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CIRM for article

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This is a si
## Preparations
### Loading the RBBGCMuso package and the necessary functions
```{r}
library(RBBGCMuso)
source("make_individual_trees.R") # The DT creation and update algorithms
source("glue.R") # GLUE optimizer algorithms
```
The file containing the path to the observation files (Martonvasar_maize.obs), and the parameter intervals (Martonj)
### Reading the observations
The mean yield had to be adjust. see in art.
```{r}
measureFile <- "Martonvasar_maize.obs"
measurements <- read.csv2(measureFile, stringsAsFactors=FALSE)
measurements$mean <- measurements$mean / 10000 * 0.85
measurements$sd <- measurements$sd / 10000 * 0.85
```
### Define conditioning functions
constraints.json
```{json}
{
"constraints": [
{
"Expression": "SELECT(harvest_index, max)|median",
"Min": 0.45,
"Max": 0.55
},
{
"Expression": "SELECT(proj_lai, max)|quantile(.,0.5)",
"Min": 2.7,
"Max": 5
},
{
"Expression": "SELECT(rootdepth5, max)|quantile(.,0.5)",
"Min": 1.40,
"Max": 1.80
},
{
"Expression": "SELECT(flower_date, max)|quantile(.,0.5)",
"Min": 180,
"Max": 190
}
],
"treshold": 80
}
```
```{r}
constraints <- jsonlite::read_json("constraints.json",simplifyVector=TRUE)
```
### Cal file:
```{verbatim}
Martonvasar_maize.obs
Martonvasar_maize.set
site
Martonvasar_maize;211
```

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zero_var <- function(m){
apply(m,2, function(v){
var(v) != 0
})
}
glue <- function(results="result.csv",res_r="results.RDS",output ="gplot.pdf",epcname="maize_glue.epc"){
res <- read.csv(results)[-1]
res <- res[-1,]
colnames(res)
non_zero <- res[,1:(ncol(res)-4)]
colnames(non_zero) <- colnames(res)[1:(ncol(res)-4)]
impvars <- zero_var(non_zero)
nonzero <- non_zero[,impvars]
likelihoods <- res[,(ncol(res)-3)]
rmse <- res[,(ncol(res)-2)]
const <- res$Const
namess <- gsub("__.*","",colnames(nonzero))
likelihoods <- likelihoods[res$Const==1]
goods <- res[res$Const==1,]
medlik <- median(likelihoods[likelihoods >= quantile(likelihoods,0.95)])
medlik_place <- which.min(abs(likelihoods - medlik))
parameters <- readRDS(res_r)
glue_opt <- goods[medlik_place, 1:(ncol(res)-4)][impvars]
nonka <- goods[likelihoods >= quantile(likelihoods,0.95),1:(ncol(res)-4)]
med_opt <- apply(nonka,2,median)[impvars]
# med_opt <- apply(nonka,2,mean)[impvars]
ml_opt <- goods[which.max(likelihoods),1:(ncol(res)-4)][impvars]
calibrationPar <- parameters$calibrationPar[impvars]
changemulline(src="maize.epc", calibrationPar = calibrationPar, contents=glue_opt, outFiles = epcname)
changemulline(src="maize.epc", calibrationPar = calibrationPar, contents=med_opt, outFiles = "maize_median.epc")
changemulline(src="maize.epc", calibrationPar = calibrationPar, contents=ml_opt, outFiles = "maize_ml.epc")
print(output)
pdf(output)
for(i in 1:ncol(nonzero)){
