AN ABSTRACT OF THE THESIS OF
A. A. L. Amerasinghe for the degree of Doctor of Philosophy in Horticulture
presented on July 9. 1992.
Title: Modeling Purple Nutsedge (Cypems mtundus L.) Growth: Preliminary
Evaluation of Light Intensity. Soil Moisture, and Herbicide Effects.
Abstract approved: ^ j ^. ~ ^.^^ , i--^ Garvin Crabtree
Purple nutsedge [Cypems rotundas L.) has been recognized as one of the most troublesome perennial weeds of agricultural lands in tropical and some temperate regions. This research sought to determine the effects of timing of herbicides, shading, and soil moisture on plant population growth and tuber production of purple nutsedge through field and greenhouse experiments. The results of these experiments were used to validate a purple nutsedge population matrix model constructed with observed and reported data. Purple nutsedge control options were evaluated with model simulations.
Glyphosate reduced shoot number, tuber number, and tuber viability of purple nutsedge, and the herbicide efficacy was higher when applied from 2 to 4 weeks after shoot emergence as compared to the first 2-week growth period. The more effective period for the herbicide coincided with the tuber initiation phase of purple nutsedge growth. Metolachlor caused only temporary suppression of purple nutsedsge. Sunlight intensity by 30%, 47%, 63%, and 90% caused in successively
greater reductions in shoot number, tuber number, leaf area, and total dry weight
of purple nutsedge. Shading decreased partitioning of plant biomass into tubers
and increased partitioning into leaves. These responses remained essentially the
same irrespective of timing of shading from early emergence through the first 4
weeks of plant growth. Depletion of available soil moisture from 25% to 75%
also reduced the number and dry weights of shoots and tubers produced.
However, proportional biomass allocation to shoots, leaves, and tubers and
relative growth and net assimilation rates remained unaltered with soil moisture
depletion, suggesting that purple nutsedge is fairly well adapted to low soil
moisture levels.
The importance of intraspecific competition on population regulation of
purple nutsedge was evident from model simulations. Model predictions of
maximum population size closely agreed with reported plant and tuber densities
of purple nutsedge. Model simulations of proportional changes in population size
of purple nutsedge, as influenced by soil moisture depletion or shading, also
closely followed the field results. Model simulations indicated that seasonal
application of herbicides resulting in 90% shoot kill will provide a successful level
of control and that herbicide efficiency will be higher when shoots are killed during the second to fourth week of the growing period than from earlier applications. However, model simulations showed that a better strategy than using a highly effective, short duration herbicide is to provide a moderate level of purple nutsedge control extending through the growing season. Modeling Purple Nutsedge (Cypeius rotundus L.) Growth: Preliminaiy Evaluation of Light Intensity, Soil Moisture and Herbicide Effects
by
A. A. L. Amerasinghe
A THESIS
submitted to
Oregon State University
in partial fulfillment of the requirements for the degree of
Doctor of Philosophy
Completed July 9, 1992
Commencement June 1993 APPROVED:
^ -— ' V_^ f L' f Professor of horticulture in charge of major
U LS ^ ^_ Head of department of Horticulture
\a.i i|im i i- n<»T>rty—^^_ Dean of Graduat^School
Date thesis is presented July 9. 1992
Typed by A. A. L. Amerasinghe ACKNOWLEDGEMENT
I wish to express sincere gratitude to my major professor Dr. Garvin
Crabtree for his continued interest, guidance, and encouragement throughout this study and for his constructive criticisms in preparing the thesis manuscript
I am grateful to the members of my graduate committee. Dr. Arnold P.
Appleby, Dr. Steven R. Radosevich, Dr. Jack Stang, and Dr. Frank Conklin for their advice, time and interest throughout my graduate studies. I extend my thanks to Mr. C. M. Ghersa and Dr. Mary Lynn Roush for their intereset and guidance in constuction of population matrix models.
My thanks are also to all those who helped me in various ways to complete my Held research at the Agricultural Research Station, Maha
Huppallama, Sri Lanka. A special word of thanks goes to my colleague, Koshala
Jayawardana, for her assistance in field observations.
Finally, my deepest and heartful appreciation to my wife Pemaseeli, daughter Anuradha, and son Hemantha, for their love, patience, and understanding throughout my study period.
The financial support provided by the Department of Agriculture, Sri
Lanka, through a USAID program is gratefully acknowledged. Note: Chapters 3, 4 and 5 of this thesis are presented in the manuscript format of Weed Science, the Journal of the Weed Science Society of America. TABLE OF CONTENTS Page
Chapter 1. INTRODUCTION 1
Chapter 2. LITERATURE REVIEW 5
I. Biology and Life Histoiy 5
A. Morphology 5 B. Growth and Development 7 C. Factors Affecting Tuber Production and Survival 10 D. Dormancy of Tubers and Apical Dominance 11
n. Interference with Crop Plants 12
HI. Control of Purple Nutsedge 14
A. Cultural Control 14 B. Mechanical Control 15 C. Biological Control 16 D. Chemical Control 16 1. Growth Regulators 16 2. Photosynthetic Inhibitors 18 3. Pigment Synthesis Inhibitors 19 4. Cell Membrane Disruptors 19 5. Shoot Inhibitors of Seedlings 20 6. Root/shoot Inhibitors 21 7. Herbicides Inhibiting Amino acid Synthesis 23 8. Miscellaneous Herbicides 27 E. Integrated Control 29
VLPlant Population Models 30
A. Difference Equation Models 33 B. Logistic Equation Models 34 C. Matrix Models 35 Chapter 3. HERBICIDAL CONTROL OF PURPLE NUTSEDGE (Cypenis mtundus) DURING ITS EARLY GROWTH STAGES 38
Introduction 39 Materials and Methods 41 Results and Discussion 44 Literature cited 65
Chapter 4. EFFECT OF SHADE AND MOISTURE ON GROWTH OF PURPLE NUTSEDGE (Qpems mtundus) 68
Introduction 69 Materials and Methods 71 Results and Discussion 76 Literature dted 98
Chapter 5. A PRELIMINARY POPULATION MODEL FOR PURPLE NUTSEDGE {Cypems mtundus) 104
Introduction 105 Primary Model Development 107 Results of the Primary Model 110 Secondaiy Model that Characterizes Asymptotic Growth . 112 Comparison of Secondaiy Model Output with Some Field Observations 115 Using the Seconadry Model for Weed Management 118 Literature dted 136
BIBLIOGRAPHY 140 LIST OF FIGURES
Figure Page Chapter 3
3.1 Rainfall at Maha Iluppallama, Sri Lanka, May 25, 1990 to September 23, 1990 61
3.2 Soil moisture content in untreated control plot for the Yala Season field study. May 25, 1990 to September 23, 1990 62
33 Rainfall at Maha Iluppallama, Sri Lanka, October 19, 1990 through February 19, 1991 63
3.4 Soil moisture content in untreated control plot for the Maha season field study, October 19, 1991 to February 19, 1991 64
Chapter 4
4.1 Number of purple nutsedge plants produced under different shade levels in the field 92
4.2 Total leaf area of nutsedge plants produced under different shade levels in the field 93
43 Leaf dry weights of purple nutsedge produced under different shade levels in the field 94
4.4 Number of purple nutsedge tubers produced under different shade levels in the field 95
4.5 Weight of puiple nutsedge tubers produced under different shade levels in the field 96
4.6 Total purple nutsedge plant dry weight produced under different shade levels in the field 97
Chapter 5
5.1 Diagrammatic model of growth and development of purple nutsedge populations 123 5.2 Column vector and the transition matrix of the diagrammatic model of purple nutsedge population growth 124
53 The five transition matrices constructed for the different development periods in the primary model for purple nutsedge 125
5.4 Simulation of purple nutsedge population growth without density dependent population regulation 126
5.5 Effect of plant density on shoot production from a single plant of purple nutsedge during three consecutive growth periods 127
5.6 Simulation of a purple nutsedge population with density dependent population regulation 128
5.7 Diagrammatic submodel from the purple nutsedge population model, including factors expected to influence shoot production from existing shoots 129
5.8 Population simulation of purple nutsedge at different light intensities 130
5.9 Simulated and observed (during the two growing seasons, Yala and Maha) relative suppression of purple nutsedge population growth at different shade levels 131
5.10 Simulation of population growth of purple nutsedge under different soil moisture depletion levels 132
5.11 Simulated and observed relative suppression of purple nutsedge population growth with different soil moisture depletion levels 133
5.12 Population simulation of purple nutsedge assuming that 90% of shoots were killed with a herbicide 134
5.13 Simulation of purple nutsedge populations under different management options 135 LIST OF TABLES
Table Page
Chapter 3
3.1 Effect of herbicides on nutsedge shoot number m"2, 1990 Yala season 52
3.2 Effect of herbicides on nutsedge shoot number m"2, 1990/91 Maha season 53
33 Effect of herbicides on nutsedge shoot dry weight m"2, 1990 Yala season 54
3.4 Effect of herbicides on nutsedge shoot dry weight m"2, 1990/91 Maha season 55
3.5 Effect of herbicides on nutsedge tuber number m'2, 1990 Yala season 56
3.6 Effect of herbicides on nutsedge tuber number m*2, 1990/91 Maha season 57
3.7 Effect of herbicides on nutsedge tuber diy weight m'2, 1990 Yala season 58
3.8 Effect of herbicides on nutsedge tuber diy weight m"2, 1990/91 Maha season 59
3.9 Effect of herbicides on purple nutsedge tuber sprouting after four months in Yala and one, three and five months in Maha Season 60
Chapter 4
4.1 Effect of shade on purple nutsedge specific leaf area (SLA), leaf area ratio (LAR) and leaf weight(LWR) during two growing seasons 85
4.2 Effect of shade on purple nutsedge on average dry weight per tuber and tuber weight ratio (TWR) during two growing seasons 86 43 Purple nutsedge growth responses as represented by plant number, tuber number and leaf area pot*1 in two growing seasons 87
4.4 Purple nutsedge growth response as represented by leaf dry weight, tuber dry weight and total dry weight pot in two growing seasons 88
4.5 Purple nutsedge growth responses as represented by leaf area ratio (LAR), specific leaf area (SLA), leaf weight ratio (LWR) and tuber weight ratio (TWR) in two growing seasons 89
4.6 Effect of soil moisture regimes on plant number, tuber number and leaf area of purple nutsedge pot"1 in two growing seasons 90
4.7 Effect of soil moisture regimes on leaf dry weight, tuber dry weight and total plant dry weight of purple nutsedge pot"1 in two growing seasons 91
Chapter 5
5.1 Sensitivity values derived from purple nutsedge transition parameters resulting from a 10% decrease in each transition parameter in five projection matrices of the primary purple nutsedge model after a ten-season simulation time 121
52 Effect of timing of glyphosate application on purple nutsedge shoot number m"2 in the two growing seasons, Yala and Maha 122 MODELLING PURPLE NUTSEDGE GROWTH: PRELIMINARY
EVALUATION OF UGHT INTENSnY, SOIL MOISTURE AND
HERBICIDE EFFECTS
Chapter 1
INTRODUCTION
Purple nutsedge (Cypems mtondus) is considered the world's worst
weed (Holm et al., 1977). A single plant produces a number of shoots connected
by an underground network of bulbs, rhizomes, and tubers. Since these plants,
including the tubers, are not cold hardy (Stoller, 1973), its distribution is mainly
restricted to tropical and subtropical regions. Although of relatively short stature
(Holm et al., 1977), 10 to 80 cm, its aggressive growth over a range of soil and
climatic conditions enables purple nutsedge to compete with more crops in more countries of the world than any other weed (Bendixen and Nandihalli, 1987).
Agricultural practices of reduced tillage, increased cropping frequency, and the control of annual weeds with herbicides have dramatically increased purple nutsedge populations in both irrigated and rain-fed agriculture (Bendixen and
Stroube, 1977; Hauser, 1963b; IRRI, 1974; IRRI, 1979; Parker, 1985).
Since tubers play a central role in the establishment and proliferation of purple nutsedge, efforts to develop rational control methods should be based on means of inhibiting tuber sprouting and/or new tuber formation.
Perhaps the earliest attempt to chemically control purple nutsedge 2 was in 1925 by Ranade and Bums (1925). Since then, herbicides from several
chemical families have been tested but most have provided only poor or marginal
control. The reasons for failure includes marginal translocation of herbicides to
the site of action, only temporary inhibition of sprouting, and lack of control of
new tuber formation (Pereira et al., 1987). Herbicides that have given some level
of control were those that translocated to basal bulbs and tubers (Chase and
Appleby, 1979b; Hauser, 1963a; Zandstra et al., 1974). Evidence indicates that
the effectiveness of postemergent herbicide treatments is dependent on proper
timing of the herbicide application relative to purple nutsedge growth stages
(Pereira et al., 1987), but few studies include an evaluation of herbicide application timing relative to purple nutsedge growth stages (Gosette at al., 1975;
Hauser, 1963a; Suwunnamek and Parker, 1975; Zandstra et al., 1974). The results of these investigations are inconsistent and do not provide adequate information to understand the effects of herbicides on production and subsequent survival of tubers.
In addition to herbicides, shade (Bantillan et al., 1974; Jorden-
Molero and Stoller, 1978; Patterson, 1982; Shetty et al., 1982) and soil moisture
(Bhardwaj and Verma, 1968; Davis, 1942) have a profound influence on purple nutsedge growth and tuber production. As developing crop canopies provide different degrees of shading (Keely and Thullan, 1978; William and Warren,
1975a), adaption of crops with well developed canopies is a viable alternative or a supplementary option to chemical control of purple nutsedge tuber production. 3 Since the effect of shading on tuber production is modified by temperature
(Wills, 1975b), the precise level of purple nutsedge control by shading cannot easily be predicted without appropriate field experimentation. Evidence of the influence of soil moisture on production of tubers also suggests that the reported results are inadequate to make direct practical application of soil moisture research findings.
Identification of crucial stages of a plant's life cycle is a central theme in developing strategic control methods for weeds, and plant population models can be conveniently used to identify those crucial stages that regulate the proliferation and persistence of weeds (McMahaon and Mortimar, 1980;
Mortimar et al.,1978; Mortimar et al.,1980). Moreover, model simulations are useful in evaluating different control options and for predicting long-term behavior of populations subjected to different control practices.
Field and greenhouse experiments were conducted at the
Agricultural Research Station, Maha fluppallama, Sri Lanka, to investigate the effect of herbicides, artificial shading, and soil moisture on growth and tuber production of purple nutsedge. A simple population model for purple nutsedge was developed and model simulations were performed to evaluate effects of light intensity, soil moisture, and herbicides and to compare several control options.
Biology, life cycle, tuber formation, and action of different herbicides on purple nutsedge is reviewed in Chapter 2. The influence of the herbicides glyphosate and metolachlor on growth and tuber production of purple 4 nutsedge, when applied at its early growth stages under field conditions, is discussed in Chapter 3. Effects of soil moisture on shoot growth, tuber production, total plant dry weight, resource allocation, and relative growth and assimilation rates are discussed in Chapter 4. The same chapter includes the discussion of the effect of shade on purple nutsedge growth and tuber production under field conditions. Chapter 5 includes the development of a matrix population model for purple nutsedge. Comparison of model simulations with field results and evaluation of several control options are discussed. Chapter 2
LITERATURE REVIEW
Cypems wtundus L, a perennial sedge commonly called purple nutsedge, is considered as one of the world's worst weeds (Holm et al., 1977).
Although it is native to the tropics, it has spread into subtropical regions as well.
It grows in nearly every soil type, relative humidity and soil organic matter level known to agriculture (Doll, 1986a). It has an extensive underground system of roots, rhizomes and tubers. As the tubers can remain quiescent, the plant can survive unfavorable conditions of heat, drought, or flooding. Purple nutsedge competes well with crop plants for nutrients, water and, in early stages, for light
(Rochecouste, 1956; William and Warren, 1975a). It has been shown to have allelopathic effects on seed germination and growth of some crop plants
(Friedman and Horowitz, 1971).
L Biology and Life History
A. Morphology
Purple nutsedge is a grass-like herb with unjointed triangular solid stems and three ranks of leaves with closed sheaths and without ligules. It possesses an extensive subterranean network of rhizomes and tubers. Young rhizomes are white, fleshy and covered with scaly leaves. A clearly defined cortex encloses the vascular cylinder containing vascular bundles. These central vascular bundles are continuous throughout the underground network of rhizomes, tubers and parent plant (Mercado, 1979; Wills and Brisco, 1970).
Tubers are produced in the meristematic region behind the leaf
primordia at the rhizome apex. The internode ceases to elongate and the
parenchymatous cells outside of the endodermis enlarge and accumulate much
starch. The leaf primordia remain dormant Buds in the swollen area remain
dormant or extend to produce new rhizomes (Wills and Brisco, 1970). Young
tubers are white, fleshy and covered with scaly leaves and when mature, they are
found as chains of tubers, interconnected by rhizomes. The swelling of the rhizome which bears the aerial shoot is called the basal bulb. It develops from the meristemetic region at the rhizome apex similar to tuber formation, except that leaf primordia develop into a shoot Part of the meristematic area of the basal bulb produces new rhizomes which after elongating for varying distances may develop into either more basal bulbs or tubers (Hauser, 1962).
Leaves are dark green, have a distinct midrib and are 2.5 to 7.5 mm wide. Their lengths vary from 5 to 20 cm (Holm et al., 1977). The leaf vascular bundles lead through the basal bulb and into the rhizome system. This interconnected aerial and subterranean vascular system remains intact throughout the growing season. The upper leaf surface has a vaxy cuticle and no stomates.
The lower surface is thinly cutinized and has many rows of parallel stomates
(Holm et al., 1977). The leaf anatomy is of the Kranz type with two layers of bundle sheath cells (Mercado, 1979).
The inflorescence is a reddish brown to purplish compound spike. It 7 is borne on a solid, leafless, nodeless, triangular stalk that emerges from a basal bulb. Usually the ovary is sessile. The dark brown three sided seeds (strictly speaking, firuits) have a thick, hard pericarp. Despite heavy flowering, purple nutsedge produces few seeds, most of which are often not viable (Holm et al.,
1977; Smith and Fick, 1937).
B. Growth and Development
Most purple nutsedge plants in the field are derived from tubers. A tuber may have up to 12 buds, each capable of producing an aerial shoot Tubers exhibit a dormancy when they are interconnected with rhizomes. Within the subterranean network of purple nutsedge, sprouting of the dormant tubers is regulated by apical dominance. Segmenting the chain of tubers removes the apical dominance (Smith and Fick, 1937) and when temperature and moisture conditions are favorable, the single tubers thus separated readily sprout and form aerial shoots (Horowitz, 1972).
The sprout is actually a rhizome which gives rise to a new shoot of rosette leaves. Usually one or two rhizomes grow to the soil surface from a single tuber. A basal bulb develops at the junction of the rhizome and leaves below the ground surface. Usually the basal bulb forms near the ground surface, but may be as deep as 20 cm (Hauser, 1962b). The apices of rhizomes produced from the nodes of the basal bulb are transformed either into tubers or new shoots. Usually rhizomes grow 1-30 cm horizontally before the tip turns upward to form a new aerial shoot with another basal bulb. Basal bulbs give rise to more rhizomes which 8 after elongating for varying distances, terminate in more basal bulbs. These bulbs
in turn repeat the cycle, leading to the development of a complex of
interconnected shoots, roots and bulbs (Hauser, 1962a).
Tuber production usually commences within 6-8 weeks after
sprouting (Hauser, 1962). Muzik and Cruzado (1953) divided the process of tuber
production into four stages;
1. elongation of the rhizome from a bud
2. cessation of longitudinal growth
3. formation of tuber, recognized by a swelling 2-3 cm back from the apex
4. renewed growth of the rhizome.
Successive repetition of these four stages leads to a chain of tubers.
Subterranean organs produced by the parent tuber spread centrifugaUy around the parent plant and 60-70% of the tubers are in soil depths of 0-20 cm. No tubers have been observed below 40 cm (Horowitz, 1972), though the roots of puiple nutsedge can penetrate to a depth of 1 m (Andrews, 1940). There has been considerable variation among reports in the time of dormancy development in tubers. Hauser (1962a) observed dormant tuber production only after 18 weeks from time of sprouting while Sierra (1969) indicated that only 7 weeks was required to produce dormant tubers. This discrepancy can be attributed to different environmental conditions for each study.
The growth pattern of purple nutsedge is largely influenced by environmental factors. In heavy soils, growth ceases when moisture content falls 9 below 20% (3). Rochecouste (1956) found that plant growth and tuber production was greater in the humid zone (1250 mm to 2500 mm annual rainfall) when compared to sub humid (less than 1250 mm annual rainfall) and super humid
(over 2500 mm annual rainfall) zones. By increasing the soil moisture level from 9 to 15% Davies (1942) obtained a 150% increase in tuber growth and 259% increase in aerial growth of purple nutsedge. Although nutsedge grows better at higher levels of soil moisture, it is highly adapted to low moisture conditions.
The development pattern of purple nutsedge varies according to climatic conditions. Seasonal development is apparent in areas with extreme weather conditions. Bhardwaj and Verma (1968) observed that shoot growth was rapid during August to September in India by which time mean temperature ranged from 28C to 31C and the soil moisture was always near field capacity. A close relationship between temperature and regrowth behavior in purple nutsedge was reported by Horowitz (1972). He observed rapid growth during the warm season and senescence of the aerial shoots during the cool season.
Purple nutsedge is sensitive to shade (Bantillan et al., 1974;
Patterson, 1982; Sierra, 1969; William et al., 1977). Under shady conditions, it produces fewer and thinner leaves. Bantillan et al. (1974) observed that reduction in light available to the parent plant can cause drastic reduction in the vegetative growth. Patterson (1982) reported that growing nutsedge in 40, 70, and 85% shade for 62 days reduced total dry weight by 51, 67 and 84% respectively. This sensitivity to shading is an important factor to be considered in managing purple 10 nutsedge growing in annual crops. The use of a rapidly growing crop which forms a canopy early would render purple nutsedge less competitive.
Maximum growth of purple nutsedge is found in a medium day length of around 12 hrs, while longer or shorter day lengths tend to reduce its biomass (William, 1978).
The plant appears to have a wide tolerance to pH with optimal growth observed at pHs between 3.5 and 7 (Al-Ali et al., 1978).
C. Factors Affecting Tuber Production and Survival
Tuber production is most intensive between 26 to 31 C (Bhardwaj and Verma, 1968) while there is no production below 20 C (Horowitz, 1972).
Tuber formation approximates one tuber per plant for the first 90 to 140 days
(Doll, 1986a) with variations resulting from differences in light and photoperiod.
Short photoperiod and short day lengths favor tuber production (Berger and Day,
1967) with an optimum photoperiod of approximately 12 hrs (William, 1978).
However, under tropical conditions, where there is very little variation in day length throughout the year, effect of day length may have little significance.
Reduction in light available to the plant reduces tuber production. Bantillan et al.
(1974) observed that tuber production in 50 and 20 per cent of available light was reduced by 25 and 90% respectively. Thus adoption of fast growing crops can be an effective component of a system for the management of purple nutsedge by reducing its tuber production. Patterson (1982) reported that production of fewer number of tubers under shade was due to a decrease in partitioning of plant 11 biomass into tubers.
Sprouting of tubers occurs between temperatures of 10 to 45 C with
30 to 35 C being the optimum (Bhardwaj and Verma, 1968). Continuous submergence of tubers completely checks their sprouting ability (Raju and Reddy,
1990) but does not cause them to lose their viability, even after 200 days under water (Ueki, 1969). This suggests that inclusion of lowland rice in an upland cropping rotation does not guarantee a reduced purple nutsedge population in the upland crops.
Purple nutsedge shows a plastic response to increasing density. With low plant densities more dry weight is partitioned into inflorescence than into tubers, while the converse becomes true at higher densities (William et al., 1977).
Moisture content appears to be important in maintaining the viability of puiple nutsedge tubers (Andrews, 1940; Raju and Reddy, 1990). Baker
(1964) observed that tubers kept at 3% moisture for 15 days lost their viability.
Prolonged exposure to the sun can be a good means of killing tubers. Rao and
Nagarajan (1963) showed that sun drying for 12 hrs reducing moisture content from 50% to 36% and decreased sprouting from 100% to 79%. They found a high correlation between moisture content and sprouting.
