Critical Period of Interference Between

Home , Solanum americanum, Solanum sarrachoides

CRITICAL PERIOD OF INTERFERENCE BETWEEN AMERICAN BLACK NIGHTSHADE (Solanum americanum Mill.) AND TRIPLOID (SEEDLESS) WATERMELON [Citrullus lanatus (Thunb.) Matsumura and Nakai]

JOSHUA IRA ADKINS

A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE

UNIVERSITY OF FLORIDA

2009

To my Parents

ACKNOWLEDGMENTS

Numerous individuals have helped me during the completion of my master’s degree. I would especially like to thank some of them in this thesis. I thank my advisor, Dr. William Stall, for his guidance throughout my graduate studies. I also express my gratitude to the other members of my committee: Dr. Stephen Olson, Dr. Bielinski Santos, and Dr. Jason Ferrell.

I appreciate the efforts of everyone at the Plant Science Research and Education Unit and

North Florida Research and Education Center, especially the efforts of John Morris. I am grateful for the assistance of Theodore McAvoy, Aparna Gazula, and Camille Esmel during multiple stages of this project.

I thank my parents for supporting me throughout my education. I am grateful for the inspiration of Kristen Hohenstein. Most importantly, I thank God for providing me with this wonderful opportunity.

TABLE OF CONTENTS

page

ACKNOWLEDGMENTS ...... 4

LIST OF TABLES ...... 7

LIST OF FIGURES ...... 8

ABSTRACT ...... 11

1 LITERATURE REVIEW ...... 13

Weed Competition ...... 13 Experimental Design of Competition Studies ...... 14 Additive Designs ...... 14 Neighborhood Designs ...... 15 Substitutive (Replacement Series) Designs ...... 16 Systematic Designs ...... 17 Critical Period Designs ...... 18 Solanum Sect. Solanum (S. nigrum Complex) ...... 19 American Black Nightshade ...... 20 Nightshade Competition in Crops ...... 21 Control of Nightshade ...... 22 Overview and History of Watermelon ...... 23 Watermelon Production Practices ...... 25 Polyethylene Mulch ...... 26 Transplants ...... 26 Fumigants ...... 27 Triploid (Seedless) Watermelons ...... 28 Impact of Weeds in Florida Watermelon Production ...... 29 Field Experiments ...... 29 Weed Control in Watermelon Production ...... 31

2 MAXIMUM PERIOD OF COMPETITION BETWEEN AMERICAN BLACK NIGHTSHADE AND TRIPLOID (SEEDLESS) WATERMELON ...... 32

Introduction ...... 32 Materials and Methods ...... 32 Measured Variables ...... 33 Statistical Analysis ...... 34 Results...... 34 Total Fruit Number ...... 35 Total Fruit Weight ...... 35 Marketable Fruit Number ...... 36 Marketable Fruit Weight ...... 36 Weight per Fruit ...... 37

Early Yield ...... 37 SOLAM Dry Weight ...... 37 Total Soluble Solids (SS) ...... 38 Discussion ...... 38

3 MINIMUM WEED-FREE PERIOD OF AMERICAN BLACK NIGHTSHADE IN TRIPLOID (SEEDLESS) WATERMELON ...... 46

Introduction ...... 46 Materials and Methods ...... 46 Measured Variables ...... 47 Statistical Analysis ...... 48 Results...... 48 Total Fruit Number ...... 49 Total Fruit Weight ...... 49 Marketable Fruit Number ...... 50 Marketable Fruit Weight ...... 50 Weight per Fruit ...... 51 Early Yield ...... 51 SOLAM Dry Weight ...... 52 Total Soluble Solids (SS) ...... 53 Discussion ...... 53

4 CRITICAL PERIOD OF INTERFERENCE BETWEEN AMERICAN BLACK NIGHTSHADE AND TRIPLOID (SEEDLESS) WATERMELON ...... 63

Introduction ...... 63 Materials and Methods ...... 64 Results...... 64 Total Fruit Number ...... 64 Total Fruit Weight ...... 65 Marketable Fruit Number ...... 65 Marketable Fruit Weight ...... 65 Discussion ...... 65

LITERATURE CITED ...... 73

BIOGRAPHICAL SKETCH ...... 78

LIST OF TABLES

Table page

2-1 Effect of SOLAM removal treatments on early yield of seedless watermelons from the 2007 PSREU study, 2007 NFREC study, and 2008 PSREU study, combined...... 44

3-1 Effect of SOLAM plant-back treatments on weight per watermelon for the 2007 PSREU study, 2007 NFREC study, and 2008 PSREU study, combined...... 59

3-2 Effect of SOLAM plant-back treatments on average total soluble solids expressed as degrees brix for the 2007 PSREU study, 2007 NFREC study, and 2008 PSREU study, combined...... 62

LIST OF FIGURES

Figure page

2-1 Effect of SOLAM removal treatments on total fruit number of seedless watermelons from the 2007 PSREU study, 2007 NFREC study, and 2008 PSREU study, combined...... 40

2-2 Effect of SOLAM removal treatments on total fruit number of seedless watermelons from the 2008 PSREU study...... 40

2-3 Effect of SOLAM removal treatments on total fruit weight of seedless watermelons from the 2007 PSREU study, 2007 NFREC study, and 2008 PSREU study, combined...... 41

2-4 Effect of SOLAM removal treatments on total fruit weight of seedless watermelons from the 2008 PSREU study...... 41

2-5 Effect of SOLAM removal treatments on marketable fruit number of seedless watermelons from the 2007 PSREU study, 2007 NFREC study, and 2008 PSREU study, combined...... 42

2-6 Effect of SOLAM removal treatments on marketable fruit number of seedless watermelons from the 2008 PSREU study...... 42

2-7 Effect of SOLAM removal treatments on marketable fruit weight of seedless watermelons from the 2007 PSREU study, 2007 NFREC study, and 2008 PSREU study, combined...... 43

2-8 Effect of SOLAM removal treatments on marketable fruit weight of seedless watermelons from the 2008 PSREU study...... 43

2-9 Effect of SOLAM removal treatments on the average weight per SOLAM plant from the 2007 PSREU study and 2007 NFREC study, combined...... 44

2-10 Effect of SOLAM removal treatments on the average weight per SOLAM plant from the 2008 PSREU study...... 45

3-1 Effect of SOLAM plant-back treatments on total fruit number of seedless watermelons from the 2007 PSREU study, 2007 NFREC study, and 2008 PSREU study, combined...... 55

3-2 Effect of SOLAM plant-back treatments on total fruit number of seedless watermelons from the 2008 PSREU study...... 55

3-3 Effect of SOLAM plant-back treatments on total fruit weight of seedless watermelons from the 2007 PSREU study, 2007 NFREC study, and 2008 PSREU study, combined...... 56

3-4 Effect of SOLAM plant-back treatments on total fruit weight of seedless watermelons from the 2008 PSREU study...... 56

3-5 Effect of SOLAM plant-back treatments on marketable fruit number of seedless watermelons from the 2007 PSREU study, 2007 NFREC study, and 2008 PSREU study, combined...... 57

3-6 Effect of SOLAM plant-back treatments on marketable fruit number of seedless watermelons from the 2008 PSREU study...... 57

3-7 Effect of SOLAM plant-back treatments on marketable fruit weight of seedless watermelons from the 2007 PSREU study, 2007 NFREC study, and 2008 PSREU study, combined...... 58

3-8 Effect of SOLAM plant-back treatments on marketable fruit weight of seedless watermelons from the 2008 PSREU study...... 58

3-9 Effect of SOLAM plant-back treatments on total early fruit number for the 2007 PSREU study, 2007 NFREC study, and 2008 PSREU study, combined...... 59

