The Container Tree 

Nursery Manual

Volume Seven

Chapter 2

Assessing Plant Quality

by Gary A. Ritchie and Thomas D. Landis

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In his prophetic work “Planting the Southern Pines”,

Wakeley (1954) foresaw what we now hold as axiomatic

—restoration, including forestation, can never be entirely

successful until nurseries are able to produce crops of

“high quality” plants consistently and reliably. But how to

distinguish a high quality plant from a low quality one

has not always been obvious, so the concept of plant

quality remained obscure for many years. Wakeley also

recognized that “morphological grades” often fell short in

their ability to predict performance, and he hypothesized

that “physiological grades” may be a better criterion of

viability (Wakeley 1949). What exactly constituted a

physiological grade, however, and how to measure it,

eluded Wakeley and his contemporaries. 

During the past 30 years worldwide, nursery researchers

and managers convened numerous workshops and symposia and published many reports on the subject of plant

quality and how to measure it (for example, Duryea 1985),

Colombo (2005)]. This work generated a variety of quality

tests and although many are ingenious, most failed to meet

expectations. A few, however, stand the test of time and

remain in operational use. In this chapter, we discuss the

most practical ways of measuring plant quality and how

they can be used in container nurseries.

7.2.1 Introduction
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7.2.2 Classes of Plant Quality Attributes

Forestry researchers have labored to identify quantifiable

traits that could be used as indicators of plant quality, and

better yet, predictors of performance after outplanting.

While an impressive list of such attributes has been

assembled (for example, Grossnickle 2000), relatively few

are used operationally in nurseries or on the outplanting

site. In our view, plant quality can be divided into broad

categories: 

Morphological attributes—These traits can be readily

seen and easily measured, such as stem height, stem (root

collar) diameter, root volume, and root and shoot dry

weight. During the harvesting-to-outplanting process,

these traits do not change appreciably.

Physiological attributes—These traits are not readily visible and need to be measured with instruments or laboratory procedures. In contrast to morphological characteristics, physiological attributes change often and sometimes

dramatically during the harvesting-to-outplanting process.

Therefore, any measurement of physiological quality is a

“snap-shot” relevant for only a brief point in time. Some

common physiological attributes include cold hardiness

and bud dormancy. 

Performance attributes—These traits can be assessed only

by subjecting plants to certain pre-defined testing protocols and observing how they perform. Performance tests

have great value because they assess and integrate a wide

spectrum of morphological and physiological traits at

once. Unfortunately, performance tests are laborious, time

consuming, and therefore expensive. Nevertheless,

because of their intuitive appeal, performance tests have

found wide use in plant quality assessment. One of the

oldest and still most commonly used performance tests is

the root growth potential (RGP) test.
7.2.3.1 Introduction

Most nursery stock produced in the United States,

Canada, and Europe during the 1970s was bareroot, and

so most seedling morphology literature focuses on bareroot stock types (Frampton and others 2002, Ritchie and

others 1997). The effects of morphology on performance

of bareroot stock have been summarized in the literature

(Thompson 1985, Mexal and Landis 1990, Wilson and

Jacobs 2006); height, stem diameter, root system “quality”

(volume or mass), and the ratio of the mass of the shoot to

that of the root system are typically the best predictors of

outplanting performance. Survival is best forecast by stem

diameter, while shoot growth tends to be more related to

initial seedling height. With bareroot stock, when stem

diameter increases above about 5 mm (0.2 in.), other

morphological indicators become less important (Mexal

and Landis 1990). Additionally, bareroot seedlings with

larger root volumes at the time of outplanting have greater

subsequent growth and survival than those with smaller

root volumes (Rose and others 1997).

7.2.3.2 Morphological characteristics of container

seedlings 

Let’s discuss the major morphological factors that

describe container stock quality in order of importance.

Container volume. The most important morphological

factor affecting plant quality in container nurseries is container size or volume. Container volume controls the

amount of roots that a plant can produce, which in turn,

determines how large a shoot can be produced in a given

amount of time. In addition, the size of the container

“plug” limits the moisture and mineral nutrient reserves

that will be taken to the outplanting site. Compared to

bareroot stock with their extremely variable root systems,

it is easy to characterize the volume and depth of container root plugs and most container nursery stock is

described by container volume. For example, in the

northwestern United States, a “Styro 20” refers to a plant

that has been produced in a StyrofoamTM block container

with cells that are 340 cm3 (20 in3) in volume. 

Container volume is the most important factor controlling

root egress after outplanting (Figure 7.2.1A). As container

volume increases, the amount of exterior surface area of

the root plug also increases (Figure 7.2.1B), which means

that plugs of larger containers have more surface contact

with the surrounding soil. 

Among different container sizes, volume and growing

density have the most significant effect on plant morphology (Table 7.2.1). In studies with interior spruce (Picea

glauca x engelmanii complex) (Grossnickle 2000);

Douglas-fir, western hemlock (Tsuga heterophylla), and

Sitka spruce (Picea sitchensis) (Arnott and Beddows

1982); black spruce (Picea mariana) (Jobidon and others

1998); and cherrybark oak (Quercus pagoda) (Howell and

Harrington 2004), every morphological trait measured

increased in value as container volume increased. In

every case, container stock with larger root plugs produced larger plants after outplanting. 

Because block containers have fixed cell spacing, it is

more difficult to study the effects of changing plant density at the same cell volume. In contrast, the Ray Leach

Conetainer® system allows cell spacing to be changed,

allowing a few good research trials to be done. Douglasfir seedlings grown at densities ranging from 270 to1,080

plants/m2 (25 to 100/ft2) showed that shoot height

increased with increasing density because of the competition for light in response to crowding (Figure 7.2.2). Stem

diameter decreased, however, which shows that quality

can be lessened by growing plants too closely together

(Timmis and Tanaka 1976).

Within containers of the same size, stem diameter and

shoot height have proven to be the most important morphological traits affecting quality and therefore are the

two factors most often used in grading specifications

(Figure 7.2.3A). More discussion on measuring height and

stem diameter is provided in Volume One, section

1.5.4.2. 

Stem diameter (“caliper”). Stem diameter is typically

measured, using a small caliper, at the root collar where

the stem meets the root system. Root-collar diameter, or

stem diameter, is always reported in millimeters.

Numerous studies show that stem diameter is the best

predictor of outplanting performance and therefore plant

quality. When Engelmann spruce container seedlings with

a range of stem diameters were outplanted on a high elevation site in Utah, survival after two growing seasons

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7.2.3 Morphological Attributes
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Figure 7.2.1—Root growth out of the plug and into the surrounding soil

(“egress”) is critical to plant survival and growth after outplanting (A).

Container volume is important not only because it determines the amount

of roots that a container plant has, but also the surface area of the plug in

contact with the surrounding soil (B). (A – modified from Grossnickle

2000).

Table 7.2.1—Effect of container volume on seedling morphology of two-year-old interior spruce 

(Picea glauca x Picea engelmannii)*

StryroblockTM Cell Volumes

Seedling Morphological 105 cm3 170 cm3 340 cm3

Attributes (6.6 in3) (10 in3) (20 in3)

Shoot Height–cm (in.) 24.2 (9.5) 29.7 (11.7) 33.3 (13.1) 

Root-Collar Diameter–mm 4.4 5.0 6.8 

Shoot Dry Weight–g (oz) 2.8 (0.10) 4.5 (0.16) 6.4 (0.23)

Root Dry Weight–g (oz) 1.1 (0.04) 1.4 (0.05) 2.1 (0.07)

Number of Branches 18 24 33

Number of Buds 50 67 86

* Source: Grossnickle (2001)

Figure 7.2.2—When plants are grown in the

same volume container but at different densities, shoot height increases with closer spacing

whereas stem diameter decreases (modified

from Timmis and Tanaka 1976).

A

B
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was strongly correlated with initial stem diameter (Figure

7.2.3B). This information was used to develop grading

standards; in this case, seedlings with stem diameters >

2.5 mm were shippable whereas smaller ones were not

(Hines and Long 1986). Of course, this relationship varies

with conditions on the outplanting site so standards must

be developed for each species and for different outplanting conditions. 

Shoot height. Height is the distance from the root collar

to the tip of the terminal bud or shoot. It is usually reported in centimeters or millimeters, but in the United States

it is often reported in inches. This results in the peculiar

situation where plants are characterized using both

English and metric measuring systems; for example, a

plant shoot that is 12 in. high with a 5 mm caliper.

Height is correlated with the number of needles on the

stem and is therefore a good estimate of photosynthetic

capacity and transpirational area.

“Rootbound” plugs. The fact that excessive root growth

becomes a quality issue in container plants has been

known for decades but, until recently, no morphological

index or rating system had been developed. Rootbound

nursery stock can be defined as plants that have grown

too large for their container, resulting in severe matting

and tangling of the root system (Figure 7.2.4A). From a

quality standpoint, this condition reduces plant survival or

growth after outplanting (South and Mitchell 2005).

Several studies have related rootbinding to the length of

time that the plant has been in the container. Usually, the

larger the container, the longer it takes for the plant to

become rootbound. But time alone is not really useful

because root growth is also affected by cultural conditions at the nursery. A species growing rapidly in one

nursery will become rootbound faster than the same

species growing slower in another nursery. Similarly, a

species in a large container given large amounts of fertilizer may become rootbound as fast as the same species in

a smaller container given smaller amounts.

For a given species in the same volume container, outplanting survival has been shown to decrease after an

optimum root-collar diameter is exceeded (Figure 7.2.4B).

South and Mitchell (2005) propose a “root-bound index”

based on root-collar diameter divided by container diameter or volume that must be calculated for each container

type. From an operational standpoint, however, establishing a maximum stem diameter along with a visual assessment of root binding might be the most practical culling

system.

Other morphological indices. Several other morphological criteria, such as biomass, shoot-to-root ratio, sturdiness, and appearance, have been used to describe plant

quality.

Figure 7.2.3—Shoot height and stem diameter are the most common grading criteria in container nurseries (A), but stem

diameter has proven to be the best single morphological indicator of seedling quality. When Engelmann spruce (Picea

engelmannii) container stock were outplanted, plants with stem diameters larger than 2.5 mm outperformed smaller ones

after the second year (B, modified from Hines and Long 1986).
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Figure 7.2.4—Container plants that have grown too long

in the same container become “rootbound” which greatly

reduces their quality (A). For a given species and container size, an optimum stem diameter exists that can be used

for grading-out rootbound plants (B). (B, modified from

South and Mitchell 2005).

Biomass can be determined using volume or dry weight

methods. Shoots and roots are usually measured separately. Dry weight is determined by cleaning, oven drying,

and weighing plants. Volumes are determined using water

displacement (Harrington and others 1994; Burdett 1979).

Shoot-to-root ratio (shoot:root) is the ratio of the dry mass

or volume of the shoot to the dry mass or volume of the

root system and provides an indicator of the “balance” of

the plant. When the shoot:root is “1” the size of the root

mass equals the size of the shoot mass. More often, however, the ratio is greater than 1 because the shoot often

outweighs the root system. Shoot-to-root ratios less than

2.5 are usually deemed most desirable. A sturdiness ratio

is calculated by dividing shoot height (cm) by diameter

(mm). It attempts to capture the idea of “sturdiness” (low

value) in contrast to “spindliness” (high value). This ratio

has found particular use in container stock, which can

become tall and thin when grown at high densities and/or

under lower lighting conditions. Color, form, and damage

should also be accounted for when evaluation morphological quality. Foliar color is a general indicator of plant

quality and can vary by species and time of season.

Yellow, brown, or pale-green foliage indicate lower vigor

and/or chlorophyll content than dark green foliage.

Existence of multiple shoots, stem sweep, root deformity,

stiff lateral roots, physical damage, and any other noticeable characteristics that can affect plant performance are

also important factors to note when assessing morphological quality. A single, but comprehensive study with Italian

stone pine (Pinus pinea) container seedlings measured

various morphological characteristics. The best single

indicator of plant quality was the ratio of container depth

to stem diameter, and target plants had a container

depth-to-stem diameter ratio of 4 (Dominguez-Lerena and

others 2006). 

7.2.3.3 Effects of container size on outplanting 

performance

The main objective of measuring plant morphological

traits is to predict performance after outplanting—specifically survival and growth.

So what traits, or combinations of traits, have the greatest

positive effect on plant performance? The conventional

wisdom is that bigger is generally better than smaller. All

other factors being equal, big plants with proportionately

A

B
large stem diameters and root systems normally exhibit

higher survival and greater growth than small plants or

plants with poor root systems. In general, outplanting survival is more related to stem diameter, whereas shoot

growth after outplanting depends more on initial plant

height (Arnott and Beddows 1982).

As discussed in Chapter 7.1, survival and growth also

depend strongly on environmental conditions of the outplanting site. After reviewing the literature on container

size and performance, Grossnickle (2005) concluded that

”large” seedlings performed better than “small” seedlings

on moist sites where vegetative competition was severe.

Conversely, smaller seedlings fared better on sites prone

to water stress. On sites with heavy vegetative competition, the ability to access and process sunlight strongly

determines survival and growth. Hence taller, branchier

seedlings with a large photosynthetic area have an advantage over smaller seedlings that tend to become shaded

by competing vegetation. For example, large white spruce

(Picea glauca) seedlings outplanted in a boreal British

Columbia forest were better equipped for competition

than smaller seedlings (McMinn 1982). Similarly, tall container seedlings of Douglas-fir, western hemlock, and

Sitka spruce exhibited greater height growth after outplanting on a coastal British Columbia site than shorter

seedlings (Arnott and Beddows 1982). In a study in

Quebec, large spruce seedlings grew better than smaller

ones on mesic sites with heavy plant competition (Figure

7.2.5). Larger stock with thick stems also perform better

on sites with animal predation and heavy snow, as shown

with Engelmann spruce seedlings (Hines and Long 1986). 

Contrast this with an outplanting site where hot and dry

conditions cause high evapotranspirational demand.

Here, the advantage lies with plants that have a relatively

small transpirational surface area relative to a large,

absorptive root system. Under these conditions, nursery

plants with a large shoot and small root system (high

shoot:root) are at a disadvantage because they transpire

faster than they can absorb water from the soil. For these

high stress sites, specifying larger volume containers at

lower growing densities (wider cell spacing) will produce

plants that have a short shoot and thick stem diameter

(Grossnickle 2005). 

Miniplug transplants are a stock type producing large

plants in a short time (Landis 2007). Growers sow miniplugs (approximately 16 cm3 [1 in3] cavity volume) in a

greenhouse during mid-winter and then transplant them a

few months later to larger, wider-spaced containers that

are moved to outdoor growing areas or bareroot nursery

beds. These “plug + plug” transplants have proven to be

popular stock types for hot and dry outplanting sites

(Figure 7.2.6).

Although much less research has been done on

broadleaved (hardwood) species, the review by Wilson

and Jacobs (2006) notes that, as with conifers, height and

stem diameter are the most frequently used grading criteria for hardwoods, with stem diameter usually providing

the most consistent predictions of field performance. 

7.2.3.4 Morphological attributes: Summary and recommendations

Shoot height and stem diameter are the most frequently

measured morphological traits and the most common

grading criteria. Morphological attributes are easily

assessed and do not change appreciably during the harvesting-to-outplanting process. Nearly all morphological

traits reflect container volume and/or growing density;

large container volumes and low growing density promote development of large stock.

Effects of morphology on performance of container stock

mirror those for bareroot stock: 

• Initial stem diameter tends to be correlated with survival.

• Initial height tends to be correlated with shoot growth.

• Morphological traits can interact. For example, stem 

diameter may influence survival in plants with poor 

root systems, but not in those with good root systems. 

• Larger stock generally performs better than smaller

stock, but this depends on conditions on the outplanting site.

• Tall stock with thick, stiff stems and a large photosynthetic surface are best for sites with plant competition, animal predation, or heavy snow loads.

• Short stock with thick, stiff stems and extensive root 

systems are best for droughty sites.

As discussed earlier, physiological traits of nursery plants

differ significantly from morphological characteristics in

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Figure 7.2.5—Large black spruce and white spruce container stock outperformed smaller plants when measured

8 years after outplanting in southeastern Quebec (modified from Thiffault 2004).

Figure 7.2.6—For hot and dry outplanting sites, these “Q-plug + one” Jeffrey pine (Pinus jeffreyi)

have the ideal morphology - short stocky shoots (A) with large stem diameters and root mass (B).

A B
that they are usually invisible, they change often and

sometimes dramatically throughout the harvest-to-outplanting process, and they must be measured with laboratory equipment. 

Most physiologically based quality tests measure only one

plant function, such as cold tolerance, water status, or

photosynthetic efficiency. It is helpful to think of plant

quality in layers: morphological characteristics are the

base layer whereas physiological traits are the second

layer. A batch of plants may have ideal shoot height and

stem diameter but these morphological traits alone are

insufficient to guarantee high quality. Physiological tests

are needed to provide a more comprehensive picture.

Next we will discuss four tests of physiological quality:

plant moisture stress, cold hardiness, root electrolyte leakage, and chlorophyll fluorescence.

7.2.4.1 Plant Moisture Stress (PMS)

Plant moisture stress, or PMS, is one of the oldest and

most commonly used tests to measure quality. Its popularity rests on its simplicity and robustness, and the fact that

PMS equipment is relatively inexpensive, intuitive, and

portable. While PMS measurements are easily made,

however, their interpretation can be more difficult.

What is PMS? Without a steady supply of good quality

water, plants cease growing and ultimately die. The

amount of water needed to meet the basic metabolic

needs of a plant is quite low. During photosynthesis,

atmospheric carbon dioxide (CO2) diffuses into leaves

through stomata and, once inside the leaf, this CO2 is

converted to sugars. Photosynthesis is, however, a very

“leaky” process because while CO2 is diffusing into the

leaves, water is diffusing out—this loss of water is called

transpiration. Plants can reduce transpiration by closing

stomata but this impedes photosynthesis. So, in order to

grow, plants must transpire vast amounts of water. 

Transpiration generates a tension (or “stress”), which due

to water’s high cohesion, is transmitted through vascular

tissue from the leaf down through the stem and into the

roots. During daylight, when stomata are open, transpiration typically exceeds the plant’s ability to extract water

from the soil. Therefore, during the day, plants are always

under some degree of water stress. This stress is perfectly

normal and not injurious unless it reaches high levels for

a prolonged period of time. 

In very simple terms, plant moisture stress can be modeled as:

PMS = A – T + S 

where A is the absorption of water from the soil, T is transpirational loss, and S is storage of water in the plant’s

stem and roots, which is negligible in seedlings but

important in large trees. During daylight, T almost always

exceeds A.

Water potential. A more precise way to model the state of

water in plants is the thermodynamic approach, which is

based on water potential and represented by the Greek letter psi (ψ). The total water potential (ψW ) is a measure of

the free energy or chemical potential of water. In plants,

ψW is the sum of two component potentials: the pressure

potential (ψP), which can be either positive or negative, and

the osmotic potential (ψO), which is always negative: 

ψW = ψP + ψO

Potentials are expressed in units of pressure, and although

MegaPascals (MPa) are the official SI units, bars are most

commonly used by nursery and reforestation personnel. By

definition, the ψW of pure water at standard temperature

and pressure is 0 bars, or 0 MPa. ψP and ψO are continually

changing as transpiration and osmosis cause water to move

across membranes, in and out of cells, and up the transpiration stream. 

The components of water potential have different properties

depending on where the water is located within the plant

tissues. Water is contained within cell membranes as part of

the symplast and outside cell membranes as part of the

apoplast. In the apoplast, water is nearly always under

hydrostatic tension from transpirational pull, so pressure

potential (ψP) is always negative (Table 7.2.2). In the symplast, however, ψP is normally positive owing to the inward

turgor pressure that cell membranes and walls exert on cell

contents. The exception would be for a cell that has lost all

turgor (wilted), in which case ψP = 0. This is often called the

“zero turgor point”, which will be discussed below. 

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7.2.4 Physiological Attributes
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Table 7.2.2—Properties of component water potentials in

the symplast and apoplast

Component potential Apoplast Symplast

(outside cells) (inside cells) 

Pressure potential (ψP) Always negative Generally positive 

Zero when wilted 

Osmotic potential (ψO) Generally zero Always negative 

Water potential (ψW) Always negative Variable

Figure 7.2.7—The interrelationships between plant water

potential (ψW) and its components, osmotic potential

(ψO) and pressure potential (ψP), change over the range

of plant water contents from turgidity (A) to the permanent wilting point (PWP) (C) (modified from Ritchie

1984b).

