To the Graduate School:

May 2, 1986

Herewith is submitted a dissertation written by Lambert BlanchardMcCarty entitled "Quantifying Environmental and Cultural ParametersInfluencing Daily Growth and Development of Two Turfgrasses." Irecommend that it be accepted in partial fulfillment of the requirementsfor the degree of Doctor of Philosophy, with a major in Plant Physiology.

We have reviewed this dissertationand recommend its acceptance:

L~~77;7~Dissertation Advisor

Accepted for the Graduate School:

QUANTIFYING ENVIRONMENTAL AND CULTURAL PARAMETERS

INFLUENCING DAILY GROWTH AND DEVELOPMENT

OF TWO TURFGRASSES

A Dissertation

Presented to

the Graduate School of

Clemson University

In Partial Fulfillment

of the Requirements for the Degree

Doctor of Philosophy

Plant Physiology

by

Lambert Blanchard McCarty

May 1986

ABSTRACT

Quantifying turfgrass daily growth and development without

destructive harvests has traditionally been by physical measurements such

as plant height. A non-destructive, accurate method of measuring daily

leaf growth as influenced by environmental and cultural practices (e.g.

pesticide or fertilizer application) could aid 'researchers in evaluating

growth-limiting variables.

A method is presented for quantifying the daily growth and

development of tall fescue [Festuca arundinacea Schreb.] and bermudagrass

[Cynodon dactylon (L.) Pers.] in 1984 and 1985. The method measured

regular leaf appearance at the growing point. The progress of each new

leaf growing from tip emergence at the coleoptile to complete unrolling

was divided into ten equal fractions. Total growth was the sum of all

unrolled leaves plus a fractional portion of the next leaf. The rate of

growth was determined by subtracting the growth of the previous day from

that of the current day. Quantitative stages of leaf development were

recorded, transformed into growth rates, and used as dependent variables

for regression on a variety of daily environmental parameters, plant age

and herbicidal effects. The tall fescue growth-rate-regression equation

successfully projected growth on data from a different year (1984).

The growth-rate equations for common, 'Tifway' and 'Tifgreen'

bermudagrass successfully projected the growth of the other two

.cultivars. This indicates that these three cultivars respond comparably

when grown under similar environmental conditions. In addition, there is

iii

potential use of a single equation for projecting the daily growth of

common, Tifway and Tifgreen bermudagrass. The common bermudagrass

growth-rate equation also was tested on unrelated data from July through

September in 1984. Although this fit was not as accurate in projecting

daily leaf growth on unrelated data as was the tall fescue equation,

seasonal growth trends of bermudagrass were successfully followed by the

equation. Improved growth-rate equations could be achieved by developing

season-specific equations and by incorporating multiple-year data into

the model. Standardizing cultural practices for specific crops and

planting sites would provide additional accuracy.

The herbicides MSMA [monosodium salt of methylarsonic acid] at 3.4-1 -1kg ha. and 2,4-D [(2,4-dichlorophenoxy)-acetic acid] at 2.2 kg ha

neither retarded the growth of nor proved phytotoxic to treated common

bermudagrass. Growth was stimulated slightly in 1984 but was unaffected

in 1985. Adjacent MSMA-treated large crabgrass [Digitaria sanguinalis

(L.) Scop] slowed in growth after initial application and died after two

applications. This shows the potential use of the described procedure

for detecting daily plant growth following herbicide application.

Environmental conditions during herbicide treatment, differential

responses of bermudagrass cultivars to herbicide application or the

ability of bermudagrass to deactivate this chemical prior to symptom

development may explain why no detectable daily retardation of leaf

growth resulted.

VITA

The author was born on October 26, 1958, to Mr. and Mrs. Tyrone

McCarty and raised in Batesburg, South Carolina. He attended

Batesburg-Leesville schools and graduated in 1977. During summers and

holidays he worked on a peach farm. In the fall of 1977 he entered

Clemson University and during this time worked at various golf courses

and at the Clemson Ornamental Gardens. A Bachelor of Science degree in

Agronomy and Soils was received in 1981. In the fall of 1981 he entered

the Crop Science Department of North Carolina State University to obtain

a Master of Science degree. His advisors were Drs. Joseph M. DiPaola,

William M. Lewis, and William B. Gilbert. During this time he assisted

in a wide variety of turf-management and highway-roadside research

projects and also assisted in teaching. After graduating in 1983 the

author entered the Horticulture Department of Clemson University to

pursue a Doctor of Philosophy degree in plant physiology with a minor in

plant pathology. During this time the author was inducted into Gamma

Sigma Delta and was responsible for organizing and implementing a pilot

.project in integrated pest management on golf courses, referred to as

Turf Information and Pest Scouting. In addition the author did research

on potential herbicides for use in turf, adapted a growth model for

quantifying daily grass growth, studied the effects of plant growth

retardants on tall fescue root growth, and assisted in many extension

meetings and in the writing of papers dealing with a wide variety of

topics for golf course managers.

ACKNOWLEDGMENTS

The author wishes to express his sincere gratitude to Dr. Landon C.

Miller, Chairman of his Advisory Committee, for his guidance throughout

the course of this investigation. Additional appreciation is extended to

other members of the Advisory Commitee, Drs. Joseph R. Haun, Jere A.

Brittain, and Graydon C. Kingsland, for their constructive suggestions

and help in the preparation of this manuscr~pt and ready support during

the course of this study.

Sincere thanks are extended to Mr. Carey Frick, Thomas Boucounis,

and Jeffrey Higgins plus the rest of the Horticulture graduate students

and faculty for their assistance and special times for the author while

at Clemson University.

