Environmental analysis of crop trials - Van Etten
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The document outlines methods for conducting environmental analyses of crop trials using weather data, highlighting the installation of necessary R packages and retrieval of weather data from stations. It details the process of selecting geographic locations, downloading relevant datasets, and interpolating weather variables linked to crop trial data. Additionally, it describes techniques for analyzing the data through redundancy analysis and predicting yield values based on derived ecophysiological variables.
![Selecting stations firstSelect stations within a geographic extentdata(stations)locsExtent <-c(0,20,40,60)stationsSelected <- stationsExtent(locsExtent, stations)Show on a mapplot(stationsSelected[c("LON","LAT")], pch=3, cex=.5)library(maps)map("world",add=TRUE, interior=F)](https://image.slidesharecdn.com/analysiscroptrialsusingclimatedatajacob-110930050455-phpapp01/85/Environmental-analysis-of-crop-trials-Van-Etten-7-320.jpg)
![Make a quick mapstationsSelected <- stationsExtent(c(-110,-60,5,25), stations)plot(stationsSelected[c("LON","LAT")], pch=3, cex=.5)points(locs[c("LON","LAT")], pch=15)map("world",add=TRUE, interior=F)](https://image.slidesharecdn.com/analysiscroptrialsusingclimatedatajacob-110930050455-phpapp01/85/Environmental-analysis-of-crop-trials-Van-Etten-11-320.jpg)
![InterpolateInterpolate weather for the years 2003, 2004 and 2005.ipW2003 <- interpolateDailyWeather(tableGSOD = weatherCA, locations = locs[c("ID", "LON", "LAT", "ALT")], startDate="2003-5-15", endDate="2003-9-25", stations = stationsSelected)Repeat for the other years and then combine:ipW <- rbind(ipW2003,ipW2004,ipW2005)](https://image.slidesharecdn.com/analysiscroptrialsusingclimatedatajacob-110930050455-phpapp01/85/Environmental-analysis-of-crop-trials-Van-Etten-13-320.jpg)
![Do ANOVAm <- lm(Yield ~ Variety + Location + Plant.m2, data=tr2005) G + GxE are left over, the rest is filtered outtr2005$Yield <- residuals(m)tr2005 <- tr2005[,c("Variety","Location","Yield")]](https://image.slidesharecdn.com/analysiscroptrialsusingclimatedatajacob-110930050455-phpapp01/85/Environmental-analysis-of-crop-trials-Van-Etten-17-320.jpg)
![Make table ready for RDAtr2005 <- melt(tr2005)tr2005 <- acast(tr2005, Location ~ Variety)env2005 <- trial[trial$Year == 2005, c("Location", "TEMPSTRESS30", "PRECSUM", "PRECCV", "RADIATION")]env2005 <- unique(env2005)rownames(env2005) <- env2005$Locationenv2005 <- env2005[,-1]](https://image.slidesharecdn.com/analysiscroptrialsusingclimatedatajacob-110930050455-phpapp01/85/Environmental-analysis-of-crop-trials-Van-Etten-18-320.jpg)
Environmental analysis of crop trials - Van Etten
- 1. Environmental analysis of crop trialsusing weather dataJacob van Etten
- 2. weatherDataThis package helps us to:1. Get data from weather stations2. Interpolate weather data for any location
- 3. Get the packagesinstall.packages("weatherData", repos="http://R-Forge.R-project.org")library(weatherData)install.packages(“cropData", repos="http://R-Forge.R-project.org")library(cropData)OR:http://dl.dropbox.com/u/18619554/cropData_1.0.ziphttp://dl.dropbox.com/u/18619554/weatherData_1.0.zip
- 4. Get additional packagesinstall.packages(c(“maps”, “vegan”, “reshape”))library(maps)library(vegan)library(reshape)
- 6. Get the dataWe will use the Global Summary of Day (GSOD) data of NCDC.ftp://ftp.ncdc.noaa.gov/pub/data/gsod/Downloading takes a lot of time.However, we can selectively download part of the data, in an automatic way.We will show how to do it with a toy example.Then we will use data from disk to continue.
