Significant Findings from the PINEMAP Program on SE Forest Productivity

Research and Tools for Managing Southern Pine Forests for the Future Tim Martin1, Carlos GonzalezBenecke2, Robert Teskey3, Lisa Samuelson4, Gary Peter1, Jason Vogel5, Dan Markewitz3, Heather Aldridge6, and many others 1University

of Florida 2Oregon State University 3University of Georgia 4Auburn University 5Texas A&M University 6North Carolina State University Pine Integrated Network: Education, Mitigation, and Adaptation project (PINEMAP) is a Coordinated Agricultural Project funded by the USDA National Institute of Food and Agriculture, Award #2011-68002-30185

Forests can provide services in perpetuity

Once upon a time…

• Mid 2000s, potential for carbon markets drove increased funding for forest carbon research • Carbon Resource Science Center at UF • Department of Defense Strategic Environmental Research and Development Program • USDA National Institute for Food and Agriculture

“Hybrid” model development • Use exising (loblolly and slash) or develop new (longleaf) growth and yield functions to “grow” stems • Use existing (loblolly and slash) or develop new (longleaf) functions to relate growth of branches, leaves, and roots to stem growth • Leverage huge, existing datasets in all three species to develop and test models • Model production of wholeforest biomass / carbon

Note: Input Values in English Units

Units (Output)

Type of Simulation

English

From Establishment (Planting)

Metric

RUN Growth & Yield

RUN C Balance

From Inventory Post Establishment Simulation From Inventory Rotation Age (yr.)

60

Required

Go To Inventory

From Inventory Tree List From Inventory Summary

Simulation From Planting

Simulation From Inventory (Data From Inventory List or from external summary

TPA planting

500

Req. for Mult. Rot.

INPUTS

Survival year 1

95%

Req. for Mult. Rot.

Age

Site Index (ft) base age =50

75

From Inventory Tab

Stem Density (trees / acre) Not. Req. for Mult. Rot.

Enter Inventory Summary 40

Required

162

Required Optional

BA (ft2 / acre)

Use SI from Inventory

Site Index (ft) base age =50

75.5

Optional

Simulate Thinning?

Already Thinned ?

Required

Burning Residues at Site Prep.?

Required

Optional

Assessing C Balance?

Simulate Multiple Rotations # Rotations to Simulate ?

Thinning Type

4

Select Type

Prescribed Burning

Fixed Age

Age first prescribed burned

Target BA

Burning Frequency (yrs.)

Required Required

Target SDI

Burned Before ?

Required

Thinning at Fixed Age

Age (years)

% Removal

Th1

50

33%

Required

Th2 Th3 Th4

Upper Stocking Limit BA

Residual BA

(ft2 / acre)

(ft2 / acre)

113.256

78.408

Not Needed

Not Needed

Target SDI - Limits (% of Max.) Self-thinning threshold

Lower-Limit Full Site Occupancy

55%

30%

Maximum SDI =

450

Trees per acre

Required

Outputs – Growth & Yield Initial Values Age Stem Density Dq Hdom BA Site Index

(years) (trees / acre) (inch) (ft) (ft2 / acre) (ft)

1 475 1 75

# Thinnings BA removed GT-OB removed N removed

(ft2 / acre) (green tons / acre) (trees / acre)

1 20 1 150

At Rotation Age Age Stem Density Dq Hdom BA Total Merchantable OB Sawtimber OB Chip-and-Saw OB Pulpwood OB

(years) (trees / acre) (inch) (ft) (ft2 / acre) (green tons / acre) (green tons / acre) (green tons / acre) (green tons / acre)

60 212 10.6 81 130 120 36 71 13

Thinning

Outputs - Carbon

So what am I supposed to do with a hybrid carbon model?

So what am I supposed to do with a hybrid carbon model?

• Simulate growth and yield under different scenarios • Simulate biomass for alternative markets • Simulate litter accumulation for pinestraw production • Be prepared should C markets materialize

Gainesville renewable energy center

http://carboncenter.ifas.ufl.edu/models.shtml

Increased productivity through technology development 50

6000 40

4000

30 20

2000 10 0

Rotation age (years)

60

Volume at Harvest (ft3 / ac)

8000

0 1940 1950 1960 1970 1980 1990 2000 2010

Establishment Decade Natural Stand Weed Control

Planting Tree Improve

Site Prep Biotech/Clonal

Fertilization Rotation Age

Redrawn from: Fox, T.R., E.J. Jokela and H.L. Allen. 2007. The development of pine plantation silviculture in the southern United States. J. Forestry 105:337-347.