Finally, the author especially wishes to thank his parents, Mr. and

Mrs. W. Tyrone McCarty, and also Lt. Patrick E. McCarty and Dr. and Mrs.

Michael T. McCarty, for their encouragement, support, and understanding.

TITLE PAGE

ABSTRACT

VITA

TABLE OF CONTENTS

..........................................................................................................

..........................................................ACKNOWLEDGMENTS ...............................................LIST OF TABLES

LIST OF FIGURES

CHAPTER

III.

I. INTRODUCTION ••••••••••••.•••••••••••••••••••••••••••••

II. LITERATURE REVIEW

Temperature Effects on Plant Growth •••••••••••••••••Light Effects on Plant Growth •••••••••••••••••••••••Plant Growth Nutrients •••••••••••••••••••••••••••••••Effects of Plant Genotypes on GrowthCrop Tolerance to PesticidesEnvironmental Interactions and

Plant GrowthPlant-Growth-Determination Techniques

..................................

QUANTIFYING ENVIRONMENTAL PARAMETERS INFLUENCINGTALL FESCUE DAILY GROWTH AND DEVELOPMENT ••••••••••••

In troduc tion ••••••••••••••••••••••••••••••••••••••••Materials and Methods ...............................Results and Discussion ........... - .

IV. COMPARING GROWTH AND DEVELOPMENT RESPONSES OFBERMUDAGRASS CULTIVARS TO ENVIRONMENTALPARAMETERS ..........................................Introduction ••••••••••••••••••••••••••••••••••••••••Materials and Methods ...............................Results and Discussion ..............................

Page

i

ii

iv

v

viii

ix

1

3

35667

1011

15

151722

28

282930

Tables of Contents (Cont'd.)

V. DAILY LEAF GROWTH AND DEVELOPMENT RESPONSES OF COMMONBERMUDAGRASS TO REPEAT MSMA AND 2,4-DTREATMENTS ..........................................Introduction ••••••••••••••••••••••••••••••••••••••••Materials and Methods ...............................Results and Discussion

APPENDICES

VI. CONCLUSIONS

•••••••••••••••••••••••••••••••••••••••••••••. e ••••••

A.B.

Environmental Data,Environmental Data,

REFERENCES CITED

Spring 1985Sunnner 1985 . .

Page

43

434546

53

55

5661

66

LIST OF TABLES

Table Page

1. Analysis of variance, regression coefficients, andstatistics of fit for the dependent variable:tall fescue leaf-growth rate,Clemson, SC, 1985 •••••••••••••••••••••••••••••••••••• 23

2. Analysis of variance, regression coefficients, andstatistics of fit for th~ dependent variable:common bermudagrass leaf-growth rate,Clemson, SC, 1985 •••••••••••••••••••••••••••••••••••• 31

3. Analysis of variance, regression coefficients, andstatistics of fit for the dependent variable:'Tifgreen' bermudagrass leaf-growth rate,Clemson, SC, 1985 •••••••••••••••••••••••••••••••••••• 34

4. Analysis of variance, regression coefficients, andstatistics of fit for the dependent variable:'Tifway' bermudagrass leaf-growth rate,Clemson, SC, 1985 .......•..............•............. 35

5. Common bermudagrass calculated growth-rate equation(1985) tested on common, 'Tifway,' and 'Tifgreen'bermudagrass data, Clemson, SC, 1985 ••••••••••••••••• 36

6. 'Tifway' bermudagrass calculated growth-rate equation(1985) tested on common, 'Tifway,' and 'Tifgreen'bermudagrass data, Clemson, SC, 1985 ••••••••••••••••• 37

7. 'Tifgreen' bermudagrass calculated growth-rateequation (1985) tested on common, 'Tifway,'and 'Tifgreen' bermudagrass data,Clemson, SC, 1985 •••••••••••••••••••••••••••••••••••• 38

8. Analysis of variance measuring common bermudagrassdaily leaf growth in 1984 following threeMSMA and 2,4-D treatments •••••••••••••••••••••••••••• 47

9. Analysis of variance measuring common bermudagrassdaily leaf growth in 1985 following threeMSMA and 2,4-D treatments •••••••••••••••••••••••••••• 48

LIST OF FIGURES

Figure Page

1. Diagrammatic scale for quantifying leaf changesduring grass growth ••••••••••••••••••••••••••••••••• 19

2. Actual 1985 tall fescue leaf growth and calculatedby 1985 growth equation (Table 1) based on22 Feb. - 30 Apr. data •••••••'....................... 25

3. Actual 1984 tall fescue leaf growth and projected1984 growth utilizing 1985 growth model ••••••••••••• 26

4. Actual 1984 common bermudagrass leaf growth andcalculated by 1985 growth equation (Table 2)based on 18 July - 28 Sept. data •••••••••••••••••••• 32

5. Actual 1984 common bermudagrass leaf growth andprojected growth utilizing 1985 model ••••••••••••••• 33

6. Common, 'Tifway' and 'Tifgreen' bermudagrass growth,summer 1985 ........••....••.........•.•....•....•... 41

7. Influence of MSMA on common bermudagrass and largecrabgrass daily leaf growth in 1984 ••••••••••••••••• 50

A-I. Environmental data, spring 1985 ••••••••••••••••••••••• 56thruA-4.B-1. Environmental data, summer 1985 ••••••••••••••••••••••• 61

thruB-4.