The osmotic component (ψO) is normally near 0 in the

apoplast whereas, in the symplast, ψO is always negative

owing to effects of dissolved solutes (ions) in the cells

(Table 7.2.2). These component potentials are continually

changing as water moves across cell membranes due to

osmosis or up through the plant due to transpiration.

Because ψW is the sum of these two components, it is

almost always negative and the plant is almost always

under some level of water deficit, or stress.

The interplay of these component potentials in the symplast can be visualized with a Höfler diagram (Figure

7.2.7). The X-axis is the water content of the cell

expressed as a percentage of full turgor. The Y-axis gives

the component potentials. At full hydration (A in Figure

7.2.7), the plant is turgid and the positive turgor pressure

of the cell walls (ψP) balances the negative osmotic potential (ψO) of the cell contents. At this point, ψW = 0 MPa.

As the cell loses water, ψP falls and the concentration of

solutes in the cell increases. This drives ψO down, so ψW

also falls. When ψP reaches 0 MPa (B in Figure 7.2.7),

cells collapse and the plant wilts. The value of ψW at

which this occurs is known as the “zero turgor point” or,

as it is more commonly known, the “permanent wilting

point” (C in Figure 7.2.7). 

Units of water potential. Thermodynamic water potential

terminology (Slatyer 1967) has sometimes been troublesome for growers because negative values are hard to

visualize and tricky to manipulate algebraically.

Fortunately, someone somewhere had the idea to express

water potential as a positive value and call it “Plant

Moisture Stress” (PMS). These values can be easily converted because -1.0 MPa equals 10 bars. This relationship

and some examples are shown in Table 7.2.3. For example, a PMS value of 10 bars indicates a “moderate” level

of stress and is equivalent to ψW of -1.0 MPa. From a theoretical standpoint, however, thermodynamic terminology

is useful because it is consistent through the soil-plantatmosphere continuum (Figure 7.2.8). 

Diurnal patterns of plant water potential. As already

mentioned, ψW is dynamic and this affects its usefulness

as an index of plant quality. Consider, for example, a container plant whose growing medium is at field capacity

with water. During daylight, while stomata are open, low

humidity (high vapor pressure deficit) draws moisture
29

from the leaves. This creates an imbalance between transpiration and water absorption resulting in the development of PMS at mid-day (ψW decreases). During nighttime, stomata tend to close, relative humidity rises to

nearly 100%, and transpiration ceases. The negative ψW

in the plant pulls water from the soil or growing medium

thereby relieving the stress. By early the next morning

pre-dawn ψW reaches a dynamic equilibrium with soil

moisture potential (ψW = ψsoil).

If no water is added to the container, the growing medium dries out, and pre-dawn and mid-day plant moisture

stress increase daily as ψsoil decreases. After a few days,

the plant will close its stomata during midday to retard

transpiration. This can be seen occurring in days 4 and 5

in Figure 7.2.9, and results in a moderating of the midday

PMS. ψsoil will eventually become so negative that the

plant will be unable to equilibrate during the night.

Throughout this time, the mid-day stress will continue to

increase. When irrigated, the system will return to the initial state shown in Day 1 unless the plant has experienced

irreversible damage from the high PMS.

Note that the ability to track moisture stress levels of both

soil and plant in Figure 7.2.9 shows the advantage of

using water potential units rather than PMS, which

reflects only plant stress.

Measurement of plant moisture stress. Over the years, as

plant physiologists labored to understand the dynamics of

plant water relations, many attempts were made to develop methods of measuring ψW (Lopushinsky 1990). As far

as nursery work goes, the most significant development

was invention of the “Scholander Pressure Chamber”

(Scholander and others 1965), based on an earlier glass

pressure chamber devised by Dixon (1914). Wareing and

Cleary (1967) modified the chamber for trees and

seedlings and outlined basic measurement procedures. 

The modern pressure chamber consists of a metal pressure vessel connected to a nitrogen gas source through a

pressure regulator. To measure plant moisture stress, the

stem is cut and inserted through a rubber or compression

gasket. This is then sealed into a hole in the chamber lid

with the foliage inside the chamber and the cut stem protruding (Figure 7.2.10). Nitrogen gas is slowly bled into

the chamber while the cut stem is closely observed.

Table 7.2.3—Comparison of units and terms for plant

water potential and plant moisture stress 

Figure 7.2.8—Water is pulled along a gradient of water

potential that is driven by evapotranspiration, from higher

(less negative) levels in the growing medium, through the

plant to lower (more negative) levels in the surrounding

air (modified from McDonald and Running 1979).
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Figure 7.2.9—For a plant growing in a nonirrigated container, the plant water potential (ψW) gradually decreases

as the growing medium (ψsoil) dries (modified from

Slatyer 1967).

When a droplet of water appears at the end of the stem,

the chamber pressure is noted. The gas pressure required

to force water to the surface is equal to the moisture stress

of the plant. For a detailed theoretical description and

procedural guide see Ritchie and Hinckley (1975).

The pressure chamber is the standard technique used for

measuring PMS in forest nurseries, on outplanting sites, and

in plant research facilities. For example, the USDA Forest

Service J.H. Stone nursery in Central Point, Oregon uses

pressure chambers to measure PMS for scheduling bareroot

seedling irrigation and to detect dangerous PMS levels during lifting and packing operations (JH Stone Nursery 1996).

Pressure chambers and supplies are available from:

PMS Instrument Company

1725 Geary Street SE

Albany, OR 97322 USA

Telephone: 541.704.2299

Fax: 541.704.2388

E-mail: info@pmsinstrument.com

URL: http://pmsinstrument.com/

Interpretation of PMS values. PMS measurements are

used extensively in plant physiology and ecological

research because they are robust, easy to obtain, and

their relationship to plant physiology is easy to demonstrate. For example, when container white spruce was

subjected to extended moisture stress, stomata closed and

photosynthesis ceased abruptly at -2MPa (20 bars) (Figure

7.2.11). Unless this stress is relieved, plant growth will

most certainly be restricted and death may occur.

The relationship between PMS readings and plant quality is

not, unfortunately, always as straightforward as one might

hope. This is partly because PMS, as an estimate of ψW,

integrates several variables into one reading and therefore

much information is lost. In addition, because the components of water potential change seasonally, a given value of

PMS might have a different interpretation if taken in spring

as opposed to winter. For example, Figure 7.2.12 shows

how the “zero turgor point” changes seasonally in roots

and stems of Douglas-fir seedlings (Ritchie and Shula

1984). Looking at stem values, a PMS reading of -2.5 MPa

(25 bars) would be a potentially lethal value if taken in

April because it would be near the zero turgor point. But

the same value, if measured in January, would be of little

concern. On the other hand, root systems with PMS near 

-2.0 MPa (20 bars) would be suspect most of the year.

As illustrated in Figure 7.2.9, PMS can vary sharply

throughout the day and from day to day. Daytime PMS

values can fluctuate widely on days with intermittent sunshine and wind, providing only brief “snap shots” of PMS

that have little diagnostic value. Probably the most useful

PMS value is what is known as “pre-dawn PMS”. This is

the PMS that occurs just before sunrise when ψW is in

dynamic equilibrium with ψsoil (Figure 7.2.9) and provides

an estimate of the minimum stress the plant might experience that day. If this minimum value is high, it may be

cause for concern. With the above caveats in mind, we

present some suggested guidelines for interpretation of

pre-dawn PMS measurements as they relate to plant

growth and cultural implications (Table 7.2.4). 

Is PMS an Indicator of Plant Quality As pointed out by

Lopushinsky (1990), the commonly-used plant quality

indicators (root growth potential, cold hardiness, stress

resistance, and dormancy intensity) are not correlated

with PMS. Therefore, PMS can not be used as a proxy

indicator of any of these. So, can PMS alone be a useful

indicator of quality? 
31

In our opinion, PMS reflects quality only when stress is

moderately high and sustained for several days. For example, nursery stock with pre-dawn PMS values in the 

-1.5 to -2.5 MPa (15 to 25 bar) range is under severe

stress (Table 7.2.4), especially if these readings persist

after irrigation. We should also point out that dead plants

can exhibit very low PMS values because dead roots

retain the ability to absorb water. So, low PMS values are

not necessarily indicators of healthy stock.

PMS is also used operationally to monitor plant condition

during the harvest-to-outplanting process. For example,

stock that has a plant water potential (PMS) value of, say,

-1.0 MPa (10 bars) coming out of refrigerated storage

would certainly be cause for concern. Likewise, nursery

stock should have low PMS values immediately prior to

outplanting, when high values indicate overheating or

exposure to sun or wind. 

You may have noted that all research has been done with

conifers. Use of PMS as a performance predictor for deciduous hardwoods also shows some promise, although

Wilson and Jacobs (2006) point out that much work is

needed to define critical PMS values for a given species.

PMS as a snapshot of plant water status. The fact that

PMS is not always a good predictor of plant quality

should not be interpreted to mean that monitoring PMS is

a waste of time. Pressure chambers should be used to

check plant moisture status at several times during nursery tenure. Using pre-dawn PMS readings to fine-tune

nursery irrigation practices is a good idea because pressure chamber measurements are an easy way to truly

know the water status of plants at a given time.

PMS measurements during harvesting can alert nursery

managers to dangerously dry conditions or excessive

plant exposure (MacDonald and Running 1979). PMS can

also be used to check the moisture status of stock immediately before outplanting. For example, a very strong

relationship was found between PMS readings taken

immediately before outplanting of radiata pine (Pinus

radiata) seedlings and root growth potential (RGP) (MenaPetite and others 2001) (Figure 7.2.13).

Plant moisture stress: Conclusions and recommendations.

Plants normally lose water more rapidly through transpiraFigure 7.2.10—How to measure plant moisture stress

(PMS) with a pressure chamber. A plant stem is severed,

and the cut end forced through a hole in the center of a

rubber gland, which is then inserted into the lid of the

chamber. Nitrogen gas is slowly introduced into the

chamber until a drop of water is forced to the surface of

the cut stem. The gauge pressure at which this occurs is

equal and opposite to the forces holding the water in the

stem, and is known as PMS.
32

Figure 7.2.11—Plant moisture stress can give an instantaneous indication of nursery stock water status. When different families of white spruce seedlings were placed

under increasing water stress, the stomata closed (A) and

all photosynthesis ceased at -2 MPa (20 bars) (B)

(Modified from Bigras 2005).

Figure 7.2.12—The value of water potential at zero turgor

varies differently through the year for roots and stems of

Douglas-fir seedlings (modified from Ritchie and Shula 1984).

Table 7.2.4—Growth response and cultural implications of

inducing moisture stress in conifer seedlings in northwestern

United States nurseries (modified from Landis and others 1989)

Predawn Moisture Seedling Response &

PMS value (bars) stress rating Cultural Implications

0 to 5 Slight Rapid growth

5 to 10 Moderate Reduced growth

Best for hardening

10 to 15 High Restricted growth 

Variable hardening 

may result

15 to 25 Severe Potential for injury

> 25 Extreme Injury or mortality

tion than they absorb from the soil, so they are almost

always under some level of water stress, commonly

known as plant moisture stress (PMS). PMS is linearly correlated to, but differs in sign from, plant water potential

(ψW). PMS shows strong diurnal variations as transpiration

rates adjust in response to changes in temperature, vapor

pressure deficit, and stomatal aperture. The most useful

value of PMS is that which occurs just before dawn (predawn PMS), when ψW is in near equilibrium with ψsoil.

The Scholander pressure chamber, introduced in the mid

1960s, remains the most robust and useful method for

measuring PMS. In this test, a stem is severed from a

plant, sealed in a pressure chamber, and gas under pressure is introduced into the chamber until a water drop

forms at the cut surface. The pressure at which this occurs

is equal and opposite to the forces holding the water in

the stem and provides an estimate of PMS. Although there

are strong seasonal variations in critical PMS (plant water
33

Figure 7.2.13—In some studies, plant moisture stress was

found to be a good predictor of the ability to grow new

roots after outplanting (modified from Mena-Petite and

others 2001).

potential) values, readings in the range of -0.5 to -1.5

MPa (5 to 15 bars) are normal whereas those below -1.5

MPa (above 15 bars) can be cause for concern.

PMS is not directly correlated with any of the classical

plant quality indicators, but pre-dawn PMS measurements

can be used in nurseries to determine irrigation amount

and timing, and are the best way to monitor stress levels

during hardening. PMS reading during harvesting can

alert nursery managers to stressful conditions, and plant

users can use PMS to check moisture status of their stock

immediately before outplanting.

7.2.4.2 Cold hardiness 

Cold hardiness (CH) testing has been used in horticulture

since the early 1900s as a method of selecting cold hardy

cultivars. Its use as a plant quality test in forest and conservation nurseries has developed over the past 30 or so

years, but it stands now as perhaps the second most-often

used test of forest planting stock quality.

Concepts behind the test. During the growing season,

most temperate zone plants are killed when the air temperature drops below freezing. As winter approaches and

growth slows, however, plants respond to the changing

photoperiod (lengthening nights) and develop tolerance to

cold (Weiser 1970, Glerum 1976, 1985, Bigras and others

2001). In general nursery terminology, this is known as

“hardening” and this cold tolerance is indicative of general stress resistance. When winter arrives, plants that

would have been killed at slightly below 0 °C (32 °F) during the growing season are able to survive temperatures

far below that. As winter draws to a close and the growing season nears, this resistance to low temperatures is

rapidly lost and plants resume growth. 

What happens when plant tissues freeze. To understand

how plants withstand sub-freezing temperatures, it is first

necessary to understand what happens inside a plant when

it freezes. Consider a generalized cross section of plant tissue showing the cellular structure (Figure 7.2.14A). Cells are

enclosed by flexible walls made primarily of cellulose,

which is stiff and strong. Cells are typically packed tightly

together, but occasionally spaces occur between them

(intercellular) that contain only air and/or water.

Plant tissue is composed of many types of cells that have 

different functions. Some cells, such as vessels and tracheids,

are empty and transport water from roots to the leaves, or

photosynthate back down from leaves. Living cells that

function in photosynthesis and other physiological processes are filled with cytoplasm, which is surrounded by a semipermeable membrane composed of a fatty material called

lipid in which protein molecules are embedded. This membrane plays a key role in plant cold hardiness; everything

within it is referred to as symplast and is living tissue.

Everything outside this membrane (cell walls, vessels, intercellular spaces, empty cells, etc.) is referred to as apoplast

and is not living (Figure 7.2.14A). 

Both the symplast and apoplast normally contain some

water. Apoplast water is nearly pure, so its freezing point

is close to 0 °C (32 °F). In contrast, the symplast contains

dissolved sugars and salts, suspended starch granules, and

protein molecules. These solutes act as “anti-freeze”,

depressing the freezing point of the symplast to considerably below 0 °C. So, when cells are exposed to sub-freezing temperatures, the apoplastic water begins to freeze.

As it does, small ice crystals form within the cell walls,

intercellular spaces, and other voids within the apoplast

(Figure 7.2.14B). The symplast water, with its lower freezing point, resists freezing. Therefore, the ice that forms

within the plant tissue is contained in the apoplast and

does little or no damage. 

Ice has, however, a very strong affinity for water—so

strong that ice crystals pull water tenaciously across the
34

Figure 7.2.14—Living cell contents (symplast) are separated from non-living cell contents (apoplast) by the cell

membrane (A). When temperatures fall below freezing, ice

crystals begin to form in the apoplast. As these crystals

grow, they draw water across the cell membrane causing

dehydration of the cell contents (B). If the cytoplasm

becomes severely dehydrated, the membrane can rupture,

and cell contents leak into the apoplast causing cell injury.

membrane and out of the symplast. Because the membrane is permeable only to water, the dissolved sugars

and other materials remain in the symplast even as water

is drawn out. This raises the concentration of the dissolved solutes, further lowering the freezing point of the

symplast water. When plant tissues are not cold hardy, or

when the temperature falls below their seasonal level of

hardiness, the cytoplasm can become severely dehydrated

to the point at which: 1) proteins denature; 2) membranes

are killed or damaged allowing cell contents to leak into

the apoplast; 3) cells plasmolyze; and 4) cytoplasmic cell

volume decreases sharply, signaling cell death. It is not

clear whether low temperature itself, or desiccation, or

both actually incite the damage (Adams and others 1991;

Sutinen and others 2001).

Cold hardiness mechanism. In order for plants to resist

freezing, several changes must occur in the physical and

chemical properties of the membranes and the cytoplasm

during the hardening process (Sutinen and others 2001,

Öquist and others 2001). First, membranes change physically, becoming more permeable to water. This enables

water molecules to move out of the cells rapidly, permitting intracellular solute concentrations to increase quickly. In addition, the membranes become physically more

rigid. This helps protect them from being pierced by ice

crystals that are rapidly growing in the apoplast, while

enabling them to resist being torn and pulled away from

the cytoplasm and/or cell walls as the cytoplasm dehydrates and shrinks. The cytoplasm itself undergoes profound physical-chemical changes that enable it to survive

severe dehydration. These adaptations take place in

response to changes in photoperiod and lowering temperatures and are orchestrated by suites of genes that are

“turned on” or “off” by these environmental signals.

An important hardiness avoidance mechanism is deep

supercooling of water (Quamme 1985, Burr and others

2001). Pure water can cool to nearly - 40 °C (- 40 °F)

without forming ice crystals when no ice nuclei are present, and some plants exploit this property. When supercooled water does freeze, however, it is nearly always

lethal. The observation that many plant species do not

occur north of the – 40 °C mid-winter isotherm suggests

they avoid cold damage primarily by this mechanism

(George and others 1974). This same midwinter isotherm

commonly coincides with timberline, causing Becwar
35

and others (1981) to speculate that supercooling may also

limit survival of certain species to below timberline. Many

conifers (pines excluded) employ supercooling as a

method of avoiding cold damage. However, many tree

species can survive temperatures far below – 40 °C so

they are able to resist cytoplasmic desiccation by other,

less well understood, mechanisms.

Stages in cold hardening. Cold hardening (also known as

cold acclimation) occurs in a series of stages depending

on plant species (Timmis 1976; Timmis and Worrall 1975;

Cannell and Sheppard 1982). Table 7.2.4 gives a generalized cold hardiness pattern for coastal Douglas-fir shoots

and root systems, which is illustrated in Figure 7.2.15. The

Y-axis represents the LT50 value—the temperature that is

lethal to 50% of a sample population—which is the most

common index of cold hardiness.

Further information on the environmental cues that trigger

and sustain the various stages of hardening and dehardening are discussed in Greer and others (2001).

Hardiness variation in plant tissues, species, and 

ecotypes. Different plant tissues harden and deharden at

different rates (Bigras and others 2001, Rose and Haase

2002). In particular, the fact that roots do not harden as

deeply as shoots (Figure 7.2.15) has very important implications for container growers (Colombo and others 1995).

Burr and others (1990) tested cold hardiness of

Engelmann spruce seedlings throughout winter and separately examined buds, needles, and lateral cambium

(Figure 7.2.16). Stems and needles hardened more rapidly, and achieved greater midwinter hardiness than buds.

All three tissues dehardened very rapidly in late winter. 

Tree species and ecotypes exhibit a vast range of midwinter hardiness levels depending on the regional climate

where they naturally occur (Sakai and Weiser 1973). 

Boreal conifers, such as black and white spruce, jack pine

(Pinus banksiana) and others attain hardiness levels below

-80 °C (-112 °F). Many Rocky Mountain conifers, such as

lodgepole pine (Pinus contorta) and Engelmann spruce,

also reach this cold hardiness level. In contrast, Pacific

coast conifers, such as Douglas-fir, coast redwood

(Sequoia sempervirens), and western redcedar (Thuja plicata), rarely acclimate to below -20 °C (-22 °F). Note that

the cold tolerance of wide-ranging species, such as

Douglas-fir, varies by ecotype (-20 °C [-4 °F] for

Washington State, but interior sources from the Rocky

Mountains can tolerate -20 to -30 °C [-4 to -22 °F]).

Cold hardiness testing methods. Although plants can be

tested for cold hardiness by several methods (Burr and

others 2001), two tests are widely used: the whole plant

freezing test (WPFT) (Tanaka and others 1997) and the

freeze-induced electrolyte leakage test (FIEL) (Dexter and

others 1932, Burr and others 1990, McKay 1992). Both

tests employ two steps (Ritchie 1991, Burr and others

2001). First, plants or plant parts are exposed to a freezing stress and, second, the amount of cold injury is rated.

