HORIZON-CL6-2026-02-FARM2FORK-05

The sources collectively examine the impact of climate change and environmental pressures on the body size and genetic adaptation of various organisms, particularly focusing on the widespread phenomenon of declining body size in response to warming. The first source proposes that declining body size is a potential third universal response to climate warming, alongside changes in species phenology and distribution, emphasizing the need for integrative analyses using historical data and theoretical models to understand the underlying physiological mechanisms and consequences for species resilience. The second source applies landscape genomics and selection signature analyses to diverse sheep breeds to identify specific genes and biological pathways, such as those related to energy metabolism, heat response, and endocrine regulation, that illustrate genetic adaptation to varied climatic conditions globally. This research proposal highlight the importance of understanding the genetic and physiological factors that determine how species respond to both gradual and extreme climatic shifts.

Another aim of this project is to investigate the effects of different roughage ratios and fat levels used in ruminant diets on rumen fermentation processes and microbial structure under in vitro conditions through a multilayered analytical approach. In this study, fermentation outputs such as gas production and methane formation, along with compositional changes in the rumen microbiome, will constitute the main components of the experimental dataset. Experimentally, rations will be formulated with nine different roughage ratios and five different fat levels and subjected to in vitro incubation to determine gas and methane production quantitatively. In parallel, microbial community changes will be analyzed at the taxonomic level. The physiological data (gas production, methane emission) and microbial data (abundance and distribution of microbial taxa) obtained will be analyzed using state-of-the-art machine learning algorithms. By modeling the relationships between feed composition and ruminal responses, an artificial intelligence–supported analytical framework capable of producing highly accurate predictive outcomes will be developed. Through this approach, the effects of different feed compositions on the ruminal ecosystem will be addressed from a holistic and predictive perspective, surpassing the limitations of conventional statistical methods. The project outcomes are expected to contribute both scientifically and practically to environmental sustainability (through methane mitigation) and to improved efficiency in feed formulation.