D. Dormancy of Tubers and Apical Dominance
Most purple nutsedge tubers remain dormant as long as they are connected to the mother plant Dormancy, however, is lost once the tuber is severed from the chain. Hence, cultivation followed by favorable conditions will 12 cause an increase in the number of emerging shoots. Puiple nutsedge shows two forms of apical dominance; apical dominance exercised by the terminal tuber over the other tubers in the chain and apical dominance exercised by the apical bud of a tuber over the other buds (Mercado, 1979). The degree of apical dominance is influenced by age of the tuber chain. Zandstra and Nishimoto (1977) found young tuber chains to be less dormant than older chains. Berger (1966) reported that foliage extracts of puiple nutsedge contained tuber sprouting inhibitors and that salicylic add was the most active inhibitor in the extracts. However, salicylic acid has not been found in dormant tubers (Jangaard et al., 1971). Although several authors have suggested the presence of growth inhibitors in dormant tubers
(Palmer and Porter, 1959; Teo et al., 1973), the exact mechanism involved in dormancy of tubers has not been well established.
IL Interference with Crop Plants
Purple nutsedge is well suited to thrive under varied agroclimatic conditions. Its relative size, when compared with crops, does not reflect the potential to reduce crop yields. It competes well with all the major agricultural crops for nutrients, water and during early growth stages for light (Holm et al.,
1977). In humid zones, it can mobilize and store 815 kg of ammonium sulphate,
320 kg of potash and 200 kg of super phosphate per hectare (Rochecouste, 1956).
Bhardwaj and Verma (1968) reported that purple nutsedge removed 94.6 kg of N,
11.6 kg of P205 and 96.4 kg of K2O per ha and more than 50% of these nutrients 13 were stored in the tubers.
Rice is one of the major crops that suffers from serious competition with purple nutsedge. Okafor and De Datta (1976b) and Kwesi and De Datta
(1989) found that purple nutsedge competes strongly with rice for nutrients, specifically nitrogen. While annual weeds can reduce rice yields by 67%, purple nutsedge alone can reduce the yields by 51% (Okafor and De Datta, 1974).
The critical period of competition between purple nutsedge and crops can vary. Usually less competitive crops have a relatively longer critical period with heavy yield losses than fast growing crop species. William & Warren
(1975a) in a two year study estimated that purple nutsedge could reduce the yields by 89% in garlic, 66% in okra, 39 to 50% in cucumber, 35% in cabbage and 35% in tomatoes. The critical period for slow growing crops may vaiy from 3 to 13 weeks while this may last for only 3 to 5 weeks for fast growing species.
Exudates of purple nutsedge can inhibit germination and growth of some crop plants and the suppressive effects may persist in soil for 1-3 months
(Horowitz and Freedman, 1971). It has been suggested that these suppressive effects are caused by phenolic compounds present in exudates.
Most of the studies conducted on interference of purple nutsedge with crops do not address the issues of why, when and under what conditions, specific interactions occur. Therefore a knowledge of the effects of various crop management practices on growth and development of purple nutsedge is important for shifting the competitive advantage in favor of the crop. 14 IIL Control of Purple Nutsedge
Control of purple nutsedge has received much attention over the past several decades. This includes cultural, mechanical, chemical, biological and integrated methods.
A. Cultural Control
While purple nutsedge is an extremely noxious weed in most crops, it is susceptible to competition from other plant species because it rarely grows taller than 40 cm (Holm et al., 1977). It's growth is considerably reduced under shade. Ranade and Bums (1925) reported that Indian farmers occasionally allowed native vegetation to become re-established in fields that were heavily infested with purple nutsedge. After several years only a few tubers are present and crops can again be planted. Cultural practices which result in the rapid development of a cover during the growing season would greatly suppress purple nutsedge. Honess (1960) found that radish competed better than finger millet against purple nutsedge because the broadleaf radish shaded the weed. Studies conducted at the International Rice Research Institute (1974) revealed that changing from a low leaf area index to high leaf area index cropping pattern considerably reduced the population of purple nutsedge. Thus investigation of fast growing crops, using narrower row spacings and planting at higher crop densities may be productive in reducing the competitive growth of purple nutsedge. Such investigations are important in developing cropping patterns with the goal of managing purple nutsedge on the long term basis. 15 Maintaining a continuous level of standing water reduces the growth of nutsedge in lowland rice (1986b).
Ranade and Bums (1925) studied several methods of artificially covering the soil to control purple nutsedge. They found that bamboo matting and paper mulches were ineffective in reducing tuber populations whereas a grass or cotton stubble mulch was effective (but not feasible) if more than 10 cm thick.
B. Mechanical Control
Deep plowing to a depth of 30-40cm followed by several harrowings during the dry season resulted in 80 to 90% control of tubers (Honess, 1960;
Ranade and Burns, 1925). Andrews (1940) developed a horizontal blade that separates the basal bulbs from the roots and tubers which supply water and nutrients from deeper in the soil. This method has given a 99% level of tuber control (Pothecary and Thomas, 1968). Patel (1962) reported good control of purple nutsedge in India when the soil was plowed 15 to 23 cm deep in the summer. Deep summer plowing and repeated tillage at 3 to 4 week intervals has been reported by several workers for control of purple nutsedge (Day and Russel,
1955; Sinha and Thakur, 1967). Since separation of tubers from their chain may break the apical dominance exercised by the sprouted shoot, cultivation can often increase the level of infestation. Rao and Nagarajan (1963) reported that the population in cultivated areas was more than twice the population of purple nutsedge in uncultivated areas. However, Baker (1967) reported eradication of purple nutsedge after fallow cultivation for 3 years. Yet this approach would not 16 be acceptable in areas where land for arable cropping is limited.
C. Biological Control
Three moths Bactm vemtana Zeller, Rminima Meyric and
Rvenosana Zeller and a weevil Athesapeuta cyperi Marshall have been studied in detail to explore their potential to control purple nutsedge. Despite their adequate host plant specificity, none have proved effective in controlling the weed (Phatak et al., 1987).
D. Chemical Control
Earliest studies on chemical control have been reported by Ranade
& Burns (1925) in which they tested common salt and copper sulphate. Since the advent of selective herbicides in the 1940s, numerous investigations have been made to evaluate the potential of herbicides to suppress purple nutsedge. Pereira et al. (1987) have recently reviewed these works with results being reported as based on the mode of action of various herbicides.
1. Growth Regulators
A widely investigated growth regulator type herbicide is 2,4-D. It has given inconsistent and often unsatisfactory control of purple nutsedge. The degree of control from a single application can vary from 50% to 90% (Burgis, 1969;
Wifret and Burgis, 1976) while repeated application may provide better control
(Hammerton, 1971; Hauser, 1963b; Standifer, 1974). However, repeated applications do not always ensure better control than a single application (Wilfret 17 and Burgis, 1976). Control of purple nutsedge by 2,4-D is temporary (Hammerton,
1974; Parker et al., 1969; Ray and Wilcox, 1969). It is primarily effective in killing
top growth (Hamilton, 1971; Hammerton, 1975) with occasional temporary
reduction of tubers (Hammerton, 1974; Hammerton, 1975). 2,4-D, which is
applied when purple nutsedge is actively growing, translocates to active growth
sites by passing through the tubers (Doll, 1986b). Consequently, it has no major
effect on developed tubers (Eames, 1949; Hammerton, 1975). Efficacy of 2,4-D on purple nutsedge could be influenced by climatic factors. Application during the rainy season provides better control than application during the dry season (Sinha
and Thakur, 1970).
Addition of surfactants (Pembroke, 1970) and undiluted isoparaffinic oil carriers (Burr and Warren, 1972) to 2,4-D may improve the level of control.
Isoparaffinic oil carriers help to increase both rate and quantity of 2,4-D penetration into purple nutsedge. Application of 2,4-D followed by paraquat improves the shoot control markedly and this could be further enhanced by cultivation after the application of herbicides (Hammerton, 1971). Two to four plowings followed by an application of a mixture of 2,4-D and dalapon provides better nutsedge control including tubers (Gopal and Mani, 1968).
MCPA may also provide temporary control (Parker et al., 1969) while 2,4,5-T and 2,3,6-TBA are less effective than MCPA on purple nutsedge
(Singh et al., 1968). 18 2. Photosynthetic Inhibitors
Several herbicides in the photosynthesis inhibitor group have been
tested on purple nutsedge. Bromacil and terbadl, either soil incorporated or foliar
applied at 33 kg ha"1 have been reported as effective for purple nutsedge control
in citrus orchards (Leydon, 1967). Terbacil is more effective when applied to 4 to
6 weeks old plants than when applied to more mature purple nutsedge (Ray and
Wilcox, 1969). Tubers sprouting after terbacil application grow to the 3 to 4 leaf
stage and then become chlorotic and die. The herbicide is translocated in the
plant and appears to be absorbed through the root system (Ray and Wilcox, 1969;
Waters and Burgis, 1968). Terbacil gives some control of purple nutsedge for up
to 10 to 12 months but does not give total control (Ray and Wilcox, 1969; Waters
and Burgis, 1968). This is due to the presence in the treated soil of unsprouted
tubers which will sprout once the herbicide level in the soil is reduced (Waters
and Burgis, 1968). Terbacil application after the induction of tuber sprouting with
growth regulators has shown greater promise for killing dormant tubers, eventually
leading to better control of purple nutsedge (Parker and Dean, 1972). However
this approach has received little attention in the field.
Although bromacil at 2 lbs ac"1 gives efficient control under glass
house conditions (Parker and Dean, 1972), substantially higher rates, from 4 to 8 lbs ac , and a 5-inch rain fall within 30 days after herbicide application are required for excellent control under field conditions (Escoro, 1969). One major concern with higher rates is the toxicity to crop plants. 19 Atrazine and simazine give erratic control of purple nutsedge
(Balasubramanian and Sundararaj, 1968; Horowitz, 1965). They are only moderately effective and high rates are needed for a satisfactory level of control
(Balasubramanian and Sundararaj, 1968; Parker et al., 1969).
Diuron provides only temporary control of purple nutsedge
(Balasubramanian and Sundararaj, 1968; Whitehead, 1965).
3. Pigment Synthesis Inhibitors
Two foliar applications of amitrol during the first 4 weeks after emergence of purple nutsedge give excellent control of top growth with a 90% control of tubers (Hauser, 1963a). This success could be due to the ability of amitrol to translocate readily and accumulate at the growing points of purple nutsedge (Anderson, 1958). Application more than 6 weeks after shoot emergence is not effective (Hauser, 1963a). Despite repeated applications, amitrol fails to completely control purple nutsedge (Hamilton, 1971) with the reason being that most of the tubers are not affected by amitrol (Ray and Wilcox, 1969).
Fluridone and norflurazon have been reported as successfully controlling purple nutsedge in cotton (Parker, 1981).
4. Cell Membrane Disrupters
Control of purple nutsedge with paraquat has been inconsistent
(Hammerton, 1974; Homg and Leu, 1979; Standifer, 1974; Suwunnamek and
Roanowski, 1967). Repeated application would reduce, but not eliminate purple 20 nutsedge (Hamilton, 1971; Standifer, 1974). Paraquat causes rapid desiccation of foliage followed by rapid killing of young shoots (Parker et al., 1969). Yet all of these effects are only temporary and purple nutsedge shows a rapid recovery of shoot growth from tubers (Hammerton, 1974; Standifer, 1974) as paraquat has no effect on either tubers or basal bulbs (Teo et al., 1973). Killing aerial parts of purple nutsedge by paraquat stimulates dormant tubers to produce new shoots
(Hammerton, 1974).
Repeated application of dinoseb has given temporary control of purple nutsedge (Standifer, 1974). Nitrofen has given selective control in carrots
(William and Warren, 1975b) and application at night appeared to be more effective than daytime application. In no case did the herbicide provide complete control.
5. Shoot Inhibitors of Seedlings
EPTC is a herbicide that has been widely reported for purple nutsedge control. It kills nutsedge shoots and inhibits emergence for a long period
(Balasubramanian and Sundararaj, 1968; Ray and Wilcox, 1969; William et al.,
1976). Repeated application is better than a single application for extended suppression of tuber sprouting (Hammerton, 1975; Holt et al., 1962; Parker et al.,
1969). Even with repeated applications the herbicide provides only temporary or seasonal control (Parker et al., 1969; Ray and Wilcox, 1969). Since EPTC is highly volatile, it has to be incorporated into the top layers of soil soon after application
(Doll, 1986b). Incorporation into dry soil provides a long residual effect when 21 compared to application to moist soils (William et al., 1976). Poor control of tubers with EPTC is due to a lack of translocation into tubers (Holt et al., 1962).
EPTC vapor does not penetrate into meristemetic regions of tubers and, as a result, tubers can sprout irrespective of the rate of application. However, EPTC stops cell division and subsequent cell elongation of shoots of the sprouted tubers
(Holt et al., 1962; Rincon and Warren, 1978). Hence placement of EPTC in soil around or just above the tubers gives better control than when placed below the tubers (Rincon and Warren, 1978). Prolonged control of purple nutsedge with
EPTC depends on its persistence in the soil (Parker et al., 1969). Soil persistence is affected by volatility, leaching and microbial degradation (Gray and Weierich,
1985).
Like EPTC, butylate and vemolate, when soil incorporated, also provide temporary control of nutsedge (Hammerton, 1975; Rincon and Warren,
1978).
6. Root/Shoot Inhibitors
Alachlor can delay purple nutsedge emergence for about 8 weeks
(Keely et al., 1972) and its effect is much more pronounced when incorporated around the tubers (Rincon and Warren, 1978). Since herbicide activity rapidly decreases in soil, its suppressive effect prevails only for a short duration (Wills,
1976).
Metalochlor is relatively ineffective in controlling purple nutsedge since it is poorly translocated inside the plant (Obrigawitch et al., 1980). 22 Napropamide was reported to be very effective on purple nutsedge
(Keely et al., 1972; Mercado et al., 1974; Ray and Wilcox, 1969) The primary basis of control is an inhibition of root development and shoot growth, preventing further tuber production (Mercado et al., 1974). Conflicting results have been reported on the degree of soil persistence and the control of purple nutsedge by this herbicide. Keely et al. (1972) observed control for 56 days while Mercado et al. (1974) reported inhibition of tuber emergence and tuber production for 90 days. Parker et al. (1969) obtained 60% reduction in vigor for 4 weeks.
Conversely, Rincon & Warren (1979) observed no effect even with application of
4 to 8 kg ha"1. These variable effects of the herbicide on purple nutsedge may be associated with soil moisture variations as napropamide is effective on nutsedge only when there are high soil moisture conditions (Wilfret and Burgis, 1976).
Lower rates of dichlobenil provided fair control while higher rates controlled purple nutsedge for one year (Ray and Wilcox, 1969). However, the use of higher rates has little practical importance as it may pose phytotoxicity and residue problems for most crops. Because of its high volatility, it needs soil incorporation. Even with incorporation it is not persistent enough under tropical conditions to exert a prolonged effect (Parker et al., 1969). Increased concentration of the herbicide causes a progressive inhibition of purple nutsedge sprouting. This response does not appears to be a function of chemical influence on dormant tubers, but rather the result of the chemical's effect at the inception of tuber bud growth (Hardcastle and Wilkinson, 1968). Diclobenil can move 23 basipetaly and acropetaly, accumulating in tubers and leaves. It causes destruction of phloem in the basal portion of the leaf sheath (Akobundu et al., 1971).
7. Herbicides Inhibiting Amino Add Synthesis
Glyphosate has been widely reported as successful in controlling purple nutsedge under various conditions (Andrews et al., 1974; Gosette et al.,
1975; Horng and Leu, 1979; Magambo and Teny, 1973; Standifer, 1980; Terry,
1974; Zandstra et al., 1974). Glyphosate is foliar applied and moves from the foliar to the underground organs. It inhibits the shikimate pathway, interfering with the enzyme EPSP synthase, and preventing the formation of the aromatic amino acids phenyl alanine, tyrosine and tiyptophan (Duke, 1990; LaRossa and
Falco, 1984). Glyphosate, which translocates into underground parts of the plant, moves through mature tubers to the newly forming tubers at the rhizome apex.
There is a steady increase in translocation up to 4 days after application
(Zandstra and Nishimoto, 1977). While glyphosate shows rapid movement in young plants, it moves less rapidly and differentially in the underground parts of older plants. There is no evidence of glyphosate metabolism in purple nutsedge leaves or in tubers (Zandstra and Nishimoto, 1977). Repeated application has resulted in a greater reduction of nutsedge stands, including the tuber population
(Standifer, 1980; Wilfret and Burgis, 1976; Zandstra et al., 1974). Though glyphosate reduces the sprouting of tubers (Hammerton, 1975), complete eradication has not been observed, particularly under field conditions (Doll and
Piedrahita, 1982; Toth and Smith, 1979; Wills, 1975a; Zandstra et al., 1974). 24 Glyphosate kills only the foliage and the young tubers of purple nutsedge, leaving the dormant tubers unaffected (Doll and Piedrahita, 1982). Cools and Locassio
(1977) observed poor control of purple nutsedge in spring application when compared with either a fall or summer application. This difference was attributed to the presence of a large number of dormant tubers in the spring.
Conflicting results have been reported on the efficacy of glyphosate relative to the time of application. Suwannamek and Parker (1975) observed that the activity was greater when glyphosate was applied 3 weeks after emergence as compared to 9 weeks. In contrast, Gosette et al. (1975) found that it was more effective when applied 7 weeks after foliar emergence while Zandstra &
Nishimoto (1977) observed their best results 12 weeks after emergence. These observations suggests that optimum time for glyphosate application is not closely related to the chronological age of the plant It has been suggested that the best time for application is when most of the tubers in the soil have sprouted and new tubers produced are connected to healthy foliage (Zandstra and Nishimoto, 1977).
Many researchers have reported that glyphosate activity is affected by environmental factors. Application during the dry season provided less control than in the wet season (Chase and Appleby, 1979a) and the reason was attributed to differences in moisture relationships within the purple nutsedge plant during the two seasons. A three-fold increase in translocation into the underground parts of purple nutsedge occurred at 90% as compared to 50% relative humidity, and twice as much translocation occurs at -2 bars as compared to -11 bars of soil 25 water potential (Chase and Appleby, 1979b). Reduced effects of glyphosate due to
high soil moisture stress has also been reported by Moosavi- Nia and Dore
(1979a). Glyphosate is more toxic to purple nutsedge at 25 C than at 35 C and
this effect is influenced by relative humidity and soil moisture regime (Wills,
1974). In general, higher temperature and lower relative humidity result in
reduced activity of glyphosate.
Effectiveness of glyphosate is enhanced by shading (Moosavi-Nia
and Dore, 1979b). Also glyphosate application followed by tillage provides a
greater degree of control of purple nutsedge than the herbicide application alone.
A period of 3 days after application is sufficient for maximum translocation of the
herbicide to the tubers (Chase and Appleby, 1979a). Response of tubers to
glyphosate appears to be rate dependent Translocation of a phytotoxic dose was
achieved within 72 hours at 1 kg ha"1 application rate whereas 36 hours was
sufficient at 2 kg ha*1 (Doll and Piedrahita, 1982).
Glyphosate has been applied to purple nutsedge in combination with
a range of other herbicides and non herbicidal additives. Ammonium sulphate has given a strong activating effect for some researchers (Suwunnamek and Parker,
1975; Wills and Mcwhorter, 1985). In contrast Cools and Locassio (1977) observed no such enhancement with the same additive. Generally photosynthetic inhibitors such as diuron, atrazine and terbacil are antagonistic when used with glyphosate
(Suwunnamek and Parker, 1975). This antagonism is more pronounced at threshold levels of glyphosate and can be overcome by increasing the glyphosate 26 rate (Appleby and Somabhi, 1978). This antagonistic effect is primarily due to the physical bonding of glyphosate that occurs within the spray solution (Appleby and
Somabhi, 1978).
Monovalent cations of NH4, K and Na have been reported to enhance the toxicity of glyphosate applied to purple nutsedge while salts of divalent and tri valent cations of Zn and Fe reduced its activity (Wills and
14 Mcwhorter, 1985). Studies with C glyphosate showed that NH4C1 increased the translocation while FeClj increased the retention of glyphosate in the tissues at point of application.
Imazaquin has been reported as being effective on purple nutsedge.
It is absorbed by nutsedge propagules through shoots, roots and rhizomes and moves both basipitaly and acropetaly (Nandihalli and Bendixon, 1988). It inhibits acetolactate synthase(ALS), thus preventing formation of the amino acids valine, leucine and isoleucine (Shaner et al., 1984). Rate of application of the herbicide is relatively low when compared with other herbicides. Lower rates reduce nutsedge shoot growth while higher rates check the development of tubers and rhizomes
(Nandihalli and Bendixon, 1988). Placement of the herbicide above the tubers gives more effective control. Imazaquin has given successful purple nutsedge control in turf grass and its activity was enhanced by addition of MSMA (Coats,
1985; Coats et al., 1987). Addition of bentazon to imazaquin results in an antagonism. 27 8. Miscellaneous Herbicides
Several arsenicals, AMA (ammonium methane arsenate), DSMA
(disodium monomethyl arsenate) and MSMA( monosodiun methyl arsenate) have been reported as effective for nutsedge control (Burleson, 1971; Everest and
Patterson, 1988; Hamilton, 1971; Hammerton, 1975; Holt et al., 1967). In some studies repeated application of MSMA was effective in accomplishing complete control (Hamilton, 1971), including top growth and tuber germination
(Hammerton, 1975). However, in other trials repeated applications have resulted in only a considerable reduction, but not a total elimination of tubers
(Suwunnamek and Romanowski, Jr., 1967; Zandstra et al., 1974). The translocation of arsenicals was increased by the addition of NH4C1 (Wills and
Mcwhorter, 1985), eventually leading to increased control of purple nutsedge.
Application of MSMA in the evenings resulted in less control than when applied either in morning or at noon (Suwunnamek and Romanowskijr., 1967).
Repeated application of arsenical herbicides resulted in translocation of arsenic from leaves through basal bulbs and into tubers of purple nutsedge (Holt et al., 1962). The killing effect is not apparently due to accumulation of arsenic in tubers but rather the depletion of food reserves as a result of increased sprouting (Holt et al., 1967; Zandstra et al., 1974).
Trifluralin when applied as a subsurface layer at the same level as tubers has resulted in good control of nutsedge under greenhouse conditions
(Baker, 1976). In contrast Rincon and Warren (1978) observed little effect from 28 this herbicide, even at very high doses. Parker et al. (1969) observed inconsistent results with trifluralin on purple nutsedge. The herbicide effect is poor when placed either below or above the tubers (Baker, 1976). Erratic control with trifluralin under field condition could be due to the distribution of tubers at variable depths in the soil.
Nitofen is only partially effective in controlling purple nutsedge but the effect is much greater with night applications than with day applications
(William and Warren, 1975b). Bentazon can provide control if applied at early stages of growth (Boswell, 1971). Okafore & De Datta (1976a) reported that bentazon has selectively controlled purple nutsedge in rice when applied 7 days after rice emergence. Repeated applications of dinoseb have killed portions of aerial parts of purple nutsedge, leaving the tubers unaffected and allowing for regeneration from the original shoots (Standifer, 1974). Dalapon has given excellent control of nutsedge for 15 to 20 days (Balasubramanian and Sundararaj,
1968). Dicamba has given inconsistent control (Balasubramanian and Sundararaj,
1968; Escoro, 1969).
Reliable control has been obtained by fumigating the soil with methyl bromide applied under a gas tight seal (Leonard and Harris, 1950).
However, its applicability is only for limited areas as the gas is expensive and considerable labor is required for its application.
Despite the large number of herbicides tested on purple nutsedge, most herbicides provided only poor control. Pereira et al. (1987), after reviewing 29 the reported information, concluded that the failure to control purple nutsedge by
most of the herbicides was due to marginal translocation of herbicides to site of
action, temporary inhibition of tuber sprouting and inconsistent control when
applied under different stages of growth and under various environmental
conditions. The authors suggested that research with herbicides should be
designed to explain or to improve control strategies aimed at vulnerable parts of
the life cycle.
E. Integrated Control
Despite various attempts to control purple nutsedge it still remains a
serious weed in many cropping situations. Classical control practices have been
based on starvation of underground organs, prevention of new tuber formation
and desiccation of existing tubers. Most of these methods have not been entirely
satisfactory when used alone because each has its own limitations (Doll, 1986b;
Glaze, 1987). Major difficulties in attempt to control this weed include rapid
growth and proliferation from the rhizomes and tubers and the production of
dormant tubers. Most dormant tubers act as weak sinks to herbicides (Zandstra
and Nishimoto, 1977). Since the tuber plays a central role in the establishment of
new shoots and underground organs, any measure by which a herbicide is better
translocated and accumulated in toxic amounts in the tubers offers a greater potential for nutsedge control. This requires integration of several control
methods (Glaze, 1987).
Stimulation of tuber sprouting either by chemical or mechanical 30 means offers a greater potential for systemic herbicides to control purple nutsedge. Investigations on the effects of many growth regulators on tuber dormancy, sprouting and growth modification of nutsedge have shown that there is little chance to include growth regulators in purple nutsedge control strategies
(Burgis, 1969; Hammerton, 1975; Parker et al., 1969; Teo et al., 1973). Thus most suggested control programs are based on repeated applications of systemic herbicides such as MSMA and glyphosate with occasional cultivations or the use of presowing application of thiocarbomates (Hammerton, 1975).