Corporate/Government/University Cooperative Research Model Research Results

$, in-kind, context

External $

leverage

Corporate and Government Forestland Owners/Mgrs

University Researchers

Forestry Research Cooperative

Host University (year founded)

Cooperative Forest Genetics Research Program

University of Florida (1953)

Cooperative Tree Improvement Program

North Carolina State University (1955)

Forest Biology Research Cooperative

University of Florida (1996)

Forest Modeling Research Cooperative

Virginia Polytechnic Institute and State Univ. (1979)

Forest Nutrition Cooperative

Virginia Polytechnic Institute and State Univ. / NC State Univ. (1969)

Plantation Management Research Cooperative

University of Georgia (1975)

Southern Forest Resource Assessment Consortium

North Carolina State University (1994)

Western Gulf Forest Tree Improvement Program

Texas A&M Univ. / Texas Forest Service (1969)

Cooperative Research Legacy • Multi-fold increases in productivity through – improved genetics: growth rate and disease resistance – multiple silvicultural technologies

• Most gains have been achieved through cooperative forestry research and associated tech transfer to cooperators • Non-corporate landowners benefit from genetic and silvicultural innovations funded by corporate investments • Forward-looking focus on sustainable production from intensively-managed systems

Pine Integrated Network: Education, Mitigation and Adaptation Project

PINEMAP Outcome Themes Increased C Sequestration through productivity and resilience

Engaged and literate public

Public policy supporting sustainable forest management

Enhanced capacity for collaboration

Robust/resilient regional forestbased economy

Enhanced connections

Monitoring networks • Baselines + model parameterization and validation • Tier I: ~ 700 sites with previously unshared data • Tier II: 123 sites / 450 plots with newly-measured C and nutrient pool data • Tier III: New experimental manipulation of H2O and nutrients on four intensively monitored sites

Climate Variation in Network

Silvicultural Treatments in Network Treatments: Chemical competition control (Chem), Fertilization (Fert), Thinning (Thin) Chem+Fert All Fert+Thin Thin Fert

Chem No treatment Chem+Thin

Soil Classification from SSURGO

Bacon and Grunwald

Carbon Sampling • Aboveground biomass and coarse roots – Cooperative mensurative data and allometry

• • • •

Understory Coarse woody debris and stumps Forest Floor Soil Samples – 0-10 cm, 10-20 cm, 20-50 cm, 50-100 cm – Rocks > 2mm, roots, and woody debris at all depths

Soil C and N by Soil Order

A. Bacon

Forest Floor and Soil Archive • Every sample has been air dried and is being held in a plastic container • Long-term archive space is being investigated

PINEMAP Regional Modeling Suite of complementary models focused on understanding opportunities and risks, and comparing management alternatives under future predicted climate • Growth and yield (climate-responsive) • Water Supply Stress Index (WaSSI) • Physiological Principles Predicting Growth (3-PG) • Community Land Model (CLM-BGC) • Sub-regional Timber Supply Model (SRTS)

The PINEMAP Modeling Family STAND (yield-focused) Uses individual tree measurements for parameterization

EMPIRICAL (data-driven)

Uses eddycovariance data for parameterization; focus on fluxes

Growth & Yield

3-PG

How are productivity and carbon storage going to change over the next 100 years?

CLMBGC

WaSSI

BIOGEOCHEMICAL (carbon-focused)

Hybrid between a process and empirical model

PROCESS (physiological)

Scales leaf processes to large-scales; Coarse resolution with global parameterizations

State-of-the-art model inputs • 20 CMIP5 climate projection models • Multivariate Adaptive Constructed Analogs (MACA) Downscaling • SSURGO soils aggregated by weighted area to hydrologic unit code (HUC-12) resolution (10,000 – 40,000 acres) • Soil HUC units intersected with climate data grid

Percent Sand

0

100

3-PG Model (Landsberg and Waring, 1997)

Physiological Principles that Predict Growth

Key to colours & shapes Carbon Water Trees State variables

T

 +

ET

VPD

f

Losses

+

Subsidiary variables

SLA

Subsidiary variables



Material flow s Influences

CO2 _

_ R

+ Soil H20

+

Climate & site Inputs

+

gC

+

H20

LAI

GPP

_ +

+

+

FR

+

+

LUE

F/S

NPP

Stocking

_

+

Rain

_

DBH B

+ Roots

_

wSx

A+

_

Stem

+

Foliage +

C,N

+

_

Dead trees

wS

_ +

w S >w Sx

+  N +

Stress

_

Litter

Diagram by Peter Sands

Example simulation: slash pine

Slash Pine simulation locations Covington MS

Covington AL

Santa Rosa FL

Jasper TX Washington LA

Dooly GA

Colleton SC

Brantley GA

Alachua FL Jefferson FL Polk FL

11 sites in SE US 4 sites in Northern Limit

Historical Mean Annual Temperature (째C)

