Improving global coordination of crop modeling efforts.
Digital warning system and coupling with crop model to boost wheat farmers’ resilience in Bangladesh and Brazil
A new digital early warning system for wheat blast disease integrates mathematical models that, when combined with weather forecasts, can simulate disease growth and risks to forewarn against potential outbreaks.
Webinar – Combining crop and disease modeling with numerical weather forecasting to inform wheat blast early warning systems in Bangladesh, Brazil, and beyond
CGIAR Webinar by the Platform for Big Data in Agriculture’s Community of Practice on Crop Modeling – Combining crop and disease modeling with numerical weather forecasting to inform wheat blast early warning systems in Bangladesh, Brazil, and beyond
Scientists develop an early warning system that delivers wheat rust predictions directly to farmers’ phones
New research describes a revolutionary early warning system that can predict and mitigate wheat rust diseases in Ethiopia.
A new paper published in BMC Biology by the MARPLE diagnostics team, a 2018 Inspire Challenge Scale-up winner, shows how the research partnership reduced the speed of diagnostics from many months in high-end labs, to just two days from the side of an Ethiopian field.