United States

Department of

Agriculture

Forest Service

Southern

Research Station

General Technical

Report SRS–72

Production of Short-Rotation

Woody Crops Grown with a

Range of Nutrient and Water

Availability: Establishment

Report and First-Year

Responses

M.D. Coleman, D.R. Coyle, J. Blake,K. Britton, M.

Buford, R.G. Campbell, J. Cox, B. Cregg, D. Daniels,

M. Jacobson, K. Johnsen, T. McDonald, K. McLeod, E.

Nelson, D. Robison, R. Rummer, F. Sanchez, J.

Stanturf, B. Stokes, C. Trettin, J. Tuskan, L. Wright,

and S. Wullschleger
Jennifer D. Knoepp, Research Soil Scientist, and

James M. Vose, Project Leader, U.S. Department of

Agriculture, Forest Service, Southern Research Station,

Coweeta Hydrologic Laboratory, 3160 Coweeta Lab Road,

Otto, NC 28763.

The Authors:

M.D. Coleman and D.R. Coyle, Biological Scientist and Biological Science Technician, USDA Forest

Service, Southern Research Station, New Ellenton, SC; J. Blake, Assistant Manager, Research, USDA

Forest Service, Savannah River, New Ellenton, SC; K. Britton, Project Leader, USDA Forest Service,

Southern Research Station, Athens, GA; M. Buford, National Program Leader for Quantitative Analysis,

USDA Forest Service, Vegetation Management and Protection Research, Washington, DC; R.G. Campbell,

Team Leader, Southern Productivity Research, Weyerhaeuser Corporation, New Bern, NC; J. Cox, College

Forest Manager, Department of Forestry, North Carolina State University, Raleigh, NC; B. Cregg, Assistant

Professor, Department of Horticulture and Department of Forestry, Michigan State University, East Lansing,

MI; D. Daniels, Professor of Forestry, D.B. Warnell School of Forest Resources, University of Georgia,

Athens, GA; M. Jacobson, Manager, Forest Productivity, Plum Creek Timber Company, Watkinsville, GA;

K. Johnsen, Project Leader, USDA Forest Service, Southern Research Station, Research Triangle Park, NC;

T. McDonald, Associate Professor, Biosystems Engineering Department, Auburn University, Auburn, AL;

K. McLeod, Associate Research Ecologist, Savannah River Ecology Laboratory, University of Georgia,

Aiken, SC; E. Nelson, Principal Scientist, Environmental Sciences and Technology Department, Savannah

River Technology Center, New Ellenton, SC; D. Robison, Associate Professor, Department of Forestry,

North Carolina State University, Raleigh, NC; R. Rummer, Project Leader, USDA Forest Service, Southern

Research Station, Auburn, AL; F. Sanchez, Team Leader for the Soils Productivity Unit, USDA Forest

Service, Southern Research Station, Research Triangle Park, NC; J. Stanturf, Project Leader, USDA Forest

Service, Southern Research Station, Athens, GA; B. Stokes, National Program Leader for Forest Operations

Research, Vegetation Management and Protection Research, Washington, DC; C. Trettin, Project Leader,

USDA Forest Service, Southern Research Station, Charleston, SC; J. Tuskan, Senior Scientist, Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN; L. Wright, Bioenergy Systems

Group Leader, National Bioenergy Center, Oak Ridge National Laboratory, Oak Ridge, TN; and S.

Wullschleger, Senior Staff Scientist, Environmental Sciences Division, Oak Ridge National Laboratory, Oak

Ridge, TN.

January 2004

Southern Research Station

P.O. Box 2680

Asheville, NC 28802

The use of trade or firm names in this publication is for reader information and does not imply endorsement

of any product or service by the U.S. Department of Agriculture or other organizations represented here.

PESTICIDE PRECAUTIONARY STATEMENT

This publication reports research involving pesticides. It does not contain recommendations

for their use, nor does it imply that the uses discussed here have been registered. All uses of pesticides must

be registered by appropriate State and/or Federal agencies before they can be recommended.

CAUTION: Pesticides can be injurious to humans, domestic animals, desirable plants, and

fish or other wildlife if they are not handled or applied properly. Use all herbicides selectively and carefully.

Follow recommended practices for the disposal of surplus pesticides and their containers.

Cover Photo: Aerial photograph of Short Rotation Woody Crop Project at the Savannah River

Site, near Aiken. SC. Individual plots are evident within each of the five large blocks. Surrounding longleaf

and loblolly pine plantations represent the cover types removed prior to installing the multi-species research

experiment testing forest growth response to a range of nutrient and soil moisture treatments.
1

Production of Short-Rotation Woody Crops Grown

with a Range of Nutrient and Water Availability:

Establishment Report and First-Year Responses

M.D. Coleman, D.R. Coyle, J. Blake, K. Britton, M. Buford, R.G. Campbell, J. Cox,

B. Cregg, D. Daniels, M. Jacobson, K. Johnsen, T. McDonald, K. McLeod, E. Nelson,

D. Robison, R. Rummer, F. Sanchez, J. Stanturf, B. Stokes, C. Trettin, J. Tuskan,

L. Wright, and S. Wullschleger

Abstract

Many researchers have studied the productivity potential of

intensively managed forest plantations. However, we need to learn more

about the effects of fundamental growth processes on forest productivity;

especially the influence of aboveground and belowground resource

acquisition and allocation. This report presents installation, establishment,

and first-year results of four tree species (two cottonwood clones,

sycamore, sweetgum, and loblolly pine) grown with fertilizer and

irrigation treatments. At this early stage of development, irrigation and

fertilization were additive only in cottonwood clone ST66 and sweetgum.

Leaf area development was directly related to stem growth, but root

production was not always consistent with shoot responses, suggesting

that allocation of resources varies among treatments. We will evaluate

the consequences of these early responses on resource availability in

subsequent growing seasons. This information will be used to:

(1) optimize fiber and bioenergy production; (2) understand carbon

sequestration; and (3) develop innovative applications such as

phytoremediation; municipal, industrial, and agricultural wastes

management; and protection of soil, air, and water resources.

Keywords: Allocation, fertigation, fine-root growth, intensive

management, interspecific comparisons, leaf area.

Introduction

Intensive management practices now lead to large productivity gains in forest plantations. Practices used to grow

short-rotation woody crops include selection of superior

genetic material, intensive site preparation, competition

and pest control, irrigation, and fertilization (Allen and

others 1990, Borders and Bailey 2001, Ceulemans and

others 1992, Dickmann and Stuart 1983, Heilman and

Stettler 1986, Linder and others 1987, Stanturf and others

2001, Steinbeck and Skinner 1985, Tuskan 1998, Yin and

Sedjo 2001). When using intensive culture techniques for

such large productivity gains, process-level understanding

is essential for making informed plantation management

decisions. Ecophysiological research has provided an

understanding of the relationship between stem growth

and such processes as light interception, uptake of water

and nutrients, and carbon metabolism (Cannell 1989,

Landsberg and Gower 1997); but there is still much to

learn. For instance, few complete carbon and nutrient

budgets exist that include both aboveground and belowground components for any forest type (Gower and others

1992, Vogt 1991). Without this information it is difficult to

assess rudimentary questions of nutrient requirements,

carbon storage, or carbon and nutrient acquisition. There

also is poor understanding of belowground processes,

allocation of carbon and nutrients within the plant, and

acquisition of available nutrients from the soil, despite the

clear evidence that both light interception and nitrogen

availability are positively correlated with productivity

(Cannell and Dewar 1994, Cannell and others 1988, Vose

and Allen 1988). Information on resource allocation, root

production, and nutrient acquisition is critical to

understanding nutritional controls over productivity, as

well as the impact of nutrition on essential root-mediated

processes such as carbon assimilation and transpiration.

We installed a set of intensively managed stands to clarify

carbon and nutrient mass balance and to define critical

processes controlling tree-growth response to resource

availability. We established a range of nutrient and water

availability levels resulting in different levels of productivity

in five tree genotypes [two eastern cottonwood (Populus

deltoides Bartr.) clones (ST66 and S7C15), sycamore

(Platatus occidentalis L.), sweetgum (Liquidambar

styraciflua L.), and loblolly pine (Pinus taeda L.)]. These

productivity levels also relate to resource acquisition and

allocation of carbon and nitrogen both aboveground and

belowground.

Throughout the rotation we will measure aboveground and

belowground tree growth, nutrient accumulation, resource

allocation, water use, and cycling of carbon and essential

mineral nutrients in the plant and soil system. Hypothetical

plant and environmental controls over productivity will be

tested with growth process models. In summarizing the

establishment of this long-term study, this report presents

site conditions, treatments, and initial growth responses.

