The recessive mastitis can meet the requirement of 10 ten thousand mL of milk of dairy cows -1 < SCC ≤ 50 ten thousand-mL -1 。
In the system, the system may further comprise a risk prediction module or a computing device provided with the risk prediction module, and the risk prediction module may be capable of predicting the incidence risk of mastitis of the dairy cow according to the size and season of the farm where the dairy cow to be detected is located, the number of births, the lactation period, and the SCC or SCS.
In the above system, the cow mastitis is clinical cow mastitis, and the risk prediction module may predict the risk of the cow clinical mastitis according to formula I:
Logit P face ＝-4.3759-0.3926x 1 +0.4070x 2 +0.2340x 3 +0.2367x 4 +0.6596 x 5 (formula I);
wherein, P Face Predicting the probability for clinical mastitis;
x1 is the field scale, the value of x1 is 1 when the number of the lactating cows is less than or equal to 800 in the field scale, and the value of x1 is 2 when the number of the lactating cows is more than 800 in the field scale;
x2 is the number of births, and the values of x2 are 1, 2 and 3 when the number of births is one, two and three;
x3 is a measuring season, x3 takes a value of 1 when the measuring season is 6, 7 or 8 months, and x3 takes a value of 2 when the measuring season is not 6, 7 or 8 months;
x4 is a lactation stage, and the values of x4 are 1, 2, 3 and 4 when the lactation stage is 1-100 days, 101-200 days, 201-300 days and more than 300 days;
x5 is SCS value = log2 (SCC/100,000) +3.
In the above system, the cow mastitis is cow subclinical mastitis, and the risk prediction module may predict the risk of cow subclinical mastitis according to formula II:
Logit P concealed ＝-2.3966-0.1683x 1 +0.2618x 2 +0.0879x 3 +0.2507x 4 +0.4432x 5 (formula II);
wherein, P Concealed Predicting probability for subclinical mastitis;
x1 is the field scale, the value of x1 is 1 when the number of the lactating cows is less than or equal to 800 in the field scale, and the value of x1 is 2 when the number of the lactating cows is more than 800 in the field scale;
x2 is the number of births, and the values of x2 are 1, 2 and 3 when the number of births is one, two and three;
x3 is a measuring season, x3 takes a value of 1 when the measuring season is 6, 7 or 8 months, and x3 takes a value of 2 when the measuring season is not 6, 7 or 8 months;
x4 is a lactation stage, and the values of x4 are 1, 2, 3 and 4 when the lactation stage is 1-100 days, 101-200 days, 201-300 days and more than 300 days;
x5 is SCS value = log2 (SCC/100,000) +3.
The system may further include a readable carrier; the readable carrier can predict the clinical mastitis incidence risk of the dairy cow according to the field scale and season, the delivery and abortion, the lactation stage and SCC or SCS of the dairy cow to be detected, and the readable carrier can be recorded as the following formula I:
Logit P face ＝-4.3759-0.3926x 1 +0.4070x 2 +0.2340x 3 +0.2367x 4 +0.6596x 5 (formula I);
wherein, P Face Predicting probability for clinical mastitis;
x1 is the field scale, the value of x1 is 1 when the number of the lactating cows is less than or equal to 800 in the field scale, and the value of x1 is 2 when the number of the lactating cows is more than 800 in the field scale;
x2 is the number of births, and the values of x2 are 1, 2 and 3 when the number of births is one, two and three;
x3 is a measuring season, x3 takes a value of 1 when the measuring season is 6, 7 or 8 months, and x3 takes a value of 2 when the measuring season is not 6, 7 or 8 months;
x4 is a lactation stage, and the values of x4 are 1, 2, 3 and 4 when the lactation stage is 1-100 days, 101-200 days, 201-300 days and more than 300 days;
x5 is SCS value = log2 (SCC/100,000) +3.