Herbicide application coupled with tillage was more effective than herbicide alone for purple nutsedge control (Chase and Appleby, 1979a; Everest and Patterson, 1988). Gopal and Mani (1968) reported that combinations of mechanical, chemical and cropping methods have resulted in better control of purple nutsedge. As nutsedge is highly sensitive to shading, adoption of fast growing complete canopy crops such as sweet potato or loofa could reduce purple nutsedge significantly if early season weed competition could be minimized by other methods (William and Warren, 1975a). Adoption of combinations of crop rotations, planting arrangements and planting density to provide a better shading effect, and the use of other control methods has reduced purple nutsedge significantly in the Philippines (Harwood and Bantillan, 1974).
IV. Plant Population Models
Mathematical models offer a convenient way to describe and 31 understand the relative importance of various demographic processes in the life
cycle of plants (Cousens et al., 1987; Mortimar et al, 1978; Mortimar et al., 1980;
Silvertown, 1987). Mathematical symbols provide a useful shorthand for describing
various states/ stages in plant life cycles and equations permit formal statements of
how various states/ stages of demographic processes are interrelated and likely to
interact Translating demographic processes into sets of mathematical
relationships constitutes the building of population models. In general a model is
an abstract, and often imperfect, representation of the real world. Regardless of
its imperfections it is a powerful tool to reach tentative answers and make predictions about the behavior of the plant for which the model has been
developed (Cousens et al., 1987).
It is convenient to think of these mathematical models as having four basic elements. System variables are a set of numbers which are used to represent the state or age-state of the life cycle of the plant at any given time.
Flows or interrelationships between different states or stages are represented by equations called transfer functions or functional relationships. Inputs to the system or factors affecting, but not affected by, the components of the system are represented by equations called forcing functions. Finally constants of the mathematical equations are called parameters.
The models could take two major forms as either stochastic or deterministic. Stochastic models attempt to include effects of random variability in forcing functions and parameters. Deterministic models ignore this chance of 32 variation. Usually stochastic models are mathematically difficult to deal with and
most population models are of the deterministic type.
The primary role of population models is to forecast the future size
of plant populations and to make predictions within the larger context of a bioeconomic model (Mortimar, 1983). Such models should posses the following attributes:
- the model should provide descriptions of both spatial and temporal dynamics
of target plant species,
- provisions should be made for the calculation of both transient dynamics and
the asymptotic properties of the populations. Most important is the finite
rate of increase (A.) or net reproductive rate,
- the ability to perform sensitivity analysis on parameters of the model to
enable evaluation of the magnitude of regulatory processes,
- in the final form the model should appear stochastic to evaluate the analysis
of risk control programs.
Though most of the population models reported in the literature do not satisfy all of the above conditions, they are fairly successful in predicting some aspects of the population dynamics of the plants for which they have been developed.
While equations or functional relationships in mathematical models can take a variety of forms, the most frequently used tools to develop population models are difference and differential equations and matrix algebra (Mortimar, 33 1983; Silvertown, 1987; Walters, 1973).
A. Difference Equation Models
Only 4 basic demographic processes determine how the total number in a population changes in time. They are birth(B), death(D), imniigration(I) and emigration(E). How these processes change the size (N) of a population between one time interval (t) and the next (t+1) is given by the equation,
Nt+l = Nt +B -D +I -E The above equation is technically known as a difference equation. Models based upon difference equations are appropriate for populations in which generations are discrete and do not overlap (Silvertown, 1987). The net rate at which a population is increasing or decreasing in size is called the net reproductive rate,
RQ, and is given by,
N Ro = t+i' Nt If Ro> 1, the population will increase generally by a factor of Ro in each time
< interval. If Ro l» the population is decreasing, while RQ = 1 suggests that it is at an equilibrium. In reality, RQ is unlikely to have a constant value and will change with the density of the population (Maxwell et al., 1988; Mortimar et al., 1978).
The general equation in which RQ is a function of Nt is,
N t+i = W^t Mcdonald and Watkinson (1981) used difference equation models to study the population dynamics of annual plant species with only one generation in 34 each growing season. The model took the form of a nonlinear first order equation.
These models have been further extended to investigate the effert of seed banks
on the population dynamics of annual plants with discreet generations (Gonzalez-
Andujar and Femandez-Quintanilla, 1991; Watkinson et al., 1989). Difference
equation models are useful only in understanding the population behavior of
plants having discreet generations. They cannot be legitimately extended to
describe populations of perennials or plants with overlapping generations.
Moreover difference equation models assume that different life stages/ states in
the life cycle have a constant or equal growth rate even though this is not true for
most plant populations.
B. Logistic Equation Models
When generations overlap, population dynamics are more appropriately modeled with an equation that is able to describe a continuous process (Silvertwon, 1987). If birth and death rates in a population are constant, one can calculate the instantaneous rate of increase r, using the parameters birth rate (b) and death rate (d),
r = b-d
The rate of change of population size is dN/dt is given by the differential equation,
dN/dt = rN where N is the size of the population at a particular instant of time. To obtain the size of the population Nt at some time t, the above equation is integrated to give, 35 n Nt = N0e
where NQ is the initial size of the population at some time designated zero, e is a
constant (the base of natural logarithms), r is the intrinsic rate of increase and t is
the time elapsed since time zero. This model has several assumptions. It considers population increase as spontaneous, all individuals are extremely similar and there
is no spatial structure of the population. These limitations make logistic equation
models less useful, particularly, for plants having a spatial structure.
C. Matrix Models
Most annual and perennial plants possess the following characteristics. They may have overlapping generations; the individuals may fall into several different age/size classes or developmental states; and reproductive and death rates may vary with age or size of the individuals. Behavior of the demographic processes of plant populations having these features can be conveniently described by matrix models (Caswell, 1989; McMahaon and
Mortimar, 1980; Mortimar et al., 1980; Watkinson, 1986). Matrix models are quite relevant for studies involving the control of weeds. They allow for the encapsulation of a multistate population structure and have the precision for simulation of the transient dynamics and the calculation of net reproductive rate
(A.) (Caswell, 1989; Mortimar, 1983). Construction, analysis and interpretation of matrix population models have been treated explicitly by Caswell (1989).
Initial steps in constructing a matrix model involves the development of a diagrammatic life table consisting of stages or state variables of the life cycle. 36 Conversion of state variables in the diagrammatic life table to actual values and development of equations that describe flow rates between life histoiy stages/ states follows organization of the conceptual framework. Flow rates between life history stages/ states may be measured in terms of transition probabilities (Mortimar et al., 1978) for a defined time period. These transition probabilities constitute the elements of a square matrix which is commonly referred to as either a transition or a projection matrix (Lesli, 1945). The number of individuals in each age/ state class are presented as an array of numbers termed a column vector. The transition matrix when multiplied by a column vector at one time period gives the number of individuals of each state present at the next time period.
In matrix form, a matrix model could be depicted as,
Nt+i - M N, where Nt is a column vector( N^ Ntl, Ng Njj) representing the structure at time t of a population divided into s 'stages' and M is a square matrix of order s.
M defines the Leslie matrix. Life table data are used to calculate the elements of the matrix M (Caswell, 1989; Watkinson, 1986).
Several investigators reported on the use of matrix models to predict the behavior of plant populations (Lapham et al., 1985; Mortimar et al. 1978;
Mortimar et al., 1980; Sarukhan and Gadgil, 1974; Watson, 1985). Most of the first generation models did not include density dependence functions. This resulted in a continuous increase in populations predicting an exponential growth 37 (McMahaon and Mortimar, 1980; Sarukhan and Gadgil, 1974; Watson, 1985).
Although these models have the limitations for making quantitative predictions of
the population size, they were able to make qualitative evaluations of the
population size as influenced by various forcing factors. The most noteworthy
were the identification of the most vulnerable stages in the plant's life cycle with
the use of sensitivity analysis and evaluation of different management practices in
relation to control (Mortimar et aL, 1980; Watson, 1985). Models developed later
included density dependent factors (Maxwell et al., 1988; Mortimar et al., 1980).
These models have been used to simulate the effect of different management
practices and to evaluate the response of the population when subjected to
several control methods. The results of such evaluations are extremely helpful in
designing appropriate control strategies, particularly for weeds (Maxwell et al.,
1988; McMahaon and Mortimar, 1980). Matrix models are also useful in identifying information gaps in plant population dynamics data and help to focus future investigations on the most productive lines of inquiry. 38 Chapters
Herbicidal Control of Purple Nutsedge (Cypews mtundus) During
Its Early Growth Stages1
LAKSHMAN AMERASINGHE and GARVIN CRABTREE2
Abstract Field studies were conducted for two growing seasons in the Diy Zone of Sri Lanka to determine the optimum time of application of glyphosate and metolachlor for purple nutsedge control. Each herbicide was applied at 3.0 kg ai ha"1 when each parent tuber was attached to (I) a single shoot without rhizomes,
(11) a single shoot with developing rhizomes, (HI) a single tuber with a secondaiy shoot and (IV) a shoot with a developing tuber. Metolachlor was also applied when parent tubers were sprouting. Average reductions by glyphosate in shoot numbers was 40%, shoot weight 45%, tuber number 45%, tuber weight 42%, and tuber viability 60%. The degree of suppression increased with delayed application, with the greatest suppression occuring when glyphosate was applied at tuber initiation. The degree of control differed in the two seasons. Metolachlor caused only temporaiy reduction of purple nutsedge growth, which again was not
1 Received for publication , and in revised form.
2 Grad. student and Prof., Dept Horticulture, Oreg. State Univ., Corvallis, OR.
97331. Current address of senior author: Regional Agricultural Research Station,
Maha Huppallama, Sri Lanka. 39 consistent between the seasons. Variable response of purple nutsedge to the
herbicides is discussed in the light of environmental and plant physiological
factors. Nomenclature: glyphosate, N-(phosphonomethyl)glycine; metolachlor, 2-
chloro-N-(2-ethyl-6-methylphenyl)-N-(2-methojq'-l-methylethyl)acetaniide; purple
nutsedge, (Qpews wtundus) L. #3 CYPRO.
Additional index words. Plant number, tuber count, tuber viability, glyphosate,
metolachlor, CYPRO.
INTRODUCTION
Classical control of purple nutsedge has been based on starvation of
underground organs, reduction of new tuber formation and desiccation of existing
tubers. Very often these methods have not been entirely successful because
without eradication rapid vegetative growth accompanied by profuse tuber
production soon reestablish the infestation. Also tuber dormancy makes most of
these control methods less effective in achieving a practical level of control.
Although purple nutsedge produces viable seeds, the principal mode
of reproduction is through tubers (12, 21). Therefore, control attempts with
herbicides necessarily demands control of tubers or tuber-forming meristemetic
3 Letters following this symbol are a WSSA-approved computer code for
Composite List of weeds, Revised 1989. Available from WSSA, 309, West Clerk
Street, D 61820. 40 tissues. Evidently, foliar-applied herbicides that have given some level of control are only those that translocated to basal bulbs and tubers (10, 14, 27). The efficacy of foliar-applied herbicides for perennial weed control is related to stage of plant growth, source-sink relationships, and the amount of herbicide absorbed
(23). In purple nutsedge, as initial centers of high metabolic activity mature, they are replaced by new, actively growing rhizomes, tubers, and roots (1).
Consequently, mature underground tubers become less active as sinks for foliar- applied herbicides. Hence, timing of application in relation to tuberization becomes a crucial factor in the control of purple nutsedge with foliar applied herbicides.
Usually the optimal period to apply herbicides for perennial plants is shortly before or during early bloom stage. However, for a tuber forming perennial it is necessary to apply before the commencement of tuber production/maturation, as mature tubers are usually unaffected by herbicides (18).
While several reports indicate that the best time to apply herbicides is around 3 to 4 weeks after nutsedge emergence (24, 10), others suggest an interval of from 7 to 12 weeks is best (7, 28). It is not clear whether these differences were due to seasonal variations in growth or due to physiological differences of the plants.
Zandstra & Nishimoto (28) suggested that the best time to apply all herbicides is during the period when most of the young underground tubers are connected to the aerial parts of the plant It is important to characterize the tuberization of nutsedge at the time of herbicide application as several reports suggest that 41 herbicide application should be before the production of dormant tubers (5, 4).
There is considerable variation among the reports in the time of formation of
dormant tubers. Sierra (22) indicated that dormant tubers begin to form 50 days
after sprouting; Hauser(8, 9) observed dormant tuber production 3 weeks after
sprouting. These discrepancies can be attributed to different environmental
conditions during each study together with physiological differences in the parent
tubers (15).
The objectives of the present investigation were (1) to determine the
optimum timing of metolachlor and glyphosate applications relative to purple
nutsedge growth stage and, (2) to ascertain the effectiveness of metolachlor and
glyphosate for suppression of purple nutsedge tuber production and survival under
field conditions.
MATERIALS & METHODS
The experiment was conducted for two growing seasons (Yala and
Maha) in fields where annual crops are being grown. Natural populations of purple nutsedge in these fields were treated with glyphosate and metolachlor, each at 3 kg ai ha"1 at different stages of growth of the plant. These growth stages were: sprouting tubers (May 25th in Yala, Oct 19th in Maha), tubers with a single shoot (May 30th in Yala, Oct. 22nd in Maha), single shoot at rhizome initiation
(June 4th in Yala, Oct 26th in Maha), shoot with a secondary shoot (June 15th in
Yala, Nov. 6th in Maha) and shoot with the first new tuber (June 23rd in Yala, 42 Nov. 12th in Maha). Metolachlor was applied at each of the growth stages while
glyphosate was applied at all the stages except for the tuber sprouting stage.
Observations were made at monthly intervals for 4 months and for statistical
analyses each observation was considered as a subtreatment The treatments were
included in a split plot design with either five or six replicates. Each main plot
was 2 x 12 m while a subplot was 3x2 meters. Herbicide treatments were applied
to main plots and observations collected from sub-plots by destructive sampling.
The experiment was initiated in early May, 1990, and carried
through August, 1990 (Yala season), and repeated from late October, 1990,
through February 1991 (Maha season). The periods in which the experiment was
conducted coincided with the two normal growing seasons for Sri Lanka.
Supplementary irrigation was given to plots during the Yala season to simulate
farmer conditions while the Maha season experiment was conducted totally under
rain-fed conditions. Nitrogen totalling 50 kg N ha"1, applied in three equal
amounts, was the only fertilizer used during each growing season.
Since different growth stages have not been previously identified
and characterized, growth was continuously assessed through visual observation,
starting with the onset of each growing season. From these observations, the respective growth stages were identified for the application of herbicide treatments. To initiate treatments, the fields were plowed and leveled 3 to 4 weeks before the onset of rains. All herbicide treatments were applied during the first three weeks after initiation of nutsedge growth. 43 To assess the effectiveness of treatments for the control of puiple nutsedge, the number of shoots in four randomly distributed quadrats of 0.25 m were counted monthly in each subplot For each subplot, all shoots from one 0.25 m2 quadrat was removed to determine biomass (dry weight). The top 30 cm soil in each harvested quadrat was excavated for underground parts of the plants. All tubers were counted and the dry weights of underground parts removed from the excavated soil were recorded. The same observations were made during the second season except terminal and dormant tubers were counted separately.
Tubers were removed from the top 30 cm of soil from another
0.25m2 sample area four months after initial tuber sprouting in the Yala season's experiment to check viability of the tubers. All of the tubers in the soil sample taken from each herbicide-treated and untreated plot were separated from their rhizomes, counted, and planted in sand trays. Tuber sprouting was recorded for six weeks at which time no further sprouting was observed. At this time the unsprouted tubers were sliced and examined for degenerated tissue to assess viability of these tubers. Tuber viability was monitored during the Maha season similarly to the Yala season, but at three different times: 1, 3 and 5 months from initial tuber sprouting in the field.
Data were subjected to analysis of variance separately for each season since sample variances between the two seasons were significantly different
Data were transformed into logarithmic form before analysis if the variances were found to be heterogeneous; but original data are presented. Means were compared 44 using Duncan's multiple range test, with a probability equal to 0.05.
RESULTS AND DISCUSSION
No toxicity symptoms were observed in metolachlor treated purple nutsedge, while green parts of purple nutsedge became completely necrotic about
1 week after glyphosate application. Metolachlor and glyphosate significantly reduced shoot numbers in both seasons, however control by glyphosate was more consistent (Tables 3.1 and 32). The degree of control was related to the stage of purple nutsedge growth at the time of treatment Application of glyphosate after nutsedge initiation of secondary shoots or tubers resulted in better control of shoots than when the herbicide was applied before plants reached these growth stages. The consistent reduction in shoot density became evident only after two months in the Yala season while the effect was apparent from the first month's observation in the Maha season. However, in both seasons, all glyphosate treatments showed similar responses when evaluated at the end of four months.
One noteworthy feature in shoot numbers in the untreated control plots was that this value was relatively constant throughout the Maha season but dropped sharply between the three and four months observation dates in the Yala season.
Glyphosate caused a significant reduction but not a total elimination of nutsedge shoots. Generally, shoot numbers gradually increased in glyphosate treated plots after the dramatic initial reduction. This was true for both growing seasons except for the drop in shoot numbers for the last observation date of the Yala season. 45 While glyphosate significantly reduced the shoot dry weights in both
seasons (Tables 33 and 3.4), metolachlor effects were inconsistent and much less than shoot diy weight reductions measured in glyphosate-treated plots. In the
Maha season, the suppression of shoot growth by metolachlor was more evident when it was applied at the tuber initiation stage and this suppression was evident until the end of the growing season. There was less effect of metolachlor on shoot dry weight during the Yala season. In contrast, shoot dry weights were greatly reduced by glyphosate treatments in both seasons and these values were significantly lower than those recorded from metolachlor treated plots.
Glyphosate's effect on dry weight was apparent from the first month of growth in both seasons, although the degree of suppression was different between the two seasons. While application at any of the growth stages in the Yala season provided a similar level of reduction, application late in the Maha season caused in significantly lower dry weights when compared to applications at earlier stages of nutsedge growth. Total above ground biomass production in untreated plots during the two seasons differed. During the Maha season, harvested dry matter in the untreated control plots was somewhat consistent throughout the growing season; in the Yala season, shoot weights steadly declined from the first month of growth to the end of the growing season.
The effect of herbicides on tuber numbers was evident from the first month of growth during the Maha season (Table 3.5). Both herbicides significantly reduced tuber production. However, the effect of metolachlor was temporary and 46 was not apparent after one month into the Maha season. Metolachlor resulted in
no reduction in tuber numbers during the Yala season (Table 3.5). Conversely,
glyphosate plots had significantly reduced tuber numbers in both seasons, although
the response patterns differed. In general, application at later growth stages caused
a greater reduction in tuber number than when applied at early stages. This
reduction was evident beginning only after two months of the Yala season, but was
evident from the first observation date in the Maha season. In the Yala season
trial much of glyphosate's suppressive effects on tuber production diminished after
three months. The relative increase in tuber number in glyphosate treatments after
three months of nutsedge growth in the Yala season was not evident in the trial
conducted during the Maha season. Instead, the suppression of tuber production
remained throughout the Maha growing season. Noteworthy is the fact that purple
nutsedge tuber production rate reached a maximum by the end of the first month
in both growing seasons. In many plots, after the first month, there was a decline
in total tuber numbers even in the untreated control plots.
Tuber weights were somewhat less responsive to treatments (Tables
3.7 and 3.8) than numbers of tubers but response patterns were similar. The effects
of herbicides on tuber dry weights became evident after three months of growth
although metolachlor did not cause any consistent pattern of reduction of tuber dry weights (Tables 3.7 and 3.8). Glyphosate showed a significant effect of reducing tuber dry weights when applied at later growth stages.
Effect of the herbicides on tuber sprouting differed between the two 47 seasons. During the Yala season, only glyphosate significantly reduced tuber sprouting capacities (Table 3.9). Application of glyphosate before rhizome initiation did not significantly effect tuber sprouting while applications at later growth stages reduced the sprouting capacity. In the Maha season, both metolachlor and glyphosate caused a significant reduction in tuber sprouting.
However, glyphosate's effect was much greater than that of metolachlor and lasted throughout the growing season, while the effect from metolachlor was temporary.
The glyphosate application timing most effective in reducing tuber sprouting was when purple nutsedge was either at the stage of initiation of secondary shoots or during the tuber production stage.
In general, overall growth of purple nutsedge during the two growing season differed. This was clearly evident from shoot number, shoot diy weight and tuber production. The effectiveness of the herbicides too were not consistent between the two seasons. These differences in growth and herbicide response could mainly be attributed to differences in moisture availability in the two seasons. The Maha season received sufficient rainfall, well distributed over the growing season, for unimpeded growth of purple nutsedge (Figure 3.3). This is confirmed by the fairly high and uniformly distributed soil moisture content during the growing season (Figure 3.4). Conversely, the Yala season received very little rain (Figure 3.1) and purple nutsedge growth was almost totally dependent upon supplementary irrigation which was provided when plants were near the wilting stage. The variability in seasonal soil moisture levels (Figures 3.2 and 3.4) shows 48 that nutsedge was exposed to relatively lower moisture levels during the Yala season than during the Maha season. These low Yala season moisture levels may have limited plant growth. The sharp drop in shoot number and weight from the
3rd to the 4th month of the Yala season was probably due to low soil moisture induced early senescence.
It is clear that metolachlor did not effectively control purple nutsedge under the conditions of this study. Metolachlor is effective only on meristematic tissues of emerging shoots and roots (6) with very little movement into the purple nutsedge tubers (7). Hence, metolachlor would have an affect on purple nutsedge only through its emerging shoots and roots. In the present study, metolachlor was applied to the soil surface, but not incoiporated. Since the vapor pressure of metolachlor is relatively high, a considerable loss of herbicides could have occurred due to high temperatures that prevailed during the growing seasons.
This loss could be a major contributing factor to the poor control obtained with metolachlor. It has been reported that preplant, incorporated applications of metolachlor have provided seasonal control of yellow nutsedge (17, 13) and the importance of the method of placement for shoot inhibiting herbicides for effective weed control emphasized (19). Hence there is potential for control of purple nutsedge with metolachlor through proper soil placement The inconsistent reduction in shoot number and shoot dry weight when metolachlor was applied postemergence could be due to the inconsistent exposure of meristems in shoot apices to the herbicide. 49 There are several reports indicating that glyphosate effectively
controls purple nutsedge (14, 25, 27, 26) and the herbicide translocates rapidly in purple nutsedge (28, 3). The present study also showed that glyphosate is effective in controlling purple nutsedge. The degree of control is dependent on timing of glyphosate applications relative to nutsedge growth stage. Application at the initial growth stages was not as effective as application at later growth stages in controlling shoot growth, tuberization and tuber viability. The significance of proper timing of application in relation to growth stage has been noted by other researchers (21, 7, 27). However, most of these reports do not characterizes the physiological stage of purple nutsedge in relation to timing of application.
Zandstra & Nishimoto (28) reported that the best time of application is when most of the tubers are sprouted and the young tubers are connected by healthy foliage.
In most studies reported in the literature maximum control of shoots ranged from
80 to 100%. However, tuber control never reached total eradication. In the present study, the degree of control obtained was close to the previously reported levels.
Our results also show that the degree of control is more related to the physiological, rather than the chronological, age of the plant Accordingly, application of glyphosate at tuber initiation provides a reasonable level of control of purple nutsedge and has potential for practical, commercial use.
Effectiveness of foliar applied herbicides for the control of perennial weeds depends upon their rapid absorption and translocation to meristematic regions of roots, rhizomes etc. in quantities sufficient to kill those organs (20, 23). 50 The movement of foliar applied herbicides within a plant is strongly affected by
the relative strengths of sources and sinks for carbohydrates within the plant (11).
Since assimilate transport from shoots to underground parts is minimal at early
growth stages in purple nutsedge (1), it can be concluded from the present study
that most of the herbicides applied at early growth stages did not reach
underground sinks in quantities sufficient to provide effective control. This is
probably the reason for relatively poor control of purple nutsedge with glyphosate
when applied at early growth stages. Conversely the greater degree of control of
purple nutsedge when glyphosate was applied to plants either with secondary
shoots or with tubers could be attributed to the development of stronger sinks as
the plants mature (28) and greater absorptive surfaces. Although the effect of
glyphosate on shoot number and shoot dry weight was evident in both seasons, the
effect on tuber production and tuber viability was less pronounced in the Yala
season. Moosavi-Nia and Dore (16) reported that stress from low soil moisture
conditions reduced the effect of glyphoste on purple nutsedge growth, tuber production and tuber survival. Similar reductions in purple nutsedge control has
also been observed by Chase & Appleby (2) under low rainfall conditions and the poor control was attributed to poor translocation of glyphosate. Thus the relatively low moisture levels that prevailed in the Yala season of this study may have reduced or slowed the herbicide movement to underground meristems and eventually caused poor control of tuber production. Conversely, relatively higher moisture levels that prevailed in the Maha season may be responsible for better 51 translocation of the herbicide into underground parts, eventually resulting in a consistently better level of control of tubers over the season.