As the study continues, we will report on growth, carbon

and nutrient allocation, canopy development, leaf-level

physiology, whole-tree transpiration, and fine-root

production and turnover.
2

Materials and Methods

Site Description

The experimental plot is located on the U.S. Department of

Energy’s Savannah River Site, a national environmental

research park near Aiken, SC, in the Carolina sandhills

physiographic region (33°23’ N.; 81°40’ E.) (fig. 1). The

climate is humid continental with warm summers, mild

winters, and an average annual temperature of 17.9 °C.

Average minimum temperature in January is 1.4 °C;

average maximum temperature in July is 32.8 °C.

Growing season potential evaporation rates average 0.52

cm/day (U.S. Department of Commerce, National

Oceanographic and Atmospheric Administration 1993,

1997).

A soil survey conducted in December 1998 identified five

different soil series in two soil orders on the 40-ha site

(fig. 2). The predominate soil is of the Blanton soil series,

which developed in siliceous sand originating from

Tertiary period beach deposits, and is classified as thermic

Grossarenic Paleudults. An argillic subsoil occurs between

120 and 200 cm across the site, but the 105-cm soil depth

monitored for the described experiment is above this

subsoil across the entire study site.

Previous vegetation was pine plantations with a sparse

upland oak (Quercus spp.) understory. The eastern portion

of the site contained pulp-quality loblolly pine planted in

1988, while the western portion contained pole-timber

quality longleaf pine (P. palustris Mill.) planted in 1964.

Stands were harvested in April through May of 1999.

Figure 1—Location of the short-rotation woody crops study site operated by the U.S. Department of Agriculture

Forest Service at the Savannah River Site near Aiken, SC (33°23’ N. 81°40’ E.).

Southeastern

United States

Short-rotation woody

crops study site

m

Savannah

River Site

•

1000 0 1000 2000
3

Site Preparation

Site preparation consisted of debris removal, tilling, and

site amendments. We piled the slash measuring > 15 cm in

diameter and the debarking debris that remained following

the harvesting of research plots. An RS-500 Reclaimer/

Stabilizer (CMI Corp., Oklahoma City) (fig. 3) prepared

the 21.9-ha area, pulverizing and incorporating the

remaining debris, including stumps, to a depth of 30 cm.

We applied dolomite lime at a rate of 3.4 Mg/ha to

achieve a target soil pH of 6.5. Finally, we pulled a Rome

disc in two directions and planted winter rye for erosion

control.

Field Layout and Experimental Design

Treatment plots were 0.22 ha (fig. 4). Tree spacing was 2.5

by 3 m for a planting density of 1,333 trees/ha so that 294

trees made up each treatment plot. Each treatment plot

contained a central 0.04-ha measurement plot containing

54 trees, and large end borders served as harvest plots for

destructive sampling. The border between the edge of the

Figure 2—Soil survey of the short-rotation woody crops study site (Personal Communication. 1998. Dennis DeFrancesco, Soil Scientist,

USDA Natural Resources Conservation Service, 301 University Ridge, Suite 4900, Greenville, SC 29601). Ultisols including Blanton, Fuquay,

and Troup series occur within the study area. Entisols of the Lakeland and Foxworth series also occur. Blanton soils with subsoil depth of 102

to 152 cm were separated from those having depths from 150 to 203 cm. Refer to http://www.ortho.ftw.nrcs.usda.gov/osd/dat/B/

BLANTON.html for a complete description of Blanton soils. The Foxworth series included in the study area is similar to Blanton except that

it is sandy throughout the 203-cm profile.

	
  



 	



 









 





















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treatment plot and the measurement plot was >12 m,

minimizing interaction among nutrient treatment plots.

The six trees at the center of each plot were used for

repeated growth measurements.

The experiment tested discrete treatment differences using

analysis-of-variance (ANOVA) and determined response

surfaces by regression. This robust composite method

accommodates tradeoffs among experimental designs and

operational constraints. The core experiment was a threeway, complete factorial, split-plot design including

fertilization, irrigation, and five tree genotypes (fig. 5). We

split genotype whole plots into four subplots, which

included fertilization and irrigation treatments. We

randomly assigned genotypes to whole plots and

treatments to subplots. This experiment tested main effects

and interactions among species, irrigation, and

fertilization. However, the core factorial ANOVA

experiment could not define the fertilizer response

function that was required for modeling. We defined the

fertilizer response function by including nonreplicated

intermediate fertilizer levels for the primary irrigation

treatment. Increasing the number of fertilizer levels in the

replicated ANOVA experiment would have required

greater resources and placed undue emphasis on lessdesired treatment combinations as nonirrigated

cottonwood or irrigated pine. The regression approach

tests important treatment combinations on nonreplicated

plots, and efficiently defines response surfaces in

agricultural experiments (Anderson and McLean 1974,

Box and Draper 1987, Gomez and Gomez 1984). The

regression approach used less than half the plot count

required for the full factorial design, but more precisely

defined the response surface.

Tree Stock and Planting Information

We tested five tree genotypes in this experiment: two

eastern cottonwood clones (ST66, Issaquena County, MS,

and S7C15, Brazos County, TX); sycamore (Westvaco

orchard run); sweetgum (Westvaco family WV340); and

loblolly pine (International Paper family 7-56). Two

cottonwood clones gave a broader genetic representation

of the species than would have been possible with a single

clone. Other species represented by a single half-sibling

family had more genetic diversity than cottonwood. The

cottonwood cuttings came from Crown Vantage (Fitler,

MS), the pine seedlings from International Paper

Figure 3—An RS-500 Reclaimer/Stabilizer (CMI Corp., Oklahoma City) prepared the site following shearing and

raking. Remaining litter, vegetation, and stumps were ground and incorporated into the top 30 cm of soil in one pass.

Cost was comparable to stump removal.
5

Figure 4—Individual treatment plot having 14 by 21 tree rows (294 trees). A central measurement and two destructive harvest plots were included in

each plot. Periodic height, diameter, and leaf area measurements were taken on six central trees.

(Lumberton, NC), and the sycamore and sweetgum

seedlings from Westvaco (Summerville, SC).

Nursery personnel lifted bare-root loblolly pine, sycamore,

and sweetgum seedlings (1-0) in mid-January 2000 and we

planted them during the first week of February 2000.

Nursery personnel collected cottonwood cuttings (1- to

2-cm diameter by 40-cm long) from stool beds in early

January 2000. We held them at 3.3 °C, and soaked them

during the second week of April 2000 for at least 48 hours

prior to planting using round metal dibbles (2 by 45 cm).

Irrigation Treatment

Plots received either an irrigated or nonirrigated treatment

using an automated drip irrigation system (B.B. Hobbs,

Darlington, SC). The irrigated treatments (1 and 2, 5

through 10) (fig. 5) received 0.5 cm/day, 6 days a week (3

cm/week) from April to October in 2000, regardless of

rainfall. Average regional daily evaporation for the period

between April and October is 0.5 cm/day; therefore, the

irrigation amount was designed to eliminate the

evaporation deficit and ensure favorable soil moisture. In

addition to precipitation, nonirrigated treatments (3 and 4,

11 through 16) (fig. 5) received only enough water to

Harvest plots

30 m

(98.4 feet)

8 m

(59.0 feet)

52.5 m (172.2 feet)

15 m (49.2 feet) 22.5 m (73.8 feet) 5 m

(16.4 feet) 2.5 m

(8.2 feet)

12 m (39.4 feet)

42 m (137.8 feet)

3 m (9.8 feet)

N
6

apply liquid fertilizer and flush lines in the most distant

plot (0.5 cm/week).

Fertilizer Treatment

We applied fertilizer weekly to both irrigated and

nonirrigated treatments through the automated drip

irrigation system. The concentrated liquid fertilizer used

was 7-7-7 nitrogen-phosphorus-potassium (N-P-K) plus

0.22 percent boron (B), 0.01 percent copper (Cu), 0.05

percent manganese (Mn), 0.001 percent molybdenum (Mo),

and 0.03 percent zinc (Zn). Starting in April we divided the

annual amount into 26 equal doses applied weekly. Site

preparation lime applications met calcium (Ca) and

magnesium (Mg) requirements.

We included two fertilizer levels in the replicated ANOVA

experiment: a nonfertilized control and 120 kg N/ha/year.