The system may further include a readable carrier; the readable vector can predict the recessive mastitis onset risk of the dairy cow according to the field scale and season, the birth and fetal times, the lactation stage and SCC or SCS of the dairy cow to be detected, and the readable vector is described as the following formula II:
Logit P concealed ＝-2. 3966-0.1683x 1 +0.2618x 2 +0.0879x 3 +0.2507x 4 +0.4432x 5 (formula II);
wherein, P Concealed Predicting probability for subclinical mastitis;
x1 is the field scale, the value of x1 is 1 when the number of the lactating cows is less than or equal to 800, and the value of x1 is 2 when the number of the lactating cows is more than 800;
x2 is the number of births, and the values of x2 are 1, 2 and 3 when the number of births is one, two and three;
x3 is a measuring season, x3 takes a value of 1 when the measuring season is 6, 7 or 8 months, and x3 takes a value of 2 when the measuring season is not 6, 7 or 8 months;
x4 is a lactation stage, and the values of x4 are 1, 2, 3 and 4 when the lactation stage is 1-100 days, 101-200 days, 201-300 days and more than 300 days;
x5 is SCS value = log2 (SCC/100,000) +3.
In the above system, the parameter-collecting substance may include an apparatus for measuring SCC by microscopy, electron particle counting (Coulter counting) or fluorescence photoelectric counting, such as a somatic cell detector (e.g., a somatic cell detector of Foss, denmark or Bentley, USA) for microscopy, coulter counting or fluorescence photoelectric counting.
The system can be composed of the parameter acquisition substance only, the parameter acquisition substance and the risk prediction module, the parameter acquisition substance and the computing equipment provided with the risk prediction module, and the parameter acquisition substance and the readability carrier.
The invention also provides a using method of the system for predicting or assisting in predicting the incidence risk of dairy cow mastitis, wherein the method comprises the following steps:
1) Collecting the scale and season of a cow to be detected, the number of births and births, the lactation stage and SCC or SCS;
2) Calculating a clinical mastitis prediction probability according to formula I or calculating a recessive mastitis prediction probability according to formula II;
Logit P face ＝-4.3759-0.3926x 1 +0.4070x 2 +0.2340x 3 +0.2367x 4 +0.6596x 5 (formula I), P Face Predicting the probability for clinical mastitis;
Logit P concealed ＝-2.3966-0.1683x 1 +0.2618x 2 +0.0879x 3 +0.2507x 4 +0.4432x 5 (formula II), P Concealed Predicting probability for subclinical mastitis;
x1 is the field scale, the value of x1 is 1 when the number of the lactating cows is less than or equal to 800 in the field scale, and the value of x1 is 2 when the number of the lactating cows is more than 800 in the field scale;
x2 is the number of births, and the values of x2 are 1, 2 and 3 when the number of births is one, two and three;
x3 is a measuring season, x3 takes a value of 1 when the measuring season is 6, 7 or 8 months, and x3 takes a value of 2 when the measuring season is not 6, 7 or 8 months;
x4 is a lactation stage, and the values of x4 are 1, 2, 3 and 4 when the lactation stage is 1-100 days, 101-200 days, 201-300 days and more than 300 days;
x5 is SCS value = log2 (SCC/100,000) +3.
The invention also provides application of the system for predicting or assisting in predicting the incidence risk of the dairy cow mastitis in preparing a product for predicting or assisting in predicting the incidence risk of the dairy cow mastitis.
The cow mastitis can be clinical mastitis or recessive mastitis of the cow.
In the present invention, the system may be a product. The product may be a kit.
The dairy cow may be a Chinese Holstein cow.
In the invention, the parameter acquired by the parameter acquisition substance can be the parameter of one month of the dairy cow, and the system can be used for predicting the incidence risk of mastitis in the next month.
P Concealed The larger the value, the greater the risk of occult mastitis; p Face The larger the number, the greater the risk of clinical mastitis onset.
Experiments prove that the system for predicting or assisting in predicting the incidence risk of the mastitis of the dairy cattle can not only predict the incidence risk of clinical mastitis, but also predict the incidence risk of recessive mastitis, and has the advantages of high accuracy, high sensitivity, high specificity and simplicity and quickness in prediction. The method can realize the early discovery of the dairy cows with high mastitis incidence risk, further prevent the dairy cows from suffering from mastitis in production, has important guiding significance for the early detection and prevention of the mastitis of the dairy cows, and is suitable for popularization and application.
Drawings
FIG. 1 is a ROC curve for a recessive mastitis risk assessment model.
FIG. 2 is a ROC curve of a clinical mastitis risk assessment model.
