O congresso mundial da IUFRO (International Union of Forest Research Organizations) que acontece esse ano em Salt Lake City, nos Estados Unidos em sua 24ª edição, contou com a apresentação do pesquisador da Universidade Técnica de Lisboa Dr. João Palma no dia 07 de Outubro (terça-feira), sobre os resultados realizado na International Paper do Brasil. O projeto mostra os impactos no planejamento da empresa num cenário de mudanças climáticas, utilizando dados de pesquisa da empresa, modelos processuais de crescimento e modelagem de planejamento otimizado, foi possível construir um cenário futuro que aponta para 3 a 5% de diminuição da produtividade em um cenário de mudanças extremas nas condições climáticas no futuro.

1. Universidade de Lisboa
Instituto Superior de Agronomia
Preliminary assessment of climate change impact on optimized
strategic plans of eucalyptus plantations in Brazil
Palma JHN1, Lemos C2, Weber K2, Hakamada R2, Nobre S3, Estraviz LC4
October 2014
1 Forest Research Centre, School of Agronomy, University of Lisbon, Portugal
2 International Paper, Mogi Guaçu, São Paulo, Brazil
3 Atrium Forest Consulting Ltda, Piracicaba, São Paulo, Brasil
4 Departamento de Ciências Florestais, Escola Superior de Agronomia Luiz Queiroz, Piracicaba, São Paulo, Brasil

2. Universidade de Lisboa
Instituto Superior de Agronomia
Evaluate the impact of climate change in the optimized
management plan wood supplying for International Paper
Brasil pulp mills
Access information on climate change
Use of 3PG to assess impact of climate change in tree growth
Integration of tree growth information in WoodStock© optimization model
Evaluation of main differences in the optimized plan
Objectives

3. Universidade de Lisboa
Instituto Superior de Agronomia
IP – 5 Distinct regions
5 distinct climate datasets
5 coordinates were sent to the Instituto Nacional de
Pesquisa Espacial to retrieve daily datasets for future
climate:
1) Control Dataset (current climate)
2) MIDI dataset (Moderate changes in climate)
Climate Model: HadRM3Q, scenario A1B (Chou et al,
2011; Marengo et al, 2011)
Monthly averaging and formatting for 3PG
Methodology – Climate datasets
CHOU, S.C., MARENGO, J.A., LYRA, A.A., SUEIRO, G., PESQUERO, J.F., ALVES, L.M., KAY, G., BETTS, R., CHAGAS, D.J., GOMES, J.L., BUSTAMANTE, J.F.,
TAVARES, P., 2011. Downscaling of South America present climate driven by 4-member HadCM3 runs. CLIMATE DYNAMICS 38 635-653.
Marengo, J.A., Chou, S.C., Kay, G., Alves, L.M., Pesquero, J.F., Soares, W.R., Santos, D.C., Lyra, A.A., Sueiro, G., Betts, R., Chagas, D.J., Gomes, J.L.,
Bustamante, J.F., Tavares, P., 2011. Development of regional future climate change scenarios in South America using the Eta CPTEC/HadCM3 climate
change projections: climatology and regional analyses for the Amazon, São Francisco and the Paraná River basins. Climate Dynamics DOI:
10.1007/s00382-00011-01155-00385.

4. Universidade de Lisboa
Instituto Superior de Agronomia
Definition of hypothetical stands for testing each region climate
scenario
Clay soils
Sandy Soils
Plantation density: 1212 trees ha-1
Calibration of 3PG
PhD thesis from Lemos (2012) calibration was used
Set of parameters for one clone and for each soil type
This study assumes all clones having the same relative growth response to changing
climate
Methodology – 3PG
Lemos, C, 2012, Aprimoramento, teste e uso do modelo 3-PG em plantios clonais de eucalyptus no nordeste do Estado de São Paulo, Escola
Superior de Agronomia Luiz Queiroz, São Paulo, Brasil. PhD Thesis. In portuguese