The NUTREM model (Anonymous 1997) was used to

consider nutrient uptake requirements for loblolly pine. We

supplied the model with data from an 11-year-old stand

producing 13 Mg/ha/year of stem dry weight (Albaugh

and others 1998). The “soil resource uptake” value output

from the model was 118 kg N/ha/year. Four-year-old hybrid

Figure 5—Treatments at the short-rotation woody crops study site. The 3 central analysis-of-variance blocks and the 2 peripheral regression blocks

contain a total of 16 treatments of 5 tree genotypes. Treatments 1, 2, 3, and 4 were replicated in the central blocks for all genotypes. Block 1 includes

irrigated treatments for cottonwood and sycamore, and block 5 includes nonirrigated treatments for pine and sycamore. A range of nonreplicated

fertilizer levels was applied in both blocks 1 and 5.

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poplars producing 14 Mg/ha/year contained 116 kg N/ha/

year aboveground (Heilman and Stettler 1986, Nelson and

others 1987). Clones producing over twice this amount of

wood will contain over 250 kg N/ha/year aboveground

(Heilman and Stettler 1986). In other fertilized forests, rates

of N uptake exceed 400 kg N/ha/year (Johnson 1992).

Mineralization is expected to supply 20 to 80 kg N/ha/year

(Pastor and others 1984, Reich and others 1997); therefore,

the 120-kg-N/ha/year application rate we used exceeded

the amount of N required for pine, but was low for fastgrowing hardwoods. Applying a relatively high rate for

pine and relatively low rate for hardwoods was justified

because we applied a common fertilizer treatment level to

all genotypes. In the ANOVA experiment an optimal

fertilizer rate was not critical because the regression

experiment was designed to identify the optimal nutrient

amendment rate.

For the primary irrigation treatment, we applied six

additional fertilizer levels to nonreplicated plots for each

species; i.e., irrigated cottonwood and sycamore,

nonirrigated sweetgum and pine (fig. 5). Cottonwood and

sycamore received three levels below and three levels

above 120 kg N/ha/year ANOVA level. Loblolly pine and

sweetgum received five levels below and one above 120 kg

N/ha/year. The highest fertilizer levels were expected to

exceed optimum nutrition rates. To help define the

regression curve for the secondary irrigation treatment, we

included a single plot at 60 kg N/ha/year for each species.

To ensure nutritional balance, we maintained other

nutrients in proportion with N as amendment rates varied.

During the 2000 growing season, fertilizer rates were onethird of the target rate based on nutrient requirement. The

proportion of the final rate will increase during the first 3

to 5 establishment years because demand is less during

establishment. For cottonwood and sycamore, we applied

one-third the designated level in the first year; i.e., 40 kg

N/ha/year for ANOVA plots, we will apply two-thirds in

the second year and the full treatment rate thereafter. For

loblolly pine and sweetgum, we will apply one-third of the

treatment rate the first 2 years, two-thirds will be applied

the third and fourth years, and the full treatment

subsequently. The increase will approximate the initial

low nutrient demand of young plantations and the

maximum demand following establishment.

Pest and Weed Control

Pest and weed control were applied as needed. Weed control

treatments included a glyphosate (Roundup® Pro, Monsanto

Corp., St. Louis, MO) application to cottonwood plots

2 weeks prior to planting and an oxyflourfen (Goal® 2XL,

Rohm and Haas Co., Philadelphia) application to bare-root

species within 1 month postplanting. Oxyflourfen was

applied to cottonwoods 5 to 8 days postplanting. We

directed glyphosate applications in rows between irrigation

lines during the last week of June and the second week of

September. We tilled weeds between irrigation rows during

the first week of August. We applied Hydramethylnon

(Amdro®, American Cyanamid Co, Parsippany, NY) and

dicofol (Kelthane® 50, Rohm and Haas Co., Philadelphia)

to cottonwood plots for fire ant and leaf mite control,

respectively; we applied permethrin (Ambush®, Zeneca Ag

Products, Wilmington, DE) to loblolly pine plots for tip

moth control. Pest monitoring determined the need for

insecticide applications.

Minirhizotrons

In March and April 2000, we installed 5-cm-diameter

acrylic minirhizotrons at a 45° angle to a depth of 1.05 m

to measure fine-root growth. We placed five minirhizotron

tubes per plot at one of five predetermined locations around

five trees (fig. 6) in each loblolly pine and cottonwood

ST66 plot within ANOVA blocks (treatments 1 to 4). One

of the five locations was assigned to a randomly selected

tree within the measurement plot. We painted the exposed

minirhizotron ends black to prevent light from reaching

roots, and then white to avoid heat buildup. We secured

tubes in place using 5-cm pipe hangers bolted to a 1.3-cm

metal conduit driven 90 cm into the ground. We constructed

tube caps by gluing a 35-cm long piece of pipe insulation

(designed for 1.9-cm copper pipe) inside the bottom of a

355-ml aluminum beverage can (with top removed). Pipe

insulation extended belowground inside the tube to prevent

light penetration and minimize heat transfer from the

surface to the roots. The aluminum can protected the tube

end from moisture.

We used a video camera (BTC2; Bartz Technology, Santa

Barbara, CA) equipped with an indexing handle to collect

observations. One-hundred-four frames (180.96 mm2

)

were monitored per tube for a total of 188.2 cm2/

minirhizotron. This monitored area, 75 percent of captured

image area (13.5 by 18 mm), allows image rectification.

We captured digital video images in the field (ICAP Bartz

Technology, Santa Barbara, CA) and quantified them

using Rootracker (Duke University, Durham, NC). We

measured new roots appearing on the outside upper

surface of the acrylic tube. Because we tried to eliminate

competing vegetation, we assumed all observed roots to be

from plantation trees.
8

Environmental Monitoring

A weather station (Campbell Scientific, Inc., Logan, UT)

installed in a cleared area (50 m west of block 1) recorded

hourly temperature, solar radiation, soil moisture, soil

temperature, relative humidity, rainfall, and wind speed

and direction. We calculated potential evapotranspiration

with hourly weather data (Van Bavel 1966). Additional

data loggers recorded soil temperature measurements

hourly (Onset Computer Corp., Pocasset, MA) at eight

locations and four depths per location (0, 15, 45, and 105

cm). We also installed rain gauges (Tru-Chek, Albert Lea,

MN) at 12 locations across the site and recorded data after

each rain event to evaluate distribution.

We measured soil moisture weekly at a 15-cm depth in

each plot using a TRIME-FM Mobile Moisture Meter

(IMKO, Ettlingen, Germany). Soil moisture was measured

under drip irrigation lines and between drip lines in each

plot.

Soil Samples

In April 1999 before harvesting preexisting vegetation, we

collected soil samples at 40 locations on a 100- by 100-m

grid for 0- to 15-, 15- to 45-, and 45- to 105-cm depths. In

April 2000 following planting, we sampled soil from each

plot at the same depths. The Clemson University

Agricultural Extension Soil and Plant Analysis Laboratory

conducted soil analyses. They acid-extracted samples (0.05

N HCl and 0.025 N H2SO4) and analyzed for P, K, Ca, Mg,

Zn, Mn, Cu, B, and sodium (inductively coupled plasma

atomic emmission spectrometer). They determined organic

matter from loss on ignition and measured soil pH and

buffer pH (8.0) for evaluating cation exchange capacity,

base saturation, and lime requirements (Sims 1996). They

also determined soil texture on samples collected in April

1999.

Growth Measurements

We recorded height and root-collar diameter monthly on

the six central trees per plot. Where multiple shoots or

trees were present, we measured the tallest.

Mortality

We quantified cottonwood and loblolly pine mortality on

May 25, 2000. We classified dead cottonwoods as

nonrooters (having a dead shoot over 2.5 cm in length),

herbicide damaged (dead bud or shoot under 2.5 cm in

length), or previously dead (no shoot growth). Pine

mortality was classified as pine weevil (Pales and

Hylobius spp., Coleoptera: Curculionidae) feeding (dead

tree; girdling present) or previously dead because of stock

quality or planting (dead tree; no weevil girdling). We

observed no mortality in sweetgum or sycamore.

Figure 6—Five minirhizotron locations (dark circles) adjacent to randomly selected trees in cottonwood ST66 and loblolly pine measurement plots.

Locations are selected to define expected variation in root production due to tree and drip-tube proximity. Locations 1 and 2 vs. 4 and 5 contrast the

drip-tube proximity effect. Locations 3 and 4 vs. 1, 2, and 5 contrast the tree proximity effects.

Trickle tubes

Tree

N

1 2

3

5 4

1.5 m

0.75 m

1.25 m

0.625 m
9

Leaf Area

We measured leaf area monthly in each plot from June to

October 2000. Due to rapid growth we modified the

number of trees measured at each sampling event. Leaf

area estimates for secondary stems were based on

allometry of the main stems. For cottonwoods, we

measured lengths of all fully expanded leaves >3 cm in

length (Leaf Plastochron Index = 0) (Larson and Isebrands

1971) on the six centermost trees per plot in July and one

tree per plot in August, September, and October. For

sycamore, we recorded the length of every leaf in June.

