Meta-Analysis in Plant Pathology: Publication Patterns, Methodological Trends, and Potential Future Directions.

Meta-analysis (MA) is increasingly adopted in agricultural and ecological sciences, fields at the interface with plant pathology. Our review of the use of MA in the field since 1999 identified 79 original research papers. Fifty percent of these works were published in two American Phytopathological Society journals, emphasizing their central role in disseminating MA research.

The use of fixed study main effects in arm-based network meta-analysis.

Methods of network meta-analysis (NMA) can be classified as arm-based and contrast-based approaches. There are several arm-based approaches, and some of these have been criticized because they recover inter-study information and hence do not obey the principle of concurrent control. Here, we point out that recovery of inter-study information in arm-based NMA can be prevented by fitting a fixed main effect for studies.

Into the Trees: Random Forests for Predicting Fusarium Head Blight Epidemics of Wheat in the United States.

Constructing models that accurately predict Fusarium head blight (FHB) epidemics and are also amenable to large-scale deployment is a challenging task. In the United States, the emphasis has been on simple logistic regression (LR) models, which are easy to implement but may suffer from lower accuracies when compared with more complicated, harder-to-deploy (over large geographies) model frameworks such as functional or boosted regressions. This article examined the plausibility of random forests (RFs) for the binary prediction of FHB epidemics as a possible mediation between model simplicity and complexity without sacrificing accuracy.

Environmental Conditions After Fusarium Head Blight Visual Symptom Development Affect Contamination of Wheat Grain with Deoxynivalenol and Deoxynivalenol-3-Glucoside.

Fusarium head blight (FHB) of wheat, caused by the fungus , is associated with grain contamination with mycotoxins such as deoxynivalenol (DON). Although FHB is often positively correlated with DON, this relationship can break down under certain conditions. One possible explanation for this could be the conversion of DON to DON-3-glucoside (D3G), which is typically missed by common DON testing methods.

Environment, Grain Development, and Harvesting Strategy Effects on Zearalenone Contamination of Grain from Fusarium Head Blight-Affected Wheat Spikes.

Fusarium head blight (FHB), caused by the fungus , is associated with grain contamination with mycotoxins such as deoxynivalenol (DON) and zearalenone (ZEA). Unlike DON, less is known about factors affecting ZEA production during FHB epidemics. The objective of this study was to quantify ZEA contamination of wheat grain as influenced by temperature, relative humidity, FHB index (IND), grain maturation, simulated late-season rainfall, and harvest timing.

How to observe the principle of concurrent control in an arm-based meta-analysis using SAS procedures GLIMMIX and BGLIMM.

Network meta-analysis is a popular method to synthesize the information obtained in a systematic review of studies (e.g., randomized clinical trials) involving subsets of multiple treatments of interest.

Impact of Gibberella Ear Rot on Grain Quality and Yield Components in Maize as Influenced by Hybrid Reaction.

The impact of Gibberella ear rot (GER; caused by ) on deoxynivalenol (DON) contamination of grain and yield components in maize were investigated using data from 30 environments in Ohio (3 years by 10 locations). Fifteen hybrids, later classified as susceptible (SU), moderately susceptible (MS), or moderately resistant (MR), based on the magnitude of differences in mean arcsine square-root-transformed GER severity (arcSEV) and log-transformed DON (logDON) relative to a reference SU check, were planted in each environment, and 10 ears per hybrid were inoculated with a spore suspension of . Relationships between GER severity and DON were well described by a Kono-Sugino-type nonlinear equation.

Efficacy of Genetic Resistance and Fungicide Application Against Fusarium Head Blight and Mycotoxins in Wheat Under Persistent Pre- and Postanthesis Moisture.

