COMPARISON OF TECHNIQUES FOR BIOMASS

ESTIMATION IN SHORTGRASS PLAINS

by

AMY CHRISTINE GANGULI, B.S.

A THESIS

IN

RANGE SCIENCE

Submitted to the Graduate Faculty of Texas Tech University in

Partial Fulfillment of the Requirements for

the Degree of

MASTER OF SCIENCE

Approved

Accepted

December, 1999

ACKNOWLEDGMENTS

I would like to thank each of my committee members for their support throughout

my studies at Texas Tech University. Dr. Kent Rylander, a remarkable naturalist, shared

his tremendous scientific curiosity with me and provided support and friendship at crucial

times. Dr. David Wester, a wonderful teacher and statistician, has provided substantial

advice and an example for me to follow in the ftiture. I truly admire your humbleness and

talents. Dr. Mark Wallace brought me to the west and Texas Tech University, an

experience that has opened a lot of doors. Dr. Rob Mitchell, who introduced me to the

wonderful world of fire, provided essential support and encouragement during this project

and shared insightful "truck talks" to and from study sites and events.

The Department of Range, Wildlife and Fisheries Management provided my

assistantship and other resources that made this project possible. I would like to thank

Dr. Ernest Fish and Dr. Ron Sosebee for having an open door and sharing their wisdom

with me. Dr. Carlos Villalobos and Dr. Carlton Britton are appreciated for fielding

questions and offering advice on my project. I would like to thank all of the secretaries,

especially Kay Arellano, for being so supportive and making sure all of my paperwork

was eventually filed and taken care of

A special thanks is extended to my colleague and friend Lance T. Vermeire.

Lance gets the credit for introducing me to the field of range and has provided research

support at crucial times throughout this project. Along with your wife Leah, you have

been my family away from home. Thank you!

u

I would like to extend my gratitude to those who helped with various aspects of

my project: Deanna Oberheu and Lisa Wrinkle for assistance with the quad pod adventure

and showing me the ropes of Texas; Brandee Williams and Jennifer Bowers for field

assistance; Jennifer Davidson for the many miles we shared, constant encouragement and

assistance in the field and with equipment; Bruce Meyer provided assistance with field

equipment; Corey Moffet provided technical assistance; and Erin Atkinson provided

geospatial technology expertise. I would like to thank all the graduate students in our

department for my experiences especially my officemates: Wayne Brown, Jennifer

Davidson, Andy Forbes, Joanna Hahm, Susan Rupp and Lance Vermeire. Cindy Caplen,

Irene Tiemaim-Boege and Ozlen Konu are fiiends who added to my memorable

experiences at Tech.

My friends and family back east are appreciated for the support they offered. June

Kinigstein has been a great friend and helped me keep everything in perspective. Sharon

Fish, Missy Watts and Karla Ganswindt are appreciated for their fiiendship across the

miles. Final and most important thanks are graciously offered to my parents, Fran and

Prabhash, and my brother Greg for the constant love and support you have given me

throughout this endeavor. Even though you were -2,000 miles away you still made it to

every game.

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TABLE OF CONTENTS

ACKNOWLEDGEMENTS ii

ABSTRACT vi

LIST OF TABLES vii

LIST OF FIGURES ix

I. LITERATURE REVIEW 1

Introduction 1

Biomass Estimation 1

Vegetation Sampling 2

Double Sampling 3

LAI-2000 4 Visual Obstruction 8 Canopy Height 10 Weighted Plate 12

Botanical Composition 14

Literature Cited 17

II. CAN GRASSLAND BIOMASS BE INDIRECTLY

PREDICTED THROUGH LIGHT ATTENUATION? 22

Abstract 22

Introduction 22

Materials and Methods 24

Results and Discussion 25 Conclusion 26

Literature Cited 28

iv

III. COMPARISON OF FOUR NON-DESTRUCTIVE TECHNIQUES TO ESTIMATE STANDING CROP IN SHORTGRASS PLAINS 32

Abstract 32

Introduction 33

Materials and Methods

Study Area 37 Methods 38 Statistical Analysis 40

Results Cost and Sampling Time 41 Plot Standing Crop Estimation 41 Pasture Standing Crop Estimation 42

Discussion 42

Summary 45

Literature Cited 46

APPENDIX: DATA 55

ABSTRACT

Double sampling procedures are commonly used as alternatives to standing crop

estimation techniques that rely on clipping alone. These methods are often faster than

direct techniques and have the ability to provide immediate results in the field. Studying

the performance of LAI-2000 (plant canopy analyzer; PC A), visual obstruction (VOM),

canopy height (CH), and weighted plate (WP) measurements in a community dominated

by mid and short grasses should help researchers and producers workiijg in these areas

identify tools to estimate standing crop based on their objectives and available resources.

The objectives of this study were to: (1) determine if PC A measurements can be

used to non-destructively estimate biomass, and, (2) evaluate plot and pasture estimates

of standing crop using PCA, VOM, CH and WP measurements.

This study was conducted in 1998 and 1999 in Lubbock County, TX. In 1998

PCA investigations were conducted through establishing two trials. In both trials, a

single LAI measurement (A/B) was taken with a 90° view cap. Following LAI

measurements, standing vegetation and litter rooted in the plots were clipped to ground

level and oven dried at 53°C to a constant weight. Trial I measurements were made from

the center of 25 nested 0.25 and 1.0-m^ square plots. Trial II measurements were made

on a 90° quadrat 1.0-m in length. After LAI measurements were made, the 90° quadrat

was clipped 0.25, 0.50, 0.75 and 1.0-m away from the sensor, representing arced sub­

plots with areas of 0.05, 0.20, 0.44 and 0.79-m^ respectively. Estimates of plot and

pasture standing crop were evaluated for PCA, VOM, CH and WP instruments in 1999.

vi

Five hundred trials for plot estimation were conducted for each method along 25

transects, where each transect mean was used for the pasture estimation trials.

Early investigations with the PCA showed a weak relationship between standing

crop and LAI in 0.25-m^ (r = 0.63) and 1.0-m^ (r = 0.66) square plots. Sampling

modifications that resulted in isolation of the area that the PCA's sensor was actually

measuring improved this correlation. The best relationship observed was in the 0.05-m

arced sub-plot (r = 0.83).

Coefficients of determination improved as we moved from plot (0.34, 0.85, 0.37,

and 0.70) to pasture (0.67, 0.87, 0.59, and 0.83) estimation for PCA, VOM, CH and WP

measurements, respectively. The PCA was the only purchased instrument ($4800),

whereas VOM ($6), CH ($14) and WP ($14) instruments were constructed from readily

available materials. Each instrument provided fast measurements, especially when

considering the time required to hand clip the respective measurement areas. Pasture

estimation root mean square error (RMSE) values indicate the WP and VOM as the most

accurate models (445 and 446 kg ha*') followed by the PCA and CH models (613 and 691

kg ha*'). The VOM and WP instruments both provided fast, inexpensive measurements

with reasonable accuracy. Due to the rapid, inexpensive, and accurate properties of

VOM, and its current widespread use for wildlife habitat mesisurements of vertical

structure, the VOM technique is recommended as the best method for estimating standing

crop in the shortgrass plains.

vu

LIST OF TABLES

3.1. Means, ranges and coefficients of variation for pasture SC estimations using PCA, VOM, CH, and WP instruments and the SC determinations for each area measured 50

A. 1. Measurements of leaf area index (LAI-2000), visual obstruction (VOM), canopy height (CH), forage density (WP) and the oven-dried weight of hand clipped standing crop estimates for each measurement taken during the 1999 growing season in Lubbock, Texas. Measurement units for each method and the area clipped for biomass estimations are in parentheses 56

A.2. Measurements of leaf area index (LAI), mean tilt angle (MTA), and diffiise non-interceptance (DIFN) for each LAI-2000 measurement taken during the 1999 growing season in Lubbock, Texas 72

A.3. Botanical composition estimates for each plot determined through the dry weight rank procedure. For each 0.16-m^ plot the major species were recorded and given a rank of 1, 2 or 3 corresponding to the species representing 70, 21 and 9 % of the standing crop on a dry weight basis. The multipliers (0.7, 0.21 and 0.09) were used to determine the standing crop botanical composition in kg ha*' for each plot. Species codes are as follows [BOLA, silver bluestem {Bothrichloa laguroides); (ARsp, Aristida spp.); [BODA, buffalograss (Buchloe dactyloides)]; [BOGR, blue grama {Bouteloua gracilis)]', [DICA, Arizona cottontop {Digitaria californica)]; [GUSA, broom snakeweed {Gutierrezia sarothrae)]; [MUTO, ring muhly {Muhlenbergia torreyi)]; PAOB, vine-mesquite (Panicum obtusum)]; [SCPA, tumblegrass (Schedonnarduspaniculatus)]; [SIHY, bottlebrush squirreltail {Sitanion hystrix)]; [SPCR, sand dropseed {Sporobolus cryptandrus)]; [TRFL, purpletop {Tridens flavus)]; and [SPAI, alkali sacaton {Sporobolus airoides) 84

vui

LIST OF FIGURES

2.1. Approximate field of view for the optical sensor (LI-COR, Inc. 1992). The instrument reads out to a distance approximately three times the height of the vegetation 29

2.2. Quadrat design used in Trial II. The quadrat is 90° and 1.0 m in length, and the LAI 2000 is fitted with a 90° view cap to restrict its field of view to the area within the quadrat. The sensor is placed at the origin, and the 90° view cap is oriented along the X and Y axes 30

2.3. Leaf area index and biomass within the 0.05 m^ subplot for shortgrass plains in Lubbock, Texas 31

3.1 Nested quadrat design used for the comparison of each standing crop estimation technique. The area clipped for the plant canopy analyzer PCA was 0.05 m^ visual obstruction (VOM) 0.10 m^ and 0.16 m^for the canopy height (CH) and weighted plate (WP) measurements. The symbols illustrate the point where each measurement was taken 51

3.2. Diagram of the instrument used to quantify herbage bulk density and canopy height of standing crop, adapted from Raybum and Raybum (1998). The left diagram provides the dimensions of the plate and the right shows how the instrument was used to measure canopy height or allowed to settle and measure vegetation density 52

3.3. Relationship between instrument readings and standing crop for plot estimation using (a) plant canopy analyzer, (b) visual obstruction, (c) canopy height, and (d) weighted plate measurements 53

3.4. Relationship between instrument readings and standing crop for pasture estimation using (a) plant canopy analyzer, (b) visual obstruction, (c) canopy height, and (d) weighted plate measurements with 95% confidence bounds for individual points connected for presentation purposes. The open symbols in (a) tested as outliers based on Tietjen et al. (1973) and were excluded from the analysis 54

IX

CHAPTER I

LITERATURE REVIEW

Introduction

Estimates of aboveground biomass are necessary for the management of wild and

cultivated land. Biomass and standing crop are often used synonymously to refer to the

dry weight of standing vegetation and litter. Biomass is one of the most important

characteristics of range vegetation (Cook and Stubbendieck, 1986). It is a component

that is used to determine stocking rates and match forage resources to appropriate levels

of utilization, evaluate different management strategies, investigate forage or crop

production, and quantify habitat characteristics.

There are a variety of methods, both direct and indirect in nature, to estimate

standing crop (Cook and Stubbendieck, 1986; Catchpole and Wheeler, 1992). All of

these methods have benefits and drawbacks and vary in their overall level of difficulty.

The method of choice depends on: (1) research objectives, (2) vegetation structure and

diversity (community heterogeneity), (3) research funding, (4) amount of time available,

and, (5) the experience of the researcher.

Biomass Estimation

Traditionally, direct methods such as hand or mechanical clipping have been the

most widely used methods to determine biomass. Clipping is an accurate measure on

individual plots but it is time and labor intensive, may require numerous samples to

obtain reliable pasture estimates. Extensive effort has been allocated to the development

of indirect methods that rely on the relationship between specific vegetative attributes of

the foliage and biomass as alternatives or supplements to direct samples. Generally,

indirect methods are less tedious and faster to use than direct methods but they require

some sort of direct measurement for creation of new models, or calibration and validation

of their estimates.

In most range and wildlife studies, we are interested in estimating the biomass of

a large area (i.e., pasture or management unit). Individual measurements recorded from

quadrats or plots are used to estimate biomass of larger areas. Because we rarely know

what the biomass of a large area truly is, we are left with the option of comparing our

measures of biomass to sub-samples (i.e., clipped or mowed quadrats or plots).

Vegetation Sampling

Clipping or mowing is generally accepted to provide the most accurate estimates

of biomass in a given area (Catchpole and Wheeler, 1992). However, errors can be

introduced from inconsistencies among observers in things such as the stubble clipping

height, the determination of what vegetation is considered to be included in the measured

area, and in the extrapolation of plot estimates to a larger area. In plot estimation, Reese

et al. (1980) used three-dimensional clipping to overcome the vegetation "hangover

effect" that occurs when vegetation not rooted in the plot being measured hangs into the

measurement plot. Sicklebar mowers and Carter harvesters can also introduce error

through contamination of samples with soil (Stringer and Peiffer, 1981).

Quadrat size and shape for biomass estimation are selected for efficiency in

different vegetation communities. Small quadrats are more efficient statistically, but

require larger sample sizes for reliable estimates of standing crop (Wiegert, 1962;

Brummer et al., 1994). Because this can be very costly in terms of time and money, the

accuracy or precision level desired by the researcher may need to be lowered (Brummer

etal., 1994).

Clipping is an important aspect in the investigation of biomass. However, in

many sampling situations clipping alone is not feasible because the costs of sampling are

too high. For many producers and managers it is not a practical tool because of the

money and time investment required for accurate estimates. Estimates derived through

clipping can not be immediately viewed in the field because of the time required to oven

dry the vegetation which limits the use of this method when immediate decisions are

required in the field. Some investigations may demand non-destructive estimators

because destructive sampling may not be permitted.

Double Sampling

Methods that use double sampling function by developing a regression

relationship of standing crop to predictive variables such as height, leaf area, vegetation

density, age, cover or visual obstruction through a small amount of destructive sampling

(Cochran, 1977). A predictive variable is used to measure a variable (i.e., height); then

the area that the predictive variable is measuring or the area the variable is suspected to

be correlated with is subsequently clipped and weighed to describe the predictive

relationship. When a relationship has been developed, less emphasis is placed on clipped

samples, using them only for calibration and validation within trials. Examples of

methods or instruments that use double sampling include capacitance meters (Terry et al.,

1981), point frames (Frank and McNaughton, 1990), weighted or rising plates and disks

(Gourley and McGowan, 1991; Raybum and Raybum, 1998), canopy height sticks and

disks (Whitney, 1974; Gonzalez et al., 1990), visual obstmction poles and boards (Robel

et al., 1970b), plant canopy analyzers (Welles and Norman, 1991), and ocular estimation

techniques (Daubenmire, 1959). The value of a double sampling method depends on the

precision of the regression relationship and the cost of obtaining direct measurements in

comparison to the cost of obtaining indirect measurements (Ahmed and Bonham, 1982).

LAI-2000

The LAI-2000 Plant Canopy Analyzer (LI-COR, Inc., Lincoln, NE, USA) was

developed to indirectly measure canopy architecture, specifically leaf or foliage area

index (LAI) and foliage orientation or mean tilt angle (MTA). The instrument has been

used in many native and agronomic investigations. Leaf area and yield have been

measured in cotton {Gossypium hirsutum L.; Hicks and Ljiscano, 1995), cocoa

(Erythroxylum spp. L.; Acock et al., 1994), native prairie (Welles and Norman, 1991),

seeded monocultures (Mitchell et al., 1998), bushes (Brenner et al., 1995), and trees

(Stenberg et al., 1994). Miller-Goodman et al. (1999) used the LAI-2000 to quantify

forage utilization by cattle and it has been used to estimate biomass in native prairie

(Ganguli et al., 1999; Miller-Goodman et al., 1999) and homogeneous pastures

(Harmoney et al., 1997).

The LAI-2000 consists of a control box, optical sensor, and view caps. View caps

are used to obscure 90°, 180°, 270°, 315°, or 375° of the sensor's field of view. The

utilities in the view caps are to prevent inclusion of the observer or sun in the measured

area or to limit the sensor's overall field of view (Welles and Cohen, 1996). The sensor

uses hemispherical optics and ringed sensors to measure light attenuation simultaneously

in 5 angular bands (Welles and Norman, 1991). A filter is included in the sensor to limit

the spectmm of received radiation to less than 490 nm, thereby reducing the effect of

light scattered by foliage (Welles, 1990). Direct illumination of vegetation can result in a

10 to 50% reduction of apparent LAI (Welles and Norman, 1991; Hicks and Lascano,

1995). Therefore, measurements should be taken when the vegetation is not directly

illuminated (e.g., during cloud cover, before sunrise, after sunset, or when shading the

measured area).

Measurements are made by taking a single above-canopy reading (A) and several

below-canopy readings (B), replicated as many times as necessary to account for the

variability in the vegetation. Differences in light measured by the sensors are based on

light being absorbed or reflected by the vegetation (Welles and Norman, 1991). LAI-

2000 canopy stmctural measurements are obtained through the inversion of gap fraction

data (Welles, 1990). Gap fractions are calculated by dividing B readings by their

respective A reading (Welles and Norman, 1991) and can be thought of as the fraction of

the canopy that is not blocked by foliage when looking at the sky from ground level

(Welles and Cohen, 1996). The LAI-2000 can not distinguish between objects such as

stems, leaves or fruit, making it important to recognize that LAI readings include all

elements in the canopy (Welles and Norman, 1991). Leaf area index units given by the

LAI-2000 can be interpreted as foliage area/ground area, which is a unitless measure.