Later, we measured all sycamore and sweetgum branch

diameters and all leaf lengths for the terminal and three

representative branches. We measured six sycamore and

sweetgum trees per plot in June and July and one tree per

plot in August, September, and October. For loblolly pine

in June and July, we measured the diameter of all branches

and the terminal for six trees per plot. On three trees per

plot, we counted all fascicles on three representative

branches of known diameter and measured lengths of three

fascicles on each representative branch. We reduced

sampling to one tree per plot in August, September, and

October.

Coinciding with field measurement of leaf lengths we

destructively sampled leaves to determine leaf length-toprojected-area relationships and specific leaf area. In June

and July, we collected three representative hardwood

leaves and six pine fascicles per plot that encompassed the

entire range of leaf sizes. In August, September, and

October, 9 leaves (or 18 fascicles) per species were

collected in each plot. We measured leaf length, recorded

corresponding leaf area using a Delta-T area meter

(Decagon Devices, Inc., Pullman, WA), and recorded dry

weight after leaves had ovendried (65 °C) for 4 days.

We predicted representative branch-leaf area by summing

leaf areas calculated from leaf length-to-area relationships.

In turn, we predicted whole-tree leaf area by summing

branch areas calculated from branch diameter-to-branchleaf area estimates. The ratio of whole-tree leaf area to

basal diameter was used to determine leaf area index

(LAI) based on tree stocking and mortality.

Foliar Nutrient Analysis

In the first week of July 2000, we collected hardwood leaf

samples. One sunlit, recently matured leaf was collected

from each of the 54 measurement-plot trees. During midJanuary 2001, we collected pine leaf samples. Five

representative fascicles were collected from the last fully

expanded flush of each measurement tree. For each plot, we

combined ovendried (65 °C) ground samples to pass a 40-

mesh screen, and determined foliar N concentration by dry

combustion (Carlo-Erba model NA-1500).

Statistical Analysis

We analyzed data for the core experiment with a

randomized complete block design using a standard linear

model of the form: xij i ji = + + + μ αβ ε j

where

μ = represents the overall mean

αi = stands for the treatment effect (i = 1 to 4)

εij = for the block effect (j = 1 to 3) with εij being

uncorrelated random error and used as the denominator in

F-tests for treatment and block effects.

We separated treatment means with Tukey’s Studentized

Range Test (HSD) and orthogonal contrasts. All analyses

were conducted independently for each genotype. We

fitted fertilizer response surfaces to second-order

polynomials and compared regression equations for leaf

area as a function of diameter by testing for coincidence

and slope differences among treatments (Kleinbaum and

Kupper 1978). SAS was used for all analyses (SAS

Institute, Inc., Cary, NC).

Results and Discussion

Climate Data

Weather data for the 2000 growing season showed warmer

and drier conditions than normal (table 1). However,

compared with average pan evaporation values, Penman

calculations of potential evaporation for 2000 did not

reflect drier, warmer conditions. Nonetheless, Penman

calculations directly relate to evaporative demand and,

therefore, can estimate evapotranspiration for irrigation

scheduling.

Soil Texture and Water-Holding Capacity

Soil texture and water-holding capacity appear in table 2.

The soil classification is sand from the surface to a 105-cm

depth of samples collected because, on average, it contains

> 90 percent sand. However, 10 out of 40 samples between

45 and 105 cm were classified as loamy sand due to

increased silt and clay content. To evaluate soil water

supply capacity for this site, we calculated water-holding
10

capacity based on textural properties (Saxton and others

1986). Available water between field capacity and

permanent wilting point was similar for each depth due to

consistent sand content.

Available water, expressed on a soil depth basis, can be

compared to evaporative demand to determine soil water

supply capacity. Peak potential evaporation for this site is

5 to 6 mm/day (table 1). The upper 45 cm of this coarse

textured soil will store only 31 mm of water, a 5- to 6-day

supply during peak evaporation. The lower portion of the

rooting volume (45 to 105 cm) can supply an additional 8

to 9 days. Prior to using all the soil moisture supply, trees

will begin to regulate water loss by closing stomata and

abscising leaves. Without water input during peak

evaporative demand, such water-loss regulation will affect

growth before available soil moisture is exhausted within

this 5- to 9-day period.

Table 1—Comparison of 30-year average weather data collected in Blackville, SC, with weather data collected at the

short-rotation woody crops study site in 2000

Weather data Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec.

30-year average

Avg. max. temp. (°C) 13.7 15.2 20.0 24.8 28.7 31.8 32.8 32.3 29.7 24.9 19.7 15.3

Avg. min. temp. (°C) 1.4 2.8 6.8 11.2 15.7 19.4 21.4 21.1 18.0 12.4 6.8 3.6

Rainfall (cm) 11 11 13 9 11 11 12 12 10 6 6 9

Avg. pan evaporation

(cm/day)a 0.17 0.21 0.36 0.49 0.59 0.65 0.66 0.53 0.46 0.29 0.19 0.15

Evaporation deficit -5.7 -5.1 -1.8 5.7 9.3 8.5 8.5 4.4 3.8 3.0 -0.3 -4.4

2000

Avg. max. temp. (°C) — b — — — 28.9 32.5 32.8 31.7 27.5 25.1 17.6 8.8

Avg. min. temp. (°C) — — — — 16.8 19.1 19.1 20.4 17.8 10.5 5.6 -1.5

Rainfall (cm) — — — 0.1 Trace 8.8 9.9 8.5 18.0 8.4 4.8 4.4

Avg. calculated

evaporation (cm/day) — — — — 0.63 0.58 0.57 0.48 0.38 0.39 0.20 0.12

Evaporation deficit — — — — 19.6 8.4 7.8 6.5 -6.6 3.8 1.3 -0.6

a

 Pan evaporation values from Blackwell, SC, are compared with potential evaporation calculated from weather data (Van Bavel 1966). b

 Data were not collected before cottonwood cuttings were planted in late April.

Source: U.S. Department of Commerce, National Oceanographic and Atmospheric Administration (1993, 1997).

Table 2—Soil texture and water-holding capacity for core experiment at the

short-rotation woody crops study site

Field Wilting

Depth Sand Silt Clay capacity point Available water

 cm - - - - - percent - - - - - - - - - - - - cm3/cm3 - - - - - - - mm/layer

0–15 92.9 4.7 2.4 0.11 0.04 0.068 10

15–45 92.0 5.5 2.5 0.11 0.04 0.070 21

45–105 90.0 5.5 4.5 0.13 0.06 0.074 45

Mean of 40 soil samples collected in April 1999.
11

Soil Nutrients

Prior to treatment applications, distinct depth gradients in

soil nutrient concentration were evident from soil samples

collected across the site. The upper layers of soil

contained the greatest amount of nutrients corresponding

with the highest levels of organic matter and cation

exchange capacity (CEC) (tables 3 and 4). Nutrient levels,

organic matter content, and CEC are naturally low in this

sandy soil type. Prior to amendments, soil pH was below

5.0 across the site and required lime to raise the pH to

acceptable levels for hardwoods (pH 6.5). According to

the Adams-Evans buffer method (Sims 1996), 3.36 Mg/ha

of lime was required to bring the pH to 6.5. Lime

applications improved soil pH especially in the upper

layers, but it was still 0.75 pH units below target levels,

demonstrating the need for additional lime. Lime

application and other site-preparation activities more than

Table 3—Soil nutrient content on the short-rotation woody crops study prior to and after lime application in March

2000

Organic

Depth matter Ca Mg P K Zn Mn Cu B Na

cm percent - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - kg/ha - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Prior to lime application

 0– 15 — 202 ± 41 27 ± 2 3.4 ± 2.8 24.3 ± 2.9 1.9 ± 0.9 14 ± 2 0.9 ± 0.3 0.1 ± 0.1 —

After lime application

 0– 15 1.40 ± 0.27 563 ± 242 176 ± 79 3.6 ± 4.5 19.1 ± 6.5 1.9 ± 0.9 20 ± 10 0.5 ± 0.3 0.3 ± 0.1 12 ± 2

15– 30 1.31 ± 0.29 322 ± 221 87 ± 83 3.3 ± 3.8 22.9 ± 7.3 1.4 ± 0.5 20 ± 10 0.5 ± 0.3 0.3 ± 0.1 11 ± 2

30– 45 0.88 ± 0.29 188 ± 104 46 ± 24 1.8 ± 2.2 18.4 ± 6.0 1.0 ± 0.7 14 ± 9 0.4 ± 0.2 0.2 ± 0.1 11 ± 3

45–105 0.59 ± 0.31 114 ± 61 28 ± 14 1.9 ± 1.8 12.8 ± 3.6 0.5 ± 0.2 5 ± 5 0.3 ± 0.4 0.2 ± 0.1 11 ± 3

Ca = Calcium; Mg = magnesium; P = phosphorus; K = potassium; Zn = zinc; Mn = manganese; Cu = copper; B = boron; Na = sodium; — = no data.

