IUFRO - Preliminary assessment of climate change impact on optimized strategic plans of eucalyptus...

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



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



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.



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



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



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



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



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)



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.



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)



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%


38%

2%

60%


MG LA Overall Boiler

66% 6%

28%


98.7%

0.9%

0.4%


42%

1%

57%


82%

3% 15%


82%

4% 14%


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

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



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



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



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



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



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%



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



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)



Final considerations

Uncertainty

Climate model error

Forest growth error

No absolute values should be interpreted

Suggested trends should be considered



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?)



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

IUFRO - Preliminary assessment of climate change impact on optimized strategic plans of eucalyptus...

Economy & Finance

Transcript of IUFRO - Preliminary assessment of climate change impact on optimized strategic plans of eucalyptus...