Papers
8
Total Citations
88
H-Index
5
About
Magdalena Kolenda is a prominent researcher specializing in dairy cattle science, with a particular focus on automatic milking systems (AMS) and the application of advanced data analytics to optimize dairy farm productivity. Her work sits at the intersection of animal science, precision livestock farming, and machine learning, making her a distinctive voice in modern agricultural research. Kolenda's most significant contributions involve harnessing decision tree techniques to forecast milk yield and milking efficiency in Polish Holstein-Friesian cows, with her 2020 paper on predicting monthly milk yield accumulating 31 citations and her 2022 milking efficiency study earning 19. These works have provided farmers and researchers with actionable frameworks for improving herd management in robot-assisted environments. Her comparative analyses of AMS performance across multiple European countries and the United States have further broadened the field's understanding of international benchmarks and best practices, cited 13 times. Beyond productivity metrics, Kolenda has contributed to genetic science through heritability and genetic correlation estimations for key milking traits, advancing selective breeding strategies. Her research on somatic cell counts and udder health underscores her holistic approach to dairy welfare and quality. Collectively, her body of work, totaling over 80 citations, offers valuable tools for improving the sustainability and profitability of modern automated dairy operations.
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