Mean ± sem, n = 95.

Table 4—Soil pH and base saturation levels on the short-rotation woody crops study prior to and after lime

application in March 2000

Base saturation

Depth Soil pH Buffer pH CEC Acidity Ca Mg K Na Total

cm - - - meq 100/g - - - - - - - - - - - - - - - - - - - - - percent - - - - - - - - - - - - - - - - - -

Prior to lime application

 0– 15 4.9 ± 0.1 7.71 ± 0.04 1.5 ± 0.2 0.9 ± 0.1 28.0 ± 3.9 6.4 ± 0.6 2.0 ± 0.0 1.6 ± 0.6 38.0 ± 3.8

After lime application

 0– 15 5.8 ± 0.3 7.72 ± 0.06 4.0 ± 0.8 2.2 ± 0.4 28.0 ± 8.0 14.6 ± 4.7 0.4 ± 0.5 0.6 ± 0.5 43.5 ± 11.5

15– 30 5.4 ± 0.4 7.70 ± 0.06 3.4 ± 0.8 2.4 ± 0.5 18.4 ± 8.2 8.2 ± 5.5 0.9 ± 0.3 0.8 ± 0.4 28.3 ± 12.9

30– 45 5.2 ± 0.3 7.77 ± 0.06 2.4 ± 0.6 1.8 ± 0.5 15.7 ± 5.8 6.5 ± 3.1 0.9 ± 0.3 1.0 ± 0.3 24.0 ± 8.2

45–105 5.0 ± 0.2 7.81 ± 0.05 1.9 ± 0.6 1.5 ± 0.4 12.2 ± 4.4 5.1 ± 2.0 0.9 ± 0.3 1.1 ± 0.4 19.4 ± 6.3

CEC = Cation exchange capacity; Ca = calcium; Mg = magnesium; K = potassium; Na = sodium.

Mean ± sem, n = 95.
12

doubled CEC, resulting in higher base saturation, mainly

occupied by exchangeable Mg. As the total base saturation

increased with the improved CEC, the proportion of

acidity decreased from 63.5 percent prior to liming to 55.3

percent afterward. Such results demonstrate the value of

liming for improving soil nutrient-holding capacity.

Soil Moisture

Percent soil moisture was consistently higher under drip lines

(fig. 7); between drip lines, soil moisture was consistently

low for all treatments and species. This occurred even in

nonirrigated treatments because fertilizer delivery required

water in fertilizer-only plots, necessitating equivalent

application to maintain control in nonfertilized, nonirrigated

plots. In this sandy soil, moisture applied through drip

irrigation does not wet soil between rows. Because irrigation

applications are calculated on a land-area basis, it is likely

that a significant portion of that irrigation water applied with

drip lines percolates through the soil under saturated

conditions and is not available for transpiration. Subsequent

root development may improve distribution of soil moisture

between rows on irrigated sites, but it is likely that

predominant root development will occur under drip lines.

Mortality

A variety of management and pest-related factors caused

the wide range of mortality among species. There was no

mortality for sycamore or sweetgum; in contrast, total

cottonwood mortality was nearly 26 percent (table 5)—

22.1 percent for ST66 and 29.5 percent for S7C15. Poor

stock accounted for 9.6 percent of ST66 mortality and

23.1 percent of S7C15 mortality. Nonrooting occurred in

5.7 percent of ST66 cuttings and 2.7 percent of S7C15

cuttings. Herbicide damage killed 6.8 percent of ST66

cuttings and 3.7 percent of S7C15 cuttings.

Total mortality on loblolly pine was 7.4 percent (table 5).

Stock quality and planting accounted for 2.9 percent of the

total loblolly pine mortality. A total of 204 trees had pine

weevil girdling damage, and 4.5 percent of these were dead

(table 5). The high pine weevil incidence was attributed to

slash remaining on the site following the recent harvest

(Nord and others 1982).

Figure 7—Mean soil moisture (+SE, n = 100) in response to irrigation

and fertilization. Measurements were collected in analysis-of-variance

plots from July through September 2000. Within-row measurements

represent those taken along the irrigation drip line, while between-row

measurements were taken between lines.

Table 5—Mortality during the 2000 growing season

Tree species or clonea

Cottonwood

Loblolly

Causal agent ST66 S7C15 Sycamore Sweetgum pine

Stock/establishment 99 237 0 0 30

Herbicide 70 38 NA NA NA

Nonrooters 58 28 NA NA NA

Weevil damage NA NA NA NA 46

Total 227 303 0 0 76

NA = No mortality was attributed to specified agent.

a A total of 1,027 trees were observed for each species.

25

20

15

10

5

0

Soil moisture (percent)

Within row

Between row

Control Fertilized Irrigated Fertilized +

irrigated

Treatment
13

Plantation Growth Monitoring

Root-collar diameter (fig. 8) and height (fig. 9) measurements

show consistent fertilizer and irrigation treatment responses.

All species showed highly significant fertilizer effects for

both diameter and height, and although irrigation effects

differed among species, height and diameter responses were

consistent within species.

Cottonwood ST66 exhibited positive growth response to

both irrigation and fertilization (figs. 8 and 9). In contrast,

cottonwood S7C15 did not respond to irrigation at this

stage of stand development. The response to irrigation for

these two clones is consistent with climatic factors to

which they are adapted. Cottonwood ST66 may have high

water demand because it originated from along the

Mississippi River Delta where the growing season

environment includes high humidity and mesic soil

moisture. Clone S7C15 may be more tolerant of low soil

moisture because it originated from east Texas where the

climate is considerably drier.

The early response of these clones may represent a

difference in water use efficiency. Poplar (Populus spp.)

genotypes have previously been distinguished on their

water use efficiency, stomatal control, and drought

tolerance (Ceulemans and others 1978; Gebre and Kuhns

1991; Pallardy and Kozlowski 1979; Schulte and others

1987; Tschaplinski and others 1994, 1998). This

experiment demonstrates that those physiological

responses result in important growth impacts. Greater

tolerance for the dry conditions at Savannah River may be

Figure 8—Root-collar diameter growth of the five tree genotypes (cottonwood ST66, cottonwood S7C15, sycamore, sweetgum, and loblolly pine) in

response to varying resource availability. Significance levels for irrigation (I) and fertilizer (F) main effects and their interaction (IxF) are shown.

Treatments within genotypes not sharing the same letter are significantly different [Tukey’s Studentized Range (HSD), α = 0.05]. Diameter (mm)

30

20

10

0

20

10

0

30

20

10

0

Cottonwood ST66

Cottonwood S7C15

Sycamore

Sweetgum

Loblolly Pine

30

20

10

0

9

6

3

0 May 24 June 22 July 20 Aug. 24 May 24 June 22 July 20 Aug. 24

Control

+ Fertilizer

+ Irrigation

+ Fertilizer + Irrigation

Source p

I < 0.001

F < 0.01

I x F NS

Source p

I NS

F < 0.001

I x F NS

Source p

I < 0.01

F < 0.001

I x F < 0.10

Source p

I < 0.01

F < 0.001

I x F < 0.01

Source p

I NS

F < 0.001

I x F NS

c

bc

ab

a

b

a

b

a

c

ab b a

c

b

c

a

ab

a

b

a

Diameter (mm)
14

an important reason clone S7C15 had larger diameters.

Although we do not expect the low demand for irrigation

water by S7C15 to continue as the stands develop and

occupy the site, we do expect continued water use

efficiency differences resulting in superior growth,

especially under nonirrigated conditions.

The only other species that did not respond to irrigation

was loblolly pine (figs. 8 and 9). A similar lack of

irrigation response occurred in other studies where

loblolly pine was irrigated (Albaugh and others 1998).

Exceptional growth for loblolly pine also occurs when

management practices include only competition control

and fertilization (Borders and Bailey 2001, Jokela and

Martin 2000), suggesting that irrigation is not the limiting

factor in the Southeastern United States. Optimal growth

of loblolly pine in this region may not require

supplemental water because of high water use efficiency.