CHAPTER I

INTRODUCTION

The relationship among environmental and cultural parameters

influencing daily turfgrass growth are not fully understood. Although

temperature, available moisture and nitrogen influence growth,

researchers continue investigating the relationships of these variables

and others on daily growth.

Newman and Beard (38), studying plant growth as affected by biotic

factors, stated that "the central problem is one of finding more

accurate, quantitative measures of biological responses as they are

influenced by meteorological factors." These authors reported that this

type of periodic observation, whether it be daily, weekly, or bi-week1y

as potentially being very useful in crop research studies.

Internode elongation, stalk diameter, relative growth rate, and net

assimilation rate are examples of available methods for determining

growth. Various growth-analysis formulae and the necessary conditions

for their use have been reviewed by Radford (43).

A method for visually identifying changes in the form rather than in

the size of new-leaf development at the growing tip was suggested by

Higgins et a1. (19). Multiple regression analysis was used to separate

the effects of day1ength, temperature, solar radiation, and soil moisture

on the daily leaf-development rate. Since these successive observations

were nondestructive because they involved the same growing points, this

simplified sampling and plant-measurement statistical problems.

Experimental design and analysis also were simplified.

2

Haun and associates have expanded this method to include the crops

wheat [Triticum aestivum L. em. TheIl.] (15), carnations [Dianthus

caryophyllus L.] (29) and peaches [Prunus persica (L.) Batsch.] (18).

Klepper et al. (25), working with wheat, found this method to be a rapid

field-measurement tool permitting very specific and detailed estimates of

daily shoot development.

The objectives of this study were to:

1. measure daily growth and development of tall fescue, common,'Tifgreen' and 'Tifway' bermudagrass and to relate thesechanges to environmental parameters and plant age by (a)developing a growth-rate regression equation, and (b) testingthis equation on unrelated data,

2. compare each bermudagrass cultivar equation by testing theaccuracy of each equation on the other two cultivars,

3. evaluate if the method could detect changes in commonbermudagrass daily leaf growth following repeat applicationsof the herbicides MSMA and 2,4-D and to detect changesof MSMA-treated crabgrass.

CHAPTER II

LITERATURE REVIEW

Plant growth varies yearly, seasonally and daily. Although weather

influences this fluctuation, precise grass growth and developmental

responses have not been fully identified, The search continues for

methods to measure growth responses to environmental variables, plant age

and architecture, and pests and cultural treatments.

Variables potentially influencing growth and development of grass

are discussed. .A method for quantifying the ~aily growth and development

of tall fescue [Festuca arundinacea Schreb.] and bermudagrass [Cynodon

dactylon (L.) Pers.] leaves is described, together with methods relating

.these changes to environmental parameters. Growth characteristics of

three bermudagrass cultivars are compared, and herbicide effects on

bermudagrass leaf growth, measured by the described procedure, also are

address~d.

Temperature Effects on Plant Growth

Aerial environmental variables influencing leaf expansion include

temperature, light and carbon dioxide. Soil variables - water and

mineral nutrient availability, soil temperature and soil solution salt

concentration - also influence leaf expansion (49).

Temperature consistently has been identified as a major

.environmental parameter influencing leaf appearance and development.

Increased efficiency of water use and light-saturated photosynthetic

rates generally have been shown at high temperatures in C4 versus C3

4

plants (10). In C4 plants, the quantum yield for CO2 uptake is greater

at temperatures above 30 C than temperatures below this figure.

The optimum temperature for tall fescue leaf growth is near 25 C

(37, 44) and 35 C for bermudagrass (33). Robson (45) suggests that leaf

growth was affected more by changes in day than night temperature and

that there was no evidence for a thermoperiodic response to temperature.

Temperature minimum for bermudagrass shoot growth is approximately 10 C.

Tollenaar et ale (51) noted that the appearance rate of successive

leaves of corn [Zea mays L.] was nearly constant for a given temperature,

consequently, the rate of leaf appearance should be a meaningful para-

meter for studying the temperature and rate of development relation-

ship of corn. They also cited a curvilinear relationship between temp-

erature and rate of development. A polynomial regression analysis of

data for corn grown at constant day/night temperatures was developed by

these researchers. This cubic equation for rate of leaf appearance

(leaves day-I) versus ambient temperature (T) was used in predicting leaf

appearance rate (Y) in fluctuating temperature environments and consisted

of: Y = 0.0997 - 0.0360T + 0.00362T2 - 0.0000639T3 (51). The authors

suggested that variations in this equation might be attributed to other

environmental factors, such as soil temperature, moisture and nutrient

imbalance. This limits the application of the equation since it excludes

other environmental parameters and their interactions with temperature

relative to leaf emergence.Lowering soil temperature reduces water absorption by roots, which

is greatest on warm-climate species subjected to cool root temperatures

(51). Reduced water uptake by roots at lower temperatures may be due to:

(i) increased water viscosity in plant tissue, (ii) decreased cell

5

membrane permeability, (iii) decreased active uptake and accumulation of

salt, and (iv) root growth reduction (51).

Light Effects on Plant Growth

Virgin (52) suggests light is a major determining variable in the

unfolding of grass leaves. Red light (max. 660 nm) promotes unfolding,

and far-red light (max. 710 nm) conteract~ the effect of red light.

Templeton et ale (48) noted that tall fescue leaves appeared more rapidly

under an 8 hr compared to a 16 hr photoperiod regime. Rapid leaf

elongation of plants grown continuously at 23.9 C was apparent within 72

hr after light exposure. Wilhelm and Nelson (53) observed that leaves

continuously increased in length throughout the day, but the rate of leaf

elongation was sensitive to environmental changes during a 24 hr period.

