National forestry accounting plan for Norway EFTA LULUCF EG - 2nd meeting 20 April 2020 • General introduction • Data sources • Climate change • Dynamic age-related characteristics • Stochasticity • Cost (related to strata definition) • Harvest intensities • HWP • Natural disturbances • Assumptions
General introduction
Data driven approach The main data source of GHGI for managed forest is the NFI Forecast the development of the NFI, and apply the same methods and definitions of the GHGI 1. Stratification 2. Forest management practices 3. Management intensities in the RF 4. Simulation of development (growth, mortality, ingrowth, regeneration, other harvest and management) of NFI plots
Stratification is based on: 1. Stratification • Main species (based on NFI data) • Site index (based on NFI data) • Cost (Granhus et al. 2011) Stratification at the RP Stratification during the simulation Is assessed at each remeasurement using We used the stratification of the last NFI of the RP. NFI data The stratum assign to a plot does not change during simulation Cost same for each plot during the simulation and during the RP
1. Stratification OBJECTIVE: dividing the managed forest into strata with homogenous management activities.
1. Stratification
2. Forest management practices • Felling forest management practices are defined according to the most common management practices in the different strata. • Regeneration numbers are based on surveys. • Other felling (e.g. unplanned) are not part of the FMP, but are included as part of the simulation Clearcuts 88% of the biomass is removed (NFI data from the RP) Thinnings32% of the biomass (NFI data from the RP)
3. Management intensities in the RP Maturity was defined for each SI and species, as 20 years before the maturity according to the NFI definitions of forest development (Figure 1 of FNAP)
4. Simulation of development (growth, mortality, ingrowth, regeneration, other harvest and management) of NFI plots NFI DATA FROM RP DBH BAL SI SBA LAT fate BA inc VOL inc IMPUTATION 64 12 17 12.5 58.12 alive 111 46 SIMULATED DATA 67 12 17 12.5 58.12 alive 58 17 Variables tree level 57 12 17 12.5 58.12 alive 40 12 imputation 175 17 17 19.9 58.282 alive 40 28 DBH BAL SI SBA LAT DBH Size 94 22 17 46 58.253 245 10 17 19.9 58.282 alive 101 123 SI Site quality 96 21 17 46 58.253 251 8 17 19.9 58.282 alive 78 -17 SBA Competition 100 20 17 46 58.253 193 16 17 19.9 58.282 alive 38 16 BAL Social status 151 18 17 19.9 58.282 cut 0 0 134 13 17 46 58.253 Latitude 81 19 17 19.9 58.282 alive 0 2 138 18 17 19.9 58.282 dead 0 0 183 26 23 29.3 58.282 alive 33 -179
4. Simulation of development (growth, mortality, ingrowth, regeneration, other harvest and management) of NFI plots Management - Final felling - Thinning - Other harvest imputation
4. Simulation of development (harvest and management) of NFI plots Harvest Area to be harvested Calculate area per strata and maturity class Area for the stratum and maturity class Calculate area to be harvested per strata and maturity class Select plots to be harvested
4. Simulation of development (harvest and management) of NFI plots Harvest • Age to maturity Rank the plots by harvest probability model Antón-Fernández • Volume & Astrup (2012) fitted to RP • Distance to road • Proportion of species
Mineral soils and DOM • We use the same methodology as in the GHGI • No climate change (same as GHGI) • Mineral soils and DOM we use Yasso07
Data sources
The Norwegian NFI 5-years NFI-cycles 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 GHGI2000 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
Reference period MANAGEMENT 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 GHGI 2000 GHGI 2009 Reference period used to define management intensities GROWTH (IMPUTATION) 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 GHGI 2002 GHGI 2009 Reference period used for growth, ingrowth, mortality, and other harvest
Data used from the reference period (2000-2009) • Data used to calculate management intensity GHGI 2000 to GHGI 2009 • Data used for imputation (growth, mortality, ingrowth, and other harvest) • GHGI 2002 (2000-2004) until GHGI 2009 (2007-2011) • All plots and sub-plots that were not final felled or thinned • Why did we not used data before 2000? • Site index was not measured before year 2000, but estimated by the field crews
Starting year 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 GHGI 2009
Climate change
Climate change rcp 4.5 Best data available: Rcp 4.5 downscaled to a 1 by 1 km grid for Norway http://www.senorge.no/aboutSeNorge.html?show=on Lussana, Cristian, Ole Einar Tveito, Andreas Dobler, and Ketil Tunheim. “SeNorge_2018, Daily Precipitation, and Temperature Datasets over SI change Norway.” Earth System Science Data 11, no. 4 (October 14, 2019): 1531 – 51. https://doi.org/10.5194/essd-11-1531-2019. Climate-sensitive site index models for Norway SI model uses the "space for time" substitution approach. Antón-Fernández, Clara, Blas Mola-Yudego, Lise Dalsgaard, and Rasmus Astrup. “ Climate- Sensitive Site Index Models for Norway.” Canadian Journal of Forest Research 46, no. 6 (March 15, 2016): 794 – 803. https://doi.org/10.1139/cjfr-2015-0155.