3-10 Effect of SOLAM plant-back treatments on total early fruit weight for the 2007 PSREU study, 2007 NFREC study, and 2008 PSREU study, combined...... 60

3-11 Effect of SOLAM plant-back treatments on marketable early fruit number for the 2007 PSREU study, 2007 NFREC study, and 2008 PSREU study, combined...... 60

3-12 Effect of SOLAM plant-back treatments on marketable early fruit weight for the 2007 PSREU study, 2007 NFREC study, and 2008 PSREU study, combined...... 61

3-13 Effect of SOLAM plant-back treatments on average SOLAM dry weight per weed for the 2007 PSREU study, 2007 NFREC study, and 2008 PSREU study, combined...... 61

3-14 Effect of SOLAM plant-back treatments on average SOLAM dry weight per weed for the 2008 PSREU study...... 62

4-1 Influence of time of weed emergence or weed removal on yield expressed as percent of check and magnitude of the critical period...... 67

4-2 Three scenarios (relationships) that can exist in critical period studies...... 68

4-3 Effect of SOLAM removal and plant-back treatments on total fruit number of seedless watermelons from the 2007 PSREU study, 2007 NFREC study, and 2008 PSREU study, combined...... 69

4-4 Effect of SOLAM removal and plant-back treatments on total fruit weight of seedless watermelons from the 2007 PSREU study, 2007 NFREC study, and 2008 PSREU study, combined...... 70

4-6 The effect of SOLAM removal and plant-back treatments on marketable fruit number of seedless watermelons from the 2007 PSREU study, 2007 NFREC study, and 2008 PSREU study, combined...... 71

4-7 The effect of SOLAM removal and plant-back treatments on marketable fruit weight of seedless watermelons from the 2007 PSREU study, 2007 NFREC study, and 2008 PSREU study, combined...... 72

Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science

CRITICAL PERIOD OF INTERFERENCE BETWEEN AMERICAN BLACK NIGHTSHADE (Solanum americanum Mill.) AND TRIPLOID (SEEDLESS) WATERMELON [Citrullus lanatus (Thunb.) Matsumura and Nakai]

Joshua Ira Adkins

May 2009

Chair: William M. Stall Major: Horticultural Science

Florida has consistently been a major watermelon [Citrullus lanatus (Thunb.) Matsumura and Nakai] producing state. In 2007, Florida harvested 10,036 hectares with a production value of $152.5 million. In terms of production value, Florida has been the leading state since 2005.

Seventy-eight percent of the watermelons sold in the United States in 2007 were triploid. In

2005 and 2006, during peak watermelon production, seeded watermelons were approximately 10 cents less per kilogram than seedless varieties.

Season-long interference of American black nightshade (Solanum americanum Mill.) is known to reduce watermelon yield at two nightshade plants per square meter. Field trials were conducted in the spring of 2007 and 2008 to determine the critical period of interference between

American black nightshade and triploid watermelon. Trials were located at Citra, Florida and

Live Oak, Florida. In order to determine the critical period, the maximum period of competition and minimum weed-free period were investigated. Trials were conducted with two nightshade plants per square meter.

The maximum period of competition to prevent marketable yield loss (based on the weight of marketable fruits) greater than 10% was 3.9 weeks after transplanting. Therefore, if American

black nightshade that was established with the watermelon crop is removed by 3.9 weeks after transplanting and the crop is subsequently kept weed-free, resulting yield loss should not exceed

10% of a crop that was grown weed-free all season.

The minimum weed-free period to prevent marketable yield loss (based on the weight of marketable fruits) greater than 10% was 3.7 weeks after transplanting. Therefore, if the establishment of American black nightshade is delayed for 3.7 weeks after transplanting and then subsequently allowed to grow, resulting yield loss should not exceed 10% of a crop that was grown weed-free all season.

The critical period to conduct nightshade control measures is sometime between 3.7 and

3.9 weeks after transplanting if acceptable yield loss is set at 10%. If the acceptable yield loss were set at a different level, the period would vary accordingly.

CHAPTER 1 LITERATURE REVIEW

Weed Competition

A weed may plainly be characterized as “any plant growing where it is not wanted”

(Anderson 1996). The same plant species may be desired in one location and not in another.

Classifying a plant as a weed depends on the setting in which someone encounters it and on the perspectives and objectives of the individuals dealing with it (Radosevich et al. 1997).

Weeds are generally considered to be competitors with crops. A wide variety of definitions have been attributed to the word “competition” (Grace and Tilman 1990). The term originates from the Latin word competere, which means to ask or sue for the same items another does (Rao 2000).

Connell (1990) simply defines competition as “a reciprocal negative interaction between two organisms.” He states that the word is usually limited to circumstances involving just two general categories of mechanisms. One is direct interference, which involves the various ways that one plant may directly harm a neighboring plant. The other is an indirect exploitation of collective resources. Sometimes, competition may be more apparent than real. Apparent competition is commonly produced via interactions with natural enemies (i.e. parasites, pathogens, or herbivores) and by positive interactions among species. The latter form requiring a positive interaction among two species with a negative interaction between one of the two and a third species.

Anderson (1996) states that competition occurs between two or more neighboring plants when the supply of one or more factors required for growth and development drops below the combined needs of the plants. Furthermore, he defines the word “interference” as an all- inclusive term that denotes all the direct effects that one plant may impose upon another, such as

competition, parasitism, allelopathy, and indirect effects (generally unknown) without referring

to one specific effect.

Competition may be intraspecific (occurring within a species) or interspecific (occurring

between species). A series of papers written by a group of Japanese researchers in the 1950s and

60s provide much of the insight regarding intraspecific competition (Yoda et al. 1963, as cited in

Park et al. 2003). The papers identified three main effects resulting from intraspecific

competition in monocultures: a competition-density effect (reduction in average size of surviving plants with increasing density); alteration in the population size structure (size hierarchy

development); and density-dependent plant death (self-thinning). In respect to interspecific competition, Park et al. (2003) state that agronomic studies intended to quantify competition between two species generally consider a weed and crop species and, to less of an extent, two crops grown in an intercrop.

Experimental Design of Competition Studies

Experimental design selection is a critical element of weed competition studies

(Rejmánek et al. 1989). Competition trials often focus on proportion, density, spatial arrangement, and timing/duration of weed competition (Radosevich 1987; Weaver et al. 1992).

Some of the experimental types used to approach this subject include: additive, neighborhood, substitutive, systematic, and critical period studies (Radosevich 1987; Gibson and Liebman

2003).

Additive Designs

An additive design refers to an experiment where both proportion and density of species are varied in mixtures (Park et al. 2003). Radosevich (1987) explained that although more than two plant species can be grown together in additive studies, most experiments are conducted with just two species—a crop and a weed. The partial additive experimental design is one of the

most common methods used to study weed-crop competition (Rejmánek et al. 1989). This is the simplest and most typically applied form of an additive design (Park et al. 2003). In these experiments, one species (generally a crop) is planted at a constant density, while the density of a second species (generally a weed) is varied.

A benefit of the partial additive design is that it imitates a real agricultural situation: crops are typically sown at a specific seed rate, leading to a relatively constant crop density

(Cousens 1991). The results of such an experiment may answer weed management questions in a manner that is specific to the cropping situation. These questions may involve the economic threshold for weed control or the yield loss related to a certain weed density or species.

The partial additive design has been criticized due to the simultaneous changes of total density and proportion (Rejmánek et al. 1989). Therefore, since two factors in the experiment vary, interpretation of the effects of either factor is difficult (Radosevich 1987). Also, it does not allow the separation of intra- and inter-specific competition effects (Cousens 1991).

