Cambridge Global Food Security Lunchtime Conversations: "The dynamics of agricultural pests: leveraging data from genome to landscapes" with Dr Renata Retkute & Dr Henry North. Fri 30 Jan 2026, 1.30–2.30pm Organised by Cambridge Global Food Security IRC tinyurl.com/498vvhhp

Cost-effective early detection of banana bunchy top disease: insights from spatio-temporal modelling in Benin https://www.biorxiv.org/content/10.64898/2026.01.09.698707v1

Cost-effective early detection of banana bunchy top disease: insights from spatio-temporal modelling in Benin https://www.biorxiv.org/content/10.64898/2026.01.09.698707v1

Predicting wind-dispersed crop disease at large scale and evaluating performance with sparse observations. Pleased to have collaborated and published! iopscience.iop.org/article/10.1... @iopp-environment.bsky.social @camplantsci.bsky.social #OpenAccess #PlantDisease #FoodSecurity

Controlling #banana bunchy top #disease in Benin: Crop protection strategies with socio-economic perspectives Renata Retkute, et al. nph.onlinelibrary.wiley.com/doi/10.1002/... #PlantScience

Visiting FAO to discuss modelling of locusts & transboundary plant pests and diseases. Excited to exchange ideas on strengthening global food security. 🌍🦗🌱

New climate resilience & sustainability project co-led by @drrenataretkute.bsky.social with @bioversityciat.bsky.social aims to protect banana production in Africa from crop disease. Read more: tinyurl.com/2dutb4h2 #PlantSci 🧪 @cam.ac.uk @cambridgebiosci.bsky.social @camplantepidem.bsky.social

New preprint: Controlling Banana Bunchy Top Disease in Benin: crop protection strategies with socioeconomic perspectives doi.org/10.1101/2025...

New preprint: "A novel two-stage parameter estimation framework integrating Approximate Bayesian Computation and Machine Learning: The ABC-RF-rejection algorithm". With application to cassava brown streak disease #CBSD spread. #ParameterEstimation arxiv.org/abs/2507.02072

Now online doi.org/10.3389/fpls...

We celebrate a team member's success! Renata @drrenataretkute.bsky.social has received a BBSRC Flexible Talent Mobility Award to work with the Australian Plague Locust Commission on improved forecasting & management in Australia. @camplantsci.bsky.social @globalfoodcamb.bsky.social

Congratulations to @drrenataretkute.bsky.social from Cambridge Plant Sciences' Epidemiology & Modelling group! She has received a BBSRC Flexible Talent Mobility Award to work with the Australian Plague Locust Commission on improved forecasting & management in Australia. #PlantSciences 🧪

Excited to share that i received a BBSRC Flexible Talent Mobility Award! 🎉 Over the next 6 months, I’ll be working with the Australian Plague Locust Commission to adapt desert locust models for improved forecasting & management in Australia. Big thanks to @BBSRC 🌾🦗 #PestManagement #ClimateAdaptation

New paper: "Evaluation of the 2022 West Nile virus forecasting challenge, USA" parasitesandvectors.biomedcentral.com/articles/10....

New paper: Developing a spatio-temporal model for Banana Bunchy Top Disease: leveraging remote sensing and survey data www.frontiersin.org/journals/pla...

New preprint on the Detection of Banana Diseases Based on Landsat-8 Data and Machine Learning from the Epidemiology and Modelling group at the Department of Plant Sciences, University of Cambridge.🍌 👇 doi.org/10.20944/pre...

New preprint: Detection of Banana Diseases Based on Landsat-8 Data and Machine Learning www.preprints.org/manuscript/2...

How does a solitary insect cause riots and mass starvation? It just needs a trigger. Scientists from @camplantsci.bsky.social & @fao.org have developed new tool to help national agencies manage huge desert locust swarms before they devastate food crops in Africa & Asia 👇 tinyurl.com/327pj2cs

Predicting desert locust swarms: A new modelling framework integrates lifecycle, breeding, and movement patterns with weather data to forecast swarm behavior.

By using concepts from epidemiological modelling, we (@camplantsci.bsky.social, @metoffice.bsky.social and @fao.org) have developed an integrated modelling framework for doing just this! Paper (open access): journals.plos.org/ploscompbiol... Press release: phys.org/news/2024-12...

How do you combine simple atmospheric dispersion modelling trajectories like this with environmental data and population biology knowledge to make short- and long-term predictions of desert locust swarm migration?