The leaf-elongation rate declined during the latter part of the light

period and declined futher at the beginning of the dark period.

Interaction of solar radiation and plant moisture is a difficult

problem in light-plant response studies. Moisture-stress conditions are

often associated with periods of high solar radiation. The correlation

between corn yield and solar radiation has been low, at times even

negative (46). The greatest photosynthetic rates occurred at inter-

mediate light intensities when there was high soil-moisture tension or

high evaporation potential (34). Linvill et ale (31) suggested that

plant growth responses to solar radiation may be limited by moisture

stress induced by a combination of high potential evaporation and limited

available soil moisture. Maximum unfolding of leaves in light occurs

only when leaves are turgid (52); consequently, either plant water

potential or soil moisture and potential evaporation must be considered

in any study relating crop growth to solar radiation.

6

Plant Growth Nutrients

Plant growth may be limited by the availability of at least 13

different mineral elements. The nutrients required in greatest amounts

are nitrogen and potassium. Nitrogen is an essential component of

chlorophyll, amino acids, proteins, nucleic acids, enzymes and other

substances. Faster leaf elongation, greater leaf length and area, and

increased tillering result from a moderate increase in nitrogen levels

(49). 'Coastal' bermudagrass is more productive and competitive than C3species when grown under very low soil nitrogen levels (9). Turfgrass

plants typically contain three to five percent nitrogen and one to four

percent potassium on a dry-weight basis. Potassium is a component of

carbohydrate synthesis and translocation, catalyzes numerous enzymatic

reactions, regulates transpiration and respiration, and functions in the

controlled uptake of certain nutrients (5). Potassium does not cause as

great a visual turfgrass response as nitrogen but does influence rooting

and wear tolerance. A positive correlation of stolon and rhizome growth

to potassium levels has been shown (5).

Effects of Plant Genotypes on Growth

Differences between forage yields among tall fescue genotypes have

been cited. Nelson et ale (36) suggested that dry matter production was

influenced by parameters such as genotypic variation in leaf aging and

canopy architecture. In the field, high-yielding genotypes had leaf

growth rates approximately 52% greater than low-yielding genotypes.

High-yielding genotypes also were noted to collar approximately 4 days

earlier than low-yielding genotypes (36).

Lewis (30), using multi-factor regression analysis in field and

glasshouse studies, showed that three temperate zone cereals - wheat

7

[Triticum aestivum L.], barley [Hordeum vulgare L.], and rye [Secale

cereale L.] - respond similarly to like environmental conditions. The

growth-prediction model for each cereal was tested on the other two. A

high correlation between the observed and calculated daily growth rates

for each equation on the other two cereals suggested the potential use of

a single growth-prediction equation for wheat, barley and rye.

Because 'Tifgreen' and 'Tifway' are two completely sterile triploid

hybrid bermudagrasses, they must be vegetatively propagated. Tifgreen's

low growth habit also includes a very fine texture, soft leaf blade, and

high shoot density. Tifway has a medium-fine texture, stiff leaf blade,

high shoot density, medium-low growth habit and vigorous growth rate.

These grasses require intensive management programs for optimum turf.

Common bermudagrass has a chromosome number of 36 and can be propagated

by seed. It is relatively coarse textured, medium green in color, and

has an intermediate shoot-growth rate and density (5).

Crop Tolerance to Pesticides

Pesticides are widely used in turfgrass production. Turf managers

apply these to control specific pests while minimizing damage to desired

plant species.

Organic arsenicals and dichlorophenoxy acids are two classes of

herbicides often used in bermudagrass production. Herbicidal-mode-of-

action studies have not shown exactly how either of these control target

species. Interference with nucleic acid and protein metabolism by 2,4-D

[(2,4-dichlorophenoxy)-acetic acid] is thought to cause phloem

obstruction, which prevents translocation of food, resulting in root

starvation (26). Investigators also suggest that 2,4-D decreases either

the rate of sugar diffusion from photosynthesis sites into the main

8

translocation stream or the biochemical turnover rate of carbon (8).

Reports of the effects of 2,4-D on photosynthesis also are inconsistent.

Some researchers think phenoxy herbicides inhibit photosynthesis while

others report no relationship between 2,4-D and photosynthesis (42).

Foliar damage also may result from 2,4-D (26), thus compounding its

effect on net photosynthesis. Other possible explanations of the phenoxy

herbicide mode of action are discussed by Ashton and Crafts (1).

Organic arsenicals are believed either to regulate amino acid

concentration, to accelerate stored starch utilization (26) or to

uncouple phosphorylation by the herbicide entering into reactions in

place of phosphate (1). DubIe and Holt (12) measured high CO2utilization in arsenical-treated plants. Untreated plants accumulated

more photosynthates than treated plants.

The tolerance of certain plants to these herbicides is largely

speculative. Some plants can detoxify 2,4-D by forming conjugates with

plant constituents or by preventing absorption and translocation due to

morphological barriers. Others may be partially affected by the

herbicide only to recover and survive from root and shoot regeneration

tissue or by transporting and providing root leakage of the phenoxy

herbicides following foliar application (1).

Plant tolerance to the arsenicals also is not fully understood.

Approximately 25% of the DSMA [disodium salt of methylarsonic acid]

applied to Coastal bermudagrass was translocated to roots and rhizomes

within five days after treatment (13). The carbon-arsenic bond of this

.herbicide remained largely intact during translocation. The authors

suggest a complex formed between the DSMA and some unknown plant

component.

