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Making improved replacement decisions for dairy cattle is a complex and crucial task for farmers. Research suggests that the ideal economic productive lifespan of a cow is typically between 5 and 6 lactations, yet real-world practices fall short of this potential. Farmers often face suboptimal decisions owing to the intricate nature of the culling process, which involves numerous economic and non-economic factors that vary between farms. This complexity is compounded by a lack of comprehensive information on the economic implications of culling decisions. To address this challenge, we present an algorithm and tool designed for Swiss dairy farmers, aiming to simplify and optimize their culling decisions. This algorithm, inspired by previous models, leverages a Markov chain approach to calculate anticipated survival probabilities, considering factors such as pregnancy. Key aspects of this tool include assigning a monetary value, known as the "cow value," to each cow based on expected survival and monthly revenue. The cow value allows farmers to rank all cows comprehensively, aiding in the identification of less productive cows that merit replacement. The algorithm considers the diverse production systems and breed variations in Swiss dairy farming. It factors in variables such as the cost of acquiring replacement heifers, milk prices, protein and fat contents, fertility, and health. This tool offers Swiss dairy farmers valuable insights, especially in larger herds, where culling decisions may be less evident. By providing economic implications of culling choices, the algorithm optimizes average herd life and enhances farmers' income.
Schlebusch et al. (Mon,) studied this question.