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New okra genotypes for the management of aphids (Hemiptera: Aphididae) in the diverse ecological landscapes of Central Africa.

Albert Fomumbod AbangSrinivasan RamasamyRachid HannaSevillor KekeunouApollin Fotso KuateAbdou TenkouanoKomi Kouma Mokpokpo FiaboeCharles-Félix Bilong Bilong
Published in: Journal of economic entomology (2024)
Various aphid species, including Aphis gossypii Glover (Hemiptera: Aphididae), are considered important pests of okra and other vegetables. Previous studies under 1 environment in Cameroon, Central Africa, had found multiple okra genotypes with resistance to A. gossypii. However, the stability and yield performance of the "resistant" genotypes across different environments were unknown. Ten previously identified aphid-resistant okra genotypes along with 1 commercial variety and a local landrace (specific to a given location) were compared during 2 seasons in replicated trials in 4 agro-ecological zones of Cameroon that also represent large areas of Central Africa. Aphid populations and okra yield parameters were evaluated. Breeding values were predicted using a linear mixed model for all data, and genotypes by location interactions were identified. The area under the infestation pressure curve (AUIPC) was calculated using aphid count data and subjected to resistance analysis. The Local-the most susceptible with the highest breeding value (+2.33)-and VI060794-one of the moderately resistant-genotypes had the highest yield per hectare. The only resistant genotype VI036213 had the lowest breeding value (-2.20). Genotype × location interactions were significant for yield, pod width, and plant height, while location variance was significant for all parameters evaluated. When considering that higher aphid densities could lead to greater pesticide use and, therefore higher production and environmental costs, the high-yielding VI060794-with moderate aphid resistance across multiple environments-presents an alternative or substitute for local landraces across multiple agro-ecologies of Cameroon and (by extension) Central Africa.
Keyphrases
  • human health
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  • climate change
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  • deep learning
  • drinking water
  • health risk assessment
  • heavy metals
  • neural network
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