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Jori Uusitalo
EFFORTE Business Forum
Key Findings
Helsinki 19.6.2019
EFFORTE targets at efficiency and sustainability
2 26.6.2019
• Coordination by Luke
• 23 partners from five
countries
– Finland (6)
– Sweden (8)
– France (6)
– UK (2)
– Switzerland(1)
• 5 research institutes
• 13 large industry
companies
• 4 SMEs
Duration 2016-2019
Total budget of 4.15 M€
out of which 2.23 M€
from Horizon 2020
1) Soil mechanics and trafficability
2) Efficient silviculture
a) Increasing forest growth
b) Efficient silvicultural operations
3) Big data –applications
Consortium
Finland
• Luke (coordinator)
• Oy Arbonaut Ltd
• Metsägroup Oyj
• UPM-Kymmene Oyj
• Stora Enso Oyj
• Metsäteho Oy
Sweden
• Skogforsk
• SLU
• SCA Skog AB
• Holmen aktiebolag
• Sveaskog Förvaltning AB
• Södra skogägarna
• Stora Enso Skog AB
• Creative Optimization
France
• FCBA
• Forets et Bois de l’Est (FBE)
• Office National des Forets
(ONF)
• sarl Ste d’Exploitation
Forestière de l’Est (SEFE)
• Comptoir des Bois de Brive
(CBB)
• COPACEL
UK
• James Jones & Sons Ltd
• Woodilee Consultancy Ltd
Switzerland
• Agroscope
3 26.6.2019
Experimental series in EFFORTE (Photos Jari Ala-
Ilomäki, Philippe Ruch, Ian Stewart)
4 17.6.2019
Vihti; fine-grained
Kuru; mid-grained
France
Scotland
WP1 – Basic components of soil trafficability
• We have taken big steps in understanding
– Significance of the soil characteristics and
moisture content on soil strength relationship
• All findings confirm the importance of wetness in
predicting soil strength, especially in fine-grained
soils
5 26.6.2019
The models created
will work in boreal
forests and temperate
coniferous and
broadleaf forests
WP1 – Basic components of soil trafficability
• Soil type can to certain extent be predicted with
topography and tree species composition
• Traffic has significant impact on compaction
(Increase of Bulk density) and it will slow down
drying of soil
• Especially forest traffic in wet conditions should
planned carefully
– The significance of machine mass/ground
pressure on the soil compaction and future
growth conditions requires more research
6 26.6.2019
WP 2 – Increased efficiency in silviculture
Studies on
Syncronising site preparation and planting
Fully automatized mechanical planting
7 26.6.2019
WP 2 – Increased efficiency in silviculture
Studies on various methods to increase
Productivity of young stand management
8 26.6.2019
WP 2 – Precision forestry
Tree locations at
moment of harvest
Site index within the
stand
Optimizing growth of the
next generation
WP 3 - Big data –applications in
forestry
10
Soil type maps
Topography
Remote sensing
Weather data
Huge progress going on to employ
various data sources in forestry
Big data applications provides
excellent possibilities to
make even more environmentally
sound and efficient operations
Enhanced utilisation of Forest Big Data
11 26.6.2019
Prior to logging operations
- Trafficability/Bearing capacity
- Timing of operations
- Planning optimal places for
logging trails
- Prognosis of wood properties
- Scheduling of harvesters –
optimal machinery/accessories
While logging
- Interpretation of machine data
– soil properties
- Optimal routing of logging
trails
- Optimal tree bucking
Post-harvest
- Soil properties
- Tree species and dimensions,
past tree growth (site index)
and tree defects
- Post-harvest quality
Conclusions
• Excellent atmosphere – excellent collaboration between
Researchers and industrial partners and between countries
• Altogether 31 Deliverables – and 2-3 appendices
– www.luke.fi/efforte
• Scientifically new understanding about soil mechanics in
forest soils
• So far roughly 10 scientific papers – probably more than 10
will be submitted during the next year
• Invention, processing and validating of roughly 15 applications
in the areas of tree logging and silviculture – many of them
very promising
12 26.6.2019
Thank you!