These tests are compared in Table 7.2.5. Note that

because the FIEL test uses only small sections of tissues,

sampling is much more important.

Table 7.2.4—Stages of cold hardening and dehardening for conifer seedlings (compare to Figure 7.2.15)

Hardiness Stage Season Environmental Cues Temperature Tolerance as LT50

Hardening Begins Slowly Early Fall Shortening photoperiod -2 to -5 °C (28 to 23 °F)

Hardening Increases Rapidly Late Fall Increasing lower temperatures, -10 to -20 °C (14 to - 4 °F)

especially at night

Maximum Hardiness Mid Winter Very cold temperatures -15 to -40 °C (5 to -40 °F) 

Dehardening Happens Quickly Late Winter Rising temperatures Rapidly rising to -2 °C (28 °F)

and longer days
36

Figure 7.2.15—These typical cold hardening trends for

conifer seedlings show that shoots and roots follow the

same general pattern, reaching peak hardiness in January.

It is important to note that some species and ecotypes do

not reach Stage 3 Hardening, and roots do not attain the

same level of hardiness as shoots.

Figure 7.2.16—Plant tissues harden at different rates in

the fall, but all deharden very rapidly in the spring (modified from Burr and others 1990).

Whole plant freezing test. To begin, a representative sample of plants is subjected to a series of sub-freezing temperatures in a programmable chest freezer (Figure

7.2.17A-B) or a Thermotron for a pre-determined time

period, often a few hours. Test temperatures are selected

based on their expected ability to bracket the LT50. Next,

plants are incubated in a warm environment, such as a

greenhouse, for several days to allow symptoms to develop. Finally, the stem, buds, and foliage of test plants are

assessed for cold damage by evaluating visible damage or

“browning” in the bud, cambial, and foliar tissues (Figure

7.2.17C-E). Mortality is determined based on the severity

and position of tissue damage (Tanaka and others 1997).

Freeze-induced electrolyte leakage test. This test is based

on the fact that freeze-damaged cell membranes leak

electrolytes that can be measured with an electrical conductivity (EC) meter. To begin, sample tissues (foliage,

buds, or roots) are cut from the test plants (Figure

7.2.18A), and subjected to freezing temperatures (Figure

7.2.18B). They are then placed into deionized water,

which has zero electrical conductivity (Figure 7.2.18C).

The electrolytes that leak from damaged cells increase the

EC of the water, and this relative increase in EC

(described below) is a measure of the amount of cold

injury. Although this test can be done on any plant tissue,

samples of foliage or roots are most commonly used. 

A relative conductivity index of freeze damage (RC),

described by Ritchie (1991) and Burr and others (2001), is

determined as follows: 1) place tissue into vials containing deionized water; 2) expose the tissue to sub-freezing

temperatures; and 3) incubate the vials until the EC reading stabilizes. This point is known as the initial solution

conductivity (EC1). Finally, the sample is completely killed

by heating or freezing and the final conductivity (EC2) is

measured. Relative conductivity is calculated as:

RC (%) = (EC1 – B1) x 100 / (EC2 – B2)

where B1 and B2 are optional blanks included to

account for possible ion leakage from vials.

So, as you can see, the FIEL test provides a quick and

easy way to measure cold hardiness of plant tissues.
37

Differential thermal analysis. Differential Thermal

Analysis (DTA) is based on the theory that when supercooled water freezes it almost always indicates significant

tissue injury. Two plant tissue samples (stem or bud) are

collected and one of these is killed by heat or cold and

then dried. Two tiny thermocouples, wired in series, are

placed in the sample material—one in the dead and one

in the living tissue. The samples are placed into a freezing

cabinet capable of freezing down to about -40 °C (-40 °F). 

As the temperature is slowly lowered, the temperature difference between the samples remains at zero until a

freezing event happens. At this point a “spike” is registered. The first spike often occurs when the temperature

reaches -5 °C to -10 °C (23° to 14 °F) and represents the

freezing of intercellular (apoplastic) water. In tissues that

supercool, a second spike will occur at a lower temperature (down to -40 °C [-40 °F]). Evidence suggests that the

position of this second spike indicates the lethal temperature for that sample (Ritchie 1991).

While this method seems to offer promise for determining

hardiness levels of species that supercool, various technical problems have hindered its operational use (Burr and

others (2001).

Cold hardiness testing through gene expression. We indicated earlier that changes in environmental signals,

specifically photoperiod and temperature, trigger changes

in gene expression that ultimately result in cold hardiness

development. A novel approach to measuring hardiness,

described by Balk and others (2007), involves identifying

genes known to be implicated in this process. These

genes are responsible for production of enzymes, proteins

that trigger all physiological processes in organisms. To

create an enzyme, the cell must first transcribe the genetic

information stored in the DNA into messenger RNA

(mRNA). The strand of mRNA then moves over to a ribosome, a site of protein synthesis, where amino acids are

stitched together using the mRNA blueprint. The subsequent chain of amino acids is another enzyme that folds

into its characteristic shape, floats free, and begins performing a specific reaction (Figure 7.2.19A). Changes in

levels of enzymes triggered by these genes signal acquisition or loss of cold hardiness. An advantage is that these

signals can be detected much earlier (indicating that nursery treatments used to trigger cold hardiness development

were effective, or that plants are losing cold hardiness in

spring) than waiting for measurable changes in cold hardiness values using tests like whole plant freezing and

freeze-induced electrolyte leakage.

Research with Scots pine (Pinus sylvestris) and Norway

spruce (Picea abies) identified three indicator genes and

their subsequent enzymes that together provide enough

information to give an accurate estimate of the cold hardiness stage of nursery plants (Wordragan and others 2006).

Subsequent work with Douglas-fir showed similar results

(Wordragan and others 2008). Chemical assays were developed to detect the enzymes created by the indicator genes,

and a private company, N-Sure, now offers this test. A composite sample of bud tissue is collected by the nursery

manager, stabilized using chemicals provided in a sampling

kit, and mailed to the test laboratory (Figure 7.2.19B).

Results are available in 24 to 48 hours.

Table 7.2.5—Comparison of two main cold hardiness tests

Factor Whole Plant Freezing Test (WPFT) Freeze-Induced Electrolyte Leakage (FIEL)

Plant Tissue Tested Intact plant (foliage, buds, stem, and roots) Tissue (foliage, buds, stems, or roots)

Time Several days to a week One to two days

Required Testing Programmable freezer and Programmable freezer,

Equipment growth chamber or greenhouse electrical conductivity meter, 

autoclave or microwave

Evaluation Criteria Degree of tissue damage (browning) Numerical reading
38

Figure 7.2.17—In the whole plant freezing test, plants are

exposed to cold temperatures in a chest freezer (A) with programmable capabilities (B). After a specified exposure period,

plant tissues are rated for “browning” of buds (C), foliage (D),

and lateral cambium (E). (Photos by Diane Haase).

A B C

D

E
39

Figure 7.2.18—In the freeze-induced electrolyte leakage

test, plant tissue samples (A) are exposed to freezing temperatures (B) and then immersed in deionized water. The

relative increase in electrical conductivity is an indication

of cold injury (C). (Photos A&B by Thomas D. Landis;

Photo C courtesy of Sonia Gellert).

Applications of cold hardiness testing. Container nurseries use CH testing for several purposes.

1. CH tests can be used to track the hardiness of crops as

they go through natural hardening in the fall, or through

cultural hardening procedures such as blackout. In outdoor compounds, CH tests at regular intervals can be

used to determine when frost protection measures are

needed (Perry 1998). 

2. CH test are commonly used to determine the “lifting

window” for container crops. For example, the ability to

tolerate -18 °C (0 °F) is being used as an indication of

when conifer crops in British Columbia can be lifted for

freezer storage (Burdett and Simpson 1984). Different reference temperatures should be developed for other

species and ecotypes. 

3. CH tests provide a good estimate of overall plant stress

resistance (Ritchie 2000), which is a key quality attribute

(see Section 7.2.5.2).

Cold hardiness: conclusions and recommendations.

Plants that are easily killed by freezing temperatures during the growing season can survive much lower temperatures in winter when they are cold hardy. Cold injury

must be distinguished from winter desiccation that results

when cell water is pulled across the cell membrane to

feed ice crystals growing outside the cells. This can

severely dehydrate cytoplasm and injure membranes

causing them to leak cell contents.

The hardiness process is triggered in late summer by photoperiod, and increases during early winter as plants are

exposed to increasingly lower temperatures. The level of

hardiness can vary greatly among species and ecotypes and

is highly influenced by the climate of origin. Peak hardiness

occurs in January in temperate zone plants. Following peak

hardiness, as photoperiod begins to lengthen and temperatures begin to rise, hardiness is rapidly lost.

The most commonly used CH tests are the whole plant

freeze test in which entire plants are exposed to freezing

temperatures then evaluated for their response, and the

freeze-induced electrolyte leakage test, which is used to

test foliar and root samples. Tests based on genetic indicators are now becoming available.

B

A

C
40

Figure 7.2.19—Genomic cold hardiness tests allow early

detection of the chemical signals that trigger cold hardiness and can serve as an early indicator (A). The N-Sure

test provides a quick and accurate way to monitor cold

hardiness of nursery stock (B).

Cold hardiness tests can be used for establishing lifting

windows, for indicating when frost protection may be

needed in the nursery, and as a surrogate for stress resistance testing. 

7.2.4.3 Root electrolyte leakage 

Roots are among the most fragile parts of plants and,

hence, are sensitive to many environmental and operational stresses. This is particularly true of container stock

whose root systems are not insulated by surrounding soil.

Stresses include high and low temperatures (Lindström

and Mattson 1989, Stattin and others 2000), desiccation

(McKay and Milner 2000), rough handling (McKay and

White 1997), improper storage (McKay and Mason 1991,

McKay 1992, Harper and O’Reilly 2000), and even water

logging and disease. It is sometimes possible to detect

root damage using the time-honored thumbnail scraping

and browning examination, but often the damage is invisible or difficult to quantify. A more rigorous test is called

Root Electrolyte Leakage (REL). Because it measures the

health and function of root cell membranes, REL can be

used as an indication of root injury and therefore quality. 

REL has been used in Canada (for example, Folk and others 1999) and is currently one of a battery of plant quality

tests developed by the Ontario Ministry of Natural

Resources (Colombo and others 2001). In the United

States, electrolyte leakage has primarily been used to test

the cold hardiness of foliage, but application of this technique to roots is uncommon. 

The REL method is relatively simple, uses readily available equipment, produces results quickly, and can be useful with deciduous trees, which are leafless in winter

(Wilson and Jacobs 2006). Interpretation of REL results,

however, can be problematic due to species, seedlot, and

seasonal effects. 

Theory. REL tests are based on the same principle as the

FIEL test described in the previous section. The main difference, however, is that the REL test measures all types of

root injury, not just cold damage. The basic idea is that

measuring the quantity of ions that leak across damaged

root membranes provides an estimate of the relative “viability” of the root system (Palta and others 1977). When

damaged roots are placed in distilled water, the amount

B

A
41

of membrane leakage can be easily and quickly measured

with an electrical conductivity (EC) meter. 

The biological significance of REL. McKay (1998) offers

the following explanation for why the REL test has application as a plant quality test. After outplanting, the main

cause of plant mortality is transplant shock induced by

water stress. Plants with existing, viable roots systems are

more efficient in extracting water from soil, and REL

measures that root system viability. A low REL reading

indicates high root viability, allowing water uptake to mitigate transplant shock.

Measurement procedure. The technique most often used

(McKay 1992, 1998) has changed little from the initial

protocol described by Wilner (1955, 1960). The steps are

as follows (Figure 7.2.20):

1. Roots are first washed in water to remove soil, then in

deionized water to remove any surface ions that may

be present. 

Figure 7.2.20—In the root electrolyte leakage test, measuring the change in electrical conductivity of root tissue gives an

indication of the amount of membrane damage. Because this test reflects all types of root injury, it can be used to indicate

how well roots will grow out after outplanting.

2. A central mass of roots is removed from the plant—with

nursery plants this is often a band about 2 cm wide running across the mid-section of the root system.

Roots with diameter > 2 mm are removed from the

sample leaving only “fine” roots. 

3. Fine roots are placed into a vessel containing deionized water. 

4. The vessel is then capped, shaken, and left at room

temperature for about 24 hours. 

5. Conductivity of the solution (Clive) is measured with a

temperature-compensated electrical conductivity

meter. 

6. Root samples are removed and killed by autoclaving

for 10 minutes at 100 °C (212 °F). 

7. Conductivity of the solution surrounding the dead

root samples (Cdead) is measured.
42

8. REL is calculated as the ratio of the EC of live roots

divided by the EC of dead roots:

REL = (Clive / Cdead) x 100 

Applications of REL in nurseries. The REL test is most

often used to assess effects of cold damage, poor storage

conditions, root exposure causing desiccation, or rough

handling of nursery stock. Nearly all the published work

has been with bareroot conifer seedlings, primarily

Douglas-fir, spruces, pines, and larch. Use of REL to

detect freezing damage to roots is applied in one of two

contexts: evaluation of cold hardiness test results, and

detection of root injury following unseasonably cold

weather.

Measuring root cold hardiness. REL cold hardiness testing

is the same process as FIEL as explained in Section

7.2.4.2. For example, root samples from bareroot Norway

spruce seedlings were exposed to either -5 °C (23 °F) or 

-10 °C (14 °F) bi-weekly from September through

December in Sweden (Stattin and others 2000). As winter

progressed, the difference in REL between cold treated

and nontreated seedlings became smaller, indicating that

seedlings were becoming increasingly more cold hardy

(Figure 7.2.21).

Detecting cold or heat injury to roots. Because roots of

container plants are not protected by the thermal mass of

soil, they can be easily injured by extreme temperatures.

This is especially true when nursery stock is over-wintered

outdoors under snow, as is done in eastern Canada and

Scandinavia (Lindström and Mattson 1989). If snow fails

to accumulate, or a sudden warm period occurs, container crops are often exposed long enough for their roots to

be severely damaged. The REL test is ideally suited for

making rapid assessment of potentially damaged nursery

stock (for example, Coursolle and others 2000).

Determining lifting windows. REL has been used to indicate when it is safe to harvest bareroot nursery stock

(McKay and Mason 1991). For example, Douglas-fir

seedlings harvested during midwinter showed much

lower REL readings, and therefore less root injury, than

stock harvested earlier (Figure 7.2.22).

Monitoring quality of stored seedlings. REL can be used

to monitor quality during overwinter storage (McKay

1992, 1998; McKay and Morgan 2001). In one test

(McKay 1998), spruce and larch seedlings were harvested

throughout winter, beginning October 1, and then placed

in storage at 1 °C (33 °F). All seedlings were removed

from storage, tested for REL, and then outplanted in April.

With both species, REL decreased and survival increased

as harvesting was delayed. In another experiment (Harper

and O’Reilly 2000), Douglas-fir seedlings were harvested

in October, November, December, and January, “warm

stored” at 15 °C (59 °F) for 7 and 21 days, and then tested

for REL. The REL readings taken at the time of harvest

decreased with later harvesting dates indicating that

seedlings were becoming hardier. For each harvest date,

however, the readings increased sharply with storage

duration suggesting that warm storage contributed to fine

root degradation (Figure 7.2.22). 

Desiccation and rough handling effects. Bareroot Sitka

spruce and Douglas-fir seedlings were held in controlled

environment chambers with their roots exposed to drying

conditions for up to 3 hours (McKay and White 1997).

The REL readings increased with the intensity of the desiccation treatment indicating root injury. Injury was confirmed when the desiccation treatments had poor outFigure 7.2.21—Root electrolyte leakage measurements of

Norway spruce seedlings show the development of root

hardiness during fall. RELdiff is the increased electrolyte

leakage from roots following exposure to -5 or -10 °C (23

or 14 °F) compared with leakage from nonfrozen

seedlings (modified from Stattin and others 2000).
43

Figure 7.2.22—REL can be used to determine harvesting

(lifting) windows and monitor stock quality during storage. Douglas-fir seedlings harvested during midwinter

showed lower REL levels than stock harvested earlier in

fall. The same stock was warm-stored after harvesting, and

REL measurements at each date showed that the less

warm storage, the better (modified from Harper and

O’Reilly 2000).

planting performance on sites with low spring rainfall in

Great Britain . 

Rough handling in combination with root desiccation was

assessed in bareroot Douglas-fir, Sitka spruce, Japanese

larch (Larix kaempferi), and Scots pine using REL (McKay

and Milner 2000). Rough handling treatments were simulated by dropping bags of seedlings from a height of 3 m

(9.8 ft). Desiccation was achieved by exposing roots to

warm dry air for 5 hours. Although effects varied with

harvesting date and species, REL was significantly higher

in stressed seedlings across species and treatments. 

REL as a predictor of outplanting performance. The ultimate objective of any plant quality test is to predict how

well nursery stock will survive and grow after outplanting,

and many studies have used REL for this purpose with

mixed results. REL was closely correlated with relative

water content of radiata pine (Pinus radiata) seedlings 20

days after planting (Mena-Petite and others 2004). With

Sitka spruce and Japanese larch seedlings, REL was closely related to both survival and height growth (Figure

7.2.23). In Sitka spruce and Douglas-fir seedlings, REL

was correlated with survival on some sites, but not others

(McKay and White 1997). REL predicted establishment of

Japanese larch seedlings to some extent, but root growth

potential was a better predictor (McKay and Morgan

2001). Similar results were found with black pine (Pinus

nigra) (Chiatante and others 2002), while Harper and

O’Reilly (2000) reported that REL was a poor predictor of

survival potential in warm-stored Douglas-fir seedlings. 

Limitations of REL. Why does REL predict survival in

some cases but not all? As with many things “the devil is

in the details”. 

Genetics. REL has been shown to vary with species and

even seed sources within species. For example, jack pine

and black spruce exposed to a range of damaging root

temperatures had REL values in the range of 27 to 31 percent, while white spruce exposed to the same temperatures had REL between 36 and 38 percent (Coursolle and

others 2000). Sitka spruce seedlings from Alaska, the

Queen Charlotte Islands (QCI), and Oregon provenances

were evaluated for their ability to withstand root drying

and rough handling (McKay and Milner 2000). Oregon

and QCI seedlings exposed to root drying had lower REL

Figure 7.2.23—Root electrolyte leakage has shown good

correlation with outplanting performance in this study

with Japanese larch, but not so in many others (modified

from McKay and Mason 1991).
values than Alaska seedlings, while Alaska and QCI

seedlings, when exposed to rough handling had lower values than Oregon seedlings. In another study, Douglas-fir

had higher REL values than Sitka spruce, Scots pine, and

Japanese larch, regardless of the type of stress encountered

(McKay and Milner 2000). Two coastal seedlots of

Douglas-fir (British Columbia) gave different relationships

between REL and survival (Folk and others 1999). 

Dormancy status. McKay and Milner (2000) found that

the resistance to stresses mentioned above varied seasonally and was correlated with the intensity of bud dormancy.

A similar result was reported by Folk and others (1999) for

Douglas-fir seed lots who concluded that REL must first be

calibrated to bud dormancy status before it can be effectively used to assess root damage in Douglas-fir.

Seedling age. REL gave good correlations with survival in

two-year-old black pine seedlings, but correlations were

weak for one-year-old seedlings (Chiatante and others

2002). The authors speculate that the efficiency of REL as

a quality assessment tool could be closely related to the

developmental state of the root system.

Root Electrolye Leakage: summary and conclusions.

Electrolyte leakage from fine roots (REL) is a measure of

the ability of membranes within the root system to contain ions. Damaged membranes tend to leak ions so, if

ion leakage is quantified, it can provide an indicator of

root viability. The REL test is a robust, fast, and easy,

measurement. REL has been used successfully to evaluate

the effects of cold damage, rough handling, desiccation,

cold and warm storage, and other stresses on root viability and plant vigor. REL is sometimes closely correlated

with plant survival, but in other cases these correlations

are weak. This is because factors other than root damage

can affect REL, including species, seedlot, plant age, season, and bud dormancy intensity. Fortunately, REL can be

calibrated for these effects. 