Neighborhood Designs

A neighborhood design may be appropriate when the main experimental focus is the individual plant responses to the proximity of other plants (Radosevich et al. 1997). Utilizing a neighborhood design, performance of a target individual is recorded as a function of the biomass, number, distance and/or aggregation to neighbors (Goldberg and Werner 1983).

Goldberg and Werner (1983) describe an experimental design in which the effect of one neighboring species on one target species is studied by a series of field plots over a range of neighboring plant densities. Each plot contains one individual of the target species and individuals of just one neighboring species. The “per-amount competitive effect of a neighbor species on the target species is the slope of the regression of performance (e.g., growth rate,

survival, reproductive output) of target individuals on amount (e.g., density, biomass, leaf surface area) of the neighboring species.”

Neighborhood designs can have very resource-intensive data requirements (Park et al.

2003). Since observations are based on single target individuals, many treatments should be examined in order to accurately quantify neighbor effects (Radosevich et al. 1997). However, these experiments are of great value where competition needs to be quantified under various spatial arrangements of plants (Park et al. 2003).

Substitutive (Replacement Series) Designs

The substitutive (replacement series) design was pioneered by individuals such as C.T. de

Wit and W.H. van Dobben (Cousens 1991). In these experiments, the densities of two species are varied so that their total density stays constant but their proportions vary. The constant total density may be chosen arbitrarily or as the average density observed in a field setting (Goldberg and Werner 1983). Within this constant density, there are generally two monocultures, a 50:50 mixture, and various other species frequencies from 0% to 100% (Goldberg and Werner 1983;

Rejmánek et al. 1989).

Traditionally, the substitutive design is used for two main reasons: to determine something about the manner in which two biotypes or species interact and/or to determine which of two biotypes or species is the best competitor (Cousens 1991). The premise of this design is to determine the yields of mixtures by relating them to the yield of the monocultures

(Radosevich 1987). Experimental results are generally presented graphically as replacement diagrams in which the yield of each of the two examined species is plotted against its proportion of the mixture (Rejmánek et al. 1989).

Cousens (1991) states that this design has been criticized because its results rely on the density chosen, it varies only plant ratio and not total density, it cannot be used in the prediction

of population dynamics or to separate the intra- and inter-specific competition effects, and many of the indices are obscure in their interpretation. However, some of these issues have been overcome by design modifications. Although partitioning the absolute effects cannot be readily accomplished, Radosevich et al. (1997) state that it is possible to find the relative effects of inter- and intra-specific interference using the substitutive design. This type of experiment is quite useful to evaluate the competitive effects of species proportion kept at a single overall density

(Radosevich 1987). For decades, it has been a standard method to examine competition in two species mixtures and may be the most commonly utilized design among ecologists (Rejmánek et al. 1989; Cousens 1991).

Systematic Designs

Systematic experiments were developed due to the collective influences of proximity factors in weed-crop competition (Radosevich et al. 1997). These experiments systematically vary both relative and total plant density. Cousens (1991) refers to some systematic experiments

(such as addition series or complete additive) as response surface designs. The addition series entails a combination of multiple replacement series at a range of total densities. The complete additive design is a series of additive designs at various total densities.

Nelder experiments are another type of systematic design (Radosevich 1987). Generally, they are restricted to the study of interference among individuals of one species. The design often consists of a grid of plants, usually as an arc or circle. The spatial arrangement and planting density is varied systematically. Over the various parts of the grid, the amount of area available to each plant changes in a consistent manner.

A benefit of the Nelder experiment is that a range of densities can be examined without altering the plant arrangement pattern. Also, only a small area is needed to study the various density arrangements. Potentially negative aspects include that only individual plants can be

measured, resulting in difficulty obtaining the “stand” effect from neighbor relations.

Additionally, the arcs may be challenging to establish in the field.

Critical Period Designs

The impact concerning length of time that weeds are present in a crop on the immensity of crop yield losses has usually been examined in the context of the critical period of weed competition (Nieto et al. 1968, as cited in Weaver et al. 1992). The “critical period” of weed interference (critical period for weed control) is a specific minimum time period during which the crop must be kept weed-free to prevent loss in yield (Weaver and Tan 1983; Knezevic et al.

2002). The period represents the overlap of two separate components: (1) the duration of time that weeds can remain in a crop before interference starts and (2) the duration of time that weed emergence must be prevented so that ensuing weed growth does not diminish crop yield (Weaver and Tan 1983). These components may also be referred to as the maximum period of competition and the minimum weed-free period, respectively (Terry et al. 1997). The beginning and end of the critical period for weed control will often depend on the level of acceptable yield loss employed in the prediction (Knezevic et al. 2002).

Critical period studies may also be referred to as removal or plant-back studies (Oliver

1988). Experiments generally entail thinning a natural weed infestation or planting weed transplants or seeds to desired densities. The experimental design may either be a randomized complete block or a factorial on a randomized complete block. The trial should include four to six replications.

Knezevic et al. (2002) cite three reasons for which the critical period has historically been studied. These are: (1) the potential to lower the quantity of herbicide applied by accomplishing optimal application timing, (2) the potential to lower ecological and environmental degradation

associated with the prophylactic use of herbicides, and (3) to provide a test to decide if the

methods of weed control are grounded upon biological necessity.

Solanum sect. Solanum (S. nigrum complex)

Solanum sect. Solanum (likewise known as the S. nigrum complex) is a group composed

of approximately thirty annual or short-lived perennial, herbaceous weed species (Schilling

1981). The taxonomy of this section is very difficult and remains a topic of study and debate

(DeFelice 2003). Ogg et al. (1981) suggest that there are four main reasons that this is a

taxonomically difficult group. First, there is an obvious similarity in the overall morphology

among members. Second, the species exhibit considerable phenotypic plasticity. Hence, many

of the taxonomic features that are commonly used for identification purposes vary considerably

when the plants are grown in different environments. Third, there is considerable genetic

variability in certain species which is conveyed as multiple geographic types within a single

species. Fourth, there has been widespread nomenclature confusion within the section.

In 1981, two papers were published that assisted in the taxonomic clarification of the

weedy nightshades in the United States and Canada (Ogg et al. 1981; Schilling 1981). Prior to

this time, many weed control manuals and other forms of agricultural literature contained

misidentifications and inaccuracies on the subject (Ogg and Rogers 1989). Often times, various

nightshades were grouped together and referred to as “black nightshade.” Another problem was

that some of the early botanists were unaware of the unusual variability of Solanum sect.

Solanum (Ogg et al. 1981). Therefore, in some situations, a multitude of names were produced

for relatively few actual species.

Schilling (1981) states that eleven species of Solanum sect. Solanum occur in North

America. These include: S. americanum, S. ptycanthum, S. douglasii, S. pseudogracile, S. interius, S. sarrachoides, S. furcatum, S. nigrum, S. villosum, S. scabrum, and S. retroflexum.

S. americanum, S. ptycanthum, S. nigrum, and S. sarrachoides are commonly found on

agricultural lands (Ogg et al. 1981). To compare these four species, Ogg et al. (1981) examined

characters such as stem ridging and branching, growth habit, leaf margin type, leaf pubescence,

leaf apex type, under-leaf color, infructescence and inflorescence arrangement, pedicel type and

length, petiole length, peduncle type and length, sepal length, fruit and flower number, calyx

form and length, corolla length, anther, stamen, and style length, style posture, berry size, color,

shape, and surface type, seed size and number, pollen grain diameter, and sclerotic granule

number. Chromosome number was also considered for comparison purposes. Due to the

considerable variation of these Solanum species, very few characters are consistent and clear

indicators of a specific species. Vegetative structures are often especially variable. Flower and

fruit characteristics are more likely to be consistent and useful.