5. Universidade de Lisboa
Instituto Superior de Agronomia
Woodstock was used to prepare the optimization datasets and CPLEX
was used as the solver. Current objective function maintained.
Yields section of Woodstock had a section for applying volume and cellulose ratios
for genetic improvements accounting
The integration of climate change used this section to apply the ratios of climate
change impacts
The existing section was divided per
Region
Ownership (with genetic improvements) and third party areas (no genetic improvement)
Current optimization report graphs were used for the comparison with
the new optimization results
Methodology – Optimizer

6. Universidade de Lisboa
Instituto Superior de Agronomia
More rain when less needed (summer), Less rain when more needed (winter)
Results – Climate
Averages 2011-2040

7. Universidade de Lisboa
Instituto Superior de Agronomia
General increase of stress days in winter (Increased pest ocurrence?)
Results – Climate
Averages 2011-2040
Nr Days with a water
stress day (WSD)
WSD = if sum rain in last
15 days < 10 mm

8. Universidade de Lisboa
Instituto Superior de Agronomia
Results – 3PG, Climate
Monthly data 2011-2018
7 years (1 rotation)
Monthly values.
More rain when less
needed. (summer)

9. Universidade de Lisboa
Instituto Superior de Agronomia
Results – Tree Growth
Monthly data 2011-2018
7 years (1 rotation)
0
100
200
300
400
500
600
700
800
1 2 3 4 5 6 7
MgDryMatterha-1
Age
Brotas
Stem DM (CL-CNTRL) Stem DM (CL-MIDI)
0
100
200
300
400
500
600
700
1 2 3 4 5 6 7
MgDryMatterha-1
Age
Brotas
Stem DM (S-CNTRL) Stem DM (S-MIDI)
Differences in year 7
Mogi Guaçu CL - 4%
Mogi Guaçu S - 3%
Brotas CL - 5%
Brotas S - 6%
São Simão CL - 2%
São Simão S - 3%
Luis Antonio CL - 3%
Luis Antonio S - 4%
Fomento CL - 3%
Fomento S - 3%
Similar graphical results for other areas. Brotas region has the highest reduction.

10. Universidade de Lisboa
Instituto Superior de Agronomia
Results – Tree Growth
Behind the Scenes… Modifiers’ dynamics.
0.8
0.85
0.9
0.95
1
1 2 3 4 5 6 7
fT
Age
Temperature modifier
0
0.2
0.4
0.6
0.8
1
1 2 3 4 5 6 7
fSW
Age
Soil Water Modifier
CNTRL
MIDI
0.6
0.65
0.7
0.75
0.8
0.85
0.9
0.95
1
1 2 3 4 5 6 7
fVPD
Age
Vapour Pressure Deficit
Brotas, Sandy Soil
0
0.2
0.4
0.6
0.8
1
1 2 3 4 5 6 7fPhysMod
Age
Physiology Modifier (efficiency)

11. Universidade de Lisboa
Instituto Superior de Agronomia
Results – Man. Plan Optimization
Main differences identified
current
1) % Supply by forest type
66%8%
26%
Own Partnership LOAP
99.5
%
0.3%
0.2%
Own Partnership LOAP
38%
2%
60%
Own Partnership LOAP
MG LA OverallBoiler
66%6%
28%
Own Partnership LOAP
98.7
%
0.9%
0.4%
Own Partnership LOAP
42%
1%
57%
Own Partnership LOAP
82%
3% 15%
Own Partnership LOAP
82%
4% 14%
Own Partnership LOAP
climate
change

12. Universidade de Lisboa
Instituto Superior de Agronomia
0
100
200
300
400
500
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 AVG
m3
Partnership Own
Results – Man. Plan Optimization
Main differences identified2) Sales Volume
current
climate
change
0
100
200
300
400
500
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 AVG
m3
Partnership Own