7.2.4.4 Chlorophyll fluorescence

Although technology for measuring chlorophyll fluorescence (CF) has been in place for more than 50 years, it

has only been applied to tree seedling physiology since

the late 1980s (Mohammed and others 1995). In early trials, forestry researchers considered CF to be an important

research tool for potential applications such as assessing

effectiveness of irrigation and fertilization, determining

harvest windows, and evaluating plant vigor after storage.

CF was predicted to be a “simple, rapid, reliable and nondestructive method of evaluating seedling physiological

status during the nursery production cycle” (Vidaver and

others 1988). 

In the intervening years, CF has not lived up to all those

early expectations. Because CF has such great potential,

however, both plant producers and users should have a

basic understanding of CF and what it can and cannot do. 

What is chlorophyll fluorescence? When solar radiation

strikes a leaf, some light energy is reflected, some is transmitted through the leaf tissue, and some is absorbed.

Plants absorb much more light energy than is required for

photosynthesis. In fact, < 20 percent of the photosynthetically active radiation absorbed by a leaf is actually used

in photosynthesis (Figure 7.2.24). Red and blue wavelengths are absorbed by chlorophyll and other pigments,

but green wavelengths are reflected, giving living plants

their green color. To vent off all that excess energy, plants

have developed ingenious processes known collectively

as “energy quenching”. 

Three types of energy quenching are known. Photochemical quenching (qP) is energy used in photosynthesis.

Nonphotochemical quenching (qN) is energy dissipated

mainly as sensible heat. Fluorescence quenching (qF) is

energy emitted as fluorescence and is the basis for the

chlorophyll fluorescence test. The largest amount of the

absorbed energy is dissipated as sensible heat (qN), while

a much smaller amount is given off as fluorescent light

(qF) (Figure 7.2.24). These three quenching mechanisms

operate simultaneously and in competition with each

other.

If these quenching mechanisms are overloaded by high

light, the surplus energy drives a biochemical process

called the “Moehler reaction”. This generates free radicals,

mainly oxides and peroxides toxic to the plant. To protect

themselves, leaves synthesize scavenging molecules that

mop up free radicals and render them harmless. The yellow

carotenoid pigments, for example, serve this function.

When light intensity is so high, however, as to overwhelm

these scavenging systems then photodamage occurs

44
45

Figure 7.2.24—Only a small amount of photosynthetically

active radiation is absorbed by leaves and actually used

(quenched) by photosynthesis. The rest of the surplus

energy is quenched as heat loss or as fluorescence.

(Demig-Adams and Adams 1992). This often appears as leaf

“scorching” and is common in nursery plants that have

been moved too quickly from shade to full sun.

The manner in which a plant is able to manage the light

energy it absorbs is a sensitive indicator of stress (Krause

and Weis 1991). The CF technique, which quantifies energy quenching, is useful for studying plant responses to

stress and therefore plant quality.

Photosynthesis and chlorophyll fluorescence. Photosynthesis embodies three sequential processes (Vidaver and

others 1991): 

1) light harvesting—light energy is absorbed by light-sensitive pigments (including chlorophyll) in the leaves.

2) photochemistry—the absorbed light energy is converted into chemical energy.

3) biochemistry—chemical energy is used to drive Calvin

cycle reactions that convert atmospheric carbon into simple sugars. 

CF provides a view into the photochemistry process.

Because all three processes are intimately interconnected,

a perturbation to one part of one process affects the entire

set of reactions. These changes in the photosynthetic

process are reflected in variations in the amount and rate

of CF emissions.

Light energy enters the leaf of a plant and is “captured”

by light harvesting pigments (Figure 7.2.25). Depending

on the wavelength of the captured light, it enters one of

two reaction centers called Photosystem I (PSI) and

Photosystem II (PSII), which are located on membranes in

the chloroplasts. When a chlorophylla (Chla) molecule in

PSII absorbs a photon of energy, one of its electrons is

raised to a higher energy state. While in this excited state,

it is captured by an electron acceptor pool from which it

funnels down through an electron transport chain into

PSI, where a similar process occurs (PSI and PSII are

named in the order in which they were discovered, not

the order of the reaction). This energy transfer leads to the

generation of ATP and ultimately the reduction of NADP

to NADPH. The energy contained in ATP and the reducing power of NADPH contributes to the fixation of CO2

molecules and their ultimate conversion to simple sugars

in the Calvin Cycle.

“Water splitting” is another key part of the light reaction.

In order to replenish the electrons that are lost from Chla

in PSII, the plant splits water molecules, releasing oxygen

atoms into the atmosphere and providing electrons that

feed into PSII (Figure 7.2.25).

For any of a number of reasons, many of the excited electrons from Chla in PSII are not captured by the acceptor

pool and they decay back to their ground state. The energy lost in this decay process is given off as fluorescent

light (qF), which emanates entirely from Chla in PSII

(Kraus and Weis 1991) as it decays to its ground state.

This is shown in Figure 7.2.25 as a wavy line, and occurs

when the acceptor pool is fully reduced or when the electron transport pathway is backed up. In other words,

when more excited electrons are produced than can be

processed, they fall back to their ground state, releasing

their excitation energy as fluorescence. 

qN

qF

qP
46

This fluorescence emission is too weak to be visible to the

naked eye but can easily be detected by an instrument

called a chlorophyll fluorometer. The fluorometer measures and quantifies the nature of this fluorescence emission, and forms the basis of the CF test.

Measuring chlorophyll fluorescence. The German plant

biochemist Hans Kautsky first observed chlorophyll fluorescence in the late 1920s (Govindje 1995). Kautsky darkened

a leaf, then illuminated it with a brief flash of intense light,

and noted an emission of fluorescent light followed the

light pulse. Surprisingly, he found that in healthy tissue the

emission disappeared within a few minutes, but when the

tissue was killed with cyanide or by freezing, the fluorescence emission persisted much longer. It has since been

determined that poisoning or freezing leaf tissue disables

the electron flow pathway causing excited electrons to fall

back to their ground state and give off measurable fluorescence. In healthy tissue, by contrast, more electrons are

quenched in the electron transport pathway thereby reducing fluorescence emissions.

Figure 7.2.25—Simplified diagram of the light reaction of photosynthesis. Chlorophyll fluorescence emanates from chlorophyllα in Photosystem II. This fluorescence can be measured with a fluorometer, and can be used to diagnose stresses.

Kautsky fluorometers. Kautsky’s observation led to the

development of instruments called “Kautsky” fluorometers.

Originally large and cumbersome and the staple of laboratory research on photosynthesis, Kautsky fluorometers have

evolved into small, affordable, portable, and user-friendly

devices. They contain a light source, two sets of filters, a

microprocessor and a photosensor, and they typically interface with a laptop computer (Figure 7.2.26A). The light

source sends a pulse of photosynthetically active light

through a fiber-optic cable to the leaf surface where it activates Chla in PSII. The Chla emission returns back through

the cable and passes through a second filter that transmits

fluorescent light to the photosensor, which records the

emission. The process is controlled by the microprocessor,

which is programmed using the laptop computer.

The CF measurement process begins with “dark adapting”

the leaf for about 20 minutes. This ensures that: 1) all

chlorophyll is in an unexcited, or ground, state; 2) the

acceptor pools are empty; and 3) the electron transport

pathway is clear before the light pulse is received.

Following the light pulse, the fluorometer generates a

curve in which the intensity of the resulting fluorescence
47

emission is plotted over time (Figure 7.2.26B). In the

Kautsky curve, Fo is fluorescence emanating from the

light harvesting pigments in the leaf, not from PSII. Fm is

the maximum fluorescence, and Fv is the variable fluorescence coming from PSII. 

This curve has many diagnostic features, but the most

useful is the ratio of variable fluorescence to maximum

fluorescence, or Fv/Fm. This ratio provides a direct estimate of the efficiency of the light reaction (Genty and

others 1989) and is the most often used CF output.

Pulse amplitude modulated fluorometers. A more recent

development in fluorometry is an instrument called the

Pulse Amplitude Modulated (PAM) fluorometer (Schreiber

and others 1995). After delivering an initial excitation

light pulse, the PAM generates a rapid stream of high

intensity saturating light pulses that overwhelm acceptor

pools, thus canceling out photochemical quenching. The

fluorescence emission differences between these peaks

and the fluorescence decay curve is, therefore, nonphotochemical quenching. 

This powerful procedure enables simultaneous measurement of the three energy quenching components along

with determination of overall process efficiency at several

levels. One of these instruments, the PAM-2000, is manufactured by Heinz Walz, in Germany

(url://www.walz.com). PAMs have become an essential tool

for seedling physiology research. A PAM-2000 run produces estimates of quantum yield (Fv/Fm), effective quantum yield (Y), photochemical quenching (qP), nonphotochemical quenching (qN), electron transport rate (ETR), and

many other variables. 

Normal values of CF parameters in plants.The biochemistry of photosynthesis is essentially uniform across all

species of C4 plants. Therefore, CF parameters in “normal”

healthy plants would not be expected to vary across a

broad range of species. Discussions with other scientists,

as well as perusal of the CF literature, led to the development of Table 7.2.6. This gives what are often considered

to be “normal” values for the CF parameters and can be

used as a guide to interpreting literature values. 

Use of CF in plant quality assessment. At the present

time, CF is used more in research than in quality testing,

but here are some common applications.

Figure 7.2.26—A “Kautsky” fluorometer consists of a light

source, two filters, a photosensor, microprocessor, and a

fiberoptic cable that attaches to a leaf. Instructions are

sent to the fluorometer from a laptop computer (A). A

quenching curve is generated after a light pulse is delivered to a dark-adapted leaf. These curves are diagnostic

because healthy and stressed plants differ in the amount

and duration of their fluorescence emission (B). For example, the ratio of variable to maximum chlorophyll fluorescence (Fv/Fm) is a good indicator of photosynthetic efficiency. See text for explanation of symbols (B - modified

from Rose and Haase 2002).

B

A
48

Dormancy. Although attempts to use CF as an indicator of

plant phenological condition or dormancy status have

been done, we are not yet convinced that these studies

are verifiable or repeatable. 

Cold hardiness. Currently, the most common use of CF is

in detecting and assessing cold injury (Binder and others

1997). For example, when 17 species of Abies were tested for cold hardiness, the damage to buds, foliage, and

cambium were all well-correlated with CF ratings (Jones

and Cregg 2006). Rather than rating cold injury with visual, electrolytic, or other methods (see Section 7.2.4.2), the

CF approach uses the response of the photosynthetic

process as an index of damage. “Normal” plants will typically have Fv/Fm values from 0.700 to 0.830, or slightly

lower in winter. When this value falls to < 0.600 following freezing it indicates significant damage to the photosynthetic process (Table 7.2.6). 

Outplanting performance. Some studies have attempted to

correlate CF variables with outplanting performance. For

example, measures of effective quantum yield predicted

variations in survival and plant health of stored and nonstored Douglas-fir seedlings in an Irish nursery (Perks and

others 2001). 

Storage effects. Short term (2-week) cooler storage of radiata pine seedlings caused depressions of Fv/Fm, Fv/Fo,

and other CF parameters as leaf water potential, stomatal

conductance, and net photosynthesis also dropped

(Mena-Petite and others 2003). These reflected storagerelated damage to photosynthetic apparatus and portended reduced post-planting performance.

Drought stress. Long-term drought affects photosynthesis

directly by depressing leaf water potential, which closes

stomata. Recent evidence suggests that prolonged drought

also disrupts photosynthesis at the photochemical level.

When white spruce seedlings were exposed to 21 successive

days without water in a controlled environment chamber

(Bigras 2005), Fo and qN were unaffected, but Fm, Fv,

Fv/Fm, and qP were depressed when water potential fell

below -1.0 MPa (10 bars PMS). Fv/Fm measured in dormant

Norway spruce seedlings was unaffected by 4 weeks of postplanting drought in the field, but the same drought exposure

depressed Fv/Fm from 0.83 to about 0.28 in seedlings lacking bud dormancy (Helenius and others 2005).

Chlorophyll fluorescence: summary and conclusions.

Plants have evolved intricate mechanisms for dissipating,

or quenching, the light energy they absorb. Some of this

energy is used in photosynthesis (photochemical quenching, qP), while the remainder is dissipated by nonphotochemical (qN) or fluorescence (qF) quenching. 

Stress caused by high and low temperature, disease,

drought, inadequate nutrition, and so on impairs a plant’s

ability to manage energy quenching. Thus, by measuring

and interpreting the three components of quenching with

CF, it is possible to detect damage resulting from subtle,

transient stress as well as long term, severe stress. Three

important CF parameters that are often reported in the

nursery literature are, qP, qN, and Fv/Fm.

Damaged or stressed plants have the ability to recover

quickly, so it is important to measure CF parameters over

a course of several days following stressful events before

conclusions about plant damage can be reached. If Fv/Fm

remains low and qN high for several days, this indicates

that significant damage to the photosynthetic system has

probably occurred. Still, much more research is need

before CF will be an operational quality test.

7.2.4.5 Mineral nutrient content

Intuitively, the amount of mineral nutrients that are stored

in a plant should be related to its quality. Mineral nutrients such as nitrogen and phosphorus supply the building

materials for new growth and newly outplanted seedlings

must rely on a supply of stored nutrients until they are

established in the field. Plant tissue can be quickly and

precisely analyzed to give a complete picture of mineral

nutrient levels (Landis and others 2005), but the correlation between foliar nutrient levels and outplanting survival is not good. One problem is that a plant could be

severely stressed or even dead and still contain ideal mineral nutrient levels. Even though mineral nutrient levels

are not a guarantee of vitality, foliar nitrogen levels

appear to be a good predictor of growth after outplanting

(Landis 1985). For example, van den Driessche (1984)

found a strong correlation between foliar nitrogen and the

shoot growth of Sitka spruce seedlings when measured 3

years after outplanting (Figure 7.2.27A). This only makes

sense because once a plant is established it needs good

reserves of nitrogen to repair any injuries and build new
49

Table 7.2.6—Normal ranges of chlorophyll fluorescense emissions parameters in C4 plants (extracted from the literature)

CF Parameter Definition Description Normal Range Stress Range

Fo Ground state Fluorescence which emanates from 0.2 to 0.4 >0.7

fluorescence the light-harvesting pigments of the 

leaf; generally considered a 

“background level” fluorescence 

which is zeroed out when measuring 

PSII chlorophyll fluorescence.

Fs Steady-state Fluorescence level Low Ft indicates stress

fluorescence (sometimes referred to as Ft)

Fv Variable fluorescence Height of the fluorescence peak above 

Fo following exposure to the actinic 

light pulse (Fv = Fm – Fo)

Fm Maximal fluorescence Fv + Fo 1.2 to 1.5

Fv/Fm Maximum quantum yield An estimate of the ratio of moles of 0.70 to 0.83 <0.60

carbon fixed per mole of light energy 

absorbed (Genty and others 1989); 

theoretical maximum value for C3

photosynthesis is approximately 0.830.

Y Effective quantum yield (Fm-Fs)/Fs 0.40 to 0.60 0.10 to 0.20

qN Non-photochemical Dissipation of absorbed light energy by 0.4 to 0.6 Prolonged values > 0.6

quenching means other than photosynthesis (mainly 

as sensible heat)

qP Photochemical quenching Use of absorbed light energy via 0.7 to 0.8 Prolonged values < 0.6

photosynthesis 

ETR (in full sun) Electron transport rate Speed at which electrons are <300 μmol electrons m-2s-1

transported through the photosystem 
50

cells. Some nurseries have established foliar nitrogen targets at the time of harvest as one indication of plant quality; for instance, provincial nurseries in Quebec specify a

minimum foliar nitrogen levels for their nursery stock depending on container size (Government of Quebec 2007). 

The latest research into the relationship between seedling

nutrient levels and outplanting performance involves a

concept called “nutrient loading” with nitrogen. The idea

is that “supercharging” a seedling with nitrogen will help

it survive and grow better on the outplanting site where

mineral nutrients are usually limiting. Nutrient loading

involves fertilizing seedlings during the hardening phase

until their nitrogen content is in the luxury consumption

area of the growth curves (Figure 7.2.27B). This process

has been successful with black spruce (Picea mariana) on

sites with heavy plant competition as chronicled by

Timmer and his associates (for example, Timmer 1997).

The concept of nutrient loading with nitrogen is certainly

attractive and it is hoped that this technique will be tested

with more species on a wide variety of outplanting sites

(Landis and others 2005). 

7.2.4.6 Carbohydrate reserves

It also seems logical that the amount of food stored as

carbohydrates in nursery stock should be a good indication of plant quality. After outplanting, nursery plants

must rely on this stored “food” to fuel new growth until

the plants can start photosynthesizing. Marshall (1983)

gives an excellent review of carbohydrates in plants and

presents a good comparison of how stored carbohydrates

would be used in two different seedlings. Seedling 1 contains adequate levels when harvested, but carbohydrates

are gradually consumed during storage; after outplanting,

even more are used until the plant becomes established

and generates new carbohydrates through photosynthesis

(Figure 7.2.28A). Plants that suffered stress or injury

would use-up even more carbohydrates to repair tissues

and fuel metabolic recovery. In fact, carbohydrate

reserves were found to influence the growth of nursery

stock for up to 2 years after outplanting (Ronco 1973). 

Unfortunately, research trials have not shown carbohydrate reserves to be a good predictor of plant quality and

little has been done with container nursery stock. For

example, the carbohydrate reserves of bareroot Scots pine

seedlings were evaluated as an indicator of stock quality,

and the results followed the general trend in Figure

7.2.28A. When reserves dropped below 2% total glucose

during storage, significant mortality occurred (Figure

7.2.28B). The author concluded that difficulties in measuring carbohydrate concentrations and the dynamics of

carbohydrate metabolism make tests of carbohydrate

reserves impractical for operational use as a plant quality

index (Puttonen 1986).

Measuring performance attributes can be thought of as a

“bioassay” that integrates the functioning of all plant systems into one performance variable. Although they are

often robust indicators of plant performance potential,

performance attributes do not identify what, specifically,

is wrong when performance potential is low. They also

suffer from being very time consuming to measure directly, which can limit their usefulness to plant producers and

users.
51

Figure 7.2.27—Foliar nitrogen (N) concentration was

shown to be a good predictor of the shoot growth of Sitka

spruce seedlings when measure 3 years after outplanting

(A). “Nutrient loading” conifer seedlings with high levels

of nitrogen (B) has been shown to be beneficial on wet

outplanting sites with heavy plant competition (A,modified from van den Driessche 1984; B, modified from

Timmer 1997).

Figure 7.2.28—Nursery plants consume significant

amounts of stored carbohydrates from harvest through

storage and outplanting. Seedling 1 contained adequate

reserves and survived and became established on the outplanting site. Seedling 2 started out with inadequate carbohydrate storage and died soon after outplanting (A).

Seedling mortality increased and shoot growth decreased

after outplanting when total glucose levels dropped below

2 percent (B) (A, modified from Marshall 1983; B, modified from Puttonen 1986).

B

A

A

B
52

Figure 7.2.29—Dormancy refers to the activity of the

meristematic tissues: buds, lateral meristems in the stem,

and root tips. In the normal context of plant quality, bud

dormancy is the primary concern.

7.2.5.1 Bud dormancy 

The notion that nursery stock quality is related to its dormancy status is strongly engrained in the minds of plant

producers and users, especially foresters. When pressed

to explain this relationship and why it is important, however, few are able to articulate a clear view of what dormancy is, how it works, or how it affects quality. 

The concept of dormancy. Dormancy is one of the oldest

concepts in plant science. Nursery workers learned by trial

and error that plants could be transplanted and outplanted

most successfully when they were not actively growing. In

the temperate zone, this occurs in winter, so nurseries have

traditionally harvested stock then. The concept of the “lifting

window” was developed by harvesting and outplanting

seedlings from late fall through early spring and measuring

survival and growth (Jenkinson and others 1993). These trials supported the traditional practice of harvesting during

midwinter, and people interpreted these results to mean that

plants were most “dormant” during this period. As we will

show, however, this concept of a midwinter dormancy peak

is not correct.

Defining dormancy. Dormancy can be broadly defined as

a state of minimal metabolic activity, or any time that a

plant tissue is predisposed to grow, but does not

(Lavender 1984). In other words, dormancy is that condition in which plant growth—cell division and enlargement—is not occurring. In horticulture, dormancy can

refer either to seed dormancy or plant dormancy. In the

published literature, plant dormancy has been studied

much less than seed dormancy but it is what we are concerned with here. 