9

The herbicides MSMA [monosodium salt of methylarsonic acid], DSMA,

and 2,4-D are widely used in turfgrass management. Sensitivity of

desired plants, number and frequency of applications required, and

environmental conditions during treatment have all contributed to

failures when using these chemicals (7).

Diffe!ential growth and development responses of various bermuda-

grass cultivars have been observed following herbicide treatments.

Tifgreen bermudagrass was discolored by one to three MSMA treatments at. -12.24 kg ha each, while common bermudagrass was unaffected (35).

However, Johnson (21) noted that MSMA-treated common bermudagrass turf

displayed initial injury symptoms but recovered two to three weeks later.

Johnson (23) also observed that the quality of Tifway was reduced more

than Tifgreen, 'Tifdwarf,' or 'Ormond' following oxadiazon [3-[2,4-

dichloro-5-(I-methylethoxy)phenyl]-5-(I,I-dimethylethy1)-1,3,4-oxadiazol--1 -12-(3~)-one] applications in September at 4.5 kg ai ha yr Spring

regrowth of Tifway and Tifdwarf was retarded more than Tifgreen or Ormond

after treatment with paraquat [1,1'-dimethyl-4,4'-bipyridinium ion] at, -11.2 kg ai ha (22). In pot trials, the differential susceptibilitY'of

bermudagrass cultivars to siduron [~-(2-methylcyclohexyl)-~'-phenylurea]

was observed by Siviour and Schultz (47) to be so evident that the

authors supported the hypothesis that repeated use of the herbicide may

result in poor efficacy because of elimination of susceptible strains.

The herbicide metribuzin [4-amino-6-(I,I-dimethylethyl)-3-(methylthio)-

1,2,4-triazin-5(4~)-one] also caused the differential net photosynthesis

response of six bermudagrass cultivars (54). Based on the level of

foliar injury, recovery rate, and degree of inhibition of net photo-

10

synthesis" 'Midiron' and 'Vamont' were more sensitive to metribuzin than

common, Tifway, 'Tufcote', and C. transvaalensis cultivars.

Environmental Interactionsand Plant Growth

Utilizing only one or two environmental variables has often proven

too limiting when studying plant responses in the field. The

interactions or interrelationships of these variables are probably more

important than their individual effects (5).

Templeton et al. (48) reported that tall fescue development is

greatly influenced by temperature and light, but that development was not

limited to these two enviromental parameters. The authors further

emphasized the importance of interactions among environmental parameters

and between environmental and genetic variables. They suggested that the

tall fescue leaf-appearance rate was influenced by the following

interactions: photoperiod x temperature, photoperiod x age of plant,

temperature x age of plant, and temperature x photoperiod x duration of

treatment.

Hodgen (20), working with Helianthus annuus L. and Vicia faba L.,

incorporated the independent variables of light, temperature, and initial

leaf-area ratio in linear regression to estimate the following: net

assimilation rate, leaf-area ratio, and relative growth rate. Weekly

growth and net-assimilation rates were compared with those calculated

trom the regression equation which incorporated light, temperature and

initial leaf-area ratio variables. The author concluded that environ-

mental conditions other than these could have only minor importance in

determining growth in terms of weekly periods.

11

The multiple-factor approach was used by Jordan (24) and Beard (3)

to study the environmental influence on fructose levels in bentgrass

[Agrostis palustris Ruds.] leaf tissue. The fifteen environmental

parameters used in linear mUltiple regression-correlation analysis

accounted for 88.6% of seasonal fructose level variation in bentgrass. A

single parameter, maximum soil temperature at 1.3 cm depth, was

responsible for nearly 50% variation. When soil moisture at 2.5 cm and

light intensity parameters were added, additional increases in the R~

resulted. Temperature was the only one of these three variables

significantly correlated with fructose level.

Beard (4) utilized the multiple-factor approach to measure the

seasonal variation of certain nitrogen fractions in bentgrass. Fifteen

environmental parameters accounted for 57.2% of bentgrass amides,

glutamine and asparagine levels. Maximum soil temperature at 15.2 cm was

responsible for 29.2% predicted amide level, while the four-way

interaction, (max. soil temp. at 15.2 cm) + (min. air temp. at 243.8 cm)

+ (light intensity) + (soil moisture at 2.5 cm) accounted for 43.1% of

this amide variation.

Plant-Growth-Determination Techniques

A traditional problem for researchers has been measuring vari-

ability in individual plants affected by the environment or pesticides.

Selecting a pertinent dependent-plant-growth statistic is the first

problem in any study of weather and crop growth. Typical measured

responses of plants to these variables include parameters such as

phytotoxicity (commonly visually estimated), physical growth (usually

defined as height or stem diameter), periodic clipping weights, or final

harvest weight. Problems may occur when only one or a few measurement

12

increments such as final yield, are taken. When plant responses to

treatments cannot be adequately explained by terminal data, it is often

necessary to have additional data on the progress of plant development

during the season prior to final harvest. Increasing the frequency of

such measurements would help to explain adequately and accurately,

periodic growth variations. Daily measurements would be ideal because ofdaily weather fluctuations.

A sampling technique also should be available which quickly and

accurately determines periodic changes in plant growth and development.

Newman and Beard (38) included this point when they raised the following

questions about using phenological observation to measure biological

responses:

"(1) Can the observation be expressed quantitatively, both withrespect to time and state of the organism?

(2) How often with respect to time and state of organism change isit necessary to repeat the observation?

(3) What are the possible causal physical factors within theenvironment?

(4) How should each of these factors be measured with respect totime and space?

