The paper concentrated on the creation of a prognostic model based on a large dataset. It evaluated a user-friendly tool which assist dairy farmers in reproduction management decisions by forecasting the chance of conception success for a specific insemination using herd- and cow-level parameters.
The Modulos AutoML platform was used to predict the insemination outcomes of individual cows based on phenotypic data. The paper examined data on health, reproduction, production, and breeding from 2,500 Swiss farms during a five-year period from 2015 to 2020.
The study's findings suggest that using information about health, reproductive history, production level, and other environmental characteristics can help identify highly fertile cows and classify those animals most likely to become pregnant, allowing farmers to optimize their breeding programs from an economic standpoint.
The paper is selected as Best Paper in 2022 by the Committe presided by Prof. Dr. Dirk Morschett. The Committee particularly emphasised the perfect execution of the EMBA thesis work and the originality of the thesis topic. The iimt congratulates its two EMBA Alumni on this award!