Climate change in Norway
Climate change effect on SI
• Climate change effect in Mineral soils and DOM (Yasso) Climate change was not included on our simulations with Yasso Consistency with GHGI
Dynamic age-related characteristics
Dynamic age-related forest characteristics
Dynamic age-related forest characteristics
Dynamic age-related forest characteristics: Age to deveopment class 5 Here I need a figure showing maturity as defined in the FRL distribution among strata... proving the point that our forest is getting "mature"
Dynamic age-related forest characteristics
Dynamic age-related forest characteristics
Stochasticidty
Stochasticity
Cost (strata)
Cost used to define strata (Granhus et al. 2011) Transport to roadside: Felling and delimbing For each terrain class (operating system) The harvesting costs per cubic meter calculated according to a methodology the costs were calculated using the described by Granhus et al (2011) and Bergseng (2013). function of Dale and Stamm (1994). The costs connected to the felling and delimbing operations depends largely on stand density, and the operating system in use, which is dependent on Plots with need of sea transport: terrain steepness. An additional transportation cost of NOK100 per cubic meter. Harvesting costs per cubic meter for the terrain classes 1-3 were calculated based on the work of Dale et al. (1993), Omnes (1984) and Lileng (2009), respectively, with machine hour costs assumed to be representative as of 2018.
Cost used to define strata References: Bergseng, E., Eid, T., Løken, Ø. & Astrup, R. 2013. Harvest residue potential in Norway – A bioeconomic model appraisal. Scand. J. For. Res. 28: 470-480. Dale, Ø., Kjøstelsen, L. & Aamodt, H. E. 1993. Mekaniserte lukkede hogster. In:13 Aamodt, H. E. (Ed.) Flerbruksrettet driftsteknikk. Rapp. Skogforsk 20: 3- 23. Dale, Ø. & Stamm, J. 1994. Grunnlagsdata for kostnadsanalyse av alternative hogstformer. Rapp. Skogforsk 7: 1-37. Granhus, A., Andreassen, K., Tomter, S., Eriksen, R. & Astrup, R. 2011. Skogressursene langs kysten. Tilgjengelighet, utnyttelse og prognoser for framtidig tilgang. Oppdragsrapport fra Skog og landskap 11: 1-35. Lileng, J. 2009. Avvirkning med hjulgående maskiner i bratt terreng. Oppdragsrapportfra Skog og landskap15: 1-7.
Harvest intensities
Harvest intensity
Thinning intensity
Harvest wood products (HP)
Harvested wood products The projectionHWP for the simulation period are based on a modification of the existing model used in the NIR GHG reporting for calculation of HWP (production approach, Tier Deforestation Roundwood from 2) using the reference period 2000-2009. deforestation was excluded from the estimation of the ratios of semi-finite products
Natural disturbances
Natural disturbances • Included as part of mortality in imputation • Natural disturbances were not excluded of the reference dataset (from RP) of imputation
Assumptions
Assumptions concerning 2010-2020 Throughout the full simulation (2010-2100) • Areas of each stratum were kept constant • FMP were applied consistently • HWP was calculated in the same way
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