plot(nonzero[,i],res[,(ncol(res)-3)],main="",col="lightgray", pch=20, cex=0.4, xlab=namess[i],ylab="logLikelihood")
points(nonzero[const==1,i],res[const==1,(ncol(res)-3)],pch=20, cex=0.6, col="red",type="p",xlab=namess[i],ylab="logLikelihood")
abline(v=glue_opt[i],col="green")
abline(v=med_opt[i],col="blue")
abline(v=ml_opt[i],col="black")
}
dev.off()
}

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start_intervals <- read.csv("1/Martonvasar_maize.set",skip=1,stringsAsFactors=FALSE)
indices <- which(start_intervals[,3] != start_intervals[,4])
png("kichen_sink.png",width=30,height=30,res=600,units = "cm")
par(mfrow=c(5,4))
for(i in indices){
ranges <- start_intervals[i,3:4]
optimes <- numeric(10)
for(j in 1:10){
base_table <- read.csv(paste(j,"Martonvasar_maize_after_tree.set",sep="/"),
skip=1, stringsAsFactors=FALSE)
ranges <- rbind(ranges,base_table[i,3:4])
optimes[j] <- unlist(readRDS(paste0(j,"/results.RDS"))$parameters[start_intervals[indices,1]][indices==i])
}
plot(ranges[,1],11:1,type="l",xlim=range(ranges),main=base_table[i,1],xlab="",ylab="iterations",yaxt="n")
axis(2,at=11:1,labels = 0:10)
points(optimes,10:1)
lines(ranges[,2],11:1,type="l")
}
dev.off()
postscript("kichen_sink.eps",paper="a4")
par(mfrow=c(5,4))
for(i in indices){
ranges <- start_intervals[i,3:4]
optimes <- numeric(10)
for(j in 1:10){
base_table <- read.csv(paste(j,"Martonvasar_maize_after_tree.set",sep="/"),
skip=1, stringsAsFactors=FALSE)
ranges <- rbind(ranges,base_table[i,3:4])
optimes[j] <- unlist(readRDS(paste0(j,"/results.RDS"))$parameters[start_intervals[indices,1]][indices==i])
}
plot(ranges[,1],11:1,type="l",xlim=range(ranges),main=base_table[i,1],xlab="",ylab="iterations",yaxt="n")
axis(2,at=11:1,labels = 0:10)
points(optimes,10:1)
lines(ranges[,2],11:1,type="l")
}
dev.off()

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library(rpart)
library(rpart.plot)
zero_var <- function(m){
apply(m,2, function(v){
var(v) != 0
})
}
decbin <- function(decnum){
if(decnum < 2){
return(decnum)
}
c(decbin((decnum %/% 2)),decnum %% 2)
}
decpad <- function(decnum,len){
binrep <- decbin(decnum)
c(rep(0,len-length(binrep)),binrep)
}
tree_per_const <- function(results="result.csv",output ="tree_per_const.pdf",
parameters_file="Martonvasar_maize.set"){
varname <-readLines(parameters_file)[1]
parameters <- read.csv(parameters_file,skip=1,stringsAsFactors=FALSE)
results <- read.csv(results, stringsAsFactors=FALSE)
# likelihoods <- results[,ncol(results)-3]
# results <- results[likelihoods>=quantile(likelihoods,0.95),]
len <- round(log(max(results$failType),2))
failTypes <- do.call(rbind,lapply(results$failType,function(x){decpad(x,len)}))
pdf(output)
sapply(1:len, function(const){
nonzero <- results[,1:(ncol(results)-4)]
nonzero <- nonzero[,-1]
nonzero <- nonzero[,zero_var(nonzero)]
colnames(nonzero) <- gsub("__.*","",colnames(nonzero))
constraint <- failTypes[,const]
baseTable <- cbind.data.frame(nonzero,constraint = as.factor(constraint))
