Prediction of first test day milk yield using historical records in dairy cows

Salamone, Matthieu; Adriaens, Ines; Vervaet, A.; Opsomer, G.; Atashi, H.; Fievez, Veerle; Aernouts, Ben; Hostens, Miel

doi:10.1016/j.animal.2022.100658

Cited by 6 publications

(6 citation statements)

References 25 publications

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“…The calculated performance of all models demonstrates the potential of using historical production data to predict milk production and milk components in young cows. The MPIs observed in all models are similar to the results of Dallago et al (2019) and Salamone et al (2022), including RMSEs that fall within the margins of the theoretical minimum of 6 kg proposed by Cole et al (2012) for predicting daily milk production. This is due to variability resulting from environmental and health changes, as explained by Cole et al (2012).…”

Section: Discussionsupporting

confidence: 85%

“…The small differences in MPIs in our case can be explained in part by the limited contribution of the characteristics of Tunisian herds raised, for the most part, in low‐input systems, which limits prediction. In such situations, Salamone et al (2022) suggest including less parametrized models to assess when a real difference can be observed. According to Olori, Hill, and Brotherstone (1999), improved fit can also be achieved through more accurate RV estimation when more independent estimates at different stages of lactation are allowed with the removal of the constraint on variation, especially at the beginning and end of lactation.…”

Section: Discussionmentioning

confidence: 99%

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Random regression models to estimate genetic parameters for milk yield, fat, and protein contents in Tunisian Holsteins

Soumri

Carabaño

González-Recio

et al. 2023

J Animal Breeding Genetics

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This study aimed to find the parsimonious random regression model (RRM) to evaluate the genetic potential for milk yield (MY), fat content (FC), and protein content (PC) in Tunisian Holstein cows. For this purpose, 551,139; 331,654; and 302,396 test day records for MY, FC, and PC were analysed using various RRMs with different Legendre polynomials (LP) orders on additive genetic (AG) and permanent environmental (PE) effects, and different types of residual variances (RV). The statistical analysis was performed in a Bayesian framework with Gibbs sampling, and the model performances were assessed, mainly, on the predictive ability criteria. The study found that the optimal model for evaluating these traits was an RRM with a third LP order and nine classes of heterogeneous RV. In addition, the study found that heritability estimates for MY, FC, and PC ranged from 0.11 to 0.22, 0.11 to 0.17, and 0.12 to 0.18, respectively, indicating that genetic improvement should be accompanied by improvements in the production environment. The study also suggested that new selection rules could be used to modify lactation curves by exploiting the canonical transformation of the random coefficient covariance (RC) matrix or by using the combination of slopes of individual lactation curves and expected daily breeding values.

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Section: Discussionsupporting

confidence: 85%

Section: Discussionmentioning

confidence: 99%

Random regression models to estimate genetic parameters for milk yield, fat, and protein contents in Tunisian Holsteins

Soumri

Carabaño

González-Recio

et al. 2023

J Animal Breeding Genetics

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“…Model evaluation was carried out on test data with four metrics frequently used in similar research: coefficient of determination (R 2 ), RMSE, the mean absolute error (MAE) and the mean absolute percentage error (MAPE) ( 45–47 ). R 2 indicates the proportion of the variance of decay-305 explained by the independent variables.…”

Section: Methodsmentioning

confidence: 99%

Prediction of persistency for day 305 of lactation at the moment of the insemination decision

Chen,

Steeneveld,

Nielen

et al. 2023

Front. Vet. Sci.

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When deciding on the voluntary waiting period of an individual cow, it might be useful to have insight into the persistency for the remainder of that lactation at the moment of the insemination decision, especially for farmers who consider persistency in their reproduction management. Currently, breeding values for persistency are calculated for dairy cows but, to our knowledge, prediction models to accurately predict persistency at different moments of insemination are lacking. This study aimed to predict lactation persistency for DIM 305 at different insemination moments (DIM 50, 75, 100, and 125). Available cow and herd level data from 2005 to 2022 were collected for a total of 20,508 cows from 85 herds located in the Netherlands and Belgium. Lactation curve characteristics were estimated for every daily record using the data up to and including that day. Persistency was defined as the number of days it takes for the milk production to decrease by half during the declining stage of lactation, and calculated from the estimated lactation curve characteristic ‘decay’. Four linear regression models for each of the selected insemination moment were built separately to predict decay at DIM 305 (decay-305). Independent variables included the lactation curve characteristics at the selected insemination moment, daily milk yield, age, calving season, parity group and other herd variables. The average decay-305 of primiparous cows was lower than that of multiparous cows (1.55 *10−3 vs. 2.41*10−3, equivalent to a persistency of 447 vs. 288 days, respectively). Results showed that our models had limitations in accurately predicting persistency, although predictions improved slightly at later insemination moments, with R2 values ranging between 0.27 and 0.41. It can thus be concluded that, based only on cow and herd milk production information, accurate prediction of persistency for DIM 305 is not feasible.

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“…Salamone et al [37] aimed to predict the milk yield on the first test day, starting with the routine data of previous lactations. They employed the random forest regression (RFR) algorithm on three different datasets to denote the best configuration for achieving higher results in terms of root mean squared error (RMSE), mean absolute error (MAE), and R 2 .…”

Section: Prediction Of Performancementioning

confidence: 99%

“…Several works reported the main limitations of their analysis. For example, Salamone et al [37] noted that the main limitation of their work is the absence of a high-quality disease registration dataset. The latter condition has negatively influenced the quality of their results.…”

Section: Limitation and Obstacles In Machine Learningmentioning

confidence: 99%

Short review of current limits and challenges of application of machine learning algorithms in the dairy sector

Trapanese,

Hostens,

Salzano

et al. 2024

Acta IMEKO

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In the last years, the livestock sector is moving towards a more sustainable animal-based industry, mitigating the environmental impact of livestock while meeting the demand for high-quality food. To achieve these goals, farms are using a more technological approach, adopting algorithms to manipulate the vast amount of data from sensors and routine operations. The results will be useful for making more objective decisions. In this context, machine learning - a branch of Artificial Intelligence applied to the study of prediction, inference, and clustering algorithms - can be successfully employed. Nowadays, machine learning algorithms are successfully used to solve many issues in the livestock sector, such as early disease detection, and they are expected to be employed in the future for welfare monitoring. This brief review gives an overview of the current state of the art of the most popular applications for dairy science and the most widely used and best-performing algorithms, highlighting the challenges and obstacles for broad acceptance of these techniques in the dairy sector.

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Prediction of first test day milk yield using historical records in dairy cows

Cited by 6 publications

References 25 publications

Random regression models to estimate genetic parameters for milk yield, fat, and protein contents in Tunisian Holsteins

Random regression models to estimate genetic parameters for milk yield, fat, and protein contents in Tunisian Holsteins

Prediction of persistency for day 305 of lactation at the moment of the insemination decision

Short review of current limits and challenges of application of machine learning algorithms in the dairy sector

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