When tubers that failed to germinate were cut open and examined all were found to contain degenerated tissues. Thus, essentially all viable tubers had germinated and the percent germination can be considered equivalent to percent viability of tubers. Greater reduction of tuber viability when glyphosate was applied at later growth stages is again explained by its greater movement into meristematic tissues of rhizomes and tubers.
Our research indicates the importance of timing of herbicide application and the interaction of some environmental factors for purple nutsedge control in the Dry Zone of Sri Lanka. Foliarly applied herbicides with systemic action, like glyphosate, effectively control purple nutsedge shoots as well as tubers when herbicide application timing coincides with tuber initiation. In the Dry Zone this period is usually 3 to 4 weeks after shoot emergence. The seasonal soil moisture variation in the Dry Zone can have a considerable effect on herbicide efficacy. Lower soil moisture levels and resultant plant stress tend to reduce the efficacy of foliarly applied herbicides. High soil temperatures common in the Dry
Zone of Sri Lanka reduce the effectiveness of pre-emergent herbicides, particularly those having a fairly high vapor pressure, on purple nutsedge. Soil incorporation may enhance the effectiveness and residual effect such herbicides. Observation date Herbicides Application Growthr-"* -*.l_ date stageb 28th June 28th July 27th Aug. 23rd Sept. 1990 1990 1990 1990 Metolachlor 25th May '90 I 239 ab 202 b 227 ab 45 a Metolachlor 30th May '90 n 359 a 197 b 312 a 23 be Metolachlor 4th June '90 in 258 ab 180 b 250 ab 36 ab Metolachlor 15th June '90 IV 214 b 165 b 179 be 45 a Metolachlor 23rd June '90 V 188 b 198 b 141 cd 35 ab Glyphosate 30th May '90 n 61c 52 b 173 d 17 cd Glyphosate 4th June '90 m 49 c 76 c 81 e 16 cd Glyphosate 15th June '90 IV 52 c 31c 62 ef 19 cd Glyphosate 23rd June '90 V 54 c 45 c 43 f lid Check 351a 329 a 277 a 45 a a Means within columns followed by the same letter are not significantly different at the 5% level as determined by Duncan's multiple range test. b I, sprouting tuber; II, tuber with a single shoot; in, single shoot at rhizome initiation, IV, shoot with secondary shoot; V, shoot with the first new tuber en Table 3.2. Effect of herbicides on nutsedge shoot number m"2, 1990/91 Maha season8.
Observation date Herbicides Application Growth date stageb 16th Nov. 17th Dec. 20th Jan. 16th Feb. 1990 1990 1991 1991 Metolachlor 19th Oct. '90 I 234 a 268 abc 266 a 330 a Metolachlor 22nd Oct. '90 n 246 a 344 a 276 a 314 a Metolachlor 26th Oct. '90 m 251a 248 be 264 a 309 ab Metolachlor 6th Nov. '90 IV 196 b 248 be 234 a 238 be Metolachlor 12th Nov. '90 V 246 a 304 ab 226 a 132 de Glyphosate 22nd Oct. '90 II 144 b 112 d 146 c Hide Glyphosate 26th Oct. '90 m 62 c 88 d 112 c 131 de Glyphsoate 6th Nov. '90 IV 12 d 33 e 52 e 84 e Glyphsoate 12th Nov. '90 V 280 a 25 e 84d 104 e Check 251a 228 c 200 a 182 cd a Means within columns followed by the same letter are not significantly different at the 5% level as determined by Duncan's multiple range test. b I, sprouting tuber; II, tuber with a single shoot; III, single shoot at rhizome initiation; IV, shoot with secondary shoot; V, shoot with the first new tuber Table 3.3. Effect of herbicides on nutsedge shoot diy weight m"2, 1990 Yala season8.
Observation date Herbicides Application Growth date stageb 28th June 26th July 27th Aug. 23rd Sept. 1990 1990 1990 1990 Metolachlor 25th May '90 I 23.7 ab 18.1a 20.0 a 5.4 a Metolachlor 30th May '90 II 22.1 ab 18.7 a 18.7 ab 5.8 a Metolachlor 4th June '90 III 17.1b 9.5 b 15.4 ab 4.6 a Metolachlor 15th June '90 IV 21.8 ab 18.5 a 19.4 a 4.4 a Metolachlor 23rd June '90 V 19.7 b 17.3 a 24.6 a 5.5 a Glyphosate 30th May '90 II 6.8 c 4.5 bed 4.6 c 1.7 b Glyphosate 4th June '90 III 4.9 cd 4.7 be 3.7 c 1.7 b Glyphsoate 15th June '90 IV 2.7 d 1.6 d 3.1c 2.0 b Glyphsoate 23rd June '90 V 2.7 d 2.3 cd 3.0 c 1.8 b Check 31.7 a 23.8 a 15.3 b 7.1a a Means within columns followed by the same letter are not significantly different at the 5% level as determined by Duncan's multiple range test. b I, sprouting tuber; II, tuber with a single shoot; III, single shoot at rhizome initiation, IV, shoot with secondary shoot; V, shoot with the first new tuber
4k. -2 1 Table 3.4. Effect of herbicides on nutsedge shoot dry/ weight _ C7_ _ m , 1990/91— Maha season
Observation date Herbicides Application Growth date stageb 16th Nov. 17th Dec. 20th Jan. 16th Feb. 1990 1990 1991 1991 Metolachlor 19th Oct. *90 I 44.0 b 46.0 bed 86.8 a 69.2 a Metolachlor 22nd Oct. '90 II 49.2 b 53.6 abed 71.2 ab 62.0 a Metolachlor 26th Oct. '90 in 44.0 b 63.6 abc 89.6 a 71.6 a Metolachlor 6th Nov. '90 IV 32.6 cb 42.8 cd 58.8 ab 27.2 b Metolachlor 12th Nov. '90 V 48.0 b 74.8 a 61.2 ab 26.8 b Glyphosate 22nd Oct. '90 II 25.8 c 36.4 d 43.6 b 22.8 b Glyphosate 26th Oct. '90 III 11.4 c 13.6 e 25.2 c 15.6 be Glyphsoate 6th Nov. '90 IV 16.0 c 4.2 f 11.6 d 11.6 c Glyphsoate 12th Nov. '90 V 42.8 b 1.9 e 10.4 d 17.2 be Check 73.6 a 73.2 a 66.0 ab 65.6 a a Means within columns followed by the same letter are not significantly different at the 5% level as determined by Duncan's multiple range test. b I, sprouting tuber; II, tuber with a single shoot; in, single shoot at rhizome initiation, IV, shoot with secondary shoot; V, shoot with the first new tuber en 2 8 Table 3.5. Effect of herbicides on nutsedge o tuber—' number m" , 1990 Yala season .
Observation date Herbicides Application Growth date stageb 28th June 26th July 27th Aug. 23rd Sept. 1990 1990 1990 1990 Metolachlor 25th May '90 I 660 a 788 a 656 ab 660 a Metolachlor 30th May '90 II 776 a 552 ab 884 a 380 bed Metolachlor 4th June '90 III 636 a 516 be 700 ab 652 a Metolachlor 15th June '90 IV 548 a 688 a 836 a 536 ab Metolachlor 23rd June '90 V 856 a 544 ab 784 a 500 abed Glyphosate 30th May '90 II 708 a 204 d 340 bed 256 cd Glyphosate 4th June '90 111 584 a 236 d 164 d 240 d Glyphsoate 15th June '90 IV 820 a 293 d 216 cd 364 bed Glyphsoate 23rd June '90 V 876 a 408 c 240 cd 284 bed Check 856 a 596 ab 580 abc 520 abc a Means within columns followed by the same letter are not significantly different at the 5% level as determined by Duncan's multiple range test.
b I, sprouting tuber; II, tuber with a single shoot; III, single shoot at rhizome initiation, IV, shoot with secondary shoot; V, shoot with the first new tuber
ON C7-
Observation date Herbicides Application Growth date stageb 16th Nov. 17th Dec. 20th Jan. 16th Feb. 1990 1990 1991 1991 Metolachlor 19th Oct. '90 I 620 a 604 ab 636 a 652 a Metolachlor 22nd Oct. '90 II 504 b 540 ab 484 ab 708 a Metolachlor 26th Oct. '90 III 504 b 476 abc 580 a 600 ab Metolachlor 6th Nov. '90 IV 540 b 764 a 552 a 624 a Metolachlor 12th Nov. '90 V 588 b 624 ab 664 a 596 ab Glyphosate 22nd Oct. '90 II 444 be 111 be 320 b 364 be Glyphosate 26th Oct. '90 III 552 b 316 cd 284 be 280 c Glyphsoate 6th Nov. '90 IV 324 c 288 d 172 c 208 c Glyphsoate 12th Nov. '90 V 492 b 520 ab 332 b 320 be Check 744 a 628 ab 628 a 660 a a Means within columns followed by the same letter are not significantly different at the 5% level as determined by Duncan's multiple range test. b I, sprouting tuber; II, tuber with a single shoot; III, single shoot at rhizome initiation, IV, shoot with secondary shoot; V, shoot with the first new tuber Table 3.7. Effect of herbicides on nutsedge tuber dry weight m , 1990 Yala season8
Observation date Herbicides Application Growth date stageb 28th June 26th July 27th Aug. 23rd Sept. 1990 1990 1990 1990 Metolachlor 25th May '90 I 115.6 a 79.2 abc 101.2 ab 178.0 ab Metolachlor 30th May '90 II 90.8 abc 110.0 a 310.8 a 110.0 be Metolachlor 4th June '90 ffl 60.4 abc 52.9 abc 72.4 bed 102.0 be Metolachlor 15th June '90 IV 82.8 abc 65.2 abc 127.6 ab 140.0 abc Metolachlor 23rd June '90 V 71.6 abc 106.8 ab 130.4 ab 143.0 abc Glyphosate 30th May '90 II 64.8 abc 51.2 abc 63.6 bed 60.4 c Glyphosate 4th June '90 UI 47.2 c 36.7 c 39.6 de 74.2 c Glyphsoate 15th June '90 IV 99.2 ab 46.4 be 25.6 e 104.4 be Glyphsoate 23rd June '90 V 128.8 a 68.4 be 43.6 de 87.2 c Check 129.4 a 74.7 abc 90.8 abc 210.0 a a Means within columns followed by the same letter are not significantly different at the 5% level as determined by Duncan's multiple range test. b I, sprouting tuber; II, tuber with a single shoot; III, single shoot at rhizome initiation, IV, shoot with secondary shoot; V, shoot with the first new tuber
CO 00 c? —■ —J ' CT » —
Observation date Herbicides Application Growth date stageb 16th Nov. 17th Dec. 20th Jan. 16th Feb. 1990 1990 1991 1991 Metolachlor 19th Oct. '90 I 97.2 ab 102.8 ab 89.2 be 132.4 a Metolachlor 22nd Oct. '90 II 68.4 b 87.2 be 101.2 b 109.6 ab Metolachlor 26th Oct. '90 III 81.6 ab 99.6 ab 105.2 b 116.6 ab Metolachlor 6th Nov. '90 IV 87.6 ab 99.2 ab 105.6 b 95.2 be Metolachlor 12th Nov. '90 V 116.0 a 116.4 a 150.4 a 80.8 cd Glyphosate 22nd Oct. '90 II 73.2 b 76.4 be 52.4 de 50.4 ef Glyphosate 26th Oct. '90 III 104.4 ab 60.0 c 57.6 de 56.4 ef Glyphsoate 6th Nov. '90 IV 68.8 b 26.4 d 28.8 e 35.6 f Glyphsoate 12th Nov. '90 V 79.6 ab 92.1 ab 64.4 cd 69.6 cde Check 94.8 ab 93.2 ab 86.4 be 119.6 ab a Means within columns followed by the same letter are not significantly different at the 5% level as determined by Duncan's multiple range test. b I, sprouting tuber; II, tuber with a single shoot; III, single shoot at rhizome initiation, IV, shoot with secondary shoot; V, shoot with the first new tuber Table 3.9. Effect of herbicides on purple nutsedge tuber sprouting after four months in Yala and one, three and five months in Maha season8.
Percent sprouting Herbicide Growth stageb Yala, after Maha, after Maha, after three Maha, after five four months one month months months Metoalchlor I 69.0 b 66.4 ab 72.4 be 67.4 ab Metolachlor II 80.0 a 59.4 b 72.0 be 70.4 b Metolachlor III 88.8 a 63.4 ab 86.2 a 73.0 ab Metolachlor IV 84.5 a 65.6 ab 72.4 be 73.6 ab Metolachlor V 54.8 c 62.0 b 78.8 abc 79.6 a Glyphosate II 53.6 c 49.6 c 67.8 cd 52.4 c Glyphosate III 26.8 d 29.6 d 56.2 de 50.0 c Glyphosate IV 25.4 de 12.0 e 48.8 e 20.4 d Glyphosate V 14.4 e 15.2 e 27.4 f 15.6 d Check 56.8 be 72.0 a 84.0 ab 76.6 ab a Means within columns followed by the same letter are not significantly different at 5% level as determined by Duncan's multiple range test.
b I,Sprouting tuber; II, tuber with a single shoot; HI, single shoot at rhizome initiation; IV, shoot with a secondary shoot; V, shoot with the first new tuber. 61 Figure 3.1. RainfaU at Maha DuppaUama, Sri Lanka, May 25, 1990 to September
23, 1990.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 Weeks from slart of experiment (25, May 1990 - 25, Sept 1990). 62
Figure 3.2. Soil moisture content in untreated control plot for the Yala season field study, May 25, 1990 to September 23, 1990.
20
0-6" depth o 16- 6-12' depth
CO "5 E 12- "o CA «-c■ 8-
4-
1 1 1 1 1 1 1 1 1- 2 4 6 8 10 12 14 16 18 20 Weeks firom start of experiment (25, May 1990 - 23, Sept. 1990). 63
Figure 3.3. RainfaU at Maha Duppallama, Sri lanka, October 19, 1990 through
February 19, 1991.
E
123 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
Weeks from stan of expeximent (19, OcL 1990 - 19, Feb. 1991) 64 Figure 3.4. Soil moisture content in untreated control plot for the Maha
season field study, October 19, 1990 to February 19, 1991.
20
Q 0-6" depth 16- ♦— 6-12" depth | t/i | 12
c 8- o CL,
—t— —I— —T— -1— T- 2 4 6 8 10 12 14 16 18 20 Weeks from start of experiment (19.Oct.1990 -19^66.1991) 65 LITERATURE CITED
1. Akobundu, O. I., D. E. Bayer and O. Leonard. 1970. The effects of dichlobenil
on assimilate transport in purple nutsedge. Weed Sci. 17:403-408.
2. Chase, R. L. and A. P. Appleby. 1978. Effect of interval between application
and tillage on glyphosate control of Cypems mtundus L. Weed Res. 19:207-11.
3. Chase, R. L. and A. P. Appleby. 1979. Effects of humidity and moisture stress
on glyphosate control of Cypems mtundus L. Weed Res. 19:241-246.
4. Cools, W. G. and S. J. Loccascio. 1977. Control of purple nutsedge
{Cypems mtundus L) as influenced by season of application of glyphosate and
nitrogen rate. Proc. South. Weed Sci. Soc. 30:158-164.
5. Doll, J. D and W. Piedrahita. 1982. Effect of glyphosate on the sprouting of
Qpems mtundus L tubers. Weed Res. 22: 123- 128.
6. Fuerst, E. P. 1987. Understanding the mode of action of the
chloroacetamide and thiocarbamate herbicides. Weed Tech. 1:270-277.
7. Gossette, B. J., R. L. Stephens and J. T. Gillingham. 1975. Response of
Puipk nutsedge to glyphosate. Proc. South. Weed Sd. Soc. 28:146.
8. Hauser, E. W. 1962a. Establishment of nutsedge from space-planted tubers.
Weeds. 10:209-212.
9. Hauser, E. W. 1962b. Development of purple nutsedge under field conditions.
Weeds. 10:315-321.
10. Hauser, E..W. 1963. Response of purple nutsedge to amitrol, 2,4-D and EPTC.
Weeds. 11:251-252. 66 11. Hay, J. R. 1976. Herbicide transport in plants. Pages 365-396 in, L. J. Audus,
ed. Vol. I. Herbicides Physiology, Biochemistry and Ecology. Acdemic Press,
London.
12. Holm, L .G., D..L..Plucknett, J. V. Pancho and J. P. Herberger. 1977. The
world's worst weeds-Distribution & Biology. Univ. Press of Hawaii, Honolulu.
Pages 8-24.
13. Higgins, E. R., M. G. Schnappinger and S. W. Pruss. 1976. CGA-24705 and
atrazine for yellow nutsedge control. Proc. Northeast Weed Sri. Soc. 30:65.
14. Magambo, M. J. and P. J. Terry. 1973 Control of purple nutsedge (Qpems
mtundus) with glyphosate. Proc. Fourth Asian-Pacific Weed Sri. Soc. 1:191-
194.
15. Mercado, B. I. 1979. Monograph on Qpems mtundus L. Biotrop Bull. No. 15,
SEAMEO, Regional Center for Tropical Biology, Bogor, Indonesia. 63pp.
16. Moosavi-Nia and J. Dore. 1979. Factors affecting glyphosate activity in
Impemta cylindrical) Beu. and Qpems mtundus L. I: Effect of soil moisture.
Weed Res. 19:137-143.
17. Obngawitch, T., J. R. Abemathy and J. R. Gipson. 1980. Response of yellow
(Qpems escukntus) and purple (Qpems mtundus) nutsedge to metolachlor.
Weed Sri. 28: 708-715.
18. Pereira, W. and G. Crabtree. 1985. Timing glyphosate application relative to
growth stage of yellow nutsedge. Proc. Northeast Weed Sri. Soc. 39:99.
19. Rincon, D. J. and G. E. Warren. 1979. Effect of soil placement on the activity 67 of herbicides on Cypems mtundus. Weed Res. 19:81-87.
20. Sandberg, C .L.,W. F. Meggitt and Penner. D. 1980. Absorption, translocation
and metabolism of 14C-glyphosate in several weed species. Weed Res. 20:195-
200.
21. Smith, E. V. and G. L. Fick. 1937. Nutgrass eradication studies. I. Relation of
the life history of nutgrass, Cypems mtundus L. to possible methods of control.
J. Am. Soc. Agron. 29:1007-1013.
22. Sierra, A. N. 1969. Some aspects of the biology of Cypems mtundus L.
Biotrop Bull. No. 11:71-86.
23. Sprankle, P., W. F. Meggitt and D. Penner. 1975. Absorption, action and
translocation of glyphosate. Weed Sci. 23:235-240
24. Suwannamek, U and C. Parker. 1975. Control of Cypems mtundus with
glyphosate: the influence of ammonium sulphate and other additives. Weed res
15: 13-19.
25. Teny, P. J. 1974. Long-term control of Cypems mtundus with glyphosate.
Proc. East African Weed Control Conf. 5: 1-13.
26. Wills, G. D. 1975. Evaluation of glyphosate for post emergence control of
purple nutsedge in cotton. Proc. South. Weed Sd. Soc. 28: 54-57.
27. Zandstra, H., C. K. H. Teo and R. K. Nishimoto. 1974. Response of purple
nutsedge to repeated application of glyphosate. Weed Sci. 22:230-232.
28. Zandstra, B. and R. K. Nishimoto. 1977. Movement and activity of glyphosate
in purple nutsedge. Weed Sci. 25: 268-274. 68 Chapter 4
Effect of Shade and Moisture on Growth of Purple Nutsedge (Cypeius rutundus)1
LAKSHMAN AMERASINGHE and GARVIN CRABTREE2
Abstract Effects of shade and soil moisture on growth and biomass partitioning in
purple nutsedge were determined in field and green house experiments
respectively. Reducing light intensity from 0% through 30%, 47%, 63%, and 80%
of full sunlight with shade cloth, significantly reduced shoot production, tuber
production, and leaf area. Shading decreased partitioning of plant biomass into
tubers and increased partitioning into leaves. The degree of growth reduction and
changes in biomass allocation pattern as a result of increasing shade remained
essentially the same when shade levels were imposed at three different growth
stages, viz. at emergence of purple nutsedge and two and four weeks later.
Depletion of soil moisture from field capacity to 75% of available moisture did
not cause any appreciable change in growth but further depletion below 75% of
available moisture significantly reduced shoot and tuber numbers and leaf area.
Although total plant dry weight was reduced, biomass partitioning into tubers,
1 Received for publication and in revised form .
2 Graduate student and Prof., Dept, Horticulture, Oreg. State Univ., Corvallis,
OR 97331. Current address of senior author: Regional Agricultual Research
Station, Maha Duppallama, Sri Lanka. 69 shoots andleaves remained unchanged with depletion of soil moisture. The presence of relatively constant net assimilation rates and relative growth rates with decreasing soil moisture levels from 0% to 75% of field capacity suggests that purple nutsedge is relatively well adapted for survival under low soil moisture conditions. Nomenclature: purple nutsedge, Cypems mtundus L #3 CYPRO;
Additional index words. Shade tolerance, biomass partitioning, tuber number, shoot number, soil moisture depletion, growth analysis, CYPRO.
INTRODUCTION
Light is one of the most important variables controlling plant growth. A number of investigations have reported on the effect of shade in reducing growth of plants (8, 9, 26, 28, 30) including purple nutsedge (19, 35, 49).
Based upon biochemical characteristics. Black et al (5) classified plants into two groups as photosyntheticaUy efficient and non-efficient and included purple nutsedge in the efficient group. These latter plants continue to fix increasing amount of CO2 as light intensity increases to nearly full sunlight Shading from rapid development of a plant canopy would decrease photosynthetic rates and suppress the growth of understoiy plants. Competition for light has been shown to
3 Letters following this symbol are a WSSA-approved computer code from
Composite list of weeds; Revised 1989. Available from WSSA, 309 West Clark,
Champaign, IL 61820. 70 be of major importance in many weed crop interactions (14, 51) and the
competitive ability of plants can be altered by the level of irradiance (18).
Experiments conducted under controlled environments showed that
decreased light intensity greatly reduced shoot and tuber production in purple
nutsedge (42). In another study, it was reported that at 20% of available light
intensity tuber production and shoot production was reduced about 90% and 25%
respectively, when compared to growth in full light (3). Field studies have shown
that purple nutsedge growth is influenced by the intensity of canopy shade (32,
25). In upland rice and com, purple nutsedge has increased as a problem weed
where annual weeds were controlled by herbicides (22, 23). This has been largely
attributed to reduced shading from taller weeds that were controlled by
herbicides. Purple nutsedge competes vigorously in crops that either do not
provide or slowly produced a dense canopy (25, 46). Conversely crops with fast
growing canopies seriously affects its growth (27, 46). Shading decreases the partitioning of plant biomass into tubers and rhizomes and increases partitioning into leaves in purple nutsedge (35). Such a response may be of adaptive significance in shade grown plants because it represents a greater investment of plant biomass in photosynthetic tissues.
Apart from shade, soil moisture is another key factor controlling plant growth. Effect of water stress on plant growth is a widely studied subject
(44, 29) This is not surprising because depletion of soil moisture, associated with periodic drought, is the most important single factor limiting plant growth. The 71 majority of these studies deal with cultivated plants with very little information related to weeds such as purple nutsedge. Yet, information on the effect of soil moisture on weeds such as purple nutsedge would be very useful because crop- weed competition could be altered by varying the soil moisture regime. In a green house study, Wise and VanDiver (48) investigated the effects of soil moisture on competition between sorghum and eight weed species. They found that relative competitive abilities of the eight species varied with soil moisture level. Davies
(12) reported that an occasional decrease in soil moisture from 15% to 6% of soil mass can decrease shoot growth and tuber formation considerably in purple nutsedge. Reduction of purple nutsedge growth due to moisture stress has also been reported by Bhardwaj and Verma (48). Several studies have adopted growth analysis techniques to evaluate water relations and growth of some weed species
(37, 36).
The objective of the present investigations were to find the effect of different shade and soil moisture levels on purple nutsedge growth under dry zone agroclimatic conditions of Sri Lanka.
MATERIALS & METHODS
Field experiments were conducted at research fields of the
Agricultural Research Station, Mahailuppallama, Sri Lanka from May to August
1990 and November 1990 to February 1991. These two periods closely followed the two growing seasons, Yala and Maha, respectively. The soil was a clay loam 72 belonging to the Reddish Brown Earth group (Hapustulfs) with 0.5 to 2% organic
matter and pH of 5.5 to 65 (13). Fields infested with uniform populations of
purple nutsedge were selected for the study.