try({
rp = rpart(constraint ~ .,data = baseTable)
})
try({
parameters <<- update_parameters_based_on_tree(rp, parameters)
})
try({
rpart.plot(rp)
})
})
dev.off()
outname <- paste0(tools::file_path_sans_ext(parameters_file),"_after_tree.",
tools::file_ext(parameters_file))
writeLines(varname,outname)
write.table(parameters,outname,row.names=FALSE,append=TRUE,sep=",",quote=FALSE)
}
update_parameters_based_on_tree <- function(rp, parameters){
frm <- rp$frame
nodes <- labels(rp)
names(nodes) <- row.names(frm)
node <- get_start_node(frm)
parameters <- change_parameters_on_node(nodes,node,parameters)
while(node !=1){
node <- get_parent_node(node)
if(node == 1){
break()
}
parameters <- change_parameters_on_node(nodes,node,parameters)
}
parameters
}
parse_rule_row <- function(string){
rule_row <- regmatches(string,regexec("([a-zA-Z_0-9]+)([>=< ]+)(.*)",string,perl=TRUE))[[1]][-1]
if(rule_row[2] == ">="){
rule_num <- 1
} else {
rule_num <- 2
}
rule <- list(c(rule_num,as.numeric(rule_row[3])))
names(rule) <- rule_row[1]
return(rule)
}
get_start_node <- function(frm){
nfrm <- frm[frm$yval == 2,]
nfrm <- nfrm[nfrm[,"var"] == "<leaf>",]
pot_start <- as.numeric(row.names(nfrm))[which.max(nfrm$n)]
pot_start
}
get_parent_node <- function(node_id){
as.integer(node_id/2)
}
change_parameters_on_node <- function(nodes,node,parameters2){
crule <- parse_rule_row(nodes[as.character(node)])
minmax <- unlist(parameters2[parameters2[,1] == names(crule),c(3,4)])
if(crule[[1]][1] == 1){
if(minmax[1]<=crule[[1]][2]){
minmax[1] <- crule[[1]][2]
if(minmax[1] <= minmax[2]){
parameters2[parameters2[,1] == names(crule),c(3,4)] <- minmax
} else {
write(sprintf("WARNING: %s's minimum(%s) > maximum(%s)", parameters2[,1],
minmax[1], minmax[2]), "errorlog.txt", append=TRUE)
}
}
} else {
if(minmax[2]>=crule[[1]][2]){
minmax[2] <- crule[[1]][2]
if(minmax[1] <= minmax[2]){
parameters2[parameters2[,1] == names(crule),c(3,4)] <- minmax
} else {
write(sprintf("WARNING: %s's minimum(%s) > maximum(%s)", parameters2[,1],
minmax[1], minmax[2]), "errorlog.txt", append=TRUE)
}
}
}
parameters2
}

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library(RBBGCMuso)
file.copy("../../start_set/maize.epc","./start_set/maize.epc",overwrite=TRUE)
setwd("start_set/")
rmse <- function(modelled, measured){
sqrt(mean((modelled-measured)**2))
}
r2 <- function(modelled, measured){
summary(lm("mod ~ meas", data= data.frame(mod=modelled,meas=measured)))$r.squared
}
modeff <- function(modelled, measured){
1 - (sum((modelled-measured)**2) / sum((measured - mean(measured))**2))
}
bias <- function(modelled, measured){
mean(modelled) - mean(measured)
}
get_stats <- function(modelled,measured){
c(r2=r2(modelled,measured),
rmse=rmse(modelled,measured),
bias=bias(modelled,measured),
modeff=modeff(modelled,measured))
}
get_modelled <- function(obsTable,settings, ...){
simulation <- runMuso(settings, ...)