26.6.201913

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Key findings of EFFORTE project

  • 1. Jori Uusitalo EFFORTE Business Forum Key Findings Helsinki 19.6.2019
  • 2. EFFORTE targets at efficiency and sustainability 2 26.6.2019 • Coordination by Luke • 23 partners from five countries – Finland (6) – Sweden (8) – France (6) – UK (2) – Switzerland(1) • 5 research institutes • 13 large industry companies • 4 SMEs Duration 2016-2019 Total budget of 4.15 M€ out of which 2.23 M€ from Horizon 2020 1) Soil mechanics and trafficability 2) Efficient silviculture a) Increasing forest growth b) Efficient silvicultural operations 3) Big data –applications
  • 3. Consortium Finland • Luke (coordinator) • Oy Arbonaut Ltd • Metsägroup Oyj • UPM-Kymmene Oyj • Stora Enso Oyj • Metsäteho Oy Sweden • Skogforsk • SLU • SCA Skog AB • Holmen aktiebolag • Sveaskog Förvaltning AB • Södra skogägarna • Stora Enso Skog AB • Creative Optimization France • FCBA • Forets et Bois de l’Est (FBE) • Office National des Forets (ONF) • sarl Ste d’Exploitation Forestière de l’Est (SEFE) • Comptoir des Bois de Brive (CBB) • COPACEL UK • James Jones & Sons Ltd • Woodilee Consultancy Ltd Switzerland • Agroscope 3 26.6.2019
  • 4. Experimental series in EFFORTE (Photos Jari Ala- Ilomäki, Philippe Ruch, Ian Stewart) 4 17.6.2019 Vihti; fine-grained Kuru; mid-grained France Scotland
  • 5. WP1 – Basic components of soil trafficability • We have taken big steps in understanding – Significance of the soil characteristics and moisture content on soil strength relationship • All findings confirm the importance of wetness in predicting soil strength, especially in fine-grained soils 5 26.6.2019 The models created will work in boreal forests and temperate coniferous and broadleaf forests
  • 6. WP1 – Basic components of soil trafficability • Soil type can to certain extent be predicted with topography and tree species composition • Traffic has significant impact on compaction (Increase of Bulk density) and it will slow down drying of soil • Especially forest traffic in wet conditions should planned carefully – The significance of machine mass/ground pressure on the soil compaction and future growth conditions requires more research 6 26.6.2019
  • 7. WP 2 – Increased efficiency in silviculture Studies on Syncronising site preparation and planting Fully automatized mechanical planting 7 26.6.2019
  • 8. WP 2 – Increased efficiency in silviculture Studies on various methods to increase Productivity of young stand management 8 26.6.2019
  • 9. WP 2 – Precision forestry Tree locations at moment of harvest Site index within the stand Optimizing growth of the next generation
  • 10. WP 3 - Big data –applications in forestry 10 Soil type maps Topography Remote sensing Weather data Huge progress going on to employ various data sources in forestry Big data applications provides excellent possibilities to make even more environmentally sound and efficient operations
  • 11. Enhanced utilisation of Forest Big Data 11 26.6.2019 Prior to logging operations - Trafficability/Bearing capacity - Timing of operations - Planning optimal places for logging trails - Prognosis of wood properties - Scheduling of harvesters – optimal machinery/accessories While logging - Interpretation of machine data – soil properties - Optimal routing of logging trails - Optimal tree bucking Post-harvest - Soil properties - Tree species and dimensions, past tree growth (site index) and tree defects - Post-harvest quality
  • 12. Conclusions • Excellent atmosphere – excellent collaboration between Researchers and industrial partners and between countries • Altogether 31 Deliverables – and 2-3 appendices – www.luke.fi/efforte • Scientifically new understanding about soil mechanics in forest soils • So far roughly 10 scientific papers – probably more than 10 will be submitted during the next year • Invention, processing and validating of roughly 15 applications in the areas of tree logging and silviculture – many of them very promising 12 26.6.2019