In order for the LAI determinations made by the LAI-2000 to be correct the

following assumptions must be met: (1) radiation is not reflected or transmitted by the

foliage, (2) distribution of the foliage is random, (3) foliage viewed by the sensor is

small, and (4) foliage has a random azimuth orientation (Welles and Norman, 1991). The

first and most important assumption is met by taking readings when the vegetation is not

directly illuminated. The second and fourth assumptions rarely create problems because

canopies are generally randomly distributed and azimuth orientation tends to be

problematic in heliotrophic species. The third assumption is met by making sure that the

foliage closest to the sensor is a distance of at least four times its width from the sensor.

Engel et al. (1987) used the LAI-3000 area meter (LI-COR, Inc., Lincoln, NE,

USA) to directly determine that orchardgrass {Dactylis glomerata L.) has a linear

relationship between leaf area and biomass. Since the LAI-2000 measures foliage

area/ground area, it should be possible to indirectly predict biomass. The LAI-2000 has

been used to estimate the biomass of grasslands in Iowa (Harmoney et al., 1997), Texas

(Ganguli et al., 1999), and Nebraska (Miller-Goodman et al., 1999). Harmoney et al.

(1997) found that measurements from the LAI-2000 had a poor relationship (r = 0.32)

with mesic grsissland biomass. They made LAI measurements by recording eight

readings around the center of a 0.21-m^ circular frame and correlated the mean of the

eight readings to the clipped quadrat standing crop. Information about the light

conditions during their measurements or the size view cap used was not provided. They

suggested the low correlation resulted from the LAI-2000 reading areas outside of their

clipped plots. Miller-Goodman et al. (1999) received similar results (r = 0.42) on mid

and tallgrasses in the Sandhills of Nebraska and suggested that the relationship they

observed supported the findings of Harmoney et al. (1997). They used mean pasture

production and mean pasture LAI values to obtain their relationship. Ganguli et al.

(1999) reported a correlation coefficient of r = 0.83 in a semi-arid grassland. Their trials

were designed to isolate the area that the LAI-2000 was reading when using a 90°

viewcap with one above-canopy and one below-canopy reading. Measurements were

made when there was no direct illumination of the vegetation or when the plots were

manually shaded. The LAI-2000 was correlated best to a 0.05 m^ arced subplot.

The LAI-2000 provides fast, non-destmctive, objective measurements. Data can

be obtained in the field through the control unit but for efficient data transmission a

computer is required to download readings. The LAI-2000 can be used in a variety of

vegetative and topographic communities. The LAI-2000 is not reported to be

temperature, humidity or moisture sensitive, so measurements can be made in most

weather conditions provided the optical sensor is protected with the same care given to

camera lenses (LI-COR Inc., 1992). The LAI-2000 has not been used extensively as a

tool to predict biomass. Previous exploration has produced varying results due to

illogical sampling designs (e.g., compgiring instrument readings to areas not read by the

sensor), instrument/observer variability, and violation of assumptions (e.g., making

measurements in situations of direct illumination). Further research is required to

determine if this instrument can accurately estimate biomass.

Visual Obstmction

Visual obstmction measurements are used in studies of habitat (Robel et al.,

1970a; Nudds, 1977; Griffith and Youtie, 1988; Haukos et al. 1998) and yield (Robel et

al., 1970b; Michalk and Herbert, 1977; Harmoney et al., 1997; Vermeire and Gillen in

review). A variety of tools such as poles, boards and cutout shapes of animals have been

used to measure horizontal visual obstmction by vegetation. These techniques involve

the relationship between vegetation and the amount of area on the board or pole that is

blocked when viewing from a fixed position.

Robel et al. (1970b) correlated herbaceous yield and visual obstmction

measurements made with a self-supporting 3-cm x 150-cm pole marked off in altemating

brown and white 10-cm bands. Each band was marked at its midpoint with a narrow

black band. They recorded the highest 5-cm section on the pole that was visually

obstmcted by vegetation from distances of 2, 3 and 4-m from the pole and 0.5, 0.8, and

1.0-m height from ground level to determine which strategy could explain the most

variation in biomass. Their experimental units were transects and 10 measurements of

each distance and height combination were made. On homogeneous vegetation they

found the highest coefficient of determination (r^= 0.95) when making measurements

from a distance of 4 m and a height of 1 m.

8

Harmoney et al. (1997) used a modified visual obstmction pole and made

measurements on homogeneous introduced pastures. Their pole differed in that it was

painted in red and white altemating 10-cm bands and was further divided into 2-cm

sections for increased accuracy. They fixed a telescope to a pole that was attached to the

visual obstmction pole by 4 m of string to make their observations from at a height of 1

m. When taking a mean of the 4 measurements made in 90° angles from the center of a

circular quadrat (0.21 m ) they obtained an overall r of 0.63. To increase their predictive

ability they presented different models for each species measured. Vermeire and Gillen

(in review) used a modified pole to measure native tallgrass prairie in Oklahoma. Their

pole was painted in red and white altemating 10-cm bands divided into 2.5-cm sections.

Their experimental unit was a transect where 20 measurements were made by placing a

pole towards the back of a 0.10 m^ quadrat and recording one visual obstmction value

from a height of 1-m and distance of 4-m to the pole. They found that the pole performed

well and required separate models for burned (r^= 0.95) and non-bumed (r^= 0.90)

pastures. They also investigated the claim that dividing the bands on the pole into

smaller increments would improve accuracy. They found that the accuracy was similar

for measurements made to 2.5-cm (r = 0.64) and 5.0-cm (r = 0.63) increments,

suggesting little benefit to using smaller increments.

Visual obstmction poles and boards have been used extensively to characterize

habitat. Research has shown that visual obstmction poles can provide useful information

for the non-destmctive estimation of biomass (Robel, 1970b; Vermeire and Gillen, in

review). However, Higgins et al. (1996) suggested that poles and boards would not

provide useful information in the shortgrass prairie or other habitats with sparse

vegetation. This claim or investigations of how visual obstmction measurements perform

in other vegetation types has not been evaluated.

Canopv Height

Canopy height measurements have been used to characterize canopy attributes

such as growth, vigor, adaptability, resistance to grazing and aboveground biomass

(Heady, 1957). Plant height can be a difficult canopy characteristic to measure because it

is often hard to determine and disagreement exists over which plants should be

considered to form an estimate of mean canopy height (Heady, 1957). To accurately

measure the canopy height, extensive measurements must be recorded from plots unless

the canopy measured has low stmctural heterogeneity. Investigations of large areas

require rapid estimates so appropriate averages can be determined. Ocular estimates have

been used but tend to be subjective and often are not repeatable (Heady, 1957).

Rapid measurements have been made with a measuring stick in orchardgrass

{Dactylis glomerata L.)-clover {Trifolium spp. L.) pastures (Alexander et al., 1962),

alfalfa {Medicago sativa L.\ Griggs and Stringer, 1988), bermudagrass [Cynodon dactylon

(L.) Pers.] (Gonzalez et al., 1990), and mesic grasslands (Harmoney et al., 1997).

Methods that add an area dimension to produce more accurate measurements of mean

canopy height have been made with plastic disks (Sharrow, 1984) and plates (Whitney,

1974) in commercial oat {Avena sativa L.) / grass-sub clover {Trifolium subterraneum L.)

10

pastures and the tropical grasses kikuyugrass {Pennisetum clandestinum Hochst. ex.

Chiov.) and pangola digitgrass {Digitaria decumbens Stent.) pastures.

Alexander et al. (1962) took a mean of several readings to estimate canopy height

on quadrats and had varying levels of success when predicting biomass with separate

models for individual trials (r = 0.60 to 0.92). Griggs and Stringer (1988) reported

coefficients of determination ranging from 0.59 to 0.86 in their trials when measuring

canopy height to the nearest mm with a meter stick that had a sliding 1-m-crossbar that

could be lowered to measure the mean canopy height. For both plot and pasture biomass

estimation, Gonzalez et al. (1990) reported a coefficient of determination of 0.86 using

mean height. Harmoney et al. (1997) found that canopy height poorly explained the

variation of the biomass they sampled (r^= 0.55) when measuring the height of the tallest

leaf tissue present within their frame to the nearest 2 cm. Whitney (1974) measured

canopy height to the nearest cm by lowering a plastic (28 x 28-cm) lens until several

leaves were just touching the lens. They found this method to have high coefficients of

determination (r = 0.94) but required separate models for each species. Sharrow (1984),

in three separate small trials (n= 20, 20 and 30), also found good results (r = 0.90, 0.72,

and 0.86, respectively) when using a plastic disk (0.2 m ) with the same sampling strategy

used by Whitney (1974).

Rapid techniques to predict yield through height have not been extensively

explored in native grasslands. Time intensive methods involving pin-drops have been

used in California annual grasslands (Heady, 1957). Most of the research involving

measurements of height has been conducted on introduced pastures. More investigation

11

of using canopy height measurements to predict biomass is required to determine if it can

be a useful tool in native rangeland sampling.

Weighted Plate

Several different instruments have been used to measure forage density. It is

defined as the volume of the aboveground forage when compressed and is a function of

the vegetation's height, density and compressibility (Bransby et al., 1977). The earliest

instmments used were simple and included a cardboard box or plywood plank (Alexander

et al., 1962) that was dropped on the canopy and its mean height determined by

measuring the height of each side's midpoint. Instruments that are now more commonly

used include weighted disks and plates that are either dropped or allowed to settle on the

canopy (Santillan et al., 1979). Another variation of this instrument is a rising disk or

plate meter. Rising meters allow vegetation to push a plate or disk up a pole it is

supported on, as it is lowered into the vegetation.

Rising disk and plate meters have the advantage of automatically recording the

total resting height and the number of observations made, allowing for more rapid

measurements than traditional weighted plates and disks (Earle and McGowan, 1979;

Gourley and McGowan, 1991). The drawback of commercially available rising disk or

plate meters is the cost, as opposed to weighted plate or disk meters that can be

manufactured by the user. Inexpensive, easy to construct weighted plates and disks have

been made from acrylic plastic for use in trials (Sharrow, 1984; Karl and Nicholson,

1987; Raybum and Raybum, 1998) with good success.

12

Considering previous inconsistencies, caution should be taken when comparing

results or applying models from study to study because of observer variability (Aiken and

Bransby, 1992), different instmments used, and different methods of comparing meter

readings to clipped estimates of standing crop. Investigations have shown that trial

results remained consistent when using disks of different sizes and weight (Bransby et al.,

1977). Santillan et al. (1979) found that lowering a disk onto the forage as opposed to

dropping it from a fixed height resulted in a small increase in the correlation, likely due to

the reduced disturbance to the vegetation. Researchers who have used these instmments

have needed to calibrate their models when moving to different vegetation types or

pastures (Santillan et al., 1979; Baker et al., 1981) and when the vegetation changed

growth form or state (Bransby et al., 1977; Michell, 1982). While a majority of the

models that have been presented are linear, previous models created required quadratic

terms due to curvilinear relationships (Baker et al., 1981; Michell, 1982; Karl and

Nicholson, 1987; Gonzalez et al., 1990).

Precision of models is affected by management practices. Stockdale and Kelly

(1984) found that trampling in grazed pastures caused increases in the magnitude of their

intercept. Lodged vegetation, due to animals bedding or some other disturbance, has

presented problems (Vartha and Matches, 1977; Gonzalez et al., 1990). Under-grazing

that facilitates mulch accumulation alters predictive relationships (Vartha and Matches,

1977). Hoof indentations in the soil and other microtopographical effects have also been

blamed for reduced precision in models (Sharrow, 1984; Murphy et al., 1995). Weighted

13

plates and disks have been used extensively on introduced pastures. Few studies have

investigated how well this method works on native rangeland.

Botanical Composition

Knowledge of botanical composition is necessary for many vegetation studies and

is essential for range monitoring projects. When investigating biomass estimation

techniques, measures of botanical composition provide the opportunity to test for

differences due to different species or growth form. Hand clipping and sorting vegetation

is recognized as an accurate technique but is very time consuming and tedious to perform.

As with biomass measurements, there may be circumstances that prevent this type of

destmctive sampling. Methods have been developed to estimate botanical composition

without having to cut, hand-separate and weigh vegetation. Examples of non-destructive

methods include canopy point intercept (Frank and McNaughton, 1990), ocular

estimation (Daubenmire, 1959), and estimation through dry weight ranks (DWR;

Mannetje and Haydock, 1963).

The canopy point intercept method uses pins that have been passed through the

vegetation to determine the botanical composition. It has been very accurate, but requires

significant time to perform the estimates (Frank and McNaughton, 1990). Methods that

rely on ocular estimation involve investigators estimating the percent each species

occupies within a given area, and have been recognized as being subjective and

inconsistent when multiple observers are used. Botanical estimates through DWR have

been developed as a rapid measure where an observer records the species that are present

14

in a quadrat and estimates which species takes first, second or third place (Mannetje and

Haydock, 1963) in terms of dry weight. The ranks are then ordered and converted to a

proportion. To obtain the percent dry weight of the species observed, the multipliers

70.19, 21.08 and 8.73, derived by Mannetje and Haydock (1963), are multiplied by the

proportions.

Jones and Hargreaves (1979) modified the DWR method through application of a

weighting factor to DWR estimates by using the yield clipped from the quadrat measured.

They also developed a process of cumulative ranking to reduce bias in pastures that tend

to be dominated by one species. Cumulative ranking is done by assigning more than one

rank to the dominant species if it amounts to more than 70% of the quadrat (Jones and

Hargreaves, 1979).

The creation of new multipliers that are specific for vegetation types have been

explored, but not pursued because the advantage received is minimal (Jones and

Hargreaves, 1979; Gillen and Smith, 1986) thus reducing the simplicity of the method.

Several studies have shown that botanical composition estimates through DWR do not

significantly differ from direct measures of clipped, sorted, and weighed vegetation

(Mannetje and Haydock, 1963; Jones and Hargreaves, 1979; Gillen and Smith, 1986;

Friedel et al., 1988). Sandland et al. (1982) reported that DWR estimates are unbiased in

most sampling situations where you have trained observers.

The advantage of DWR is that subjectivity is reduced (as compared to other

indirect methods) because the observer only has to decide if one species has a greater dry

weight than another. Additionally, it is a fast method that does not require clipping and

15

sorting of the vegetation which is beneficial in terms of saving time and reducing risk of

losing vegetation in the sorting and drying process.

Studying the performance of the LAI-2000, visual obstmction, canopy height and

weighted plate measurements in a community dominated by short and mid grasses should

help researchers and producers working in these areas identify tools to estimate standing

crop based on their objectives and resources. The objectives of this study were to: (1)

determine if the LAI-2000 (plant canopy analyzer) can be used to non-destmctively

estimate herbaceous biomass, and (2) evaluate LAI-2000, visual obstmction, canopy

height and weighted plate estimates of plot and pasture standing crop. The following

chapters are organized to address each of these objectives: Chapter II: " Can grassland

biomass be indirectly predicted through light attenuation?", and Chapter III: "Comparison

of four non-destmctive techniques to estimate standing crop in shortgrass plains."

16

Literature Cited

Acock, M. C , C. S. T. Daughtry, G. Beinhart, E. Hirschmann, and B. Acock. 1994. Estimation leaf mass from light interception measurements on isolated plants of Erythroxylum species. Agron. J. 86:570-574.

Ahmed, J., and C. D. Bonham. 1982. Optimum allocation in multivariate double sampling for biomass estimation. J. Range Manage. 35:777-779.

Aiken, G. E., and D. I. Bransby. 1992. Observer variability for disk meter measurements of forage mass. Agron. J. 84:603-605.

Alexander, C. W., J. T. Sullivan, and D. E. McCloud. 1962. A method for estimating forage yields. Agron. J. 54:468-469.

Baker, B. S., T. V. Eynden, and N. Bogress. 1981. Hay yield determinations of mixed swards using a disk meter. Agron. J. 73:67-69.

Bransby, D. I., A. G. Matches, and G. F. Krause. 1977. Disk meter for rapid estimation of herbage yield in grazing trials. Agron. J. 69:393-396.

Brenner, A. J., M. C. Romero, J. G. Haro, M. A. Gilabert, L. D. Incoll, J. Martinex Femandez, E. Porter, F. I. Pugnaire, and M. T. Younis. 1995. A comparison of direct and indirect methods for measuring leaf and surface areas of individual bushes. Plant, Cell, Environ. 18:1332-1340.

Brummer, J. E., J. T. Nichols, R. K. Engel, and K. M. Eskridge. 1994. Efficiency of different quadrat sizes and shapes for sampling standing crop. J. Range Manage. 47:84-89.

Catchpole, W. R., and C. J. Wheeler. 1992. Estimating plant biomass: a review of techniques. Australian J. Ecol. 17:121-131.

Cochran, W. G. 1977. Sampling Techniques. John Wiley and Sons, Inc., New York.

Cook, W. C , and J. Stubbendieck. 1986. Range research: basic problems and techniques. Society for Range Management. Denver, CO.

Daubenmire, R. 1959. A canopy-coverage method of vegetation analysis. Northwest Sci. 33:43-64.

17

Earle, D. F., and A. A. McGowan. 1979. Evaluation and calibration of an automated rising plate meter for estimation dry matter yield of pasture. Australian J. Exp. Agr. Anim. Husb. 19:337-343.

Engel, R. K., L. E. Moser, J. Stubbendieck, and S. R. Lowry. 1987. Yield accumulation, leaf area index and light interception of smooth bromegrass. Crop Sci. 27:316-321.

Frank, D. A., and S. J. McNaughton. 1990. Aboveground biomass estimation with the canopy intercept method: a plant growth form caveat. Oikos 57:57-60.

Friedel, M. H., V. H. Chewings, and G. N. Bastin. 1988. The use of comparative yield and dry-weight rank techniques for monitoring arid rangeland. J. Range Manage. 41:430-434.