yield <- simulation[,"fruit_DM"]
modelled <- yield[match(as.Date(obsTable$date),as.Date(names(yield),"%d.%m.%Y"))] * 10
modelled
}
obsTable <- read.csv2("Martonvasar_maize.obs",stringsAsFactors=FALSE)
obsTable$mean <- obsTable$mean / 1000 * 0.85
obsTable$sd <- obsTable$sd / 1000
measured <- obsTable$mean
# apriori
settings <- setupMuso(iniInput=c("n.ini","n.ini"))
modelled <- get_modelled(obsTable, settings)
results <- matrix(ncol=4,nrow=11)
colnames(results) <- c("r2","rmse","bias","modeff")
row.names(results) <- 0:10
results[1,] <- get_stats(modelled,measured)
# max_likelihood_stats
for(i in 1:10){
file.copy(sprintf("../%s/maize_ml.epc",i),"maize.epc",overwrite=TRUE)
settings <- setupMuso(iniInput=c("n.ini","n.ini"))
modelled <- get_modelled(obsTable, settings)
results[i+1,] <- get_stats(modelled, measured)
}
# median_stats
results_med <- results
for(i in 1:10){
file.copy(sprintf("../maize_median_step%02d_corrected.epc",i),"maize.epc",overwrite=TRUE)
settings <- setupMuso(iniInput=c("n.ini","n.ini"))
modelled <- get_modelled(obsTable, settings)
results_med[i+1,] <- get_stats(modelled, measured)
}
results_med
succes_ratio <- numeric(10)
for(i in 1:10){
succes_ratio[i] <- sum(read.csv(sprintf("../%s/result.csv",i),stringsAsFactors=FALSE)$Const[-1])/10000
}
names(succes_ratio) <- 1:10
png("../success_rate.png",height=30,width=30,res=300, units="cm")
barplot(succes_ratio,ylim=c(0,1),xlab="iteration number",ylab="Succes rate")
dev.off()
postscript("../success_rate.eps")
barplot(succes_ratio,ylim=c(0,1),xlab="iteration number",ylab="Succes rate")
dev.off()

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library(rpart)
library(rpart.plot)
accuracy <- function(x,rp){
# Accuracy = (TP + TN)/(TP + TN + FP + FP)
# TP: True Positive
# TN: True Negative
# FP: False Positive
# FN: False Negative
predicted <- rpart.predict(rp,type = "vector")
predicted[predicted==1] <- 0
predicted[predicted==2] <- 1
(sum(x*predicted) + sum((x + predicted) == 0)) / length(x)
}
zero_var <- function(m){
apply(m,2, function(v){
var(v) != 0
})
}
decbin <- function(decnum){
if(decnum < 2){
return(decnum)
}
c(decbin((decnum %/% 2)),decnum %% 2)
}
decpad <- function(decnum,len){
binrep <- decbin(decnum)
c(rep(0,len-length(binrep)),binrep)
}
tree_per_const <- function(results="result.csv",output ="tree_per_const.pdf",
parameters_file="Martonvasar_maize.set"){
varname <-readLines(parameters_file)[1]
parameters <- read.csv(parameters_file,skip=1,stringsAsFactors=FALSE)
results <- read.csv(results, stringsAsFactors=FALSE)
# likelihoods <- results[,ncol(results)-3]
# results <- results[likelihoods>=quantile(likelihoods,0.95),]
len <- round(log(max(results$failType),2))
failTypes <- do.call(rbind,lapply(results$failType,function(x){decpad(x,len)}))
sapply(1:len, function(const){
nonzero <- results[,1:(ncol(results)-4)]
nonzero <- nonzero[,-1]
nonzero <- nonzero[,zero_var(nonzero)]
colnames(nonzero) <- gsub("__.*","",colnames(nonzero))
constraint <- failTypes[,const]
baseTable <- cbind.data.frame(nonzero,constraint = as.factor(constraint))
tryCatch({
rp <- rpart(constraint ~ .,data = baseTable)
accuracy(constraint, rp)
}, error = function(e){NA})
})
}
results <- matrix(nrow=10,ncol=4)
row.names(results) <- 1:10
colnames(results) <- c("Harvest Index", "LAI", "Root depth in phen. 5", "Flowering date")
for(i in 1:10){
setwd(as.character(i))
results[i,] <- tree_per_const(parameters_file="Martonvasar_maize_after_tree.set")
setwd("../")
}
results

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parameters <-
getOption("RMuso_constMatrix")[["epc"]][["6"]]
NAME
yearday to start new growth
yearday to end new growth
transfer growth period as fraction of growing season
litterfall as fraction of growing season
base temperature