In the figure, model represents a mastitis risk assessment Model, herd _ scale represents a field scale, parity represents a fetal number, test _ Season represents a measurement Season, DIM represents a lactation stage, and SCS _ This _ Month represents a SCS value in This Month.
Detailed Description
The invention provides a risk assessment system for predicting the incidence of Chinese Holstein cow mastitis, which is used for assessing and predicting the risk of mastitis in the next month of cows; the system comprises:
a factor analysis module: the method is used for selecting and analyzing risk indexes for predicting cow mastitis;
a calculation module: the system is used for calculating the prediction probability according to the record of the risk index and a Logistic regression equation;
a prediction module: and predicting the incidence of mastitis of the dairy cow in the next month according to the prediction probability.
A factor analysis module, further comprising:
a factor selection submodule: for selecting a mastitis risk factor; mastitis risk factors include: field scale, fetal frequency, season, lactation stage, SCS in this month;
a classification factor judgment submodule: the risk assessment model is used for judging and assigning the form of the classified risk factors entering the risk assessment model; the factor forms include: the field scale is divided into small size, medium size (1) and large size (2); the number of the tires is divided into a first tire (1), a second tire (2) and a third tire (3); measuring seasons which are divided into summer (1) and non-summer (2); the lactation stage is divided into 1-100 days (1), 101-200 days (2), 201-300 days (3) and more than 300 days of pregnancy (4);
a factor analysis submodule: for analysis of the more likely form of the risk factor for occurence of occult, clinical mastitis; selecting field scale, fetal times, measuring seasons, lactation stage and the like as variables to substitute into a multi-factor Logistic regression equation, and determining a factor form more prone to occurence of recessive mastitis and clinical mastitis according to an OR value; where the OR value of a form indicates that the at risk form is more prone to occurence of occult OR clinical mastitis.
In the calculation module, the Logistic regression equation of clinical mastitis is as follows:
LogitP face ＝-4.3759-0.3926x 1 +0.4070x 2 +0.2340x 3 +0.2367x 4 +0.6596x 5 (formula I), P Face Predicting the probability for clinical mastitis; wherein x1 is field size, x2 is fetal number, x3 is season, x4 is lactation stage, and x5 is SCS value;
the Logistic regression equation of the recessive mastitis is as follows:
Logit P concealed ＝-2.3966-0.1683x 1 +0.2618x 2 +0.0879x 3 +0.2507x 4 +0.4432x 5 (formula II), P Concealed Predicting probability for subclinical mastitis; wherein x1 is the field scale, x2 is the fetal number, x3 is the season, x4 is the lactation stage, and x5 is the SCS value;
P concealed The larger the value, the greater the risk of occult mastitis; p Face The larger the number, the greater the risk of clinical mastitis onset.
The present invention is described in further detail below with reference to specific embodiments, which are given for the purpose of illustration only and are not intended to limit the scope of the invention. The examples provided below serve as a guide for further modifications by a person skilled in the art and do not constitute a limitation of the invention in any way.
The experimental procedures in the following examples, unless otherwise specified, were carried out in a conventional manner according to the techniques or conditions described in the literature in this field or according to the product instructions. Materials, reagents, instruments and the like used in the following examples are commercially available unless otherwise specified. The quantitative tests in the following examples, all set up three replicates and the results averaged.
Example 1 construction of Risk assessment System for Breast inflammation of Chinese Holstein cattle
1. Milk cow production performance measurement record collection
Selecting DHI data (from the cow center of Beijing city) in Beijing area, covering 78,386 Chinese Holstein cows in 121 cattle farms for 1998-2016, and totaling 1,967,310 DHI determination records. The data content comprises individual number, fetal times, calving date, measuring date, milk production days, SCC and the like. Among them, SCC can be measured by microscopy, electron particle counting (coulter counter) or fluorescence photoelectric counting.
Recording of the obtained raw DHI: deleting the SCC-missing cattle records; deleting the SCC value of the previous 4 days to avoid deviation caused by overhigh SCC value of the previous 4 days; screening records that the same cow has DHI for two consecutive months, and classifying Chinese Holstein cows into a healthy group, a recessive mastitis group and a clinical mastitis group according to SCC in the next month; using the international conversion formula SCS = log 2 (SCC/100,000) +3 the SCC value is converted into SCS, and the SCS is taken as a data set of 0-9.9.
