Using Process-Based Modeling to Inform SRWC Species Selection and Management in the Southeastern USA

William Headlee1

Richard Hall1

Ronald Zalesny, Jr.2

Matthew Langholtz3

1Iowa State University Department of Natural Resource Ecology & Management, Ames, IA2USFS Northern Research Station Institute for Applied Ecosystem Studies, Rhinelander, WI3DOE Oak Ridge National Laboratory, Oak Ridge, TN

•Project overview▫Goals and objectives▫Model description

•Current progress ▫Literature review and data compilation▫Model fitting▫Mapping productivity▫Economic analyses

Modeling Short-Rotation Woody Crops

• Southeastern USA has suitable climate for a variety of SRWCs ▫ Which woody crops are best-suited for different combinations of

climate and soil conditions? • Process-based models allow for comparisons of crops, in lieu

of extensive side-by-side testing ▫ 3-PG model already calibrated for three candidate SRWCs (poplars,

loblolly pine, and eucalypts)

Poplars (photo R. Zalesny)

Loblolly pine (photo W. Ciesla)

Eucalypts(photo D. Haugen)

Goals & Objectives

• Primary objective: Within range of overlap, model productivity and compare economics for each species on marginal lands.

• Additional objective: Develop methods to account for differences in silvicultural practices (site prep, weed control, fertilization, etc). Geographic ranges for (A) poplars, (B) loblolly pine, and (C)

eucalypts. Adapted from USDA Plant Hardiness Zone map.

Goals & Objectives (continued)

• “Physiological Principles Predicting Growth” model (Landsberg & Waring 1997) accounts for key drivers of growth

▫ Available resources (sunlight, water, nutrients)

▫ Tree physiology (resources → biomass)

• Uses site-specific inputs for climate and soils to estimate available pools of key resources

▫ Sunlight (solar radiation)

▫ Water (precipitation, temperature, soil water holding capacity, water table depth, etc.)

▫ Nutrients (site fertility)

Overview of 3-PG ModelSunlight

Water Nutrients

• Species-specific physiological parameters determine the amount of photosynthate produced from available resources, and its allocation to tree components

▫ Quantum canopy efficiency

▫ Respiration

▫ Leaf litterfall rate

▫ Root turnover rate

▫ Biomass partitioning (stem, branches, foliage, bark, roots)

▫ The list goes on… 60 parameters in all!

Turnover

CO 2

Leaf

fall

Overview of 3-PG (continued)

Literature Review & Data Compilation

• Identified 57 previously-published growth and yield studies for crops of interest in MS, AL, FL, GA, and SC

• Contain 441 unique combinations of soils, management, planting density, etc. (stands)

Model Fitting• Use previously-published data to validate

existing base calibrations for poplars (Headlee et al 2013), loblolly pine (Bryars et al 2013), and eucalypts (Sands 2004)

• Accounting for differences in silvicultural practices

▫ Planting density – direct input

▫ Irrigation – roll into precipitation

▫ All others – quantify responses with linear regression, roll into fertility rating

Sunlight

Water Nutrients

Model Fitting (continued)

20% change in slope

20% change in intercept 20% change in intercept and slope

• Silviculture regression model asssumptions

▫ Practices can influence slope and/or intercept of cumulative growth curve

▫ Increasing application rates associated with diminishing rates of return

Cum

ulati

ve G

row

th

Time

Model Fitting (continued)• Regression models based on typical cumulative growth function (Avery and

Burkhart 2002) where “G” is cumulative growth, “a” is the intercept (growth ceiling), and “b” is the slope (sigmoid curve):

• To predict impacts of treated stands (subscript T) relative to untreated controls (subscript C):

• Log-transform and separate ∆a and ∆b into components for fertilizer (F), weed control (W), site preparation (S), pest control (P), and residuals (R):

• Silviculture linear regression – results▫ Responses varied by SRWC and growth parameter ▫ Regression model fit (r2) ranged from 0.57 to 0.88

Model Fitting (continued)

Crop PracticeHeight Regression DBH Regression

∆ Intercept ∆ Slope ∆ Intercept ∆ Slope Eucalypts N ns <0.0001 <0.0001 ns

P ns <0.0001 ns nsPoplars N <0.0001 0.0091 <0.0001 ns

P ns ns ns nsK ns ns ns ns

Site Prep. ns <0.0001 ns <0.0001Loblolly N ns ns ns ns

P <0.0001 ns ns <0.0001K ns ns ns ns

Site Prep. ns ns ns ns Weed Ctrl. <0.0001 0.0037 ns <0.0001Pest Ctrl. ns ns ns ns

• Silviculture linear regression – examples▫ Eucalypt response to fertilization (Rockwood et al

2008)▫ Poplar response to site preparation (Francis 1982,

Baker and Blackmon 1978) ▫ Loblolly pine response to fertilization (Samuelson

et al 2008 & 2004)

Model Fitting (continued)

