Prediction of growth and feed efficiency in mink using machine learning algorithms
The feed efficiency (FE) expresses as the amount of feed required per unit of BW gain. Since feed cost is the major input cost in the mink industry, evaluating of FE is a crucial step for competitiveness of the mink industry. However, the FE measures have not been widely adopted for the mink due to...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-02-01
|
| Series: | Animal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1751731124002672 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823856798549934080 |
|---|---|
| author | A. Shirzadifar G. Manafiazar P. Davoudi D. Do G. Hu Y. Miar |
| author_facet | A. Shirzadifar G. Manafiazar P. Davoudi D. Do G. Hu Y. Miar |
| author_sort | A. Shirzadifar |
| collection | DOAJ |
| description | The feed efficiency (FE) expresses as the amount of feed required per unit of BW gain. Since feed cost is the major input cost in the mink industry, evaluating of FE is a crucial step for competitiveness of the mink industry. However, the FE measures have not been widely adopted for the mink due to the high cost of periodically measuring BW and daily feed intake. Measuring individual daily feed intake and BW is time-consuming, labor-intensive, and stressful for the animals and mink producers. The main objectives of this study were to (1) evaluate the application of machine learning (ML) algorithms to predict the average daily gain (ADG), feed conversion ratio (FCR), and residual feed intake (RFI) values during the whole growing and furring period (15 weeks from August 1st to November 14th) using less expensive features such as sex, color type, age, BW and length; (2) find the most significant contributing feature within the growth and furring period to predict the ADG, FCR and RFI. The color and sex features were recorded on 1 088 mink and mink’s age, BW and length were measured every 3 weeks from August 1st to November 14th which is called P1–P5. The ADG, FCR, and RFI were then predicted by the selected ML algorithms using multiple combinations of the observed and measured features from P1 to P5. By comparing the calculated ADG, FCR, and RFI values with the predicted values, it was determined that the most accurate combination of features was to include all features such as sex, color, age, BW and body length on August 1st (at the beginning of the P1). Among selected ML algorithms, the extreme gradient boosting (XGB) algorithm provided the most accurate and reliable prediction for the ADG (R2 = 0.71, RMSE = 0.10), FCR (R2 = 0.74, RMSE = 0.14), and RFI (R2 = 0.76, RMSE = 0.10). The XGB algorithm can be an accurate algorithm to predict the ADG, FCR, and RFI values without measuring costly daily feed intake. In addition, sex was identified as the most significant feature to predict the ADG, FCR, and RFI values with the importance scores of 0.85, 0.67, and 0.79, respectively. |
| format | Article |
| id | doaj-art-0e3f1fe1262744b98a4ac24cfe480911 |
| institution | Kabale University |
| issn | 1751-7311 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Animal |
| spelling | doaj-art-0e3f1fe1262744b98a4ac24cfe4809112025-02-12T05:30:50ZengElsevierAnimal1751-73112025-02-01192101330Prediction of growth and feed efficiency in mink using machine learning algorithmsA. Shirzadifar0G. Manafiazar1P. Davoudi2D. Do3G. Hu4Y. Miar5Department of Animal Science and Aquaculture, Dalhousie University, Truro, Nova Scotia B2N 5E3, Canada; Biosystems Engineering Department, Shiraz University, Shiraz, IranDepartment of Animal Science and Aquaculture, Dalhousie University, Truro, Nova Scotia B2N 5E3, CanadaDepartment of Animal Science and Aquaculture, Dalhousie University, Truro, Nova Scotia B2N 5E3, CanadaDepartment of Animal Science and Aquaculture, Dalhousie University, Truro, Nova Scotia B2N 5E3, CanadaDepartment of Animal Science and Aquaculture, Dalhousie University, Truro, Nova Scotia B2N 5E3, CanadaDepartment of Animal Science and Aquaculture, Dalhousie University, Truro, Nova Scotia B2N 5E3, Canada; Corresponding author.The feed efficiency (FE) expresses as the amount of feed required per unit of BW gain. Since feed cost is the major input cost in the mink industry, evaluating of FE is a crucial step for competitiveness of the mink industry. However, the FE measures have not been widely adopted for the mink due to the high cost of periodically measuring BW and daily feed intake. Measuring individual daily feed intake and BW is time-consuming, labor-intensive, and stressful for the animals and mink producers. The main objectives of this study were to (1) evaluate the application of machine learning (ML) algorithms to predict the average daily gain (ADG), feed conversion ratio (FCR), and residual feed intake (RFI) values during the whole growing and furring period (15 weeks from August 1st to November 14th) using less expensive features such as sex, color type, age, BW and length; (2) find the most significant contributing feature within the growth and furring period to predict the ADG, FCR and RFI. The color and sex features were recorded on 1 088 mink and mink’s age, BW and length were measured every 3 weeks from August 1st to November 14th which is called P1–P5. The ADG, FCR, and RFI were then predicted by the selected ML algorithms using multiple combinations of the observed and measured features from P1 to P5. By comparing the calculated ADG, FCR, and RFI values with the predicted values, it was determined that the most accurate combination of features was to include all features such as sex, color, age, BW and body length on August 1st (at the beginning of the P1). Among selected ML algorithms, the extreme gradient boosting (XGB) algorithm provided the most accurate and reliable prediction for the ADG (R2 = 0.71, RMSE = 0.10), FCR (R2 = 0.74, RMSE = 0.14), and RFI (R2 = 0.76, RMSE = 0.10). The XGB algorithm can be an accurate algorithm to predict the ADG, FCR, and RFI values without measuring costly daily feed intake. In addition, sex was identified as the most significant feature to predict the ADG, FCR, and RFI values with the importance scores of 0.85, 0.67, and 0.79, respectively.http://www.sciencedirect.com/science/article/pii/S1751731124002672Extreme gradient boostingFeed conversion ratioMachine learningPrecision livestock managementResidual feed intake |
| spellingShingle | A. Shirzadifar G. Manafiazar P. Davoudi D. Do G. Hu Y. Miar Prediction of growth and feed efficiency in mink using machine learning algorithms Animal Extreme gradient boosting Feed conversion ratio Machine learning Precision livestock management Residual feed intake |
| title | Prediction of growth and feed efficiency in mink using machine learning algorithms |
| title_full | Prediction of growth and feed efficiency in mink using machine learning algorithms |
| title_fullStr | Prediction of growth and feed efficiency in mink using machine learning algorithms |
| title_full_unstemmed | Prediction of growth and feed efficiency in mink using machine learning algorithms |
| title_short | Prediction of growth and feed efficiency in mink using machine learning algorithms |
| title_sort | prediction of growth and feed efficiency in mink using machine learning algorithms |
| topic | Extreme gradient boosting Feed conversion ratio Machine learning Precision livestock management Residual feed intake |
| url | http://www.sciencedirect.com/science/article/pii/S1751731124002672 |
| work_keys_str_mv | AT ashirzadifar predictionofgrowthandfeedefficiencyinminkusingmachinelearningalgorithms AT gmanafiazar predictionofgrowthandfeedefficiencyinminkusingmachinelearningalgorithms AT pdavoudi predictionofgrowthandfeedefficiencyinminkusingmachinelearningalgorithms AT ddo predictionofgrowthandfeedefficiencyinminkusingmachinelearningalgorithms AT ghu predictionofgrowthandfeedefficiencyinminkusingmachinelearningalgorithms AT ymiar predictionofgrowthandfeedefficiencyinminkusingmachinelearningalgorithms |