Wherein, the mastitis judgment standard is as follows: healthy group, SCC ≤ 10 ten thousand-mL -1 (ii) a Group of recessive mastitis, 10 Wan. ML -1 Less than SCC less than or equal to 50 ten thousand mL -1 (ii) a Clinical mastitis group, SCC>50 ten thousand mL -1 。
After the elimination, 781,611 DHI records are obtained.
2. Selection of mastitis risk factor
Using the DHI data of Beijing area, using SAS 9.2 software to perform single factor analysis on each factor influencing the cow mastitis prediction, primarily screening the single factor, researching the influence of the single factor on the cow mastitis, and determining indexes with statistical significance among a healthy group, a recessive mastitis group and a clinical mastitis group, namely, the risk factors of the cow mastitis.
3. Formal determination and assignment of mastitis risk factor
And (3) analyzing by using the DHI data in Beijing area, and determining the form of entering the classification variables into the model and assigning values at the same time according to the logit (p) of the cow recessive mastitis group and clinical mastitis group. The classification factor forms include: the field scale is divided into small size, medium size (1) and large size (2); the number of the tires is divided into a first tire (1), a second tire (2) and a third tire (3); measuring seasons which are divided into summer (1) and non-summer (2); the lactation stage is divided into 1-100 days (1), 101-200 days (2), 201-300 days (3) and more than 300 days (4); the numbers in parentheses are assigned to different indices.
Wherein, the small and medium size of the farm scale means that the number of the lactating cows is less than or equal to 800, and the large size of the farm scale means that the number of the lactating cows is more than 800.
The first, second and third fetus of the gestation is the gestation of the cow.
Summer means 6, 7 and 8 months, and non-summer means 6, 7 and 8 months.
The lactation stage refers to the lactation time after the dairy cows are produced.
The results of the single factor analyses are shown in table 2 and fig. 2.
4. Analysis of single factor in recessive mastitis group and clinical mastitis group
The risk assessment of cow mastitis was performed using DHI data provided by the beijing dairy center. And (3) analyzing by using the field scale, the fetal times, the season measurement and the lactation stage as variables, and defining and assigning the factors in the same step.
The results of the single factor analysis are shown in table 1 and fig. 1.
Determining the more likely form of the factor for occurence of occult, clinical mastitis from the OR value; where the magnitude of the OR value indicates that the at risk form is more prone to occurence of occult OR clinical mastitis. Wherein, when the field scale is small or medium, the risks of recessive mastitis and clinical mastitis of the dairy cows are obviously increased; the risks of subclinical mastitis and clinical mastitis of the dairy cattle after the second and third births are obviously increased; the risk of subclinical mastitis and clinical mastitis is obviously increased in summer; the risks of recessive mastitis and clinical mastitis are obviously increased after 101-200 days, 201-300 days and 300 days of lactation (namely more than 300 days).
TABLE 1 Single factor analysis of occult mastitis groups
Note: 1 is a reference category; * P < 0.001.
TABLE 2 analysis of clinical mastitis group factors
Note: 1 is a reference category; * P < 0.001.
5. Establishment of mastitis risk assessment model
Aiming at the data set screened in the step 1, taking each risk index (field scale, fetal number, measuring season, lactation stage and SCS value) as an independent variable and mastitis diseased condition as a dependent variable, adopting a non-conditional multi-factor Logistic regression analysis method to establish a model, and respectively obtaining a Logistic regression equation (namely an mastitis risk assessment model) of recessive mastitis and clinical mastitis as follows:
LogitP face ＝-4.3759-0.3926x 1 +0.4070x 2 +0.2340x 3 +0.2367x 4 +0.6596x 5 (formula I), P Face Predicting the probability for clinical mastitis;
wherein x1 is the field scale, x2 is the number of births, x3 is the season, x4 is the lactation stage, and x5 is the dairy cow production performance, and the SCS value in this month is recorded;
Logit P concealed ＝-2.3966-0.1683x 1 +0.2618x 2 +0.0879x 3 +0.2507x 4 +0.4432x 5 (formula II), P Concealed Predicting probability for subclinical mastitis;
wherein x1 is the field size, x2 is the number of births, x3 is the season, x4 is the lactation stage, and x5 is the dairy cow production performance.