Change in Above Ground Biomass -1 (Mg ha )

Future Slash Pine Productivity 50 SI=62 ft SI = 19 SI=75 ft SI = 23 SI=92 ft SI = 28

40

30

20

10

0 18

20

22

Mean Annual Temperature (C)

RCP 8.5

Notes: • All effects positive • Growth stimulation greater on low quality sites • Growth stimulation greater in cool sites on northern edge of range • Interacting effects 24 of fertilizer not considered here

Example simulation: loblolly pine

Relative increase in biomass future vs. present 3D Contour Plot of 85p against Tmean and SI rcp_out.sta 27v*216c 85p = Spline 110

100

90

SI

80

70

60

Relative Increase 50

40 14

15

16

17

18 Tmean

19

20

21

22

> 0.4 < 0.35 < 0.25 < 0.15 < 0.05 < -0.05

RCP 8.5

Decision Support System (DSS) • Primary outlet for PINEMAP tools and resources to help users identify opportunities and mitigate risk associated with changing and variable climate • Intended for professional foresters • Tools are derived from PINEMAP research and are Beta tested with a small user group

Current USFS Seed Deployment Guidelines

R.C. Schmidtling, 2001, Southern Pine Seed Sources, USFS General Technical Report SRS-44

Seed Movement

R.C. Schmidtling, 2001, Southern Pine Seed Sources, USFS General Technical Report SRS-44

Seedling Deployment Tools: Background

• Seedling markets tool: nurseries can see where current seed may be most effective in the future • What does it show?

– projected migration of historical location-specific isotherm for 20yr future periods, e.g. 2020-2039

38

Seedling Deployment Tools: Background • Seedling sources tool: local growers can see where they could pull seedlings from for their site in the future • What does it mean?

– Over a 20-year stand, current temp of light blue band will migrate to Athens so may want to consider pulling from there

Seedling source for 20-year stand (light blue)

Seedling Deployment Tools: Background

• Ability to visualize risk

– Display temperature range (5˚) around any isotherm

Why + 5˚? Schmidling says good balance: ↑ growth & ↓ negative effects from less cold tolerance

Seedling source for 20-year stand (light blue -1˚ to +5˚) 41

Some Initial Take Home Messages From the Crystal Ball â&#x20AC;˘ Where CO2 and temperature rises and water balance remains favorable, there will likely be opportunities for increased productivity, especially in combination with careful nutrient management â&#x20AC;˘ There may be opportunities for moving genetic material further north in the medium term

A few more thoughts: The bigger picture

Mitigating disturbance risks

Southern Pine Beetle East Texas

2007 Georgia Wildfires GA Forestry Commission

We grow trees on marginal agricultural land

We grow trees on marginal agricultural land “The concentration of grain production in the Midwest and dependence on agricultural production in the arid West leaves the country’s food and fiber production vulnerable to climate change.

Solution we propose is to migrate agriculture back to the East and Southeast under an irrigation-assisted rain-fed system for a more distributed production.”

Dr. Richard McNider (and others), University of Alabama, Huntsville

Regional impacts of increased productivity and demand

Regional impacts of increased productivity and demand

Additional take-home messages â&#x20AC;˘ We need to pay attention to the policy environment to protect forestland and avoid unintended consequences â&#x20AC;˘ Disturbance risk will remain an important management consideration â&#x20AC;˘ Adaptive management of stands for resilience remains one of the best responses to future uncertainty

After PINEMAP • 30-year outcome goals, 5year project • Data and tools • Outreach and educational resources • Network coordination • Southeast Regional Climate Hub will be central to our efforts • Cooperative advisory board consisting of coop directors will advise SERCH on outreach needs of corporate community

Acknowledgements • The Pine Integrated Network: Education, Mitigation, and Adaptation project (PINEMAP) is a Coordinated Agricultural Project funded by the USDA National Institute of Food and Agriculture, Award #2011-6800230185 • Department of Defense Strategic Environmental Research and Development Program • UF/IFAS Carbon Resources Science Center • Forest Biology Research Cooperative • Florida Forest Service