Two kinds of plant dormancy are recognized:

External dormancy, also known as “quiescence”,

occurs when environmental conditions (for example,

severe water stress) will not support growth (Lavender

1984). Plants exhibiting imposed dormancy will

resume growth when these unfavorable conditions

improve (when it rains). 

Internal dormancy, or “deep dormancy”, is a condition in which plants will not resume growth until they

have experienced a long period of exposure to low

temperatures (Perry 1971). This condition is also

called “winter rest”. In this chapter, we are concerned

with deep dormancy and how this physiological condition affects nursery culture and outplanting success. 

Dormancy refers to tissues, not entire plants. In everyday

nursery jargon we talk about plants, or even entire crops,

being dormant. While this is common terminology, it is

important to understand that plant dormancy refers to a

specific meristematic tissue, usually buds (Figure 7.2.29).

In the same plant, buds may be dormant while the lateral

meristem may not. Root meristems never truly go dormant

and will grow anytime that environmental conditions,

especially temperature, are favorable. Because we are

concerned with quality testing, we will be discussing bud

dormancy, which is most clearly observed in the behavior

of terminal buds.

7.2.5 Performance Attributes
53

The dormancy cycle. Perennial plants that grow in temperate regions exhibit a pronounced seasonal “cycle of

dormancy” (Figure 7.2.30A). In spring, as day length and

temperature increase, plant buds begin to exhibit dimensional increases reflecting both cell division and expansion—in other words, they begin to grow. Shoot growth

persists through spring and into summer. In early summer,

as day length (photoperiod) begins to shorten, the increasing length of the dark period is perceived by the phytochrome system in leaves as a signal to begin preparing for

winter. At this point shoot growth slows and winter bud

development proceeds (Burr 1990). 

The chilling requirement. In late summer, plant buds

enter the condition of imposed dormancy. As summer surrenders to autumn, imposed dormancy gradually gives

way to deep dormancy and buds reach maximum dormancy in late fall (Figure 7.2.30A). As we just mentioned,

dormancy is then released by exposure of the plants to an

extended period of low temperatures; this is known as a

“chilling requirement” and is sensed by the buds. This

evolutionary adaptation ensures that plants will not

resume shoot growth (break bud) during a midwinter

Figure 7.2.30—The buds of perennial plants in the temperate zone, including forest and conservation nursery stock,

undergo a seasonal cycle of shoot growth and dormancy. Note that peak dormancy occurs in late fall instead of midwinter, as is often believed, and that dormancy is released by cumulative exposure to cold (“chilling requirement”). Older

nursery stock will form firm “winter buds” and conifer foliage turns bluish due to waxy deposits (B).

warm spell only to be killed by a return of cold weather.

Once this chilling requirement is satisfied, warm spring

temperatures and, to a lesser extent, lengthening photoperiod, will trigger and sustain a resumption of shoot

growth (Campbell 1978). Although temperatures in the

range of about 3 to 5°C (37 to 41°F) are most efficient at

releasing bud dormancy (Anderson and Seeley 1993),

temperatures above and below this range also are effective to a lesser degree (Figure 7.2.31). 

Orchardists, and other horticulturalists, have developed

elaborate models to predict the date of flower bud opening in cold sensitive crops such as peaches (see, for

example, Richardson and others 1974). These models take

into account the efficiency of chilling and the fact that

warm interruptions during late fall can negate some chilling that has occurred up to that time. In forest and conservation nurseries, however, a simpler process for calculating chill sums or chilling hours is often used. The

details are given in the following section.

Measuring dormancy. Because of its tremendous importance to nursery management, many attempts have been

A B
54

Figure 7.2.31—Chilling temperatures and their efficiency

at breaking bud dormancy (modified from Anderson and

Seeley 1993). Note that temperatures in the range of

refrigerated storage (-1 to +1 °C [30 to 33 °F]) release

dormancy very slowly.

Figure 7.2.32—The “dormancy meter” was an attempt to

find a simple and easy way to measure dormancy and

determine when plants were ready for harvest. Operational testing showed that such devices were unreliable.

made to develop a simple way to measure dormancy. As

we will now discuss, this objective has been elusive.

Dormancy meters. In the 1970s, researchers observed

that changes in electrical resistance of plant tissue provided a useful way to determine whether tissues were injured

or dead. Building on these observations, they constructed

a “dormancy meter” (Figure 7.2.32) with the objectives of

measuring dormancy in fall and telling nursery managers

when it was safe to harvest their stock. Unfortunately,

subsequent tests showed that these meters were unreliable (Timmis and others 1981). The idea of a simple

“black box” quality test is still attractive but it is doubtful

that any equipment or technique will be able to instantaneously measure bud dormancy.

Chilling sums. This is the easiest and most practical

method for estimating the intensity of bud dormancy, and

is based on the chilling requirement just discussed.

Chilling sums have immediate application because they

can be used to establish harvesting windows or monitor

bud dormancy as it weakens during winter. The concept

is logical enough; the cumulative exposure of plants to

cold temperatures controls the release of dormancy. So,

by measuring the duration of this exposure, it is possible

to estimate the intensity of dormancy indirectly. 

In actual practice, chilling hours, or degree-hardeningdays (DHD) have been used. The process involves measuring the temperature each day and calculating the

amount of time below a specific reference temperature. A

method that is sometimes used in forest and conservation

nurseries is to simply count the number of hours during

which the air temperature is at or below a threshold

value, such as 5 °C (41 °F) (Ritchie and others 1985). One

shortcut method is to record the daily maximum and minimum temperatures, average them, and subtract this average from the base temperature. Note that, when calculating chilling sums, only negative values are recorded

(Table 7.2.7).

Bud break test. The more dormant a plant is, the more

slowly the terminal buds will resume growth (break)

under ideal growing conditions. This phenomenon forms

the basis of the only direct way of measuring dormancy

intensity—the bud break test. With access to a greenhouse or other growth-promoting structure that can main-
55

Table 7.2.7—An example of how to calculate chilling sums using degree days, calculated from an average of daily 

maximum and minimum temperatures, and a 40 °F (4.5 °C) base temperature

Day Base Temp. (°F) Daily Temperatures (°F) Degree Days Chilling Sum

Maximum Minimum Average

One 40 40 20 30 10 10

Two 40 45 35 40 0 10

Three 40 50 40 45 0 10

Four 40 40 30 35 5 15

Figure 7.2.33—The only reliable test for bud dormancy

intensity is a “bud break” test that can be performed by

harvesting plants at regular intervals during late fall and

winter, and bringing then into a greenhouse. As they

break bud, the number of days to bud break (DBB) is

plotted against the chilling sum for each lift date. The data

shown are typical of Douglas-fir nursery seedlings (modified from Ritchie 1984a).

tain ideal growing environment through the winter, the

intensity of dormancy in nursery stock can be measured

by observing days to bud break (DBB) in this “forcing”

environment. 

The procedure is relatively simple. Grow plants to shippable size and, in the late summer, harden them to the

fully dormant condition by exposing them to ambient

conditions. By early fall, plants typically have formed a

dormant bud and exhibit the other morphological

changes such as leaf color change and abscission in hardwood stock (Figure 7.2.30A), and increased needle waxes

on conifer needles (Figure 7.2.30B). Place a temperature

recording device at plant height and check temperatures

at least weekly to compute chilling sums (Table 7.2.7). 

Set the environmental controls in the testing greenhouse

to maintain spring forcing conditions with warm days,

cool nights, and long photoperiods created with photoperiod lights. Then, beginning around Halloween, harvest

a sample of plants, pot and label them, and bring them

into the forcing greenhouse. Keep the sample plants

watered and count the number of days required for the

terminal buds to resume growth—this is days to bud

break (DBB). Repeat this process at every major holiday:

Thanksgiving (late November), Christmas (late December),

New Year’s Day (early January), Valentine’s Day (midFebruary), and St. Patrick’s Day (late March). Starting at

the first sample date in September, keep track of the sum

of chill hours, all hours when the temperature was, say, 5

°C (41°F) or lower throughout this test period.
56

When finished, plot the DBB values over the chilling

sums. The number of days required for the terminal buds

to break is a direct measure of dormancy intensity. (Note:

the Halloween plants may never break bud). It is likely

results will be similar to those shown in Figure 7.2.33,

which came from coastal Douglas-fir in western

Washington and Oregon (Ritchie 1984a) and are in agreement with the general curve proposed by Lavender

(1984). As the chilling sum accumulates during winter, the

number of days to bud break will shorten dramatically.

Similar experiments with many tree species, including several hardwoods (birch, dogwood, hawthorn, and oak) have

yielded similar results (Sorensen 1983, Lindqvist 2000).

Once this curve has been developed for a nursery, it can

be used subsequently to estimate dormancy intensity for a

given species and seed zone directly from chilling sums. 

From this experiment, it is clear that bud dormancy intensity is very high in fall and drops sharply in early winter,

in contrast to the common misconception that deepest

dormancy occurs in midwinter when plants are most

stress resistent. In addition, this test illustrates that there is

no simple “chilling requirement” for any species. Rather,

there is a curvilinear relationship between chilling and

dormancy in which more chilling will result in more rapid

budbreak under forcing conditions. For example,

Douglas-fir seedlings with only 800 hours of chilling

exposure will eventually break bud, but not nearly as rapidly as those exposed to 2,000 hours of chilling (Figure

7.2.33).

Calculating the dormancy release index. Now that the

days to bud break (DBB) for a given crop can be estimated from chill sums, how is this information used? If DBB

was measured on a group of Douglas-fir seedlings that

were fully released from dormancy (that is, the chilling

requirement was completely fulfilled) the buds would

break in about 10 days. Taking this number as the denominator, an index can be calculated that expresses the dormancy intensity on a linear scale:

Dormancy release index (DRI) = 10/DBB 

DBB is the days to budbreak of a test group of plants as

described in the experiment above.

Buds at peak dormancy have a DRI value near zero (for

example, DRI = 10/300 = 0.03). As dormancy weakens,

DRI approaches 1 (for example, DRI = 10/15 = 0.67). This

relationship is shown in Figure 7.2.34. DRI is useful

because it transforms the curvilinear relationship between

dormancy intensity and chilling sum to a more useable

linear form. This linear regression can then be used to

provide a benchmark and common scale for comparing

stock lots in a given plant species.

McKay and Milner (2000) developed a variation on this

approach; they estimated DRI by counting the days

required for 50 percent of the terminal buds to break in

Sitka spruce, Douglas-fir, Japanese larch, and Scots pine.

Their results also closely resemble those of Figure 7.2.34.

The Dormancy Release Index has been particularly useful

as an indicator of plant stress resistance—a key performance attribute. We will discuss this relationship and how

it is used in Section 7.2.5.2.

Measuring mitotic index. In our definition of dormancy,

we stressed that dormancy referred only to buds or other

plant meristems (Figure 7.2.29). Laboratory techniques

Figure 7.2.34—Because days to bud break (DBB) over chill

sum is a curvilinear relationship, it is useful to convert it to a

linear dormancy release index (DRI). In this example, DRI =

10/DBB because Douglas-fir seedlings resumed growth

(broke bud) in 10 days when their full chilling requirement

was satisfied (modified from Ritchie 1984a).
57

have been developed to measure the number of meristematic cells that are dividing at any given time (Figure

7.2.35A). Although primarily used for research purposes,

these measurements also illustrate dormancy patterns. 

For example, the tips of terminal shoots and long roots of

bareroot Douglas-fir seedlings were excised. Using 400X

microscopes, a mitotic index was calculated (O’Reilly

and others 1999). The results indicate that terminal bud

activity shows a definite seasonal pattern; cell division

slows gradually in fall and stops completely during winter. With warmer temperatures and longer days in late

winter and early spring, cell division begins to increase

rapidly (Figure 7.2.35B). This is in direct contrast to the

patterns of root meristem activity, showing that roots

never become truly dormant but will grow whenever soil

temperatures permit (Figure 7.2.35C). Although useful to

researchers, this test is too time-consuming to be used

operationally.

Bud size and development. While bud size and development are not, in themselves, indicative of the intensity of

bud dormancy, they have traditionally been viewed by

nursery managers as an indicator of plant quality. For

example, a bud length measuring protocol was developed

by the Ontario Ministry of Natural Resources as part of

their former quality testing service. The process involves

Figure 7.2.35 —Measuring cell division rates in buds (A) is a laboratory measure of dormancy. Shoot activity over 4 years

shows a characteristic pattern of inactivity during winter (B), but roots (C) continue to grow whenever conditions are

favorable (modified from O’Reilly and others 1999).

cutting buds in half and counting needle primordia. At the

end of the hardening phase, low numbers of primordia

were interpreted to indicate stressful conditions and

increased susceptibility of overwinter damage. Conversely,

seedlots having buds with large numbers of needle primordia were rated as being of higher quality (Colombo

and others 2001). 

Dormancy: conclusions and recommendations. Although

the term “dormant plants” is common in nursery jargon,

dormancy refers only to meristematic tissues—buds, lateral cambium, and root tips. Of these, bud dormancy has

been studied most intensively and is of major interest to

plant producers and users. 

Forest and conservation nursery crops, like all perennial

plants, undergo an annual cycle of activity. In late summer, shortening photoperiods trigger plants to begin the

bud dormancy process that culminates in late fall. This

condition is known as deep dormancy and can be

released by exposure to a period of low temperatures.

This process is known as satisfying the chilling requirement, and temperatures in the range of about 3 to 5 °C

(37 to 41 °F) are most efficient. By late winter, the chilling

requirement has been met and buds will break whenever

temperatures permit. 

A B C
58

Figure 7.2.36—This classic illustration shows that bud

dormancy and stress resistance follow similar bell-shaped

trajectories but occur at different times. Comparison to

the traditional midwinter lifting window shows that stress

resistance is a better indicator of when to harvest (“lifting

window”) and store nursery crops (modified from

Lavender 1984).

Unfortunately, bud dormancy cannot be quickly or easily

measured. The only reliable method is to conduct a bud

break test by bringing samples of plants into a forcing

greenhouse at regular intervals throughout winter, and

recording the days required for the buds to break (DBB).

Once the relationship between DBB and chilling has

been developed for a nursery, it can be used to establish

harvesting windows and estimate the dormancy intensity

of crops during subsequent winters.

A useful index of dormancy intensity, called the

Dormancy Release Index, makes the DBB information

more practical by converting the data to a straight line.

Although we lack a quality test for bud dormancy, it can

be estimated from the known relationship between chilling and dormancy intensity as measured by DBB.

Nurseries can measure the chilling requirement for their

various crops and use this information to monitor the

release of bud dormancy. 

7.2.5.2 Stress resistance

In the previous section, we indicated that dormancy is

closely related to stress resistance (SR). From an operational standpoint, we will introduce some techniques that

nursery managers can use to estimate the relative SR of a

crop at any point during the harvesting-to-outplanting

process. 

The concept of stress resistance. Plants are subjected to a

variety of stresses (mechanical stresses, root exposure,

rough handling, and desiccation to just name a few) from

the time they are harvested in the nursery to when they

are outplanted. Nursery managers use a variety of cultural

techniques, collectively termed “hardening-off”, to prepare their stock to tolerate these stresses. Realizing its

importance and practical applications, plant physiologists

have been studying SR for almost 40 years. 

Hermann (1967) determined that SR seems to be primarily

a property of root systems in bareroot stock, and Lavender

(1984) showed that SR varies seasonally, reaching a midwinter peak after bud dormancy intensity has begun to

decline (Figure 7.2.36). The data for this seasonal curve

came mainly from outplanting trials and that is why it

corresponds exactly with the traditional midwinter lifting

season. 

Obviously, nursery managers want to maximize SR in their

crops and maintain this condition until they are shipped to

their customers for outplanting or transplanted back into

the nursery. But how can we measure or estimate SR, and

how can we culture our crops to reach this peak?

Measuring stress resistance. A quick and easy way to

measure the SR of nursery stock would be an invaluable

tool, and there have been many attempts to develop a test

to ascertain this important aspect of quality. 

Stress tests. During the 1970s and 1980s, several

attempts were made to develop quick tests of SR. For

example, a stress test was developed at Oregon State
59

consists of three sequential steps:

1. Exposing plants to a controlled stress treatment. The

most commonly used stress treatments employ some sort

of controlled trauma to root systems. This might involve

exposure to high or low temperatures, prolonged drying,

or another simulation of rough handling, such as dropping or tumbling. 

2. Outplanting stress–treated plants into a natural environment where their growth response to the treatment can

be expressed. By “natural”, we mean the plants should be

growing in soil and exposed to the ambient outdoor environment, but they must be able to express growth potential without confounding effects of browsing, water stress,

or weed competition. A bareroot nursery bed that is

watered regularly and kept weed free is ideal. The test

plants are set out in replicated blocks along with nonstressed controls of similar initial size from the same seedlots or families.

3. Evaluating the impact of the stress treatment by comparing the performance of the stressed plants to that of

nonstressed controls after a predefined time period, typically one complete growing season. The assessment can

be as simple as measuring shoot growth or as complicated as destructively sampling the entire plant and measuring total biomass. We have found that removing the shoot

of the plant and determining its dry weight is a good basis

for comparison. In this approach, SR is characterized as

the difference in growth between the stressed plants and

nonstressed controls. A helpful way of expressing this difference numerically is by calculating a Stress Injury Index

(SII) using the first year shoot growth of the stressed (Gs)

and nonstressed control seedlings (Gc):

SII = 100 – (Gs/Gc x 100)

The SII expresses the percentage reduction in top growth

resulting from stress injury and so, the lower the value,

the higher the stress resistance of the test plants (Ritchie

and others 1985). 

Figure 7.2.37—Stress tests involve harvesting seedlings

and exposing them to a stressful environment. At Oregon

State University, the stress was a dry, hot greenhouse.

University (McCreary and Duryea 1984) that consisted of

harvesting plants, potting them, and exposing them to

stressful conditions, mainly high temperature, low humidity, and low soil moisture. After a pre-determined time,

plants would then be moved into a greenhouse and

assessed for survival, root growth, bud break, and other

indicators of vigor (Figure 7.2.37). Despite some promising early results, the outcomes of literally hundreds of

such tests proved difficult to interpret and not very repeatable. Accordingly, this quality test was abandoned. 

Another more elaborate and more time-consuming, but

more accurate, method of measuring SR involves a procedure similar to cold hardiness testing (Ritchie 1986). It
60

Figure 7.2.38—Both bud dormancy, as measured as days

to bud break (DBB), and stress resistance, as measured

by cold hardiness tests, can be used to determine the best

time to harvest nursery stock (“lifting window”). Cold

hardiness tests, however, are so much quicker and easier

that they have become the standard test for lifting and

subsequent refrigerated storage.

Using cold hardiness tests to estimate overall stress 

resistance. Decades of nursery experience have shown

that, when plants are at their maximum state of hardiness,

they are the most resistant to the many stresses of harvesting, handling, storage, shipping, and outplanting. In fact,

recent genetic research has revealed that some of the

same (dehydrin) gene complexes that are involved in cold

acclimation also play a key role in resistance to water

stress (Wheeler and others 2005). 

Container nurseries in western Canada use a “storability

test” to determine if plants are physiologically ready for

harvesting, packaging, and cold storage (Simpson 1990).

Essentially, if plants are cold hardy to a threshold temperature of -18 °C (0 °F), then they are ready to withstand

the stresses of storage. A more recent modification uses

chlorophyll fluorescence (see Section 7.2.4.4) to determine if tissue damage has occurred and produces results

up to 6 days earlier than visual evaluation (L’Hirondelle

and others 2006). Because this method tests plant samples directly, it has proven to be a reliable predictor of

outplanting performance (Kooistra 2003). This storability

test should be verified with other species. To use this test

in a more temperate or coastal area, a higher temperature

threshold would need to be determined. 

Table 7.2.8—Seedling quality classes based on dormancy

release index (DRI) and stress resistance (SR) (modified

from Ritchie 1989).

Quality class DRI Value Degree of SR 

Class 2 < 0.25 Seedlings are below 

peak SR, but are 

increasing. 

Class 1 0.26 to 0.40 Seedlings are at 

peak SR.

Class 3 > 0.40 Seedlings are beyond 

peak and B SR is 

decreasing.