6.P threshold determination
The method for predicting recessive mastitis and clinical mastitis according to the P value obtained by the regression equation comprises the following steps:
P concealed The larger the numerical value, the greater the risk of onset of subclinical mastitis; p Face The larger the number, the greater the risk of clinical mastitis onset.
In particular, such as P Concealed If the milk cow is more than 0.5, the dairy cow to be tested suffers from recessive mastitis in the next month, such as P Concealed Less than or equal to 0.5, the dairy cow to be detected does not suffer from recessive mastitis in the next month; such as P Face If the milk cow is more than 0.5, the milk cow to be detected is lowerClinical mastitis in months, e.g. P Face Less than or equal to 0.5, the dairy cow to be detected does not suffer from clinical mastitis in the next month.
Such as P Face > 0.5 and P Concealed If the milk cow is more than 0.5, the milk cow to be detected suffers from clinical mastitis in the next month; such as P Face Less than or equal to 0.5 and P Concealed If the milk cow is more than 0.5, the dairy cow to be detected suffers from recessive mastitis in the next month; such as P Face Less than or equal to 0.5 and P Concealed Less than or equal to 0.5, the dairy cow to be detected does not suffer from clinical mastitis or recessive mastitis in the next month; not seen in P Face > 0.5 and P Concealed Less than or equal to 0.5.
7. Testing verification model using actual data
And (3) evaluating the accuracy of the mastitis risk evaluation model for predicting the mastitis of the dairy cow by utilizing 781,611 DHI records in the step 1.
The result shows that the accuracy of predicting the recessive mastitis by using the recessive mastitis risk assessment model is 67.6%, the sensitivity is 50.7%, the specificity is 80.3%, and the area under the curve is 0.7214; predicting the area under the curve of the recessive mastitis by using the field scale to be 0.5130; predicting the area under the curve of the recessive mastitis by using the fetal number to be 0.5645; predicting the area under the curve of the recessive mastitis by using the measured season to be 0.5112; predicting the area under the curve of the recessive mastitis by utilizing the lactation stage to be 0.5749; the area under the curve for predicting recessive mastitis by SCS is 0.7133. The prediction of the recessive mastitis by using the recessive mastitis risk assessment model is superior to each individual index.
The accuracy of predicting clinical mastitis by using the clinical mastitis risk assessment model is 83.6 percent, the sensitivity is 42.5 percent, the specificity is 94.9 percent, and the area under the curve is 0.8251; predicting the area under the curve of clinical mastitis by using the field scale to be 0.5356; predicting the area under the curve of clinical mastitis by using the fetal number to be 0.6105; predicting the area under the curve of clinical mastitis by using the measured season to be 0.5282; predicting the area under the curve of clinical mastitis by utilizing the lactation stage to be 0.5762; the area under the curve for clinical mastitis predicted by SCS was 0.8157. The clinical mastitis risk assessment model is used for predicting clinical mastitis to be superior to each individual index.
Example 2 measuring, recording, testing and testing the accuracy of the test model by using the production performance of the dairy cow
The accuracy of predicting cow mastitis by the mastitis risk assessment model of example 1 was evaluated using DHI data of chinese holstein cows in the pasture. The DHI data content comprises individual number, fetal number, calving date, measuring date, milk production related records, SCC and the like. DHI data was sourced from another exemplary cattle farm in beijing.
And (3) utilizing R3.4.1 software to sort the DHI data, analyzing each measurement index in the DHI data of the pasture by using a Logistic regression model, and comparing the consistency between the monthly predicted condition and the actual condition of the mastitis of the dairy cows in the next month to verify the accuracy of the model. The standard for judging the incidence of the mastitis of the dairy cattle is as follows: SCC > 50 ten thousand/ml.
The results are shown in Table 3, and the results show that the accuracy is over 65%.