However, there are cases where supplemental irrigation

does provide benefits (Samuelson and others 2001). As

pine stands fully occupy plots in this study, water

requirement will increase. Unless taproots locate a

subterranean water source, a significant irrigation effect

may develop. The B horizon acts as a confining layer

where subsurface flow occurs at this site (Dosskey and

Bertsch 1994). At a depth of 120 to 200 cm, this water

source may be available to loblolly pine.

The growth response of sycamore and sweetgum to

irrigation differed between fertilizer treatments; i.e. the

ANOVA shows a significant fertilizer-by-treatment

interaction (figs. 8 and 9). Both responded more to

Figure 9—Height growth of the five tree genotypes (cottonwood ST66, cottonwood S7C15, sycamore, sweetgum, and loblolly pine) in response to

varying resource availability. Significance levels for irrigation (I) and fertilizer (F) main effects and their interaction (IxF) are shown. Treatments

within genotypes not sharing the same letter are significantly different [Tukey’s Studentized Range (HSD), α = 0.05]. Height (m)

2.5

2.0

1.5

1.0

0.5

0.0

2.0

1.5

1.0

0.5

0.0

2.0

1.5

1.0

0.5

0.0

Cottonwood ST66

Cottonwood S7C15

Sycamore

Sweetgum

Loblolly Pine

Height (m)

1.6

1.2

0.8

0.4

0.0

0.6

0.4

0.2

0.0 May 24 June 22 July 20 Aug. 24 May 24 June 22 July 20 Aug. 24

Control

+ Fertilizer

+ Irrigation

+ Fertilizer + Irrigation

Source p

I < 0.001

F < 0.05

I x F NS

Source p

I NS

F < 0.01

I x F NS

Source p

I < 0.01

F < 0.001

I x F < 0.10

Source p

I < 0.01

F < 0.001

I x F < 0.01

Source p

I NS

F < 0.01

I x F NS

c

bc

ab

a

b

a

b

a

c

b

ab

a

b

b b

a

abab

b

a
15

fertilization than to irrigation, especially for diameter, but

the response of sycamore to combined irrigation and

fertilization was small compared with that of sweetgum,

suggesting that these two species may require different

combinations of water and nutrients. As the stands develop,

monitoring differences in resource demand will be critical

to defining the utility of these woody crops under various

growing conditions.

Supplying optimal nutrient requirements is important for

maximizing the return on fertilizer applications. We

Figure 10—Root-collar diameter for the five tree genotypes (cottonwood ST66, cottonwood S7C15, sycamore, sweetgum, and loblolly pine) grown

with or without irrigation across a range of fertilization. Measurements were collected on August 24, 2000. Error bars (SE, n = 3) are for replicated

analysis-of-variance plots. Points without error bars are for nonreplicated regression plots. Plotted lines are least squares, second-order polynomial

regressions.

determined optimal requirements by identifying maximum

growth responses to various fertilizer levels applied in the

regression plots. Again, the response of diameter (fig. 10)

and height (fig. 11) growth was similar for each species.

Maximum growth response occurred at moderate fertilizer

levels for each species except cottonwood S7C15,

suggesting that the rates of fertilization will provide

valuable information on optimum nutrient requirements

among these species. We anticipate the optimum to shift

with stand development and as fertilizer treatments are

increased to target levels during establishment years.

Cottonwood ST66

Cottonwood S7C15

Sycamore

Sweetgum

Loblolly Pine

Diameter (mm)

30

20

10

0

30

20

10

0

30

20

10

0

30

20

10

0

9

6

3

0

Diameter (mm)

0 30 60 90 120 150 180 210 0 20 40 60 80 100 120 140

Fertilizer N (kg/ha)

Irrigated

Nonirrigated
16

Minirhizotron – Root Growth

Fine-root density, or live-root standing crop, varied with depth

in the soil profile and between species. Fine-root length was

greatest at the surface (0 to 15 cm) and least at 45- to 105-cm

depth (fig. 12A). Jackson and others (1996) observed similar

responses in a wide range of ecosystems. Cottonwood ST66

produced measurable root length at the lowest depth

increment but loblolly pine did not. Greater cottonwood root

length at lower depths is likely a reflection of different tree

sizes between species (figs. 8 and 9).

Cottonwood ST66 produced significantly more roots than

loblolly pine in each treatment (P = 0.014), with greatest

differences at lower depths (fig. 12B). Similarly, in other

studies pine and poplar appeared to have such differences

in root growth (Coleman and others 2000, Steele and

others 1997). Species differences were greatest with

nutrient amendments. Cottonwood had more than twice

the root length density of loblolly pine with fertilizer

additions, but on average, had only 27 percent more root

length without nutrient additions. For both species, root

length declined with nutrient-only or water-only additions,

but root growth increased with combined nutrient and

water additions. This interaction between irrigation and

fertilization (P = 0.082 for cottonwood, P = 0.115 for pine)

masked the treatments’ main effects (P > 0.8). Lower

growth in the single-resource treatments may reflect a

decrease in the relative proportion of root-to-shoot

production compared to controls, despite larger overall

tree growth. Both irrigation and fertilization decrease root

production or standing crop relative to unamended

Figure 11—Height for the five tree genotypes (cottonwood ST66, cottonwood S7C15, sycamore, sweetgum, and loblolly pine) grown with or without

irrigation across a range of fertilization. Measurements were collected on August 24, 2000. Error bars (SE, n = 3) are for replicated analysis-ofvariance plots. Points without error bars are for nonreplicated regression plots. Plotted lines are least squares, second-order polynomial regressions.

Cottonwood ST66

Cottonwood S7C15

Sycamore

Sweetgum

Loblolly Pine

Irrigated

Nonirrigated

Height (m)

2.0

Height (m)

1.5

1.0

0.5

2.0

1.5

1.0

0.5

0.0

2.0

1.5

1.0

0.5

0.0

0 30 60 90 120 150 180 210 0 30 60 90 120 150 180 210

1.6

1.2

0.8

0.4

0.0

0.4

0.2

0.0

Fertilizer N (kg/ha)
17

treatments (Gower and others 1992, Joslin and Wolfe 1998,

Keyes and Grier 1981). Perhaps relative declines in root-toshoot production due to single-resource additions caused

an absolute decrease in fine-root density. However, for the

plots receiving both irrigation and fertilization, the

absolute growth was large enough to overcome the relative

root-growth decline caused by available resources.

Evaluating changes in root production and turnover will

help to assess the processes controlling fine-root density

under the conditions of this experiment. Quantifying the

dynamics of fine-root production is critical for evaluating

the surface area available for acquisition of water and

nutrient resources, and the allocation of resources for

continued acquisition.

Foliar Nitrogen

Foliar nitrogen concentrations were seldom different

among treatments (table 6). Cottonwood ST66 and

sweetgum showed a significant, but small, fertilizer effect.

Figure 12—Mean (±SE) root length of cottonwood ST66 and loblolly pine per unit minirhizotron surface: (A) at three depth zones with all treatments

combined, and (B) in response to fertilizer and irrigation treatments with all depths combined.

Table 6—Foliar nitrogen concentrations for hardwoods (sampled in July) and loblolly pine (sampled in December)

Tree species or clone

Cottonwood

Treatment Source ST66 S7C15 Sycamore Sweetgum Loblolly pine

 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - percent - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 

Control 2.76 ± 0.16 ab 3.25 ± 0.12 a 2.43 ± 0.08 a 1.90 ± 0.09 a 1.64 ± 0.06 a

Fertilized 3.06 ± 0.04 ab 3.33 ± 0.12 a 2.45 ± 0.06 a 2.18 ± 0.09 a 1.60 ± 0.07 a

Irrigated 2.61 ± 0.12 bb 3.33 ± 0.06 a 2.23 ± 0.17 a 1.95 ± 0.10 a 1.70 ± 0.05 a

Fertilization

+ irrigation 3.07 ± 0.10 ab 3.33 ± 0.10 a 2.43 ± 0.18 a 2.11 ± 0.02 a 1.75 ± 0.08 a

I NS NS NS NS NS

F P < 0.05 NS NS P < 0.05 NS

FxI NS NS NS NS NS

I = Irrigation; F = fertilization; FxI = interaction; NS = not significant.

Mean ± sem, n = 12.

Significant analysis-of-variance probabilities are included for irrigation, fertilization, and the interaction.

Means followed by the same letter in each column are not significantly different [Tukey’s Studentized Range (HSD), α = 0.1].

