Overview simulated data

Simulated Climate Change: CC85

Names of the restructured GPKGs

gpkg <- unique(rslt$gpkg)
print(gpkg)

##  [1] "MV_Hartola"   "MV_Kitee"     "MV_Korsnas"   "MV_Parikkala" "MV_Pori"     
##  [6] "MV_Pyhtaa"    "MV_Raasepori" "MV_Simo"      "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                                    "Selection cut_1"                    
##  [3] "Selection cut_2"                     "Selection cut_3"                    
##  [5] "Selection cut_4"                     "Tapio thinning"                     
##  [7] "Tapio thinning nature"               "Short rotation thinning 5"          
##  [9] "Long rotation thinning 5"            "Long rotation thinning 10"          
## [11] "Long rotation thinning 15"           "Long rotation thinning 30"          
## [13] "Tapio harvest without thinnings -20" "Short rotation harvest 5"           
## [15] "Tapio harvest"                       "Tapio harvest nature scene"         
## [17] "Tapio harvest without thinnings"     "Long rotation harvest 5"            
## [19] "Long rotation harvest 10"            "Tapio harvest without thinnings 10" 
## [21] "Long rotation harvest 15"            "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	1.1113674
MV_Kitee	29	0.316	2.404	1.1518026
MV_Korsnas	297	0.109	11.691	1.0387372
MV_Parikkala	284	0.091	4.483	1.0451327
MV_Pori	207	0.112	7.130	1.0637268
MV_Pyhtaa	120	0.154	8.052	1.2882144
MV_Raasepori	200	0.025	5.350	1.0884667
MV_Simo	290	0.070	10.892	1.4445900
MV_Vaala	24	0.183	4.682	1.6917989
MV_Voyri	190	0.078	4.524	0.9040756

Development of average stand volume V (m3/ha)

for certain regimes

meanV <- rslt[rslt$regime %in% c("BAU", "SA", "CCF_2", "BAUwGTR"), ] %>% 
   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)

only for CCF regimes

meanV_CCF <- rslt[rslt$regime %in% c("CCF_1", "CCF_2", "CCF_3", "CCF_4"), ] %>% 
   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_CCF)

Development of average V_total_deadwood (m3/ha) for certain regimes

meanDW <- rslt[rslt$regime %in% c("BAU", "SA", "CCF_2", "BAUwGTR") , ] %>% 
   group_by(year, regime, gpkg) %>% 
   summarise(mean_DW = mean(V_total_deadwood)) %>% 
   ggplot(aes(year, mean_DW)) + 
   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(meanDW)

Development of average CARBON_STORAGE (kg/ha) for certain regimes

meanCS <- rslt[rslt$regime %in% c("BAU", "SA", "CCF_2", "BAUwGTR")  , ] %>% 
   group_by(year, regime, gpkg) %>% 
   summarise(mean_CS = mean(CARBON_STORAGE, na.rm = TRUE )/1000) %>% 
   ggplot(aes(year, mean_CS)) + 
   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(meanCS)

Average harvested timber volume (m3/ha) for certain regimes

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

meanHarvested_V <- rslt[rslt$regime %in%c("BAU", "SA", "CCF_2", "BAUwGTR") , ] %>% 
   group_by(year, regime, gpkg) %>% 
   mutate(Harvested_V = ifelse(is.na(Harvested_V), 0, Harvested_V)) %>% 
   summarise(meanHarvested_V = mean(Harvested_V), na.rm = TRUE) %>% 
   ggplot(aes(year, meanHarvested_V)) + 
   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(meanHarvested_V)

Average cash flow (Euro/ha) for certain regimes