Ganguli, A. C , R. B. Mitchell, M. C. Wallace, and L. T. Vermeire. 1999. Can grassland biomeiss be indirectly predicted through light attenuation? Proc. of the 5th Intemational Symposium on the Nutrition of Herbivores. San Antonio April 11-16.

Gillen, R. L., and E. L. Smith. 1986. Evaluation of the dry-weight rank method for determining species composition in tall grass prairie. J. Range Manage. 39:283-285.

Gonzalez, M. A., M. A. Hussey, and B. E. Conrad. 1990. Plant height, disk and capacitance meters used to estimate bermudagrass herbage mass. Agron. J. 82:861-864.

Gourley, C. J. P., and A. A. McGowan. 1991. Assessing differences in pasture mass with an automated rising plate meter and a direct harvesting technique. Australian J. Exp. Agr. 31:337-339.

Griffith, B., and B. A. Youtie. 1988. Two devices for estimating foliage density and deer hiding cover. Wildl. Soc. Bull. 16:206-210.

Griggs, T. C , and W. C. Stringer. 1988. Prediction of alfalfa herbage mass using sward height, ground cover and disk technique. Agron. J. 80:204-208.

Harmoney, K. R., K. J. Moore, J. R. George, E. C. Bmmmer, and J. R. Russell. 1997. Determination of pasture biomass using four indirect methods. Agron. J. 89:665-672.

18

Haukos, D. A., H. Z. Sun, D. B. Wester, and L. M. Smith. 1998. Sample size, power, and analytical considerations for vertical stmcture data from profile boards in wetland vegetation. Wetlands 18:203-215.

Heady, H. F. 1957. The measurement and value of plant height in the study of herbaceous vegetation. Ecol. 38:313-320.

Hicks, S. K., and R. J. Lascano. 1995. Estimation of leaf area index for cotton canopies using the LI-COR LAI-2000 plant canopy analyzer. Agron. J. 87:458-464.

Higgins, K. F., J. L. Oldemeyer, K. J. Jenkins, G. K. Clambey, and R. F. Hariow. 1996. Vegetation sampling and management. Pages 567-591 in T. A. Bookhout, ed. Research and management techniques for wildlife and habitats. Fifth ed., rev. The Wildlife Society, Bethesda, MD.

Jones, R. M., and J. N. G. Hargreaves. 1979. Improvements to the dry-weight rank method for measuring botanical composition. Grass Forage Sci. 34:181-189.

Karl, M. G., and R. A. Nicholson. 1987. Evaluation of the forage-disk method in mixed-grass rangeland of Kansas. J. Range Manage. 40:467-471.

LI-COR, Inc. 1992. LAI-2000 Plant Canopy Analyzer Operating Manual. LI-COR, Inc., Lincoln, NE, USA.

Mannetje, L. 'T, and K. P. Haydock. 1963. The dry-weight rank method for the botanical analysis of pasture. J. Brit. Grassl. Soc. 18:268-275.

Michalk, D. L., and P. K. Herbert. 1977. Assessment of four techniques for estimating yield on dryland pastures. Agron. J. 69:864-868.

Michell, P. 1982. Value of a rising-plate meter for estimating herbage mass of grazed perennial ryegrass-white clover swards. Grass Forage Sci. 37:81-87.

Miller-Goodman, M. S., L. E. Moser, S. S. Waller, J. E. Bmmmer, and P. E. Reece. 1999. Canopy analysis as a technique to characterize defoliation intensity on Sandhills range. J. Range Manage. 52:357-362.

Mitchell, R. B., L. E. Moser, K. J. Moore, and D. D. Redfearn. 1998. Tiller demographics and leaf area index of four perennial pasture grasses. Agron. J. 90:47-53.

19

Murphy, W. M., J. P. Silman, and A. D. Mena Barreto. 1995. A comparison of quadrat, capacitance meter, HFRO sward stick, and rising plate for estimating herbage mass in a smooth-stalked, meadowgrass-dominant white clover sward. Grass Forage Sci. 50:452-455.

Nudds, T. D. 1977. Quantifying the vegetative stmcture of wildlife cover. Wildl. Soc. Bull. 5:113-117.

Raybum, E. B., and S. B. Raybum. 1998. A standardized plate meter for estimating pasture mass in on-farm research trials. Agron. J. 90:238-241.

Reese, G. A., R. L. Bayn, and N. E. West. 1980. Evaluation of double-sampling estimators of subalpine herbage production. J. Range Manage. 33:300-306.

Robel, R. J., J. N. Briggs, J. J. Cebula, N. J. Silvy, C. E. Viers, and P. G. Watt. 1970a. Greater prairie chicken ranges, movements and habitat usage in Kansas. J. Wildl. Manage. 34:286-306.

Robel, R. J., J. N. Briggs, A. D. Dayton, and L. C. Hulbert. 1970b. Relationships between visual obstmction measurements and weight of grassland vegetation. J. Range Manage. 23:295-297.

Sandland, R. L., J. C. Alexander, and K. P. Haydock. 1982. A statistical assessment of the dry-weight-rank method of pasture sampling. Grass Forage Sci. 37:263-272.

Santillan, R. A., W. R. Ocumpaugh, and G. O. Mott. 1979. Estimating forage yield with a disk meter. Agron. J. 71:71-74.

Sharrow, S. H. 1984. A simple disc meter for measurement of pasture height and forage bulk. J. Range Manage. 37:94-95.

Stenberg, P., S. Linder, H. Smolander, and J. Flower-Ellis. 1994. Performance of the LAI-2000 plant canopy analyzer in estimation leaf area index of some Scots pine stands. Tree Physiol. 14:981-995.

Stockdale, C. R., and K. B. Kelly. 1984. A comparison of a rising-plate meter and an electronic capacitance meter for estimating the yield of pastures grazed by dairy cows. Grass Forage Sci. 39:391-394.

Stringer, W. C , and R. A. Peiffer. 1981. Soil contamination of forage samples by forage plot harvesters. Agron. J. 73:65-66.

20

Terry, S. W., D. H. Hunter, and B. F. Swindel. 1981. Herbage capacitnce meter:an evaluation of its accuracy in Florida rangelands. J. Range Mange. 34(3):240-241.

Vartha, E. W., and A. G. Matches. 1977. Use of a weighted-disk measure as an aid in sampling the herbage yield on tall fescue pastures grazed by cattle. Agron. J. 69:888-890.

Vermeire, L. T., and R. L. Gillen. in review . Measuring herbage standing crop in tallgrass prairie with the visual obstmction method. J. Range Manage.

Welles, J. M. 1990. 3. Some indirect methods of estimating canopy structure. Remote Sens. Rev. 5:31-43.

Welles, J. M., and S. Cohen. 1996. Canopy stmcture measurement by gap fraction analysis using commercial instmmentation. J. Exp. Bot. 47:1335-1342.

Welles, J. M., and J. M. Norman. 1991. Instmment for indirect measurement of canopy architecture. Agron. J. 83:818-825.

Whitney, A. S. 1974. Measurement of foliage height and its relationships to yields of two tropical forage grasses. Agron. J. 66:334-336.

Wiegert, R. G. 1962. The selection of an optimum size for sampling the standing crop of grasses and forbs. Ecol. 43:125-129.

21

CHAPTER II

CAN GRASSLAND BIOMASS BE INDIRECTLY PREDICTED

THROUGH LIGHT ATTENUATION?

Abstract

Numerous methods have been used to determine herbaceous biomass. Direct

methods are the most accurate, but are time and labor intensive and may be precluded

where destmctive sampling is prohibited. We used the LAI-2000 (plant canopy analyzer,

LI-COR, Inc., Lincoln, NE, USA) to indirectly estimate herbaceous biomass in a

shortgrass plains site. Preliminary exploration and isolation of measurements using the

LAI-2000 resulted in a fair correlation to herbaceous biomass (r = 0.83). More research

is proposed to evaluate this relationship and identify the sources of variation in the

estimation technique. The LAI-2000 will be evaluated in sampling situations and

compared to other non-destmctive techniques to assess its performance in relation to

other commonly used biomass estimation techniques.

Introduction

The LAI-2000 was developed to indirectly measure canopy architecture,

specifically leaf or foliage area index (LAI). Some advantages of this indirect method are

that it is a fast, non-destructive, objective measurement that can be rapidly and easily

applied to large areas. If the relationship between LAI and herbaceous biomass can be

identified, biomass could be rapidly and non-destmctively assessed. The ease of

sampling allows for more samples to be taken, providing a better estimation of the

22

variation in sampled areas. The LAI-2000 may also provide an altemative method when

destmctive sampling is not an acceptable option.

The LAI-2000 consists of a control box, optical sensor, and view caps that

prevent inclusion of the observer in the measured area (Welles and Cohen, 1996). It uses

hemispherical optics and ringed sensors to measure light attenuation simultaneously in 5

angular bands (Welles and Norman, 1991). Measurements are made by taking a single

above-canopy reading (A) and several below-canopy readings (B), replicated as many

times as necessary to account for the variability in the vegetation. Differences in light

attenuation, measured by the sensors, are based on the incidental light absorbed or

reflected by the vegetation (Welles and Norman, 1991). The LAI is obtained from gap

fractions measured at each angle and calculated by dividing B readings by their

respective A reading (Welles, 1990). The LAI-2000 can not distinguish between objects

such as stems, leaves or fruit making it important to recognize that the LAI readings

include all elements in the canopy (Welles and Norman, 1991). Leaf area index values

given by the LAI-2000 can be interpreted as foliage area/ground area, which is a unitless

measure.

Readings on vegetation that is directly illuminated can result in a 10 to 50%

reduction of apparent LAI (Welles and Norman, 1991; Hicks and Lascano, 1995).

Therefore, measurements should be taken when the vegetation is not directly illuminated

(e.g., during cloud cover, before sunrise, after sunset or when shading the measured area).

The LAI-2000 measures out a distance approximately three times the canopy height (Fig.

2.1; LI-COR, Inc., 1992). Consequenfly, if measurements were taken in a canopy that is

23

0.5 m in height, the sensor would measure light attenuation approximately 1.5 m in any

direction, unless restricted by a view cap.

Herbaceous biomass has been correlated with canopy LAI. Harmoney et al.

(1997) found that measurements from the LAI-2000 had a poor relationship (r = 0.32)

with mesic grassland biomass. They suggested this relationship resulted from the LAI-

2000 reading areas outside of their clipped plots. Our hypothesis was that by adjusting

biomass sampling techniques, we could better define the relationship between LAI and

biomass estimates in semi-arid grasslands. The objective of this study was to determine

if the LAI-2000 could be used to estimate herbaceous biomass in the shortgrass plains.

Materials and Methods

Two trials were conducted from September to November 1998 on non-grazed

native shortgrass plains in Lubbock, TX (33°35' N, 101°53' W, elevation 990 m).

Sampling was conducted on a deep hardland range site with Acuff-Urban and Midessa

fine sandy loam (Mixed, thermic Aridic PaleustoUs) soils (Blackstock, 1987). Mean

annual precipitation is 450 mm with 67% of the precipitation falling from May to

September and a frost free growing period of 210 days (N.O.A.A., 1997).

Vegetation on the study area was dominated by short and mid grasses

(nomenclature used follows Hatch et al. 1990). Dominant grasses included blue grama

[Bouteloua gracilis (H.B.K.) Lag. ex Griffiths], purple threeawn [Aristida purpurea

Nutt.], silver bluestem [Bothriochloa laguroides (DC.) Herter] and buffalograss [Buchloe

dactyloides (Nutt.) Engelm.] with the dominant forbs blueweed {Helianthus ciliaris DC),

24

prairie coneflower [Ratibida columnifera (Nutt.) Woot. & Standi.] and scarlet

globemallow [Sphaeralcea coccinea (Nutt.) Rydb.].

To reduce direct illumination problems, all LAI measurements were recorded

between 0600 and 0800. In Trial I, a single LAI measurement (A/B) was recorded with a

90° view cap from the center of 25 nested 0.25 and 1.0-m^ square plots. Following LAI

measurements, only the standing vegetation and litter rooted in the plots was clipped to

ground level and oven dried at 53°C to a constant weight. In Trial II, a 90° quadrat 1.0-m

in length was used to establish plots (Fig. 2.2). LAI measurements were taken with a 90°

view cap placed over the sensor to restrict its view only to the area within the quadrat.

One A/B reading was recorded per plot. After the LAI reading was taken, each plot (N =

28) was hand clipped to ground level at a distance of 0.25, 0.50, 0.75 and 1.0 m away

from the sensor, representing arced sub-plots (Fig. 2.2) with areas of 0.05, 0.20, 0.44 and

0.79 m , respectively.

Results and Discussion

Trial I LAI readings ranged from 0.20 to 2.20. Clipped biomass estimates ranged

from 680 to 3525 kg ha"' in the 0.25-m^ subplot and 610 to 1810 kg ha"' in the 1.0-m^

plot. The correlation between LAI and biomass in the 0.25-m subplots was r = 0.63

which improved to r = 0.66 in the 1.0-m plot. These results are similar to those reported

by Harmoney et al. (1997). They conducted trials that involved taking one above-canopy

•y

reading with eight below-canopy readings around the center of a 0.21-m circular

quadrat. The quadrat was clipped to compare biomass values to the LAI readings, but

they did not provide information about the size view cap they used. They suggested the

25

low correlation resulted from the LAI-2000 reading areas outside of their clipped plots.

The sampling protocol in this study was similar to Harmoney et al. (1997) which

compared instmment readings to areas that were not seen by the sensor, resulting in a low

correlation between LAI and biomass.

Trial II LAI ranged from 0.05 to 1.50. Clipped biomass estimates ranged from

145 to 9900 kg ha"' in the 0.05 m^ subplot, 535 to 5660 kg ha"' in the 0.20 m^ subplot,

1015 to 4270 kg ha"' in the 0.44 m^ subplot and 1245 to 3715 kg ha ' in the 0.79 m^

subplot. Average vegetation height was 45 cm. Results of the correlation analysis

indicated the first subplot (0.05 m ) had the highest correlation between LAI and biomass

(r = 0.83; Fig. 2.3). As distance away from the sensor increased, the relationship became

weaker (r = 0.51 for 0.20 m^ r = 0.14 for 0.44 m^ and r = 0.03 for 0.79 m^). Decreasing

r values indicate the sensor is reading only a fraction of shortgrass plains vegetation

beyond 0.5 m and are probably not reading 3 times the average vegetation height in this

community. Possible explanations for reduced sensitivity as distance from the sensor

increases are: (1) only the fifth ring reads out to 3 times the canopy height so the other

rings are not likely able to detect changes in LAI at those distances, and (2) dense

canopies reduce the sensitivity because any part of the sensors view can only be blocked

once (Jon Welles, LI-COR, Inc., personal communication).

Conclusion

The LAI-2000 can be used to predict herbaceous biomass in the shortgrass plains.

Simple adjustments that better defined the biomass actually sampled greatly improved the

correlation between LAI and herbaceous biomass. Possible reasons for the poor success

26

previously experienced could be that vegetation was clipped outside of the area read by

the sensor. The LAI-2000 is also highly sensitive to vegetation illumination and rapidly

changing light conditions. Future investigation will focus on specific attributes of the

canopy stmcture sensed which are key components to indirect biomass determination. A

better understanding of these components is necessary to test whether this instmment can

accurately estimate herbaceous biomass in other vegetation types. Additional research is

necessary to determine the variability of LAI-2000 measurements for evaluation of

instmment precision.

27

Literature Cited

Blackstock, D. A. 1987. Soil Survey of Lubbock County, Texas. U. S. D. A., Soil Conservation Service.Washington, D.C.

Harmoney, K. R., K. J. Moore, J. R. George, E. C. Bmmmer, and J. R. Russell. 1997. Determination of pasture biomass using four indirect methods. Agron. J. 89:665-672.

Hatch, S. L., N. G. Kancheepuram, and L. E. Brown. 1990. Checklist of the vascular plants of Texas. MP-1665, TX Agric. Exp. Stn., College Station, TX.

Hicks, S. K., and R. J. Lascano. 1995. Estimation of leaf area index for cotton canopies using the LI-COR LAI-2000 plant canopy analyzer. Agron. J. 87:458-464.

LI-COR, Inc. 1992. LAI-2000 plant canopy analyzer, instmction manual. Version 2, LI-COR, Inc., Lincoln, Nebraska, USA.

National Oceanic and Atmospheric Administration. 1997. Climatological data annual summary, Texas. Vol. 102, Num. 13.

Welles, J. M. 1990. 3. Some indirect methods of estimating canopy structure. Remote Sens. Rev. 5:31-43.

Welles, J. M., and J. M. Norman. 1991. Instrument for indirect measurement of canopy architecture. Agron. J. 83:818-825.

Welles, J. M., and S. Cohen. 1996. Canopy stmcture measurement by gap fraction analysis using commercial instmmentation. J. Exp. Bot. 47:1335-1342.

28

3 times the canopy height

Fig. 2.1. Approximate field of view for the optical sensor (LI-COR, Inc. 1992). The instrument reads out to a distance approximately three times the height of the vegetation.

29

.79 m2

Total area of the sub-plots -44 m2 (inclusive)

.20 m2

.05 m2

.25 m .50 m .75 m 1.0 m Distance away from the sensor

Fig. 2.2. Quadrat design used in Trial II. The quadrat is 90° and 1.0 m in length, and the LAI 2000 is fitted with a 90° view cap to restrict its field of view to the area within the quadrat. The sensor is placed at the origin, and the 90° view cap is oriented along the X and Y axes.

30

12,000

^10 ,000- -

O) 8,000--

r = 0.83

w 6,000 --

I 4,000--o m 2,000-1-

0 0.0 0.5 1.0

LAI 1.5

•y

Fig. 2.3. Leaf area index and biomass within the 0.05 m subplot for shortgrass plains in Lubbock, Texas.

