Review: Application and Prospective Discussion of Machine Learning for the Management of Dairy Farms

Cockburn, Marianne

doi:10.3390/ani10091690

Cited by 68 publications

(47 citation statements)

References 100 publications

(175 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Applied statistical methods used in the literature [ 16 ] showed that milking frequency, lactation number (parity number), month of milking, and type of lying stall represent important factors responsible for the monthly milk yield of dairy cows in farms with AMSs. In this context, Machine Learning (ML) algorithms have been already applied in some areas of dairy research, particularly to predict data, and they represent a promising tool, useful to develop and improve decision support for farmers [ 17 ] in order to increase both milk yield and animal welfare and, on the other hand, to reduce the resources needed, hence increasing the sustainability of the sector [ 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

Random Forest Modelling of Milk Yield of Dairy Cows under Heat Stress Conditions

Bovo

Agrusti

Benni

et al. 2021

Animals

View full text Add to dashboard Cite

Precision Livestock Farming (PLF) relies on several technological approaches to acquire, in the most efficient way, precise and real-time data concerning production and welfare of individual animals. In this regard, in the dairy sector, PLF devices are being increasingly adopted, automatic milking systems (AMSs) are becoming increasingly widespread, and monitoring systems for animals and environmental conditions are becoming common tools in herd management. As a consequence, a great amount of daily recorded data concerning individual animals are available for the farmers and they could be used effectively for the calibration of numerical models to be used for the prediction of future animal production trends. On the other hand, the machine learning approaches in PLF are nowadays considered an extremely promising solution in the research field of livestock farms and the application of these techniques in the dairy cattle farming would increase sustainability and efficiency of the sector. The study aims to define, train, and test a model developed through machine learning techniques, adopting a Random Forest algorithm, having the main goal to assess the trend in daily milk yield of a single cow in relation to environmental conditions. The model has been calibrated and tested on the data collected on 91 lactating cows of a dairy farm, located in northern Italy, and equipped with an AMS and thermo-hygrometric sensors during the years 2016–2017. In the statistical model, having seven predictor features, the daily milk yield is evaluated as a function of the position of the day in the lactation curve and the indoor barn conditions expressed in terms of daily average of the temperature-humidity index (THI) in the same day and its value in each of the five previous days. In this way, extreme hot conditions inducing heat stress effects can be considered in the yield predictions by the model. The average relative prediction error of the milk yield of each cow is about 18% of daily production, and only 2% of the total milk production.

show abstract

Section: Introductionmentioning

confidence: 99%

Random Forest Modelling of Milk Yield of Dairy Cows under Heat Stress Conditions

Bovo

Agrusti

Benni

et al. 2021

Animals

View full text Add to dashboard Cite

show abstract

“…Machine learning algorithms offer new approaches for the analysis of large amount of data collected in dairy farming thanks to the use of herd management systems 25 and milk testing performed in the frame of national recording procedures. Machine learning methods are indeed a promising tool to improve decision support systems for farmers and have already been applied in different areas of dairy research such as behaviour, feeding, management, physiology and reproduction 25 . Such advanced analyses allow to predict outcomes of economic relevance.…”

Section: Discussionmentioning

confidence: 99%

“…Despite the large number of published studies on ML methods applied to animal science, a reliable practical implementation of most tested algorithms for management decision has not occurred yet. This may be due to the availability of poor training data 25 . To improve prediction accuracies, data retrieved from many different herds and recorded for longer time periods should be considered, and large integrated high-quality datasets have to be created.…”

Section: Discussionmentioning

confidence: 99%

Comparison of machine learning methods to predict udder health status based on somatic cell counts in dairy cows