0

0.5

1

1.5

2

2.5

3

A

0

0.5

1

1.5

2

2.5

3

B

Mean root length (mm/cm2

)

0 to 15 15 to 45 45 to 105

Cottonwood ST66

Loblolly pine

Depth zone (cm)

Mean root length (mm/cm2

)

Cottonwood ST66

Loblolly pine

Control Fertilized Irrigated

Treatment

Fertilized +

irrigated
18

Orthogonal contrast mean separations helped identify

differences in sweetgum. Cottonwood clones had greater N

concentration than other species, whereas loblolly pine

and sweetgum had the lowest concentrations. Other

nutrients also showed similar small differences among

treatments (data not shown). Subtle differences in nutrient

concentrations among treatments indicate that tree growth

is limited by available nutrients. Increased nutrient

availability clearly stimulated growth (figs. 8, 9, 10, and

11), yet there is no accumulation of foliar nutrient

concentrations. Therefore, it appears that the trees are

growing to the limits of available nutrients, exhibiting

dilution of acquired nutrients through proportionally

increased biomass.

Leaf Area

Leaf area development during the growing season was

closely related to stem diameter growth (fig. 13). Power

functions best fit the data (R2

 > 0.9), which is typical for

this type of allometric relationship (Jokela and Martin

2000, Landsberg and Gower 1997). Parameters from these

functions predicted LAI from plot diameter measurements

(fig. 14). LAI for this young plantation is still small, but

distinct species differences are evident. Sycamore has the

greatest development followed by cottonwood S7C15,

ST66; sweetgum; and loblolly pine. The relationship

among species is likely to change as canopies develop.

Addition of water and nutrients frequently resulted in

greater leaf area development compared with the controls.

These differences were not always statistically significant

(fig. 14). Fertilization generally increased LAI, but

irrigation did not always increase it. Greatest LAI

occurred in the combined treatment for each species

except cottonwood S7C15, where the nutrient-only

treatment was greatest. As with other studies (Cannell and

others 1988, Jokela and Martin 2000, Landsberg and

others 1997), leaf area development in our study closely

parallels growth. Therefore, we may estimate stand vigor

and productivity response to nutrient additions through

canopy analysis, but need data throughout stand

development to factor out age and ontogenetic effects.

Conclusions

In this study, each of the species responded to fertilization,

and less distinct responses occurred with irrigation. At this

early stage of stand development, tree genotypes with

greater relative drought tolerance responded much less to

irrigation than water-loving varieties. Treatment responses

are generally consistent among collected measurements,

including stem diameter, height, root density, and leaf area

development. The two main processes of nutrient

acquisition and leaf area development control growth. To

understand more fully the controls of growth and

Figure 13—Example of allometric leaf area relationships for cottonwood ST66. Whole-tree leaf area is related to stem diameter (D)

by a power function: LA = aDb

, where a and b are least-squares regression coefficients.

Total leaf area (m2

) 2.0

Stem diameter (mm)

1.6

1.2

0.8

0.4

0.0

5 10 15 20 25

Control 0.85 2.0E-04 2.86

Fertilized 0.98 5.0E-05 3.43

Irrigated 0.99 4.0E-.05 3.59

Irr + Fert 0.99 2.0E-05 3.59

Treatment R2 a b
19

productivity under varying resource availability, we need

information on uptake surface and the activity of that

surface for resource acquisition. This experimental facility

has established a variety of tree species growing with a

range of resource availability treatments. Continued

monitoring will examine the processes controlling growth

of forest plantations. This critical information will be

valuable for understanding biological processes

controlling forest growth, as well as management of forest

stands for forest products, bioenergy feedstock production,

carbon and nitrogen sequestration, and phytoremediation

potential.

Acknowledgments

Funding for this project came from the U.S. Department of

Energy, Savannah River Operations Office through the

U.S. Department of Agriculture Forest Service, Savannah

River, under Interagency Agreement DE–IA09–

00SR22188; and from sponsors of the Short-Rotation

Woody Crops Cooperative Research Program including

Weyerhaeuser, The Timber Company, Union Camp,

Champion International, Bioenergy Feedstock

Development Program, Oak Ridge National Laboratory,

and U.S. Department of Agriculture Forest Service,

Southern Research Station Research Work Units SRS–

4103, SRS–4104, SRS–4154, SRS–4505, and SRS–4703.

Many Savannah River site personnel working for

subcontracting agencies including the U.S. Department of

Agriculture Forest Service, Savannah River and Southern

Research Station; Savannah River Technology Center; and

Savannah River Ecology Laboratory. Specific individuals

have been invaluable in assisting with this project,

including Dave Wilson, Ron Bonar, Homer Gabard, Cindy

Possee, Bob Morgan, Ron Mosley, Jamie Scott, Dan

Strawbridge, Don Coulter, Kevin Korman, and Sandy

Wilkins. Individuals from other organizations that helped

in planning and initiating this project include Neil

Dulohery, Hank Page, Dan Robison, Dennis DeFrancesco,

Marianne Burke, Diane DeStevens, Chris Barton, and

Julian Singer.

Figure 14—Leaf area index (LAI) measurements for the five tree genotypes (cottonwood ST66, cottonwood S7C15, sycamore, sweetgum, and

loblolly pine) in July 2000. Means sharing a letter are not significantly different [Tukey’s Studentized Range (HSD), α = 0.05].

Cottonwood clone ST66

0

0.01

0.02

0.03

0.04

Control Fert Irr Fert+Irr

Treatment

LAI (m2/m2

)

a

ab

b

ab

Sycamore

0.00

0.05

0.10

0.15

0.20

0.25

Control Fert Irr Fert+Irr

Treatment

LAI (m2/m2

)

a

b

ab ab

Loblolly Pine

0.000

0.001

0.002

0.003

Control Fert Irr Fert+Irr

Treatment

LAI (m2/m2

)

ab ab

a

b

Cottonwood clone S7C15

0

0.01

0.02

0.03

0.04

0.05

0.06

Control Fert Irr Fert+Irr

Treatment

LAI (m2/m2

)

a

a

a

a

Sweetgum

0

0.01

0.02

0.03

0.04

0.05

0.06

Control Fert Irr Fert+Irr

Treatment

LAI (m2/m2

)

a

b

b ab
20

Literature Cited

Anon. 1997. A model for soil nutrient uptake and harvest removals

in loblolly pine plantations. Raleigh, NC: North Carolina State

University. 24 p.

Albaugh, T.J.; Allen, H.L.; Dougherty, P.M. [and others]. 1998. Leaf area

and above- and belowground growth responses of loblolly pine to

nutrient and water additions. Forest Science. 44: 317-328.

Allen, H.L.; Dougherty, P.M.; Campbell, R.G. 1990. Manipulation of

water and nutrients - practice and opportunity in Southern U.S. pine

forests. Forest Ecology and Management. 30: 437-453.

Anderson, V.; McLean, R. 1974. Design of experiments, a realistic

approach. New York: Marcel Dekker. 418 p.

Borders, B.E.; Bailey, R.L. 2001. Loblolly pine - pushing the limits of

growth. Southern Journal of Applied Forestry. 25: 69-74.

Box, G.; Draper, N. 1987. Empirical model-building and response

surfaces. New York: John Wiley. 669 p.

Cannell, M.G.R. 1989. Physiological basis of wood production: a review.

Scandinavian Journal of Forest Research. 4: 459-490.

Cannell, M.G.R.; Dewar, R.C. 1994. Carbon allocation in trees: a review

of concepts for modelling. Advances in Ecological Research. 25: 59-

104.

Cannell, M.G.R.; Sheppard, L.J.; Milne, R. 1988. Light use efficiency and

woody biomass production of poplar and willow. Forestry. 61: 125-

136.

Ceulemans, R.; Impens, I.; Lemeur, R. [and others]. 1978. Water

movement in the soil-poplar-atmosphere system II. Comparative

study of the transpiration regulation during water stress situations in

four different poplar clones. Oecologia Plantarum. 13: 139-146.

Ceulemans, R.; Scarascia-Mugnozza, G.; Wiard, B.M. [and others]. 1992.

Production physiology and morphology of Populus species and their

hybrids grown under short rotation. I. Clonal comparisons of 4-year

growth and phenology. Canadian Journal of Forest Research. 22: 12.

Coleman, M.D.; Dickson, R.E.; Isebrands, J.G. 2000. Contrasting fineroot production, survival and soil CO2

 efflux in pine and poplar

plantations. Plant and Soil. 225: 129-139.

Dickmann, D.I.; Stuart, K.W. 1983. The culture of poplars in

Eastern North America. East Lansing, MI: Michigan State

University Press. 168 p.

Dosskey, M.G.; Bertsch, P.M. 1994. Forest sources and pathways of

organic matter transport to a blackwater stream: a hydrologic

approach. Biogeochemistry. 24: 1-19.