31

CHAPTER III

COMPARISON OF FOUR NON-DESTRUCTIVE TECHNIQUES TO

ESTIMATE STANDING CROP IN SHORTGRASS PLAINS

Abstract

Non-destructive estimators of standing crop (SC) are important for efficient

monitoring of rangeland. They are often faster than direct measurements and have the

ability to provide immediate results in the field. The objectives of this study were to

evaluate plot and pasture estimates of SC using LAI-2000 (plant canopy analyzer; PCA),

visual obstruction (VOM), canopy height (CH) and weighted plate (WP) measurements.

This study was conducted from June through August 1999 in Lubbock County, TX on

shortgrass plains vegetation. Five hundred trials for plot estimation were conducted for

each method along 25 transects, where each transect mean was used for the pasture

estimation trials. Coefficients of determination improved as we moved from plot (0.34,

0.85, 0.37, and 0.70) to pasture (0.67, 0.87, 0.59, and 0.83) estimation for PCA, VOM,

CH and WP measurements, respectively. The PCA was the only purchased instrument

($4800), whereas the VOM ($6), CH ($14) and WP ($14) instruments were constructed

fi-om readily available materials. Each instrument provided fast measurements, especially

when considering the time required to hand clip the respective measurement areas.

Pasture estimation root mean square error (RMSE) values indicate that the WP and VOM

were the most accurate models (445 and 446 kg ha" ) followed by the PCA and CH

models (613 and 691 kg ha"'). The VOM and WP instruments both provided fast,

inexpensive measurements with acceptable accuracy. Because of the rapid, inexpensive

32

and accurate properties of VOM, and its current widespread use for wildlife habitat

measurements of vertical structure, VOM is recommended as the best method for

estimating SC in the shortgrass plains.

Introduction

Measurements of SC are essential for the determination of stocking rates,

investigation of forage or crop production, and the evaluation of different management

strategies. A variety of methods, both direct and indirect, have been used to estimate SC

(Cook and Stubbendieck, 1986; Catchpole and Wheeler, 1992). Traditionally, estimates

from hand or mechanically clipped quadrats have been used to estimate SC for pastures

or management units. Although clipping provides accurate measurements for the area

measured (Catchpole and Wheeler, 1992), it is a time intensive, laborious technique that

may require numerous samples to obtain reliable pasture estimates (Brummer et al.,

1994).

Several techniques use double sampling procedures as an altemative to clipping.

These methods ftmction by developing a regression relationship of SC to predictive

variables such as height, leaf area, vegetation density, age, cover or visual obstruction

(Cochran, 1977). Double sampling requires some destructive sampling to develop a

predictive relationship. However, after a relationship has been developed, less emphasis

can be placed on clipping, using it only for calibration and validation within trials. The

value of a double sampling method depends on the precision of the regression

relationship and the cost of obtaining direct measurements in comparison to the cost of

obtaining indirect measurements (Ahmed and Bonham, 1982).

33

The PCA (LI-COR, Inc., Lincoln, NE, USA) is a fast, non-destructive instrument

for the indirect estimation of leaf area index (LAI). Direct measurements of LAI using

the LAI-3000 area meter (LI-COR, Inc., Lincoln, NE, USA) on orchardgrass {Dactylis

glomerata L.) have shown a linear relationship between leaf area and SC (Engel et al.,

1987). Since the PCA measures foliage area per unit of ground area, it should be able to

indirectly predict SC. The instrument measures differences in light attenuation through

the canopy based on light being absorbed or reflected by the vegetation (Welles and

Norman, 1991) and uses gap fraction analysis to derive its canopy structural

measurements (Welles, 1990). Instrument design and measurement theory is thoroughly

reviewed by Welles and Norman (1991).

The PCA has been used to estimate SC of grasslands in Iowa (Harmoney et al.,

1997), Nebraska (Miller-Goodman et al., 1999; Volesky et al., 1999), and Texas (Ganguli

et al., 1999). When using the mean of eight readings taken around the center of a 0.21 -

m^ circular quadrat, Harmoney et al. (1997) found that measurements from the PCA had

•y

a poor relationship (r = 0.32) with mesic grassland standing crop. Miller-Goodman et al.

(1999) obtained similar results (r = 0.42) on mid and tallgrasses in the Nebraska

Sandhills when correlating mean pasture production and mean pasture LAI values. In an

additional study conducted in the Nebraska Sandhills, Volesky et al. (1999) explained

33% of the variation in SC with the PCA, which increased to 59% when sampling

modifications were made. Ganguli et al. (1999) reported a correlation coefficient of 0.83

in trials designed to isolate the specific area that the PCA was measuring. They found the

PCA was best correlated to a 0.05-m^ plot when using a 90° viewcap in a semi-arid

grassland.

34

Visual obstruction measurements have been used to estimate SC (Robel et al.,

1970b; Michalk and Herbert, 1977; Harmoney et al., 1997; Vermeire and Gillen in

review). Measurements involve the relationship between SC and the amount of area on a

board or pole that is blocked when looking at it from a fixed position. Robel et al.

(1970b) used a 3 x 150-cm pole marked in altemating brown and white 10-cm bands with

a narrow black band used to delineate 5-cm increments. They recorded the highest 5-cm

section on the pole that was visually obstmcted by vegetation from distances of 2, 3 and

4-m fi-om the pole and 0.5, 0.8 and 1.0-m height from ground level to determine which

strategy could explain the most variation in SC. Using means of 10 readings, they found

the highest coefficient of determination (0.95) when measuring from a distance of 4-m

and a height of 1 -m. Research has shown that visual obstmction poles can provide usefiil

information for non-destmctive estimation of SC in homogeneous prairie (Robel et al.,

1970b) and tallgrass prairie vegetation (Vermeire and Gillen, in review). However,

investigations of how well this method performs in different vegetation types are limited.

Rapid measurements of CH for the prediction of SC have been made with

measuring sticks (Griggs and Stringer, 1988; Gonzalez et al., 1990; Harmoney et al.,

1997), and plastic disks (Sharrow, 1984) or plates (Whitney, 1974). Canopy height can

be difficult to measure due to the subjectivity associated with measurements and

disagreement over which plants or plant parts should be considered to form a mean CH

estimate (Heady, 1957). On introduced pastures, researchers have had success using

disks or plates (Whitney, 1974; Sharrow, 1984) which incorporate an area dimension to

the measurement. When used for CH measurements, disks and plates are thought to

reduce bias associated with random tillers.

35

Forage "density" is the volume of aboveground forage when compressed and is a

ftmction of vegetation height, density and compressibility (Bransby et al., 1977). Several

different instruments have been used to measure forage density for the prediction of SC.

The earliest instruments used were simple and included a cardboard box or plywood

plank (Alexander et al., 1962) that was dropped on the canopy and its mean height

determined by measuring the height of the midpoint of each side. Other instmments now

more commonly used include weighted disks and plates that are either dropped or

allowed to settle on the canopy. Another variation of this instrument is a rising disk or

plate meter that allows the forage to support the plate or disk as the support pole is

lowered to the soil surface. Rising disk and plate meters have the advantage of

automatically recording the total resting height and the number of observations made,

allowing for more rapid measurements than traditional weighted plates and disks

(Gourley and McGowan, 1991). Weighted plates and disks are more economical because

they can be manufactured by the user. Inexpensive, easy to constmct instruments have

been made fi"om acrylic plastic for use in several trials (Sharrow, 1984; Raybum and

Raybum, 1998).

Considering previous inconsistencies, caution should be used when comparing

results or applying models fi"om study to study because of observer variability (Aiken and

Bransby, 1992), different instruments used, and different methods of comparing meter

readings to clipped estimates of SC. Investigations have shown that trial results remained

consistent when using disks of different sizes and weight (Bransby et al., 1977).

Santillan et al. (1979) found that lowering a disk onto the forage as opposed to dropping

it fi-om a fixed height resulted in a small increase in the correlation, likely due to reduced

36

vegetation damage. Researchers who have used these instruments have needed to

calibrate their models when moving to different vegetation types or pastures (Santillan et

al., 1979; Baker et al., 1981) and when the vegetation changed growth form (Bransby et

al., 1977; Michell, 1982). While a majority of the models presented are linear, some

models required quadratic terms because of curvilinear relationships (Baker et al., 1981;

Michell, 1982; Karl and Nicholson, 1987; Gonzalez et al., 1990). Our objective was to

compare PCA, VOM, CH and WP estimates of plot and pasture SC in native shortgrass

plains vegetation.

Materials and Methods

Study Area

This study was conducted on the Southem High Plains, Lubbock County, TX (33°

35' N, 101° 53' W, elevation 990 m). The climate is dry steppe with an average frost-free

growing period of 210 days (NOAA, 1998). Mean annual precipitation is 450 mm with

67% of the precipitation falling fi-om May to September. Sampling was conducted on a

deep hardland range site with Acuff-Urban and Midessa fine sandy loam (Mixed, thermic

Aridic PaleustoUs) soils (Blackstock, 1987).

Vegetation on the study area was dominated by short and mid grasses

(nomenclature used follows Hatch et al. 1990). This site has not been grazed by large

herbivores since November 1982, but some of the areas have been mown at different

intervals as part of on-going management. Dominant grasses included blue grama

[Bouteloua gracilis (H.B.K.) Lag. ex Griffiths], purple threeawn [Aristida purpurea

Nutt.], silver bluestem [Bothrichloa laguroides (DC.) Herter] and buffalograss [Buchloe

37

dactyloides (Nutt.) Engelm.] with lesser amounts of vine mesquite [Panicum obtusum

(Kunth in H.B.K.)], ring muhly [Muhlenbergia torreyi (Kunth) A.S. Hitchc. ex Bush],

prairie threeawn [Aristida oligantha Michx.] and sand dropseed [Sporobolus cryptandrus

(Torr.) Gray]. Dominant forbs included blueweed {Helianthus ciliaris DC), prairie

coneflower [Ratibida columnifera (Nutt.) Woot. & Standi.], cutleaf germander [Teucrium

laciniatum Torr.], lamb's quarter [Chenopodium album L.] and scarlet globemallow

[Sphaeralcea coccinea (Nutt.) Rydb.].

Methods

A total of 500 quadrats were sampled along 25 transects from June to August

1999. To maximize sampling effort a nested quadrat design was used (Fig. 3.1) that

allowed each method to be compared to the same sampled area. Plot estimation trials

were conducted on individual quadrats (N=500), whereas pasture estimation trials

involved calculating the mean of the 20 measurements that were recorded along each

transect (N=25). We were interested in herbaceous SC so we avoided inclusion of yucca

{Yucca glauca Nutt.) and honey mesquite {Prosopis glandulosa Torr.) in our sample

plots. All measurements in the field were made in order fi-om the least to most

destmctive measure (PCA, VOM, CH, and WP) to minimize errors that may arise from

manipulating the vegetation.

Leaf area measurements were made with the LAI-2000 (plant canopy analyzer,

LI-COR, Inc., Lincoln, NE, USA). To reduce instrumental bias, PCA measurements

were made between 0600-0715 hours, when the sun was not directly illuminating the

vegetation. For this study, the PCA was programmed to calculate LAI with 1 above-

38

canopy and 1 below-canopy reading for each plot. The reading was taken in the

designated position of the nested quadrat (Fig. 3.1). A 90° view cap was used to restrict

the optical sensor's view to the area within the quadrat and block the observers from each

measurement.

Visual obstmction was measured with a modified visual obstmction pole (Robel

et al., 1970b). The pole was 2.5 x 120-cm marked with 10-cm-wide altemating white and

red bands ftirther divided with narrow black bands every 2 cm. A spike was affixed to

the bottom of the wooden pole so that it would stand on its own. Readings were made

fi"om a distance of 4 m and a height of 1 m from the fixed pole. To maintain consistency

we used a 1-m pole with 4-m of rope to ensure that readings were always taken from the

same height and distance. The highest band that was visually obstmcted by vegetation

was recorded to the nearest 2-cm.

Height of herbaceous biomass was measured to the nearest 1 mm with a modified

WP instrument (Fig. 3.2). A 40 x 40-cm plate with a hole in the center was lowered

down a measuring pole by strings attached to the comers of the plate. The plate was

lowered until it touched vegetative leaves in a minimum of three places (Whitney, 1974)

where the height of the top of the plate was recorded on the measuring pole.

Immediately following canopy height measurements, vegetation density was

measured to the nearest 1 mm with a modified WP instrument (Fig. 3.2; Raybum and

Raybum, 1998). The plate was 6 mm thick, weighed 1,000 g and exerted a force of 6.23

kg m' . Measurements were made by lowering the plate on the vegetation by the four

strings attached to the comers of the plate and allowing it to settle for 10 to 15 seconds,

and measuring the height of the top of the plate on the measuring pole.

39

Botanical composition of each plot was estimated through dry-weight rank

procedures (Mannetje and Haydock, 1963; Jones and Hargreaves, 1979). The three most

dominant species or groups were assigned a rank of one, two or three. If necessary,

multiple ranks were assigned to a dominant species that occupied more than one of the

ranks. Ranks were converted to SC values by converting them to a ratio and multiplying

by the multipliers 70, 21 and 9 derived by Mannetje and Haydock (1963).

After botanical composition was determined, plots were measured by each

method and the quadrats hand-clipped to ground level with the nested quadrats (Fig. 3.1)

separated so that each method could be correlated to the area it was measuring. Only the

vegetation rooted in the plot was clipped to ground level. Vegetation was oven-dried at

53°C to a constant weight to determine dry matter production. The cost and time of each

method were investigated by assembling the cost of the materials for each instrument and

recording the time required to perform each measurement.

Statistical Analysis

Data were analyzed with analysis of regression procedures with Statistical

Analysis Systems (SAS) software (SAS Inst., 1985). Linear regressions were calculated

relating PCA, VOM, CH and WP values to clipped, oven-dried measures of SC.

Regression values for each technique were derived for all individual plots and pasture

estimates of SC. Weighted least-squares procedures were used in place of ordinary least-

squares if variances among samples were not constant (Neter et al., 1996). The RMSE

was used to assess the accuracy of each pasture estimation model (Zar, 1974). Single

outlier data points were tested with the procedure described by Tietjen et al. (1973).

40

Results

Cost and Sampling Time

Material costs for each method are based on 1999 prices. The most expensive

instrument was the PCA ($4800) purchased from LI-COR, Inc., Lincoln NE, USA, but it

provided the fastest measurements (10 seconds). The rest of the instmments were

constructed from locally purchased materials. The VOM pole was constmcted for $6 and

required 20 seconds for each measurement. The WP meter was constmcted for $14 and

took 20-25 seconds for measurements depending on the time required for settling.

Canopy height determinations in this study were made with the same instrument we used

for the WP measurements and required 15 seconds. The time required to clip PCA,

VOM, and CH/WP measurement areas averaged 1.5, 2.0 and 2.5 minutes, respectively.

Plot Standing Crop Estimation

Plot measurements recorded with the PCA ranged from 0.06 to 3.53 LAI, with

corresponding plot SC values ranging from 0 to 17866 kg ha''. VOM measurements

were between 0 and 40 cm, with SC values from 0 to 13920 kg ha''. CH and WP meter

values ranged from 6.0 to 51.0 cm and 3.0 to 29.0 cm with the range in SC values ranging

from 31 to 9863 kg ha"'. Each method violated the assumption of constant variance and

was therefore analyzed with weighted least-squares procedures except for the PCA

model. The PCA model violated the assumption of constant variance when the weighted

least squares procedure was used, so ordinary least squares were used with the

understanding that the model was not biased (Montgomery and Peck, 1982) but the

associated p-values are only approximate. Linear equations relating plot SC and each

41

technique were significant {P < 0.01). The VOM model accounted for the most variation

in plot SC followed by WP, CH and PCA models (Fig 3.3).

Pasture Standing Crop Estimation

Coefficients of determination for pasture SC were highest in the VOM model

followed by WP, PCA, and CH models (Fig. 3.4). The model for each technique had

significant slopes {P < 0.01), whereas only the PCA and VOM models had significant

intercepts {P < 0.04). Open points on the PCA graph (Fig. 3.4) represent data that tested

as outliers (Tietjen et al., 1973) and were removed from the analysis. The PCA had the

most variable measurements followed by VOM, WP and CH measurements (Table 3.1).

Coefficients of variation for SC determinations followed the same order as the instrument

readings (PCA, VOM, WP and CH; Table 3.1). The RMSE was lowest in the WP model

(445 kg ha'') followed by VOM (446 kg ha''), PCA (613 kg ha''), and CH models (691

kg ha"'). The maximum and minimum distance from the line of best fit for 95%

confidence bounds calculated for each method are 576 and 266 kg ha'' for the PCA, 427

and 184 kg ha'' for the VOM model, 585 and 286 kg ha'' for the CH model, and 377 and

184 kg ha'' for the WP model.

Discussion

The PCA was the only instrument that was purchased from a manufacturer and

was by far the most expensive method. The VOM pole, CH and WP instruments were

constmcted from readily available materials and were inexpensive. All of the

instmments provided fast measurements and were all substantially faster than the time

42

required to hand-clip the areas measured. The easiest method to use was the VOM. Both

the CH and WP techniques were easy to use but the plastic plate was awkward in the

field and in transport, and canopy height determinations tended to be subjective. The

PCA required the most technical knowledge of all the instmments and therefore had the

highest leaming curve. For efficient use of the PCA, a computer is required for data

transmission. From a sampling perspective, the conditions that the PCA's measurements

should be made in can be limiting because of direct illumination of plots. Direct

illumination problems can be solved by manual shading (Hicks and Lascano, 1995), but

that would reduce the simplicity of the technique.

