Document Type : Research Paper
Authors
1 Department of Animal Science, Faculty of Agriculture, Abhar Branch, Islamic Azad University
2 PhD of animal genetic & biotechnology Assistant Professor. Department of Animal Sciences. Agricultural Research, Education and Extension Organization (AREEO) Agricultural institute of education and extension (IATE)
3 Department of Animal Science, Faculty of Agriculture, Varamin Branch, Islamic Azad University
Abstract
Objective: This research aims to systematically identify and analyze the key factors that influence the longevity of Holstein dairy cattle within herds by employing advanced data mining algorithms. Understanding and predicting longevity is vital because it directly impacts dairy farm productivity and profitability. Longer-lasting cows tend to produce more calves over their lifetime, contribute to higher milk yields, and thus enhance the overall economic efficiency of dairy operations. Furthermore, extended longevity is associated with reduced replacement costs.
Materials and Methods: In recent years, the integration of machine learning techniques into agricultural and livestock management has gained significant momentum. This study utilizes detailed phenotypic data collected from 37,009 daughter animals belonging to 664 sires across 82 distinct herds, representing a comprehensive dataset that spans a decade. Data includes eight milk production records, alongside other relevant variables such as animal age, sire number, shelf life in months, somatic cell count, lactation days, milk production (kg), protein and fat content, calving cycle length, milking frequency, geographic location (province), birth date, calving date, calving interval, herd code, and age at first calving. The data preparation phase involved processing and organizing the dataset using Excel 2016, ensuring data quality and consistency. Subsequent data analyses were conducted using R software (version 4.3.3), employing relevant packages specialized for machine learning and statistical modeling.
Results: The results showed that, the Support Vector Machine has the best accuracy (0.987). The Random Forest was the second most efficient algorithm. The accuracy of the Gradient Boosting Machine was slightly lower than that of the Random Forest but still showed good performance. The Decision Tree provided the least accuracy among these algorithms. The Decision Tree and Support Vector Machine achieved this performance with fewer input variables compared to the Gradient Boosting Machine and Random Forest.
Conclusion: The results showed that none of the algorithms used for survival classification, despite acceptable accuracy, are error-free, but on the other hand, it was shown that the decision tree is simpler and less expensive. The most important features of these methods are the lack of statistical assumptions and requirements that linear regression and interpolation methods require, the lack of normality assumptions, robustness to missing values and values, and the ability to detect complex nonlinear relationships between variables and prediction objectives, which makes them suitable for various applications in the livestock industry. Accurate data recording protocols as well as precise algorithm settings are essential for accurate prediction.
Keywords
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