Bobbo

Biffani

Taccioli

et al. 2021

Sci Rep

View full text Add to dashboard Cite

Bovine mastitis is one of the most important economic and health issues in dairy farms. Data collection during routine recording procedures and access to large datasets have shed the light on the possibility to use trained machine learning algorithms to predict the udder health status of cows. In this study, we compared eight different machine learning methods (Linear Discriminant Analysis, Generalized Linear Model with logit link function, Naïve Bayes, Classification and Regression Trees, k-Nearest Neighbors, Support Vector Machines, Random Forest and Neural Network) to predict udder health status of cows based on somatic cell counts. Prediction accuracies of all methods were above 75%. According to different metrics, Neural Network, Random Forest and linear methods had the best performance in predicting udder health classes at a given test-day (healthy or mastitic according to somatic cell count below or above a predefined threshold of 200,000 cells/mL) based on the cow’s milk traits recorded at previous test-day. Our findings suggest machine learning algorithms as a promising tool to improve decision making for farmers. Machine learning analysis would improve the surveillance methods and help farmers to identify in advance those cows that would possibly have high somatic cell count in the subsequent test-day.

show abstract

“…In our analysis, we find that chopped straw as litter in free-stalls for lactating cows (OR=1. 26 Environmental conditions play a secondary role in the prediction of periparturient hypocalcemia. The association of a high standard deviation of temperature with increased risk of periparturient hypocalcemia (OR=1.48 [1.34; 1.63] is consistent with a similar finding in the literature 91 .…”

Section: /30mentioning

confidence: 99%

“…Several machine learning (ML) approaches have been considered for the prediction of breeding values 15 , insemination success 16 , feed intake 17 and calving 18 , as well as milk yield [19][20][21] , modelling of physiological and behavioural animal parameters 22,23 and estimating BCS 24,25 -see also Cockburn 2020 26 for a recent review. The literature on the prediction of diseases is frequently based on black-box sensor systems in which prediction algorithms are used that are of commercial interest and not publicly known or evaluated 26 or the reported sensitivity and specificity of prediction algorithms varies widely 9 . Diseases for which successful applications of ML approaches have been reported include lameness [27][28][29] , mastitis [30][31][32][33] , metabolic status, i.e.…”

Section: Introductionmentioning

confidence: 99%

Integrating diverse data sources to predict disease risk in dairy cattle

Lasser

Matzhold

Egger-Danner³

et al. 2021

Preprint

View full text Add to dashboard Cite

Livestock farming is currently undergoing a digital revolution and becoming increasingly data-driven. Yet, such data often reside in disconnected silos making it impossible to leverage their full potential to improve animal well-being. Here, we introduce a precision medicine approach, bringing together information streams from a variety of life domains of dairy cattle to predict eight common and economically important diseases. Dairy cows are part of a highly industrialised environment. The animals and their surroundings are closely monitored and environmental, behavioural and physiological observations are readily accessible yet seldomly integrated. We use random forest classifiers trained on data from 5,828 animals in 166 herds in Austria to predict occurrences of lameness, acute and chronic mastitis, anoestrus, ovarian cysts, metritis, ketosis (hyperketonemia) and periparturient hypocalcemia (milk fever). To assess the importance of specific cattle life domains and individual features for these predictions, we use multivariate logistic regression and feature permutation approaches. We show that disease in dairy cattle is a product of the complex interplay between a multitude of life domains such as housing, nutrition or climate, and identify a range of features that were previously not associated with increased disease risk. For example, we can predict anoestrus with high sensitivity and specificity (F1=0.72) and find that housing, feed and husbandry variables such as barn design and time on pasture are most predictive of this disease. We also find previously unknown associations of features with disease risk, for example humid conditions, which significantly decrease the odds for ketosis. Our findings pave the way towards data-driven point-of-care interventions and demonstrate the added value of integrating all available data in the dairy industry to improve animal well-being and reduce disease risk.

show abstract

Review: Application and Prospective Discussion of Machine Learning for the Management of Dairy Farms

Cited by 68 publications

References 100 publications

Random Forest Modelling of Milk Yield of Dairy Cows under Heat Stress Conditions

Random Forest Modelling of Milk Yield of Dairy Cows under Heat Stress Conditions

Comparison of machine learning methods to predict udder health status based on somatic cell counts in dairy cows

Integrating diverse data sources to predict disease risk in dairy cattle

Contact Info

Product

Resources

About