Simulated Climate Change: without

Names of the restructured 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_1"                    
##  [3] "Selection cut_2"                    
##  [4] "Selection cut_3"                    
##  [5] "Selection cut_4"                    
##  [6] "Tapio thinning"                     
##  [7] "Tapio thinning nature"              
##  [8] "Short rotation thinning 5"          
##  [9] "Long rotation thinning 5"           
## [10] "Long rotation thinning 10"          
## [11] "Long rotation thinning 15"          
## [12] "Long rotation thinning 30"          
## [13] "Tapio harvest without thinnings -20"
## [14] "Short rotation harvest 5"           
## [15] "Tapio harvest"                      
## [16] "Tapio harvest nature scene"         
## [17] "Tapio harvest without thinnings"    
## [18] "Long rotation harvest 5"            
## [19] "Long rotation harvest 10"           
## [20] "Tapio harvest without thinnings 10" 
## [21] "Long rotation harvest 15"           
## [22] "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 154 0.126 8.209 1.1101635
MV_Kitee 29 0.316 2.404 1.1518026
MV_Korsnas 296 0.109 11.691 1.0440740
MV_Parikkala 284 0.091 4.483 1.0518901
MV_Pori 207 0.112 7.130 1.0634400
MV_Pyhtaa 120 0.154 8.052 1.2779667
MV_Raasepori 200 0.025 5.350 1.0895248
MV_Simo 290 0.070 10.892 1.4250971
MV_Vaala 24 0.183 4.682 1.7068152
MV_Voyri 190 0.078 4.524 0.9030168

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

meanV <- rslt[rslt$regime %in% c("BAU", "SA", "CCF_1") , ] %>% 
   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)) +
   scale_y_continuous(limit = c(0,700)) +
   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_1") , ] %>% 
   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)