Linking Animal Feed Formulation to Milk Quantity, Quality, and Animal Health Through Data-Driven Decision-Making
The global demand for high-quality animal products, particularly dairy, has intensified the need for more precise and efficient livestock feed formulation. This review connects data-driven decision-making in optimizing feed formulation to enhance milk quantity and quality while addressing animal hea...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Animals |
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| Online Access: | https://www.mdpi.com/2076-2615/15/2/162 |
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| author | Oreofeoluwa A. Akintan Kifle G. Gebremedhin Daniel Dooyum Uyeh |
| author_facet | Oreofeoluwa A. Akintan Kifle G. Gebremedhin Daniel Dooyum Uyeh |
| author_sort | Oreofeoluwa A. Akintan |
| collection | DOAJ |
| description | The global demand for high-quality animal products, particularly dairy, has intensified the need for more precise and efficient livestock feed formulation. This review connects data-driven decision-making in optimizing feed formulation to enhance milk quantity and quality while addressing animal health implications. Modern feed formulation has evolved into a sophisticated, data-centric process by integrating diverse data sources such as nutritional databases, environmental data, and animal performance metrics. Leveraging advanced analytical techniques, such as machine learning and optimization algorithms, have created highly accurate feed formulations tailored to specific livestock needs. These innovations increase milk yield and contribute to developing dairy products with higher nutritional value. Decision Support Systems play a complementary role by offering real-time decision-making capabilities, enabling farmers to make data-informed adjustments composition based on changing conditions. However, despite its potential, the widespread adoption of data-driven feed formulation faces challenges such as data quality, technological limitations, and industry resistance, mostly disjointed processes. The objectives of this review are: (i) to explore the current advancements and challenges of data-driven decision-making in feed formulation, focusing on its connection to milk quantity and quality, and (ii) to highlight how this optimized feed formulation strategy improves sustainable dairy production. |
| format | Article |
| id | doaj-art-460c9a1c60004173a3379bcb2fe5874c |
| institution | Kabale University |
| issn | 2076-2615 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Animals |
| spelling | doaj-art-460c9a1c60004173a3379bcb2fe5874c2025-01-24T13:17:50ZengMDPI AGAnimals2076-26152025-01-0115216210.3390/ani15020162Linking Animal Feed Formulation to Milk Quantity, Quality, and Animal Health Through Data-Driven Decision-MakingOreofeoluwa A. Akintan0Kifle G. Gebremedhin1Daniel Dooyum Uyeh2Department of Biosystems and Agricultural Engineering, Michigan State University, East Lansing, MI 48824, USADepartment of Biological and Environmental Engineering, Cornell University, Ithaca, NY 14853, USADepartment of Biosystems and Agricultural Engineering, Michigan State University, East Lansing, MI 48824, USAThe global demand for high-quality animal products, particularly dairy, has intensified the need for more precise and efficient livestock feed formulation. This review connects data-driven decision-making in optimizing feed formulation to enhance milk quantity and quality while addressing animal health implications. Modern feed formulation has evolved into a sophisticated, data-centric process by integrating diverse data sources such as nutritional databases, environmental data, and animal performance metrics. Leveraging advanced analytical techniques, such as machine learning and optimization algorithms, have created highly accurate feed formulations tailored to specific livestock needs. These innovations increase milk yield and contribute to developing dairy products with higher nutritional value. Decision Support Systems play a complementary role by offering real-time decision-making capabilities, enabling farmers to make data-informed adjustments composition based on changing conditions. However, despite its potential, the widespread adoption of data-driven feed formulation faces challenges such as data quality, technological limitations, and industry resistance, mostly disjointed processes. The objectives of this review are: (i) to explore the current advancements and challenges of data-driven decision-making in feed formulation, focusing on its connection to milk quantity and quality, and (ii) to highlight how this optimized feed formulation strategy improves sustainable dairy production.https://www.mdpi.com/2076-2615/15/2/162animal feed formulationdecision support systemsmilk qualitymilk yieldprecision nutrition |
| spellingShingle | Oreofeoluwa A. Akintan Kifle G. Gebremedhin Daniel Dooyum Uyeh Linking Animal Feed Formulation to Milk Quantity, Quality, and Animal Health Through Data-Driven Decision-Making Animals animal feed formulation decision support systems milk quality milk yield precision nutrition |
| title | Linking Animal Feed Formulation to Milk Quantity, Quality, and Animal Health Through Data-Driven Decision-Making |
| title_full | Linking Animal Feed Formulation to Milk Quantity, Quality, and Animal Health Through Data-Driven Decision-Making |
| title_fullStr | Linking Animal Feed Formulation to Milk Quantity, Quality, and Animal Health Through Data-Driven Decision-Making |
| title_full_unstemmed | Linking Animal Feed Formulation to Milk Quantity, Quality, and Animal Health Through Data-Driven Decision-Making |
| title_short | Linking Animal Feed Formulation to Milk Quantity, Quality, and Animal Health Through Data-Driven Decision-Making |
| title_sort | linking animal feed formulation to milk quantity quality and animal health through data driven decision making |
| topic | animal feed formulation decision support systems milk quality milk yield precision nutrition |
| url | https://www.mdpi.com/2076-2615/15/2/162 |
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