Early warning system and device for the risk of developing mastitis in dairy cows

Abstract

The application discloses a dairy cow mastitis disease risk early warning system and device, the system comprises an acquisition module, a prediction module and an alarm module; the acquisition module is used for acquiring the content of microorganisms at key positions in a dairy cow breeding environment; the prediction module is used for predicting the dairy cow mastitis disease risk according to the content of microorganisms obtained by the acquisition module; and the alarm module is used for sending an alarm signal according to the prediction result of the prediction module. The early warning system predicts the dairy cow mastitis disease risk according to the content of microorganisms at key positions in the dairy cow breeding environment, thereby guiding the staff of the dairy farm to carry out targeted disinfection on the key positions.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of dairy cow disease prevention, and relates to a dairy cow mastitis disease risk early warning system and device. BACKGROUND

[0002] Dairy cow mastitis is a worldwide recognized problem, which causes huge economic losses to the dairy industry. Effective control of dairy cow mastitis is of great significance to improve the production performance of dairy cows, reduce economic losses and promote the healthy and sustainable development of the dairy industry.

[0003] At present, the main means for controlling mastitis is still antibiotic treatment, and the main drugs include penicillin, streptomycin, tetracycline, cephalosporin drugs, etc. However, with the large use of antibiotic drugs, drug-resistant strains continue to appear, and the therapeutic effect of drugs has decreased. Investigation and research shows that the common pathogenic bacteria of dairy cow mastitis have developed serious drug resistance to penicillin, streptomycin, compound sulfamethoxazole, etc., which has caused certain difficulties in drug use in the later stage of mastitis. In addition, many dairy farms do not perform drug treatment in time even if the cow herd is found to be diseased, or only perform maintenance treatment, which leads to a large amount of harmful substances, such as bacteria, toxins, inflammatory exudates, etc., being mixed into fresh milk, thereby causing great potential threat to the health of consumers. Therefore, solving the problem of dairy cow mastitis is very important for ensuring the healthy development of the dairy farming industry and the safety of dairy products.

[0004] With the increasing attention and research efforts of researchers on dairy cow mastitis, in recent years, the research on mastitis mainly focuses on the classification and identification of various pathogens and drug sensitivity tests, and the research idea is mostly a "terminal diagnosis index system", that is, discovering pathogenic bacteria through the milk of sick cows and finding corresponding drugs. There is a lack of "microbial early warning index system" for "pre-disease prevention". Research shows that the occurrence of mastitis is related to a variety of pathogenic microorganisms. The more the number of microorganisms in the environment, the greater the risk of cow infection and outbreak of mastitis. Although the environmental pathogenic microorganisms in the dairy farm are difficult to be eliminated, the probability of infection of pathogenic bacteria of mastitis can be significantly reduced by keeping the environment clean and dry. In the prior art, some dairy farms excessively disinfect cowsheds in order to prevent the infection of dairy cow diseases, and the excessive disinfection not only does not play a corresponding role, but also causes harm and pollution to humans, animals and the environment, and even there is a potential risk that cannot be quantitatively evaluated. SUMMARY

[0005] In order to solve the problem of mastitis prevention in dairy farms, the present application provides a dairy cow mastitis disease risk early warning system and device. The early warning system predicts the risk of dairy cow mastitis according to the content of microorganisms at key positions in the dairy cow breeding environment, and then guides the dairy farm staff to carry out targeted disinfection at the key positions.

[0006] In a first aspect, embodiments of this application provide a risk warning system for mastitis in dairy cows, characterized in that it includes:

[0007] The acquisition module is used to acquire the content of microorganisms in key locations of the dairy farming environment;

[0008] A prediction module is used to predict the risk of mastitis in dairy cows based on the content of microorganisms obtained by the acquisition module.

[0009] An alarm module is used to issue an alarm signal based on the prediction result of the prediction module;

[0010] The acquisition module, the prediction module, and the alarm module are connected wirelessly and / or via wired means.