American Black Nightshade

American black nightshade (Solanum americanum Mill.) is one of the most variable

species within the S. nigrum complex (Ogg et al. 1981). Confusion has commonly occurred

when discerning this weed species from others in the complex. Plants are commonly

misidentified as eastern black nightshade (S. ptycanthum). Until the 1980s, the species was commonly called S. nodiflorum (Schilling 1981; Ogg and Rogers 1989; Ogg et al. 1981).

American black nightshade is found throughout much of the southern United States and up the west coast into Canada (Ogg and Rogers 1989; USDA 2008). The weed is also found through

Mexico to Central and South America (Schilling 1981).

Plants are annual to short-lived perennial herbs or subshrubs (Ogg et al. 1981). They may grow in a spreading or erect manner and reach up to 1.2 m in height (Ogg et al. 1981; Ogg and Rogers 1989). Stems are usually herbaceous and slender, turning somewhat woody with maturity (Ogg et al. 1981). They can be round, ridged, angular, or ridged with small teeth. The

underside of seedling leaves is green (Ogg and Rogers 1989). Mature leaves may be ovate or

ovate-lanceolate with crenate margins. They may be glabrous or have a varied amount of eglandular hairs.

Ogg and Rogers (1989) provide a good description of the flowers on p. 28.

The stellate corolla is white (infrequently tinted or streaked purple-white) with a yellow star, 4.3 to 6.2 mm long. Corolla lobes are 2.8 to 4.5 mm long. Stamens are 1.6 to 2.6 mm long and anthers are 1.3 to 1.8 mm long. The style may be straight or bent, 2.5 to 3.3 mm long with the lower 1/2 to 2/3 pubescent. The inflorescence is usually unbelliform.

The seeds of American black nightshade are small at 1.2-1.6 mm long (Schilling et al.

1992). Fifty to 110 light-tannish seeds are found in each berry along with 0-5 sclerotic granules

(Ogg and Rogers 1989). Immature berries almost always have white flecks on the fruit surface

(Ogg et al. 1981). Mature berries are purplish-black, shiny, and generally detach from the plant

at the receptacle (Ogg and Rogers 1989).

The size of pollen grains is a very useful identification character. American black

nightshade has small grains from 15.0 to 25.0 µm (Ogg et al. 1981). The species is one of the

diploid (2n chromosome number equal to 24) species of nightshade (Schilling et al. 1992; Ogg

and Rogers 1989). Examining the characteristics mentioned in this section can help to identify

American black nightshade and distinguish it from other Solanum spp. However, it is important

to remember that many of the features may vary considerably when grown under different

environmental conditions (Ogg et al. 1981).

Nightshade Competition in Crops

Solanum spp. have been found to cause yield reduction in various crops. When

watermelons were grown on bare ground, Gilbert (2006) found that two American black

nightshade plants/m2 reduced watermelon (Citrullus lanatus) yield by an average of 84% when season-long competition occurs. Roos (1999) determined that one American black nightshade

plant/m2 caused a 20% loss in early and total marketable yield of bell pepper (Capsicum annuum

L.) under season-long competition. Blackshaw (1991) found that two hairy nightshade (S.

sarrachoides) plants per meter of row resulted in a 13% reduction of dry bean (Phaseolus

vulgaris) seed yield. Eastern black nightshade caused a 25-60% yield loss in transplanted

processing tomatoes and an 80% yield loss in direct-seeded processing tomatoes when there were four nightshades growing/m2 (Weaver et al. 1987; Ogg and Rogers 1989).

Roos (1999) found that the critical period of American black nightshade interference in bell pepper to avoid total marketable yield loss of greater than 10% was from 2.7 to 4.6 weeks after pepper planting. Buckelew et al. (2006) determined that the critical weed-free period of eastern black nightshade to avoid greater than 20% tomato yield loss (extra large and jumbo grades) was 28 to 50 days after tomato transplanting. Blackshaw (1991) found that up to 9 weeks of hairy nightshade-free maintenance was required after crop emergence in order to avoid dry bean yield losses. Hairy nightshade interference during the first 3 weeks after bean emergence was sufficient to lower bean yields.

Control of Nightshade

Weed-control techniques can be grouped into five basic categories: preventive, cultural, biological, mechanical (physical), and chemical (Anderson 1996). Preventive weed control involves the steps taken to prevent the introduction, establishment, and/or spread of certain weed species into areas that aren’t currently infested. Cultural weed control is concerned with using proper crop, land, and water management practices. Biological weed control utilizes natural enemies to control a specific weed species. Mechanical (physical) strategies of weed control involve practices such as hoeing, mowing, hand-pulling, burning, smothering with nonliving material, using artificially high temperatures, and machine tillage. Chemical weed control involves the use of phytotoxic chemicals. The phytotoxic chemicals used for weed control are

called “herbicides.” Preventive, cultural, mechanical, and chemical weed control strategies have

all been used to manage nightshade species. This is highly dependent on the type of crop and the

location at which it is grown. Research shows that biological control may one day be an option

to control certain nightshades in specific areas of crop production (Wapshere 1988).

Paraquat is commonly utilized to control nightshade in Florida vegetable production.

However, paraquat control of American black nightshade became unacceptable in certain areas

of the state. Research has shown a couple of reasons why this is probably happening.

Experiments show that application of cupric hydroxide, a copper-containing fungicide, to

American black nightshade prior to paraquat treatment reduced herbicide phytotoxicity (Bewick

et al. 1990). Studies also show that certain Florida populations of this weed are less sensitive to

paraquat treatment, and that sensitivity is altered with previous application of cupric hydroxide.

Overview and History of Watermelon

In 2007, the United States harvested 60,986 hectares (NASS 2008) of watermelons

[Citrullus lanatus (Thunb.) Matsumura and Nakai]. This area had a production value of $476.2

million. The same year, Florida harvested 10,036 hectares with a production value of $152.5

million. In terms of production value, Florida has been the leading state since 2005. Following

Florida, the leading states in 2007 were Georgia, California, Texas, and Arizona. In recent years,

average watermelon yield in Florida has been 28,021 kilograms per hectare (Olson et al. 2007).

Watermelon is a dicotyledonous angiosperm in the order Cucurbitales and the family

Cucurbitaceae (Freeman 2007 and Buker 1999). Within the Cucurbitaceae family, watermelon belongs to the Cucurbitoideae subfamily and the tribe Benincaseae (Robinson and Decker-

Walters 1997). Native to central Africa, watermelons have been cultivated in Africa and the

Middle East for thousands of years (Mills 2008 and Wehner 2005). In China, watermelons have been grown at least as far back as 900 AD (Wehner 2005). The plants were brought to the

Americas in the early 1600s, first being cultivated in Massachusetts in 1629 (Mills 2008). By the mid 1600s, watermelons made it to Florida.

In ancient times, watermelons were utilized as a source of water (fruit is 90% water), staple food, fermentation for alcohol production, and for animal feed (Mills 2008). Wild

watermelons were very bitter, although this was eliminated quickly under cultivation with cross- pollination and the selection of seed. Plant breeders have released varieties having larger fruit, disease resistance, dwarf vines, higher lycopene content, higher sugar content, seedlessness, and new flesh colors such as orange, yellow, and dark red (Wehner et al. 2001). Today, watermelons are mostly eaten fresh (Wehner 2005). However, they are also eaten cooked, pickled, boiled for syrup, and dried for candy. In India, watermelon seeds are powdered and baked like bread

(Robinson and Decker-Walters 1997). Seeds are often roasted in the Middle East and the Orient.