minimum temperature for growth displayed on current day
optimal1 temperature for growth displayed on current day
optimal2 temperature for growth displayed on current day
maxmimum temperature for growth displayed on current day
minimum temperature for carbon assimilation displayed on current day
optimal1 temperature for carbon assimilation displayed on current day
optimal2 temperature for carbon assimilation displayed on current day
maxmimum temperature for carbon assimilation displayed on current day
annual leaf and fine root turnover fraction
annual live wood turnover fraction
annual fire mortality fraction
whole-plant mortality paramter for vegetation period
C:N of leaves
C:N of leaf litter
C:N of fine roots
C:N of fruit
C:N of softstem
C:N of live wood
C:N of dead wood
dry matter content of leaves
dry matter content of leaf litter
dry matter content of fine roots
dry matter content of fruit
dry matter content of softstem
dry matter content of live wood
dry matter content of dead wood
leaf litter labile proportion
leaf litter cellulose proportion
fine root labile proportion
fine root cellulose proportion
fruit labile proportion
fruit cellulose proportion
softstem labile proportion
softstem cellulose proportion
dead wood cellulose proportion
canopy water interception coefficient
canopy light extinction coefficient
potential radiation use efficiency
radiation parameter1 (Jiang et al.2015)
radiation parameter1 (Jiang et al.2015)
all-sided to projected leaf area ratio
ratio of shaded SLA:sunlit SLA
fraction of leaf N in Rubisco
fraction of leaf N in PeP
maximum stomatal conductance
cuticular conductance
boundary layer conductance
maximum height of plant
stem weight corresponding to maximum height
plant height function shape parameter (slope)
maximum depth of rooting zone
root distribution parameter
root weight corresponding to max root depth
root depth function shape parameter (slope)
root weight to rooth length conversion factor
growth resp per unit of C grown
maintenance respiration in kgC/day per kg of tissue N
theoretical maximum prop. of non-structural and structural carbohydrates
prop. of non-structural carbohydrates available for maintanance resp
symbiotic+asymbiotic fixation of N
time delay for temperature in photosynthesis acclimation
critical VWCratio (prop. to FC-WP) in germination
critical photoslow daylength
slope of relative photoslow development rate
critical vernalization temperature 1
critical vernalization temperature 2
critical vernalization temperature 3
critical vernalization temperature 4
slope of relative vernalization development rate
required vernalization days (in vernalization development rate)
critical flowering heat stress temperature 1
critical flowering heat stress temperature 2
theoretical maximum of flowering thermal stress mortality
VWC ratio to calc. soil moisture limit 1 (prop. to FC-WP)
VWC ratio to calc. soil moisture limit 2 (prop. to SAT-FC)
minimum of soil moisture limit2 multiplicator (full anoxic stress value)
vapor pressure deficit: start of conductance reduction
vapor pressure deficit: complete conductance reduction
maximum senescence mortality coefficient of aboveground plant material
maximum senescence mortality coefficient of belowground plant material
maximum senescence mortality coefficient of non-structured plant material
lower limit extreme high temperature effect on senescence mortality
upper limit extreme high temperature effect on senescence mortality
turnover rate of wilted standing biomass to litter
turnover rate of cut-down non-woody biomass to litter
turnover rate of cut-down woody biomass to litter
drought tolerance parameter (critical value of day since water stress)
effect of soilstress factor on photosynthesis
crit. amount of snow limiting photosyn.