[0011] In conjunction with the first aspect, in one embodiment, the key location includes a feed trough and / or bedding material, and the microorganisms include Proteobacteria and / or Bacteroides.

[0012] In conjunction with the first aspect, in one embodiment, the method of predicting the risk of mastitis in dairy cows based on the content of microorganisms obtained by the acquisition module includes:

[0013] The risk score for the incidence of mastitis in dairy cows is calculated based on the content of microorganisms obtained by the acquisition module.

[0014] When the risk score is higher than the risk threshold, the alarm module issues an alarm signal.

[0015] In conjunction with the first aspect, in one embodiment, the method of predicting the risk of bovine mastitis based on the content of microorganisms obtained by the acquisition module includes: calculating a risk score for the incidence of bovine mastitis based on the content of microorganisms obtained by the acquisition module.

[0016] When the risk score is higher than the risk threshold, the alarm module issues an alarm signal.

[0017] In conjunction with the first aspect, in one implementation, the prediction module calculates a risk score for the incidence of mastitis in dairy cows based on a Logistic regression model.

[0018] Secondly, embodiments of this application provide a bovine mastitis risk warning device, which includes the aforementioned bovine mastitis risk warning system.

[0019] Thirdly, embodiments of this application provide the application of reagents for detecting Proteus and / or Bacteroides in the preparation of a bovine mastitis risk warning product, wherein the bovine mastitis risk warning product predicts the risk of bovine mastitis by detecting the content of one or more of Proteus and Bacteroides in feed troughs and bedding.

[0020] In combination with the third aspect, in an implementation, the mastitis risk early warning product comprises a chip, a kit and / or an instrument.

[0021] In a fourth aspect, the embodiments of the present application provide a dairy cow breeding method, comprising:

[0022] Obtaining the content of microorganisms at key positions in the dairy cow breeding environment; the microorganisms at the key positions comprise at least one of the following: Proteobacteria in a feed trough, Proteobacteria in bedding, and Bacteroides in the feed trough;

[0023] Predicting the mastitis risk of the dairy cow according to the obtained content of the microorganisms;

[0024] Issuing an alarm signal according to the prediction result;

[0025] Performing environmental management or drug prevention according to the alarm signal.

[0026] In combination with the fourth aspect, in an implementation, the environmental management comprises: disinfecting the microorganisms at the key positions that exceed a risk threshold; or keeping the breeding environment clean and dry;

[0027] The drug prevention comprises: performing preventive medication on the dairy cow.

[0028] The technical scheme provided by the embodiments of the present application has the following beneficial effects:

[0029] The present application provides a dairy cow mastitis risk early warning system and device, which predicts the mastitis risk of the dairy cow according to the content of microorganisms at key positions in the dairy cow breeding environment, and then guides the dairy farm staff to perform targeted disinfection on the key positions, thereby solving the problem of the dairy farm in preventing mastitis. BRIEF DESCRIPTION OF DRAWINGS

[0030] Figure 1 Dilution curves of various samples;

[0031] Figure 2 Mastitis prediction ROC curve of environmental Proteobacteria;

[0032] Figure 3 Mastitis prediction ROC curve of feed trough Bacteroides. DETAILED DESCRIPTION

[0033] The technical scheme of the present application will be further described in detail below in combination with specific embodiments. It should be understood that the following embodiments are only illustrative and explanatory of the present application, and should not be interpreted as limiting the scope of protection of the present application. Any technology realized based on the above description of the present application is covered within the scope of protection intended by the present application.

[0034] In order to make the purposes, technical solutions and advantages of the present application clearer, the embodiments of the present application will be further described in detail below with reference to the drawings.

[0035] In a first aspect, the embodiments of the present application provide a dairy cow mastitis disease risk early warning system, characterized in that it comprises:

[0036] An acquisition module is configured to acquire the content of microorganisms at a key position in a dairy cow breeding environment.

[0037] A prediction module is configured to predict the dairy cow mastitis disease risk according to the content of microorganisms acquired by the acquisition module.

[0038] An alarm module is configured to send an alarm signal according to the prediction result of the prediction module.

