The project, led by the University of Sheffield, aims to produce heat and drought- tolerant rice that require less water to grow, reducing methane emissions a by-product of traditional flooded rice fields. Ultimately the researchers hope the discoveries will expand and encourage the adoption of sustainable rice farming practices and increase food security.
“As rice is one of the world's most significant crops the impact that this research will potentially have on sustainability is enormous - it is an exciting project that will allow us to take our research from the laboratory and out into the rice fields.” Professor Julie Gray
Rice is a staple food for over half of the world's population and is a major focus of biological research because of its high water requirements and its vulnerability to heat and drought stress, factors that are increasing due to climate change.
The research team have already screened the genomes of nearly 1,000 varieties of rice and have identified a number of genes linked to resilience in challenging climates. From this work, only six genes have been chosen for the project, focusing on their impact on stomata. Stomata are the tiny, adjustable pores on leaves that regulate water loss and cooling in plants and by understanding how different varieties of rice naturally control these pores, it is hoped that more water-efficient crops can be developed.
The three-year £800,000 project funded by the Biotechnology and Biological Sciences Research Council (BBSRC), is led by Professor Julie Gray from the School of Biosciences and will collaborate with researchers from Penn State University, University of Würzburg and the International Rice Research Institute in the Philippines.
Professor Julie Gray and her team will grow and compare different genetically enhanced rice varieties in both controlled environments and tropical field trials in the Philippines. Through this they will be able to identify the specific traits that have a positive effect on resilience and use advanced tools such as machine learning to predict which plants will perform the best under stressful conditions.