limit1 (under:full constrained) of HEATSUM index
limit2 (above:unconstrained) of HEATSUM index
limit1 (under:full constrained) of TMIN index
limit2 (above:unconstrained) of TMIN index
limit1 (above:full constrained) of VPD index
limit2 (under:unconstrained) of VPD index
limit1 (under:full constrained) of DAYLENGTH index
limit2 (above:unconstrained) of DAYLENGTH index
moving average (to avoid the effects of extreme events)
GSI limit1 (greater that limit -> start of vegper)
GSI limit2 (less that limit -> end of vegper)
length of phenophase (GDD)-0
leaf ALLOCATION -0
fine root ALLOCATION-0
fruit ALLOCATION -0
soft stem ALLOCATION-0
live woody stem ALLOCATION -0
dead woody stem ALLOCATION -0
live coarse root ALLOCATION-0
dead coarse root ALLOCATION -0
canopy average specific leaf area-0
current growth proportion-0
maximal lifetime of plant tissue-0
length of phenophase (GDD)-1
leaf ALLOCATION -1
fine root ALLOCATION-1
fruit ALLOCATION -1
soft stem ALLOCATION-1
live woody stem ALLOCATION -1
dead woody stem ALLOCATION -1
live coarse root ALLOCATION-1
dead coarse root ALLOCATION -1
canopy average specific leaf area-1
current growth proportion-1
maximal lifetime of plant tissue-1
length of phenophase (GDD)-2
leaf ALLOCATION -2
fine root ALLOCATION-2
fruit ALLOCATION -2
soft stem ALLOCATION-2
live woody stem ALLOCATION -2
dead woody stem ALLOCATION -2
live coarse root ALLOCATION-2
dead coarse root ALLOCATION -2
canopy average specific leaf area-2
current growth proportion-2
maximal lifetime of plant tissue-2
length of phenophase (GDD)-3
leaf ALLOCATION -3
fine root ALLOCATION-3
fruit ALLOCATION -3
soft stem ALLOCATION-3
live woody stem ALLOCATION -3
dead woody stem ALLOCATION -3
live coarse root ALLOCATION-3
dead coarse root ALLOCATION -3
canopy average specific leaf area-3
current growth proportion-3
maximal lifetime of plant tissue-3
length of phenophase (GDD)-4
leaf ALLOCATION -4
fine root ALLOCATION-4
fruit ALLOCATION -4
soft stem ALLOCATION-4
live woody stem ALLOCATION -4
dead woody stem ALLOCATION -4
live coarse root ALLOCATION-4
dead coarse root ALLOCATION -4
canopy average specific leaf area-4
current growth proportion-4
maximal lifetime of plant tissue-4
length of phenophase (GDD)-5
leaf ALLOCATION -5
fine root ALLOCATION-5
fruit ALLOCATION -5
soft stem ALLOCATION-5
live woody stem ALLOCATION -5
dead woody stem ALLOCATION -5
live coarse root ALLOCATION-5
dead coarse root ALLOCATION -5
canopy average specific leaf area-5
current growth proportion-5
maximal lifetime of plant tissue-5
length of phenophase (GDD)-6
leaf ALLOCATION -6
fine root ALLOCATION-6
fruit ALLOCATION -6
soft stem ALLOCATION-6
live woody stem ALLOCATION -6
dead woody stem ALLOCATION -6
live coarse root ALLOCATION-6
dead coarse root ALLOCATION -6
canopy average specific leaf area-6
current growth proportion-6
maximal lifetime of plant tissue-6
INDEX UNIT DEPENDENCE MIN MAX GROUP TYPE
9.00 yday NA 0.00000 364.0000 0 0
10.00 yday NA 0.00000 364.0000 0 0
11.00 prop NA 0.00000 1.0000 0 0