Although each model generated for plot estimation was significant, it appears that

only the VOM and WP models would provide accurate estimates of SC on small plots

due to the variability associated with PCA and CH measurements. Most producers and

researchers are concemed with the SC on larger areas. Each method we investigated was

able to account for more of the variation in SC as we moved from plots to pastures. The

most accurate pasture estimation methods (lowest RMSE values) were the WP and VOM,

which were only separated by 1 kg ha"', followed by the PCA and CH methods.

Previous studies investigating the relationship between the PCA readings and SC

have produced varying results. Recently, modifications in sampling procedures have

been made to improve this relationship (Ganguli et al., 1999; Volesky et al., 1999).

Although these modifications have resulted in improvements, the apparent variability of

instrument readings may restrict its use. More information about what the sensor is

reading in vegetative communities of different stmcture as well as how the environment

43

and different observers influence the instrument's measurements are essential to assess

how this instmment should be properly used.

Following the same VOM measurement protocol used by Vermeire and Gillen {in

review) in non-bumed heterogeneous tallgrass vegetation resulted in a similar coefficient

of determination (0.90). Robel et al. (1970b) also used a similar sampling protocol on

homogeneous vegetation and obtained a coefficient of determination of 0.95. Harmoney

et al (1997) and Volesky et al. (1999) used a different sampling strategy, where 4 VOM

measurements were made, 1 in each cardinal direction, on a 0.21 m^ quadrat. Harmoney

et al. (1997) accounted for 63% of the variation in SC on the monocultures and simple

communities they evaluated, whereas Volesky et al. (1999) accounted for 36% of the

variation in SC on a site dominated by mid and tall grasses. Visual obstmction

measurements have been used as wildlife habitat measurements of vertical stmcture

(Robel et al., 1970a; Nudds, 1977; Griffith and Youtie, 1988; Haukos et al. 1998). While

visual obstmction measurements are currently being used as a tool to characterize

wildlife habitat, results from Robel et al. (1970b), Vermeire and Gillen {in review) and

the current study show that VOM can also provide useftil information for the non-

destmctive estimation of SC.

Most of the investigations using CH as a predictor of SC have been done on

introduced pastures. Researchers who have used instruments similar to the one we used

(Whitney, 1974; Sharrow, 1984) have had good success (r = 0.94) and (r = 0.72 to

0.90), respectively. In the heterogeneous community of the current study, CH only

explained 59% of the variation in pasture SC. Because of the wide variability of plant

44

heights typical in native pastures, CH may have limited use as a tool to predict SC in

native rangeland situations.

Most trials with WP instmments have been conducted in agronomic situations.

We used an instmment described by Raybum and Rayburn (1998) and obtained a 0.70

coefficient of determination as compared to the 0.52 they acquired when making

measurements made on individual plots. Caution must be made when trying to compare

results from study to study due to different instmments used, different sampling protocol

followed, and observer variability (Aiken and Bransby, 1992). In the current study, a

linear relationship was observed. Despite differences in plant growth form and

community heterogeneity, one model explained 83% of the variation in pasture SC.

Summary

Plant canopy analyzer and CH techniques would not adequately predict SC on

plots or pastures through double sampling procedures on native shortgrass plains

vegetation. The WP and VOM technique had considerably stronger relationships with

SC on plots and pastures. Considering the performance of VOM and its current

widespread use for wildlife habitat measurements of vertical stmcture, the VOM

technique is recommended as the best method for non-destmctively estimating SC in the

shortgrass plains.

45

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48

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49

Table. 3.1. Means, ranges and coefficients of variation for pasture SC estimations using PCA, VOM, CH, and WP instmments and the SC determinations for each area measured.

Technique

PCA (lai)

VOM (cm)

CH (cm)

WP(cm)

Mean

1.3

17.0

32.4

12.2

Readings Range

0.62-1.73

8.4-23.3

22.75-41.64

7.00 - 16.33

CV

(%)

41

30

17

25

Standing Crop Estimates Mean

3732

3657

3484

3484

Range

• kg ha"

1888-5223

1824-5915

1604-4922

1604-4922

CV

(%)

59

40

34

34

50

40-cm

40 cm

25 cm

PCA

VOM

I CH&WP

25 cm

Fig. 3.1 Nested quadrat design used for the comparison of each standing crop estimation technique. The area clipped for the plant canopy analyzer (PCA) was 0.05 m , visual obstmction measurements (VOM) 0.10 m and 0.16 m for the canopy height (CH) and weighted plate (WP) measurements. The symbols illustrate the point where each measurement was taken.

51

4.5 cm I

40 cm

O O

4 cm diameter hole

O Plate weight

1,000 g

40 cm

u, 5 cm Plastic plate 6 mm thick

Measuring pole / visual obstruction pole

:'/

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52

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54

APPENDIX

DATA

55

Table A. 1. Measurements of leaf area index (LAI-2000), visual obstmction (VOM), canopy height (CH), forage density (WP) and the oven-dried weight of hand clipped standing crop estimates for each measurement taken during the 1999 growing season in Lubbock, Texas. Measurement units for each method and the area clipped for biomass estimations are in parentheses.

Date 6/1/99

6/1/99

6/1/99

6/1/99

6/1/99

6/1/99

6/1/99

6/1/99

6/1/99

6/1/99

6/1/99

6/1/99

6/1/99

6/1/99

6/1/99

6/1/99

6/1/99

6/1/99

6/1/99

6/1/99

6/3/99

6/3/99

6/3/99

6/3/99

6/3/99

6/3/99

6/3/99

6/3/99

6/3/99

Transect

2

2

2

2

2

2

2

2

2

Plot 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

1

2

3

4

5

6

7

8

9

LAI-2000 (LAI) 1.03

1.17

1.04

1.45

1.38

1.03

0.89

2.04

1.51

2.10

1.28

1.40

1.56

3.50

2.47

3.53

2.22

0.60

0.84

1.40

1.04

1.11

0.94

1.02

1.01

0.54

1.13

0.88

1.20

Method VOM (cm)

16

28

28

18

20

26

18

32

20

22

14

12

12

36

26

32

26

8

14

18

20

22

28

34

20

16

8

8

8

CH (cm) 35.0

43.5

39.9

42.2

36.0

31.9

39.5

38.4

37.1

43.0

30.2

28.1

34.1

39.8

44.1

44.9

40.5

24.5

33.2

38.5

35.0

42.8

37.1

32.9

32.6

31.3

24.9

22.6

23.2

WP (cm) 14.0

20.0

24.0

29.5

20.2

11.9

12.8

15.3

15.4

17.4

9.1

9.9

11.1

18.7

18.9

25.7

20.2

10.0

9.6

12.8

15.6

20.0

20.5

16.4

15.4

12.0

9.2

9.8

10.0

Weight (kg/ha) LAI

(0.05m^) 2261

5419

1304

6071

3321

3076

1915

4298

4889

6906

2893

3952

3015

13038

8067

15320

7517

5032

1304

4869

3484

7436

3422

5439

2241

3545

7252

2221

4217

VOM (O.lOm )

2190

4940

3660

6330

3750

3570

3930

6440

4090

5750

2130

4510

3260

8730

6710

10460

5300

3050

1100

4550

3860

6760

7580

3930

4320

3790

4630

2140

4360

CH&WP (0.16m^)

3094

5125

4656

5256

4613

4425

3494

6294

3606

7381

2525

3669

3069

6513

4381

9038

7750

3313

1994

3856

5519

6388

6463

4106

3894

4100

3894

2581

3769

56

Table A. 1. Continued.

Date 6/3/99 6/3/99

6/3/99

6/3/99

6/3/99

6/3/99

6/3/99

6/3/99

6/3/99

6/3/99

6/3/99

6/3/99

6/3/99

6/3/99

6/3/99

6/3/99

6/3/99

6/3/99

6/3/99

6/3/99

6/3/99

6/3/99

6/3/99

6/3/99

6/3/99

6/3/99

6/3/99

6/3/99

6/3/99

6/3/99

6/3/99

6/3/99

Transect 2 2

2

2

2

2

2

2

2

2

2

3

3

3

3

3

3

3

3

3

3

3

3

3

3

3

3

3

3

3

3

4

Plot 10

11

12

13

14

15

16

17

18

19

20

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

1

LAI-2000 (LAI) 1.14

0.39

2.16

0.42

0.63

0.64

2.18

1.40

0.34

0.96

1.92

0.51

0.63

2.41

0.15

1.43

1.67

1.44

1.36

2.39

3.02

0.72

1.57

1.46

0.93

1.42

0.80

0.95

2.31

1.73

2.59

0.78

Method VOM (cm)

16

8

20

20

14

18

20

22

8

18

10

12

14

16

10

18

18

22

20

22

16

14

10

18

10

18

22

10

24

34

20

28

CH (cm) 31.0

21.7

35.6

30.2

30.1

37.0

33.9

37.5

24.5

30.6

41.4

27.4

32.8

32.8

27.0

30.7

29.0

28.3

32.3

34.0

30.8

33.5

24.5

32.6

35.3

39.1

31.1

24.5

36.1

39.9

30.7

38.4

WP (cm) 12.5

10.3

14.0

12.5

8.5

13.5

17.5

19.8

7.5

13.8

14.9

8.0

11.0

11.7

9.8

13.2

14.2

10.3

26.0

17.0

9.6

14.1

10.5

11.0

13.4

16.5

11.7

8.5

16.3

19.3

13.6

13.9

Weight (kg/ha) LAI

(0.05m^) 3585

367

7049

3076

1508

204

2893

4869

1202

3158

3300

1039

1385

4502

306

4197

9473

1895

3035

7965

4910

1283

4482

3871

3932

1874

917

2445

2689

2037

9432

1467

VOM (O.lOm )

3380

760

5860

1530

1730

3140

2750

3790

1020

3930

2030

1520

1980

2620

1380

3660

6820

3190

4160

5450

2770

3990

2880

2450

3580

2050

3110

1780

4380

3960

5140

3750

CH&WP (0.16m^)

2806

1813

4606

1294

1375

1981

4019

4813

813

3863

2819

1838

2875

2600

1756

3031

4638

4075

3644

5256

2431

3544

2031

2519

3869

2050

2113

1775

4869

6350

3669

2775

57

Table A. 1. Continued.

Date 6/3/99

6/3/99

6/3/99

6/3/99

6/3/99

6/3/99

6/3/99

6/3/99

6/3/99

6/3/99

6/3/99

6/3/99

6/3/99

6/3/99

6/3/99

6/3/99

6/3/99

6/3/99

6/3/99

6/9/99

6/9/99

6/9/99

6/9/99

6/9/99

6/9/99

6/9/99

6/9/99

6/9/99

6/9/99

6/9/99

6/9/99

6/9/99

Transect 4 4

4

4

4

4

4

4

4

4

4

4

4

4

4

4

4

4

4

5

5

5

5

5

5

5

5

5

5

5

5

5

Plot 2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

1

2

3

4

5

6

7

8

9

10

11

12

13

LAI-2000 (LAI) 0.80

0.84

0.54

1.01

1.46

1.25

1.17

0.94

1.76

1.89

1.84

1.35

1.71

1.13

0.98

1.33

0.93

0.84

1.14

1.14

0.21

0.27

0.84

0.89

1.40

2.53

1.56

1.57

2.11

0.31

1.65

2.59

Method VOM (cm) 20

24

24

20

18

22

20

28

18

38

20

16

20

18

18

20

24

8

14

22

0

16

16

2

14

16

16

20

20

4

18

14

CH (cm) 38.4

38.8

32.7

37.0

34.1

36.3

37.0

35.3

33.0

43.2

41.6

38.3

38.2

39.7

40.4

48.7

47.0

24.3

34.5

37.5

21.7

22.6

29.1

18.0

37.0

37.3

37.3

41.2

40.8

34.5

35.1

30.4

WP (cm) 13.9

19.5

14.2

19.3

18.7

16.0

13.3

17.3

15.4

21.2

20.5

14.1

14.1

12.7

16.2

21.3

14.9

11.0

10.0

13.2

4.7

6.2

8.2

4.9

7.5

12.0

11.2

12.4

13.1

5.6

10.5

10.6

Weight (kg/ha) LAI

(0.05m^) 13832

4319

2098

5399

3382

5011

17866

4115

3647

3178

3259

1976

2832

3178

2037

1589

1263

4034

4360

4869

306

448

1039

1304

4808

8149

3178

2058

5358

41

1528

5073

VOM (0. lOm ) 11290

3880

3430

6750

5260

4660

9110

3240

3080

10340

3680

1540

3150

1910

4760

2380

3120

3270

2790

4000

150

1430

2140

970

3520

4960

1700

3120

3670

770

5080

2910

CH&WP (0. 16m )

7644

3481

4400

5788

4769

3906

7156

3325

4863

7331

3619

2650

3938

4119

4200

4825

3525

2600

2650

3331

588

1544

1781

913

2413

4138

2456

3813

2688

513

3206

3100

58

Table A. 1. Continued.

Date Transect 6/9/99

6/9/99

6/9/99

6/9/99

6/9/99

6/9/99

6/9/99

6/16/99

6/16/99

6/16/99

6/16/99

6/16/99

6/16/99

6/16/99

6/16/99

6/16/99

6/16/99

6/16/99

6/16/99

6/16/99

6/16/99

6/16/99

6/16/99

6/16/99

6/16/99

6/16/99

6/16/99

6/17/99

6/17/99

6/17/99

6/17/99

6/17/99

5

5

5

5

5

5

5

6

6

6

6

6

6

6

6

6

6

6

6

6

6

6

6

6

6

6

6

7

7

7

7

7

Plot 14

15

16

17

18

19

20

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

1

2

3

4

5

LAI-2000 (LAI) 1.27

0.23

1.88

1.57

1.78

0.94

0.89

1.64

0.79

1.79

1.83

2.01

1.18

2.35

1.34

1.03

0.06

1.88

0.68

1.56

1.05

2.20

1.46

0.67

2.57

2.82

3.10

0.18

0.93

0.77

0.98

1.37

Method VOM (cm)

14

0

6

10

8

6

16

20

26

10

22

28

18

20

26

8

0.0

20

0

20

16

16

28

16

40

38

20

16

18

8

20

22

CH (cm) 37.9

36.9

29.9

35.3

31.7

28.0

31.1

41.6

40.7

46.1

48.8

49.6

46.0

46.3

44.5

43.6

6.0

43.0

46.5

45.0

32.0

40.5

43.4

28.4

49.1

46.9

44.8

32.9

35.0

34.1

36.8

38.2

WP (cm) 9.2

10.8

6.4

11.0

10.4

6.8

9.6

16.1

13.2

17.8

18.7

17.9

14.3

19.2

16.2

12.6

3.0

16.2

13.5

13.2

8.0

15.9

20.1

5.7

14.2

16.5

16.3

7.6

12.3

8.5

11.8

15.3

Weight (kg/ha) LAI

(0.05m^) 1813

81

1956

1935

3687

3097

3158

5765

3973

4461

5623

4563

2608

3545

1406

957

0

2363

244

1976

1833

2424

1833

183

11286

10492

5602

1650

4380

2343

3810

4971

VOM (0. lOm^)

3150

40

2220

1330

2240

2680

2790

4600

4460

2860

3020

4590

2570

3990

4370

480

0

3490

120

4330

2090

2150

3260

1430

7170

10250

3360

2020

4590

1840

4250

4760

C H & W P (0. 16m )

3263

1106

1938

2938

2625

2013

2506

4475

3456

3806

3644

5144

1700

4663

3863

1850

31

3625

2713

3319

1463

3119

3713

2013

5625

6669

3263

1975

4106

1775

3700

4513

59

Table A. 1. Continued.

Date Transect 6/17/99

6/17/99

6/17/99

6/17/99

6/17/99

6/17/99

6/17/99

6/17/99

6/17/99

6/17/99

6/17/99

6/17/99

6/17/99

6/17/99

6/17/99

6/26/99

6/26/99

6/26/99

6/26/99

6/26/99

6/26/99

6/26/99

6/26/99

6/26/99

6/26/99

6/26/99

6/26/99

6/26/99

6/26/99

6/26/99

6/26/99

6/26/99

7

7

7

7

7

7

7

7

7

7

7

7

7

7

7

8

8

8

8

8

8

8

8

8

8

8

8

8

8

8

8

8

Plot 6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

LAI-2000 (LAI) 0.89

0.86

1.25

0.62

1.35

1.07

1.00

2.67

1.81

1.54

0.67

0.55

1.52

0.91

1.50

0.79

0.82

0.64

0.40

1.22

1.27

0.86

0.85

1.04

1.00

1.15

0.23

1.60

1.50

2.54

2.17

1.25

Method VOM (cm)

16

18

18

4

20

18

14

28

26

20

14

18

10

8

20

18

18

16

18

20

22

18

16

10

20

20

8

28

18

24

20

18

CH (cm) 32.0

32.6

30.5

28.9

27.6

35.6

28.2

34.4

35.5

37.9

30.4

32.4

28.2

32.4

39.3

41.5

46.4

43.5

39.5

37.3

48.8

36.0

45.0

45.6

38.5

39.0

32.7

34.6

35.0

34.5

40.0

41.3

WP (cm) 12.9

12.6

11.4

15.5

10.1

12.1

11.3

14.7

12.0

16.7

9.5

9.7

10.7

12.8

13.2

12.4

16.4

12.0

13.5

13.3

17.3

13.0

12.4

7.0

11.3

15.5

8.0

15.5

13.2

15.2

13.8

13.8

Weight (kg/ha) LAI

(0.05m^) 1243

2709

6275

1691

2098

1813

2180

8841

13344

4991

1385

815

2465

1976

3382

2934

4849

3850

3422

3810

4523

4237

2098

3178

3137

5276

285

3097

5500

6560

4665

2037

VOM (0. lOm )

1830

3530

3240

1030

1650

4410

2530

5310

7690

3810

2180

2350

1870

2050

3380

3180

5630

4060

3980

4770

5060

4040

2980

1740

2730

4910

710

5830

3890

4020

3810

5930

C H & W P (0.16m^)

3331

2713

2600

1244

1788

3338

4231

6025

5244

3938

2213

1844

1969

1875

3700

3194

5275

3769

4219

3513

4650

3669

3544

1156

2913

4338

1206

5450

3788

5650

4419

5638

60

Table A. 1. Continued.