(5) What skills are necessary on the part of the observer?"

In a discussion review on meteorology in agriculture, Penman (41)

said that "the state of the plant is one of the main technical

difficulties encountered in crop-weather statistics: It is futile to

attempt to find a relation of Y = (xl' x2' x3' etc.) if Y cannot be

measured as well as xl, x2' etc., and the cure is not to increase the

number of variables on the right-hand side of the equation but instead to

give extra attention to the left-hand side." Newman and Beard (38)

reiterated that "the central problem is one of finding more accurate,

13

quantitative measures of biological responses as they are influenced by _

meteorological factors." Newman and Beard added that this periodic

observation, whether it be daily, weekly, or bi-weekly~ could be very

useful in crop-research studies.

Berbecel used internode elongation, stalk diameter, and height of

corn as growth measurements (6). Relative Growth Rate (RGR), defined as

the dry-weight increase over a period diyided by the average weight of

the plant during the period, was used by Cowan and Milthorpe (11). Net

Assimilation Rate (NAR), Crop Growth Rate (CGR), and RGR are three

measures used by Koller et al. (27) for soybean [Glysine ~ (L.)

Merrill] growth analyses. Increase in plant dry weight in a period

divided by average leaf area in the period is the NAR, while the CGR is

simply the increase in plant dry weight. Various growth-analysis

formulae and the necessary conditions for their use have been reviewed by

Radford (43).

Higgins et al. in 1964 (19) suggested a method to visually identify

changes in form rather than size of new leaf development at the growing

tip. Multiple-regression analysis was used to quantify the effects of

daylength, temperature, solar radiation and soil moisture on the daily

leaf-development rate of crambe [Crambe abyssinica Hochst.], tephrosia

[Tephrosia vogel Hook. f.] and corn. This method has been expanded by

Haun and associates to include wheat [Triticum aestivum L. em. TheIl.]

(15), carnations [Dianthus caryophyllus L.J (29) and peaches [Prunus

persica (L.) Batsch.] (18). The procedure (18) involves counting the

number of leaves plus visually estimating the developmental stage of the

youngest leaf. The sta'tus of plant development is determined daily by

averaging the stage of the youngest leaf on a number of marked plants.

14

Sampling and plant measurement statistical problems are greatly

minimized since these successive observations involve the same growing

points. Klepper et al. (25), working with wheat, found this particular

method to be a rapid, non-destructive field-measurement tool that permits

a very specific and detailed designation of small daily shoot

development.

Many regression models have been criticized in the literature for

lacking independent-data testing, (28). Linvill et al. (31), utilizing

simple linear regression for independent data in 1972, attempted to

compute the rate of corn growth during a 1969-72 experiment. Solar

radiation, pan evaporation, percent available soil moisture, previous

growth rate and growing degree days were included in this equation. The

derived variables were inconsistent when tested on various years.

Feyerherm and Paulsen (14) proposed a wheat-yield prediction model

based on 12 independent and four possible dependent variables. This

model was tested for accuracy against 21 years of USDA winter and spring

yield records and had some successful predictions. The authors suggest

the lack of fit of their model was related more to weather/disease/pest

variables than to technology.

Haun's goal was a practical yield-prediction system for wheat

production (15). The universality of this system was shown when it was

applied to spring-planted wheat areas in the USSR. Further work by Haun

(17) on corn and Haun and Coston (18) on peach provided predictive growth

models applicable for differing yearly growth and yield.

CHAPTER III

QUANTIFYING ENVIRONMENTAL PARAMETERS INFLUENCING

TALL FESCUE DAILY GROWTH AND DEVELOPMENT

Introduction

Yearly, seasonal and daily plant growth variation occurs. -Even

though this variation is influenced by weather, precise responses of

grass growth and development have not been fully identified. A more

accurate method of measuring and relating growth responses to

environmental variables, plant age and architecture, pests and cultural

treatments would provide researchers a better understanding of what is

influencing daily plant development.

Temperature, light and carbon dioxide are aerial environmental

variables influencing leaf expansion. Soil variables influencing leaf

expansion include water and mineral nutrient availability, soil

temperature and soil solution salt concentration (49).

Tall fescue leaf growth occurs at a temperature optimum of

approximately 25 C (37, 44). Leaves appear more rapidly under an 8 hr

photoperiod regime than a 16 hr (48). Templeton et a1. (48) reported

accelerated leaf elongation when the 8 hr photoperiod and a continuous

temperature at 23.9 C were combined.

Nitrogen and potassium are nutrients required in the largest

amounts for plant growth. Faster leaf elongation, greater leaf length

and area, plus increased tillering resulted when ryegrass [Lolium sp.]

was grown under increased nitrogen levels (49).

16

Genotypic responses in tall fescue forage yields have also been

shown. High-yielding genotypes had leaf-growth rates approximately 52%

greater than low-yielding genotypes (36). The authors suggested that

variation in leaf aging and canopy architecture were possible genotypic

responses influencing this dry matter production.

Interactions among environmental parameters and between environ-

mental and genetic variables have important effects on tall fescue

development. Templeton et al. (48) suggested that the tall fescue

leaf-appearance rate was influenced by: [photoperiod x temperature],

[photoperiod x plant age], [temperature x plant age] and by the three-

way interaction [temperature x photoperiod x duration of treatment].

