Login / Signup

Predicting Phenology of Four Major Hemipteran Pests to Enhance Integrated Pest Management Programs in Potatoes in the Lower Columbia Basin.

Tiziana OppedisanoGovinda ShresthaSarah AndersonIra D ThompsonSilvia I Rondon
Published in: Journal of economic entomology (2022)
The potato crop (Solanum tuberosum L.) is affected by various hemipteran insect pests including Circulifer tenellus Baker, Lygus spp., Myzus persicae Sulzer, and Macrosiphum euphorbiae Thomas. These pests can cause direct foliage damage or vector plant pathogens, and consequently reduce potato yield. Gaining insights into which factors have the greatest impact on seasonal population growth of insect pests is key for improving integrated pest management strategies. Moreover, abiotic and biotic cues such as temperature and crop growth stage can strongly influence insect population growth. Hence, the seasonal population dynamics of C. tenellus, Lygus spp., M. persicae, and M. euphorbiae, and temperature, were monitored weekly throughout potato growing seasons in commercial fields located in the lower Columbia Basin (USA). Using a multi-year dataset, we developed phenology models of each pest based on the accumulated degree days (DD) and potato days (PD). Temperature-mediated population growth models suggest that C. tenellus and Lygus spp. are the first of the pests to colonize the potato crop fields, with 90% of cumulative catch by 2,823 and 1,776 DD, respectively. In contrast, M. persicae and M. euphorbiae populations increased more gradually over the course of the season, with 90% cumulative catch by 5,590 and 5,047 DD, respectively. PD-mediated population growth models suggest that 50% of the populations of C. tenellus, Lygus spp., and M. persicae can be collected at potato tuber growth stage, while 50% of the M. euphorbiae population at tuber initiation stage. The results presented here will help in improving hemipteran potato pests' management.
Keyphrases
  • climate change
  • magnetic resonance
  • magnetic resonance imaging
  • oxidative stress
  • computed tomography
  • transcription factor
  • contrast enhanced
  • genome wide analysis
  • plant growth