[0039] The acquisition module, the prediction module and the alarm module are connected through wireless and / or wired modes.

[0040] In combination with the first aspect, in an embodiment, the key position comprises a feed trough and / or bedding, and the microorganisms comprise Proteobacteria and / or Bacteroidetes.

[0041] In combination with the first aspect, in an embodiment, the prediction of the dairy cow mastitis disease risk according to the content of microorganisms acquired by the acquisition module comprises:

[0042] Calculating a risk score of the dairy cow mastitis disease according to the content of microorganisms acquired by the acquisition module.

[0043] When the risk score is higher than a risk threshold, the alarm module sends an alarm signal.

[0044] In combination with the first aspect, in an embodiment, the prediction of the dairy cow mastitis disease risk according to the content of microorganisms acquired by the acquisition module comprises:

[0045] When the risk score is higher than a risk threshold, the alarm module sends an alarm signal.

[0046] In combination with the first aspect, in an embodiment, the prediction module calculates the risk score of the dairy cow mastitis disease according to a Logistic regression model.

[0047] In a second aspect, the embodiments of the present application provide a dairy cow mastitis disease risk alarm device, which comprises the dairy cow mastitis disease risk early warning system.

[0048] In a third aspect, the embodiments of the present application provide application of the reagent for detecting Proteobacteria and / or Bacteroidetes in preparation of a dairy cow mastitis disease risk early warning product. The dairy cow mastitis disease risk early warning product predicts the risk of dairy cow mastitis by detecting the content of one or more of Proteobacteria in the feed trough, litter, and Bacteroidetes in the feed trough.

[0049] In combination with the third aspect, in an embodiment, the dairy cow mastitis disease risk early warning product comprises a chip, a kit, and / or an instrument.

[0050] In a fourth aspect, the embodiments of the present application provide a dairy cow breeding method, comprising:

[0051] obtaining the content of microorganisms at key positions in the dairy cow breeding environment; the microorganisms at the key positions include at least one of Proteobacteria in the feed trough, Proteobacteria in the litter, and Bacteroidetes in the feed trough;

[0052] predicting the risk of dairy cow mastitis according to the obtained content of microorganisms;

[0053] sending an alarm signal according to the prediction result;

[0054] performing environmental management or drug prevention according to the alarm signal.

[0055] In combination with the fourth aspect, in an embodiment, the environmental management comprises: disinfecting and killing the microorganisms at the key positions that exceed the risk threshold; or keeping the breeding environment clean and dry;

[0056] The drug prevention comprises: performing preventive medication on the dairy cows.

[0057] Embodiments

[0058] 1. Dairy cow health condition grouping

[0059] The health conditions of dairy cows were grouped according to the somatic cell counts (SCC) in the DHI data of 1020 Chinese Holstein dairy cows from two dairy farms in Hubei Province.

[0060] Table 1. Dairy cow health condition grouping

[0061]

[0062] 2. A total of 620 environmental samples of feed, litter, cow dung, and water trough were collected from the healthy area and mastitis area of two dairy farms in Hubei Province, and the DNA of the environmental samples was extracted using a kit.

[0063] The extracted DNA was used as a template for PCR amplification of bacterial 16S rDNA. The specific primers for the V3-V4 region of bacterial 16S rDNA were selected for PCR amplification,

[0064] 338F (5'-ACTCCTACGGGAGGCAGCA-3') (seq_1), 806R (5'-GGACTACHVGGGTWTCTAAT-3') (seq_2).

[0065] The reaction procedure is as follows: after the components required for the PCR reaction are configured, the template DNA is denatured sufficiently at 98°C for 30 s in a PCR instrument, and then the amplification cycle is entered. In each cycle, the template is denatured at 98°C for 15 s, and then the temperature is reduced to 50°C for 30 s to allow the primers to anneal to the template sufficiently; at 72°C for 30 s, the primers are extended on the template to synthesize DNA, completing one cycle. The above cycle is repeated 25-27 times to accumulate a large amount of amplified DNA fragments. Finally, the product is extended completely at 72°C for 5 min, and is stored at 4°C.