Using chilling hours to predict stress resistance. It is intuitive that SR is very closely related to dormancy, and this

has been verified by plant physiology research (Ritchie and

others 1985, Ritchie 1986, 1989). As dormancy intensity

weakens through winter in response to chilling, SR gradually increases to a midwinter high. Then it falls rapidly as

dormancy is fully released and spring approaches (Figure

7.2.38). The physiological mechanisms behind this relationship are not fully understood, but it is repeatable from

year to year with different crop types (bareroot and container) and species (Douglas-fir, pines, spruces, some hardwoods) and across nurseries (Burr and others 1989,

Cannell and others 1990, Ritchie and others 1985). This

means that if you can track the dormancy status of a crop

through winter, this information can be used to estimate

SR without measuring it directly. 

As discussed in the previous section, bud dormancy

peaks in fall and is released gradually during winter as

plants are exposed to low temperatures—the “chilling

requirement”. Transforming this curvilinear relationship

into a linear “Dormancy Release Index” (DRI) makes it

much easier to use. The DRI = 0 at peak dormancy in fall,

and approaches 1 as dormancy is released in spring.
61

Figure 7.2.39—Graph showing how the chill sum at time

of lifting, combined with time in cooler or freezer storage,

can be used to predict the dormancy intensity (DRI) and

stress resistance class (Table 7.2.8), of planting stock. The

graph is entered on the X-axis at the nursery chill sum at

which seedlings were placed into storage. The storage duration is found on the Y-axis. These lines intersect at the DRI

value of the seedlings at that time. Their quality class can

then be read from the Y-axis (modified from Ritchie 1989).

Research with Douglas-fir has revealed a consistent relationship between DRI and SR (Ritchie 1986). In early winter, when DRI is in the range between 0 and about 0.25,

SR is low but increasing. Between DRI 0.26 and 0.40

(midwinter) SR reaches a seasonal high, but when DRI

exceeds 0.40 (early spring) SR declines and plants

become very susceptible to damage. These results lead to

the definition of three seedling quality classes based on

dormancy intensity and SR (Table 7.2.8). 

Once the relationship between chilling and DRI has been

established for a given species in a given nursery, it can be

used to estimate SR at any point during the winter for subsequent crops at that nursery. Let us say, for example, that it

is late December and your nursery chilling sum is about

1,000 hours. Using Figure 7.2.39, you would estimate that

DRI was approaching 0.2. From Table 7.2.8, we see that

stock at this time is in SR Class 2—not yet peaked, but will

improve with more chilling. Now, let’s say it is February

and you have about 2,000 hours of chilling at your nursery.

DRI is about 0.38 indicating that SR is in the seasonal high

range, but will soon begin to decline. 

Adjusting for the added effect of refrigerated storage. For

crops transplanted or outplanted without cooler or freezer

storage (“hot-planted”), DRI is very useful. You simply

look at the chilling sum at any point and, from it, estimate

stress resistance. But many nursery crops are refrigerated

from a few weeks to several months before transplanting

or outplanting. So, how does that affect SR? 

The low temperatures in refrigerated storage are within

the chilling range, hence they contribute to dormancy

release. They do so inefficiently, however, because storage

temperatures are below the optimum chilling temperature

(Ritchie 1984a, van den Driessche 1977). Therefore,

refrigerated storage has the effect of slowing the release of

dormancy. This means that plants that are harvested and

placed into refrigerated storage will pass through SR

Classes 2, 1, and 3 more slowly than they would if left in

open container storage (see Chapter 7.4). Plants that are

kept in freezer storage accumulate very little chilling

because temperatures are well below optimum. These

plants must have already accumulated an adequate level

of chilling prior to being placed in storage.
62

To use the graph, select total ambient chilling hours from

a nursery on the X-axis. For this example, let us use 1,000

hours. At this point, the stock will have a DRI value of

about 0.20, placing it in Quality Class 2 (Table 7.2.8).

Now, if the plants are held in refrigerated storage for

about 4 weeks, they will enter Class 1 and have even

higher SR. However, if these same plants had been held

in the nursery for a few more weeks until they accumulated over 1,300 hours of chilling, they would exceed the

DRI limit of 0.25 and enter Class 1 and have maximum

SR. Then, if they were placed in freezer storage, they

could be held for at least 15 weeks (left axis) before their

DRI approached 0.40 and their quality dropped to Class

3. (Note: as a rule of thumb, cooler storage should not

exceed 6 weeks. If storage longer than 6 weeks is needed

freezer storage should be used—see Chapter 7.3).

On a practical basis, Figure 7.2.39 integrates the effect of

both harvesting date and storage duration on DRI and

hence, stress resistance. If the chill sum at the time of harvesting is known, then storage duration can be planned to

deliver stock when it is at maximum SR: Class 1. If the

planned outplanting date is known, then lift date and time

in storage can be pre-arranged to deliver stock to the outplanting site so it will be in Class 1. This graph illustrates

the very important point that for outplanting sites that

cannot be accessed until late, early winter lifting with

overwinter freezer storage is preferable to late spring lifting with or without storage.

Application to other species and regions. The data that

were used to produce Figure 7.2.39 came from coastal

Douglas-fir seedlings from four different seedlots (high

and low elevation lots in both Washington and Oregon)

that were grown in two different coastal nurseries

(Washington and Oregon). These results have been operationally tested with Douglas-fir crops from other seedlots

and during other growing seasons with consistent results.

Therefore, for west coast nurseries raising Douglas-fir,

Figure 7.2.39 is a very handy way of estimating SR from

chilling hours. 

For interior or northern nurseries, however, the relationship between chilling and DRI may be quite different.

This was tested in an interior west Canadian nursery with

lodgepole pine and interior spruce (Ritchie and others

1985). The results showed that chilling began to accumulate earlier in fall and that more chilling accumulated

throughout winter. The results also suggested that these

species may required more chilling hours for full dormancy release than coastal Douglas-fir, similar to results with

ponderosa pine (Pinus ponderosa) (Wenny and others

2002). Nevertheless, the overall relationships (if not the

same numbers) shown in Figure 7.2.38 were similar to

what has been found with Douglas-fir. Therefore, in order

to accurately predict SR from chilling hours for other

species and nurseries, a chilling-DRI “calibration curve”

needs to be developed.

Stress resistance: conclusions and recommendations.

Stress resistance (SR) is an important, but elusive, performance attribute that describes a plant’s ability to tolerate the stresses associated with harvesting, handling, storing, and outplanting. SR varies seasonally; it is low in fall,

high in midwinter, and low in spring.

SR is very laborious to measure; there is no known “quick

test” of SR. Because the seasonal pattern of SR closely

coincides with the pattern of cold hardiness, however,

standard cold hardiness tests can provide quick and useful estimates of SR.

Studies have shown that SR is related to dormancy intensity expressed as a dormancy release index (DRI). When

DRI is in a range between 0 and about 0.25, SR is low

but improving. Between DRI 0.25 and 0.40, SR is at a

seasonal high. Above DRI 0.40, SR is declining. Most

importantly, this relationship tends to be consistent

whether or not plants have been stored.

Because cooler and freezer storage slow the release of

dormancy, storage prolongs the period of high stress

resistance. These relationships can be used to schedule

harvesting and storage in order to deliver stock to the

planting site that has very high stress resistance. Although

most of this stress resistance research was done with bareroot stock of commercial conifers, the basic principles

should apply to container plants of other species. 

7.2.5.3 Root growth potential 

Although Wakeley (1954) published the first account of

the relationship between new root growth and plant quality, it was Stone (1955), who after experimentation,
63

coined the term “root regenerating potential” to describe

his new indicator of seedling physiological quality. 

Based on Stone’s original research, other workers began

developing and using this method of plant assessment (for

example, Jenkinson 1975, Burdett 1979). A comprehensive review of root growth potential (RGP) by Ritchie and

Dunlap (1980) was responsible for a flurry of new

research and adoption of RGP as the first performance

quality test used operationally in forest nurseries. Because

of this wide interest, a chapter on Assessing Seedling

Quality in the Forest Nursery Manual (Duryea and Landis,

1984) featured a discussion and strong endorsement of

RGP (Ritchie 1984b). Further reviews (Duryea 1985,

Ritchie 1985, Ritchie and Tanaka 1990) made this test the

most popular and widely used quality test (Figure

7.2.38A). RGP tests have been employed worldwide and

have been the subject of much discussion (Binder and

others 1988, Sutton 1983, Landis and Skagel 1988) and

even debate (Simpson and Ritchie 1997). 

RGP test procedure. The RGP test consists of placing a

random sample of plants into an environment that promotes rapid root growth. After 14 to 28 days, the plants

are evaluated for new root growth. In the following section, we examine each step in the process.

Sampling. As with all tests, if sampling is biased (not random) test results will be meaningless. The number of

plants used in a typical RGP test is quite small and should

be randomly selected from the population at large in

order to be as representative as possible. A sample of 60

seedlings, which is the number usually required by testing

laboratories, is only 0.12 percent of a moderately-sized

seedlot of 50,000 seedlings. A 25- to 30-plant sample

would be a minimum number to evaluate.

It is simple in principle to collect a random sample when

plants are still in containers or on the grading table, but

sampling becomes more difficult once stock has been

packaged and stored. When cooler stored, it is operationally difficult to sample from bagged plants, because a

number of bags must be accessed, opened, and the sample collected from throughout the bag, not just from the

top layer of plants. Sampling during freezer storage would

be impossible (Landis and Skakel 1988).

Time of sample collection. Tests performed on plants at

the time of harvesting are useful to evaluate nursery cultural practices, but may not reflect the condition of the

plant at time of outplanting. If you are interested in outplanting performance, then the best time to sample is as

close to the time of outplanting as operationally possible

(Landis and Skakel 1988).

Test environment. The testing environment is particularly

important because it must provide conditions that are

near “optimum” for root growth (Landis and Skagel

1988). The temperature should be 19 to 20 °C (66 to 68

°F). The rooting medium should be well aerated and

watered, and there should be adequate light and long

days. Because these factors will affect test results, it is

important to maintain consistent conditions across tests.

This can be difficult. 

Three types of test environments have been used: 

1. Pots in Greenhouse—Most quality testing facilities use

this method, in which plants are potted in 3.8 to 7.6 liter

(1 to 2 gal) containers filled with a well-drained artificial

growing medium. The pots are kept well irrigated in a

greenhouse (Figure 7.2.38B) for the duration of the testing

period (Ritchie 1985, Tanaka and others 1997). After 2 to

4 weeks, the growing media is washed from the roots

(Figure 7.2.38C) and the amount of new root growth is

rated.

2. Hydroponic—Plants are suspended with their roots in

warm, aerated water such as an aquarium. This method

has found use with several deciduous hardwood species

(Wilson and Jacobs 2006).

3. Aeroponic—Plants are suspended in a closed chamber

while warm water mists the roots (Figure 7.2.38D). USDA

Forest Service nurseries have used this technique with

good results (Rietveld and Tinus 1990). One benefit is that

the rack of plants can be easily removed from the misting

chamber to monitor root development during the test

period (Figure 7.2.37E)
64

Figure 7.2.40—Because the relationship between new roots and outplanting success is intuitively attractive (A), the root

growth potential test quickly became the most popular and widely-used assessment of plant quality. One testing procedure

involves growing test plants in pots in a greenhouse (B), washing roots (C), and then rating the amount of new root

growth. In the second procedure, test plants are supported in a mist chamber (D), and then measured for the length and

number of new roots (E). All photos by Thomas D. Landis

A B

C

D E
65

Evaluation. Once the test is completed, new root growth

must be quantified. Researchers have attempted to shortcut this tedious process using photography, dyes, root volume measurements, and other approaches. Despite this,

the tried-and-true “root count” technique has prevailed.

This involves visually estimating the number of new roots

greater than 1 cm (0.4 in) long on the plant. An experienced technician can do this in a few minutes. This count

can be reported as a raw number (for example,120

roots/plant) or transformed into an index such as reported

by Burdett (1979) or Tanaka and others (1997) (Table

7.2.9). Root numbers and total root length are usually

well correlated.

RGP as a predictor of performance. Interpretation of the

results of RGP tests remains challenging. A common misconception has been to assume that RGP results directly

predict outplanting performance. In other words, high

RGP always ensures high survival, while low RGP always

ensures low survival (Figure 7.2.41A). At best, RGP is positively correlated with survival only about 75 percent of

the time (Ritchie and Dunlap 1980, Ritchie and Tanaka

1990). Sometimes these correlations are weak, sometimes

strong. Binder and others (1988) found no correlation

between RGP and outplanting mortality in 8,600 operational trials in British Columbia. This is because the outplanting environment (which is usually very different than

the RGP testing environment) has an overriding influence

on performance (Sutton 1983, Binder and others 1988,

Landis and Skakel 1988, Simpson and Ritchie 1997).

Performance of low-RGP stock on harsh sites, and of

high-RGP stock on mild sites is usually predictable.

However, performance of low-RGP stock on mild sites

and high-RGP stock on harsh sites is not (Figure 7.2.41B). 

It seems intuitive that for a newly outplanted plant to survive and grow, it must rapidly regenerate new roots in

order to maintain an adequate water balance. This logic

has been used to explain why RGP can be expected to

predict survival. Simpson and Ritchie (1997), however,

point out that newly planted stock is almost never able to

grow roots after outplanting because, although soil moisture may be high, soil temperature during the winter

planting season in most places is far below the threshold

temperature for root growth (Figure 7.2.41C). Under these

conditions, the existing root system is adequate to supply

water to the plant until the soil warms and roots begin to

grow (McKay 1998). Therefore, whether or not new root

growth occurs immediately after planting is of little consequence to field performance.

Why RGP often works. The discovery that many conifer

seedlings, especially Douglas-fir, require mainly current

photosynthate for new root growth (van den Driessche,

1987, 1991) has provided a rationale for interpreting RGP

test results. In order for a plant to grow new roots in the

test environment, the foliage must be photosynthesizing

(Figure 7.2.42). Therefore, the stomata must be open, the

leaves must be healthy, and the photosynthetic apparatus

must be functioning properly. Photosynthate must move to

the root system, so the phloem pathway to the roots must

be intact, and the roots themselves must be metabolizing

Table 7.2.9—Root growth index (RGI) scale developed by Tanaka and others (1997) to quantify root growth 

following an RGP test

Root Growth Index (RGI) Number of new roots 1 cm or longer

0 None

1 Some roots but none > 1 cm

2 1-3

3 4-10

4 11-30

5 31-100

6 101-300

7 More than 300
66

Figure 7.2.42—Root growth in many conifers depends on

a supply of current photosynthate from the shoot (van

den Driessche 1987, 1991). Any factors that depress 

photosynthesis, or impede the flow of photosynthate from

leaves to roots, will result in reduced RGP.

Figure 7.2.41—Although a good relationship between

RGP test values and outplanting success sometimes exists

(A), limiting factors on the outplanting site often prevents

good predictability. Performance of low-RGP stock planted on a harsh site, or high-RGP stock on a mild site is

generally predictable. However, performance of high-RGP

stock on harsh sites, or low-RGP stock on mild sites is not

(B). One frequent problem is that soil temperatures on

the outplanting sites are much lower than the ideal temperatures used in the testing environments (C). A – modified from Grossnickle 2000; C – modified from

Lopushinsky and Max 1990)

A

B

C
67

normally. If any of these systems has been compromised

by, say, cold damage, water stress, disease, photodamage,

or other agents, it will result in a depression of RGP.

Taken in that light, then, a more realistic view is that RGP

testing is analogous to seed testing, which provides a

snap-shot of seed viability at the time seeds are tested. No

one would expect seeds that had 95 percent laboratory

germination always to give 95 percent emergence in the

nursery. But if the test gave an abnormally low value it

would indicate poor seed viability. This is the model to

use when interpreting RGP test results. The RGP test is a

“red flag” test that identifies stock lots that, for whatever

reason, are not up to par. 

Root growth potential: conclusions and recommendations.

RGP remains the most popular quality test because it is

intuitive, robust, and simple. Like any test, however, RGP

has its limitations. The major drawback of the RGP test is

the long testing period and the limited predictive ability.

RGP tests provide only a “snapshot in time” because

plant physiological quality can change right up until the

stock is outplanted. 

RGP sometimes predicts survival and sometimes does not.

This is because site conditions, which are very different

than the testing conditions, can override stock quality,

RGP does not predict root growth after outplanting, and

root growth after outplanting generally has little to do

with survival.

The RGP test is a valuable test of viability—that is, it

determines whether plants are alive and functional at the

time the test is conducted. RGP test results integrate many

physiological systems in plants, such as stomatal function,

the photosynthetic mechanism, phloem integrity, root viability, seedling nutrition, and so on. If any of these systems have been compromised, it will show up as a

depression of RGP.

Results of an RGP test should be interpreted in the same

way as results of a seed germination test. It is a “red flag”

test that identifies sub-par lots, and may or may not predict field performance.
68

Figure 7.2.43—Measuring root growth potential and

chlorophyll fluorescence proved to be a good predictor of

total outplanting performance (survival + shoot growth)

of conifer seedlings (modified from L’Hirondelle and others 2007).

As you should have deduced by now, nursery plant quality is a complicated subject. So, instead of trying to predict

outplanting performance with just one variable, it makes

sense to attempt correlations with two or more plant quality indices. Recent research in British Columbia measured

root growth potential, chlorophyll fluorescence, and

stomatal conductance of conifer seedlings and then correlated them singly and in combination with survival and

growth after outplanting (L’Hirondelle and others 2007).

They found that, while survival was highly correlated with

root growth potential (R2 = 0.72), the combination of root

growth potential and chlorophyll fluorescence was a good

predictor of survival plus shoot growth as measured by

dry weight (Figure 7.2.43). Hopefully, more research will

be done in this area to further refine our ability to mathematically predict nursery stock quality. 

7.2.6 Correlating Combinations of Plant Quality Tests to Predict Outplanting Performance
69

Figure 7.2.44—Plant quality testing can be done by both

nursery managers and seedling users. The timing of the

various tests will vary with the desired interpretation.

most useful data. Many growers are resistant to spending

the time or money to collect and test samples in this manner. If you think about it, however, spending a relatively

small amount of time and money on a single, biased test

is simply wasted time and money to generate meaningless

data whereas spending a little more time and money

using a “three R” sampling protocol produces valuable

data which can assist with management decisions.

7.2.7.3 Unreasonable expectations

It is important that plant producers and users employ the

right test at the right time and that they remain aware of

the pitfalls of reading too much into test results. A discussion of this topic can be found in Simpson and Ritchie

(1997) who propose the following conceptual model of

field performance:

7.2.7.1 Timing 

Each plant quality test that we have discussed should be

done at a particular time in the nursery through outplanting cycle. Morphological attributes change as the

crops grow in the nursery but remain constant after harvesting. Physiological and performance attributes, however, vary considerable depending on when the measurements are taken. For instance, plant moisture stress has a

pronounced diurnal pattern whereas cold hardiness

increases during the fall and can be lost during refrigerated storage (Sundheim and Kohmann 2001). Root electrolyte leakage and chlorophyll fluorescence are used

mainly to detect damage following a stress event.

Therefore, they should be measured immediately after the

event while keeping two important considerations in

mind. One is that, in order to know whether test results

are “normal” or not, baseline information on these variables must be available. That often calls for routine monitoring of these variables in healthy crops prior to the

stress event. The second, and very important, point is that

plants may require time to exhibit stress symptoms and

also have the ability to recover from stress. So, for example, CF values measured the day after a cold event may

not give an accurate picture of the damage sustained by

the crop or of its longer term response.

Both nursery managers and seedling users can use plant

quality tests but would do so at different times. For example, a nursery manager would use plant moisture stress to

schedule irrigation and cold hardiness tests to determine

lifting windows and storability, whereas a seedling user

might use plant moisture stress to ensure that nursery

stock was not moisture-stressed prior to outplanting and

cld hardiness tests to indicate overall stress resistance

before outplantings. (Figure 7.2.44). 

7.2.7.2 Sampling

Proper sampling is critical to effective seedling quality

testing. If the sample is biased, the test results will be

biased and therefore worthless. One wonders how many

of the quality tests that failed to predict field performance

were conducted on samples that did not adequately represent the populations from which they were drawn. It is

important to follow the “three Rs” of sampling: random,

replicated, and representative. Multiple samples collected

randomly from throughout a given crop will yield the

7.2.7 Limitations of Plant Quality Tests
70

Field performance = f(SC, PM, SR, PV)

where:

SC = site conditions (all physical, chemical, and biological characteristics of the site during and after planting),

PM = plant morphological attributes (stem diameter and

height, shoot-to-root ratio, root quality, and so on),

SR = stress resistance (ability to withstand stresses associated with harvesting, storage, handling, and planting), and 

PV = plant viability (freedom from disease, injury, or

stress-induced disorders: plant “functional integrity”

(Grossnickle and Folk 1993) is a good way to express this

idea.

