In AndySouth/climcropr: Simple Crop Modelling at a Global Scale

knitr::opts_chunk$set(echo = TRUE, fig.width=7)

# library(tidyverse)
# library(forcats)
# library(sp) #for maps
# library(tmap) #for maps
library(climcropr)
library(raster)
library(rnaturalearth) #for maps
library(dismo) #for ecocrop
library(knitr) #for tables with kable

# extent object for use in plots
ext <- raster::extent(-180,180,-60,85)

#BEWARE names seem to be different in new df_ecocrop$COMNAME
#e.g. broom-corn is sorghum
test_crops <- c('potato','broom-corn','maize','oat','barley','apple','lentil','groundnut','sugarbeet', "rice","chickpea")

#test_crops <- c('potato','broom-corn','maize','barley')

looking at soil ph and other limits from ecocrop & applying to world harmonised soil database

other soil parameters on end of document

ecocrop has drainage, depth and texture. Each seems to have a required (R) and optimal value.

They don't quite fit with data available in the harmonised world soil database and not clear to me whether they will be useful for us.

min & max pH for selection of crops from new scraped ecocrop data base

df_crop_ph <- data.frame(cropname=test_crops)

for(cropnum in 1:length(test_crops))
{
  cropname <- test_crops[cropnum]
  df_crop_ph$phmin[cropnum] <- get_ecocrop(cropname,'phmin')
  df_crop_ph$phmax[cropnum] <- get_ecocrop(cropname,'phmax')
  df_crop_ph$phopmn[cropnum] <- get_ecocrop(cropname,'phopmn')
  df_crop_ph$phopmx[cropnum] <- get_ecocrop(cropname,'phopmx')  
  #cat(cropname, " min,max = ", df_crop_ph$phmin[cropnum], ",", df_crop_ph$phmax[cropnum])
}

kable(df_crop_ph)

crop pH suitability maps, 1=suitable, 2=optimal

#TODO re-enable eval=TRUE, disabling now to look at non pH params

#test_crops <- c('potato','broom-corn','maize','barley')


for(cropnum in 1:length(test_crops))
{
  cropname <- test_crops[cropnum]


  rst <- suit_soil_ph(cropname, plot=FALSE)

  par(mar=c(0,0,2,0)) #bltr
  plot(rst, main=paste0(cropname," soil pH suit=1, opt=2"), ext=ext)
  plot(ne_countries(), add=TRUE, border='grey', lwd=0.1)

}

\pagebreak

crop drainage requirements from ecocrop

don't seem that they will be very useful for discrimintaing areas

dfvals <- data.frame(values=levels(df_ecocrop$DRA), stringsAsFactors = FALSE)
kable(dfvals)

df_crop_drain <- data.frame(cropname=test_crops)

for(cropnum in 1:length(test_crops))
{
  cropname <- test_crops[cropnum]
  df_crop_drain$DRA[cropnum] <- get_ecocrop(cropname,'DRA')
  df_crop_drain$DRAR[cropnum] <- get_ecocrop(cropname,'DRAR')
  #cat(cropname, " min,max = ", df_crop_ph$phmin[cropnum], ",", df_crop_ph$phmax[cropnum])
}

#for example crops
kable(df_crop_drain)

The harmonised world soil database has drainage data in these categories which don't seem to relate directly.

1. 150 mm water per m of the soil unit
2. 125 mm
3. 100 mm
4. 75 mm
5. 50 mm
6. 15 mm
7. 0 mm

\pagebreak

soil depth requirements from ecocrop

don't seem that they will be very useful for discrimintaing areas

dfvals <- data.frame(values=levels(df_ecocrop$DEP), stringsAsFactors = FALSE)
kable(dfvals)

df_crop_depth <- data.frame(cropname=test_crops)

for(cropnum in 1:length(test_crops))
{
  cropname <- test_crops[cropnum]
  df_crop_depth$DEP[cropnum] <- get_ecocrop(cropname,'DEP')
  df_crop_depth$DEPR[cropnum] <- get_ecocrop(cropname,'DEPR')
  #cat(cropname, " min,max = ", df_crop_ph$phmin[cropnum], ",", df_crop_ph$phmax[cropnum])
}

#for example crops
kable(df_crop_depth)

World harmonised soil database has :

REF_DEPTH
Reference soil depth of all soil units are set at 100 cm, except for Rendzinas and Rankers of FAO-74 and Leptosols of FAO-90, where the reference soil depth is set at 30 cm, and for Lithosols of FAO-74 and Lithic Leptosols of FAO-90, where it is set at 10 cm.

ROOTS
Depth of obstacles to roots.

1. no information,
2. no obstacles to roots between 0 and 80 cm depth,
3. obstacles to roots between 60 and 80 cm depth,
4. obstacles between 40 and 60 cm,
5. 20 and 40 cm,
6. 0 and 80 cm,
7. 0 and 20 cm.

\pagebreak

soil texture requirements from ecocrop

dfvals <- data.frame(values=levels(df_ecocrop$TEXT), stringsAsFactors = FALSE)
kable(dfvals)

df_crop_texture <- data.frame(cropname=test_crops)

for(cropnum in 1:length(test_crops))
{
  cropname <- test_crops[cropnum]
  df_crop_texture$TEXT[cropnum] <- get_ecocrop(cropname,'TEXT')
  df_crop_texture$TEXTR[cropnum] <- get_ecocrop(cropname,'TEXTR')
  #cat(cropname, " min,max = ", df_crop_ph$phmin[cropnum], ",", df_crop_ph$phmax[cropnum])
}

#for example crops
kable(df_crop_texture)

harmonised world soil database has separate layers for % weight of :

1. clay
2. gravel
3. sand
4. silt
5. organic carbon

Not clear how this could be related to ecocrop texture.

AndySouth/climcropr documentation built on May 20, 2019, 5:08 p.m.