l~~77;7~ - michigan state universityarchive.lib.msu.edu/tic/thesdiss/mccarty1986a.pdf · clemson...
Post on 07-Jul-2020
0 Views
Preview:
TRANSCRIPT
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
Il) 0 Il) 0 Il) 0 Il)l"- ll) N 0 r-- ll) N- 0 0 0. . . . . . .0 0 0 0 0 0 0
...JLlJa:1.L. cue: C:z t-:::: a::a:t-~ (....II4Ja:>l4Jen,ca:>-)
25
~0
.f""l
0+J
CI) m;:l0-QJ
lI) ...c"r- +J
~0~
0 bOr-
I./')
co0'\
Il) ~<.0 :>..
..c
0"'0
<.0 QJ+Jmr-i
lI);:l
s CJ11) r-i:z: m-:z CJ
c0 :c "'0
II) ~:c mca:: ...cu,lI) +J
=' en ~>- 0a: ~s bOm0 +J
=' I.LI 4-1 m(.!) m "'0
a: QJr-i
lI) HQJ 0..
(I') ;:l <CJCI) 0QJ C""")
0 4-1(I')
r-ir-i
C'd ..cII) +J QJN Pr-I
I./')
co N0'\ N
0 ~N ~
r-i 0C'd;:l"'O
lI) +J QJCJ CI)
< m..c
0. -..
N ~0 QJ aJ00 ~r-i. ;:l..c0 . bO C'd
.f""l HPr-I'-'
0CD
11)r--
0,....
11)to
0(,C)
It) s1J) :z:-:::z:
cl:0It) 2::
Ca::l&..
-..--":-:.~ It) (f):t >-
~ a:e..c0 1LJ
CD :t C!)a:
-0 '"0CD 0 11)- E
(I')
0
~ C'U CD---'fJ LO 0
;:r .•.• ::J (!')
G--_______ - 0 CO
0 ~ (j)tI)
C'U c, ~ N
~ 00N
tI)-
26
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.
top related