Fffart nf s>ifl sunlight were imposed at three different growth stages, viz, at emergence, at two weeks and at four weeks after emergence of purple nutsedge. Shade levels also included a full sunlight treatment as the control. Shade levels were imposed with coverings of commercial black polypropylene shade cloth of specified shade densities. Measured irradiance under the 30%, 47%, 63% and 80% shades, as used in the field were 75%, 55%, 33% and 24%, respectively of unshaded sunlightThe shade cloth was attached to 2 x 2 m frames which were supported approximately 30 cm above the plant canopy and periodically raised as the plants grew in height The five light and three stages of plants were arranged factorially in randomized complete blocks with four replications. Weeds in the experimental plots were removed by hand at two week intervals to avoid other weeds from shading the purple nutsedge plants. All plots received 50 kg N ha'1 through a top dressing of urea during the first week of purple nutsedge growth. Supplementary irrigation was provided for the plots in the first experiment which was conducted during the short rainy season, Yala. But the experiment when repeated in the long rainy season, Maha, was conducted totally under rainfed conditions. After a period of three months growth, plant 73 counts were taken from four 0.25 m"2 quadrats from each plot Plants were removed from a single 0.25 m"2 quadrat from each plot to measure the leaf area and to determine the diy weight of leaves and stems. From the same quadrat, tubers were removed from the soil to a depth of 30 cm, counted and their diy weights recorded. Leaf areas were measured with a leaf area meter4 from a subsample taken from the plants sampled from each plot These values and the diy weight of leaves in the subsample were used to estimate the total leaf area for each sample taken from a plot All data values were expressed on a m'2 basis before statistical analysis. Fffect nf soil moisture regime. Purple nutsedge was grown under four different soil moisture regimes in wooden boxes (60 x 60 x 45 cm) in a greenhouse for 13 weeks. The treatments were maintained as different soil moisture depletion levels from field capacity as follows: 1. no stress - soils were continuously maintained at field capacity 2. low stress - soil water restored to field capacity at 25% depletion of available moisture 3. medium stress - soil water restored to field capacity at 50% depletion of available moisture 4. high stress - soil water restored to field capacity at 75% depletion of available moisture 4 Delta T area metering device, model AM 82, Cambridge, England. 74 The boxes were filled with air dried soil of predetermined soil moisture content An equal amount of soil was added to each box and compared to a required height to bring the soil bulk density close to 1.5 to simulate Held conditions. Two gypsum blocks were placed, one at a 7.5 cm and the other at a 15cm depth in each box. They were used with a moisture meter to measure the soil moisture level. Soil moisture levels ranging from field capacity to wilting point were measured and the moisture meter readings recorded. A plot of moisture readings and soil moisture levels was used as a calibration curve to determine the various available moisture depletion levels from field capacity for different treatments. 50 kg N ha'1 was added to the soil in each box through urea at the beginning of the experiment Two to three 1-week-old purple nutsedge plants of uniform size were planted in each box and watered adequately for one week until the plants got established. At two weeks, plants were thinned to one per box and the moisture treatments were started. Soil moisture were monitored periodically and when the respective moisture stress levels were reached the soils were brought back to field capacity by adding a sufficient amount of water. The plants were harvested four times at approximately 3-week intervals. The four harvests and the four moisture levels were arranged in a randomized block with four replications. At each harvest total number of shoots and tubers were counted for each box and the leaf area for all the plants in each box was determined using a subsampling procedure as described for the shade experiment The total dry weight of leaves, tubers, shoots, roots and rhizomes 75 were determined for the plants in each box. The experiment was conducted twice in a greenhouse under natural daylight Growth analysis. Classical growth analysis techniques (41, 38, 10) were used to evaluate the effect of soil moisture and shade levels on diy matter production, leaf area expansion and partitioning of biomass into different plant parts. Data collected from four harvests of the moisture level experiment were used to calculate the net assimilation rate (NAR) and relative growth rate (RGR) at the end of 6, 9, and 12 weeks after planting of purple nutsedge. The following relationships were used in growth analysis: leaf weight ratio (LWR)= leaf dry weight/total plant diy weight specific leaf area (SLA)= leaf area/leaf diy weight leaf area ratio (LAR)= leaf area/ total plant diy weight tuber weight ratio (TWR)= tuber diy weight/total plant diy weight root weight ratio (RWR)= root diy weight/total plant dry weight rhizome weight ratio (RhWR)= rhizome diy weight/total plant diy weight net assimilation rate (NAR)= (W2 - WjXlnLj - InL^/Oj - ^(Lj - L^ relative growth rate (RGR)= (lnW2 - InWj)/^ -1^ where, Vf1 and W2 are the initial and final plant diy weight for time ^ and time t2 respectively and Lj and L2 are the total leaf area for the time tj and time ^ respectively. All data were analyzed by analysis of variance. Whenever the data departed from normality and homogeneous variance, appropriate transformations 76 were done. Duncan's multiple range test were used to determine the significant differences among mean values for different shade and moisture levels. In the shade experiment, regression components were determined for growth parameters with significant effects as determmed by analysis of variance. RESULTS AND DISCUSSION Fffftfrt flf shafa Timing of shading, ie when shading was imposed at different growth stages of purple nutsedge, had no significant effect on the vegetative growth of purple nutsedge. Also there were no significant interactions between timing and different shade levels on any of the growth responses measured (results not shown). Significant differences were observed in growth responses only among different shade levels. When interaction among factors are not significant, it is appropriate to discuss the results based on the means of individual factors separately (40). Therefore the results are discussed on the mean values of various growth parameters at different shade levels, averaged over the three different times of shading. Shading significantly reduced plant number, leaf area and leaf dry weight in both the Yala and Maha seasons. The responses of these growth parameters to shading were best described as either a negative linear or quadratic relationship and are analogous to Patterson's (34) findings with itchgrass. Plant number showed a significant negative linear relationship to increased shading in both seasons (Figure 4.1). Leaf area showed a significant negative quadratic 77 relationship to increased shade in both seasons (Figure 4.2). Leaf dry weights were reduced either in a linear or quadratic manner with increased shade (Figure 43). Plant number at different shade levels were comparable in both seasons and the leaf area and leaf dry weight responses also followed a similar trend in the two seasons. Leaves produced in the shade were thinner than those produced in full sunlight and this was reflected in the greater SLA with increased shade (Table 4.1). Patterson (35) also observed a similar increase in specific leaf area with reduced light in purple nutsedge. The LAR and LWR also increased significantly with increased shade (Table 4.1). Increasing shade caused a significant reduction in purple nutsedge tuber number(Figure 4.4), total tuber weight (Figure 4.5) and mean tuber weight (Table 42). Reductions in tuber weights with increased shade, thus, were due to the reduction of both tuber number as well as size of tubers. In both seasons, shading reduced tuber number and size as a result of reduced biomass allocation to tubers and as evidenced by reduced TWR (Table 4.2). Total tuber production, particularly at lower shade levels was higher during the Yala than in the Maha season. This may be explained by the relatively greater allocation to tubers in the Yala season. Total dry matter production was reduced significantly as shade increased in both seasons (Figure 4.6) and the response assumed a negative quadratic relationship. This suggests that overall growth of purple nutsedge was more sensitive to higher shade levels than to lower shade levels. Total dry matter 78 production during the Yala season, particularly at lower shade levels, was relatively greater than during the Maha season (Figure 4.6). Reduced purple nutsedge growth due to shade, as exhibited by the number of shoots, tubers, leaf area and total plant dry weight, has been reported by other researchers (35, 24, 19, 49, 42). The degree of plant response to shade, however, was not similar in all of these investigations. Patterson (35) observed reductions in tuber number, leaf area and plant dry weight with increased shade and Jorden-Mollero and Stoller (24) reported significant negative linear relationships. Conversely, William and Warren (46) observed no significant effect on plant number or dry matter production when the degree of shading was increased from 0% to 63% of full sunlight. The inconsistent responses to shade by purple nutsedge could be due to the interaction of temperature and shade. Following his observation of this interaction. Wills (49) reported that maximum growth of purple nutsedge for all portions of the plant occurred at 32 C and 19 klux illumination while minimum growth occurred at 40 C and 9 klux illumination; growth at 24 C was not significantly affected by increased illumination above 9 klux. In the present investigation, the temperature ranged from 28-33 C throughout the growing period and the actual light intensities at different shade levels ranged 100%, 75%, 55%, 33%, and 24% of full sunlight Differences in LWR reflect the changes in partitioning of plant biomass into leaf component whereas differences in SLA reflect changes in the structure of leaves themselves or distribution of leaf biomass as leaf surface area. 79 Significant increase in LWR (Table 4.1) with shading in this investigation indicates that relative allocation of biomass to leaves increased with increasing shade. Since LAR is the product of LWR and SLA the concomitant increase of SLA and LWR resulted in a significant increase in LAR with increased shade. LAR has been shown to respond dramatically to shading in a number of plant species (6, 15, 34) including purple nutsedge (35). Increased LAR with shading has an adaptive value under conditions of low irradiance for the whole plant because a greater LAR represents a greater investment of plant material in photosynthetic tissues (16). However, despite the increase of LAR, drastic reductions in total purple nutsedge biomass as a response to shading suggests that increased LAR was unable to compensate for the decreased irradiance. Increased shading altered the biomass allocation pattern in purple nutsedge, reducing allocation to tubers (Figures 4.4 and 4.5) and increasing that to leaves (Figure 4.3). A similar response in allocation for purple nutsedge has also been reported in several other studies (35, 49, 3). This alteration of allocation to tubers and leaves due to shading in purple nutsedge is of considerable importance in relation to its control and management,. The primary mode of propagation in purple nutsedge occurs through the production of tubers (20, 43). Since shading drastically reduced tuber production, adoption of crops with rapid canopy development favors the suppression of purple nutsedge. In fact this has been emphasized on several occasions (45, 46, 49). It was evident that timing of shading, ie when shading was imposed 80 at 0, 2 and 4 weeks of purple nutsedge emergence, had no significant effect on the vegetative growth of purple nutsedge (results not shown). This suggests that shading of purple nutsedge is not really critical during the first four weeks of its growth. One possible reason for this could be that the development of maximum leaf area might not have occurred during the first 4 weeks of growth. William and Warren (46) reported that leaf area index (LAI) for purple nutsedge was very low (0.4 to 0.5) during the first four weeks of its growth and the plant reached to its maximum LAI, 1.0, only after 7 to 9 weeks into the growth period. This relatively low LAI and rather slow canopy development of puiple nutsedge provide further insight in selecting more suitable crops for its management William and Warren (46) observed a close relationship between yield loss due to purple nutsedge competition and the rate of canopy development in several vegetables. Fast growing crops such as green beans and cucumber incurred relatively low yield losses compared to slow growing crops such as garlic and okra. They found that garlic never formed a good canopy while okra took more than 9 weeks to reach its full canopy development, a period longer than purple nutsedge required to reach its maximum LAI. Conversely, green beans and cucumber competed well for light with purple nutsedge as they either reach their maximum LAIs simultaneously or sooner than purple nutsedge. The results from the study reported here suggest that puiple nutsedge is highly sensitive to shade under dry zone agroclimatic conditions and there is a great potential to suppress and manage this plant with crops that would provide rapid canopy development within 81 4-5 weeks after purple nutsedge emergence. However, shading may not provide complete control as purple nutsedge continued to grow and produce some tubers even at 80% shade. Fffer^ flf soil moisture. There was no significant interaction between soil moisture level and age or stage of development of purple nutsedge. This indicates that the various stages of growth of purple nutsedge responded similarly to different soil moisture levels. However, there were significant responses to moisture levels and significant developmental changes through the growing seasons. Number of plants and tubers, leaf area, dry weight of leaves and tubers and total plant biomass of purple nutsedge showed significant increases with age (Tables 4.3 and 4.4). LAR and SLA decreased significantly with plant age while LWR remained essentially unchanged with plant age (table 4.5). There was a progressive increase in dry matter allocation to tubers with plant age as indicated by TWR (Table 45). A similar response was also observed by Parker (33). Moisture stress significantly reduced plant number, tuber number and leaf area (Table 4.6). Tuber weight also was reduced with lower soil moisture (Table 4.7) and a similar response was found for leaf dry weight Growing purple nutsedge under conditions of low soil moisture significantly reduced plant weight (Table 4.7) but despite this reduction, biomass allocation to leaves remained constant over the different soil moisture regimes. This was evident from constant LWRs (not shown) over the different soil moisture levels. When calculated values 82 of LAR, SLA, LWR, RWR, RhWR, NAR and RGR were compared for moisture stress level treatments, no significant differences (P>0.05) between the values for each ratio were found and these data are not reported. With the components of these ratios being affected similarly by soil moisture content, as purple nutsedge growth was suppressed by moisture stress all growth parameters were reduced and these ratios remained relatively constant Thus purple nutsedge maintained its allocation pattern to different plant parts more or less constant as the soil moisture level was reduced. The classical equation for mean NAR involves the assumption that plant dry weight is linearly related to leaf area (41). With all moisture levels this held true throughout the period of monitored growth of purple nutsedge in this study. Estimates of NAR for periods from 3 to 6, 6 to 9 and 9 to 12 weeks appear to be reasonably unbiased. NAR declined with plant age, but showed no significant differences with decreasing soil water. RGR also showed no significant change with a decrease in available soil water, though this ratio also declined with plant age. A decline in NAR and RGR with plant age has been reported in soybean (10). The overall behavior of purple nutsedge to reduced available soil water appears to be a plastic response involving the reduction of total biomass or the gross plant size. This decline in biomass is directly correlated with and may be attributed to reduced leaf area. Long term water stress to plants results in less extensive leaf canopies (16, 4). A less developed canopy would tend to reduce the quantity of 83 solar radiation intercepted thereby reducing the plant's potential for dry matter production. Despite the decrease in purple nutsedge total biomass production, there were no changes in allocation pattern, in NAR, or in RGR with reduced soil moisture. The adaptive nature of biomass allocation in plants has been the subject of many investigations (2, 11, 21, 31). Abrahamson and Gadgil (1) suggested that the pattern of allocation will depend on the nature of the limiting factor. They gave the example that with water as the limiting factor, a larger fraction of the biomass would be allocated to roots. If this hypothesis is true, the absence of any increase in allocation to roots in the present study suggests that purple nutsedge response to moisture stress was not limiting and reduced growth effects were still additive with the other factors. In other words, there seems to be a certain degree of adaptability by purple nutsedge to imposed moisture levels in this investigation. RGR has been proposed as a measure of the integration of a plant's physiological attributes while other growth parameters are used to partition growth into more specific physiological responses to environmental changes (15, 17). NAR is a measure of the net gain of plant's dry matter relative to the leaf area, and is usually regarded as an indicator of mean photosynthetic efficiency (47). Thus, a lack of significant differences in NAR, indicates that the photosynthetic efficiency of purple nutsedge has not been impaired with the depletion of soil moisture to the extent tested (75 percent of available moisture) in the present investigation. One contributory factor for this behavior could be the photosynthetic pathway of purple nutsedge. This species belongs to the C4 group of plants (50) and they are 84 considered to be more adapted to compete well in hot, dry environments (16). Moreover, their water use efficiency is relatively high when compared to C3 plants (39). Thus the absence of differences in RGR and NAR in purple nutsedge with soil moisture depletion suggests that this plant is physiologically adapted for survival under low soil moisture conditions. The results of this investigation emphasize the significance of shade and soil moisture on population growth and tuber production of purple nutsedge. Increased shade reduced overall population growth and tuber production. Since tubers play a central role in perpetuation and expansion of purple nutsedge populations, inclusion of fast growing crops with rapid canopy development into cropping patterns is potentially useful in managing this weed in the Dry Zone of Sri Lanka. Considerable fluctuations in soil moisture during the growing seasons are quite common in the Dry Zone. Our results suggest that such moisture fluctuations may not seriously affect purple nutsedge growth, as it is adapted to survive under low soil moisture levels. This characteristic of puiple nutsedge may be a leading causative factor for its dominance among crop and weed species in the Diy Zone of Sri Lanka. 85 Table 4.1. Effect of shade on purple nutsedge specific leaf area (SLA), leaf area ratio (LAR) and leaf weight ratio (LWR) during two growing seasons3. Percent shade SLA LAR LWR Yala Maha Yala Maha Yala Maha 2 cm •S"1 g •g"1 0 76.6a 88.9a 12.5a 18.1a 0.16a 0.20a 30 82.3a 90.1a 12.6a 18.8a 0.15a 0.21a 47 106.2b 983ab 16.8b 21.8ab 0.16ab 023b 63 102.8b 107.4b 18.8b 23.7b 0.18b 0.23b 80 105.8b 135.7c 18.8c 31.3c 0.26c 0.23b aValues in column followed by the same letter are not significantly different (p>0.05) by the Duncan's multiple range test. 86 Table 4.2. Effect of shade on purple nutsedge average dry weight per tuber and tuber weight ratio (TWR) during two growing seasons*. Percent shade Average dry weight per TWR tuber Yala Maha Yala Maha g g-g-1 0 028a 028a 0.66a 0.5a 30 029a 0.25ab 0.70a 0.47a 47 022b 0.21abc 0.61b 0.47a 63 0.20b 0.18bc 032c 0.43b 80 0.18b 0.17c 0.50c 0.36c aValues in a column followed by the same letter are not significantly different P>0.05) by the Duncan's multiple range test 87 Table 4.3. Purple nutsedge growth responses as represented by plant number, tuber number and leaf area pot in two growing seasons3. Plant age Plant density Tuber production Leaf area Yala Maha Yala Maha Yala Maha weeks no. pot -1 cm2 .pot"1 3 12a 5a 5a 4a 875a 349a 6 51b 22b 29b 5a 5220b 2233b 9 114c 111c 111c 59b 14742c 6265c 12 252d 303d 246d 325c 26737d 26447d a Values in a column followed by the same letter are not significantly different (p>0.05) by the Duncan's multiple range test 88 Table 4.4. Purple nutsedge growth responses as represented by leaf dry weight, tuber dry weight and total dry weight pot"1 in two growing seasons3. Plant age Leaf dry weight Tuber dry weight Total dry weight Yala Maha Yala Maha Yala Maha weeks g. pot"1 3 2.7a 1.1a 1.7a 0.29a 5.7a 1.8a 6 17.6b 8.8b 17.4b 3.01b 42.9b 15.2b 9 49.5c 61.2c 52.3c 36.7c 120.6c 122.3c 12 1123d 148.3d 127.6d 154. Id 276.8d 356.3d a Values in a column followed by the same letter are not significantly different (p>0.05) by the Duncan's multiple range test. 89 Table 4.5. Purple nutsedge growth responses as represented by leaf area ratio (LAR), specific leaf area (SLA), leaf weight ratio (LWR), and tuber weight ratio (TWR) in two growing seasons3. Plant LAR SLA LWR TWR age Yala Maha Yala Maha Yala Maha Yala Maha 2 1 i weeks cm .g- g-g' 3 144a 195a 317a 321a 0.46 0.46 0.29a 0.16a 6 132a 146b 315a 251b 0.40 0.44 0.40b 0.2b 9 122ab 75c 301a 180c 0.41 0.43 0.42b 0.29c 12 101b 54d 247b 104d 0.43 0.41 0.46c 0.43d "Values in a column followed by the same letter are not significantly different (p>0.05) by the Duncan'd multiple range test. Table 4.6. Effect of soil moisture regimes on plant number, tuber number, and leaf area of purple nutsedge pot"1 in two growing seasons8. Available Plant densitv Tuber production Leaf area soil moistureb Yala Maha Yala Maha Yala Maha % no. pot*1. cm2 .pot"1 100 136a 126a 173a 115a 18114a 11372a 75 126ab 114ab 113b 103ab 14066b 9197a 50 92b 107ab 67c 91b 9098c 8421ab 25 75c 95b 38d 85b 6297d 6303b * Values in a column followed by the same letter are not significantly different (p>0.05) by the Duncan's multiple range test b Soil moisture regimes were established by allowing soil moisture levels to be depleted to levels indicated, as a percent of available moisture. o 91 Table 4.7. Effect of soil moisture regimes on leaf dry weight, tuber dry weight, and total plant dry weight of purple nutsedge pot"1 in two growing seasons3. Available Leaf dry weight Tuber drv weight Total dry weight soil moistureb Yala Maha Yala Maha Yala Maha % g 100 75.1a 77.1a 86.8a 60.1a 188a 167a 75 53.7b 53.2ab 53.7b 49.8ab 126b 118b 50 32.1c 49.9bc 36.2c 44.7b 80c 117b 25 21.1d 393c 22. Id 33.4b 50d 92b Values in a column followed by the same letter are not significantly different (p>0.05) by the Duncan's multiple range test ' Soil moisture regimes were established by allowing soil moisture levels to be depleted to levels indicated, as a percent of available soil moisture. 92 Figure 4.1. Number of purple nutsedge plants produced under different shade levels in the field. 600 ^— YALA 500 ▼-- MAHA IB 400 O Z YALA: Y=568.3-4.56X J 300 a, r2 = 0.94 MAHA: Y=593.3-5.21X 200 r2 = 0.94 20 40 60 PERCENT SHADE 93 Figure 4.2. Total leaf area of purple nutsedge plants produced under different shade levels in the field. 1 7000 1-- T 6000 L ^ ^X --T-- MAHA _ 's - NX X ^s 5000 N.\ - 4000 _- _ YALA: y=7036.7+338x-0.87x2 \ N - \ \ ^=0.97 T - — \ \ 3000 - MAHA: y= 6253.5+1078X-0.6166X2 — ■ ^=0.99 \ 1 2000 - . i . . . I . . . r . . i Vli -1 20 40 60 80 PERCENT SHADE 94 Figure 4.3. Leaf dry weights of purple nutsedge produced under different shade levels in the field. r 100 ~\ " ■^ r ■ i i T-^l — YALA 's - MAHA H ac o 60 - YALA: Y=96.274 - 0.899X R2=0.91 ^ 40 MAHA: Y=70.41+0.111X-0.00878X2 R2=0.99 20L_L 20 40 60 PERCENT SHADE 95 Figure 4.4. Number of purple nutsedge tubers produced under different shade levels in the field. [ ' ' ' l -■—■—■—I— ■ T , , , ' 1 1400 ■ V ^^ V 1200 V --T-- MAHA ts 1000 _ _ s V\ ■ o 7, 800 - - B -*._ 600 YALA: y=1339+1.971x-0.195x2 ^^Av^ 400 ^=0.97 MAHA: y=727+1.285x-O.0648x2 v- ^=0.96 200 i ... I ... i . i . . 