Obviously, quality testing does not provide information

on SC, but it can yield detailed information on PM, and

can offer insights on SR through monitoring of cold hardiness and dormancy intensity. PV can also be approximated using root growth potential, chlorophyll fluorescence,

root electrolyte leakage and, to some extent, plant moisture stress. 

With this “package” of available quality tests and protocols, nursery managers have sufficient tools to make more

than an educated guess about the quality of any given

stock lot at any given time. But, it should be remembered

that quality must be viewed within the context of site

conditions that can never be fully predicted. 
71

7.2.7 Commercial Plant Quality Testing Laboratories

Company Address Types of Tests Offered

Morphology Root Growth Cold Others

Potential Hardiness

Nursery Technology Oregon State University X X

Cooperative Dept. of Forest Science

Richardson Hall 321

Corvallis, OR 97331

TEL: 541.737.6576

FAX: 541.737.1393

http://ntc.forestry.oregonstate.edu/sqes

KBM Forestry SQA Coordinator X X X X

Consultants 349 Mooney Avenue

Thunder Bay, ON P7B 5L5

TEL: 807.345.5445 ext. 34

FAX: 807.345.3440

E-mail: sgellert@kbm.on.ca

Laboratory for Forest Soils Tweeddale Centre for X X X X

and Environmental Quality Industrial Forest Research

1350 Regent Street

Fredericton, NB E3C 2G6

TEL: 506.458.7817

FAX: 506.453.3574

E-mail: jestey@unb.ca

Franklin H. Pitkin Center for Forest Nursery X X X X

Nursery and Seedling Research

College of Natural Resources

University of Idaho

Moscow, ID 83844-1137

TEL: 208.885.7023

FAX: 208.885.6226

E-mail: seedlings@uidaho.edu

Several of the tests enumerated above can be administered on the nursery site (for example, root electrolyte

leakage, root growth potential, chill sum accumulation,

and so on). Certain tests (for example, cold hardiness and

chlorophyll fluorescence), however, require elaborate and

expensive equipment. At the time of this writing (2008)

we are aware of four laboratories in North America that

provide quality testing services (Table 7.2.10).

Table 7.2.10—Seedling quality testing facilities and their procedures
72

Plant quality is divided into three broad categories of

attributes: morphological, physiological, and performance. Morphological attributes are easy to see and measure and do not change readily once plants are harvested

and stored. Container size and plant density have the

most pronounced effects on morphology. Although many

characteristics may be measured (for example, shoot

height, stem diameter, biomass) and ratios of those characteristics can be calculated (for example shoot-to-root

ratio), shoot height, and stem diameter are the most frequently measured morphological traits and the most commonly used grading criteria. Initial shoot height tends to

be correlated with height growth after outplanting, whereas initial stem diameter is better correlated with survival. 

Physiological attributes are not readily visible and require

specialized equipment and testing to ascertain. Evaluations of plant moisture stress, cold hardiness, root electrolyte leakage, and chlorophyll fluorescence are most

common. 

Plants lose water more rapidly through transpiration than it

can be absorbed from the soil, putting the plants under

“plant moisture stress” (PMS). This level of stress can be

quantified by using a pressure chamber. Although a direct

correlation between PMS and any of the classical plant

quality indicators is lacking, nursery managers can use predawn PMS measurements to schedule irrigation and monitor stress during hardening, harvesting, and outplanting. 

Development of cold hardiness in nursery stock is triggered by changes in photoperiod in late summer and

increases rapidly in late fall and early winter as plants

experience lower temperatures. For temperate zone

plants, peak hardiness occurs in January and is quickly

lost as photoperiods lengthen and temperatures increase.

Different plant parts may have different cold hardiness

levels; buds are generally most cold hardy while roots are

the least. Cold hardiness levels can be determined using a

whole plant freeze test, freeze-induced electrolyte leakage (FIEL), or analysis of genetic indicators. Results from

testing can be used by nursery managers to decide safe

windows for harvesting, provide necessary frost protection, and as a surrogate for estimating overall stress resistance. 

Assessing root electrolyte leakage (REL) is similar to FIEL,

but broader in that this test looks at potential loss of root

viability from many factors, such as disease, rough 

handling, desiccation, and not just damage from cold

temperatures. It is difficult to correlate REL with plant survival because many other factors other than root damage

can affect REL.

Chlorophyll fluorescence provides a means of determining a plant’s ability to efficiently photosynthesize.

Stresses, whether they be short term, subtle, long term, or

severe, can impair this important physiological process.

This measurement can identify when significant damage

to the photosynthetic system has occurred, and that a

plant’s performance may be compromised. More work is

needed before this test will be an operational quality test. 

Performance attributes integrate both morphological and

physiological attributes. Although testing performance

attributes has great value, these tests can be laborious and

expensive. Measures of dormancy, stress resistance, and

root growth potential (RGP) are the common tests. 

Although nursery managers talk about dormant plants, dormancy can only refer to meristimatic tissues, and only bud

dormancy has been extensively studied. Shoots may cease

to elongate and form buds in response to changing environment conditions that are less favorable to growth (quiescence), or in response to reduced photoperiod (deep dormancy) that culminates in fall. Once deeply dormant, buds

require a specific duration of exposure to cold temperatures (chilling) before shoot growth will resume. The chilling requirement is the length of exposure to cold temperatures that buds need before they are once again quiescent

and ready to resume growth when temperatures permit.

The only reliable way to estimate the intensity of bud dormancy is to measure how much chilling buds have been

exposed to, and then record how many days are required

for those buds to resume shoot growth (days to bud break;

DBB) when they are returned to favorable growing conditions. The relationship between chilling and DBB is curvilinear, but a simple dormancy release index (DRI) can be

used to convert the data to a straight line and make it easier to use, for example, in establishing harvesting windows

and estimating the dormancy intensity of crops during subsequent winters. 

7.2.8 Summary and Conclusions
73

Measuring stress resistance (SR) can be a very laborious

process, but important because it describes a plant’s ability

to tolerate the stresses associated with the harvesting-tooutplanting process. Because the seasonal pattern of SR

closely coincides with the pattern of cold hardiness, standard cold hardiness tests can provide quick and useful estimates of SR. Moreover, SR is related to dormancy intensity

expressed as a dormancy release index (DRI). Because

refrigerated storage slows the release of bud dormancy,

storage prolongs the period of high stress resistance. 

Root growth potential (RGP) is the most popular performance test. This test provides an indication of the overall viability of the plant at the time of testing because many integrated physiological processes in plants are responsible for

new root production. This test only provides a “snapshot in

time” evaluation of the plant; it is important to remember

that physiological quality can change right up until the

stock is outplanted. RGP may or may not be well correlated with survival because site conditions can override stock

quality, but plants having low RGP should be further evaluated with respect to potential site conditions.

In general, morphological attributes, because they seldom

change during the harvest-to-outplanting process, may be

measured any time. Physiological attributes, because they

can change frequently, however, provide only a momentary analysis of plant quality. Testing plant moisture stress

at different stages of the harvest-to-outplanting process

can ensure that plant stress is minimized. Chlorophyll fluorescence and root electrolyte leakage tests may be used

immediately after an unexpected stress event to ascertain

damage levels, or recovery from those events. Cold hardiness testing can be done to determine proper harvesting

windows and prior to outplanting to ensure that stress

resistance is still high. Performance attributes such as

stress resistance may be done anytime during the harvesting-to-outplanting process, but root growth potential is

probably best done immediately prior to outplanting to

ensure overall plant viability.

None of these plant quality tests will yield meaningful

results unless the population of plants is sampled randomly and thoroughly. Plant producers and users must be

cognizant of what each test does and does not infer about

plant quality, and to be mindful that test results must be

considered within the context of expected, but never fully

predicted, site conditions.
Adams, G.T., T.D. Perkins, and R.M. Klein. 1991.

Anatomical studies on first-year winter injured red

spruce foliage. Amer. J. Bot. 78:1199-1206.

Anderson, J.L. and S.D. Seeley. 1993. Bloom delay in

deciduous fruits, Pp 97-144, In: Horticultural Reviews,

(Ed.) J. Janick, John Wiley and Sons.

Arnott, J.T. and D. Beddows. 1982. Influence of

StryroblockTM container size on field performance of

Douglas-fir, western hemlock and Sitka spruce. Tree

Planters’ Notes, Summer 1982: 31-34.

Becwar, M.R., C. Rajashekar, Hanson Bristow, K.J., and

M.J. Burke. 1981. Deep undercooling of tissue water

and winter hardiness limitations in timberline flora.

Plant Physiol. 68:111-114.

Bigras, F.J. 2005. Photosynthetic response of white spruce

families to drought stress. New Forests 29:135-148.

Bigras, F.J., A. Ryyppo, A. Lindstrom and E. Stattin. 2001.

Cold acclimation and deacclimation of shoots and

roots of conifer seedlings. Pp 57-88, In: Conifer Cold

Hardiness, F. J. Bigras and S.J. Colombo (Eds.), Kluwer

Academic Publishers, the Netherlands.

Binder W.D., R.K. Skagel and G.K. Krumlik. 1988. Root

growth potential: facts, myths, value? In: Landis, TD,

ed. Proceedings, Combined Meeting of the Western

Forest Nursery Associations; 1988, August 8-11;

Vernon, BC. Ft. Collins, CO: Rocky Mountain Forest

and Range Experiment Station. General Technical

Report RM-167: 111-118.

Binder, W.D., P. Fielder, G.H. Mohammed and S.J.

L’Hirondelle. 1997. Applications of chlorophyll fluorescence for stock quality assessment with different types

of fluorometers. New Forests 13:63-89.

Burdett AN. 1979. New methods for measuring root growth

capacity: their value in assessing lodgepole pine stock

quality. Canadian Journal of Forest Research 9:63-67.

Burdett, A.N. and D.G. Simpson. 1984. Lifting, grading,

packaging and storing. Pp. 227-234 In: Forest Nursery

Manual: Production of bareroot seedlings. M.L. Duryea

and T.D. Landis (Eds.), Martinus Nijhoff/Dr.W. Junk,

Publishers. For Oregon State University, Corvallis.

Burr, K.E. 1990. The target seedling concepts: bud dormancy and cold hardiness. Rose, R, Campbell SJ,

Landis TD. (Eds.) Pp 79-90 In: Target seedling symposium: proceedings, combined meeting of the Western

Forest Nursery Associations, p. 79-90. USDA Forest

Service, Rocky Mountain Forest and Range Experiment

Station, General Technical Report RM-200.

Burr, K., R. W. Tinus, S. J. Wallner and R. M. King. 1989.

Relationships among cold hardiness, root growth

potential and bud dormancy in three conifers. Tree

Physiology 5:291-306.

Burr, K.E., R.W. Tinus, S.J. Wallner and R.M. King. 1990.

Comparison of three cold hardiness tests for conifer

seedlings. Tree Physiol. 6:351-369.

Burr, K.E., C.D.B. Hawkins, S.J. L’Hirondelle, W.D. Binder,

M.F. George and R. Tapani. 2001. Methods for measuring cold hardiness of conifers. F.J. Bigras and S.J.

Colombo (eds.), Conifer Hardiness, p. 369-401, Kluwer

Academic Press, The Netherlands.

Campbell, R.K. 1978. Regulation of bud burst timing by

temperature and photoperiod regime during dormancy.

Pp 19-34 In: Proceedings of Fifth North American

Forest Biology Workshop, (Eds.) C.A. Hollis and A.E.

Squillace, Southeastern Forest Experiment Station,

USDA Forest Service.

Cannell, M.G.R. and L.J. Sheppard. 1982. Seasonal

changes in the frost hardiness of provenances of Picea

sitchensis in Scotland. Forestry 55:137-153.

Cannell, M.G. R., P.M. Tabbush, J.D. Deans, M.K.

Hollingsworth, L.J. Sheppard, J.J. Phillipson and M. B.

Murray. 1990. Sitka spruce and Douglas-fir seedlings in

the nursery and in cold storage: root growth potential,

carbohydrate content, dormancy, frost hardiness and

mitotic index. Forestry 63:9-27. 

Chiatante, D., A. Di Iorio, M. Sarnataro and G.S. Scippa.

2002. Improving vigour assessment of pine (Pinus nigra

Arnold) seedlings before their use in reforestation.

Plant Biosystems 136:209-216. 

Colombo SJ. 2005. The thin green line: a symposium on

the state-of-the-art in reforestation, proceedings.

Ontario Ministry of Natural Resources, Forest Research

Information Paper 160. 175 p.

Colombo, S.J., S. Zhao, and E. Blumwald. 1995. Frost hardiness gradients in shoots and roots of Picea mariana

seedlings. Scand. J. For. Res. 9:1-5.

Colombo S.J., P.H. Sampson, C.W.G. Templeton, T.C.

McDonough, P.A. Menes, D. DeYoe and S.C.

Grossnickle. 2001. Assessment of nursery stock quality

in Ontario. Pp 307-323 In: Regenerating the Canadian

forest: principles and practice for Ontario, (Eds.) R.G.

Wagner and S.J. Colombo, Ontario Ministry of Natural

Resources and Fitzhenry & Whiteside Ltd. 

74

7.2.9 Literature Cited
Coursolle, C. F. J. Bigras, and H.A. Margolis. 2000.

Assessment of root freezing damage of two-year-old

white spruce, black spruce and jack pine seedlings.

Scandinavian Journal of Forest Research 15:343-353.

Demig-Adams, B. and W.W. Adams. III. 1992.

Photoprotection and other responses of plants to high

light stress. Annual Review of Plant Physiology and

Plant Molecular Biology. 43:599-626.

Dexter, S.T., W.E. Tottingham, and L.F. Graber. 1932.

Investigations of the hardiness of plants by measurement of electrical conductivity. Plant Physiol. 7:63-78.

Dixon, H.H. 1914. Transpiration and the ascent of sap in

plants. MacMillan, New York.

Dominguez-Lerena S, Herrero Sierra H, Carrasco

Manzano I, Ocana Bueno L, Penuelas Rubira JL,

Mexal, JG. 2006. Container characteristics influence

Pinus pinea seedling development in the nursery and

field. Forest Ecology and Management 221(1-3): 63-71.

Duryea M.L. 1985. Evaluating Seedling Quality: principles, procedures and predictive abilities of major tests.

For. Res. Lab., Oregon State Univ., Corvallis. 143 p.

Duryea, M.L. and T.D. Landis (Eds.) 1984. Forest nursery

manual: production of bareroot seedlings. Martinus

Nijhoff/Dr W. Junk Publishers, The

Hague/Boston/Lancaster, for Forest Research

Laboratory, Oregon State University, Corvallis, 385 p.

Folk, R.S., S.C. Grossnickle, P. Axelrod and D. Trotter.

1999. Seed lot, nursery, and bud dormancy effects on

root electrolyte leakage of Douglas-fir (Pseudotsuga

menziesii) seedlings. Canadian Journal of Forest

Research 29:1269-1281.

Frampton, J. F. Ksik and B. Goldfarb. 2002. Effects of

nursery characteristics on field survival and growth of

loblolly pine rooted cuttings. Southern Journal of

Applied Forestry. 26:207-213.

Genty, B., J.-M. Briantais and N.R. Baker. 1989. The relationship between the quantum yield of photosynthetic

electron transport and quenching of chlorophyll fluorescence. Biochemica et Byophysica Acta 990:97-92.

George, M.F., M.J. Burke, H.M. Pellett, and A.G. Johnson.

1974. Low temperature exotherms and woody plant

distribution. HortScience 9:519-522.

Glerum, C. 1976. Frost hardiness of forest trees. Pp. 403-

420 In: Tree Physiology and Yield Improvement. Ed.

M.G.R. Cannell and F.T. Last. Academic Press, New

York, London.

Government of Quebec, 2007. [Field guide : Grading of

containerized conifer stock]. Guide terrain : Inventaire

de qualification des plants résineux cultivés en récipient. Québec, QC (Canada). Ministère des Ressources

Naturelles et de la Faune, Direction de la production

des semences et des plants, 128 pages.

Govindje. 1995. Sixty-three years since Kautsky:

Chlorophylla fluorescence. Australian Journal of Plant

Physiol. 22:131-160.

Greer, D.H., I. Leinonen, and T. Repo. 2001. Modelling

cold hardiness development and loss in conifers. Pp.

437-460 In: Conifer Cold Hardiness, F. J. Bigras and

S.J. Colombo (Eds.), Kluwer Academic Publishers, The

Netherlands

Grossnickle, S.C. 2000. Ecophysiology of Northern

Spruce Species: The Performance of Planted Seedlings,

NRC Research Press, National Research Council of

Canada, Ottawa, Ontario, Canada 409 p.

Grossnickle, S. C. 2005. Seedling size and reforestation

success. How big is big enough? IN: Colombo SJ,

comp. The Thin Green Line : a symposium on the stateof-the-art in reforestation, proceedings. Forest Research

Information Paper 160. Sault Ste. Marie, ON: Ministry

of Natural Resources, Ontario Forest Research Institute:

144-149.

Grossnickle, S.C. and R.S. Folk. 1993. Stock quality

assessment: forecasting survival or performance on a

reforestation site. Tree Planters Notes 44:113-121.

Harper, C.P. and C.O. O’Reilly. 2000. Effect of warm storage and date of lifting on the quality of Douglas-fir

seedlings. New Forests 20:1-13.

Harrington JT, Mexal JD, Fisher JT. 1994. Volume displacement method provides a quick and accurate way

to quantify new root production. Tree Planters’ Notes

45:121-124.

Helenius, P., J Luoranen and R. Rikala. 2005.

Physiological and morphological responses of dormant

and growing Norway spruce container seedlings to

drought after planting. Annals of Forest Science

62:201-207.

Hermann, R.K. 1967. Seasonal variation in sensitivity of

Douglas-fir seedlings to exposure of roots. Forest

Science 13:140-149.

Hines FD, Long JN. 1986. First and second-year survival

of containerized Engelmann spruce in relation to initial

seedling size. Canadian Journal of Forest Research

16(3): 668-670.

75
Howell, K.D. and T.B. Harrington. 2004. Nursery practices influence seedling morphology, field performance, and cots efficiency of containerized cherrybark

oak. Southern Journal of Applied Forestry. 28:152-162.

Jenkinson JL. 1975. Seasonal patterns of root growth

capacity in western yellow pines. Washington, DC:

Proceedings of Society of American Foresters.. 75th

National Convention. 9 p.

Jenkinson, J.L.; Nelson, J.A.; Huddleston, M.E.1993.

Improving planting stock quality — the Humboldt

experience. General Technical Report PSW-143.

Berkeley, CA: USDA Forest Service, Pacific Southwest

Research Station. 219 p.

Jobidon, R., L. Charette and P.Y. Bernier. 1998. Initial size

and competing vegetation effects on water stress and

growth of Picea mariana (Mill.) seedlings planted in

three different environments. Forest Ecology and

Management 103:293-305.

J.H. Stone Nursery. 1996. Nursery Handbook- Folder

6075 Quality Monitoring. USDA-Forest Service, J.H.

Stone Nursery, Central Point, OR.

Jones GE. Cregg BM. 2006. Budbreak and winter injury in

exotic firs. HortScience 41(1): 143-148.

Kooistra CM. 2003. Seedling storage and handling in

western Canada. In: Riley LE, Dumroese RK, Landis

TD, technical coordinators. National Proceedings:

Forest and Conservation Nursery Associations—2003.

Ogden (UT): USDA Forest Service, Rocky Mountain

Research Station. Proceedings RMRS-P-33: 15-21. 

Krause GH and Weis E. 1991. Chlorophyll fluorescence

and photosynthesis: the basics. Annual Review of Plant

Physiology and Plant Molecular Biology 42:313-349.

Landis, TD. 1985. Mineral nutrition as an index of

seedling quality. IN: Duryea ML, ed. Evaluating

seedling quality: principles, procedures, and predictive

abilities of major tests: proceedings of a workshop.

Oregon State University, Forest Research Lab. p. 29-48.

Landis TD. 2007. Miniplug transplants: Producing Large

Plants Quickly. In: Riley LE, Dumroese RK, Landis TD,

technical coordinators. National Proceedings: Forest

and Conservation Nursery Associations—2006. Ogden

(UT): USDA Forest Service, Rocky Mountain Research

Station. Proceedings (In Press).

