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Field Assessment of Aphid Doubling Time and Yield of Sorghum Susceptible and Partially Resistant to Sugarcane Aphid (Hemiptera: Aphididae).

John W GordyNicholas J SeiterDavid L KernsFrancis P F Reay-JonesRobert D BowlingM O WayMichael J Brewer
Published in: Journal of economic entomology (2021)
Since outbreaks were first detected in grain sorghum, Sorghum bicolor (L.) Moench (Cyperales: Poaceae), in 2013, sugarcane aphid, Melanaphis sacchari Zehntner has become a major annual pest in grain sorghum-producing regions of North America. Economic thresholds have been recommended for susceptible hybrids, but these recommendations may not be suitable for grain sorghum hybrids partially resistant to sugarcane aphid. The objectives were to evaluate the grain yield-aphid density relationship and field-based population growth rates of the aphid on sorghum hybrids susceptible and partially resistant to sugarcane aphid across multiple years, locations, and hybrids. These data verified previously established economic injury levels for susceptible hybrids. The observed maximum aphid density ranged from 6 to 451 aphids per leaf for resistant hybrids and from 67 to 1,025 for susceptible hybrids. Across 50 location-year combinations, the maximum aphid density observed on resistant hybrids decreased by 0-99%, compared to a susceptible hybrid at the same location (mean reduction = 80%). Doubling time for sugarcane aphid populations on partially resistant hybrids was up to 6.4-fold higher than on known susceptible hybrids. For 48 of the 50 location-years, yield loss attributable to sugarcane aphid was not detected on the partially resistant hybrids; therefore, an economic injury level was unable to be estimated. If an economic injury level exists for resistant hybrids, it is likely at an aphid population level that exceeds the levels experienced in this study. It remains prudent to monitor resistant hybrids for unusual leaf decay associated with aphid densities that exceed current economic injury levels used for susceptible hybrids.
Keyphrases
  • machine learning
  • electronic health record
  • clinical practice
  • life cycle
  • genetic diversity