Comparison of Frequentist and Bayesian Meta-Analysis Models for Assessing the Efficacy of Decision Support Systems in Reducing Fungal Disease Incidence

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.

Decision support systems halve fungicide use compared to calendar-based strategies without increasing disease risk

“…Based on Lázaro et al 30 , where frequentist and Bayesian models were compared, here both meta-analyses (MI and MIS) were specified considering betabinomial mixed-effect regression modelling framework 31 . Beta-binomial models are more adequate than the binomial generalised linear models used by Lázaro et al 30 to deal with overdispersed observations [32][33][34] , which result in discrepancies between the theoretical and empirical variances. Both MI and MIS were based on the following beta-binomial distribution:…”

“…For both meta-analyses (i.e., MI and MIS) we considered the disease incidence difference (DID) and disease incidence ratio (DIR) as the effect sizes to assess the efficacy of calendar-based and DSS-based strategies compared to untreated controls. The fitted models were used to compute effect sizes with two different approaches: i) by including only the fixed-parameter estimates in order to compute the expected disease incidence across the experiments included in the dataset (i.e., expected effect size values) 30,41 , and ii) by including both the fixedparameter estimates and the random effect estimates in order to predict the disease incidence for a new experiment, not yet conducted (i.e., the predicted effect size values) 42,43 .…”

“…where b 0,new , b cal,new , b dss,new are the effects for a new experiment computed from Eq. (3) 30,44 . Likewise, DID and DIR were computed according to Eqs.…”

“…Sensitivity analysis. A sensitivity analysis 45 was conducted to evaluate the behaviour of the Bayesian inferential methods in relation to the setting of prior specification 30,41 . The MI models (Supplementary Table S5) as well as the MIS models (Supplementary Table S7) were also fitted using a frequentist method by maximum likelihood through Laplace approximation using the glmmTMB() function of the package glmmTMB 46 implemented in the R software 47 .…”

A framework for evaluating the value of agricultural pest management decision support systems

“…Ideally, by incorporating knowledge of pest dynamics and impacts in a DSS, pesticides can be applied only when they are needed, increasing pesticide use efficiency, maintaining control of the pest without allowing an impact on yield. The use of DSS often leads to a reduction in the amount of pesticide applied (Lázaro et al, 2020), or recommendations for alternative control strategies (e.g. Mensah, 2010;Zhang & Swinton, 2009), reducing the negative impacts on the environment and human health.…”