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Home>Current Affairs>Scientists Develop Model to Combat Future Locust Outbreaks
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Scientists Develop Model to Combat Future Locust Outbreaks

Context:

Scientists from the University of Cambridge, the United Nations’ Food and Agriculture Organization (FAO), and the UK’s Met Office developed a forecasting tool to predict locust swarm movements and behaviours.

More on the News

  • Locusts are one of the most dangerous migratory pests, threatening millions of smallholder farmers and global food security.
  • The new tool, with an accuracy of 83%, can be crucial in predicting breeding areas that may be missed during ground monitoring. 
  • The tool combines environmental data, weather models, and remote sensing to anticipate swarm movements and identify potential breeding sites. 

  • It aims to help governments, farmers, and agencies prepare for future locust upsurges.
  • The tool can predict locust movements across multiple countries, critical for managing locust invasions in affected regions.
  • Integrating satellite data to assess vegetation availability and predict how long swarms will feed at each location.
  • The model was tested across five sub-Saharan countries (Kenya, Ethiopia, Somalia, Eritrea, and Djibouti), heavily impacted during the 2019 upsurge, and accurately predicted swarm movements

Key Features of the Predictive Tool

  • Breeding Site Identification: Predict suitable locust breeding areas.
  • Lifecycle Monitoring: Tracks stages from egg to adult.
  • Swarm Movement Prediction: Uses weather models to forecast migration paths.
  • Vegetation Analysis: Estimates swarm feeding duration based on food availability.
  • Swarm Trajectory Analysis: Determines likely origins and landing sites of swarms.

India’s Measures to Combat Locusts

  • New Equipment: Procured 70 new locust control tools from the UK and increased ground control teams to 104.
  • Drone Usage: Drones were used for locust control for the first time globally, marking a significant step in locust management.
  • Collaborative efforts: It helped to reduce vegetation damage in previously affected regions.

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