Landis T.D. and R.G. Skakel. 1988. Root growth potential

as an indicator of outplanting performance: problems

and perspectives. In: Landis TD, ed. Proceedings,

Combined Meeting of the Western Forest Nursery

Associations; 1988, August 8-11; Vernon, BC. Ft.

Collins, CO: Rocky Mountain Forest and Range

Experiment Station. General Technical Report RM167:106-110.

Landis TD, Tinus RW, McDonald SE, Barnett JP. 1989.

Seedling Nutrition and Irrigation, Vol. 4, The Container

Tree Nursery Manual. Agric. Handbk. 674.

Washington, DC: U.S. Department of Agriculture,

Forest Service. 119 p.

Landis, T.D.; Haase, D.L.; Dumroese, R.K. 2005. Plant

nutrient testing and analysis in forest and conservation

nurseries. IN: National proceedings, Forest and

Conservation Nursery Associations, 2004. Ft. Collins,

CO: USDA Forest Service, Rocky Mountain Research

Station, Proceedings RMRS-P-35, p.76-84.

Lavender DP. 1984. Bud dormancy. Pages 7-15 In:

Evaluating seedling quality: principles, procedures, and

predictive abilities of major tests. (Ed.) M. L. Duryea.

Workshop held October 16-18, 1984. Forest Research

Laboratory, Oregon State University, Corvallis. 

L’Hirondelle SJ, Simpson DG, Binder WD. 2007.

Chlorophyll fluorescence, root growth potential, and

stomatal conductance as estimates of field performance potential in conifer seedlings. New Forests 34:

235-251.

Lindqvist, H. 2000. Plant vitality in deciduous ornamental

plants affected by lifting date and cold storage.

Doctoral Thesis, Swedish University of Agricultural

Sciences, Alnarp, Sweden.

Lindström, A. and A. Mattson. 1989. Equipment for freezing roots and its use to test cold resistance of young

and mature roots of Norway spruce seedlings.

Scandinavian Journal of Forest Research 4:59-66.

Lopushinsky, W. 1990. Seedling moisture status. Pages

123-138 In: Target Seedling Symposium Proceedings,

Combined meeting of Western Forest Nursery

Associations: (Ed. R. Rose, S.J. Campbell and T.D.

Landis), Aug 13-17, 1990. Roseburg, OR, U. S. Forest

Service General Technical Report RM-200.

Lopushinsky W. Max TA. 1990. Effect of soil temperature

on root and shoot growth and on budburst timing in

conifer seedling transplants. New Forests 4(2):107-124.

Marshall JD. 1983. Carbohydrate status as a measure of

seedling quality. IN: Duryea ML, ed. Evaluating

seedling quality: principles, procedures, and predictive

abilities of major tests: proceedings of a workshop.

Oregon State University, Forest Research Lab. p. 49-58.

76
McCreary, D and M.L. Duryea. 1984. OSU vigor tests:

principles, procedures and predictive ability. Pp 85-92

In: Evaluating seedling quality: principles, procedures,

and predictive abilities of major tests. (Ed.) M. L.

Duryea. Workshop held October 16-18, 1984. Forest

Research Laboratory, Oregon State University,

Corvallis. 

McKay, H.H. 1998. Root electrolyte leakage and root

growth potential as indicators of spruce and larch

establishment. Silva Fennica 32:241-252. 

McKay, H.H. 1992. Electrolyte leakage from fine roots of

conifer seedlings: a rapid index of plant vitality following cold storage. Canadian Journal of Forest Research

22: 1371-1377.

McKay, H.H. and I.M.S. White. 1997. Fine root electrolyte leakage and moisture content: indices of Sitka

spruce and Douglas-fir seedling performance after desiccation. New Forests 13:139-162.

McKay, H.H. and W.L. Mason. 1991. Physiological indicators of tolerance to cold storage in Sitka spruce and

Douglas-fir seedlings. Canadian Journal of Forest

Research 21:890-901.

McKay, H.H. and A. D. Milner. 2000. Species and seasonal

variability in the sensitivity of seedling conifer roots to

drying and rough handling. Forestry 73:259-270.

McKay, H.H. and J.L. Morgan. 2001. The physiological

basis for the establishment of bare-root larch seedlings.

Forest Ecology and Management 142:1-18.

McDonald, S.E., and S.W. Running. 1979. Monitoring irrigation in western forest tree nurseries. General

Technical Report RM-61. Ft. Collins, CO: USDA Forest

Service, Rocky Mountain Forest and Range Experiment

Station. 8 p.

McMinn, R.G. 1982. Size of container-grown seedlings

should be matched to site conditions. Pages 307-312

In: Proceedings, Canadian Containerized Tree Seedling

Symposium, September 14-16, 1981, Toronto, Ontario,

J.B. Scarratt, C. Glerum, and C.A. Paxman (Eds.),

COJFRC Symposium Proceedings O-P-10. Canadian

Forestry Service, Great Lakes Forestry Center, Sault Ste.

Marie, Ontario.

Mena-Petite, A., U. Ortega-Lasuen, M.B. González-Moro,

M. Lacuesta and A. Muñoz-Rueda. 2001. Storage duration and temperature effect on the functional integrity

of container and bare-root Pinus radiata D. Don

seedlings. Trees 15:289-296.

Mena-Petite, A., A. Robreto, S. Alcalde, M.K. Duñabeitia,

M.B. González-Moro, M. Lacuesta and A. MuñozRueda. 2003. Gas exchange and chlorophyll fluorescence responses of Pinus radiata D. Don seedlings during and after several storae regimes and their effects on

post-planting survival. Trees 17:133-143.

Mena-Petite, A., J.M. Estavillo, M. Duñabeitia, B.

González-Moro, A. Muñoz-Rueda and M. Lacuesta.

2004. Effect of storage conditions on post planting

water status and performance of Pinus radiata D.Don

stock-types. Annals of Forest Science. 61:695-704. 

Mexal, J.G. and T.D. Landis. 1990. Target seedling concepts: height and diameter. Pages 17-35 In: Target

Seedling Symposium, Proceedings, Combined Meeting

of the Western Forest Nursery Associations; 1990

August 13-17; Roseburg, Oregon. R. Rose, S.J.

Campbell and T.D. Landis (eds.); Gen. Tech. Rep. RM200, Ft. Collins, CO: U.S. Department of Agriculture,

Forest Service.

Mohammed, G.H., W.D. Binder and S.L. Gillies. 1995.

Chlorophyll fluorescence: a review of its practical

forestry applications and instrumentation. Scandinavian

Journal of Forest Research. 10:383-410.

Öquist, G., P. Gardeström, and N.P.A. Huner. 2001.

Metabolic changes during cold acclimation and subsequent freezing and thawing. Pp.137-163, In: Conifer

Cold Hardiness, F. J. Bigras and S.J. Colombo (Eds.),

Kluwer Academic Publishers, The Netherlands.

O’Reilly, C., N. McCarthy, M. Keane, C.P. Harper, and J.J.

Gardiner. 1999. The physiological status of Douglas fir

seedlings and the field performance of freshly lifted

and cold stored stock. Annals of Forest Science

56:297-306.

Palta, J.P., J. Levitt and E.J. Stadlemann. 1977. Freezing

injury in onion bulb cells. I. Evaluation of the conductivity method and analysis of ion and sugar efflux from

injured cells. Plant Physiology 60:393-397.

Perry, K. 1998. Basics of frost and freeze protection for

horticultural crops. HortTechnology 8:10-15.

Perry, T.O. 1971. Dormancy of trees in winter. Science

171:29-36.

Perks, M.P., S. Monaghan, C.O’Reilly, B.A. Osborne and

D.T. Mitchell. 2001. Chlorophyll fluorescence characteristics, performance and survival of freshly lifted and

cold stored Douglas-fir seedlings. Annals of Forest

Science. 58:225-235.

77
Puttonen P. 1986. Carbohydrate reserves in Pinus

sylvestris seedling needles as an attribute of seedling

vigor. Scandinavian Journal of Forest Research 1(2):

181-193. 

Quamme, H.A. 1985. Avoidance of freezing injury in

woody plants by deep supercooling. Acta Horticultura,

168:11.

Richardson, E.A., S.D. Seeley and D.R. Walker. 1974. A

model for estimating the completion of rest for

“Redhaven” and “Elberta” peach trees. HortScience

9:331-332.

Rietveld, W.J. and R.W. Tinus. 1990. An integrated technique for evaluating root growth potential of tree

seedlings. Research Note RM-497. Ft. Collins, CO:

USDA Forest Service, Rocky Mountain Forest and

Range Experiment Station. 11p.

Ritchie, G.A. 1984a. Effect of freezer storage on bud dormancy release in Douglas-fir seedlings. Canadian

Journal of Forest Research 14:186-190.

Ritchie, G. A. 1984b. Assessing seedling quality. Pages

243-259 In: Forest nursery manual: production of bareroot seedlings (Ed. M.A. Duryea and T.D. Landis),

Martinus Nijhoff/Dr. W Junk Publ. The

Hague/Boston/Lancaster, for Forest Research

Laboratory, Oregon State University, Corvallis.

Ritchie GA. 1985. Root growth potential: principles, procedures and predictive ability. Pages 93-104, In:

Proceedings - Evaluating seedling quality: principles,

procedures and predictive ability of major tests.

Duryea, ML, (Ed.), Workshop held October 16-18,

1984. Forest Research Laoratory, Oregon State

University. Corvallis.

Ritchie, G. A. 1986. Relationships among bud dormancy

status, cold hardiness, and stress resistance in 2+0

Douglas-fir. New Forests 1:29-42.

Ritchie, G.A. 1989. Integrated growing schedules for

achieving physiological uniformity in coniferous planting stock. Forestry (Suppl.) 62: 213-226.

Ritchie, G. A. 1990. A rapid method for detecting cold

injury in conifer seedling root systems. Canadian

Journal of Forest Research 20:26-30. 

Ritchie, G.A. 1991. Measuring cold hardiness. Pages 557-

582, In: Techniques and approaches in forest tree ecophysiology (J.P. Lassoie and T. M. Hinckley, Eds). CRC

Press, Boca Raton, FL.

Ritchie, G.A. 2000. The informed buyer: understanding

seedling quality. Pages 51-56 In: Advances and

Challenges in Forest Regeneration, (Eds.) R. Rose and

D.L. Haase, Conference Proceedings, Nursery

Technology Cooperative, Oregon State University and

Western Forestry and Conservation Association.

Ritchie, G.A. and T.M. Hinckley. 1975. The pressure

chamber as an instrument for ecological research. Adv.

Ecol. Res. 9:165-254.

Ritchie G.A., and J.R. Dunlap. 1980. Root growth potential: its development and expression in forest tree

seedlings. New Zeal. J. For. Sci. 10:218-248.

Ritchie, G.A. and R.G. Shula. 1984. Seasonal changes of

tissue-water relations in shoots and root systems of

Douglas-fir seedlings. For. Sci. 30:538-458.

Ritchie, G.A., J.R. Roden and N. Kleyn. 1985.

Physiological quality of lodgepole pine and interior

spruce seedlings: effects of lift date and duration of

freezer storage. Canadian Journal of Forest Research

15:636-645.

Ritchie, G.A., Y. Tanaka, R. Meade and S.D. Duke. 1987.

Field survival and early growth of Douglas-fir rooted

cuttings: relationship to stem diameter and root system

quality. Forest Ecology and Management. 60:237-256.

Ritchie GA, and Tanaka Y. 1990. Root growth potential and

the target seedling. In: Rose R, Campbell SJ, Landis TD.

eds. Target Seedling Symposium: roceedings, combined

meeting of the western forest nursery associations; 1990

/aug 13-17; Roseburg, OR. Ft. Collins, CO: USDA Forest

Service, Rocky Mountain Forest and Range Experiment

Station, Gen. Tech. Rep. RM-200.: 37-51.

Ronco F. 1973. Food reserves of Engelmann spruce planting stock. Forest Science 19: 213-219.

Rose, R.R. and D. Haase. 2002. Chlorophyll fluorescence

and variations in tissue cold hardiness in response to

freezing stress in Douglas-fir seedlings. New Forests

2381-96.

Rose R, Haase DL. 2005. Root and shoot allometry of

bareroot and container Douglas-fir seedlings. New

Forests 30 (2-3): 215-233.

Rose, R., D.L. Haase, F. Kroiher, and T. Sabin. 1997. Root

volume and growth of ponderosa pine and Douglas-fir

seedlings: A summary of eight growing seasons.

Western Journal of Applied Forestry. 12:69-73.

Sakai, A. and C.J. Weiser. 1973. Freezing resistance of

trees in North America with reference to tree regions.

Ecology 54:118-126.

78
79

Scholander, P.F., H.T. Hammel, E.D. Bradstreet and E.A.

Hemmingson. 1965. Sap pressure in vascular plants.

Science 148:339-346.

Schreiber, U., W. Bilger and C. Neubauer. 1995.

Chlorophyll fluorescence as a nonintrusive indicator of

rapid assessment of in vivo photosynthesis. Pp. 48-70

In: E-O Schultze and M.M. Caldwell, (Eds.)

Ecophysiology of Photosynthesis. Springer-Verlag,

Berlin, Heidelberg, New York. 

Simpson DG. 1990. Frost hardiness, root growth capacity,

and field performance relationships in interior spruce,

lodgepole pine, Douglas-fir, and western hemlock

seedlings. Canadian Journal of Forest Research 20:566-

572. 

Simpson DG, and G.A. Ritchie. 1997. Does RGP predict

field performance? A Debate. New Forests 13:253-277.

Slatyer, R.O. 1967. Plant water relationships. Academic

Press, London and New York.

Sorensen, F. C. 1983. Relationship between logarithms of

chilling period and germination or bud flush rate is linear for many tree species. Forest Science 29:237-240.

South DB, Mitchell RG. 2005. A Root-Bound Index for

Container-Grown Pines. IN: Colombo SJ, comp. The

Thin Green Line : a symposium on the state-of-the-art

in reforestation, proceedings. Forest Research

Information Paper 160. Sault Ste. Marie, ON: Ministry

of Natural Resources, Ontario Forest Research Institute:

88-93.

Stattin, E., C. Hellqvist and A. Lindström. 2000. Storability

and root freezing tolerance of Norway spruce (Picea

abies) seedlings. Canadian Journal of Forest Research

30:964-970.

Stone EC 1955. Poor survival and the physiological condition of planting stock. Forest Science 1:90-94.

Sutinen, M-L, R. Arora, M. Wisniewski, E. Ashworth, R.

Strimbeck and J. Palta. 2001. Mechanisms of frost survival and freeze-damage in nature. Pp. 89-120, In:

Conifer Cold Hardiness, (Eds.) F. J. Bigras and S.J.

Colombo, Kluwer Academic Publishers, the

Netherlands.

Sutton RF. 1983. Root growth capacity: relationship with

field root growth and performance in outplanted jack

pine and black spruce. Plant and Soil 71:111-122.

Sundheim, I., and K. Kohmann. 2001. Effects of thawing

procedure on frost hardiness, carbohydrate content and

timing of bud break in Picea abies. Scandinavian

Journal of Forest Research 16:30-36.

Tanaka Y, Brotherton P, Hostetter S, Chapman D, Dyce S,

Belanger J, Johnson B, Duke S. 1997. The operational

planting stock quality testing program at Weyerhaeuser.

New Forests 13:423-437.

Thiffault N. 2004. Stock type in intensive silviculture: a

(short) discussion about roots and size. Forestry

Chronicle 80(4): 463-468.

Thompson, B.E. 1985. Seedling morphological evaluation:

what you can tell by looking. Pages 59-71, In:

Proceedings - Evaluating seedling quality: principles,

procedures and predictive ability of major tests.

Duryea, ML, (Ed.), Workshop held October 16-18,

1984. Forest Research Lbaoratory, Oregon State

University. Corvallis.

Timmer, VR. 1997. Exponential nutrient loading: a new

fertilization technique to improved seedling performance on competitive sites. New Forests 13:279-299.

Timmis, R. 1976. Methods of screening tree seedlings for

frost hardiness. Pp. 421-435 In: Tree Physiology and

Yield Improvement. (Eds.) M.G.R. Cannell and F.T. Last.

Academic Press, New York, London.

Timmis, R. and J. Worrall. 1975. Environmental control of

cold acclimation in Douglas-fir during germination,

active growth and rest. Can. J. For. Res. 5:464-477.

Timmis R, Tanaka, Y. 1976. Effects of container density

and plant water stress on growth and cold hardiness of

Douglas-fir seedlings. Forest Science 22(2): 167-172.

Timmis K.A., Fuchigami LH, Timmis R. 1981. Measuring

dormancy: the rise and fall of square waves.

HortScience 16: 200-202.

Tyree, M.T., and H. T. Hammel. 1972. The measurement

of the turgor pressure and the water relations of plants

by the pressure-bomb technique. Journal of

Experimental Botany 23:267-282.-bomb technique.

Journal of Experimental Botany 23:267-282.

van den Driessche, R. 1984. Relationship between spacing and nitrogen fertilization of seedlings in the nursery, seedling mineral nutrition, and outplanting performance. Can. J. For. Res. 14:431-436.

van den Driessche, R. 1977. Survival of coastal and interior Douglas-fir seedlings after storage at different temperatures, and effectiveness of cold storage in satisfying

chilling requirements. Canadian Journal of Forest

Research 7:125-131.
80

van den Driessche R. 1987. Importance of current photosynthate to new root growth in planted conifer

seedlings. Canadian Journal of Forest Research 17:776-

782.

van den Driessche R. 1991. New root growth of Douglasfir seedlings at low carbon dioxide concentration. Tree

Physiology 8:289-295.

Balk PA, Bronnum P, Perks M, Stattin E, van der Geest

LHM, van Wordragen MF. 2007. Innovative Cold

Tolerance Test for Conifer Seedlings. IN: Riley, L.E.;

Dumroese, R.K.; Landis, T.D., tech. coords. National

Proceedings: Forest and Conservation Nursery

Associations—2006. Fort Collins, CO: U.S. Department

of Agriculture, Forest Service, Rocky Mountain

Research Station. Proceedings, RMRS-P-50: 9-12.

Vidaver W, p. Toivonen, G. Lister, R. Brooke and W.

Binder 1988. Variable chlorophyll-A fluorescence and

its potential use in tree seedling production and forest

regeneration. In: Landis TD, technical coordinator.

Proceedings, combined meeting of the western forest

nursery associations; 1988 August 8-11; Vernon, British

Columbia. General Technical Report RM-167. Fort

Collins, CO: U.S. Department of Agriculture, Forest

Service, Rocky Mountain Forest and Range Experiment

Station: 127-132.

Vidaver, W.E., G.R. Lister, R.C. Brooke and W.D. Binder.

1991. A manual for the use of variable chlorophyll fluorescence in the assessment of the ecophysiology of

conifer seedlings. FRDA Report 163, British Columbia,

Canada.

Wakeley P.C. 1954. Planting the Southern Pines.

Agricultural Monograph No. 18. Washington, DC:

USDA-Forest Service. 233 p.

Wareing, R.H. and B. D. Cleary. 1967. Plant moisture

stress: evaluation by pressure bomb. Science 155:

1248-1254.

Weiser, C.J. 1970. Cold resistance and injury in woody

plants. Science 169:1269-1278.

Wenny DL, Swanson DJ, Dumroese RK. 2002. The chilling optimum of Idaho and Arizona ponderosa pine

buds. Western Journal of Applied Forestry 17:117–121.

Wheeler, N.C., K.D. Jermstad, K. Krutovsky, S.N. Aitken,

G.T. Howe, J. Krakowski, and D.B. Neale. 2005.

Mapping of quantitative trait loci controlling adaptive

traits in coastal Douglas-fir. IV. Cold-hardiness QTL

verification and candidate gene mapping. Molecular

Breeding 15:145-156.

Wilner, J. 1955. Results of laboratory tests for winter hardiness of woody plants by electrolyte methods. Proc.

Amer. Hort. Sci. 66:93-99. 

Wilner, J. 1960. Relative and absolute electrolyte conductance tests for frost hardiness of apple varieties.

Canadian Journal of Plant Science 40:630-637.

Wilson, B.C., and D.F. Jacobs. 2006. Quality assessment

of temperate and deciduous hardwood seedlings. New

Forests 31:417-433.