TABLE 3 measuring, recording, testing and inspecting model accuracy rate for dairy cow production performance in pasture
| Month of the year | 2 month | 3 month | 4 month | Month 5 | 6 month | Average |
| Number of heads of lactating cattle | 1234 | 1339 | 1329 | 1263 | 1165 | 1266 |
| Rate of accuracy | 67％ | 69％ | 81％ | 66％ | 65％ | 70％ |
The present invention has been described in detail above. It will be apparent to those skilled in the art that the invention can be practiced in a wide range of equivalent parameters, concentrations, and conditions without departing from the spirit and scope of the invention and without undue experimentation. While the invention has been described with reference to specific embodiments, it will be appreciated that the invention can be further modified. In general, this application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. The use of some of the essential features is possible within the scope of the claims attached below.
Claims (3)
1. The system for predicting or assisting in predicting the incidence risk of the mastitis of the dairy cow comprises a parameter acquisition substance and risk prediction module or a computing device provided with the risk prediction module;
the dairy cow mastitis is clinical mastitis or recessive mastitis of dairy cows;
the parameter collecting substance is used for collecting the field scale and season, delivery times, lactation stage and SCC or SCS of the dairy cow to be detected, wherein the SCC is the number of somatic cells of milk, the SCS is the score of the somatic cells, and the SCS value = log2 (SCC/100,000) +3;
the risk prediction module can predict the incidence risk of the mastitis of the dairy cow according to the field scale and season, the birth and abortion, the lactation stage and the SCC or SCS of the dairy cow to be detected;
the risk prediction module predicts the incidence risk of the clinical mastitis of the dairy cow according to the formula I:
Logit P face ＝-4.3759-0.3926x 1 +0.4070x 2 +0.2340x 3 +0.2367x 4 +0.6596x 5 (formula I);
the risk prediction module predicts the incidence risk of the cow subclinical mastitis according to formula II:
Logit P concealed ＝-2. 3966-0.1683x 1 +0.2618x 2 +0.0879x 3 +0.2507x 4 +0.4432x 5 (formula II);
wherein, P Face Predicting the probability for clinical mastitis; p is Concealed Predicting probability for subclinical mastitis;
x1 is the field scale, the value of x1 is 1 when the number of the lactating cows is less than or equal to 800 in the field scale, and the value of x1 is 2 when the number of the lactating cows is more than 800 in the field scale;
x2 is the number of births, and the values of x2 are 1, 2 and 3 when the number of births is one, two and three;
x3 is a measuring season, x3 takes a value of 1 when the measuring season is 6, 7 or 8 months, and x3 takes a value of 2 when the measuring season is not 6, 7 or 8 months;
x4 is a lactation stage, and the values of x4 are 1, 2, 3 and 4 when the lactation stage is 1-100 days, 101-200 days, 201-300 days and more than 300 days;
x5 is the SCS value.
2. The system of claim 1, wherein: the system further comprises a readability carrier;
the readable carrier can predict the clinical mastitis onset risk or recessive mastitis onset risk of the dairy cow according to the field scale and season, the birth and abortion, the lactation stage and SCC or SCS of the dairy cow to be detected.
3. The system according to claim 1 or 2, characterized in that: the parameter acquisition material includes an instrument for measuring SCC by microscopy, electron particle counting, or fluorescence photoelectric counting.
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| CN105137011A (en) * | 2015-07-23 | 2015-12-09 | 山东省农业科学院奶牛研究中心 | System for screening dairy cow having recessive mastitis among cattle |
| CN106355033A (en) * | 2016-09-27 | 2017-01-25 | 无锡金世纪国民体质与健康研究有限公司 | Life risk assessment system |
| CN109711549A (en) * | 2018-12-27 | 2019-05-03 | 中国农业大学 | A kind of mastitis for milk cows detection method based on genetic algorithm optimization BP neural network |
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| CN105861657B (en) * | 2016-04-11 | 2019-03-05 | 中国农业大学 | One kind CNV segment relevant to mastitis for milk cows resistance and its application |
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| CN106355033A (en) * | 2016-09-27 | 2017-01-25 | 无锡金世纪国民体质与健康研究有限公司 | Life risk assessment system |
| CN109711549A (en) * | 2018-12-27 | 2019-05-03 | 中国农业大学 | A kind of mastitis for milk cows detection method based on genetic algorithm optimization BP neural network |
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