Analysis crop trials using climate data
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Transcript of Analysis crop trials using climate data
Environmental analysis of crop trials using weather data
Jacob van Etten Alberto Labarga
weatherData
This package helps us to: 1. Get data from weather stations 2. Interpolate weather data for any location
Get the packages
install.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.zip http://dl.dropbox.com/u/18619554/weatherData_1.0.zip
Get additional packages
install.packages(c(“maps”, “vegan”, “reshape”)) library(maps) library(vegan) library(reshape)
Get the script
http://dl.dropbox.com/u/18619554/maizeCA.R http://goo.gl/Y6h7m
Get the data
We 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.
Selecting stations first
Select stations within a geographic extent data(stations) locsExtent <-c(0,20,40,60) stationsSelected <- stationsExtent(locsExtent, stations) Show on a map plot(stationsSelected[c("LON","LAT")], pch=3, cex=.5) library(maps) map("world",add=TRUE, interior=F)
Download the data Make 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.
Read the data into R
Copy 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)
Getting some trial data
The 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"))
Make a quick map
stationsSelected <- 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)
Interpolation
We 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.
Interpolate
Interpolate 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)
Duration of T > 30 °C = 4.8 h
Maximum is assumed to be 2 h after solar noon.
Minimum is assumed to be at sunrise.
Thermal stress
Time
Tem
pera
ture
(°C)
Derive ecophysiological vars
?thermalStressDaily Run 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)
Do RDA on residuals
Instead 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)
Do ANOVA
m <- lm(Yield ~ Variety + Location + Plant.m2, data=tr2005) G + GxE are left over, the rest is filtered out tr2005$Yield <- residuals(m) tr2005 <- tr2005[,c("Variety","Location","Yield")]
Make table ready for RDA
tr2005 <- melt(tr2005) tr2005 <- acast(tr2005, Location ~ Variety) env2005 <- trial[trial$Year == 2005, c("Location", "TEMPSTRESS30", "PRECSUM", "PRECCV", "RADIATION")] env2005 <- unique(env2005) rownames(env2005) <- env2005$Location env2005 <- env2005[,-1]
RDA
rda2005 <- rda(tr2005, env2005) summary(rda2005) plot(rda2005)
Putting GxE on map
It is possible to use the resulting RDA model to predict for any locations. The steps would be: 1. Interpolate weather variables for new
location 2. Derive ecophysiological variables 3. Predict yield value for this new location
(not taking into account additive environmental effect)
Final remarks
Trial data are often noisy – extracting the signal from the data is the objective Many environmental variables are difficult to measure, but can be taken to be “random” in the analysis Many statistical tools exist to link weather data to crop trial data.