Jordan (24) and Beard (3) utilized linear mUltiple regression-

correlation analysis when studying the influence of environment on

bentgrass [Agrostis palustris Huds.] fructose levels. Fifteen

environmental parameters accounted for 88.6% of seasonal fructose level

variations in bentgrass. A single variable, maximum soil temperature at

1.3 cm depth, was responsible for nearly one-half of this variation. In

further work, Beard, again using the multiple factor approach, accounted

for 57.2% of bentgrass amides, glutamine and asparagine by 15 measured

enviromental variables (4). In this study, maximum soil temperature at

15.2 cm was responsible for 29.3% of predicted amide-level variation.

The four-way interaction of [max. soil temp. at 15.2 cm] + [min. air

temp. at 243.8 cm] + [light intensity] + [soil moisture at 2.3 cm]

accounted for 43.1% of this amide variation.

The literature criticizes many growth models because all

experimental data are usually needed to formulate the parameters; thus

no independent-data are available to be tested on (28). Other problems

17

may occur when only one or a few increments of measurements, such as

final yield, are taken. Additional data on the progress of plant

seasonal development are often necessary when terminal data cannot

adequately explain results. Periodic growth variation may more

accurately be accounted for by increasing the frequency of such

measurements during the growth of plants. Ideally such measurements

would be taken daily because of daily weather variations.

Research was conducted with the following objectives: (i) adapting

a method for quantifying daily tall fescue leaf growth and development,

(ii) constructing a growth model relating the influence of plant age and

previous one- to three-day environmental parameters to this daily

growth, and (iii) testing this equation on unrelated data.

Materials and Methods

Plant material. Established 'Kentucky 31' tall fescue was used

to develop a growth equation in 1985, and the equation was tested on

independent sets of measurements made at the same location in 1984. The

grass sward was adjacent to Clemson University's weather station,

Clemson, SC, on a Cecil series (clayey, kaolinitic, thermic Typic

Hapludults). A soil test prior to the commencement of the experiment

indicated medium to high levels of P, K, Mg, and Ca. Soil pH was 6.1;

no lime was added prior to the experiment. Nitrogen fertilizer was-1applied at 49 kg N ha in October and February for both years. Mowing

to 6.4 cm in height was performed approximately every two weeks and one

day prior to the beginning of each experiment. Except for the modified

mowing schedule, tall fescue cultural practices were similar to those

typically suggested for a lawn.

18

Growth evaluation procedure. Between 900 hand 1000 h daily leaf

development was obtained by visually measuring growth of the youngest

leaf tip visible above the rolled portion of the next older leaf.

Tollenaar et ale (51) suggest that the leaf appearance rate is nearly

constant for a given temperature; therefore, the rate of leaf appearance

should be a meaningful parameter for studying the relationship between

temperature and rate of development. Readings were observed 22 February

through 30 April each year. The leaves completely unrolled were

counted; those unrolling were scored on the basis of the scales shoWn in

Fig. 1. Each new complete leaf represented a unit of development,

visually subdivided into ten equal fractional stages. Each stage

represented 0.1 of development during leaf emerging and/or unrolling.

Stage 0 indicated first visible leaf emergence. Stages 0.1 through 0.4

represented leaf extension only. The 0.5 stage indicated initial leaf

tip unrolling, while subsequent stages represented progressive tip

unrolling. Stage 1.0 included one completely unrolled leaf plus initial

tip indication of the next emerging leaf. Daily growth rate was

obtained by averaging the growth rates of 20 marked plants and was

computed by the equation (18):R = ~a - ~b

Ntwhere:

R = daily rate of leaf growth,

a = current total number of leaves and fractional portion of unrollingleaf,

b previous day's total,

N = total number of observations,

t = number of days between observations.

~~/1/1

f\ ~

0.1 0.3 0.5 0.7 1.0

Figure 1. Diagrammatic scale for quantifying leaf changes duringgrass growth. (The leaf is emerging from the coleoptile at 0.1.The leaf is in early stage of unrolling at 0.5. The leaf iscompletely unrolled plus the tip of the next leaf is barely exposedfrom the coleoptile at 1.0.)

19

20

This span of growth provides a continuous succession of points to

monitor the middle stages of development for each leaf.

Environmental data. Maximum and minimum air temperature, soil

temperature at 10.2 cm, and precipitation were obtained from Clemson

University's weather service. Solar radiation was calculated in

langleys from a Belfort weekly recording pyrheliograph. Estimated soil

moisture was computed by the Palmer-Havens adaptation (39) of the

,Thornthwaite-Mather method (50). In this method, daily precipitation is

added to the storage capacity, and evapotranspiration (read from tables)

is subtracted.

Appendix A presents the environmental data during the time the

growth-rate equation was constructed. Environmental extremes included:

(i) a total precipitation of 11.8 cm, (ii) five days with a minimum

temperature below freezing and a maximum low of -5 C, and (iii) twelve

days with a maximum temperature greater than or equal to 26.7 C.

Statistical analysis. To obtain a growth-equation model, the

average daily growth rate was regressed on the following basic

independent variables: maximum and minimum air temperature (C), mean

soil temperature (C) at 10.2 cm, precipitation (cm), age (in days of

leaf development) from mowing, solar radiation (langley) and estimated

soil moisture (%) in upper 61 cm. Lags of one to three days for

temperature, moisture, and leaf-growth data were also included as

independent variables (i.e., lagged variables). These variables were

included because plant responses to environment are not necessarily best

correlated with the environment occurring the same day as observations,

but instead may be influenced by the environment of several previous

21

days (29). Additional variables to account for curvilinear effects were

formed by selected two-factor cross products. Stepwise or multiple

regression analyses of independent variables were performed with a

computer program designed by the SAS Institute (2). In this program,

growth rate was regressed on each independent variable. The variable

producing the highest R2 was selected for the first step. In the next

step, this variable was paired with all others to obtain the best R2.