20 40 60 80 PERCENT SHADE 96 Figure 4.5. Weight of purple nutsedge tubers produced under different shade levels in the field. 1 1 T 400 V— YALA I s ▼-- MAHA 300 o 200 - YALA: y=395.4-2.089x-0.031 lx2 ^ "* ■ 100 1^=0.93 MAHA: y=180.6-0.396x-0.0167x2 12=0.99 _■ i , I , , . L ' ' ■ ■ ' ■ 20 40 60 PERCENT SHADE 97 Figure 4.6. Total purple nutsedge plant dry weight produced under different shade levels in the field. 1 , . . . 1 . . . , - -T T ' 1 ' ■ ' 1 - ^^^^ V V " ifdJK "e 500 --T-- MAHA ~ 00 H g 400 — r ▼" --- \ J i. ""---. ^T N V ^\ : g 300 [ ^ ^ \ i- ^ "^ \ \ •• - YALA: y= 567.5-1.614x-0.0558x2 o^ 200 r2 =0.94 ~-?\N \ \^ iJ ; MAHA : y= 360.9-0.530x-0.033x2 V 100 ^=0.98 > i ... i ... i . 1 ... 1 1 20 40 60 80 PERCENTAGE SHADE 98 LITERATURE CITED 1. Abrahamson, W. G. and M. Gadgil. 1973. Growth forms and reproductive effort in goldenrods (Solidago, Compositae). Am. Nat 107:651-661. 2. Abrahamson, W. G. and B. J. Hershey. 1977. Resource allocation and growth of Impatiens capensis (Balsaminaceae) in two habitats. Bull. Torrey BoL Club. 104:160-164. 3. Bantilan, R. T., M. C. Palada and R. R. Harwood. 1974. Integrated weed management: I Key factors affecting crop-weed balance. Philippine Weed Sci. Bull 1:14-36. 4. Begg, J. E and N. C. Turner. 1976. Crop water deficits. Adv. Agronomy. 28:161-217. 5. Black, C. C, T. M. Chen and R. H. Brown. 1969. Biochemical basis for plant competition. Weed Sd. 17:338-334. 6. Blackman, G. E. and G. L. Wilson. 1952. Physiological and ecological studies in the analysis of plant environment VE. An analysis of the differential effects of light on NAR, LAR and RGR of different species. Ann. Bot 59:373-408. 7. Bhardwaj, R. B. L. and R. D. Verma. 1968. Seasonal development of nutgrass (Cypems mtundus L) under Delhi conditions. Indian J. Agric. Sd. 38:950-957. 8. 8. Boyd, J. W. and D. S. Murry. 1982. Effects of shade on silverleaf night shade (Solanum elaegnfolium). Weed Sd. 30:264-269. 9. Buber, J. C. and I. N. Morrison. 1984. Growth response of green and yellow foxtail (Setaiia viridis and S. ktescens) to shade. Weed Sd. 32:774-780. 99 10. Buttery, B. R. (1969). Analysis of growth of soybean as affected by plant population and fertilizer. Can. J. Plant Sci. 49:675-684. 11. Cartica, R. J. and J. A. Quinn. 1982. Resource allocation and fecundity of populations of SoUdago sempewiiem along a coastal dune gradient Bull, of Torrey Bot Club. 109:299-305. 12. Davies, C. H. 1942. Response of Cypems mtundus to five moisture levels. Plant Physiol. 17:311-316. 13. De Alwis, K. A. and C. R. Panabokke. 1972-73. Handbook of the Soils of Sri Lanka (Ceylon). J. Soil Science Soc. of Sri Lanka. Vol.n. 97pp. 14. Donald, C. M. 1963. Competition among crop and pasture plants. Adv. Agronomy. 15:1-118. 15. Evans, G. C. 1972. The quantitative analysis of plant growth. University of California Press, Berkly. 734pp. 16. Fitter, A. H. and R. K. M. Hay. 1989. Environmental physiology of plants. Academic Press, London. 421pp. 17. Grime, J. P. and R. Hunt 1975. Relative growth rate, its range and adaptive significance in a local flora. J. of Ecology. 63:393-422. 18. Harper, J. L. 1977. Population Biology of Plants. Academic Press, London. 892pp. 19. Hauser, E. W. 1962. Establishment of nutsedge from space-planted tubers. Weeds. 10:209-212. 20. Holms, L. G., D. L. Plucknette, J. V. Pancho, and J. P. Herberger. 1977. The 100 world's worst weeds-Distribution and biology. Univ. Press, Hawaii. Pages 8-24. 21. Hickman, J. C. 1975. Environmental unpredictability and plastic energy allocation strategies in at the annual Pofygomm cascadensis (Polygonaceae). J. of Ecology 63:689-701. 22. International Rice Research Institute. 1976. Weed management Pages 325-329 in Int Rice Res. Inst(Los Banos) Annual Report, 1974. 23. International Rice Research Institute. 1979. Weed control in rice. Pages 302- 306 in Int Rice Res. Inst(Los Banos) Annual Report, 1978. 24. Jorden-Molero, J. E. and E. W. Stoller. 1978. Seasonal development of yellow and purple nutsedge : (Cypems escukntus and Cypems wtundus). Weed Sci. 26:614-618. 25. Keely, P. E. 1987. Interference and Interaction of purple and yellow nutsedge (Qpems wtundus and C escukntus) with crops. Weed Technol. 1:74-81. 26. Keely, P. E. and R. J. Thullan. 1978. Light requirements of yellow nutsedge (Qpems escukntus) and light interception by crops. Weed Sci. 26:10-16. 27. Kondap, S. M., K. Ramakrishna, S. B. Reddy and A. N. Rao. 1982. Investigations on the competitive ability of certain crops against purple nutsedge (Cypems wtundus L). Indian J. Weed Sd. 14:124-126. 28. Knake, E. L. 1972. Effects of shade on giant foxtail. Weed Sci. 20:588-592. 29. 29. Kozlowski, T. T. 1968. Water deficits and plant growth. Vol. n. Academic Press, London. 339pp. 30. Lee, S. M. and P. B. Cavers. 1981. The effect of shade on growth. 101 development and resource allocation pattern on three species of foxtail (Setaria). Can. J. Bot 59:1776-1786. 31. Ogdon, J. 1974. The reproductive strategy of higher plants, n. The reproductive strategy of TussOago faifam L. J. of Ecology 62:291-324. 32. Okafor, L. I. and S. K. DeDatta. 1976. Competition between upland rice and purple nutsedge for nitrogen, moisture, and light Weed Sd. 42:43-46. 33. Parker, C. 1985. Growth patterns in Cypems wtundus. Proc. 1985 British Crop Protection Conference-Weeds. Pages 387-394. 34. Patterson, D. T. 1979. The effect of shading on growth and photosynthesis capacity of Itchgrass (Rottboellia exatata). Weed Sd. 27:549-553. 35. Patterson, D. T. 1982. Shading response of purple and yellow nutsedges (Cypems wtundus and Cescukntus). Weed Sd. 30:25-30 36. Patterson, D. T. 1988. Growth and water relations of cotton (Gossypium hiisutum), spurred anoda (Anoda cristata) and velvet leaf (AbutUon theophrasti) during simulated drought recovery. Weed Sd. 36:318-324. 37. Patterson, D. T. and E. P. Flint 1983. Comparative water relations, photosynthesis and growth of soybean (Gfycine max) and seven associated weeds. Weed Sd. 31:318-323. 38. Patterson, D. T., C. R. Meyer and P. C. Quimby. 1978. Effects of irradiance on relative growth rates, net assimilation rates and leaf area partitioning in cotton and three assodated weeds. Plant Physiol. 62:14-17. 39. Pearcy, R. W. and J. Ehleringer. 1984. Comparative ecophysiology of C3 and 102 C4 plants. Plant cell and Environment 7:1-13. 40. Petersen, R. 1985. Design and Analysis of Experiments. Marcel Dekker, In. New York. 429pp. 41. Radford, P. J. 1967. Growth analysis formulae - their use and abuse. Crop Science 7:171-175. 42. Sierra, J. N. 1969. Some aspects of the biology of purple nutsedge Cypems mtundus L. Biotrop Bull. 15: 76-86. 43. Smith, E. V. and G. L. Fick. 1937. Nutgrass eradication studies: I. Relation of the life history of nutgrass Cypems mtundus L to possible methods of control. J. Amer. Soc. Agron. 29:1007-1013. 44. Turner, N. C. and P. J. Kramer. 1980. Adaptation of plants to water and high temperature stress. John Wiley and Sons. Inc., New York. 482pp. 45. William, R. D. 1976. Vegetable crop losses caused by purple nutsedge competition. Proc. Fifth Asian-Pacific Weed Sci. Soc. Conf. Pages 78-81. 46. Williams, R. D. and G. Warren. 1975. Competition between purple nutsedge and vegetables. Weed Sci. 23:317-323. 47. William, W. A., R. S. Loomis and C.R.Lepley. 1965. Vegetative growth of com as affected by population density 11. Component of growth, net assimilation rate and leaf area index. Crop Sci. 5:215-219. 48. Wise, A. F. and C. W. VanDiver. 1970. Soil moisture effects on competitive ability of weeds. Weeds Sci. 18:518-519. 49. Wills, G. D. 1975. Effect of light and temperature on growth of purple 103 nutsedge. Weed Sri. 23:93-96. 50. Wills, G. D. 1987. Description of purple and yellow nutsedge (Cypenis mtundus and Cescukntus). Weed Technol. 1:2-9. 51. Zimdahl, R. L. 1980. Weed-crop competition. A review. Int Plant Prot Ctr., Oregon State Univ., Corvallis, Oregon. 195pp. 104 Chapter 5 A Preliminary Population Model for Purple Nutsedge {Cypems mtundus)1 LAKSHMAN AMERASINGHE, GARVIN CRABTREE, C. M. GHERSA AND S. R. RADOSEVICH2 Abstract. A matrix model for simulating population dynamics of purple nutsedge was constructed using observed and previously reported data. The model considered three life history stages: shoots (including basal bulbs and rhizomes), terminal and dormant tubers as state variables and five transition periods for a single growing season. Primary model simulation resulted in exponential growth. With density dependent functions added, the model simulated an asymptotic growth pattern and the population attained its maximum size when populations of shoots, terminal and dormant tubers reached 1537 m"2, 3220 m"2 and 7763 m"2 respectively. Production of secondary shoots from primary shoots in the first 2 to 4 weeks of the growing period, development of tubers 8 to 12 weeks after nutsedge emergence and their survival during the quiescent period between growing seasons, were emerged as important population regulatory events from 1 Received for publication , and in revised form. 2 Grad. student and Prof., Dept Horticulture and Visiting Prof., and Prof., Dept Forest Sri., Oreg. State Univ., Corvallis, OR. 97331. Current address of senior author: Regional Agricultural Research Station, Maha Duppallama, Sri Lanka. 105 sensitivity analysis. Proportional changes in population size with simulation as ofshading or soil moisture depletion closely followed field results. Optimum time for herbicide application, predicted by model simulations to be 2 to 4 weeks into the growing season, was also supported by field results. The model was used to compare several control options for long-term management of purple nutsedge. Nomenclature: purple nutsedge (Cypews rotundas L). #3 CYPRO. Additional index words. Population dynamics, matrix models, transition matrix, management options, CYPRO. INTRODUCnON Despite the large number of studies conducted on ecology, biology and various control techniques (4, 8, 24, 30), purple nutsedge has been repeatedly cited as the world's worst weed, following Holme's et al(13) ranking (22). Perennial habit, profuse growth rate and production of dormant tubers causes purple nutsedge to continue to be a persistent weed in many cropping situations. The seriousness of the problem is further exacerbated by poor or marginal control provided by various control methods. Sager and Mortimar (25) suggested that studying the demography of such perennial weeds would provide a better 3 Letters following this symbol are a WSSA-approved computer code for Composite List of Weeds, Revised 1989. Available from WSSA, 309, West Clerk Street, IL 61820. 106 understanding of the factors regulating their populations than studies of particular phases or interphases (transition probabilities between adjacent phases within the time-span of a generation) of their life cycle. From demographic studies it is possible to construct mathematical models of the life cycle of plants and such mathematical models serve as a framework to identify those characteristics which regulate the proliferation and persistence of weed populations (19, 21). The functional relationships between the system variables in population models are defined mainly either by difference equations or matrix algebra. Models based upon difference equations assume that the individuals in populations are identical, possess a similar growth rate and that their generations are discrete. These models are more appropriate for annual species as their successive generations do not overlap (27). Unlike annual species, perennial weeds are characterized by overlapping generations and their populations are composed of several hierarchies of individuals which differ either by age or structure or both. Since models based upon matrices explicitly incorporate the age or stage structure of a population with overlapping generations these models are more appropriate to describe the population dynamics of perennial weeds. While several researchers have adopted this approach to develop population models to study the population dynamics of plant species (15, 16, 21, 33) few have extended it to identify the critical stages in life cycles of weeds in relation to their control and management (19, 33). Another significant aspect of matrix 107 models is that they can serve as a framework to organize weed biology information and help to identify information gaps, to set research priorities and to develop hypothesis pertinent to weed population regulation (19). The objective of the present study was to develop a preliminary matrix model for purple nutsedge populations and to use it to identify critical stages in the life cycle of this weed and to evaluate different control options using model simulations. PRIMARY MODEL DEVELOPMENT Use of demographic information to develop population models with matrices as proposed by Leslie (18) was subsequently improved by several workers (9, 17, 34). Mortimar et al (21) further extended these models to make predictions about future infestation levels in weed populations under different control strategies. The first stage of model development involves classifying different stages in the life cycle of the plant followed by preparing a life table from demographic data (5, 27). Classification of life stages can be either size specific or age specific, however, it is often difficult to make accurate estimates of the ages of plants. Lefkovitch (17) proposed the use of size class data where the classes are not necessarily of equal size. Werner and Caswell (34) showed that division of the life cycle of a short lived perennial plant into developmental stages may help to predict its future behavior more accurately than division into true age classes. Most reported studies of matrix models follow the stage-based 108 classification of the life cycle (7, 15, 20, 21, 33) and the same approach was employed for the present investigation of purple nutsedge. Life history stages of purple nutsedge can be divided broadly into several developmental stages as seeds, shoots, terminal tubers and dormant tubers. Tubers are recognized as the primary propagation unit in purple nutsedge (29). A shoot originates from a tuber and terminal and dormant tubers subsequently form at the end of rhizomes that develop from the basal bulbs found at the base of every shoot Population expansion occurs through continuous production of shoots, rhizomes and tubers. Inclusion of seeds into the diagrammatic life table of purple nutsedge is not critical as contribution from seeds to the plant's population is insignificant (29). Therefore the diagrammatic model of purple nutsedge would consist of only three stages of the life cycle: shoots (including basal bulbs and rhizomes), terminal tubers and dormant tubers (Figure 5.1). Conversion of state variables (boxes) shown in the diagrammatic model into actual values and development of functional relationships that describe the flow rates between life history stages (state variables) follows the organization of the conceptual framework of the model. Matrices provide a way of laying out symbolic interactions among different state variables and their functional relationships. The state variables are summarized in a column vector while the functional relationships between state variables form a transition matrix (Figure 5.2). When the transition matrix is multiplied by the column vector (the number of individuals in each state variable at one time period) the number of 109 individuals in each state variable present in the subsequent time period is given. Through repetitive multiplication of the transition matrix by successive column vectors by the transition matrix, the dynamics of the population can be simulated. The whole process can be summarized with the equation: N(t+i) = M N(t) • This equation indicates that at the next observation time (t+1), the population size (the number of individuals in each life history stage) N/t+1j is the product of the transition effect (M) and the number of individuals contained in all appropriate life history stages at the current time N/ty Purple nutsedge growth in the Dry Zone of Sri Lanka follows a seasonal pattern determined by rainfall distribution. The two characteristic growing seasons extend from early April to late June and from late October to early February. During each of the 3 to 4 month long dry periods purple nutsedge shoots die from lack of water and new growth resumes from tubers with the onset of rains at the beginning of each growing season. To coincide with these field conditions, the growing season of purple nutsedge was considered as 12 weeks in the model. Each growing season was sub-divided into the four periods of 0 to 2, 2 to 4, 4 to 8 and 8 to 12 weeks and a transition matrix was constructed for each period. An additional transition matrix was also included to represent the dry period. Other researchers (21, 33) have included several transition matrices in a single life cycle as this provides a better understanding of the population dynamics of the different life history stages of the plant 110 Commonly demographic data from published literature forms the base for calculating parameters in the transition matrix (20, 21, 33). In the present study , transition parameters for the viability of dormant tubers during the growing season and the parameters for the viability of both dormant and terminal tubers during the dry period were estimated using field data collected from the Dry Zone of Sri Lanka. The rest of the parameters in the matrices were estimated using the data reported by Sierra (26). Based on these sources the five transition matrices for purple nutsedge are shown in Figure 53. RESULTS FROM THE PRIMARY MODEL When population growth was simulated starting with a single tuber, the model predicted an exponential growth for all the growth stages, shoots, dormant tubers and terminal tubers in purple nutsedge (Figure 5.4). However, under natural conditions, exponential growth in plant species, including purple nutsedge, is unlikely because of intrinsic regulating factors. The model simulations showed an exponential growth in this study because the model did not mclude any of these regulatory factors. Watson (33) and Mortimar et al (21) also observed a similar exponential growth in populations for their primary growth models developed for leafy spurge and quackgrass respectively. This was also attributed to the absence of regulatory factors in their respective models. Despite this drawback, Watson (33) successfully used his model to compare the population growth of leafy spurge by different modes of reproduction while Maxwell et al (19) used the same model, with further refinement, to identify the critical stages in the life cycle of leafy spurge. The present primary model was also used to identify the critical stages of the life cycle in purple nutsedge. Sensitivity analysis is a technique that can be conveniently used to identify the most sensitive developmental stages for population growth. It is conducted by changing the value of a parameter in the model that describes a particular transition, while keeping all other transition parameters constant. This is followed by determining the new population size as predicted by the model. The sensitivity value of the changed parameter is given by the equation: A output A parameter Sensitivity value = / (19). output parameter Accordingly, large sensitivity values mean that small changes in a transition parameter cause large changes in the model output Thus critical parameters for the regulation of the population size can be identified by sensitivity analysis. Sensitivity analysis of the primary model suggested that certain transitions were more important in determining ultimate purple nutsedge populations (Table 5.1). Production of secondary shoots from the existing shoots (S}), sprouting of terminal tubers (S2) and maintaining the viability of terminal tubers during the quiescent period (T^) were the most important processes for population regulation in purple nutsedge. Also production of shoots was relatively more important early than late in each growth cycle. A noteworthy feature was that the sensitivity analysis gave a low ranking for the importance of dormant 112 tubers. This suggests that it is not the dormant tubers, but the terminal tubers that are significant in proliferating purple nutsedge. The sensitivity analysis also inferred that any method of control, if aimed at shoots, should be applied during the early part of the growing season. SECONDARY MODEL THAT CHARACTERIZES ASYMPTOTIC GROWTH Observations of natural populations show that with favorable conditions and freely available resources all populations have the potential to increase exponentially. With time, however, the rate of population growth slows and eventually a maximum population size is reached (3, 10, 32). Control of population growth in nature is governed primarily by increasing population density (27, 32). The primary model predicted an exponential growth in purple nutsedge because the transition parameters in the matrices were constant However, density dependent population regulation suggests that in natural populations, these transition parameters are continuously changing as the population size changes. Therefore density dependent regulation is an important factor to be incorporated into the model. From the sensitivity analysis of primary model simulation, it was evident that production of shoots from other shoots is the most critical parameter in regulating the purple nutsedge population during the growing season (Table 5.1). Using data reported by Williams, Quimby and Frick (35), intraspedfic density dependent functions for shoot production in purple nutsedge were 113 estimated for the growth periods of 0-3, 3-6 and 6-9 weeks of the life cycle (Figure 5.5). The constant transition parameters for shoot production in matrices relevant for 2-4, 4-8 and 8-12 week growth periods were replaced by the three density dependent functions. Thus the model was improved with the inclusion of the effect of intraspecific competition. A similar approach was also employed by Mortimar et al (21) to include the density dependent seedling survival oiPoa annua in a transition matrix. The model simulation using this new set of transition matrices (secondary model) resulted in an asymptotic growth simulation for purple nutsedge with the population reaching an equilibrium level with a shoot density of 1537 m"2, a terminal tuber density of 3220 m"2 and a dormant tuber density of 7763 m"2 (Figure 5.6). Similar asymptotic growth simulation was observed in previous models that included density dependent functions in transition matrices (19, 21). Natural populations of purple nutsedge have been reported to vary from 1000-1800 plants m"2 and from 2000 - 10,000 tubers m*2 (1, 11, 28). Thus the secondary model predictions followed closely the reported figures for purple nutsedge populations and the model appears to have considerable validity even though the simulated population size was higher than the population size of purple nutsedge observed under Dry Zone conditions in Sri Lanka. Plant populations observed under the Dry Zone conditions ranged from 230 -660 plants m"2 and 500 -1400 tubers m*2 respectively. This discrepancy suggests that the secondary model still needs further refinement to account for the response of 114 population to environmental variability common in the Diy Zone of Sri Lanka. Although population regulation is explained by the density dependent control of plant production, the actual size of a population in a given environment is determined by the interaction between density dependent and density independent processes (32). Thus, other than the plant density, genetic variability, weather patterns, season of growth , soil factors and management options can significantly influence plant populations from year to year. Since these density independent factors have not been taken into account in the model, and also their effects on purple nutsedge growth could vary from place to place, it is quite possible to expect a considerable difference between the model output and field observations. Inclusion of the effects of these density independent regulatory factors into the model leads to testing and further refinement of the model as suggested earlier. Since there are many transitions found in the model, it is necessary to select only those density independent factors that have the most impact on population growth. Sensitivity analysis can be used to rank the transitions for further study. From the primary model, production of shoots and terminal tubers and longevity of terminal tubers during the quiescent period were found to be the most influential life history stage parameters for population growth (Table 5.1). The major