Field experiments were conducted to investigate the efficacy of fungicide treatments in combination with genetic resistance against Fusarium head blight (FHB) and its associated mycotoxins under persistently wet pre- and postanthesis conditions in plots inoculated with -colonized corn spawn. Treatments consisted of a single application of prothioconazole + tebuconazole at early anthesis (PA), or at 3 (P3), 6 (P6), or 9 (P9) days after early anthesis, or PA followed by a single application of metconazole at 3 (PA+C3), 6 (PA+C6), or 9 (PA+C9) days after early anthesis. PA and P3 were the most efficacious of the single-application treatments in terms of mean percentage control of FHB index (IND), deoxynivalenol (DON), zearalenone (ZEA), and mean increase in grain yield and test weight (TW) relative to the nontreated susceptible check (S_CK).

Characterizing Heterogeneity and Determining Sample Sizes for Accurately Estimating Wheat Fusarium Head Blight Index in Research Plots.

Because Fusarium head blight (FHB) intensity is usually highly variable within a plot, the number of spikes rated for FHB index (IND) quantification must be considered when designing experiments. In addition, quantification of sources of IND heterogeneity is crucial for defining sampling protocols. Field experiments were conducted to quantify the variability of IND ("field severity") at different spatial scales and to investigate the effects of sample size on estimated plot-level mean IND and its accuracy.

Logistic Models Derived via LASSO Methods for Quantifying the Risk of Natural Contamination of Maize Grain with Deoxynivalenol.

Models were developed to quantify the risk of deoxynivalenol (DON) contamination of maize grain based on weather, cultural practices, hybrid resistance, and Gibberella ear rot (GER) intensity. Data on natural DON contamination of 15 to 16 hybrids and weather were collected from 10 Ohio locations over 4 years. Logistic regression with 10-fold cross-validation was used to develop models to predict the risk of DON ≥1 ppm.

A Meta-Analytical Assessment of the Aggregation Parameter of the Binary Power Law for Characterizing Spatial Heterogeneity of Plant Disease Incidence.

The binary power law (BPL) is often used to characterize spatial heterogeneity of disease incidence. A hierarchical mixed model, coupled with multiple imputation to randomly generate any missing standard errors, was used to conduct a meta-analysis of >200 published values of the estimated aggregation () parameter of the BPL. Approximately 50% of estimated values ranged from 1.

Accuracy in the prediction of disease epidemics when ensembling simple but highly correlated models.

Ensembling combines the predictions made by individual component base models with the goal of achieving a predictive accuracy that is better than that of any one of the constituent member models. Diversity among the base models in terms of predictions is a crucial criterion in ensembling. However, there are practical instances when the available base models produce highly correlated predictions, because they may have been developed within the same research group or may have been built from the same underlying algorithm.

Comparing the Temporal Development of Wheat Spike Blast Epidemics in a Region of Bolivia Where the Disease Is Endemic.

Epidemics of wheat blast, caused the pathotype of , were studied in the Santa Cruz del la Sierra region of Bolivia to quantify and compare the temporal dynamics of the disease under different growing conditions. Six plots of a susceptible wheat cultivar were planted at Cuatro Cañadas (CC), Okinawa 1 (OK1), and Okinawa 2 (OK2) in 2015. Spike blast incidence (INC) and severity (SEV) and leaf blast severity (LEAF) were quantified in each plot at regular intervals on a 10 × 10 grid ( = 100 clusters of spikes), beginning at head emergence (Feekes growth stage 10.

Quantifying the Effects of Temperature and Relative Humidity on the Development of Wheat Blast Incited by the Lolium Pathotype of .

The pathotype of (MoT) that causes wheat blast has not yet been reported in the U.S., but the closely related Lolium pathotype (MoL), also capable of inciting blast, is found in several wheat growing regions.

Is Disease Intensity a Good Surrogate for Yield Loss or Toxin Contamination? A Case Study with Fusarium Head Blight of Wheat.

Sometimes plant pathologists assess disease intensity when they are primarily interested in other response variables, such as yield loss or toxin concentration in harvested products. In these situations, disease intensity potentially could be considered a surrogate of yield or toxin. A surrogate is a variable which can be used instead of the variable of interest in the evaluation of experimental treatments or in making predictions.

