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Pheromone traps and climate variations influence populations of Sahlbergella singularis (Hemiptera: Miridae) and associated damage of cocoa in Cameroon.

Hermine C MahotLeïla Bagny-BeilheRaymond J MahobAimé-Didier B BegoudéApollin Fotso KuateGertrude MembangNathalie EwaneAdolph KemgaCharles F B BilongDavid R HallKomi Kouma Mokpokpo FiaboeRachid Hanna
Published in: Environmental entomology (2024)
Knowledge of insect pest ecology and biology is important for maximizing crop protection and reducing crop losses. Currently, we lack an efficient control program for the cocoa mirid Sahlbergella singularis Haglund (Hemiptera: Miridae), the principal insect pest of cocoa in West and Central Africa. A 2-yr study was conducted in 11 plantations across Ayos and Konye, two of the largest cocoa growing areas of Cameroon. We evaluated the effects of mirid sex pheromone and climatic variations on mirid population dynamics and their associated cocoa damage. Sex pheromone traps caught 1.5-fold higher mirids in Ayos than in Konye, with more overall counts in 2015 than in 2016. Cocoa pod counts were also significantly higher in 2015 than in 2016 and were negatively correlated with temperature and relative humidity. In both localities, mirid populations and associated cocoa pod damage were suppressed in plantations where sex pheromone traps were used. Damage incidence was positively correlated with mirid counts, confirming that the cocoa pod is the preferential site for mirid feeding and reproduction. As such, damage incidence could be used as proxy for comparative mirid population level due to the mirid's cryptic habit. Of the recorded weather variables, only relative humidity was correlated (negatively) with damage severity. Our data on the relationships between damage caused by mirids and their population dynamics and sex pheromone trap catches suggest that an effective control strategy using mass trapping could be developed for mirid management in cocoa plantations.
Keyphrases
  • oxidative stress
  • climate change
  • healthcare
  • risk factors
  • peripheral blood
  • machine learning
  • quality improvement