Add the 9th tree species: Eucalyptus grandis x Eucalyptus urophylla.

Author: BayesianLearner

R/parameters.r

@@ -1,6 +1,6 @@
 ###default parameters
-speciesNam <- c("pisy","piab","beal","fasy","pipi","eugl","rops",'popu')
-pCROB <- matrix(NA,43,8,dimnames = list(NULL,speciesNam))
+speciesNam <- c("pisy","piab","beal","fasy","pipi","eugl","rops",'popu','eugrur')
+pCROB <- matrix(NA,43,9,dimnames = list(NULL,speciesNam))
 rownames(pCROB) <-  c("cR","rhow","sla","k","vf","vr","c","mf","mr","mw",
                       "z","beta0","betab","betas","rhof2","s1","kRein","s0scale","x","aETS",
                       "alfar1","alfar2","alfar3","alfar4","alfar5","sarShp","S_branchMod","conifers=1 or decidous=2","p0_ref","ETS_ref",
@@ -62,6 +62,13 @@ pCROB[,8] <- c(0.0552574805543716,146.1815811,59.47661807,0.293587959,0.98952194
                   24.97203048,0.038961807,0.000412673,-3.324,0.135,-0.03,
                   0.031652341,59.47661807,0,0.818727858,0,1.1) #Populus is based on a small dataset
 
+pCROB[,9] <- c(0.204117167,183.1185505,26.02424445,0.222587319,1.004187457,0.404366253,
+               0.205109392,0.399223504,0.361811051,0.02145648,1.75957705,0.256080148,
+               0.388792131,0.282923723,298.2159489,0.015950268,617.3465727,0.896917787,
+               0.989982075,0.951355453,0.235587077,0.219406227,0.35113714,0.506901017,
+               0.893191014,1,1,2,3.1,5800,0.014469428,39.69879172,0.045439925,0.000897902,
+               -3.324,0.135,-0.03,0.058115759,40.0468376,2.968459454,1.189177125,0.1149,1.1508) # Eucalyptus grandis x Eucalyptus urophylla
+
 pPREL = c(413.000000, 0.450000, 0.118000, 3.000000, 0.745700, 10.930000, -3.063000, 17.720000,
           -0.102700, 0.036730, 0.777900, 0.500000, -0.364000, 0.271500, 0.835100, 0.073480,
           0.999600, 0.442800, 1.200000, 0.330000, 4.970496, 0.000000, 0.000000, 160.000000,
@@ -76,7 +83,7 @@ pYAS = c(4.897147e-01, 4.913873e+00, 2.419735e-01, 9.487642e-02, 4.362893e-01, 2
          -7.913602e-05, 3.518549e-02, -2.089906e-04, -1.808920e+00, -1.172547e+00, -1.253595e+01,
          4.596472e-03, 1.302583e-03, -4.389227e-01, 1.267467e+00, 2.569142e-01)
 
-parsAWEN <- matrix(NA,12,8,dimnames = list(NULL,speciesNam))
+parsAWEN <- matrix(NA,12,9,dimnames = list(NULL,speciesNam))
 parsAWEN[,1] <- c(0.518000,0.177300,0.088700,0.216000,0.474660,0.019012,0.078308,
                   0.430248,0.670000,0.022500,0.007500,0.285000)
 parsAWEN[,2] <- c(0.482600,0.131700,0.065800,0.319900,0.474660,0.019012,
@@ -96,8 +103,11 @@ parsAWEN[,7] <- c(0.407900,0.198000,0.099000,0.295100,0.474660,0.019012,
                   0.078308,0.430248,0.715000,0.015000,0.000000,0.275000)
 parsAWEN[,8] <- c(0.407900,0.198000,0.099000,0.295100,0.474660,0.019012,
                   0.078308,0.430248,0.715000,0.015000,0.000000,0.275000)
+parsAWEN[,9] <- c(0.360000,0.281200,0.098800,0.260000,
+                  0.650000,0.022200,0.007800,0.320000,
+                  0.750000,0.030000,0.000000,0.220000)
 
-pHcM <- matrix(NA,7,8,dimnames = list(NULL,speciesNam))
+pHcM <- matrix(NA,7,9,dimnames = list(NULL,speciesNam))
 ###parameters Hcmodel pisy
 pHcM[,1] <- c(1.4628,-0.256007,0.017293,-0.089891,0.213387,-0.079094,-0.062191)
 ###parameters Hcmodel piab
@@ -114,9 +124,11 @@ pHcM[1:5,6] <- c(-1.067271, -0.017684, 0.07708,	-0.619978,0.045046)
 pHcM[1:3,7] <- c(0.04237,-0.13308,0.31382)
 ###parameters Hcmodel poplus
 pHcM[1:3,8] <- c(0.04237,-0.13308,0.31382)
+###parameters Hcmodel eugl
+pHcM[1:5,9] <- c(-1.067271, -0.017684, 0.07708,	-0.619978,0.045046)
 
-litterSizeDef <- matrix(0.,3,8,dimnames = list(NULL,speciesNam))
-litterSizeDef[1,] <- c(10,10,5,10,10,7,7,7)
+litterSizeDef <- matrix(0.,3,9,dimnames = list(NULL,speciesNam))
+litterSizeDef[1,] <- c(10,10,5,10,10,7,7,7,7)
 litterSizeDef[2,] <- 2
 
 ClCut_birch <- matrix(NA,2,4)