factors affecting the production of shoots of purple nutsedge could be broadly classified as proximity factors, physiological factors and environmental factors (Figure 5.7). Among the proximity factors the importance of intraspecific 115 competition on population regulation has already been observed. Among the environmental factors, temperature may be of least importance on purple nutsedge growth as the mean temperature in the Dry Zone varies only from 28- 32 C. However, nutrient availability, soil moisture and light intensity all could be of particular significance (6, 23). Since the purple nutsedge in this study is growing under rain-fed conditions, plants are subjected to periodic drought spells with the erratic rainfall distribution in the Dry Zone. Light, although received by the field surface in sufficient quantities throughout the growing season, could also be reduced for purple nutsedge as a result of shading from canopies produced by other plant species. Hence, apart from intraspecific competition, the most important factors influencing the population size of purple nutsedge could be light intensity and soil moisture level. COMPARISON OF SECONDARY MODEL OUTPUT WITH SOME FIELD OBSERVATION One way to validate a model is to compare of model predictions with the results obtained from field situations. However, data sets used to develop the model cannot be again used for validation purposes. The validity of the present model was examined by comparing the model responses of three density independent factors with field results. Data from Jordon-Molero and Stoller (14) and Bantilan et al (2) were used to incorporate into the secondary model the effect of shade on plant 116 growth and the model simulations were compared against the results of a field experiment Effect of soil moisture on purple nutsedge growth was incorporated into the model from the data collected from a field experiment The model results were then compared against the data collected from the same experiment but from another season. Effect of a herbicide was also simulated and the results were compared with the results obtained from a field experiment conducted in the Dry Zone of Sri Lanka. Model simulations (Figure 5.8) indicated that maximum purple nutsedge populations were reduced with shading, although model predictions did not fit closely with data obtained from the field. For example, the observed nutsedge population growing in full light in the field was equivalent to the simulated populations at the 75% shade level. It is interesting to note however, that the proportionate reduction of purple nutsedge population with increased shading as predicted by the model closely followed the field results (Figure 5.9). Model simulations showed that soil moisture depletion also decreased purple nutsedge populations in a manner similar to shading (Figure 5.10). Again the model predictions of change in response to decreased soil moisture level followed closely the second season's observations (Figure 5.11). When the effect of light or soil moisture level was incorporated into the secondary model and populations simulated, deviations from the field results suggest that the model cannot be used, without further modifications, to make quantitative predictions of population response to light or soil moisture 117 level. However, similarity between model predictions and the field data of the proportionate change of population to either light or soil moisture level suggests that the model can be used to make valid qualitative or comparative predictions on puiple nutsedge population behavior as influenced by those factors. The differences between the actual values of the model output and field results could be the result of the transition parameters used in the model inasmuch as they strongly influence the model output Since most of the parameters in the model have been estimated using data from controlled experiments, these parameters may not represent the real transition coefficients relevant to field conditions in the Dry Zone of Sri Lanka. Thus it is reasonable to expect a disparity between the model output and the field data. The effects of light and soil moisture were incorporated as proportional changes in the population into the model. Thus the model predictions, with changing light or soil moisture level, would necessarily appear as proportionate changes in the population. The similarity of proportionate change in population size between model output and field data in response to variable light or soil moisture levels indicates that the transition parameters in the model are proportionately higher than expected under field conditions in the Dry Zone. Model simulations of purple nutsedge shoot development in response to herbicide applied either at the first or third week of the growing season are shown in Figure 5.12. These simulations suggest that 90% control of shoots in the first week is less effective than in the third week. Results of the 118 field experiment also showed that third week timing of glyphosate application provided better suppression of purple nutsedge than application in the first week (Table 5.2). This is also confirmed by the sensitivity analysis in which the transition of shoots from other shoots during the second to fourth week of the growing season received the highest rank (Table 5.1). USING THE SECONDARY MODEL FOR WEED MANAGEMENT The model validation using field observations suggested that model output could be reliably used to make at least a qualitative assessment of different management options for purple nutsedge. From the sensitivity analysis it was found that there are several transitions that are important for the regulation of plant population size (Table 5.1). Therefore any attempt of controlling this weed should invariably focus on one or several of these transitions. Nutsedge management alternatives can be assessed directly by introducing into the model the effect of control practices. The effect of a herbicide that would kill 90% of the shoots of a purple nutsedge population has been simulated at two different times of applications. A single application was found to be ineffective for long term control and the population returned to its initial density in a few seasons (Figure 5.12). This suggests that for satisfactory control of purple nutsedge herbicide should be applied in every season. Model simulation indicated that seasonal applications resulting in 90% kill provided a successful level of control and that herbicide efficiency was higher when shoots 119 were killed in the third week than in the first week of growth (Figure 5.12). Several reports on the effect of herbicides on purple nutsedge control also indicate that the best time for herbicide application was between the 3rd and 4th weeks after purple nutsedge emergence (12, 31). Simulation further suggests that a lower level of control (percent kill), in the third week does not effectively control purple nutsedge (results are not shown). However, other management alternatives, including herbicides with a different mode of action can be used. One probable management alternative for purple nutsedge is to maintain a moderate level of control pressure extended over a long period of the growth cycle. Model simulation shows that a better strategy than using a highly effective, but a short duration, herbicide is to provide a moderate level of purple nutsedge control, but for a longer period. This is evident from model simulations (Figure 5.13) in which 70% kill of shoots over the entire growing season gave better long term suppression of purple nutsedge than 90% kill of shoots in every season, but timed only at the third week of growth. It also suggests that other agents besides herbicides, which provide moderate "control" but for a longer duration, may be appropriate management tactics to include into the farming practices. An advantage simulating with population models is that they can be used to incorporate the effect of several simultaneous control measures and evaluate their resultant effect on the population behavior. There may be a degree of error associated in these estimates because in model simulations it is assumed that the effect of individual factors are additive where as in reality nonadditive 120 interactions are likely to occur. Even so, there may be considerable advantages to using model output to assess the multiple factor effects. The present secondaiy model was used to simulate the effect of a herbicide that would kill 90% of purple nutsedge shoots in the 3rd week of growth and the effect of shading. The model simulation results indicate that by increasing shade at the same time an effective herbicide was used a higher level of control was obtained than if the two factors were operating independently (Figure 5.13). The present investigation demonstrates that models can act as a framework for integrating available information on plants population dynamics. We also show that models can be used efficiently to identify critical stages in the life history of a plant species. Several critical stages of purple nutsedge have been identified with the present model and the relative importance of those stages has been demonstrated through model simulations. Results also suggest that the model can be used to make population projections as influenced by different levels of regulatory factors. These results clearly demonstrate that models are powerful tools, complimenting experimentation, to 'screen' effective control strategies from a series of control options. This use through elimination of inappropriate experimentation enhances the efficacy of research programs aimed at developing weed control strategies. 121 Table 5.1. Sensitivity values derived from purple nutsedge transition parameters resulting from a 10% decrease in each transition parameter in the five projection matrices of the primary purple nutsedge model after a ten-season simulation time. Developmental period Parameter Sensitivitv value 0 - 2 weeks S2 2.42 T2 1.0 T5 0 2-4 weeks SI 3.5 12 0.9 T5 0 4-8 weeks SI 0.7 Tl 2.5 T2 0 T3 0 T5 0 8 - 12 weeks SI 0 Tl 022 T2 3.4 T3 0 T5 0 quiescent period T2 3.5 T3 0 T5 0 122 Table 5.2. Effect of timing of glyphosate application on purple nutsedge shoot number m"2 in the two growing seasons, Yala and Mahaa. Time of glyphosate application Shoots after 3 months growth Yala Maha no.m"2 First week after nutsedge 146 a 123 a emergence Third week after nutsedge 52 b 62 b emergence control 200 c 277 c a Means within columns followed by the same letter are not significantly different at the 5% level as determined by Duncan's multiple range test. 123 Figure 5.1. Diagrammatic model of growth and development of purple nutsedge populations. The boxes represent stages in the life cycle, arrows indicate processes, valve symbols represent the rate at which a process OCCUR over a specified iteration time. I SHOOTS tXD \E>1 ><»] 1r T3 TERMINAL TUBERS Model transition parameters During growth period 51 - number of secondary shoots produced per single shoot 52 - number of shoots produced by a single terminal tuber Tl - number of terminal tubers produced per single shoot T2 - number of terminal tubers produced per single terminal tuber T3 - number of dormant tubers produced per single terminal tuber T5 - proportion of dormant tubers that remain viable in the soil During quiescent period T2 - proportion of terminal tubers that remain viable in the soil T4 - proportion of dormant tubers that may convert into terminal tubers 124 Figure 5.2. Column vector and the transition matrix of the diagrammatic model of purple nutsedge population growth. COLUMN VECTOR r -\ NUMBER OF SHOOTS NUMBER OF TERMINAL TUBERS NUMBER OF DORMANT TUBERS TRANSITION MATRIX /" -\ SI S2 0 Tl T2 T4 0 T3 T5 V -/ 125 Figure 5.3. The five transition matrices constructed for the different developmental periods in the primary model for purple nutsedge. 0 - 2 WEEKS 0 1.2 0 0 1.0 0 0 0 0.8 J 2 - 4 WEEKS f^ 0 0^ 0 1.0 0 0 0 0.6 J 4-8 WEEKS 2.1 0 (A 0.8 1.6 0 0 0.7 0.6 v. J 8 - 12 WEEKS r0.9 0 0 ^ 0.05 1.7 0 0 3.7 0.6, QUIESCENT PERIOD 0 0 0 0 0.2 0 0 0.2 0.4, v 126 Figure 5.4. Simulation of purple nutsedge population growth without density dependent population regulation. TIT 30000 -A— SHOOTS -A— TERMINAL TUBERS 25000 -6— DORMANT TUBERS CM 20000 B | 15000 Z 10000 5000 °t± 3 4 5 6 NUMBER OF SEASONS 127 Figure 5.5. Effect of plant density on shoot production from a single plant of purple nutsedge during three consecutive growth periods. T I I —■— 1 1 'A in . V 0 • 3 WEEKS \ X, — ac 6 N. ■ GO Xv £ \ u,4 O A^\^ OS m . 2 0.0029X i - Y= 823 e" i l l 1 I . i 200 400 600 800 1000 PLANT DENSITY - NUMBER M"2 I ' 1 ' 1 1 ' l ' I in 3 - 6 WEEKS 8 T S3 ■ ^^■-^^ T Y= 2.97 e-000085^ ^^^-^jr^_ 1 I.I.I 200 400 600 800 1000 PLANT DENSITY - NUMBER M'2 T T T £ ▲ Ss 6-9 WEEKS §a 2.5 — '* ^^ tn * g 2.0 ▲ O S 1.5 - Y= 2.89 e-0015X _L _L _L 0 200 400 600 800 1000 PLANT DENSITY - NUMBER M"2 128 Figure 5.6. Simulation of a purple nutsedge population with density dependent population regulation. 8000 - 1 ' ' ■ 1 ' ' - 9^ A - SHOOTS - - TERMINAL TUBERS o/^ - DORMANT TUBERS 6000 f ■ 1 - CM - J £ 4000 i A A / \ 2000 A— —A— —A H 0 A 1 1 1 1 1 1 1 1 3 4 5 6 7 NUMBER OF SEASONS 129 Figure 5.7. Diagrammatic submodel from the purple nutsedge population model, including factors expected to influence shoot production from existing shoots. PROXIMITY FACTORS -\ 1. INTRASPECIHC COMPETITION 2. INTERSPECIFIC COMPETITION SHOOTS J ENVIRONMENTAL FACTORS 1. LIGHT INTENSITY ^ 2. SOIL MOISTURE REGIME SI 3. NUTRIENT AVAILABILITY > PHYSIOLOGICAL FACTORS ^ 1. BIOTYPE 2. HORMONAL REGULATION 3. PLANT AGE J 130 Figure 5.8. Population simulation of purple nutsedge at different light intensities. r ~i t i i ■ i ■» 1500 - 100% LIGHT -A— 83% LIGHT -9— 63% LIGHT -•— 48% LIGHT -B— 25% LIGHT 6 1000 - Oz £ 500 0 - 12 3 4 5 NUMBER OF SEASONS 131 Figure 5.9. Simulated and observed (during the two growing seasons, Yala and Maha) relative suppression of purple nutsedge population growth at different shade levels. 1 ' ■ . ,.,.,..., 1 100 ^^ i ^^ " ^S*.^^i^^. - Q . S ^*^jftk^^^ § 80 - NN \\ X . V \ >v . C/3 \ \ X^ g ' - u. - ^A\ \ \ H o A> \ * 6° ~ ^xV—A H w v\ \ ■j in ^^ \ H Vv^ \ O jk \ O '^ \ i M 40 OBSERVED- MAHA -\ A OBSERVED - YALA \\ J --O- SIMULATED ^ ] \ \ 1 . . i ... i ... i J 20 40 60 80 PERCENT SHADE 132 Figure 5.10. Simulation of population growth of purple nutsedge under different soil moisture depletion levels. Soil moisture at field capacity is designated as 100%FC; 75%Dep., soil moisture at 75% depletion of available moisture; 50%Dep., soil moisture at 50% depletion of available moisture; and 25%Dep., soil moisture at 25% depletion of available moisture. 1 ' I ■ 1 1 1 ■ ' A A A _ - 1400 A - 100% FC / - 75% Dep. / ^- A oi*k - 50% Dep. / 1200 - 25% Dep. / 1000 - ^> A / / 'a /^ o / z 800 ™ ™i go // g 600 a. // CO 400 -] S* 200 : i ^ 0 # 1 , , 1 4 6 8 10 12 NUMBER OF SEASONS 133 Figure 5.11. Simulated and observed relative suppression of purple nutsedge population growth with different soil moisture depletion levels. i i i . | . -. ' 1 ' ' ' T 100 \-- i I ^^•s^^ - r " ^M % 90 j- - Q -J - - w \> E \\ ■ " \ X < 80 \ N |. \ X . z \ X o • \ X ^ . \ X J < J - X^\ \ i 70 \ X -j A. x £ • •1 O ' XsX j --A-- SIMULATED ^^ H 60 - vs. | - vx. H - X J^ ] * X 1 § „ X J on 50 - . . i 1 ... 1 > 1 0 20 40 60 SOIL MOISTURE (PERCENT OF FIELD CAPACITY) 134 Figure 5.12. Population simulation of purple nutsedge assuming that 90% of shoots were killed with a herbicide. Simulations include herbicide applied in the first week, only in the second growing season, -A ; herbicide apphed in the third week, only in the second growing season, -& ; herbicide applied in the first week in every season beginning with the second growing season, ^ ; and herbicide applied in the third week in every season beginning with the second growing season,-^-. r \—r—i . r r ■ '"K-/ Z'0 /\ A i 1400 : W —-- a r~ J 1200 § I : \/ 1000 J as CO ■ V 800 - ^# j i ... i 1 4 6 NUMBER OF SEASONS 135 Figure 5.13. Simulation of purple nutsedge populations under different management options. Simulations through nine seasons are shown as follows: Purple nutsedge grown under full sunlight and 90% kill of shoots with a herbicide at 3rd week of growth in every season, starting from the 2nd growing season,-©-; growth under 63% of full sun light, shading applied continuously, starting from the second growing season,-&-; purple nutsedge grown under 63% of full sunlight and 90% kill of shoots with a herbicide at 3rd week of growth in every season, starting from 2nd growing season,-*-; continuous 70% control of shoots throughout the growing season, starting from the 2nd growing season-*-. 1600 ~ ■ i —■—r ^ ^ \ 1400 - I - 1200 „ _ /-*. vv • - ■? \ \ B — j* A i* — . 1000 V A i-iA i-i ~^A-A i \ w 800 - iN- -©^ — ~~^> | 600 - - C/5 400 - _^— - i^ __iA_ 200 - - Y-+- —• 1 1 t i i i 1 1 1 3 4 5 6 7 NUMBER OF SEASONS 136 LITERATURE CITED 1. Baker, R. S. 1964. Reproductive capacity of nutsedge (Cypeius mtundus) tubers. Abstr. Weed Sci. Soc. Am. p.62-64. 2. Bantilan, R. T., M. C. Palada and R. R. Haiwood. 1974. Integrated weed management: I Key factors affecting crop-weed balance. Philippine Weed Sci. Bull. 1:14-36. 3. Begon, M, J. L. Harper and C. R. Townsend. 1986. Ecology: Individuals, Populations and Communities. Smauer Associates, Inc. Publishers, Sunderland,Massachusetts. 876pp. 4. Bendixen, L. E. and U. B. Nandihall. 1987. Worldwide distribution of purple and yellow nutsedge (Cypems mtundus and Cescukntus). Weed Technol. 1:1:61-65. 5. Caswell, H. 1989. Matrix population models. Construction, Analysis and Interpretation. Sinauer Associates, Inc. Sunderland, Messachusetts. 328pp. 6. Davies, C. H. 1942. Response of Cypeius mtundus to five moisture levels. Plant physiol. 17: 311-316. 7. Enright, N. and J. Ogden. 1979. Application of transition matrix models in forest dynamics: Amucaria in Papua New Guinea and Nortbopbagus in New Zealand. Aust J. Ecology 4: 3-23. 8. Glaze, N. C. 1987. Cultural and mechanical manipulation of Cypeius sp. Weed Technol. 1:82-83. 9. Goodman, L. A. 1969. The analysis of population growth when the birth and 137 death rates depend upon several factors. Biometrics. 25:659-681. 10. Harper, J. L. 1977. Population Biology of Plants. Academic Press, London. 892 pp. 11. Hauser, E. W. 1962. Development of purple nutsedge under field conditions. Weeds 10:315-321. 12. Hauser, E. W. 1963. Response of purple nutsedge to amitrol, 2.4-D and EPTC. Weeds 11:8-24. 13. Holm, L. G., D. L. Plucknette, J. V. Pancho and J. P. Herberger. 1977. The World's Worst Weeds - Distribution and Biology. University Press of Hawaii, Honolulu. Pages 8-24. 14. Jordan-Molero, J. E. and E. W. Stoller. 1978. Seasonal development of yellow and purple nutsedges (Cypems escukntus and Cmtundus) in Illinois. Weed Sci. 26:614-618. 15. Lapham, J., D. S. H. Drennan and L. R. Francis. 1985. Population dynamics of Cypems escukntus L (yellow nutsedge) in Zimbabwe. Proc. 1985 British Crop Prot Conf.- Weeds. Pages 397-402. 16. Law, R. 1981. The dynamics of a colonizing population of Poa anima. Ecology 62: 1267-1277. 17. Lefkovitch, L. P. 1965. The study of population growth in organisms grouped by stages. Biometrics 21:1-18. 18. Lesli, P. H. 1945. On the use of matrices in certain population mathematics, Biometrika 35:183-212. 138 19. Maxwell, B. D., M. V. Wilson and S. R. Radosevich. 1988. Population modelling approach for evaluating leafy spurge (Eupboibia esula) development and control Weed Technol. 2:132- 138. 20. McMahon, D. J. and A. M. Mortimer. 1980. The prediction of couch infestation. A modelling approach. Proc. 1980 British Crop Prot Conf.- Weeds. Pages 601-607. 21. Mortimer, A.M., P.D.Putwain and DJ.McMahon. 1978. A theoretical approach to the prediction of weed population sizes. Proc. 1978 British Crop Prot Conf.- Weeds. Pages 467-474. 22. Parker, C. 1985. Growth patterns in Qpems rotundus. Proc. 1985 British Crop Prot Conf.- Weeds. Pages 387-393. 23. Patterson, D. J. 1982. Shading response of purple and yellow nutsedge (Qpems wtundus and C escukntus) Weed Sci. 30:25-30. 24. Pereira, W., G. Crabtree and R. D. William. 1987. Herbicidal action on purple and yellow nutsedge (Qpems wtundus and Cescukntus). Weed Technol. 1:92-98. 25. Sager, G. and A. M. Mortimer. 1976. An approach to the study of the population dynamics of plants with special reference to weeds. Pages 1-47 In Applied Biology Ed. T. H. Coaker, Academic Press, London. 26. Sierra, J. N. 1979. Some aspects of the biology of Qpems wtundus L. Biotrop Bull. 11:71-86. 27. Silvertown, J. W. 1987. Introduction to Plant Population Ecology. Longman 139 Scientific & Technical, Essex, England. 229pp. 28. Siriwardana, G. and R. K. Nishimoto. 1987. Propagules of purple nutsedge (Cypems mtundus) in soil. Weed Technol. 1:217-220. 29. Smith, P. V. and G. L. Pick. 1937. Nutgrass eradication studies. LRelation of the life history of nutgrass, Cypems mtundus L. to possible methods of control. J. Amer. Soc. Agron. 29:1007-1013. 30. Stoller, E. W and R. D. Sweet 1987. Biology and life cycle of purple nutsedge (Qpeius mtundus and Cescukntus). Weed Technol. 1:66-73. 31. Suwannamek, U. and C. Parker. 1975. Control of Cypems mtundus with glyphosate: The influence of ammonium sulphate and other additives. Weed Res. 15:13-19. 32. Watkinson, A. R. 1986. Plant Population Dynamics. Pages 137-184, In Plant Ecology Ed. M. J.Crawly, Blackwell Scientific Publication, Oxford. 33. Watson, H.K. 1985. Integrated management of leafy spurge. Pages 93-104, In Leafy Spurge. Ed. A. K. Watson, Weed Sci. Soc. Am. Champaign, IL. 34. Werner, P. A. and Caswell. H. 1977. Population growth rates and age versus stage distribution models for teasel (Dipsacus syh/estris Huds). Ecology 58:1103-1111. 35. Williams, R. D., P. C. Quimby, Jr. and K. E. Frick. 1977. Intraspecific competition of purple nutsedge (Cypems mtundus) under greenhouse conditions. Weed Sci. 25: 477-481. 140 BIBLIOGRAPHY Abrahamson, W. G. and M. Gadgil. 1973. Growth forms and reproductive effort in goldenrods (Solidago, Compositae). Am. Nat 107:651-661. Abrahamson, W. G. and B. J. Hershey. 1977. Resource allocation and growth of Impatiens capensis (Balsaminaceae) in two habitats. Bull. Torrey Bot. Club. 104:160-164. Al-Ali, F., S. Shamsi and S. Hussain. 1978. Sprouting and growth of purple nutsedge, Cypems mtundus, in relation to pH and aeration. Physiologia Planatarium. 44:373-376. Akobundu, I. O., D. E. Bayer and O. A. Leonard. 1971. The effects of dichlobenil on assimilate transport in purple nutsedge. Weed Sd. 18 :403-408. Anderson, O. 1958. Studies on the adsorption and translocation of amitrol (3- amino-l,2,4-triazol) in nut grass (Cypems mtundus). Weeds 6:370- 385. Andrews, F. W. 1940. A study of nutgrass (Cypems mtundus L) in the cotton soil of Gezira. I. The maintenance of life in the tuber. Ann. Bot 13:177-193. Andrews, O. N., R. C. Billman and F. O. Timmons. 1974. Glyphosate control of railroad rights-of-way vegetation in the southeast Proc. South. Weed. Sci. Soc. 27:251-258. Appleby, A. P. and M. Somabhi. 1978. Antagonistic effect of atrazine and simazine on glyphosate activity. Weed Sci. 26:135-139. Baker, R. S. 1964. Reproductive capacity of nutsedge (Cypems mtundus L) tubers. Abstr. Weed Sd. Soc. Am. Pages 63-64. Baker, R. S. 1967. Nutsedge suppression under different cropping practices. Abstr. Weed Sd. Soc. Am. 18(1):292. Baker, R. S. 1976. Herbiddes applied as a subsurface layer for nutsedge control. Proc. South. Weed Sd. Soc. 29:151-156. Balasubramanian, M. and D. D. Sundararaj. 1968. Studies on the control of Cypems mtundus (nutsedge) during summer fallow in red loams. Proc. 9th British Weed Control Conf. 2:814-819. 141 Bantillan. R. T., M. C. Palada, and R. R. Harwood. 1974. Integrated weed management: I. Key factors affecting crop-weed balance. Philippine Weed Sci. Bull. 1:14-36. Begg, J. E and N. C. Turner. 1976. Crop water deficits. Adv. Agronomy. 28:161-217. Begon, M., J. L. Harper and C. R. Townsend. 1986. Ecology: Individuals, Populations and Communities. Sinauer Associates, Inc. Publishers, Sunderland,Massachusetts. 876pp. Bendixen, L. E. and E. W. Stroube. 1977. Yellow and purple nutsedge: Two species of worldwide significance. Weeds Today 9(1):9-15. Bendixen, L. E. and U. B. Nandihalli. 1987. Worldwide distribution of purple and yellow nutsedge (Cyperus mtundus and Cescukntus). Weed Technol. 1:61-65. Berger, G. B. 1966. Dormancy, growth inhibition and tuberization of nutsedge. Ph.D thesis; University of California, Riverside, California. 108pp. Berger, G. and B. E. Day. 1967. Dormancy, growth inhibition and tuberization of nutsedge (Cypems mtundus L) as affected by photoperiod. Proc. First Asian-Pacific Weed Control Interchange. Page 123. Bhardwaj, R. B. C and R. D. Verma. 1968. Seasonal development of nutgrass {Cypems mtundus L) under Delhi conditions. Indian J. Agric. Sci. 38:950-957. Black, C. G, T. M. Chen and R. H. Brown. 1969. Biochemical basis for plant competition. Weed Sd. 17:338-334. Blackman, G. E. and G. L. Wilson. 