Date Transect 6/26/99

6/26/99

6/26/99

6/27/99

6/27/99

6/27/99

6/27/99

6/27/99

6/27/99

6/27/99

6/27/99

6/27/99

6/27/99

6/27/99

6/27/99

6/27/99

6/27/99

6/27/99

6/27/99

6/27/99

6/27/99

6/27/99

6/27/99

6/28/99

6/28/99

6/28/99

6/28/99

6/28/99

6/28/99

6/28/99

6/28/99

6/28/99

8

8

8

9

9

9

9

9

9

9

9

9

9

9

9

9

9

9

9

9

9

9

9

10

10

10

10

10

10

10

10

10

Plot 18

19

20

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

1

2

3

4

5

6

7

8

9

LAI-2000 (LAI) 2.33

1.71

2.12

1.50

2.07

2.19

2.14

2.05

0.82

1.98

2.30

1.33

1.68

1.01

0.84

1.01

1.06

0.98

1.05

0.44

2.00

1.47

1.50

1.10

0.34

1.10

0.93

0.99

1.69

1.05

0.80

0.86

Method VOM (cm)

18

20

22

20

20

20

24

16

16

18

20

20

16

20

22

20

18

20

20

18

16

20

24

20

14

18

16

20

26

18

16

18

CH (cm) 38.2

39.7

41.2

41.8

43.4

29.5

38.8

40.2

38.2

38.1

39.0

27.0

30.0

22.8

23.6

29.5

26.3

29.5

33.5

20.6

31.0

33.2

34.1

40.4

37.4

28.4

30.4

31.5

37.1

32.9

36.0

38.4

WP (cm) 12.6

17.0

15.8

13.2

17.2

13.5

17.6

16.2

16.2

17.5

14.4

9.4

16.5

9.6

9.4

13.0

12.5

9.5

13.2

8.5

14.2

16.1

17.5

16.6

12.3

11.1

10.5

11.6

12.2

11.0

14.2

13.3

Weight (kg/ha) LAI

(0.05m^) 3850

3361

2078

2771

5134

8719

4849

6173

2526

5073

6621

2058

4156

2363

1548

3341

2078

3056

2384

1569

3606

2628

5399

4889

1243

2180

3321

1935

4013

2078

2567

1752

VOM (0. lOm )

3540

3550

3520

5660

5090

4660

6330

5660

3470

4700

3680

3000

3480

4760

4040

4170

3610

6390

5090

4130

2970

6070

8490

3540

2200

3180

2930

2350

5180

2250

3340

2850

CH&WP (0.16m^)

3594

4075

3131

4125

5394

4613

4706

5631

4956

5456

5900

2906

5575

2625

2106

3538

4456

2969

2831

1856

3925

3275

4556

4250

2419

2763

3150

3588

4638

2275

3069

4288

61

Table A. 1. Continued.

Method Weight (kg/ha)

Date Transect LAI-2000

Plot (LAI) VOM CH WP (cm) (cm) (cm)

LAI VOM CH & WP (O.OSm ) (0. lOmO (0.16m')

6/28/99 1

6/28/99 1

6/28/99 ]

6/28/99 ]

6/28/99 1

6/28/99 ]

6/28/99

6/28/99

6/28/99

6/28/99

6/28/99

7/9/99

7/9/99

7/9/99

7/9/99

7/9/99

7/9/99

7/9/99

7/9/99

7/9/99

7/9/99

7/9/99 ]

7/9/99 ]

7/9/99 ]

7/9/99 1

7/9/99 1

7/9/99 1

7/9/99 1

7/9/99 1

7/9/99 1

7/9/99 1

7/12/99 1

0

0

10

10

10

10

10

10

10

10

10

2

10

11

12

13

14

15

16

17

18

19

20

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

1

0.70

1.54

1.78

2.05

1.92

2.35

1.44

1.37

1.93

1.86

1.88

1.37

1.44

1.51

0.87

0.89

1.29

1.43

1.07

1.03

0.94

1.30

1.50

1.02

1.42

1.64

0.79

1.05

1.27

1.46

1.35

0.67

16

18

36

20

28

22

22

30

20

20

26

20

26

20

20

24

20

18

26

20

20

34

32

20

28

28

20

22

20

20

22

10

33.9

37.3

40.0

34.0

38.0

40.5

40.5

46.0

42.7

37.3

35.0

38.5

39.2

45.4

42.6

40.1

43.9

32.5

40.6

35.5

44.3

44.5

41.7

46.3

38.0

48.0

37.3

41.1

37.0

41.0

43.5

22.0

12.2

12.7

17.0

11.8

16.9

13.6

18.0

19.7

14.2

12.0

16.4

14.5

12.5

14.0

14.1

12.2

11.3

14.8

13.3

14.7

15.2

17.3

17.0

10.5

10.0

15.7

12.3

10.2

10.2

12.5

13.0

5.5

3402

1569

8352

4584

4645

3504

5439

3158

5480

1711

5195

4074

9493

5623

3748

3708

5154

3300

4135

5358

4319

12325

7497

4380

2526

14260

3911

3606

6845

8577

6825

1283

2670

1400

5760

4690

8850

3670

5360

5620

4650

2540

5530

4930

5000

4970

4250

3830

4750

3630

5240

4340

3820

7920

6690

3410

5110

7850

4710

2300

4410

5580

6030

1050

2744

1888

4956

3575

7794

3550

5300

4338

4738

2169

5300

4331

6038

4831

3806

3531

4550

3281

4544

4356

3713

8731

6481

3475

3969

9281

4144

2544

4856

6119

5863

1038

62

Table A. 1 Continued.

Date Transect 7/12/99

7/12/99

7/12/99

7/12/99

7/12/99

7/12/99

7/12/99

7/12/99

7/12/99

7/12/99

7/12/99

7/12/99

7/12/99

7/12/99

7/12/99

7/12/99

7/12/99

7/12/99

7/12/99

7/14/99

7/14/99

7/14/99

7/14/99

7/14/99

7/14/99

7/14/99

7/14/99

7/14/99

7/14/99

7/14/99

7/14/99

7/14/99

12

12

12

12

12

12

12

12

12

12

12

12

12

12

12

12

12

12

12

13

13

13

13

13

13

13

13

13

13

13

13

13

Plot 2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

1

2

3

4

5

6

7

8

9

10

11

12

13

LAI-2000 (LAI) 1.22

0.94

1.54

0.89

0.74

0.43

1.11

0.62

1.06

1.56

1.42

1.97

1.51

1.74

0.70

1.61

2.23

0.98

1.19

1.44

1.28

1.41

1.98

2.40

1.44

1.53

1.33

1.35

1.75

1.53

1.69

1.68

Method VOM (cm)

14

18

16

10

16

10

10

16

16

18

16

12

16

14

10

20

10

6

16

20

22

26

22

20

20

26

18

20

20

26

20

26

CH (cm) 26.2

18.6

25.7

27.0

27.5

20.3

18.5

29.2

25.0

29.2

13.5

21.5

35.7

35.5

21.0

45.1

15.0

20.2

31.3

36.4

45.2

37.8

42.5

37.6

41.4

37.5

39.2

29.9

40.2

39.7

34.3

36.5

WP (cm)

8.0

10.3

7.9

8.8

8.3

7.5

7.0

10.1

10.3

9.7

9.0

8.5

9.6

11.0

9.5

14.4

7.6

8.1

9.8

16.4

18.2

18.7

17.0

10.8

19.5

13.6

17.8

13.0

14.7

18.5

16.2

16.2

Weight (kg/ha) LAI

(0.05m^) 1976

2750

4095

1813

795

1365

1670

1345

1304

1080

3687

4074

3789

7639

2282

5236

4461

1874

3076

3932

5663

6213

5562

9677

4135

2282

367

7314

4217

2893

3484

2180

VOM (0. lOm )

2280

2030

2640

1920

2130

1520

1830

2090

2890

2800

3370

3510

2920

4040

2390

4530

3350

1520

2680

6550

6150

6640

5270

6040

4660

2780

4050

5530

4670

3570

2760

5150

C H & W P (0. 16m )

2056

1544

2163

2381

1819

1913

1894

1894

2300

1969

3256

3444

2063

2981

2006

3106

3006

1600

2106

5000

6356

4956

5044

5869

3800

4738

3069

3781

4888

4438

3769

5531

63

Table A. 1. Continued.

Date Transect 7/14/99

7/14/99

7/14/99

7/14/99

7/14/99

7/14/99

7/14/99

7/15/99

7/15/99

7/15/99

7/15/99

7/15/99

7/15/99

7/15/99

7/15/99

7/15/99

7/15/99

7/15/99

7/15/99

7/15/99

7/15/99

7/15/99

7/15/99

7/15/99

7/15/99

7/15/99

7/15/99

7/16/99

7/16/99

7/16/99

7/16/99

7/16/99

13

13

13

13

13

13

13

14

14

14

14

14

14

14

14

14

14

14

14

14

14

14

14

14

14

14

14

15

15

15

15

15

Plot 14

15

16

17

18

19

20

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

1

2

3

4

5

LAI-2000 (LAI) 1.45

1.93

2.24

1.36

2.16

2.96

1.44

1.05

0.97

0.91

0.66

0.13

0.50

0.40

0.48

1.04

1.41

0.47

0.49

1.11

0.51

0.53

0.75

1.59

2.37

1.06

1.17

1.69

0.91

0.86

1.42

0.54

Method VOM (cm)

20

26

24

20

20

24

20

10

20

16

12

2

8

6

4

18

10

6

6

6

4

8

8

10

8

10

14

20

8

8

10

4

CH (cm) 36.0

39.2

43.8

36.5

38.2

45.2

40.0

23.6

33.5

37.1

24.8

19.5

30.4

23.0

28.1

29.0

26.0

30.1

18.0

22.0

26.2

19.8

28.6

28.9

30.3

25.4

33.0

36.0

29.3

28.4

26.5

25.5

WP (cm) 13.4

15.2

16.8

16.1

14.3

15.7

18.2

7.0

11.2

8.2

7.5

5.5

8.6

7.9

9.5

10.0

8.0

9.5

6.4

6.4

6.5

7.1

6.9

9.7

9.6

8.6

10.3

11.4

7.9

9.0

6.0

7.9

Weight (kg/ha) LAI

(0.05m^) 4502

6050

3789

1711

4747

4380

3911

2811

1732

1548

1609

652

1691

1446

1080

1956

2913

1059

1609

2241

1263

1772

1691

1711

5093

1609

2282

3402

1691

3035

1854

591

VOM (0. lOm )

3840

4090

4110

4020

4230

4570

4070

2770

1660

1590

1230

360

1820

1350

1740

3340

2440

1400

1430

1780

970

2230

1450

2560

3610

1510

3110

2910

2040

2310

1700

1330

C H & W P (0.16m^)

3438

4444

5069

3431

4619

4350

4331

2338

1931

1531

1725

275

2025

1331

1556

2513

2169

1794

1275

1600

875

2350

1194

2313

3213

1256

2344

2400

1806

1856

1519

1669

64

Table A. 1. Continued.

Date Transect 7/16/99

7/16/99

7/16/99

7/16/99

7/16/99

7/16/99

7/16/99

7/16/99

7/16/99

7/16/99

7/16/99

7/16/99

7/16/99

7/16/99

7/16/99

7/19/99

7/19/99

7/19/99

7/19/99

7/19/99

7/19/99

7/19/99

7/19/99

7/19/99

7/19/99

7/19/99

7/19/99

7/19/99

7/19/99

7/19/99

7/19/99

7/19/99

15

15

15

15

15

15

15

15

15

15

15

15

15

15

15

16

16

16

16

16

16

16

16

16

16

16

16

16

16

16

16

16

Plot 6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

LAI-2000 (LAI) 0.94

0.63

0.90

0.34

0.31

0.39

0.72

1.00

1.30

0.86

0.75

0.99

0.53

0.33

1.84

1.11

1.10

1.64

1.10

1.30

1.76

0.86

1.28

1.30

0.60

1.05

1.83

1.41

1.83

1.15

1.51

1.25

Method VOM (cm)

8

2

10

8

8

8

4

8

12

8

10

10

6

8

8

16

16

22

24

30

20

20

18

28

20

28

28

20

20

20

24

22

CH (cm) 24.2

18.8

21.0

16.5

13.3

17.0

21.7

15.7

25.0

23.2

21.6

28.8

20.1

18.2

24.2

23.5

20.5

23.4

36.4

40.5

33.0

35.6

32.9

36.3

32.8

42.0

43.2

42.1

47.0

44.5

30.5

38.7

WP (cm) 8.6

3.7

6.0

4.7

4.6

5.4

6.6

6.0

9.7

7.2

7.5

6.6

6.8

6.9

7.5

11.6

10.0

12.7

19.5

20.0

10.5

19.9

11.0

18.5

15.0

14.2

20.1

15.3

15.6

13.9

15.9

13.3

Weight (kg/ha) LAI

(0.05m^) 2709

570

530

530

2363

1426

1487

1202

2159

1793

1487

6743

2241

1059

2200

6458

4054

6621

4054

6865

6213

6845

5154

4523

3952

7925

10308

3626

7334

8780

3810

6295

VOM (0. lOm )

2040

740

1720

850

1700

1230

1380

990

2400

1240

1980

4340

2070

1330

2170

5150

5240

5140

6510

8410

5780

7080

3520

4560

6830

6240

8710

1990

8710

5460

7430

5810

CH&WP (0.16m^)

1681

638

1250

863

1506

1156

1194

731

1956

1119

2150

3275

1894

1369

2050

4881

5069

4506

5856

5625

5669

4838

3388

3894

4669

4519

6094

3225

5519

4413

6363

3681

65

Table A. 1. Continued.

Date Transect 7/19/99

7/19/99

7/19/99

7/20/99

7/20/99

7/20/99

7/20/99

7/20/99

7/20/99

7/20/99

7/20/99

7/20/99

7/20/99

7/20/99

7/20/99

7/20/99

7/20/99

7/20/99

7/20/99

7/20/99

7/20/99

7/20/99

7/20/99

7/21/99

7/21/99

7/21/99

7/21/99

7/21/99

7/21/99

7/21/99

7/21/99

7/21/99

16

16

16

17

17

17

17

17

17

17

17

17

17

17

17

17

17

17

17

17

17

17

17

18

18

18

18

18

18

18

18

18

Plot 18

19

20

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

1

2

3

4

5

6

7

8

9

LAI-2000 (LAI) 1.10

1.69

0.93

1.06

1.16

1.77

1.32

1.77

1.56

1.47

1.86

2.02

1.84

2.06

1.80

2.14

1.88

1.44

1.49

1.12

1.93

1.33

1.80

1.61

0.95

2.04

0.98

2.10

2.61

0.11

0.92

1.10

Method VOM (cm)

22

26

20

26

22

28

18

20

24

18

20

16

18

20

24

22

28

26

18

20

26

20

24

16

18

18

14

20

26

8

18

20

CH (cm) 42.0

39.5

36.5

36.8

43.5

39.1

38.0

34.2

36.4

35.4

36.0

32.0

30.0

47.0

41.0

30.4

45.0

37.0

34.5

38.3

34.5

34.8

32.0

28.2

33.3

30.0

31.2

30.8

46.0

28.5

34.5

28.1

WP (cm) 18.6

14.5

12.0

15.2

15.3

13.9

13.9

10.2

13.4

14.2

16.5

16.7

9.7

17.4

18.0

14.4

16.5

20.4

14.3

17.7

14.2

12.5

15.3

10.9

11.3

11.4

8.3

13.2

17.0

6.0

11.2

13.0

Weight (kg/ha) LAI

(0.05m^) 6356

6988

5337

3952

7456

6519

4278

3973

5521

3769

4115

3952

3667

3219

4482

7232

4889

4197

4360

2872

4828

3178

3504

4095

3484

4074

2200

4197

13608

1019

4400

3484

VOM (0. lOm )

4360

6850

4520

4510

6850

8260

6400

4400

4810

3650

4110

4960

3940

4400

6660

5820

6350

3690

4600

3980

6960

4120

4710

4340

2600

4660

2380

3640

9260

1480

4030

5610

C H & W P (0.16m^)

4338

4613

3975

4013

6481

6713

6044

3163

5431

3963

3994

4119

4150

4925

5825

5163

5975

5194

4250

3513

5294

4119

3994

4038

2356

3975

2731

4469

6838

1369

3181

4294

66

Table A. 1. Continued.