Effects of Pre- and Postanthesis Applications of Demethylation Inhibitor Fungicides on Fusarium Head Blight and Deoxynivalenol in Spring and Winter Wheat.

Anthesis is generally recommended as the optimum growth stage for applying a foliar fungicide to manage Fusarium head blight (FHB) and the Fusarium-associated toxin deoxynivalenol (DON) in wheat. However, because it is not always possible to treat fields at anthesis, studies were conducted to evaluate pre- and postanthesis treatment options for managing FHB and DON in spring and winter wheat. Network meta-analytical models were fitted to data from 19 years of fungicide trials, and log response ratio ([Formula: see text]) and approximate percent control ([Formula: see text]) relative to a nontreated check were estimated as measures of the effects of six treatments on FHB index (IND: mean percentage of diseased spikelets per spike) and DON.

Estimating the variance for heterogeneity in arm-based network meta-analysis.

Network meta-analysis can be implemented by using arm-based or contrast-based models. Here we focus on arm-based models and fit them using generalized linear mixed model procedures. Full maximum likelihood (ML) estimation leads to biased trial-by-treatment interaction variance estimates for heterogeneity.

Meta-Analysis of a Web-Based Disease Forecast System for Control of Anthracnose and Botrytis Fruit Rots of Strawberry in Southeastern United States.

Strawberry production in Florida and South Carolina is affected by two major diseases, anthracnose fruit rot (AFR) and Botrytis fruit rot (BFR), caused by Colletotrichum acutatum and Botrytis cinerea, respectively. The effective management of both diseases traditionally relied on weekly fungicide applications. However, to improve timing and reduce the number of fungicide sprays, many growers follow the Strawberry Advisory System (StAS), a decision support system for forecasting fungicide applications based on environmental conditions and previously developed models.

Constitutive phenolic biomarkers identify naïve Quercus agrifolia resistant to Phytophthora ramorum, the causal agent of sudden oak death.

Sudden oak death, caused by the invasive pathogen Phytophthora ramorum Werres, de Cock & Man in't Veld, can be deadly for Quercus agrifolia Neé (coast live oak, CLO). However, resistant trees have been observed in natural populations. The objective of this study was to examine if pre-attack (constitutive) levels of phenolic compounds can be used as biomarkers to identify trees likely to be resistant.

Spatiotemporal spread of grapevine red blotch-associated virus in a California vineyard.

Grapevine red blotch-associated virus (GRBaV), the causative agent of red blotch disease, is a member of the genus Grablovirus, in the family Geminiviridae and the first known geminivirus of Vitis spp. Limited information is available on the epidemiology of red blotch disease. A 2-hectare Vitis vinifera cv.

Random Plant Viral Variants Attain Temporal Advantages During Systemic Infections and in Turn Resist other Variants of the Same Virus.

Infection of plants with viruses containing multiple variants frequently leads to dominance by a few random variants in the systemically infected leaves (SLs), for which a plausible explanation is lacking. We show here that SL dominance by a given viral variant is adequately explained by its fortuitous lead in systemic spread, coupled with its resistance to superinfection by other variants. We analyzed the fate of a multi-variant turnip crinkle virus (TCV) population in Arabidopsis and N.

Combining Models is More Likely to Give Better Predictions than Single Models.

In agricultural research, it is often difficult to construct a single "best" predictive model based on data collected under field conditions. We studied the relative prediction performance of combining empirical linear models over the single best model in relation to number of models to be combined, number of variates in the models, magnitude of residual errors, and weighting schemes. Two scenarios were simulated: the modeler did or did not know the relative of performance of the models to be combined.

Multiplicative interaction in network meta-analysis.

Meta-analysis of a set of clinical trials is usually conducted using a linear predictor with additive effects representing treatments and trials. Additivity is a strong assumption. In this paper, we consider models for two or more treatments that involve multiplicative terms for interaction between treatment and trial.