Names of the restructered GPKGs

gpkg <- unique(rslt$gpkg)
print(gpkg)
##  [1] "MV_Hartola"   "MV_Kitee"     "MV_Korsnas"   "MV_Parikkala"
##  [5] "MV_Pori"      "MV_Pyhtaa"    "MV_Raasepori" "MV_Simo"     
##  [9] "MV_Vaala"     "MV_Voyri"

Simulated branching_groups

All simulated branching_groups must be listed in the file /params/regimes.csv !! Based on this the regime names are merged to the results.

##  [1] NA                                    
##  [2] "Selection cut"                       
##  [3] "Tapio thinning"                      
##  [4] "Tapio thinning nature"               
##  [5] "Short rotation thinning 5"           
##  [6] "Long rotation thinning 5"            
##  [7] "Long rotation thinning 10"           
##  [8] "Long rotation thinning 15"           
##  [9] "Long rotation thinning 30"           
## [10] "Tapio harvest without thinnings 30 n"
## [11] "Short rotation harvest 30 n"         
## [12] "Tapio harvest without thinnings -20" 
## [13] "Tapio harvest without thinnings 10 n"
## [14] "Short rotation harvest 10 n"         
## [15] "Short rotation harvest 5"            
## [16] "Tapio harvest"                       
## [17] "Tapio harvest nature scene"          
## [18] "Tapio harvest without thinnings"     
## [19] "Long rotation harvest 5"             
## [20] "Tapio harvest without thinnings 10 p"
## [21] "Long rotation harvest 10 p"          
## [22] "Long rotation harvest 10"            
## [23] "Tapio harvest without thinnings 10"  
## [24] "Long rotation harvest 15"            
## [25] "Tapio harvest without thinnings 30 p"
## [26] "Long rotation harvest 30 p"          
## [27] "Long rotation harvest 30"

Number of simulated stands per GPKG and their size

stands <- rslt %>% 
  group_by(gpkg) %>% 
  summarise(simulated_stands = n_distinct(id),
            min_stand_size = min(AREA),
            max_stand_size = max(AREA),
            mean_size = mean(AREA))

kable(stands) %>% kable_styling()
gpkg simulated_stands min_stand_size max_stand_size mean_size
MV_Hartola 5 0.771 2.467 1.5658413
MV_Kitee 5 0.496 2.404 1.1994000
MV_Korsnas 5 0.289 4.861 2.0037193
MV_Parikkala 5 0.152 1.200 0.8900909
MV_Pori 5 0.112 0.637 0.3596000
MV_Pyhtaa 5 0.498 2.448 1.2410330
MV_Raasepori 5 0.068 1.784 0.2153039
MV_Simo 5 0.141 4.792 2.9519436
MV_Vaala 5 0.454 4.682 3.5460409
MV_Voyri 5 0.355 1.238 0.7038121

Development of average stand volume (m3/ha) for certain regimes

meanV <- rslt[rslt$regime %in% c("BAU", "SA", "CCF") , ] %>% 
   group_by(year, regime, gpkg) %>% 
   summarise(meanV = mean(V)) %>% 
   ggplot(aes(year, meanV)) + 
   geom_line( aes(color = regime)) +
   theme(axis.text.x = element_text(angle = 90)) +
   scale_x_continuous(breaks=c(2016, 2026, 2036, 2046, 2056, 2066, 2076, 2086, 2096, 2106)) +
   facet_wrap(. ~gpkg)
 plot(meanV)

Average cash flow (Euro/ha) for certain regimes

Cash flow = The sum of all revenues and costs for a specific forest stand

meanCash <- rslt[rslt$regime %in% c("BAU", "SA", "CCF") , ] %>% 
   group_by(year, regime, gpkg) %>% 
   mutate(cash_flow = ifelse(is.na(cash_flow), 0, cash_flow)) %>% 
   summarise(meanCash = mean(cash_flow), na.rm = TRUE) %>% 
   ggplot(aes(year, meanCash)) + 
   geom_line( aes(color = regime)) +
   theme(axis.text.x = element_text(angle = 90)) +
   scale_x_continuous(breaks=c(2016, 2026, 2036, 2046, 2056, 2066, 2076, 2086, 2096, 2106)) +
   facet_wrap(. ~gpkg)
 plot(meanCash)