Date Transect 7/21/99

7/21/99

7/21/99

7/21/99

7/21/99

7/21/99

7/21/99

7/21/99

7/21/99

7/21/99

7/21/99

7/22/99

7/22/99

7/22/99

7/22/99

7/22/99

7/22/99

7/22/99

7/22/99

7/22/99

7/22/99

7/22/99

7/22/99

7/22/99

7/22/99

7/22/99

7/22/99

7/22/99

7/22/99

7/22/99

7/22/99

7/22/99

18

18

18

18

18

18

18

18

18

18

18

19

19

19

19

19

19

19

19

19

19

19

19

19

19

19

19

19

19

19

19

20

Plot 10

11

12

13

14

15

16

17

18

19

20

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

1

LAI-2000 (LAI) 1.27

2.73

1.50

2.31

1.56

2.18

0.11

1.90

0.91

1.37

2.15

1.59

0.74

0.87

1.62

1.18

1.65

1.99

1.42

1.52

2.61

1.20

2.52

1.54

1.70

1.69

1.83

2.02

1.05

1.13

1.55

0.90

Method VOM (cm)

12

26

20

24

30

20

4

10

14

20

12

20

18

16

24

26

20

28

20

24

36

24

38

22

20

22

30

20

16

20

22

18

CH (cm) 19.5

29.6

36.2

37.5

39.0

28.3

23.5

18.5

29.6

38.0

25.0

30.4

32.9

29.5

26.5

34.0

40.5

39.0

29.3

17.5

37.9

40.2

51.0

39.5

32.7

32.7

34.0

29.4

25.6

30.7

27.7

16.0

WP (cm) 8.1

21.5

16.7

17.9

20.2

12.7

5.5

7.9

11.3

13.5

7.9

9.8

9.1

7.4

8.4

14.3

20.3

21.5

13.2

14.5

15.2

18.0

27.5

18.6

16.0

16.0

16.8

11.9

9.8

14.7

13.5

9.7

Weight (kg/ha) LAI

(0.05m^) 2404

15503

10390

5317

5460

6132

448

4258

3769

3769

5093

7721

3178

2159

5480

9493

3259

5093

4197

4176

11286

2384

8719

3097

4747

4217

5765

6112

3361

3524

5256

5582

VOM (0. lOm )

2460

8840

6610

5280

4120

4740

450

3950

3910

5330

3600

4530

2510

4070

4770

9280

4530

5740

5090

4100

9720

2950

13920

3520

5280

4820

6420

4770

2650

3880

4650

4470

CH&WP (0.16m^)

2763

6138

5350

5813

3863

4475

881

3250

4081

4375

2663

3394

2825

3463

4131

7806

3988

5438

5075

3531

9863

3400

9438

5263

5325

4738

5325

4425

2888

3194

4519

3800

67

Table A. 1. Continued.

Date Transect 7/22/99

7/22/99

7/22/99

7/22/99

7/22/99

7/22/99

7/22/99

7/22/99

7/22/99

7/22/99

7/22/99

7/22/99

7/22/99

7/22/99

7/22/99

7/22/99

7/22/99

7/22/99

7/22/99

7/31/99

7/31/99

7/31/99

7/31/99

7/31/99

7/31/99

7/31/99

7/31/99

7/31/99

7/31/99

7/31/99

7/31/99

7/31/99

20

20

20

20

20

20

20

20

20

20

20

20

20

20

20

20

20

20

20

21

21

21

21

21

21

21

21

21

21

21

21

21

Plot 2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

1

2

3

4

5

6

7

8

9

10

11

12

13

LAI-2000 (LAI) 1.72

2.12

0.86

1.24

1.45

2.49

2.06

1.74

1.60

0.97

1.49

2.11

2.44

1.62

1.96

2.02

1.94

2.06

1.84

0.80

0.74

0.53

0.51

0.26

0.63

0.54

0.64

0.88

0.59

0.67

0.40

0.82

Method VOM (cm)

20

18

16

18

20

20

26

20

20

16

18

20

20

20

24

20

18

20

34

16

16

20

10

8

8

10

16

20

14

10

10

16

CH (cm) 30.4

29.9

29.5

30.1

27.5

31.2

31.5

17.5

27.8

25.0

26.0

30.0

32.4

29.9

28.9

29.0

32.8

26.0

46.8

32.2

29.5

29.2

25.5

27.2

25.9

20.0

22.5

24.3

29.1

25.9

21.7

17.0

WP (cm) 13.4

13.5

8.4

9.5

10.5

14.0

14.3

7.2

11.3

11.0

9.4

10.1

11.3

12.0

10.5

12.2

12.7

9.7

22.8

8.2

8.1

7.5

8.1

5.2

8.4

6.0

9.3

10.2

7.6

10.0

7.0

8.7

Weight (kg/ha) LAI

(0.05m^) 3361

4156

3015

3097

3463

4217

3748

3769

2139

2852

4747

4319

5500

4197

5704

5052

3585

6417

5786

4482

3402

1895

4604

632

1385

1691

1996

3219

1467

2302

2322

3911

VOM (0. lOm )

3980

3200

3140

3640

3880

4280

3440

3770

3830

3270

4170

3910

5720

4590

5180

6100

4310

6990

7880

3030

2270

1970

2960

1250

1390

2050

2120

2710

1490

1710

2660

3950

C H & W P (0. 16m )

4275

3163

2750

3219

3669

4175

3963

2813

3156

3269

4175

3444

5194

4131

3956

5425

5000

5681

9081

2219

1731

1781

2713

1194

1525

1950

2469

2513

1456

1900

2106

3725

68

Table A. 1. Continued.

Date 7/31/99

7/31/99

7/31/99

7/31/99

7/31/99

7/31/99

7/31/99

8/1/99

8/1/99

8/1/99

8/1/99

8/1/99

8/1/99

8/1/99

8/1/99

8/1/99

8/1/99

8/1/99

8/1/99

8/1/99

8/1/99

8/1/99

8/1/99

8/1/99

8/1/99

8/1/99

8/1/99

8/8/99

8/8/99

8/8/99

8/8/99

8/8/99

Transect 21

21

21

21

21

21

21

22

22

22

22

22

22

22

22

22

22

22

22

22

22

22

22

22

22

22

22

23

23

23

23

23

Plot 14

15

16

17

18

19

20

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

1

2

3

4

5

LAI-2000 (LAI) 0.40

0.69

0.69

0.72

0.36

0.43

1.00

0.96

0.46

0.80

0.65

0.66

0.41

0.22

0.53

1.32

0.48

0.35

0.89

0.76

1.20

0.69

0.25

0.34

0.61

0.97

1.01

0.40

0.62

0.45

0.81

0.84

Method VOM (cm)

8

20

16

18

14

6

14

20

18

14

20

8

6

8

6

14

10

8

10

14

16

16

8

2

16

10

10

8

8

10

4

8

CH (cm) 19.1

24.4

30.6

31.0

21.7

19.7

31.0

36.5

36.2

26.8

27.8

29.5

16.0

18.0

21.5

28.0

22.0

28.2

17.1

23.5

30.5

21.5

18.8

20.8

21.9

33.9

24.0

22.2

25.0

25.6

23.3

19.4

WP (cm)

7.5

9.0

9.4

8.7

9.8

5.9

11.2

9.5

10.0

9.7

9.7

8.2

5.3

6.2

5.0

8.3

7.2

7.1

5.2

9.4

7.4

8.2

5.3

4.6

5.8

11.0

7.4

6.9

7.6

6.9

5.6

7.5

Weight (kg/ha) LAI

(0.05m^) 1406

835

2384

1487

917

2058

2221

3545

3056

3117

1670

1630

1650

1732

1365

3015

1752

957

2771

4787

1732

1304

1813

0

1080

1365

2811

1711

1854

2322

1874

1732

VOM (0. lOm )

1450

3600

3250

2360

1650

2050

2170

2850

2730

3170

1860

2380

1990

2040

1440

2740

2890

1820

2910

2500

1970

2130

1460

150

1200

2440

2350

1910

2180

3620

1920

1210

C H & W P (0. 16m )

1350

2500

3431

3013

2344

2225

2350

2213

2525

2731

2438

2281

2425

2306

1256

3119

2363

1694

2781

2363

1913

1775

1650

875

1275

2344

1944

2038

1856

2950

1806

2944

69

Table A. 1. Continued.

Date Transect 8/8/99

8/8/99

8/8/99

8/8/99

8/8/99

8/8/99

8/8/99

8/8/99

8/8/99

8/8/99

8/8/99

8/8/99

8/8/99

8/8/99

8/8/99

8/11/99

8/11/99

8/11/99

8/11/99

8/11/99

8/11/99

8/11/99

8/11/99

8/11/99

8/11/99

8/11/99

8/11/99

8/11/99

8/11/99

8/11/99

8/11/99

8/11/99

23

23

23

23

23

23

23

23

23

23

23

23

23

23

23

24

24

24

24

24

24

24

24

24

24

24

24

24

24

24

24

24

Plot 6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

LAI-2000 (LAI) 0.65

0.54

0.47

0.53

0.62

0.66

0.48

0.80

1.19

1.69

0.45

1.03

0.92

0.46

1.24

1.03

0.57

0.79

0.77

0.85

1.35

0.59

0.83

1.86

0.96

1.13

1.28

1.28

0.99

1.69

0.56

1.40

Method VOM (cm)

16

10

10

14

12

6

6

16

16

16

6

16

14

10

30

28

10

26

20

20

20

14

16

20

20

8

16

20

16

20

26

16

CH (cm) 27.8

22.3

20.5

25.7

16.2

15.2

27.0

32.3

29.6

21.5

18.0

24.5

27.0

30.1

37.0

43.2

33.5

29.2

24.7

23.0

35.2

24.5

21.6

26.5

31.5

21.8

32.0

30.8

30.8

38.0

29.0

32.5

WP (cm)

8.0

8.8

5.8

6.6

4.4

6.2

6.6

6.9

8.0

6.5

3.4

9.4

7.7

7.9

14.5

25.5

7.4

10.6

13.2

10.0

15.0

8.2

9.6

11.5

12.3

8.5

8.3

15.3

8.5

13.4

17.3

10.2

Weight (kg/ha) LAI

(0.05m^) 2119

4421

1508

1548

2119

1426

1548

1833

1915

2811

1548

2180

1691

1080

2771

13038

2791

2689

4808

4176

3504

1243

2689

5500

2445

3056

3300

3422

2180

2567

1854

4095

VOM (0. lOm )

1900

3260

1900

1310

1440

1450

1180

1470

1410

2290

1140

1470

2560

1830

3140

8970

2340

6690

3560

2910

3890

2360

2720

3980

4320

2300

3180

3120

3090

2910

3260

2890

C H & W P (0.16m^)

3850

4194

2806

1219

1144

1244

1288

1406

1356

1750

1106

1681

3519

1606

2875

6538

2263

5038

4625

2781

3919

2613

2344

3350

4406

1763

3244

2725

2925

3094

3194

2706

70

Table A. 1. Continued.

Date Transect 8/11/99

8/11/99

8/11/99

8/13/99

8/13/99

8/13/99

8/13/99

8/13/99

8/13/99

8/13/99

8/13/99

8/13/99

8/13/99

8/13/99

8/13/99

8/13/99

8/13/99

8/13/99

8/13/99

8/13/99

8/13/99

8/13/99

8/13/99

24

24

24

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

Plot 18

19

20

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

LAI-2000 (LAI) 1.72

1.37

1.51

0.56

0.67

0.52

0.68

0.25

0.61

0.78

0.71

1.20

1.31

1.76

1.21

1.65

0.99

1.61

1.95

2.03

1.67

1.24

1.34

Method VOM (cm)

20

18

16

16

8

8

8

6

4

10

20

20

24

26

20

18

14

20

20

16

20

8

14

CH (cm) 30.5

26.0

25.0

37.3

21.0

22.3

34.5

27.2

14.5

27.6

26.5

35.7

34.0

35.5

36.8

36.2

22.2

31.3

34.9

28.2

31.0

26.0

33.8

WP (cm) 14.0

10.0

8.1

10.5

9.5

8.6

13.5

7.8

5.5

7.2

8.3

12.5

15.5

15.6

14.3

19.0

8.6

9.5

12.2

10.9

12.0

9.5

14.4

Weight (kg/ha) LAI

(0.05m^) 3911

1915

2689

3524

2343

1141

2872

1569

2445

3524

2893

3402

5786

4808

3341

6580

2445

4034

4095

4686

7191

2771

4502

VOM (0. lOm )

3970

2060

2640

3070

2930

1770

2890

1910

1700

2690

4000

3520

4440

5530

1880

4510

3260

5050

3540

4240

4150

3050

4120

C H & W P (0.16m^)

3863

2638

2863

2619

2838

1594

2975

1825

1363

2194

3288

3269

3819

4269

2581

3506

3681

4738

3931

4519

4150

2425

3919

71

Table A.2. Measurements of leaf area index (LAI), mean tilt angle (MTA), and diffiise non-interceptance (DIFN) for each LAI-2000 measurement taken during the 1999 growing season in Lubbock, Texas.

Date 6/1/99 6/1/99 6/1/99 6/1/99 6/1/99 6/1/99 6/1/99 6/1/99 6/1/99 6/1/99 6/1/99 6/1/99 6/1/99 6/1/99 6/1/99 6/1/99 6/1/99 6/1/99 6/1/99 6/1/99 6/3/99 6/3/99 6/3/99 6/3/99 6/3/99 6/3/99 6/3/99 6/3/99 6/3/99 6/3/99 6/3/99 6/3/99 6/3/99 6/3/99 6/3/99 6/3/99 6/3/99 6/3/99 6/3/99

Transect 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

Plot 1 2 3 4 5 6 7 8 9

10 11 12 13 14 15 16 17 18 19 20 1 2 3 4 5 6 7 8 9

10 11 12 13 14 15 16 17 18 19

LAI 1.03 1.17 1.04 1.45 1.38 1.03 0.89 2.04 1.51 2.10 1.28 1.40 1.56 3.50 2.47 3.53 2.22 0.60 0.84 1.40 1.04 1.11 0.94 1.02 1.01 0.54 1.13 0.88 1.20 1.14 0.39 2.16 0.42 0.63 0.64 2.18 1.40 0.34 0.96

MTA 74 80 90 90 69 73 69 65 90 83 66 90 80 34 76 40 90 90 90 85 67 78 90 76 81 90 77 90 76 76 90 67 90 90 90 73 81 90 90

DIFN 0.483 0.433 0.506 0.359 0.361 0.489 0.521 0.231 0.346 0.244 0.417 0.455 0.363 0.075 0.211 0.086 0.253 0.723 0.633 0.402 0.433 0.427 0.512 0.465 0.474 0.718 0.419 0.555 0.415 0.442 0.819 0.202 0.783 0.669 0.689 0.242 0.402 0.807 0.591

72

Table A.2. Continued. Date

6/3/99 6/3/99 6/3/99 6/3/99 6/3/99 6/3/99 6/3/99 6/3/99 6/3/99 6/3/99 6/3/99 6/3/99 6/3/99 6/3/99 6/3/99 6/3/99 6/3/99 6/3/99 6/3/99 6/3/99 6/3/99 6/8/99 6/8/99 6/8/99 6/8/99 6/8/99 6/8/99 6/8/99 6/8/99 6/8/99 6/8/99 6/8/99 6/8/99 6/8/99 6/8/99 6/8/99 6/8/99 6/8/99 6/8/99 6/8/99 6/8/99 6/9/99

Transect 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 5

Plot 20 1 2 3 4 5 6 7 8 9

10 11 12 13 14 15 16 17 18 19 20 1 2 3 4 5 6 7 8 9

10 11 12 13 14 15 16 17 18 19 20 1

LAI 1.92 0.51 0.63 2.41 0.15 1.43 1.67 1.44 1.36 2.39 3.02 0.72 1.57 1.46 0.93 1.42 0.80 0.95 2.31 1.73 2.59 0.78 0.80 0.84 0.54 1.01 1.46 1.25 1.17 0.94 1.76 1.89 1.84 1.35 1.71 1.13 0.98 1.33 0.93 0.84 1.14 1.14

MTA 90 90 90 69 90 74 90 74 90 86 64 90 84 90 90 90 90 85 66 90 63 69 62 75 90 90 58 90 90 90 64 66 74 90 68 90 90 90 90 90 90 63

DIFN 0.326 0.763 0.692 0.186 0.920 0.356 0.423 0.403 0.508 0.234 0.123 0.687 0.359 0.433 0.591 0.408 0.643 0.533 0.221 0.353 0.188 0.536 0.530 0.529 0.728 0.493 0.297 0.413 0.479 0.571 0.284 0.247 0.290 0.434 0.304 0.545 0.624 0.449 0.636 0.646 0.448 0.408

73

Table A.2. Continued. Date

6/9/99 6/9/99 6/9/99 6/9/99 6/9/99 6/9/99 6/9/99 6/9/99 6/9/99 6/9/99 6/9/99 6/9/99 6/9/99 6/9/99 6/9/99 6/9/99 6/9/99 6/9/99 6/9/99 6/16/99 6/16/99 6/16/99 6/16/99 6/16/99 6/16/99 6/16/99 6/16/99 6/16/99 6/16/99 6/16/99 6/16/99 6/16/99 6/16/99 6/16/99 6/16/99 6/16/99 6/16/99 6/16/99 6/16/99 6/17/99 6/17/99 6/17/99

Transect 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 7 7 7

Plot 2 3 4 5 6 7 8 9

10 11 12 13 14 15 16 17 18 19 20 1 2 3 4 5 6 7 8 9

10 11 12 13 14 15 16 17 18 19 20 1 2 3

LAI 0.21 0.27 0.84 0.89 1.40 2.53 1.56 1.57 2.11 0.31 1.65 2.59 1.27 0.23 1.88 1.57 1.78 0.94 0.89 1.64 0.79 1.79 1.83 2.01 1.18 2.35 1.34 1.03 0.06 1.88 0.68 1.56 1.05 2.20 1.46 0.67 2.57 2.82 3.10 0.18 0.93 0.77

MTA 90 90 67 76 70 53 64 68 61 90 63 48 90 90 90 90 63 90 90 65 90 90 65 68 90 61 90 90 90 90 90 70 90 67 90 62 81 64 40 90 74 90

DIFN 0.894 0.874 0.569 0.518 0.375 0.124 0.313 0.337 0.196 0.844 0.304 0.110 0.412 0.888 0.369 0.376 0.262 0.605 0.636 0.282 0.605 0.297 0.252 0.235 0.475 0.170 0.438 0.600 0.964 0.314 0.672 0.338 0.513 0.212 0.410 0.653 0.201 0.145 0.081 0.906 0.502 0.607

