ANALYSIS OF THE IMPACT OF FIVE METHANE MITIGATING FEED ADDITIVES ON MILK PRODUCTION AND ASSOCIATED PARAMETERS ACROSS MULTIPLE COMMERCIAL DAIRY FARMS

Abstract

The effect of methane-mitigating feed additives on dairy cows has been widely explored; however, confusing conclusions have been reached due to factors such as the inclusion of different doses and experimental conditions in which the additives are tested, or even a small sample size. We present the first extensive study assessing the effects of methane-mitigating feed additives on milk production across several commercial dairy farms. This study used a previously developed predictive AI-driven model based on microbiome samples; the model predicts farms where a significant reduction of methane emissions is expected due to the applied feed additives. Thus, in this study, each feed additive was supplied to a large number of farms, widely distributed across different climatic areas in Israel. The data analysis followed two simulated scenarios: (1) a naive approach, where feed additives are supplied indiscriminately, and (2) an optimized approach, where feed additives are supplied only to farms with a high likelihood of being positively impacted in terms of reduced enteric methane emissions (50% of the farms). The results show that each feed additive significantly increased milk production compared to the control groups. This increase in milk production was significantly higher in the optimized scenario. Other related parameters such as somatic cells were also improved. Our results suggest that the feed additives positively affect milk production, reaching a maximum expression when the AI-driven model is applied.