1952. Physiological and ecological studies in the analysis of plant environment VIL An analysis of the differential effects of light on NAR, LAR and RGR of different species. Ann. Bot. 59:373-408. Boswell, T. E. 1971. Post emergence weed control in peanuts. Proc. South. Weed Sd. Soc. 24:124-130. Boyd, J. W. and D. S. Murry. 1982. Effects of shade on silverleaf night shade (Sotemm elaegnfolium). Weed Sd. 30:264-269. Buber, J. C. and I. N. Morrison. 1984. Growth response of green and yellow foxtail (Setaria viridis and S. hitescens) to shade. Weed Sd. 32:774-780. 142 Burgis, D. S. 1969. Phytotoxicity to purple nutsedge (Cypenis wtundus L) and soil persistence of six forms of 2,4-dichloro phenoxy acetic acid herbicide. Proc. South. Weed Sci. Soc. 22:328-331. Burleson, C. A. 1971. Chemical weed control in cotton. Proc. 3rd Asian-Pacific Weed Sd. Soc. 2:325-331. Burr, R J. and G.F.Warren. 1972. An oil carrier for increasing purple nutsedge control. Weed sci. 29:324-327. Buttery, B. R. (1969). Analysis of growth of soybean as affected by plant population and fertilizer. Can. J. Plant Sd. 49:675-684. Cartica, R. J. and J. A. Quinn. 1982. Resource allocation and fecundity of populations of SoUdago sempeivuens along a coastal dune gradient Bull, of Torrey Bot Club. 109:299-305. Caswell, H. 1989. Matrix Population Models: Construction, Analysis and Interpretation. Sinauer Assodates, Inc. Sunderland, Massacusetts. 328pp. Chase, R. L. and A. P. Appleby. 1979 a. Effect of intervals between application and tillage on glyphosate control of Cypenis wtundus L. Weed Res. 19:207-211. Chase, R. L. and A. P. Appleby. 1979 b. Effects of humidity and moisture stress on glyphosate control of Cypenis wtundus L. Weed Res. 19:241-246. Coats, G. E. 1985. Weed control in warm season turf grass with imidazolinone herbiddes. Proc. South. Weed Sd. Soc. 38:97. Coats, G. E., R. F. Munoz, D. H. Anderson, D. Hearing and J. W. Cruggs. 1987. Purple nutsedge (Cypenis wtundus) control with imazaquin in warm season turf grass. Weed Sd. 35:691-694. Cools, W. G. and S. J. Locoscio. 1977. Control of purple nutsedge (Cypenis wtundus L) as influenced by seasons of application of glyphosate and nitrogen rate. Proc. South. Weed Sd. Soc. 30:158-164. Cousens, R., S. R. Moss, G. W. Cussans and B. J. Wilson. 1987. Modeling weed populations in cereals. Rev. Weed Sd. 3:93-112 Davis, C. H. 1942. Response of Cypenis wtundus to five moisture levels. 143 Plant Physiol. 17:311-316. Day, B. E. and R. C. Russel. 1955. The effect of drying on survival of nutgrass tubers. Calif. Agric. Exp. Stn. Bull. Page 751. De Alwis, K. A. and C. R. Panabokke. 1972-73. Handbook of the Soils of Sri Lanka (Ceylon). J. Soil Science Soc. of Sri Lanka. VoUI. 97pp. Doll, J. D. 1986 a. Ecology and control of perennial weeds in Latin America. FAO plant production and protection paper 74. Food and Agricultural Organization of the United Nations. Pages 71-89. Doll, J. D. 1986 b. Ecology and control of perennial weeds in Latin America. FAO plant production and protection paper 74. Food and Agricultural Organization of the United Nations. Pages 90-100. Doll, J. D. and W. Piedrahita. 1982. Effect of glyphosate on the sprouting of Cypems mtundus L. tubers. Weed Res. 22:123-128. Donald, C. M. 1963. Competition among crop and pasture plants. Adv. Agronomy. 15:1-118. Duke, S. O. 1990. Overview of herbicidal mechanisms of action. Environmental Health Perspectives. 87:263-271. Eames, A. J. 1949. Competitive effects of spray treatments with growth regulating substances on nut grass, Cypems mtundus L. and anatomical modifications following treatment with butyl 2,4-dichlorophenoxyacetate. Am. J. Bot 36:571-584. Enright, N. and J. Ogden. 1979. Application of transition matrix models in forest dynamics: Araucaiia in Papua New Guinea and Noithophagus in New Zealand. AusL J. Ecology 4: 3-23 Escoro, T. B. 1969. Experiments in the control of Cypems mtundus. Polomolok, South Cotabato, Mindamao, Philippines. Proc. 2nd Asian-Pacific Weed Control Interchange. Pages 322-326. Evans, G. C. 1972. The quantitative analysis of plant growth. University of California Press, Berkly. 734pp. Everest, J. W. and M. G. Patterson. 1988. Eradication of nutsedge populations for ornamental plantations. Proc. South. Weed Sd. Soc. 41:316. 144 Fitter, A. H. and R. K. M. Hay. 1989. Environmental physiology of plants. Academic Press, London. 421pp. Friedman, T. and M. Horowitz. 1971. Biologically active substances in subterranean parts of purple nutsedge. Weed Sd. 19:398-401. Fuerst, E. P. 1987. Understanding the mode of action of the chloroacetamide and thiocarbamate herbicides. Weed Technol. 1:270-277. Glaze, N. C. 1987. Cultural and mechanical manipulation of Cypems spp. Weed Technol. 1:82-83. Gonzalez-Andujar, J. L. and Femandez-Quintanilla, L. 1991. Modelling the population dynamics oiAvena sterilis under dry-land cereal cropping systems. J. of Applied Ecol. 28:16-27. Goodman, L. A. 1969. The analysis of population growth when the birth and death rates depend upon several factors. Biometrics. 25:659-681. Gopal, R and V. S. Mani. 1968. Control of Cypems mtundus and Cynadon dactylon by a combination of mechanical, chemical and cropping methods. Proc. 9th British Weed Control Conf. 9:809-813. Gosette, B. J., R. L. Stepens and J. T. Gillingham. 1975. Response of purple nutsedge to glyphosate. Proc. South. Weed Sci. Soc. 28:146. Gray, R. A. and A. J. Weierich. 1985. Factors affecting the vapor loss of EPTC from soils. Weeds. 13:141-147. Grime, J. P. and R. Hunt 1975. Relative growth rate, its range and adaptive significance in a local flora. J. of Ecology. 63:393-422. Hamilton, K. C. 1971. Repeated foliar application of MSMA on purple nutsedge. Weed Sd. 19:675-677. Hammerton, J. L. 1971. Weed control work in program at the university of West Indies. PANS. 17:226-230. Hammerton. J. L. 1974. Experiments with Cypems mtundus L. I.Growth and development and effects of 2,4-D and paraquat Weed Res. 14:365- 369. Hammerton, J. L. 1975. Experiments with Cypems mtundus L. 11 Effects of some herbiddes and growth regulators. Weed Res. 15:177-183. 145 Hardcastle, W. S. and R. E. Wilkinson. 1968. Response of purple and yellow nutsedge to dichlobenil. Weed Sci. 16:339-340. Harper, J. L. 1977. Population Biology of Plants. Academic Press, London. 892pp. Harwood, R. R. and R. T. Bantillan. 1974. Integrated weed management: n Shifts in composition of the weed community in intensive cropping systems. Philipp. Weed Sci. Bull. 1:37-59. Hauser, E. W. 1962a. The establishment of nutsedge from space planted tubers. Weeds. 10:209-212. Hauser, E. W. 1962b. Development of purple nutsedge under Held conditions. Weeds. 10:315-321. Hauser, E. W. 1963a. Effect of amitrol on purple nutsedge at different growth stages. Weeds 11:181-183. Hauser, E. W. 1963b. Response of purple nutsedge to amitrol, 2,4-D and EPTC. Weeds. 11:251-252. Hauser, E. W. 1971. Nutsedge: A worldwide plague. Weeds Today 2(l):21-23. Hay, J. R. 1976. Herbicide transport in plants. Pages 365-396 in, L. J. Audus, ed. Vol. I. Herbicides Physiology, Biochemistry and Ecology. Acdemic Press, London. Hickman, J. C. 1975. Environmental unpredictability and plastic energy allocation strategies in at the annual Pofygonum cascadensis (Polygonaceae). J. of Ecology 63:689-701. Higgins, E. R., M. G. Schnappinger and S. W. Pruss. 1976. CGA-24705 and atrazine for yellow nutsedge control. Proc. Northeast Weed Sci. Soc. 30:65. Holm, L. G., D. Plucknette, J. V. Pancho and J. Herberger. 1977. The Worlds Worst Weeds - Distribution and Biology. The University of Hawaii Press, Honolulu. Pages 8-24. Holt, E. C, J. A. Long and W. W. Allen. 1962. The toxidty of EPTC to nutsedge. Weeds 10:103-105. Holt, E. C, J. L. Faubion, W. W. Allen, and C G. Mcbee. 1967. Arsenic translocation in nutsedge tuber systems and its effect on tuber 146 viabiUty. Weeds. 15:13-15. Honess, B. D. 1960. A preliminary ecological study of nutgrass(C)pe7US mtundus L) under Trinidad conditions. Imp. Coll. of Tropical Agric. (Trinidad). Pages 1-40. Horng, L. C. and L. S. Leu. 1979. Control of five upland perennial weeds with herbicides. Proc. Seventh Asian-Pacific Weed Sri. Soc. Conf. 1:165-167. Horowitz, M. 1965. Data on the biology and chemical control of the nutsedge (Cypems mtundus) in Israel. PANS 11:389-414. Horowitz, M. 1972. Growth, tuber formation and spread of Cypems mtundus L. from single tubers. Weed res. 12:348-363. Horowitz, M. and T. Freedman. 1971. Biological activity of sub-terranean residues of Cynodon dactylon L, Sorghum halpense L and Cypems mtundus L. Weed Res. 11:88-93. International Rice Research Institute. 1974. Weed management Pages 20-31 in Rice Res. Inst (Los Banos) Annu. Report 1973. International Rice Research Institute. 1976. Weed management Pages 325-329 in Rice Res. Inst (Los Banos) Annu. Report 1974. International Rice Research Institute. 1979. Weed control in rice. Pages 202- 206 in Rice Res. Inst (Los Banos) Annu. Report 1978. Jangaard, N. O., M. M. Sekerl and R. H. Schieferstein. 1971. The role of phenolics and abscisic arid in nutsedge tuber dormancy. Weed Sri. 19:17-20. Jorden-Molero, J. E. and E. W. Stoller. 1978. Seasonal development of yellow and purple nutsedge: {Cypems escukntus and Cypems mtundus). Weed Sri. 26:614-618. Keely, P. E. 1987. Interference and Interaction of purple and yellow nutsedge (Cypems mtundus and C escukntus) with crops. Weed Technol. 1:74-81. Keely, P. E., C. H. Carter and J. H. Miller. 1972. Evaluation of the relative phytotoxirity of herbicides to cotton and nutsedge. Weed Sri. 20: 71-74. Keely, P. E. and R. J. Thullan. 1978. Light requirements of yellow nutsedge (Cypems escukntus) and light interception by crops. Weed Sri. 26:10-16. 147 Knake, E. L. 1972. Effects of shade on giant foxtail. Weed Sci. 20:588-592. Kondap, S. M., K. Ramakrishna, S. B. Reddy and A. N. Rao. 1982. Investigations on the competitive ability of certain crops against purple nutsedge (Cypems mtundus L). Indian J. Weed Sci. 14:124-126. Kozlowski, T. T. 1968. Water deficits and plant growth. Vol. n. Academic Press, London. 339pp. Kwesi, A. and S. K. De Datta. 1989. Ecophysiological studies in relation to weed management strategies in rice. Proc. Twelfth Asian-Pacific Weed Sci. Soc. Conf. 1:31-45 Lapham, J., D. S. H. Drennan and L. Francis. 1985. Population dynamics of Cypems escukntus L (yellow nutsedge) in Zimbabwe. Proc. 1985 British Crop Prot Conf.- Weeds. 1:395-402. LaRossa, R. A. and S. C. Falco. 1984. Amino acid biosynthesis enzymes as targets of herbicide action. Trends in Biotechnology 2:158-161. Law, R. 1981. The dynamics of a colonizing population of Poa annua. Ecology 62: 1267-1277. Lee, S. M. and P. B. Cavers. 1981. The effect of shade on growth, development and resource allocation pattern on three species of foxtail (Setaria). Can. J. Bot 59:1776-1786. Lefkovitch, L. P. 1965. The study of population growth in organisms grouped by stages. Biometrics 21:1-18. Leonard, O. A. and V. C. Harris. 1950. Methyl bromide eradicates nutgrass. Down to Earth. 6:13. Lesli, P. H. 1945. On the use of matrices in certain population mathematics. Biometrika. 35:183-212. Leydon, R. F. 1967. Control of nutgrass in Texas citrus orchards. Proc. South. Weed Sci. Soc. 20:130-133. Magambo, M. J. S. and P. J. Terry. 1973. Control of purple nutsedge {Cypems mtundus) with glyphosate. Proc. Fourth Asian-Pacific Weed Sci Soc. Conf. 1:191-194. Maxwell, B. D., M. V. Wilson and S. R. Radosevich. 1988. Population modeling 148 approach for evaluating leafy spurge (Euphobia esula) development and control. Weed Technol. 2:132-138. Mcdonald, N and A. R. Watkinson. 1981. Model of an annual plant population with a seed bank. J. Theo. Biol. 93:643-653. McMahaon, D. J. and A. M. Mortimer. 1980. The prediction of couch infestation - A modelling approach. Proc. 1980 British Crop Protec. Conf.- Weeds. 2:601- 607. Mercado, B. L. 1979. Monograph on Qpems mtundus L. Biotrop Bulletin 15. Regional Center for Tropical Biology, Bogor, Indonesia. Mercado, B. L., J. N. Sierra and G. B. Begonia. 1974. Control of Qpems mtundus L. with napropamide. Philippine Weed Sci. Bull. 1:32-38. Moosavi-Nia, H and J. Dore. 1979a. Factors affecting glyphosate activity in Impemta cylindrical) Beau, and Qpems mtundus L. I: Effect of soil moisture. Weed Res. 19:137-143. Moosavi-Nia, H and J. Dore. 1979 b. Factors affecting glyphosate activity in Impemta cyhndricafL) Beau, and Qpems mtundus L. I: Effect of shade. Weed Res. 19:321-327. Mortimer, A. M., P. D. Putwain and D. J. McMahon. 1978. A theoretical approach to the prediction of weed population size. Proc. 1978 British Crop Prot Conf.- Weeds. 2:467-474. Mortimer, A. M., D. J. McMahon, R. J. Manlove and P. D. Putwain. 1980. The prediction of weed infestations and cost of differing control strategies. Proc. 1980 British Crop Prot Conf.- Weeds. 2:415-422. Mortimer, A. M. 1983. On Weed Demography. Pages 1-40 in W.W.Fletcher, ed. Recent Advances in Weed Research. Commonwealth Agriculture Bureaux, England. Muzik, T. J. and H. J. Cruzado. 1953. The effect of 2,4-D on sprout formation in Qpems mtundus. American J. Bot 40:507-512. Nandihalli, U. B. and L. E. Bendixon. 1988. Absorption, translocation and toxicity of foliar applied imazaquin in yellow and purple nutsedge (Qpems escukntus and C mtundus). Weed Sd. 36:313-317. Obrigawitch, T., J. R. Abemathy and J. R. Gibson. 1980. Response of yellow 149 (Qpems esculentus) and purple (Qpems mtundus) nutsedge to metolachlor. Weed Sd. 28:708-715. Ogdon, J. 1974. The reproductive strategy of higher plants, n. The reproductive strategy of TussOago faifam L. J. of Ecology 62:291-324. Okafor, L. I. and S. K. DeDatta. 1974. Competition between weeds and upland rice in monsoon Asia. Philippine weed Sci. Bull. 1:30-45. Okafor, L. I. and S. K. DeDatta. 1976a. Chemical control of perennial nutsedge (Qpems mtundus) in tropical upland rice. Weed Res. 16:1-5. Okafor, L. I. and S. K. DeDatta. 1976b. Competition between upland rice and purple nutsedge for nitrogen, moisture, and light Weed Sci. 24:43-46. Palmer, R. D. and W. K. Porter. 1959. The metabolism of nutgrass (Qpems mtundus) HI. Polyphenol oxidase in the dormant tuber. Weeds. 7:502-510. Parker, C, K. Jolly and S. D. Hocombe. 1969. Herbicides for nutgrass control - conclusions from ten years of testing at Oxford. PANS 15:54-63. Parker, C. and M. C. Dean. 1972. The effect of some plant growth regulators on sprouting of Qpems mtundus and its response to herbicides. Proc. Eleventh British Weed ConL Conf. 11:744-751. Parker, C. 1981. The selectivity of some herbicides against Qpems mtundus in cotton. Proc. Eighth Asian-Pacific Weed Sci. Soc. Conf. 1:249-253. Parker, C. 1985. Growth patterns in Qpems mtundus. Proc. 1985 British Crop Protection Conf. - Weeds. 1:387-394. Patel, G. C. 1962. Effectiveness of some useful cultural practices of fluecured tobacco in the control of weeds. Indian Tob. 12:99-104. Patterson, D. T. 1979. The effect of shading on growth and photosynthesis capacity of Itchgrass (RottboeUia exakata). Weed Sci. 27:549-553. Patterson, D. T. 1982. Shading response of purple and yellow nutsedges (Qpems mtundus and C esculentus). Weed Sci. 30:25-30. Patterson, D. T. 1988. Growth and water relations of cotton (Gossypium hiisutum), spurred anoda (Anoda cristata) and velvet leaf (Abuttion tbeophrasti) during simulated drought recovery. Weed Sci. 36:318-324. 150 Patterson, D. T. and E. P. Flint 1983. Comparative water relations, photosynthesis and growth of soybean (Gfycine max) and seven associated weeds. Weed Sci. 31:318-323. Patterson, D. T, C. R. Meyer and P. C. Quimby. 1978. Effects of irradiance on relative growth rates, net assimilation rates and leaf area partitioning in cotton and three associated weeds. Plant Physiol. 62:14-17. Pearcy, R. W. and J. Ehleringer. 1984. Comparative ecophysiology of C3 and C4 plants. Plant cell and Environment 7:1-13. Pembroke, E. A. 1970. The control of weeds in Mackay Area. Cane Grow. Q. Bull. 33:96-99. Pereira, W., G. Crabtree and R. D. William. 1987. Herbicide action on purple and yellow nutsedge (Cypems mtundus and Cescukntus). Weed Technol. 1:92-98. Petersen, R. 1985. Design and Analysis of Experiments. Marcel Dekker, In. New York. 429pp. Phatak, S. C, M. B. Callaway and C. S. Vavrina. 1987. Biological control and its integration in weed management systems for purple and yellow nutsedge (Cypems mtundus and Cescukntus). Weed Technology 1:84-91. Pothecaiy, B. P. and W. D. Thomas. 1968. Control of Cypems mtundus in the Sudan Gezira. PANS 14:236-240. Radford, P. J. 1967. Growth analysis formulae - their use and abuse. Crop Science 7:171-175. Raju, R. A. and K. A. Reddy. 1990. Ecology of purple nutsedge Cypems mtundus). Indian J. Agric. Sci. 60:482-483. Ranade, S. B. and W. Bums. 1925. The eradication of Cypems mtundus L. (a study on pure and applied botany). Memoirs of India Dept Agric. Bot Series. 13:98-192. Rao, J. S. and N. Nagarajan. 1963. Relationship between moisture levels and viability of nutgrass tubers. Madras Agric. J. 50:120-123. Rao, J. S. 1968. Studies on the development of tubers in nutgrass and their starch content at different depth of soil. Madras Agric. J. 55:19-23. 151 Ray, B. R. and M. Wilcox. 1969. Chemical fallow control of nutsedge. Weed Res. 9:86-94. Ray, B. R., M. Wilcox, W. B. Wheeler and N. P. Thompson. 1971. Translocation of terbacil in purple nutsedge. Weed Sci. 19:306. Rincon, D. J. and G. E. Warren. 1978. Effect of five thiocarbomate herbicides on purple nutsedge (Qpems mtundus). Weed Sd. 26:127-131. Rincon, D. J. and G. F. Warren. 1979.Effect of soil placement on the activity of herbicides on Qpems mtundus L. Weed Res. 19:81-87. Rochecouste, E. 1956. Observations on nutgrass (Qpems mtundus) and its control by chemical methods in Mauritius. Proc. 9th Congress of the International Soc. of Sugar Cane Technologist. Pages 1-11. Sager, G. and A. M. Mortimar. 1976. An approach to the study of the population dynamics of plants with special reference to weeds. Pages 1-47 In Applied Biology Ed. T. H. Coaker, Academic Press, London. Sandberg, C .L.,W. F. Meggitt and D. Penner. 1980. Absorption, translocation and metabolism of^C-glyphosate in several weed species. Weed Res. 20:195-200. Sarukhan, J. and M. Gadgjl. 1974. Studies on plant demography: Ranunculus repense L, Ranunculus bulbosus L, Ranauncuhis acris L. m. A mathematical model incorporating multiple modes of reproduction. J. of Ecology 62:921-936. Shaner, D. L., P. C. Anderson and M. A. Stidham. 1984. Imidazolinones, potent inhibitors of acetohydroxy acid synthase. Plant physiol. 76:545-546. Shetty, S. V. R., M. V. K. Sivakumar and S. A. Ram. 1982. Effert of shading on the growth of some common weeds of the semiarid tropics. Agronomy J. 74:1023-1029. Sierra, J. N. 1969. Some aspects of the biology of purple nutsedge Qpems mtundus L. Biotrop bulletin 15:76-86. Silvertown, J. 1987. Introduction to Plant Population Ecology. Longman Scientific and Technical, Essex, England. 229pp Singh, M., R. K. Pandey and R. P. Singh. 1968. Chemical control of nutgrass (Qpems mtundus L). Proc. Ninth British Weed Cont Conf. 2:820-826. 152 Sinha, T. D. and C. Thakur, 1967. Control of nutgrass weed by cultivation. Indian J. Agronomy. 12:121-125. Sinha, T.D. and C. Thakur. 1970. Economical aspects of controlling nutgrass (Qpems mtundus L) weed with 2,4-D. Indian J. Agric. Sd. 40:117-123. Siriwardana, G. and R. K. Nishimoto. 1987. Propagules of purple nutsedge (Cypeius mtundus) in soil. Weed Technol. 1:217-220. Smith, E. V. and G. L. Fick. 1937. Nutgrass Eradication Studies. I. Relation of the life history of nut grass, Qpems mtundus L. to possible methods of control. J. Amer. Soc. Agron. 29:1007-1013. Sprankle, P., W. F. Meggitt and D. Penner. 1975. Absorption, action and translocation of glyphosate. Weed Sd. 23:235-240 Standifer, L. C. 1974. Control of purple nutsedge with 2,4-D, paraquat and dinoseb. Weed Sd. 22:520-522. Standifer, L. C. 1980. Control of purple nutsedge with repeated glyphosate applications. Proc. South. Weed. Sd. Soc. 33:297. Stoller, E. W. 1973. Effect of minimum soil temperature on differential distribution of Cypems mtundus and C escukntus in the United States. Weed Res. 13:209-217. Stoller, E. W and R. D. Sweet 1987. Biology and life cycle of purple nutsedge (Qpems mtundus and Cescukntus). Weed Technol. 1:66-73. Suwunnamek, U. and R. R. Romanowski, Jr. 1967. The effect of arsonates and paraquat on nutsedge (Qpems mtundus L) control. Proc. First Asian-Pacific Weed Control Interchange, University of Hawaii, Honolulu. Page 122. Suwunnamek, U. and C. Parker. 1975. Control of Qpems mtundus with glyphosate: the influence of ammonium sulphate and other additives. Weed Res. 15:13- 19. Teo, C. K., B. H. Zandstra and R. K. Nishimoto. 1973. Purple nutsedge (Qpems mtundus): its biology and control. Proc. Fourth Asian-Pacific Weed Sd. Soc. Confer. 1:184-190. Terry, P. J. 1974. Long term control of Qpems mtundus with glyphosate. Proc. East. African Weed Control Conf. 5:1-13. 153 Toth, J. and L. W. Smith. 1979. Qpeius mtundus control with glyphosate. Proc. 7th Asian-Pacific Weed Sci Confer. Pages 67-69. Turner, N. C. and P. J. Kramer. 1980. Adaptation of plants to water and high temperature stress. John Wiley and Sons. Inc., New York. 482pp. Ueki, K. 1969. Studies on the control of nutsedge (Qpeius mtundus L) on the germination of a tuber. Proc. Second Asian-Pacific Weed Control Interchange. Pages 355-369. Walters, C. J. 1973. Systems Ecology: the systems approach and mathematical models in ecology. Pages 276-292 m E. P. Odum, ed. Fundamentals of Ecology, Third Edition, Saunders, Philadelphia. Waters, W. E. and D. S. Burgis. 1968. Herbicidal persistence in soil and its effect on purple nutsedge. Weed Sci. 16:149-151. Watkinson, A. R. 1986. Plant Population Dynamics. Pages 137-184 in M. J.Crawly, ed. Plant Ecology, Blackwell Scientific Publication, Oxford. Watkinson, A. R., W. M. Lonsdale and M. Andrew. 1989. Modelling the population dynamics of an annual plant Soigbum intmns in the wet-dry tropics. J. Ecology. 77:162-181. Watson, H. K. 1985. Integrated management of leafy spurge. Pages 93-104. m A. K. Watson, ed. Leafy Spurge. Weed Sci. Soc. Am. Champaign, IL. Werner, P. A. and Caswell. H. 1977. Population growth rates and age versus stage distribution models for teasel (Dipsacus sytoestris Huds). Ecology 58:1103-1111 Whitehead, C. 1965. Sugarcane in South Africa - Its production and management Bull. S. African Asso. Exp. Stn. Weed abstr. 16:134. Wilfret G. J. and D. S. Burgis. 1976. Nutsedge (Cypems mtundus L) control using herbicides under fallow conditions. Proc. South. Weed Sci. Soc. 39:237-243. William, R. D. 1976. Vegetable crop losses caused by purple nutsedge competition. Proc. Fifth Asian-Pacific Weed Sd. Soc. Conf. Pages 78-81. William, R. D. 1978. Photoperiodic effects on the reproductive biology of purple nutsedge (Cypems mtundus). Weed Sci. 26:539-542. 154 William, R. D. and G. F. Warren. 1975 a. Competition between purple nutsedge and vegetables. Weed Sd. 23:317-323. William, R. D. and G. F. Warren. 1975 b. Suppression of Cypems mtundus L. in carrots with night application of nitrofen or herbicide oil. Weed Res. 15:285- 290. William, R. D., G. F. Wairen and B. Giordano. 1976. Seasonal activity of EPTC for Cypems mtundus L. control in a tropical climate. Weed Res. 16:217-22. William, R. D., P. C. Quimby, Jr and K. E. Frick. 1977. Intraspecific competition of purple nutsedge (Cypems mtundus) under greenhouse conditions. Weed Sd. 25:477-481. William, W. A., R. S. Loomis and C.R.Lepley. 1965. Vegetative growth of corn as affected by population density H. Component of growth, net assimilation rate and leaf area index. Crop Sd. 5:215-219. Wills, G. D. 1974. Effect of temperature, relative humidity, soil moisture and surfactant on the toxidty of glyphosate to cotton and purple nutsedge. Abstr. Weed Sd. Soc. Am. No.275. Page 119. Wills, G. D. 1975a. Evaluation of glyphosate for post emergence control of purple nutsedge in cotton. Proc. South. Weed. Sd. Soc. 28:54-57. Wills, G. D. 1975b. Effect of light and temperature on growth of purple nutsedge. Weed Sd. 23:93-96. Wills, G. D. 1976. Effect of soil incorporated herbicides on cotton and purple nutsedge. Proc South. Weed. Sd. Soc. 29:110-114. Wills, G. D. 1987. Description of purple and yellow nutsedge (Cypems mtundus and Cescukntus). Weed Technol. 1:2-9. Wills, D. and G. A. Briscoe. 1970. Anatomy of purple nutsedge. Weed Sd. 19:631- 635. Wills, G. D. and C. G. Mcwhorter. 1985. Effect of inorganic salts on the toxidty and translocation of glyphosate and MSMA in purple nutsedge (Cypems mtundus). Weed Sd. 33:755-761. Wise, A. F. and C. W. VanDiver. 1970. Soil moisture effects on competitive ability of weeds. Weeds Sd. 18:518-519. 155 Zandstra, B. H. and R. K. Nishimoto. 1977. Movement and activity of glyphosate in purple nutsedge. Weed Sci. 25:268-274. Zandstra, B. H., C. K. H. Teo and R. K. Nishimoto. 1974. Response of purple nutsedge to repeated application of glyphosate. Weed Sci. 22:230-232. Zimdahl, R. L. 1980. Weed-crop competition. A review. Int Plant Prot Ctr., Oregon State Univ., Corvallis, Oregon. 195pp.