74

Table A.2. Date

6/17/99 6/17/99 6/17/99 6/17/99 6/17/99 6/17/99 6/17/99 6/17/99 6/17/99 6/17/99 6/17/99 6/17/99 6/17/99 6/17/99 6/17/99 6/17/99 6/17/99 6/26/99 6/26/99 6/26/99 6/26/99 6/26/99 6/26/99 6/26/99 6/26/99 6/26/99 6/26/99 6/26/99 6/26/99 6/26/99 6/26/99 6/26/99 6/26/99 6/26/99 6/26/99 6/26/99 6/26/99 6/27/99 6/27/99 6/27/99 6/27/99 6/27/99

Continued. Transect

7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 9 9 9 9 9

Plot 4 5 6 7 8 9

10 11 12 13 14 15 16 17 18 19 20 1 2 3 4 5 6 7 8 9

10 11 12 13 14 15 16 17 18 19 20 1 2 3 4 5

LAI 0.98 1.37 0.89 0.86 1.25 0.62 1.35 1.07 1.00 2.67 1.81 1.54 0.67 0.55 1.52 0.91 1.50 0.79 0.82 0.64 0.40 1.22 1.27 0.86 0.85 1.04 1.00 1.15 0.23 1.60 1.50 2.54 2.17 1.25 2.33 1.71 2.12 1.50 2.07 2.19 2.14 2.05

MTA 90 90 90 90 90 90 90 67 90 59 64 90 90 90 87 86 90 73 90 90 90 68 80 90 90 90 90 90 90 90 90 63 77 90 90 90 76 90 76 62 73 79

DIFN 0.528 0.384 0.558 0.605 0.432 0.655 0.444 0.485 0.610 0.125 0.262 0.411 0.671 0.715 0.367 0.563 0.420 0.538 0.562 0.657 0.786 0.394 0.384 0.614 0.596 0.532 0.554 0.523 0.888 0.377 0.418 0.153 0.242 0.585 0.261 0.358 0.247 0.382 0.242 0.192 0.238 0.250

75

Table A.2. ( Date

6/27/99 6/27/99 6/27/99 6/27/99 6/27/99 6/27/99 6/27/99 6/27/99 6/27/99 6/27/99 6/27/99 6/27/99 6/27/99 6/27/99 6/27/99 6/28/99 6/28/99 6/28/99 6/28/99 6/28/99 6/28/99 6/28/99 6/28/99 6/28/99 6/28/99 6/28/99 6/28/99 6/28/99 6/28/99 6/28/99 7/28/99 6/28/99 6/28/99 6/28/99 6/28/99 7/9/99 7/9/99 7/9/99 7/9/99 7/9/9,9 7/9/99 7/9/99

Continued. Transect

9 9 9 9 9 9 9 9 9 9 9 9 9 9 9

10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 11 11 11 11 11 11 11

Plot 6 7 8 9

10 11 12 13 14 15 16 17 18 19 20 1 2 3 4 5 6 7 8 9

10 11 12 13 14 15 16 17 18 19 20 1 2 3 4 5 6 7

LAI 0.82 1.98 2.30 1.33 1.68 1.01 0.84 1.01 1.06 0.98 1.05 0.44 2.00 1.47 1.50 1.10 0.34 1.10 0.93 0.99 1.69 1.05 0.80 0.86 0.70 1.54 1.78 2.05 1.92 2.38 1.44 1.37 1.93 1.86 1.88 1.37 1.44 1.51 0.87 0.89 1.29 1.43

MTA 90 75 77 74 53 86 90 90 90 84 79 90 56 90 90 90 90 90 90 90 87 76 90 90 90 90 90 72 86 64 80 90 90 90 85 89 90 90 90 90 86 90

DIFN 0.608 0.259 0.215 0.405 0.238 0.468 0.553 0.479 0.473 0.495 0.454 0.771 0.193 0.382 0.380 0.449 0.821 0.485 0.594 0.543 0.316 0.499 0.644 0.646 0.648 0.433 0.397 0.244 0.291 0.189 0.345 0.513 0.386 0.332 0.309 0.412 0.414 0.403 0.594 0.556 0.423 0.411

76

Table A.2. Date

7/9/99 7/9/99 7/9/99 7/9/99 7/9/99 7/9/99 7/9/99 7/9/99 7/9/99 7/9/99 7/9/99 7/9/99 119199

7/12/99 7/12/99 7/12/99 7/12/99 7/12/99 7/12/99 7/12/99 7/12/99 7/12/99 7/12/99 7/12/99 7/12/99 7/12/99 7/12/99 7/12/99 7/12/99 7/12/99 7/12/99 7/12/99 7/12/99 7/14/99 7/14/99 7/14/99 7/14/99 7/14/99 7/14/99 7/14/99 7/14/99 7/14/99

Continued. Transect

11 11 11 11 11 11 11 11 11 11 11 11 11 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 13 13 13 13 13 13 13 13 13

Plot 8 9

10 11 12 13 14 15 16 17 18 19 20 1 2 3 4 5 6 7 8 9

10 11 12 13 14 15 16 17 18 19 20 1 2 3 4 5 6 7 8 9

LAI 1.07 1.03 0.94 1.30 1.50 1.02 1.42 1.64 0.79 1.05 1.27 1.46 1.35 0.67 1.22 0.94 1.54 0.89 0.74 0.43 1.11 0.62 1.06 1.56 1.42 1.97 1.51 1.74 0.70 1.61 2.23 0.98 1.19 1.44 1.28 1.41 1.98 2.40 1.44 1.53 1.33 1.35

MTA 90 65 90 90 90 90 90 90 90 90 90 79 90 90 90 90 68 90 90 90 90 90 67 90 90 90 90 90 90 90 90 90 90 63 84 66 60 59 75 79 90 79

DIFN 0.538 0.476 0.586 0.483 0.384 0.555 0.417 0.364 0.631 0.478 0.437 0.410 0.470 0.678 0.486 0.565 0.337 0.590 0.656 0.795 0.529 0.701 0.500 0.368 0.431 0.308 0.438 0.360 0.717 0.381 0.298 0.572 0.505 0.320 0.413 0.350 0.204 0.145 0.370 0.348 0.451 0.409

77

Table A.2. Continued. Date

7/14/99 7/14/99 7/14/99 7/14/99 7/14/99 7/14/99 7/14/99 7/14/99 7/14/99 7/14/99 7/14/99 7/15/99 7/15/99 7/15/99 7/15/99 7/15/99 7/15/99 7/15/99 7/15/99 7/15/99 7/15/99 7/15/99 7/15/99 7/15/99 7/15/99 7/15/99 7/15/99 7/15/99 7/15/99 7/15/99 7/15/99 7/16/99 7/16/99 7/16/99 7/16/99 7/16/99 7/16/99 7/16/99 7/16/99 7/16/99 7/16/99 7/16/99

Transect 13 13 13 13 13 13 13 13 13 13 13 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 15 15 15 15 15 15 15 15 15 15 15

Plot 10 11 12 13 14 15 16 17 18 19 20 1 2 3 4 5 6 7 8 9

10 11 12 13 14 15 16 17 18 19 20 1 2 3 4 5 6 7 8 9

10 11

LAI 1.75 1.53 1.69 1.68 1.45 1.93 2.24 1.36 2.16 2.96 1.44 1.05 0.97 0.91 0.66 0.13 0.50 0.40 0.48 1.04 1.41 0.47 0.49 1.11 0.51 0.53 0.75 1.59 2.37 1.06 1.17 1.69 0.91 0.86 1.42 0.54 0.94 0.63 0.90 0.34 0.31 0.39

MTA 70 69 90 77 67 69 57 90 64 61 85 90 90 90 71 90 90 90 90 90 90 90 90 90 90 90 90 90 61 90 90 79 90 78 90 90 72 90 90 90 73 90

DIFN 0.310 0.371 0.351 0.335 0.360 0.270 0.172 0.426 0.225 0.117 0.412 0.536 0.515 0.570 0.719 0.928 0.744 0.767 0.771 0.520 0.429 0.765 0.713 0.497 0.721 0.752 0.622 0.378 0.174 0.549 0.492 0.327 0.572 0.551 0.411 0.726 0.572 0.733 0.585 0.813 0.837 0.815

78

Table A.2. Continued. Date

7/16/99 7/16/99 7/16/99 7/16/99 7/16/99 7/16/99 7/16/99 7/16/99 7/16/99 7/19/99 7/19/99 7/19/99 7/19/99 7/19/99 7/19/99 7/19/99 7/19/99 7/19/99 7/19/99 7/19/99 7/19/99 7/19/99 7/19/99 7/19/99 7/19/99 7/19/99 7/19/99 7/19/99 7/19/99 7/20/99 7/20/99 7/20/99 7/20/99 7/20/99 7/20/99 7/20/99 7/20/99 7/20/99 7/20/99 7/20/99 7/20/99 7/20/99

Transect 15 15 15 15 15 15 15 15 15 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 17 17 17 17 17 17 17 17 17 17 17 17 17

Plot 12 13 14 15 16 17 18 19 20 1 2 3 4 5 6 7 8 9

10 11 12 13 14 15 16 17 18 19 20 1 2 3 4 5 6 7 8 9

10 11 12 13

LAI 0.72 1.00 1.30 0.86 0.75 0.99 0.53 0.33 1.84 1.11 1.10 1.64 1.10 1.30 1.76 0.86 1.28 1.30 0.60 1.05 1.83 1.41 1.83 1.15 1.51 1.25 1.10 1.69 0.93 1.06 1.16 1.77 1.32 1.77 1.56 1.47 1.86 2.02 1.84 2.06 1.80 2.14

MTA 90 90 90 89 90 65 90 78 83 90 90 90 90 90 90 90 90 90 90 90 78 90 90 90 70 75 90 90 90 90 90 65 71 59 74 90 87 63 87 58 90 64

DIFN 0.664 0.533 0.461 0.568 0.605 0.490 0.708 0.800 0.301 0.474 0.469 0.342 0.514 0.432 0.345 0.630 0.468 0.442 0.702 0.549 0.286 0.429 0.347 0.498 0.356 0.417 0.484 0.372 0.624 0.514 0.464 0.265 0.399 0.246 0.343 0.406 0.306 0.218 0.296 0.197 0.314 0.218

79

Table A.2. Continued. Date

7/20/99 7/20/99 7/20/99 7/20/99 7/20/99 7/20/99 7/20/99 7/21/99 7/21/99 7/21/99 7/21/99 7/21/99 7/21/99 7/21/99 7/21/99 7/21/99 7/21/99 7/21/99 7/21/99 7/21/99 7/21/99 7/21/99 7/21/99 7/21/99 7/21/99 7/21/99 7/21/99 7/22/99 7/22/99 7/22/99 7/22/99 7/22/99 7/22/99 7/22/99 7/22/99 7/22/99 7/22/99 7/22/99 7/22/99 7/22/99 7/22/99 7/22/99

Transect 17 17 17 17 17 17 17 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19

Plot 14 15 16 17 18 19 20 1 2 3 4 5 6 7 8 9

10 11 12 13 14 15 16 17 18 19 20 1 2 3 4 5 6 7 8 9

10 11 12 13 14 15

LAI 1.88 1.44 1.49 1.12 1.93 1.33 1.80 1.61 0.95 2.04 0.98 2.10 2.61 0.11 0.92 1.10 1.27 2.73 1.50 2.31 1.56 2.18 0.11 1.90 0.91 1.37 2.15 1.59 0.74 0.87 1.62 1.18 1.65 1.99 1.42 1.52 2.61 1.20 2.52 1.54 1.70 1.69

MTA 89 90 90 75 73 90 84 90 90 68 75 67 40 90 71 90 85 57 69 58 90 77 90 71 90 90 80 73 90 90 90 90 90 70 90 88 64 65 65 90 90 66

DIFN 0.298 0.434 0.373 0.471 0.274 0.481 0.315 0.364 0.524 0.241 0.516 0.228 0.110 0.937 0.534 0.538 0.426 0.114 0.358 0.173 0.375 0.248 0.938 0.282 0.562 0.398 0.256 0.315 0.616 0.563 0.340 0.467 0.359 0.247 0.409 0.384 0.151 0.397 0.163 0.372 0.337 0.317

80

Table A.2. Continued.

Date Transect Plot LAI MTA DIFN 7/22/99 7/22/99 7/22/99 7/22/99 7/22/99 7/22/99 7/22/99 7/22/99 7/22/99 7/22/99 7/22/99 7/22/99 7/22/99 7/22/99 7/22/99 7/22/99 7/22/99 7/22/99 7/22/99 7/22/99 7/22/99 7/22/99 7/22/99 7/22/99 7/22/99 7/31/99 7/31/99 7/31/99 7/31/99 7/31/99 7/31/99 7/31/99 7/31/99 7/31/99 7/31/99 7/31/99 7/31/99 7/31/99 7/31/99 7/31/99 7/31/99 7/31/99

19 19 19 19 19 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21

16 17 18 19 20 1 2 3 4 5 6 7 8 9

10 11 12 13 14 15 16 17 18 19 20 1 2 3 4 5 6 7 8 9

10 11 12 13 14 15 16 17

1.83 2.02 1.05 1.13 1.55 0.90 1.72 2.12 0.86 1.24 1.45 2.49 2.06 1.74 1.60 0.97 1.49 2.11 2.44 1.62 1.96 2.02 1.94 2.06 1.84 0.80 0.74 0.53 0.51 0.26 0.63 0.54 0.64 0.88 0.59 0.67 0.40 0.82 0.40 0.69 0.69 0.72

90 66 71 90 90 90 71 90 90 90 90 63 90 90 90 90 74 90 62 76 71 74 84 79 87 90 90 90 90 90 90 75 90 90 90 90 90 59 90 90 90 90

0.309 0.235 0.504 0.520 0.399 0.601 0.321 0.278 0.589 0.471 0.402 0.165 0.298 0.370 0.424 0.528 0.371 0.268 0.163 0.353 0.263 0.263 0.283 0.274 0.295 0.579 0.631 0.712 0.732 0.836 0.689 0.691 0.695 0.578 0.682 0.641 0.789 0.542 0.764 0.712 0.662 0.668

81

Table A.2. Date

7/31/99 7/31/99 7/31/99 8/1/99 8/1/99 8/1/99 8/1/99 8/1/99 8/1/99 8/1/99 8/1/99 8/1/99 8/1/99 8/1/99 8/1/99 8/1/99 8/1/99 8/1/99 8/1/99 8/1/99 8/1/99 8/1/99 8/1/99 8/1/99 8/8/99 8/8/99 8/8/99 8/8/99 8/8/99 8/8/99 8/8/99 8/8/99 8/8/99 8/8/99 8/8/99 8/8/99 8/8/99 8/8/99 8/8/99 8/8/99 8/8/99 8/8/99

Continued. Transect

21 21 21 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23

Plot 18 19 20 1 2 3 4 5 6 7 8 9

10 11 12 13 14 15 16 17 18 19 19 20 1 2 3 4 5 6 7 8 9

10 11 12 13 14 15 16 17 18

LAI 0.36 0.43 1.00 0.96 0.46 0.80 0.65 0.66 0.41 0.22 0.53 1.32 0.48 0.35 0.89 0.76 1.20 0.69 0.25 0.34 0.61 1.26 0.97 1.01 0.40 0.62 0.45 0.81 0.84 0.65 0.54 0.47 0.53 0.62 0.66 0.48 0.80 1.19 1.69 0.45 1.03 0.92

MTA 90 90 90 64 90 90 90 90 74 90 90 69 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 87 90 79 90 90 90 85 90 90 90 90 90 90 90 90

DIFN 0.791 0.776 0.557 0.475 0.746 0.614 0.668 0.659 0.764 0.886 0.735 0.437 0.780 0.811 0.561 0.682 0.468 0.653 0.860 0.828 0.744 0.568 0.617 0.558 0.771 0.652 0.760 0.549 0.601 0.636 0.686 0.752 0.770 0.667 0.662 0.811 0.691 0.457 0.332 0.724 0.490 0.594

82

Table A.2. Continued. Date 8/8/99 8/8/99 8/11/99 8/11/99 8/11/99 8/11/99 8/11/99 8/11/99 8/11/99 8/11/99 8/11/99 8/11/99 8/11/99 8/11/99 8/11/99 8/11/99 8/11/99 8/11/99 8/11/99 8/11/99 8/11/99 8/11/99 8/13/99 8/13/99 8/13/99 8/13/99 8/13/99 8/13/99 8/13/99 8/13/99 8/13/99 8/13/99 8/13/99 8/13/99 8/13/99 8/13/99 8/13/99 8/13/99 8/13/99 8/13/99 8/13/99 8/13/99

Transect 23 23 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25

Plot 19 20 1 2 3 4 5 6 7 8 9

10 11 12 13 14 15 16 17 18 19 20 1 2 3 4 5 6 7 8 9

10 11 12 13 14 15 16 17 18 19 20

LAI 0.46 1.24 1.03 0.57 0.79 0.77 0.85 1.35 0.59 0.83 1.86 0.96 1.13 1.28 1.28 0.99 1.69 0.56 1.40 1.72 1.37 1.51 0.56 0.67 0.52 0.68 0.25 0.61 0.78 0.71 1.20 1.31 1.76 1.21 1.65 0.99 1.61 1.95 2.03 1.67 1.24 1.34

MTA 90 90 62 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 80 90 90 90 90 90 90 90 90 90 90 90 90 68 90 80 90 78 82 90 90 80 90

DIFN 0.775 0.464 0.437 0.682 0.600 0.641 0.587 0.406 0.702 0.656 0.306 0.572 0.514 0.444 0.479 0.622 0.344 0.723 0.442 0.353 0.421 0.470 0.672 0.676 0.727 0.662 0.868 0.683 0.617 0.670 0.479 0.431 0.296 0.494 0.335 0.575 0.359 0.292 0.280 0.381 0.461 0.454

83

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100

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