12.00 prop NA 0.00000 1.0000 0 0
13.00 Celsius NA 0.00000 12.0000 0 0
14.00 Celsius 0 0.00000 10.0000 1 1
15.00 Celsius 1 10.00000 20.0000 1 1
16.00 Celsius 2 20.00000 40.0000 1 1
17.00 Celsius 3 30.00000 50.0000 1 1
18.00 Celsius 0 0.00000 10.0000 2 1
19.00 Celsius 1 10.00000 20.0000 2 1
20.00 Celsius 2 20.00000 40.0000 2 1
21.00 Celsius 3 30.00000 50.0000 2 1
22.00 1/yr NA 0.10000 0.4000 0 0
23.00 1/yr NA 0.50000 1.0000 0 0
24.00 1/yr NA 0.00000 1.0000 0 0
25.00 1/vegper NA 0.00000 0.5000 0 0
26.00 kgC/kgN 0 10.00000 100.0000 3 1
27.00 kgC/kgN 1 10.00000 60.0000 3 1
28.00 kgC/kgN 1 10.00000 60.0000 3 1
29.00 kgC/kgN 1 10.00000 60.0000 3 1
30.00 kgC/kgN 1 10.00000 60.0000 3 1
31.00 kgC/kgN 0 50.00000 100.0000 4 1
32.00 kgC/kgN 1 300.00000 800.0000 4 1
33.00 kgC/kgDM NA 0.20000 0.6000 0 0
34.00 kgC/kgDM NA 0.20000 0.6000 0 0
35.00 kgC/kgDM NA 0.20000 0.6000 0 0
36.00 kgC/kgDM NA 0.20000 0.6000 0 0
37.00 kgC/kgDM NA 0.20000 0.6000 0 0
38.00 kgC/kgDM NA 0.20000 0.6000 0 0
39.00 kgC/kgDM NA 0.20000 0.6000 0 0
40.00 prop 1 0.10000 0.6000 5 2
41.00 prop 1 0.10000 0.6000 5 2
42.00 prop 1 0.10000 0.6000 6 2
43.00 prop 1 0.10000 0.6000 6 2
44.00 prop 1 0.10000 0.6000 7 2
45.00 prop 1 0.10000 0.6000 7 2
46.00 prop 1 0.10000 0.6000 8 2
47.00 prop 1 0.10000 0.6000 8 2
48.00 prop NA 0.50000 0.9000 0 0
49.00 1/LAI/d NA 0.01000 0.1000 0 0
50.00 dimless NA 0.20000 0.8000 0 0
51.00 g/MJ NA 2.00000 2.0000 0 0
52.00 dimless NA 0.78100 0.7810 0 0
53.00 dimless NA -13.59600 -13.5960 0 0
54.00 dimless NA 2.00000 2.0000 0 0
55.00 dimless NA 2.00000 2.0000 0 0
56.00 dimless NA 0.01000 0.2000 0 0
57.00 dimless NA 0.04240 0.0424 0 0
58.00 m/s NA 0.00100 0.1000 0 0
59.00 m/s NA 0.00001 0.0001 0 0
60.00 m/s NA 0.01000 0.0900 0 0
61.00 m NA 0.10000 10.0000 0 0
62.00 kgC NA 0.10000 100.0000 0 0
63.00 dimless NA 0.50000 0.5000 0 0
64.00 m NA 0.10000 10.0000 0 0
65.00 prop NA 3.67000 3.6700 0 0
66.00 kgC/m2 NA 0.40000 0.4000 0 0
67.00 prop NA 0.50000 0.5000 0 0
68.00 m/kg NA 1000.00000 1000.0000 0 0
69.00 prop NA 0.10000 0.5000 0 0
70.00 kgC/kgN/d NA 0.10000 0.5000 0 0
71.00 dimless NA 0.00000 1.0000 0 0
72.00 dimless NA 0.00000 1.0000 0 0
73.00 kgN/m2/yr NA 0.00000 0.0010 0 0
74.00 day NA 0.00000 50.0000 0 0
79.00 prop NA 0.00000 1.0000 0 0
81.00 hour NA 14.00000 18.0000 0 0
82.00 dimless NA 0.00500 0.0050 0 0
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85.00 Celsius 1 0.00000 10.0000 9 1
86.00 Celsius 2 5.00000 15.0000 9 1
87.00 Celsius 3 10.00000 20.0000 9 1
88.00 dimless NA 0.04000 0.0400 0 0
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92.00 Celsius 1 30.00000 50.0000 10 1
93.00 prop NA 0.00000 0.4000 0 0
96.00 prop NA 0.50000 1.0000 0 0
97.00 prop NA 0.50000 1.0000 0 0
98.00 prop NA 0.00000 1.0000 0 0
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102.00 prop 1 0.00000 0.1000 0 0
103.00 prop NA 0.00000 0.1000 0 0
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105.00 Celsius NA 30.00000 50.0000 0 0
106.00 prop NA 0.00000 0.1000 0 0
