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Biology subjects

Omondi, A. B.

Publications and source records attributed to Omondi, A. B..

2 recordsLinked to original sources

Sustaining smallholder banana production in Banana Bunchy Top Disease endemic landscapes: integrating clean seed, roguing, and farmer training

Banana bunchy top virus (BBTV) continues to threaten smallholder livelihoods and food security across sub-Saharan Africa. While clean-seed programmes are widely promoted, their long-term effectiveness is often compromised by rapid reinfection in endemic landscapes. We developed an integrated framework combining spatially explicit, stochastic epidemiological modelling with additional cost-benefit analysis and use of socio-behavioural data to evaluate strategies for stabilising production. Our results demonstrate that frequent monthly inspections and accurate symptom detection are essential for disease suppression. Crucially, the economic analysis reveals that prioritizing diagnostic competence as an economic asset is necessary: improving detection efficiency can more than double farmer net revenue under realistic market conditions. Socio-behavioural findings further confirm that a farmers ability to correctly recognise symptoms is the strongest predictor of roguing adoption, far outweighing demographic characteristics. These results provide quantifiable guidance for disease management and highlight that the sustainability of clean-seed interventions hinges on shifting policy from simple seed replacement to investing in farmer diagnostic capacity. Strengthening this local surveillance capability transforms informal seed systems into resilient, durable tools for safeguarding household nutrition and regional food security.

plant biology↗

Parameterisation of epidemiological models from small field experiments: a case study of banana bunchy top virus transmission

Accurate estimation of epidemiological parameters from limited field data remains a major challenge in plant disease modeling. We present a novel data-augmented adaptive multiple importance sampling (DA-AMIS) framework that integrates Bayesian inference with stochastic epidemic modeling to estimate key transmission parameters from small field experiments. Using detailed individual-level observations from a 24-plant experiment on the natural spread of banana bunchy top virus (BBTV) in Benin, we jointly inferred infection timing, dispersal characteristics, and transmission rates for both primary and secondary infections. Model validation against independent datasets from BBTV field trials in Burundi and Malawi showed close correspondence between simulated and observed prevalence dynamics, confirming the generality of parameter estimates across regions. The inferred 12% infection rate of replanting suckers underscores the risk of disease introduction through planting material, while simulations identified April as the period of peak infection, providing actionable insights for surveillance timing.

plant biology↗