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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
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
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.
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
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
Universidade de Lisboa
Instituto Superior de Agronomia
More rain when less needed (summer), Less rain when more needed (winter)
Results – Climate
0
50
100
150
200
250
300
350
0
5
10
15
20
25
30
35
1 2 3 4 5 6 7 8 9 10 11 12
Rai
n (
mm
)
Tem
pe
ratu
re (
°C)
D – Luís Antonio
Averages 2011-2040
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
Universidade de Lisboa
Instituto Superior de Agronomia
Results – 3PG, Climate Monthly data 2011-2018
7 years (1 rotation)
0100200300400500600700800
0 12 24 36 48 60 72 84
mm
Months since 1st January 2011
D – Luis Antonio
Monthly values.
More rain when less
needed. (summer)
Universidade de Lisboa
Instituto Superior de Agronomia
Results – Tree Growth Monthly data 2011-2018
7 years (1 rotation)
0
100200300400500600700800
1 2 3 4 5 6 7
Mg
Dry
Mat
ter
ha
-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
Mg
Dry
Mat
ter
ha
-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.
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
0.60.65
0.70.75
0.80.85
0.90.95
1
1 2 3 4 5 6 7
fVP
D
Age
Vapour Pressure Deficit
Brotas, Sandy Soil
0
0.2
0.4
0.6
0.8
1
1 2 3 4 5 6 7fP
hys
Mo
d
Age
Physiology Modifier (efficiency)
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 Overall Boiler
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
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 identified 2) 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
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
Are
a
Results – Man. Plan Optimization Main differences identified 3) 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
Are
a
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
Are
a
Implantação Reforma
Results – Man. Plan Optimization Main differences identified 4) 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
Are
a
Implantação Reforma
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
Are
a
Results – Man. Plan Optimization Main differences identified 5) 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
Are
a
Universidade de Lisboa
Instituto Superior de Agronomia
Results – Man. Plan Optimization Main differences identified 6) 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
Universidade de Lisboa
Instituto Superior de Agronomia
Results – Man. Plan Optimization Main differences identified 7) 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%
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
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)
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
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?)
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