[0066] 3. The PCR product is quantified and library construction is performed, and the DNA is sequenced and data analysis is performed using the high-throughput sequencing system of the PE300 platform of Illumina.

[0067] 4. Sample sequencing and diversity index

[0068] The microbial community diversity in the sample is analyzed by 16S rRNA gene sequence sequencing. The low-quality bands in the original sequence bands of the sample are filtered, and de-redundancy processing is performed, and the rarefaction cuev of the bacteria in the sample is as shown in Figure 1 The rarefaction curve reflects the sampling depth of the sample, and can be used to evaluate whether the sequencing amount is sufficient to cover all groups. The rarefaction curve of all samples has become flat, indicating that the OTU coverage of the sample has been basically saturated, and the sequencing data amount is reasonable.

[0069] 5. Diversity analysis of microbial community in dairy farm

[0070] The sample sequencing result shows that the main dominant bacteria of the dairy farming environment samples of the feed trough, litter, cow dung and water trough at the level of phylum are Firmicutes, Proteobacteria, Bacteroides and Actinobacteria, and the main dominant bacteria at the level of family are Bacteroides, Bacillus, Flavobacterium, Sphingobacterium and Moraxella. According to the spss software analysis and statistics correlation, only Proteobacteria and Bacteroides have significance (p<0.05).

[0071] 6. Multi-factor analysis of microbial community in dairy farming environment and establishment of regression model

[0072] The main dominant bacteria at the order and family level in the dairy cow breeding environment samples of feed trough, bedding, cow dung and water trough were analyzed, the multiple factors affecting dairy cow mastitis were analyzed by spss software, and the Logistic regression model was established. The indexes with statistical significance between the healthy group and the mastitis group, that is, the risk factors of dairy cow mastitis, were determined. These indexes were used as the selected variables in the Logistic regression equation, and the spss software was used for Logistic regression analysis. The formula is as follows: Among them, The statistical index commonly used to describe the intensity of disease occurrence is called odds. When the probability of disease occurrence p is equal to the probability of non-occurrence 1-p, Odds = 1, otherwise Odds is greater than or less than 1. The odds ratio (OR) value can be calculated by Odds, which estimates the influence of the factor on the disease. Specifically, the formula is as follows: P(y=0) and P(y=1) in the mastitis Logistic risk prediction formula are the probabilities of dairy cow not suffering from mastitis and dairy cow suffering from mastitis, respectively; p1 corresponds to the probability of dairy cow mastitis; x1 represents the microbial flora in the water trough, x2 represents the microbial flora in the feed trough, x3 represents the microbial flora in the bedding, and x4 represents the microbial flora in the dung; β0 is the constant term, and β1 to β4 are the variable coefficients of the microbial content in the water trough, feed trough, bedding and dung, respectively. We select "water trough + feed trough + bedding + dung" as the variable to be substituted into the multiple factor Logistic regression equation. It is found by spss Logistic regression analysis that only the feed trough and the bedding have P<0.05, which have significant difference, and the P values of other environmental factors are all greater than 0.05, which have no significant difference. Therefore, it is finally judged that only the feed trough and the bedding are related to the risk of dairy cow mastitis. There are two explanatory variables, feed trough and bedding, which can be used to diagnose the risk of dairy cow mastitis. The mastitis risk prediction result shows that under the condition that other factors remain unchanged, if the Proteobacteria in the feed trough increases by 1 unit, the risk of mastitis increases by 1.37 times (p<0.05), and if the Proteobacteria in the bedding increases by 1 unit, the risk of mastitis increases by 1.21 times (p<0.05). Under the condition that other factors remain unchanged, if the Bacteroides in the feed trough increases by 1 unit, the risk of mastitis increases by 13.39 times (p<0.05) (Table 2).