Therefore, some Chinese cultivars have been bred to have very large seeds to be used for this purpose. In northwestern China, black watermelon seeds have been commercially produced for over 200 years.

Watermelon plants are aggressive vining annuals with long, angular branching stems

(Mills 2008 and Nonnecke 1989). Leaves are cordate at the base and usually have three to seven deep lobes (Nonnecke 1989). The extensive root system consists of a taproot and many lateral roots and grows to about 2 feet in depth (Musmade and Desai 1998). Adventitious roots may form on the vine runners (Mills 2008). Watermelons are monoecious and have flowers that are smaller and less showy than those of other cultivated cucurbits (Maynard 2001). Flowers are borne solitary in leaf axils and stay open for just one day. Honeybees are the primary flower pollinators. Fruit maturation from pollination generally requires 30-35 days for most commercial varieties.

The berrylike fruit of watermelon is a pepo (“berry”). The ovary (fruit) is fused with the receptacle tissue (basically the outer covering of the ovary) which forms the hard rind (Mills

2008). The shape of watermelon fruit ranges from long and cylindrical to spherical (Musmade

and Desai 1998). Rind coloration may be light green, often called gray, to very dark green,

almost black in appearance (Maynard 2001). Particular varieties may have stripes of a certain

design or pattern.

Watermelon Production Practices

For the best quality and production, watermelons need a long, warm growing season

(Maynard 2001). Plants prefer a mean temperature of 21˚ to 29.5˚C (Nonnecke 1989).

However, average temperatures as high as 35˚C can be tolerated. When grown from seed, a watermelon crop takes 80-100 days to reach maturity (Olson et al. 2007). Using transplants, maturity may be reached within 60-90 days (transplant to maturity). In Florida, recommended

planting dates for fields in the south, central, and north portion of the state are Dec. 15 - Mar. 1,

Jan. 15 - Mar. 15, and Feb. 15 - Apr. 15, respectively. Watermelons favor light-to-medium soils with adequate organic matter to hold moisture, yet with good drainage (Nonnecke 1989). In

Florida, watermelons may be grown on a variety of soil types (Spreen et al. 1995). However, watermelon production is not recommended on muck soils. For the best production, the pH of the soil should be 6.0-6.5 (Mills 2008).

The United States watermelon industry has seen drastic changes in production methods over the past 30 years (Egel et al. 2008). Currently, intensive watermelon farming often involves the use of black plastic mulch, transplants, and fumigants. Utilizing these practices, growers have the potential to increase both yield and profit. Over the past decade, the popularity of triploid (seedless) watermelon has been on the rise (Freeman et al. 2007a). In 2006, 78% of the watermelons sold in the United States were triploid. In 2005 and 2006, seeded watermelons

were approximately 10 cents less per kilogram (than seedless) during peak watermelon production (Freeman and Olson 2007).

Polyethylene Mulch

Polyethylene mulch has replaced bare ground culture because it reduces weed pressure,

warms soil, allows earlier production, increases soil moisture retention, reduces nutrient

leaching, decreases soil compaction, increases fumigant effectiveness, and reduces fruit rot (Egel

et al. 2008 and Olson 2007). However, disadvantages to polyethylene mulch are the requirement

of specialized equipment, higher preplant costs, and the need for removal and disposal (Olson

2007). The effects of mulching may be enhanced by integrating mulch into other production

systems. These include systems that involve transplants, drip irrigation, windbreaks, and row

covers.

Transplants

Using transplants in watermelon production offers many cultural advantages: (1) it allows planting prior to the soil being sufficiently warm to produce good germination; (2) increased seed use efficiency (especially when using expensive hybrid seed); (3) reduces or eliminates soil crusting and damping-off deterrents to seedling growth; (4) greater stand establishment

uniformity; (5) planting depth is more uniform; (6) generally results in earlier harvest; and (7) it

is the only way to cost effectively grow triploid watermelons (Vavrina 1992).

Watermelon transplants are usually ready for planting three to four weeks after seeding

(Santos 2007). They are normally planted in trays with a one inch cell size at ½ inch depth.

Germination should take place in three days with an optimum germination temperature of 90˚ F.

The disadvantages of using watermelon transplants include: (1) higher variable costs; (2)

requires more advanced planning; (3) increased labor costs; (4) fragile seedlings may be easily

broken in the transplanting operation; and (5) plant quality may be reduced if weather delays

planting (Vavrina 1992). Successful watermelon transplant production depends on four basic

requirements: (1) a weed/disease/insect free medium; (2) adequate moisture and heat; (3) high

intensity of good quality light for stocky plant growth; and (4) a “hardening off” period when

moving transplants to the field from the greenhouse.

Fumigants

Methyl bromide was classified as a Class 1 ozone depleting chemical in 1993 (Noling

and Botts 2007). Consequently, methyl bromide was mandated by the Clean Air Act of 1990 for eventual phaseout from agricultural use and production. January 1, 2005 was the final phase-out date for methyl bromide importation and production for use in the United States. Florida’s subtropical environment is conducive to the rapid accumulation of various economically important soilborne pests (Spreen et al. 1995; Santos 2007). Using methyl bromide was a consistent and effective way to control these pests in watermelon production. The fumigant allowed the growers of certain high-value vegetables to continually crop the same field without rotating to a less profitable production system (Larson et al. 2004). When used as a soil fumigant for vegetable production, methyl bromide served as a herbicide, fungicide, nematicide, and insecticide (Spreen et al. 1995). The production of certain crops still allows for the use of methyl bromide via a Critical Use Exemption (Noling and Botts 2007). However, watermelon producers in Florida do not have a Critical Use Exemption. Watermelons are sometimes grown as a second crop in a double-cropping system that follows a crop such as pepper or tomato that has a Critical Use Exemption (Mossler 2007).

A single management tactic may never be found to replace methyl bromide (Larson et al.

2004). A combination of tactics specific to the cropping situation will likely be needed to resolve the situation. This would probably entail a system using fumigants and non-fumigants.

One of the most commonly researched alternative tactic involves the combination of the

fumigants 1,3-dichloropropene (1,3-D) and chloropicrin (Santos 2007; Larson et al. 2004).

When combined with a herbicide treatment, this fumigant seems to work well in certain crop

production environments.

Triploid (Seedless) Watermelons

The fruit of diploid watermelon varieties may contain as many as 1,000 seeds per fruit

(Olson et al. 2007). Seeds are often a nuisance to the consumer. Triploid watermelon fruits produce small, white, rudimentary seeds or seedcoats that are eaten along with the fruit. With proper post harvest practices, seedless watermelons have a longer shelf life than do seeded watermelons. This might be due to the fact that flesh deterioration occurs in the vicinity of seeds.

In 1951, the idea of seedless watermelons was first described in United States literature

(Maynard 1992). It was based on experimentation that started in Japan in 1939. Triploid

watermelons have been cultivated for over 40 years in the United States (Olson et al. 2007).

However, it was not until recent years that intense marketing, improved varieties, and higher

consumer demand created the large triploid market of today.

Diploid watermelons have 22 chromosomes per cell (Stephens 1994). When diploid seedlings are treated with colchicine (a strong alkaloid that causes a doubling of the chromosome number), a tetraploid plant with 44 chromosomes may be produced (Stephens 1994; Maynard

1992). Then, by crossing a tetraploid plant with a normal diploid pollenizer, triploid (33 chromosomes) seed is produced. A sterile hybrid watermelon grows from the triploid seed.

When triploid flowers are pollinated with pollen from a diploid plant, seedless fruits develop.