13. Universidade de Lisboa
Instituto Superior de Agronomia
0.0
1.0
2.0
3.0
4.0
5.0
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030
Area
Results – Man. Plan Optimization
Main differences identified3) Sold Area
current
climate
change
0.0
1.0
2.0
3.0
4.0
5.0
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030
Area

14. Universidade de Lisboa
Instituto Superior de Agronomia
1 1.2
0
0 0 0
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030
Area
Implantação Reforma
Results – Man. Plan Optimization
Main differences identified4) LOAP Planting and Coppice
(Land Owner Assistance Program)
current
climate
change
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030
Area
Implantação Reforma

15. Universidade de Lisboa
Instituto Superior de Agronomia
0.0
0.5
1.0
1.5
2.0
2.5
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 AVG
Area
Results – Man. Plan Optimization
Main differences identified5) Areas new LOAP
(Land Owner Assistance Program)
current
climate
change
0.0
0.5
1.0
1.5
2.0
2.5
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 AVG
Area

16. Universidade de Lisboa
Instituto Superior de Agronomia
Results – Man. Plan Optimization
Main differences identified6) Capital Program Costs
About 1-2 Million R$ y-1
0
10
20
30
40
50
60
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 AVG
R$
Planting current Coppice current Maintenace current Total Current
Planting CC Coppice CC Maintenace CC Total CC
Total
Planting
Maintenance
Coppice

17. Universidade de Lisboa
Instituto Superior de Agronomia
Results – Man. Plan Optimization
Main differences identified7) Cumulated Net Present value
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030
R$
Current
Climate Change
-15%
-10%
-7% -6%

18. Universidade de Lisboa
Instituto Superior de Agronomia
Results – Man. Plan Optimization
Identified Optimization Dynamics
Climate change
3-5% lower forest
productivity
Optimization
Cost per Wood/Cellulose ton
Kept
(model constraint)
Reduce new LOAP areas
Medium term: increasing
wood selling from LOAP areas
Long term: increasing wood
selling from OWN areas
Forcing to:
Avoid selling own areas
Explained by
Genetic
improvement on
own areas
Resulting in:
Overall decrease of cumulated NPV
(Initial 15% but stabilizing at 6-8%)
Increase costs on own areas
(capital program) +1 to 2 Million $R y-1

19. Universidade de Lisboa
Instituto Superior de Agronomia
Preliminary conclusions
Climate change suggests overall higher rain depth
Higher in summer (when less needed)
Lower in winter (when more needed)
Climate change decreases 3-5% forest productivity
Optimization suggesting:
Decrease in area sold
Lower sales volume in Partnership
Higher sales volume in Own forest
Decrease in area sold
Maintenance of own areas
Decrease in new LOAP areas
Decrease in cumulated NPV, increase in costs (1-2 millions y-1)

20. Universidade de Lisboa
Instituto Superior de Agronomia
Final considerations
Uncertainty
Climate model error
Forest growth error
No absolute values should be interpreted
Suggested trends should be considered

21. Universidade de Lisboa
Instituto Superior de Agronomia
Further Research
Dryer winters
higher impact of pest incidence
higher fire ocurrences
Spatial optimization (changes in optimization model structure)
Reduce fire ocurrence (Increase landscape fire resistance)
Reduce wind damage
?
Landscape fire resistance (also wind resistance?)

22. Universidade de Lisboa
Instituto Superior de Agronomia
Acknowledgements
We thank the support of ForEAdapt (Knowledge exchange between
Europe and America on forest growth models and optimization for
adaptive forestry), a Marie Curie International Research Staff
Exchange Scheme within the 7th European Community
Framework Programme (FP7-PEOPLE-2009-IRSES).
Also the kind support of:
Adriano Almeida
Sebastião Oliveira Filho
João Morato
Chin Sou
Adan Silva
Henrique Andrade, Vinicius Bellumath, Thalita Faria, Bruno
Piana, Isis de Almeida, ...
... International Paper in General for the excelent working
conditions