- 7. Selecting stations firstSelect stations within a geographic extentdata(stations)locsExtent <-c(0,20,40,60)stationsSelected <- stationsExtent(locsExtent, stations)Show on a mapplot(stationsSelected[c("LON","LAT")], pch=3, cex=.5)library(maps)map("world",add=TRUE, interior=F)
- 8. Download the dataMake a working directory first.setwd(“yourFolder”)Now download the files to this working directory.downloadGSOD(2010, 2010, stations = stationsSelected, silent = FALSE, tries = 2, overwrite = FALSE) After a few downloads, kill the process by pressing “Esc”.Inspect what you have in “yourFolder” and delete the downloaded files.
- 9. Read the data into RCopy the data we have provided you into “yourFolder”.The following lines will make a table and remove missing observations.weather <- makeTableGSOD() weather <- na.omit(weather)fix(weather)
- 10. Getting some trial dataThe idea is to link weather data to crop trial data.We get some trial data that was incorporated in the package.trial <- read.csv(system.file("external/trialsCA.csv", package="cropData"))locs <- read.csv(system.file("external/locationsCA.csv", package="cropData"))
- 11. Make a quick mapstationsSelected <- stationsExtent(c(-110,-60,5,25), stations)plot(stationsSelected[c("LON","LAT")], pch=3, cex=.5)points(locs[c("LON","LAT")], pch=15)map("world",add=TRUE, interior=F)
- 12. InterpolationWe have already seen interpolation at work.Now we use interpolation to estimate weather variables for the trial locations.The function interpolateDailyWeather() automatically interpolates the weather surface for each day and extracts the values for each trial location.
- 13. InterpolateInterpolate weather for the years 2003, 2004 and 2005.ipW2003 <- interpolateDailyWeather(tableGSOD = weatherCA, locations = locs[c("ID", "LON", "LAT", "ALT")], startDate="2003-5-15", endDate="2003-9-25", stations = stationsSelected)Repeat for the other years and then combine:ipW <- rbind(ipW2003,ipW2004,ipW2005)
- 14. Duration of T > 30 °C =4.8 hMinimum is assumed to be at sunrise.Maximum is assumed to be 2 h after solar noon.Thermal stressTemperature (°C)Time
- 15. Derive ecophysiologicalvars?thermalStressDailyRun the example to see how this works.Then:TEMPSTRESS30 <- thermalStressSeasonal(30, ipW, trial, locs)PREC <- precipitationSeasonal(ipW, trial)RADIATION <- radiationSeasonal(ipW, trial, locs)trial <- cbind(trial, TEMPSTRESS30, PREC, RADIATION)
- 16. Do RDA on residualsInstead of a normal PCA, we constrain the axes of the PCA with linear combinations of the ecophysiological variables.This type of constrained PCA is called redundancy analysis (RDA)
- 17. Do ANOVAm <- lm(Yield ~ Variety + Location + Plant.m2, data=tr2005) G + GxE are left over, the rest is filtered outtr2005$Yield <- residuals(m)tr2005 <- tr2005[,c("Variety","Location","Yield")]
- 18. Make table ready for RDAtr2005 <- melt(tr2005)tr2005 <- acast(tr2005, Location ~ Variety)env2005 <- trial[trial$Year == 2005, c("Location", "TEMPSTRESS30", "PRECSUM", "PRECCV", "RADIATION")]env2005 <- unique(env2005)rownames(env2005) <- env2005$Locationenv2005 <- env2005[,-1]
- 19. RDArda2005 <- rda(tr2005, env2005)summary(rda2005)plot(rda2005)
- 20. Putting GxE on mapIt is possible to use the resulting RDA model to predict for any locations.The steps would be:Interpolate weather variables for new locationDerive ecophysiological variablesPredict yield value for this new location (not taking into account additive environmental effect)
- 21. Final remarksTrial data are often noisy – extracting the signal from the data is the objectiveMany environmental variables are difficult to measure, but can be taken to be “random” in the analysisMany statistical tools exist to link weather data to crop trial data.