In order to provide enough pollen to achieve optimal triploid watermelon yield, 20-33%

of the watermelon plants in the field should be diploid (Freeman et al. 2007b). Conventionally,

rows dedicated for the diploid cultivars have been set aside. However, numerous pollenizer

cultivars are now available to be planted within the row between existing triploid plants. By

eliminating the rows dedicated to diploid plants, the number of triploid plants per acre increases.

Therefore, per-acre yield of seedless watermelons should increase (Freeman et al. 2007b).

Impact of Weeds in Florida Watermelon Production

Competition from weeds can be a significant problem for Florida watermelon growers.

This can greatly be attributed to the high temperatures needed for crop establishment, which are also favorable to rampant weed growth (Buker et al. 2003). Links can also be made to low vining habit and low planting density, as well as the slow growth rate of the crop early in the season (Mossler 2007).

The spectrum of weeds that trouble watermelon growers will vary depending on region and previous land usage (Larson et al. 2004). Pressure from broadleaf and grass weeds will likely be the most severe on previously cultivated lands (Stall 1992). Some notably troublesome weeds include: nutsedge (Cyperus spp.), amaranth (Amaranthus spp.), goosegrass (Eleusine indica), crabgrass (Digitaria spp.), bristly starbur (Acanthospermum hispidum), Texas panicum

(Panicum texanum), purslane (Portulaca spp.), and pusley (Richardia spp.) (Larson et al. 2004).

The results of field experiments focusing on weed interference in Florida watermelon provide specific examples of potential yield reduction due to weeds.

Field Experiments

Terry et al. (1997) conducted a study in Gainesville, Florida to determine the critical period of smooth amaranth (Amaranthus hybridus) interference in watermelon. Allowing for a

10% yield loss, this period was determined to be between 0.5 and 3.0 weeks after watermelon emergence. In other words, to sustain 90% of normal yield, smooth amaranth interference must not be tolerated during this period. In order to prevent yield loss from exceeding 20%, smooth amaranth must be controlled from 1.9 to 3.0 weeks after watermelon emergence.

Buker et al. (2003) conducted an additive study in Gainesville, Florida and Quincy,

Florida to determine the effect of season-long interference from yellow nutsedge (Cyperus esculentus) on watermelon yield. In both transplanted and direct-seeded watermelons, two yellow nutsedge plants/m2 resulted in a 10% yield loss. In transplanted watermelons, 25 yellow

nutsedge plants/m2 reduced watermelon yield by 50 percent. Transplanted watermelons

produced much higher yields than direct-seeded watermelons. However, the competitive ability

of watermelon with yellow nutsedge was not improved by transplanting. At the particular

corresponding yellow nutsedge densities, percent yield loss was similar for both methods of establishment.

Gilbert et al. (2008) conducted a study in Gainesville, Florida and Citra, Florida to

determine the effects of season-long American black nightshade interference with watermelon.

In treatments where watermelons were grown on polyethylene mulch, two nightshade plants/m2

reduced watermelon yield by an average of 67 percent. Gilbert (2006) also carried out

treatments to examine the spatial distances between American black nightshade and watermelon

on their abilities to compete with one another. When two nightshade seedlings were established

on either side of the watermelon plants, at 15 or 30 cm, there was no significant difference in

yield relating to weed spacing. Treatments resulted in a 46% and 74% yield loss, respectively.

Monks and Schultheis (1998) conducted removal and plant-back studies in Clinton, North

Carolina to determine the critical period of large crabgrass (Digitaria sanguinalis) competition in transplanted triploid (seedless) watermelon. Although conducted in North Carolina, this study is important to Florida watermelon producers because much of Florida’s watermelon production is that of seedless varieties. They determined that a critical weed-free period between 0 and 6 weeks after transplanting must be maintained to achieve the best quality and quantity of

marketable fruit. No effect on marketable fruit weight or number of watermelons occurred from

large crabgrass that emerged after 6 weeks. Marketable fruit yield decreased by 911 fruit per ha

for every week that large crabgrass remained in watermelon. Likewise, yield increased by 151

fruit per ha for every week that large crabgrass emergence was delayed.

Weed Control in Watermelon Production

Traditional techniques of mechanical control still apply to the watermelon production of today (Stall 2007). These methods include: cultivation, plowing, disking, hoeing, mowing, and pulling weeds by hand. The process of preparing the beds for planting even helps to control weeds. The tillage involved will often induce the germination of many weeds, allowing for early season control with additional cultivation or herbicides.

The proper selection and application of herbicides can be an effective technique for weed

control (Stall 2007). Most of these products are labeled for pre-plant or pre-emergence applications. Only a few products are available that can be applied post-emergence. Generally, cucurbits have a very limited tolerance to herbicides. Soil-applied herbicides are either applied to the soil surface or incorporated (Stall 1992). For the best results, surface-applied herbicides need rainfall or irrigation soon after application. Incorporated herbicides do not depend on rainfall or irrigation, but do require greater equipment and labor investment. Incorporated herbicides generally provide more consistent and wider-spectrum weed control.

When this research began, there was not a herbicide product labeled to control American black nightshade in watermelon. However, terbacil is now available for preemergence, pretransplant, and row middle applications (Olson et al. 2007).

CHAPTER 2 MAXIMUM PERIOD OF COMPETITION BETWEEN AMERICAN BLACK NIGHTSHADE AND TRIPLOID (SEEDLESS) WATERMELON

Introduction

Information on the maximum period of competition can assist watermelon [Citrullus lanatus (Thunb.) Matsumura and Nakai] producers in knowing when in the crop’s life cycle that

American black nightshade (Solanum americanum Mill. SOLAM) should be controlled in order to prevent unacceptable yield loss. The objective of this research was to determine the maximum period of competition between SOLAM and triploid watermelon by means of a removal study.

Materials and Methods

The studies were conducted at the Plant Science Research and Education Unit, Citra, FL

(PSREU) and at the North Florida Research and Education Center, Live Oak, FL (NFREC).

They were performed in the spring of 2007 and 2008 in both locations. The soil at the PSREU is a Hague series sand (loamy, siliceous, semiactive, hyperthermic Arenic Hapludalfs), cation exchange capacity of 6.1, 1.4% organic matter, and a pH of 5.8. The soil at the NFREC is

Blanton-Foxworth-Alpin Complex, cation exchange capacity of 4.9, 1% organic matter, and a pH of 6.7.

‘Super Crisp’ triploid watermelon seedlings were transplanted into raised beds fumigated with methyl bromide and chloropicrin (50:50) at 449.3 kg/ha and covered with black polyethylene mulch. Transplanting occurred at the NFREC on 9 April, 2007 and 8 April, 2008 and at the PSREU on 11 April, 2007 and 17 April, 2008. On each planting date, ‘SP-4’ pollenizers were planted within the row with the triploid plants. Holes were made in the mulch for all watermelon plants with a sharpened 7.6 cm diameter pipe. In both locations, beds were

0.81 m wide. Beds were established on 2.13 m centers at the NFREC and on 2.44 m centers at

the PSREU. Triploid transplants were grown at an in-row spacing of one meter. Pollenizers were transplanted between every other triploid plant in the row.

University of Florida Institute of Food and Agricultural Sciences (IFAS) recommendations were followed for pesticide and fertilizer application. Plots were irrigated via drip tape that was placed under the mulch. One-fourth of the nitrogen and potassium and all of the phosphorus was applied to the bed pre-plant while the remaining amount of the fertilizer regiment was injected during drip irrigation.

SOLAM seeds were treated with a 50/50 mixture of bleach and water for 30 minutes.

