We do fundamental research to understand insect movement and migration, the way pesticides act, weeds, the mechanisms and evolution of pesticide resistance, how host plants resist to insects and pathogens and on semiochemical signalling as defence mechanism.
This is all aimed at informing better, environmental-friendly and more sustainable crop protection strategies, that minimise chemical inputs, without reducing yields. We deliver this approach via collaborations with the agrochemical industry, agri-advisors and directly with farmers/growers and policy makers.
We have a wide range of expertise across chemistry, molecular biology and biochemistry, genetics and genomics, bioinformatics, entomology, plant pathology, weed science and modelling.
By improving crop protection, we can reduce crop losses from insects, pathogens and weeds, and support the institutes’ s Science Portfolios. The department also houses the Rothamsted Insect Survey National Capability and many other resources such as an insectary, electrophysiology, the Pathogen-Host Interactions database, a Virus-Induced Gene Silencing lab and many others.
Bringing together researchers and farmers to test innovative farming systems at scale.
A database of primary arable weed vegetation records from across Europe.
Sequencing the genomes of key global pests and beneficial insects has the potential to revolutionise sustainable crop protection
A gene to landscape approach to deliver more targeted and sustainable control of insect pests, weeds and diseases in agroecosystems.
Developing and screening novel wheat germplasm for the next generation of key traits which will underpin sustainable and productive agriculture
Developing and testing innovative farming systems that increase food production & resilience to future perturbations, while reducing the environmental footprint of agriculture.
Engineering trans-kingdom RNA interference against wheat infecting Fusarium fungi
Improving the environmental footprint and resilience of the wheat crop through genetics and targeted traits analysis