[0073] Table 2 Risk factors of mastitis

[0074]

[0075] 7. Prediction of the prediction accuracy of the variable to the corresponding variable by ROC curve

[0076] The environmental samples (feed trough, bedding) are used for the multiple factor joint diagnosis of mastitis. The sample quantity is subjected to binary logistic regression, and the prediction probability value P is used for ROC analysis. The ROC curve is drawn. The Y axis is the sensitivity, and the X axis is the specificity. The sensitivity is the probability that a diagnostic test can correctly diagnose the actual sick cows as sick cows. The specificity is the probability that a diagnostic test can correctly diagnose the actual non-diseased cows as non-diseased cows. The higher the sensitivity, the greater the possibility of diagnosing the actual sick cows. The higher the specificity, the greater the possibility of excluding the actual non-diseased cows. The area under the curve (Area Under the Curve, AUC) is the diagnostic value of the model, which is generally used to evaluate the diagnostic performance of the test. The larger the area, the better the diagnostic performance of the test. Generally, AUC between 0.50 and 0.70 is considered to have general discrimination, AUC between 0.70 and 0.90 is considered to be a good model, and AUC higher than 0.90 is considered to be an excellent model. According to the analysis, in the dairy farming environment, the accuracy of the mastitis risk assessment model of Proteobacteria is 93.5%, the specificity is 100%, the sensitivity is 75.6%, and the risk score is 0.928. The risk score is the risk threshold of Proteobacteria. If the content of Proteobacteria is higher than 0.928, it is predicted that there is a risk of mastitis.

[0077] The ROC curve of Bacteroides feed trough data is selected, and the results are shown in Figure 3 The accuracy of the mastitis risk assessment model of Bacteroides in the feed trough of the dairy farm is 72.4%, the specificity is 100%, the sensitivity is 45.1%, and the risk score is 2.34. The risk score is the risk threshold of Bacteroides in the feed trough.

[0078] The present application uses Logistic regression model to evaluate the risk of inducing mastitis in another dairy farming environment in Hubei Province. According to the evaluation results, it is found that the accuracy of the Logistic regression model for the risk assessment of mastitis in the dairy farm is 73.2% on average, which has good application prospect.

[0079] The present application can help to identify the health status and risk of mastitis in dairy cows through the analysis of risk factors of dairy cow mastitis, which plays an important role in the control of dairy cow mastitis and the reduction of newly infected cows. The present application analyzes the risk factors of Holstein mastitis. The results of multiple factor Logistic regression analysis show that the higher the content of Proteobacteria and Bacteroides microorganisms in the feed trough and bedding of the dairy farm, the higher the risk of mastitis in dairy cows. The prediction value of the mastitis risk assessment model of Bacteroides and Proteobacteria using the Logistic regression model is 2.34 and 0.928, respectively, and the model prediction value is good, which can be applied to the risk assessment of mastitis in dairy farms.

[0080] The above embodiments are only used to illustrate the technical solutions of the present application but not to limit the present application. Although the present application is described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalent replaced, without departing from the spirit and scope of the technical solutions of the present application.

Claims

1. A risk warning system for mastitis in dairy cows, characterized in that, include: The acquisition module is used to acquire the content of microorganisms in key locations of the dairy farming environment; the key locations include feed troughs and / or bedding, and the microorganisms include Proteobacteria and / or Bacteroides; A prediction module is used to predict the risk of mastitis in dairy cows based on the content of microorganisms obtained by the acquisition module. An alarm module is used to issue an alarm signal based on the prediction result of the prediction module; The acquisition module, the prediction module, and the alarm module are connected wirelessly and / or via wired means. The prediction module determines whether the microbial content at key locations is higher than the microbial content that causes mastitis in healthy dairy cows; when the microbial content at key locations is higher than the microbial content that causes mastitis in healthy dairy cows, the alarm module issues an alarm signal. or, The prediction module calculates the risk score for the incidence of mastitis in dairy cows using a Logistic regression model based on the microbial content obtained by the acquisition module; when the risk score is higher than the risk threshold, the alarm module issues an alarm signal.

2. A bovine mastitis risk alarm device, characterized in that: The system includes the early warning system for the risk of mastitis in dairy cows as described in claim 1.

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