Seeds were thoroughly rinsed and placed into 2.8x2.8 cm cells of expanded polystyrene trays filled with potting mix. The weeds grew in a greenhouse under overhead irrigation until they reached approximately 8 cm in height. Holes were made in the mulch for the weeds with a 6.7 cm diameter can. Two weeds were transplanted at 15 cm on either side of the triploid watermelon plants (one on each side).

In 2007, SOLAM was established into watermelon plots at watermelon transplanting and removed at 0, 1, 2, 3, 4, and 5 weeks after transplanting (WAT). In 2008, a removal period of 6

WAT was added. The control (weed-free) plots were those with weed removal at 0 WAT.

Measured Variables

In 2007, watermelons were harvested at the NFREC on 21 June [73 days after transplanting (DAT)] and 28 June (80 DAT) and at the PSREU on 19 June (69 DAT) and 26

June (76 DAT). In 2008, watermelons were harvested at the NFREC on 19 June (72 DAT) and

26 June (79 DAT) and at the PSREU on 25 June (69 DAT) and 2 July (76 DAT). For each plot, individual watermelon weights were recorded. Data was organized and analyzed using the following yield categories: total fruit number, total fruit weight, marketable fruit number, and marketable fruit weight. All blemish-free fruits that weighed ≥ 3.63 kg were considered

marketable. Total fruit number and weight were calculated by adding together all fruit from both

harvests for each plot. Marketable fruit number and weight were calculated by adding together all marketable fruit from both harvests for each plot. Data was also analyzed by weight per fruit which was calculated by dividing total fruit weight by total fruit number for each plot. The first

harvest was also analyzed separately to determine if treatment had an affect on early yield.

All SOLAM plants were cut from each plot at the soil line and placed in a plant dryer at

the specified removal times. Dry weights of the SOLAM samples were recorded. Measurement

of total soluble solids (SS) was conducted on one watermelon from each plot. A small piece of

watermelon flesh was cut from the center of each fruit. The flesh was juiced using a garlic press

and SS were measured using a hand held brix refractometer.

Statistical Analysis

A randomized complete block design with four replications was used for all years and

locations. An Analysis of Variance (ANOVA) was conducted to test for significant treatment

effects and interactions (SAS Institute Inc., 2003). Regression analysis was then carried out on data expressed as percent of control for comparison purposes (Systat Software Inc., 2006).

Results

Statistical analysis of watermelon yield data revealed no significant interaction between the

studies at the PSREU in 2007, NFREC in 2007, and PSREU in 2008 when examining removal

treatments from 0 through 5 WAT. The study at the NFREC in 2008 was excluded due to

considerable in field variation. This was also apparent in a muskmelon study adjacent to this

trial. Yield data expressed as percent of control was combined for all studies except the 2008

study at the NFREC. The means from the combined studies were subjected to regression

analysis. The study from the PSREU in 2008 was also subjected to regression analysis by itself

to examine the removal period of 6 WAT. Regression analysis was carried out using the

equation of best fit. A linear equation was used to regress the total fruit number data for the

combined studies. For all other regression analyses, data was best fit to a quadratic equation.

Total Fruit Number

SOLAM removal time significantly affected total fruit number for the combined studies.

The regression line fitted to the data had an r2 value of 0.68 (Figure 2-1). A 10% yield loss was predicted to occur when SOLAM was removed at 3.5 WAT. Therefore, a 10% yield loss should not be exceeded as long as SOLAM removal is conducted by 3.5 WAT. A 14% yield loss was predicted at 5 WAT. SOLAM removal time also significantly affected total fruit number when the 2008 study at the PSREU was examined by itself. The regression line fitted to the data had an r2 value of 0.83 (Figure 2-2). A 10% yield loss was predicted to occur when SOLAM was

removed at 3.4 WAT. A 20% yield loss was predicted to occur when SOLAM was removed at

4.5 WAT. When SOLAM was removed at 6 WAT, there was a predicted 42% yield loss.

Total Fruit Weight

SOLAM removal time significantly affected total fruit weight for the combined studies.

The regression line fitted to the data had an r2 value of 0.86 (Figure 2-3). A 10% yield loss was predicted to occur when SOLAM was removed at 3.6 WAT. Therefore, a 10% yield loss should not be exceeded as long as SOLAM removal is conducted by 3.6 WAT. An 18% yield loss was predicted at 5 WAT. SOLAM removal time also significantly affected total fruit weight when the 2008 study at the PSREU was examined by itself. The regression line fitted to the data had an r2 value of 0.87 (Figure 2-4). A 10% yield loss was predicted to occur when SOLAM was

removed at 3.0 WAT. A 20% yield loss was predicted to occur when SOLAM was removed at

3.9 WAT. When SOLAM was removed at 6 WAT, there was a predicted 47% yield loss.

Marketable Fruit Number

SOLAM removal time significantly affected marketable fruit number for the combined

studies. The regression line fitted to the data had an r2 value of 0.77 (Figure 2-5). A 10% yield

loss was predicted to occur when SOLAM was removed at 3.9 WAT. Therefore, a 10% yield

loss should not be exceeded as long as SOLAM removal is conducted by 3.9 WAT. A 17% yield

loss was predicted at 5 WAT. SOLAM removal time also significantly affected marketable fruit

number when the 2008 study at the PSREU was examined by itself. The regression line fitted to

the data had an r2 value of 0.88 (Figure 2-6). A 10% yield loss was predicted to occur when

SOLAM was removed at 3.2 WAT. A 20% yield loss was predicted to occur when SOLAM was removed at 4.1 WAT. When SOLAM was removed at 6 WAT, there was a predicted 49% yield loss.

Marketable Fruit Weight

SOLAM removal time significantly affected marketable fruit weight for the combined studies. The regression line fitted to the data had an r2 value of 0.88 (Figure 2-7). A 10% yield

loss was predicted to occur when SOLAM was removed at 3.9 WAT. Therefore, a 10% yield

loss should not be exceeded as long as SOLAM removal is conducted by 3.9 WAT. A 19% yield

loss was predicted at 5 WAT. SOLAM removal time also significantly affected marketable fruit

weight when the 2008 study at the PSREU was examined by itself. The regression line fitted to

the data had an r2 value of 0.88 (Figure 2-8). A 10% yield loss was predicted to occur when

SOLAM was removed at 2.9 WAT. A 20% yield loss was predicted to occur when SOLAM was removed at 3.8 WAT. When SOLAM was removed at 6 WAT, there was a predicted 56% yield loss.

Weight per Fruit

Statistical analysis of the weight per fruit data revealed a significant interaction between

the studies at the PSREU in 2007, NFREC in 2007, and PSREU in 2008 when examining

removal treatments 0 through 5 WAT. Therefore, the data was analyzed separately for each

study. The studies revealed no apparent trends (data not shown).

Early Yield

Statistical analysis of early watermelon yield data revealed no significant interaction

between the studies at the PSREU in 2007, NFREC in 2007, and PSREU in 2008 when

examining removal treatments from 0 through 5 WAT. Data analyzed as total early fruit

number, total early fruit weight, marketable early fruit number, and marketable early fruit weight

was combined for the three studies. Total early fruit number was the only category that had a

significant difference between treatments (Table 2-1). However, the data did not have a trend.

SOLAM Dry Weight

Statistical analysis of average SOLAM dry weight per weed revealed no significant

interaction between both locations in 2007. Therefore, data was pooled from both locations in

2007. Data from the PSREU in 2008 was examined by independently. SOLAM removal time

significantly affected SOLAM dry weight for the combined studies and the 2008 PSREU study.

