Decoding the genetic basis of floret fertility in wheat - A high grain yield is undoubtedly a desirable trait in cereal crops. Floret fertility is a key factor which determines the number of grains per inflorescence of cereals such as bread wheat or barley. Nonetheless, until recently little was known about its genetic basis. Whilst investigating floret fertility, a group of researchers have now discovered the locus Grain Number Increase 1 (GNI1), an important contributor to floret fertility. A writeup from Science Daily of an international collaboration, including researchers from the Robert H. Smith Faculty of Agriculture, to decode the genetic basis of floret fertility in wheat.

I24 News Story about the first academic course in Israel in wine-making - the next step in growing the local wine industry

Jerusalem Post covers Food Tech Nation Conference at the Faculty - The future of healthy eating drew crowds Thursday at the Hebrew University of Jerusalem Robert H. Smith Faculty of Agriculture in Rehovot. About 300 people came to an event celebrating the institution’s 75th anniversary and giving students the opportunity to showcase their work. It featured some of the latest developments in Israeli food technology ranging from 3D printed meals to protein powder made from fly larva.

Nanotech pioneer Prof. Oded Shoseyov has already founded 11 companies based on his inventions. Now he’s on his next: a 4D printing platform to create customized meals.

in Israeli-UC Davis Vet Team-up - From jweekly: Three decades of joint research at the Faculty and UC Davis vet schools, supported by Koret grants.: A list of the collaborative work between the UC Davis School of Veterinary Medicine and the Koret School of Veterinary Medicine — the only veterinary school in Israel — reads something like a “who’s who” of the animal kingdom....

Faculty scientists Professors Ido Braslavsky and Oded Shoseyov develop technique to print food according to pre-defined criteria in a process that will serve a variety of special-needs populations.

Journal club article in PNAS about BitterPredict, a machine learning tool that predicts whether a chemical tastes bitter. BitterPredict was developed by members of Prof Masha Niv's lab.