Indicators of mastitis and milk quality in dairy cows : data, modeling, and prediction in automatic milking systems

Methods for generating predictions of important and generally accepted indicators of udder inflammation and poor milk quality, such as somatic cell count (SCC) or changes in milk homogeneity, are few. The aim of this thesis was to investigate methods to identify indicators of mastitis and poor milk quality in dairy cows using data generated by automatic milking systems (AMS). The first part of the project investigated the relationship between SCC and data regularly recorded by the AMS using models that could capture nonlinear associations between the explanatory variables and the outcome. This information could be used in modeling the SCC. Furthermore, three statistical methods, generalized additive model, random forest and multilayer perceptron, were compared for their ability to predict SCC using data generated by the AMS. The results showed that equally low prediction error was obtained using generalized additive model or multilayer perceptron for prediction of SCC based on AMS data. The second part explored the dynamics of changes in milk homogeneity in cows milked in AMS using descriptive statistics for clots collected by inline filters, scored for density. Clots were found among certain cows and cow periods and appeared in new quarters over time. Models were fitted for detecting and predicting clots in single cow milkings as well as for detecting clots in milkings over a longer period. The models successfully distinguished periods of milking free of changes in milk homogeneity, although the detection and prediction performance was poor. The prediction target and severity grade of each density category is discussed

The behaviour of the calf in different rearing systems

The purpose of this literature review was to investigate how the rearing systems influence the behaviour of dairy calves. Calves are social animals that form groups within the herd where play and social licking between calves are important social activities. Suckling is a complex behaviour and essential for the calf's survival. In the modern rearing systems the calf is usually separated from its mother soon after birth. Calves are then kept in individual pens, group pens or, more rarely, with a foster cow. Feeding system often depends upon housing system and calves in single pens are often feed through buckets or a bucket with a nipple. In group pens calves can be fed in the same manner or trough an automatic calf feeder with nipple. These types of housing systems disregard the calf's natural behaviour and suckling pattern. Due to this abnormal behaviours such as cross-sucking or non-nutritive sucking are common. The conclusion from this literature review is that in regard to the calf's welfare the most appropriate way of housing is with its mother or a foster cow.Syftet med denna litteraturstudie var att undersöka hur uppfödningssystemet påverkar beteendet hos mjölkkornas kalvar. Kalvar är sociala djur som bildar grupper inom flocken där lek och slickande kalvar mellan är viktiga sociala aktiviteter. Digivning är ett beteende av komplex natur och är essentiellt för kalvens överlevnad. I modern mjölkproduktion separerar man oftast kalven från kon strax efter födseln. Kalvar hålls i ensambox, gruppbox eller mera sällsynt, med en amko. Utfodringssystemet hör ofta samman med inhysningssystemet och kalvar i ensamboxar utfodras ofta ur spann eller spann med napp. Gruppboxhållna kalvar kan utfodras på samma vis eller genom en automatisk kalvamma med napp. Dessa typer av inhysningssystem stämmer varken överrens med kalvens naturliga beteende eller digivningsmönster. På grund av detta är onormala beteenden som sugande på andra kalvar och icke näringsgivande sugande vanligt förekommande. Slutsatsen från denna litteraturstudie är att det bästa uppfödningssystemet med avseende på kalvens välfärd är att låta kalven gå samman med sin mor eller en amko

Automatic estimation of body weight and body condition score in dairy cows using 3D imaging technique

The main aim of this MSc thesis was to investigate the possibility of using three dimensional (3D) imaging technique for automatic estimation of body weight in dairy cows of two breeds; Swedish Holstein and the Swedish Red Breed (SRB). Reference data for validation of automatic BCS in SRB has been collected in previous studies and an important part of this study was to collect reference data on one more breed; the Swedish Holstein. Data collection lasted from April to July, 2010 and was performed at Jälla agricultural school, Uppsala. The data collection included 120 dairy cows, 70 of the SRB and 40 Swedish Holstein. Body weight and 3D images were collected automatically twice daily. Manual body condition score (BCS) as reference data was performed once a week and measurements of back fat thickness were carried out at three occasions during the data collection period. The image analysis showed that the camera had difficulties to identify the shape of the body in cows with black pigment, and therefore, only cows of SRB were included in the results. Data was analyzed by linear regression and the highest correlations were found between estimated body weight by camera and measured body weight by scale (R=0.87; P< 0.001) and BSC estimated by camera and manual BCS (R=0.84; P<0.001). A day to day variation of 5.33%, 2.83 % and 7.01 % was found for body weight estimated by camera, body weight measured by scale and automatic BCS respectively. It was concluded that estimations of body weight can be performed by the 3D imaging technique and that correlation between manual BCS and automatic BCS is in agreement with previous studies. The repeatability, precision and sensitivity of the method were good but estimation of body weight would probably be improved by including BCS, milk yield and rumen fill degree in the model. Application of this product should focus on identifying changes in physical state of the animal and could then be a powerful tool monitoring heard health and fertility

