lehner.soybeanmold {agridat} | R Documentation

## Yield, white mold, and sclerotia for soybeans in Brazil

### Description

Yield, white mold, and sclerotia for soybeans in Brazil

### Usage

    data("lehner.soybeanmold")

### Format

A data frame with 382 observations on the following 9 variables.

`study`

  study number

`year`

  year of harvest

`loc`

  location name

`elev`

  elevation

`region`

  region

`trt`

  treatment number

`yield`

  crop yield, kg/ha

`mold`

  white mold incidence, percent

`sclerotia`

  weight of sclerotia g/ha

### Details

Data are the mean of 4 reps.

Original source (Portuguese) http://ainfo.cnptia.embrapa.br/digital/bitstream/item/101371/1/Ensaios-cooperativos-de-controle-quimico-de-mofo-branco-na-cultura-da-soja-safras-2009-a-2012.pdf

Data included here via GPL3 license.

### Source

Lehner, M. S., Pethybridge, S. J., Meyer, M. C., & Del Ponte, E. M. (2016). Meta-analytic modelling of the incidence-yield and incidence-sclerotial production relationships in soybean white mould epidemics. Plant Pathology. doi:10.1111/ppa.12590

### References

Full commented code and analysis https://emdelponte.github.io/paper-white-mold-meta-analysis/

### Examples

    data(lehner.soybeanmold)
    dat <- lehner.soybeanmold

    ## Not run:
      op <- par(mfrow=c(2,2))
      hist(dat$mold, main="White mold incidence")
      hist(dat$yield, main="Yield")
      hist(dat$sclerotia, main="Sclerotia weight")
      par(op)

    ## End(Not run)

    require(lattice)
    xyplot(yield ~ mold|study, dat, type=c('p','r'),
           main="lehner.soybeanmold")
    # xyplot(sclerotia ~ mold|study, dat, type=c('p','r'))

    # Example meta-analysis. Could use metafor package to construct the forest
    # plot, but it is easier with lattice and ggplot is slow/clumsy
    if(require(latticeExtra) & require(metafor)){
      # calculate correlation & confidence for each loc
      cors <- split(dat, dat$study)
      cors <- sapply(cors,
                     FUN=function(X){
                       res <- cor.test(X$yield, X$mold)
                       c(res$estimate, res$parameter[1],
                         conf.low=res$conf.int[1], conf.high=res$conf.int[2])
                     })
      cors <- as.data.frame(t(as.matrix(cors)))
      cors$study <- rownames(cors)
      # Fisher Z transform
      cors <- transform(cors, ri = cor)
      cors <- transform(cors, ni = df + 2)
      cors <- transform(cors,
                        yi = 1/2 * log((1 + ri)/(1 - ri)),
                        vi = 1/(ni - 3))
      # Overall correlation across studies
      overall <- rma.uni(yi, vi, method="ML", data=cors) # metafor package
      # back transform
      overall <- predict(overall, transf=transf.ztor)

      # weight and size for forest plot
      wi    <- 1/sqrt(cors$vi)
      size  <- 0.5 + 3.0 * (wi - min(wi))/(max(wi) - min(wi))

      # now the forest plot
      # must use latticeExtra::layer in case ggplot2 is also loaded
      segplot(factor(study) ~ conf.low+conf.high, data=cors,
              draw.bands=FALSE, level=size, centers=ri, cex=size,
              col.regions=colorRampPalette(c("gray85", "dodgerblue4")),
              main="White mold vs. soybean yield",
              xlab=paste("Study correlation, confidence, and study weight (blues)\n",
                         "Overall (black)"),
              ylab="Study ID") +
        latticeExtra::layer(panel.abline(v=overall$pred, lwd=2)) +
        latticeExtra::layer(panel.abline(v=c(overall$cr.lb, overall$cr.ub), lty=2, col="gray"))

    }

    # Meta-analyses are typically used when the original data is not available.
    # Since the original data is available, a mixed model is probably better.
    ## Not run:
      library(lme4)
      m1 <- lmer(yield ~ mold # overall slope
                 + (1+mold |study), # random intercept & slope per study
                 data=dat)
      summary(m1)

    ## End(Not run)

[Package agridat version 1.16 Index]