These two were then combined with all remaining variables until an

arbitrary limit of 15 was reached. From the large number of variables,

only a small number were represented in selecting the growth-rate

equation. If the addition of a new variable caused the significance of

a previously selected one to fall below a given point, then the previous

variable was rejected. Criteria used in selection of the best step for

the leaf-growth equation were R2, significant Student's !, and F values.

Since temperature, precipitation (mainly as soil moisture) and age are

known to be related to growth but exact quantification of this

relationship is unknown, analysis of stepwise multiple regression was

made to obtain a useful set of coefficients and transformations of

variables. Furthermore, the 1985 outcome of analysis was based not only

on significant statistical indications but also on a successful test of

the growth equation on independent (1984) data. Since there is

intercorrelation among variables, it is not possible to assign a rigid

order of significance among them; however, it may be generalized that

those selected were, as a group, more statistically important than

others not selected.

22

Results and Discussion

Statistics are presented in Table 1 for the mUltiple regression

analysis from which a model of 1985 tall fescue leaf development-

environment relationships was obtained. The growth-rate equation, leaf

growth rate = 0.016 - (0.000248 x solar radiation) + (0.0150 x

precipitation 2-day lag) + (0.117 x ESM 3-day lag) + (0.00000879 x max.

air temp. x solar radiation) - (0.0000361 x max. air temp. x age) +

(0.00307 x min. air temp. x precipitation) - (0.000439 x precipitation x

age), is potentially useful in growth-calculation systems dependent on

the input of published weather data. Other variables measured (i.e.,

soil temperature) affected leaf growth but were not included in the

equation because they did not statistically increase correlation. To

facilitate its application, the growth-rate equation includes as few

variables as possible. This system of observation and analysis provided

a means for: (i) using computer search of many empirically selected

transformations of basic variables known to be important, such as

tempera~ure, soil moisture, and their potentially delayed effects over

several days and (ii) establishing useful mathematical values for daily

plant responses to environmental variables under natural conditions.

These transformations were selected by the analysis process because they

represent curvilinear effects and interactions among variables better

than do untransformed variables. The relationship between temperature

and the rate of corn development has been shown to be curvilinear, not

linear (51). A transformed cubic equation for leaf appearance rate as

related to ambient temperature was used by Tollenaar (51) in predicting

leaf appearance rate in fluctuating temperature environments.

23

Table 1. Analysis of variance, regression coefficients, and statisticsof fit for the dependent variable: tall fescue daily leaf-growth rate,Clemson, SC, 1985.

Source df MS F Probe >F

Regression 7 0.00720 25.59 0.0001 0.759Error 57 0.00028Total 64

Variables Partial regressioncoefficients

Probe >Itl

Intercept 0.016"

Solar radiation (langley) -2.48 x 10-4 0.0001

Precipitation (cm) 2-day lag 1.50 x 10-2 0.0005"ESMt 3-day lag 1.17 x 10-1 0.0001

Max. air temp.+ x solar radiation 8.79 x 10-6 0.0001

Max. air temp. § -3.61 x 10-5 0.0001x age

Min. air temp. x precipitation 3.07 x 10-3 0.0001

Precipitation x age -4.39 x 10-4 0.0036

tESM = Estimated soil moisture (%) Thornthwaite - Mather method.

+All temperatures in C.§Days from mowing.

24

Calculated values were compared,to actual data from which the leaf

growth-rate equation was derived (Fig. 2). As previous studies with

other crops have shown (15, 16, 17, 18), statistics of fit for the

analysis of tall fescue multiple regression when applied to data from

which the equation was based was significant (Table 1). However, this

significance of fit should not necessarily be considered as final proof

of validity for the growth-rate model. Since statistical prerequisites

for an analysis of this type can never be completely met (e.g.

intercorr~lation of independent variables such as temperature and soil

moisture), validity should be established by test on unrelated data (17,

28). Comparison of the spring 1984 tall fescue daily development with

that projected by the spring 1985 growth equation (Table 1) is shown

(Fig. 3). This relatively close relationship between 1984 actual growth

and projected growth using the 1985 model could provide researchers a

method with which to identify environmental parameters promoting or

limiting tall fescue development.

Improved growth-rate equations potentially could be developed by

measuring other growth-influencing variables such as nutrient supply and

by standardizing cultural practices for specific crops and planting

sites. In this experiment the excellent vigor of plants suggested that

nutrition was not limited. Developing season (e.g. winter vs. summer

and plant species) specific equations plus incorporating multiple year

data would also probably provide improved growth equations. The reason

for this would be an expanded account of environmental extremes not

encountered during this study.

Subsequent applications of this system of observation and analysis

will provide more universal application because of the greater range of

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27

environmental variables in mUltiple-year data. Improved equations then

may be utilized to anticipate the effects of specific environmental and

cultural practices (e.g. pestici4e and fertilizer application) on the

growth and development of tall fescue with greater mathematical

accuracy. Seed head formation may require adapting a method for

measuring growth other than the one used in this study. Physical

measurements, such as shoot length, are probably more suitable since

seed head formation does not involve the unrolling of the stalk tip.

This method enables researchers to detect changes in daily growth

and also to relate this to possible growth-limiting environmental

variables. Newman and Beard (38) stated that this type of periodic

observation could be very useful in crop research studies. Terminal

data, such as final harvest, may not adequately explain the progression

of seasonal plant development.