Detecting and predicting changes in milk homogeneity using data from automatic milking systems

To ensure milk quality and detect cows with signs of mastitis, visual inspection of milk by prestripping quarters before milking is recommended in many countries. An objective method to find milk changed in homogeneity (i.e., with clots) is to use commercially available inline filters to inspect the milk. Due to the required manual labor, this method is not applicable in automatic milking systems (AMS). We investigated the possibility of detecting and predicting changes in milk homogeneity using data generated by AMS. In total, 21,335 quarter-level milk inspections were performed on 5,424 milkings of 624 unique cows on 4 farms by applying visual inspection of inline filters that assembled clots from the separate quarters during milking. Images of the filters with clots were scored for density, resulting in 892 observations with signs of clots for analysis (77% traces or mild cases, 15% moderate cases, and 8% heavy cases). The quarter density scores were combined into 1 score indicating the presence of clots during a single cow milking and into 2 scores summarizing the density scores in cow milkings during a 30-h sampling period. Data generated from the AMS, such as milk yield, milk flow, conductivity, and online somatic cell counts, were used as input to 4 multilayer perceptron models to detect or predict single milkings with clots and to detect milking periods with clots. All models resulted in high specificity (98-100%), showing that the models correctly classified cow milkings or cow milking periods with no clots observed. The ability to successfully classify cow milkings or cow periods with observed clots had a low sensitivity. The highest sensitivity (26%) was obtained by the model that detected clots in a single milking. The prevalence of clots in the data was low (2.4%), which was reflected in the results. The positive predictive value depends on the prevalence and was relatively high, with the highest positive predictive value (72%) reached in the model that detected clots during the 30-h sampling periods. The misclassification rate for cow milkings that included higher-density scores was lower, indicating that the models that detected or predicted clots in a single milking could better distinguish the heavier cases of clots. Using data from AMS to detect and predict changes in milk homogeneity seems to be possible, although the prediction performance for the definitions of clots used in this study was poor

Modeling cow somatic cell count using sensor data as input to generalized additive models

This research paper presents a study investigating if sensor data from an automatic milking rotary could be used to model cow somatic cell count (composite milk SCC: CMSCC). CMSCC is valuable for udder health monitoring and individual cow udder health surveillance could be improved by predicting CMSCC between routine samplings. Data regularly recorded in the automatic milking rotary, in one German dairy herd, were collected for analysis. The cows (Holstein-Friesian,n= 372) were milked twice daily and sampled once weekly in afternoon milkings for 8 weeks for CMSCC. From the potential independent variables, including quarter conductivity, milk flow, blood in milk, kick-offs, not milked quarters and incomplete milkings, new variables that combined quarter data were created. Past period records, i.e. lags, of up to seven days before the actual CMSCC sampling event were added in the dataset to investigate if they were of use in modeling the cell count. Univariable generalized additive models (GAM) were used to screen the data to select potential independent variables. Furthermore, several multivariable GAM were fitted in order to compare the importance of the potential independent variables and to explore how the model performance would be affected by using data from various number of days before the CMSCC sampling event. The result of the model selection showed that the best explanation of CMSCC was provided by the model incorporating all significant variables from the variable screening for the seven preceding days, including the day of the CMSCC sampling event. However, using data from only three days before the CMSCC sampling event is suggested to be sufficient to model CMSCC. Variables combining conductivity quarter data, together with quarter conductivity, are suggested to be important in describing CMSCC. We conclude that CMSCC can be modeled with a high degree of explanation using the information routinely recorded by the milking robot