107.00 prop NA 0.00000 0.1000 0 0
108.00 prop NA 0.00000 0.1000 0 0
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121.00 s 1 0.00000 0.0000 14 1
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130.60 prop 1 0.00000 1.0000 15 -3
131.60 prop 1 0.00000 1.0000 15 -3
132.60 prop 1 0.00000 1.0000 15 -3
133.60 prop 1 0.00000 1.0000 15 -3
134.60 prop 1 0.00000 1.0000 15 -3
135.60 prop 1 0.00000 1.0000 15 -3
136.60 prop 1 0.00000 1.0000 15 -3
137.60 m2/kg NA 0.00000 2.0000 0 0
138.60 prop NA 0.00000 0.0000 0 0
139.60 Celsius NA 1.00000 20000.0000 0 0
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130.61 prop 1 0.00000 1.0000 16 -3
131.61 prop 1 0.00000 1.0000 16 -3
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134.61 prop 1 0.00000 1.0000 16 -3
135.61 prop 1 0.00000 1.0000 16 -3
136.61 prop 1 0.00000 1.0000 16 -3
137.61 m2/kg NA 0.00000 2.0000 0 0
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130.62 prop 1 0.00000 1.0000 17 -3
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132.62 prop 1 0.00000 1.0000 17 -3
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134.62 prop 1 0.00000 1.0000 17 -3
135.62 prop 1 0.00000 1.0000 17 -3
136.62 prop 1 0.00000 1.0000 17 -3
137.62 m2/kg NA 0.00000 2.0000 0 0
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128.63 Celsius NA 0.00000 10000.0000 0 0
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130.63 prop 1 0.00000 1.0000 18 -3
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135.63 prop 1 0.00000 1.0000 18 -3
136.63 prop 1 0.00000 1.0000 18 -3
137.63 m2/kg NA 0.00000 2.0000 0 0
138.63 prop NA 0.00000 0.0000 0 0
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128.64 Celsius NA 0.00000 10000.0000 0 0
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130.64 prop 1 0.00000 1.0000 19 -3
131.64 prop 1 0.00000 1.0000 19 -3
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134.64 prop 1 0.00000 1.0000 19 -3
135.64 prop 1 0.00000 1.0000 19 -3
136.64 prop 1 0.00000 1.0000 19 -3
137.64 m2/kg NA 0.00000 2.0000 0 0
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128.65 Celsius NA 0.00000 10000.0000 0 0
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130.65 prop 1 0.00000 1.0000 20 -3
131.65 prop 1 0.00000 1.0000 20 -3
132.65 prop 1 0.00000 1.0000 20 -3
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134.65 prop 1 0.00000 1.0000 20 -3
135.65 prop 1 0.00000 1.0000 20 -3
136.65 prop 1 0.00000 1.0000 20 -3
137.65 m2/kg NA 0.00000 2.0000 0 0
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128.66 Celsius NA 0.00000 10000.0000 0 0
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130.66 prop 1 0.00000 1.0000 21 -3
131.66 prop 1 0.00000 1.0000 21 -3
132.66 prop 1 0.00000 1.0000 21 -3
133.66 prop 1 0.00000 1.0000 21 -3
134.66 prop 1 0.00000 1.0000 21 -3
135.66 prop 1 0.00000 1.0000 21 -3
136.66 prop 1 0.00000 1.0000 21 -3
137.66 m2/kg NA 0.00000 2.0000 0 0
138.66 prop NA 0.00000 0.0000 0 0
139.66 Celsius NA 1.00000 20000.0000 0 0