2.0 2.5 3.0 3.5 4.00

2

4

6

8

10

Predicted - FertilizedObserved - FertilizedObserved - Control

Age (years)

Euca

lypt

DBH

(cm

)

0 5 10 150

10

20

30

Predicted - FertilizedObserved - FertilizedObserved - Control

Age (years) Lo

blol

ly P

ine

DBH

(cm

)0 5 10 15

0

10

20

30

Predicted - Site PrepObserved - Site PrepObserved - Control

Age (years)

Popl

ar D

BH (c

m)

Model Fitting (continued)

• Next step: validate previous model calibrations using growth data compiled from the literature

▫ Fertility ratings estimated from regression models

▫ Climate data (temperature, precipitation, etc.) from NOAA National Climatic Data Center

▫ Soils data (water holding capacity, depth to water table, etc.) from NRCS soils database (SSURGO)

• Similar to previous poplar modeling, will focus efforts on suitable lands

• “Marginal lands” as defined by NRCS Land Capability Classes (LCC) II-IV

▫ Moorhead & Dangerfield (1998) found >95% of ag lands converted to woody crops in Georgia were LCC II-IV

Mapping Productivity

LCC Definitions (USDA-NRCS 2013) I Soils have slight limitations that restrict their useII-IV Moderate to very severe limitations restrict crop choices, require special conservation

practices, or bothV-VIII Unsuited to cultivation; limitations preclude commercial plant production

Source: Zalesny et al 2012

Economic Analyses

•Model’s productivity estimates will be used to compare:

▫Mean annual increments (MAI) for each SRWC under different combinations of climate and soils

▫Optimum rotation ages (maximum MAI)

▫ Land expectation values (LEVs)

▫Most economically feasible SRWC (maximum LEV)

• Literature Review▫ Identified 57 studies containing sufficient

data to model 441 unique combinations of site conditions and silvicultural practices

• Model Fitting ▫ Used linear regression to quantify SRWC

responses to silvicultural practices ▫ Validating model with published yield data

using fertility rating estimates, NOAA climate data, and NRCS soils data

• Mapping Productivity▫ Will focus on marginal lands (NRCS Land

Capability Classes II-IV)

• Economic Analyses ▫ Will compare crop MAIs, optimum rotation

ages, and LEVs

Summary

Avery TA, Burkhart HE (2002) Growth and yield models. In: Forest Measurements, 5th ed. McGraw-Hill, New York, pp 352-385

Baker JB, Blackmon BH (1978) Summer fallowing – a simple technique for improving old-field sites for cottonwood. Forest Service Research Paper SO-142. 5 p.

Bryars C, Maier C, Zhao D, Kane M, Borders B, Will R, Teskey R (2013) Fixed physiological parameters in the 3-PG model produced accurate estimates of loblolly pine growth on sites in different geographic regions. Forest Ecology and Management 289: 501-514

Francis JK (1982) Fallowing for cottonwood plantations: benefits carry to rotation’s end. In: Proc. North American Poplar Council 19th Annual Mtg. p 1-7.

Headlee WL, Zalesny RS, Donner DM, Hall RB (2013) Using a process-based model (3-PG) to predict and map hybrid poplar biomass productivity in Minnesota and Wisconsin, USA. BioEnergy Research 6: 196-210

Landsberg JJ, Waring RH (1997) A generalised model of forest productivity using simplified concepts of radiation-use efficiency, carbon balance and partitioning. Forest Ecology and Management 95: 209-228

Rockwood DL, Carter DR, Stricker JA (2008) Commercial tree crops for phosphate mined lands. University of Florida Publication 03-141-225. 86 p.

Samuelson LJ, Butnor J, Maier C, Stokes TA, Johnsen K, Kane M (2008) Growth and physiology of loblolly pine in response to long-term resource management: defining growth potential in the southern United States. Canadian Journal of Forest Research 38: 721-732.

Samuelson LJ, Johnsen K, Stokes T (2004) Production, allocation, and stemwood growth efficiency of Pinus taeda stands in response to 6 years of intensive management. Forest Ecology and Management 192: 59-70.

Sands PJ (2004) Adaptation of 3 PG to novel species: guidelines for data collection and parameter assignment. Technical Report 141, CRC for Sustainable Production Forestry, Hobart, Australia.

Zalesny RS, Donner DM, Coyle DR, Headlee WL (2012) An approach for siting poplar energy production systems to increase productivity and associated ecosystem services. Forest Ecology and Management 284: 45-58

References

• Funding and other support provided by: ▫ USDA-NIFA Agriculture and Food Research Initiative (CRIS 2013-03276)▫ USFS-NRS Institute for Applied Ecosystem Studies (IAES)

• Technical support ▫ Sue Lietz (IAES)

• Literature review assistance/suggestions▫ Dave Coyle ▫ John Stanturf ▫ Lynne Wright

• Questions?

Thank you for your time!