Predicting first test day milk yield of dairy heifers

Dallago, Gabriel Machado; Figueiredo, Darcilene Maria de; Andrade, Paulo César de Resende; Santos, Roseli Aparecida dos; Lacroix, René; Santschi, D.E.; Lefebvre, Daniel

doi:10.1016/j.compag.2019.105032

Cited by 21 publications

(11 citation statements)

References 35 publications

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“…The model further demonstrates the advantages of combined parameter evaluation, as both the somatic cell count (SCC) and the interaction between SCC and lactation stage affected yield prediction. Machine learning techniques also identified 15 variables from dairy herd improvement metrics that allowed to predict milk yield [ 30 ]. Artificial neural networks were hereby able to predict the first test day milk yield of heifers with a mean error below 4 kg.…”

Section: Resultsmentioning

confidence: 99%

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

Cockburn

2020

Animals

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Dairy farmers use herd management systems, behavioral sensors, feeding lists, breeding schedules, and health records to document herd characteristics. Consequently, large amounts of dairy data are becoming available. However, a lack of data integration makes it difficult for farmers to analyze the data on their dairy farm, which indicates that these data are currently not being used to their full potential. Hence, multiple issues in dairy farming such as low longevity, poor performance, and health issues remain. We aimed to evaluate whether machine learning (ML) methods can solve some of these existing issues in dairy farming. This review summarizes peer-reviewed ML papers published in the dairy sector between 2015 and 2020. Ultimately, 97 papers from the subdomains of management, physiology, reproduction, behavior analysis, and feeding were considered in this review. The results confirm that ML algorithms have become common tools in most areas of dairy research, particularly to predict data. Despite the quantity of research available, most tested algorithms have not performed sufficiently for a reliable implementation in practice. This may be due to poor training data. The availability of data resources from multiple farms covering longer periods would be useful to improve prediction accuracies. In conclusion, ML is a promising tool in dairy research, which could be used to develop and improve decision support for farmers. As the cow is a multifactorial system, ML algorithms could analyze integrated data sources that describe and ultimately allow managing cows according to all relevant influencing factors. However, both the integration of multiple data sources and the obtainability of public data currently remain challenging.

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Section: Resultsmentioning

confidence: 99%

“…Artificial neural networks were hereby able to predict the first test day milk yield of heifers with a mean error below 4 kg. Furthermore, the authors showed a positive correlation between a high bodyweight and days in milk with first-day test milk, which exhibited a higher predicted milk yield [ 30 ].…”

Section: Resultsmentioning

confidence: 99%

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

Cockburn

2020

Animals

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“…These findings agree with Bhosale and Singh 21 who reported that the performance of ANN (r 2 = 83.5) was better than the MLR (r 2 = 76.21) model for milk yield prediction. In addition, other authors have reported a better performance of ANN versus MLR when estimating milk yields from ruminants 15 , 33 , 34 .…”

Section: Discussionmentioning

confidence: 95%

Estimation of milk yield based on udder measures of Pelibuey sheep using artificial neural networks

Ángeles-Hernández

Castro-Espinoza

Peláez-Acero

et al. 2022

Sci Rep

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Udder measures have been used to assess milk yield of sheep through classical methods of estimation. Artificial neural networks (ANN) can deal with complex non-linear relationships between input and output variables. In the current study, ANN were applied to udder measures from Pelibuey ewes to estimate their milk yield and this was compared with linear regression. A total of 357 milk yield records with its corresponding udder measures were used. A supervised learning was used to train and teach the network using a two-layer ANN with seven hidden structures. The globally convergent algorithm based on the resilient backpropagation was used to calculate ANN. Goodness of fit was evaluated using the mean square prediction error (MSPE), root MSPE (RMSPE), correlation coefficient (r), Bayesian’s Information Criterion (BIC), Akaike’s Information Criterion (AIC) and accuracy. The 15–15 ANN architecture showed that the best predictive milk yield performance achieved an accuracy of 97.9% and the highest values of r2 (0.93), and the lowest values of MSPE (0.0023), RMSPE (0.04), AIC (− 2088.81) and BIC (− 2069.56). The study revealed that ANN is a powerful tool to estimate milk yield when udder measures are used as input variables and showed better goodness of fit in comparison with classical regression methods.

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“…The studies published so far have indicated multiple possibilities in this regard, starting from a basic analysis of milk production and linear type traits using economic techniques for decision-making [ 3 , 4 ], through the application of survival analysis to the prediction of longevity breeding value in dairy bulls [ 5 , 6 ], the prediction of health problems associated with metabolic diseases in cows [ 7 , 8 ], the analysis of the association between the leptin gene polymorphism and functional longevity of dairy cows [ 9 ], the objective evaluation of effective transition cow management at a herd level [ 10 ], ending up with the prediction of the first test-day milk yield of dairy heifers [ 11 ]. The previously mentioned studies have mostly focused on the analysis of relatively short time periods, i.e., a specific and important moment in cows’ life (e.g., the perinatal period), whereas research on the effective prediction of dairy cow longevity and/or their culling reasons over a longer time span based on routine herd data is still rather scarce.…”

Section: Introductionmentioning

confidence: 99%

The Use of Artificial Neural Networks and a General Discriminant Analysis for Predicting Culling Reasons in Holstein-Friesian Cows Based on First-Lactation Performance Records

Adamczyk

Grzesiak

Zaborski

2021

Animals

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The aim of the present study was to verify whether artificial neural networks (ANN) may be an effective tool for predicting the culling reasons in cows based on routinely collected first-lactation records. Data on Holstein-Friesian cows culled in Poland between 2017 and 2018 were used in the present study. A general discriminant analysis (GDA) was applied as a reference method for ANN. Considering all predictive performance measures, ANN were the most effective in predicting the culling of cows due to old age (99.76–99.88% of correctly classified cases). In addition, a very high correct classification rate (99.24–99.98%) was obtained for culling the animals due to reproductive problems. It is significant because infertility is one of the conditions that are the most difficult to eliminate in dairy herds. The correct classification rate for individual culling reasons obtained with GDA (0.00–97.63%) was, in general, lower than that for multilayer perceptrons (MLP). The obtained results indicated that, in order to effectively predict the previously mentioned culling reasons, the following first-lactation parameters should be used: calving age, calving difficulty, and the characteristics of the lactation curve based on Wood’s model parameters.

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Predicting first test day milk yield of dairy heifers

Cited by 21 publications

References 35 publications

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

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

Estimation of milk yield based on udder measures of Pelibuey sheep using artificial neural networks

The Use of Artificial Neural Networks and a General Discriminant Analysis for Predicting Culling Reasons in Holstein-Friesian Cows Based on First-Lactation Performance Records

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