Comparison of Frequentist and Bayesian Meta-Analysis Models for Assessing the Efficacy of Decision Support Systems in Reducing Fungal Disease Incidence
View/ Open
Metadata
Show full item recordDate
2020Cita bibliográfica
Lázaro, E.; Makowski, D.; Martínez-Minaya, J.; Vicent, A. (2020). Comparison of Frequentist and Bayesian Meta-Analysis Models for Assessing the Efficacy of Decision Support Systems in Reducing Fungal Disease Incidence. Agronomy, 10(4), 560.Abstract
Diseases of fruit and foliage caused by fungi and oomycetes are generally controlled by
the application of fungicides. The use of decision support systems (DSSs) may assist to optimize
fungicide programs to enhance application on the basis of risk associated with disease outbreak.
Case-by-case evaluations demonstrated the performance of DSSs for disease control, but an overall
assessment of the efficacy of DSSs is lacking. A literature review was conducted to synthesize the
results of 67 experiments assessing DSSs. Disease incidence data were obtained from published
peer-reviewed field trials comparing untreated controls, calendar-based and DSS-based fungicide
programs. Two meta-analysis generic models, a “fixed-effects” vs. a “random-effects” model within
the framework of generalized linear models were evaluated to assess the efficacy of DSSs in reducing
incidence. All models were fit using both frequentist and Bayesian estimation procedures and the
results compared. Model including random effects showed better performance in terms of AIC or DIC
and goodness of fit. In general, the frequentist and Bayesian approaches produced similar results.
Odds ratio and incidence ratio values showed that calendar-based and DSS-based fungicide programs
considerably reduced disease incidence compared to the untreated control. Moreover, calendar-based
and DSS-based programs provided similar reductions in disease incidence, further supporting the
efficacy of DSSs.