Gebre, G.M.; Kuhns, M.R. 1991. Seasonal and clonal variations in

drought tolerance of Populus deltoides. Canadian Journal of Forest

Research. 21: 910-916.

Gomez, K.; Gomez, A. 1984. Statistical procedures for agricultural

research. New York: John Wiley. 680 p.

Gower, S.T.; Vogt, K.A.; Grier, C.C. 1992. Carbon dynamics of

Rocky Mountain Douglas-fir: influence of water and nutrient

availability. Ecological Monographs. 62: 43-65.

Heilman, P.E.; Stettler, R.F. 1986. Nutritional concerns in selection

of black cottonwood and hybrid clones for short rotation.

Canadian Journal of Forest Research. 16: 860-863.

Jackson, R.B.; Canadel, J.; Ehleringer, J.R. [and others]. 1996. A

global analysis of root distributions for terrestrial biomes.

Oecologia. 108: 389-411.

Johnson, D.W. 1992. Nitrogen retention in forest soils. Journal of

Environmental Quality. 21: 1-21.

Jokela, E.J.; Martin, T.A. 2000. Effects of ontogeny and soil nutrient

supply on production, allocation, and leaf area efficiency in loblolly

and slash pine stands. Canadian Journal of Forest Research. 30: 1511-

1524.

Joslin, J.D.; Wolfe, M.H. 1998. Impacts of water input manipulations on

fine root production and mortality in a mature hardwood forest. Plant

and Soil. 204: 165, 174.

Keyes, M.R.; Grier, C.C. 1981. Above- and below-ground net production

in 40-year-old Douglas-fir stands on low and high productivity sites.

Canadian Journal of Forest Research. 11: 599-605.

Kleinbaum, D.G.; Kupper, L.L. 1978. Applied regression analysis and

other multivariable methods. Boston: Duxbury Press. 556 p.

Landsberg, J.J.; Gower, S.T. 1997. Applications of physiological ecology

to forest management. New York: Academic Press. 354 p.

Landsberg, J.J.; Prince, S.D.; Jarvis, P.G. [and others]. 1997. Energy

conversion and use in forests: an analysis of forest production in

terms of radiation utilisation efficiency. In: Gholz, H.L.; Nakane, K.;

Shimoda, H., eds. The use of remote sensing in the modeling of forest

productivity at scales from the stand to the globe. Dordrecht:

KluwerAcademic Publishers: 273-298.

Linder, S.; Benson, M.L.; Myers, B.J.; Raison, R.J. 1987. Canopy

dynamics and growth of Pinus radiata. I. Effects of irrigation and

fertilization during a drought. Canadian Journal of Forest Research.

10: 1157-1165.

Nelson, L.E.; Switzer, G.L.; Lockaby, B.G. 1987. Nutrition of Populus

deltoides plantations during maximum production. Forest Ecology

and Management. 20: 25-41.

Nord, J.C.; Ghent, J.H.; Thomas, H.A.; Doggett, C.A. 1982. Control

of pales and pitch-eating weevils in the South. For. Rep. SA-FR

21. Atlanta: U.S. Department of Agriculture, Forest Service,

Southern Region. 24 p.

Pallardy, S.G.; Kozlowski, T.T. 1979. Relationships of leaf diffusion

resistance of Populus clones to leaf water potential and environment.

Oecologia. 40: 371-380.

Pastor, J.; Aber, J.D.; McClaugherty, C.A.; Melillo, J.M. 1984. Aboveground production and N and P cycling along a nitrogen

mineralization gradient on Blackhawk Island. Ecology. 65: 256-268.
21

Reich, P.B.; Grigal, D.F.; Aber, J.D.; Gower, S.T. 1997. Nitrogen

mineralization and productivity in 50 hardwood and conifer

stands on diverse soils. Ecology. 78: 335-347.

Samuelson, L.; Stokes, T.; Cooksey, T.; McLemore, P. 2001.

Production efficiency of loblolly pine and sweetgum in response

to four years of intensive management. Tree Physiology. 21:

369-376.

Saxton, K.E.; Rawls, W.J.; Romberger, J.S.; Papendick, R.I. 1986.

Estimating generalized soil-water characteristics from texture. Soil

Science Society of America Journal. 50: 1031-1036.

Schulte, P.J.; Hinckley, T.M.; Stettler, R.F. 1987. Stomatal responses of

Populus to leaf water potential. Canadian Journal of Botany. 65: 255-

260.

Sims, J.T. 1996. Lime requirement. Methods of soil analysis. Part 3.

Chemical methods. Madison, WI: Soil Science Society of America:

491-515.

Stanturf, J.A.; van Oosten, C.; Netzer, D.A. [and others]. 2001.

Ecology and silviculture of poplar plantations. In: Dickmann,

D.I.; Isebrands, J.G.; Eckenwalder, J.E.; Richardson, J., eds.

Poplar culture in North America. Ottawa, Canada: National

Research Council: 153-206.

Steele, S.J.; Gower, S.T.; Vogel, J.G.; Norman, J.M. 1997. Root

mass, net primary production and turnover in aspen, jack pine

and black spruce forests in Saskatchewan and Manitoba, Canada.

Tree Physiology. 17: 577-587.

Steinbeck, K.; Skinner, T.M. 1985. Growing short-rotation forests in

the Southeastern USA. In: Egneus, H.; Ellegard, A., eds. Biomass

resources. Elsevier: 63-69.

Tschaplinski, T.J.; Tuskan, G.A.; Gebre, G.M.; Todd, D.E. 1998.

Drought resistance of two hybrid Populus clones grown under

irrigation in large-scale plantations. Tree Physiology. 18: 645-

652.

Tschaplinski, T.J.; Tuskan, G.A.; Gunderson, C.A. 1994. Water

stress tolerance of black cottonwood and eastern cottonwood

clones and four of their hybrid progeny. I. Growth, water

relations and gas exchange. Canadian Journal of Forest Research.

24: 364-371.

Tuskan, G.A. 1998. Short-rotation woody crop supply systems in

the United States: what do we know and what do we need to

know? Biomass and Bioenergy. 14: 307-315.

U.S. Department of Commerce, National Oceanographic and

Atmospheric Administration. 1993. Climatological data annual

summary: South Carolina. Asheville, NC. 26 p. Vol. 96.

U.S. Department of Commerce, National Oceanographic and

Atmospheric Administration. 1997. Climatological data annual

summary: South Carolina. Asheville, NC. 19 p. Vol. 100.

Van Bavel, C.H.M. 1966. Potential evaporation: the combination concept

and its experimental verification. Water Resources Research. 2: 19-31.

Vogt, K. 1991. Carbon budgets of temperate forest ecosystems. Tree

Physiology. 9: 69-86.

Vose, J.A.; Allen, H.L. 1988. Leaf area, stemwood growth, and nutrition

relationships in loblolly pine. Forest Science. 34: 547-563.

Yin, R.; Sedjo, R.A. 2001. Is this the age of intensive management? A

study of loblolly pine in Georgia’s Piedmont. Journal of Forestry. 99:

10-17.
22
Coleman, M.D.; Coyle, D.R.; Blake, J.; Britton, K.; Buford, M.; Campbell, R.G.; Cox, J.;

Cregg, B.; Daniels, D.; Jacobson, M.; Johnsen, K.; McDonald, T.; McLeod, K.; Nelson,

E.; Robison, D.; Rummer, R.; Sanchez, F.; Stanturf, J.; Stokes, B.; Trettin, C.; Tuskan,

J.; Wright, L.; Wullschleger, S. 2004. Production of short-rotation woody crops grown with

a range of nutrient and water availability: establishment report and first-year responses. Gen.

Tech. Rep. SRS-72. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern

Research Station. 21 p.

Many researchers have studied the productivity potential of intensively managed forest

plantations. However, we need to learn more about the effects of fundamental growth

processes on forest productivity; especially the influence of above- and belowground resource

acquisition and allocation. This report presents installation, establishment, and first-year results

of four tree species (two cottonwood clones, sycamore, sweetgum, and loblolly pine) grown

with fertilizer and irrigation treatments. At this early stage of development, irrigation and

fertilization were additive only in cottonwood clone ST66 and sweetgum. Leaf area development was directly related to stem growth, but root production was not always consistent with

shoot responses, suggesting that allocation of resources varies among treatments. We will

evaluate the consequences of these early responses on resource availability in subsequent

growing seasons. This information will be used to: (1) optimize fiber and bioenergy production; (2) understand carbon sequestration; and (3) develop innovative applications such as

phytoremediation; municipal, industrial, and agricultural wastes management; and protection

of soil, air, and water resources.

Keywords: Allocation, fertigation, fine-root growth, intensive management, interspecific

comparisons, leaf area.
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