The regression equation for the combined studies and the 2008 PSREU study was an exponential

growth, single, 3 parameter equation. The regression line fitted to the data from the combined

studies had an r2 value of 0.99 (Figure 2-9). The mean SOLAM dry weight from the 5 WAT removal time was 250 times larger than the mean SOLAM dry weight from the 1 WAT removal time at 53.273 g and 0.213 g, respectively. The regression line fitted to the data from the 2008

PSREU study had an r2 value of 0.99 (Figure 2-10). The mean SOLAM dry weight from the 6

WAT removal time was 938 times larger than the mean SOLAM dry weight from the 1 WAT

removal time at 226.979 g and 0.242 g, respectively.

Total Soluble Solids (SS)

Statistical analysis of total soluble solids data revealed no significant interaction between the studies at the PSREU in 2007, NFREC in 2007, and PSREU in 2008 when examining removal treatments from 0 through 5 WAT. Data was pooled for all three studies. No significant differences resulted from removal treatments (data not shown).

Discussion

A 10% yield loss was predicted for the combined studies at approximately the same removal time for the yield categories: total fruit number, total fruit weight, marketable fruit number, and marketable fruit weight. Since these categories experienced a 10% yield loss at 3.5,

3.6, 3.9, and 3.9 WAT, respectively, weed removal conducted sometime between 3.5-3.9 WAT

should be sufficient to prevent yield loss from exceeding 10% for any of the categories. This

case should hold true if weeds are kept out for the remainder of the season. When examining the

2008 study from the PSREU, weed removal had to be conducted a few days earlier to prevent a

10% yield loss. If weed removal was delayed to 4 or 5 WAT, yield loss was much greater when

examining the 2008 PSREU study alone than when the combined studies were analyzed.

Predicted yield loss was 8% greater at 4 WAT and 16% greater at 5 WAT for the respective

analyses. This was calculated using the average predicted yield losses of the following

categories: total fruit number, total fruit weight, marketable fruit number, and marketable fruit

weight. The greater yield loss is likely attributable to the larger SOLAM plants of the 2008

PSREU study. The average actual weight of SOLAM plants removed at 4 WAT from the 2008

PSREU study was 2.1 times larger than those from the 2007 PSREU study and 2007 NFREC

study, combined. At 5 WAT, the SOLAM plants from the 2008 PSREU study were 2.3 times

larger than those from the 2007 PSREU study and 2007 NFREC study, combined. The larger

SOLAM plants probably resulted in a greater intensity of weed/crop competition in comparison with the smaller SOLAM plants. When the 2008 PSREU study was examined by itself, there was an average predicted yield loss of 49% when SOLAM was removed at 6 WAT. This was also calculated using the average predicted yield losses of the following categories: total fruit number, total fruit weight, marketable fruit number, and marketable fruit weight.

110

100

60 Total Fruit Number (% of Control) of Number(% Fruit Total 50

0 1 2 3 4 5 Weeks After Transplanting Figure 2-1. Effect of SOLAM removal treatments on total fruit number of seedless watermelons from the 2007 PSREU study, 2007 NFREC study, and 2008 PSREU study, combined. The regression model is f = y0+a*x, r2 = 0.68, where y0 = 99.8825, a = - 2.7941, and x = WAT.

120

100

60 Total Fruit Number (% of Control) of Number(% Fruit Total

0 1 2 3 4 5 6 Weeks After Transplanting Figure 2-2. Effect of SOLAM removal treatments on total fruit number of seedless watermelons from the 2008 PSREU study. The regression model is f = y0+a*x+b*x2, r2 = 0.83, where y0 = 97.2298, a = 3.9471, b = -1.7583, and x = WAT.

110

100

60 Total Fruit Weight (% of Control) of (% Weight Fruit Total 50

0 1 2 3 4 5 Weeks After Transplanting Figure 2-3. Effect of SOLAM removal treatments on total fruit weight of seedless watermelons from the 2007 PSREU study, 2007 NFREC study, and 2008 PSREU study, combined. The regression model is f = y0+a*x+b*x2, r2 = 0.86, where y0 = 100.5682, a = -0.8437, b = -0.5887, and x = WAT.

120

100

60 Total Fruit Weight (% of Control) of (% Weight Fruit Total

0 1 2 3 4 5 6 Weeks After Transplanting Figure 2-4. Effect of SOLAM removal treatments on total fruit weight of seedless watermelons from the 2008 PSREU study. The regression model is f = y0+a*x+b*x2, r2 = 0.87, where y0 = 100.4017, a = 1.9630, b = -1.8127, and x = WAT.

110

100

50 Marketable Fruit Number (% of Control) of Number(% Fruit Marketable

0 1 2 3 4 5 Weeks After Transplanting Figure 2-5. Effect of SOLAM removal treatments on marketable fruit number of seedless watermelons from the 2007 PSREU study, 2007 NFREC study, and 2008 PSREU study, combined. The regression model is f = y0+a*x+b*x2, r2 = 0.77, where y0 = 99.0579, a = 0.9199, b = -0.8397, and x = WAT.

120

100

60 Marketable Fruit Number (% of Control) of Number(% Fruit Marketable

0 1 2 3 4 5 6 Weeks After Transplanting Figure 2-6. Effect of SOLAM removal treatments on marketable fruit number of seedless watermelons from the 2008 PSREU study. The regression model is f = y0+a*x+b*x2, r2 = 0.88, where y0 = 98.3708, a = 3.4305, b = -1.8954, and x = WAT.

110

100

50 Marketable Fruit Weight (% of Control) of (% Weight Fruit Marketable

0 1 2 3 4 5 Weeks After Transplanting Figure 2-7. Effect of SOLAM removal treatments on marketable fruit weight of seedless watermelons from the 2007 PSREU study, 2007 NFREC study, and 2008 PSREU study, combined. The regression model is f = y0+a*x+b*x2, r2 = 0.88, where y0 = 100.2058, a = 1.4132, b = -1.0360, and x = WAT.

120

100

60 Marketable Fruit Weight (% of Control) of (% Weight Fruit Marketable 40 0 1 2 3 4 5 6 Weeks After Transplanting Figure 2-8. Effect of SOLAM removal treatments on marketable fruit weight of seedless watermelons from the 2008 PSREU study. The regression model is f = y0+a*x+b*x2, r2 = 0.88, where y0 = 100.9961, a = 1.8353, b = -1.9002, and x = WAT.

Table 2-1. Effect of SOLAM removal treatments on early yield of seedless watermelons from the 2007 PSREU study, 2007 NFREC study, and 2008 PSREU study, combined. Means within columns followed by the same letter do not differ at the α = 0.05 as determined by LSD. Means Expressed as Percent of Control Weeks After Early Total Early Total Early Marketable Early Marketable Transplanting Fruit Number Fruit Weight Fruit Number Fruit Weight 0 100ab 100a 100a 100a 1 128a 127a 125a 125a 2 115ab 115a 117a 115a 3 104ab 104a 105a 104a 4 94b 105a 100a 107a 5 107ab 105a 104a 103a

0 Average Weight (g) per SOLAM Plant (g) perSOLAM Weight Average

1 2 3 4 5 Weeks After Transplanting Figure 2-9. Effect of SOLAM removal treatments on the average weight per SOLAM plant from the 2007 PSREU study and 2007 NFREC study, combined. The regression model is f = y0+a*exp(b*x), r2 = 0.99, where y0 = -3.1491, a = 0.8778, b = 0.8339, and x = WAT.

250

200

150

100

0 Average Weight (g) per SOLAM Plant (g) perSOLAM Weight Average

1 2 3 4 5 6 Weeks After Transplanting Figure 2-10. Effect of SOLAM removal treatments on the average weight per SOLAM plant from the 2008 PSREU study. The regression model is f = y0+a*exp(b*x), r2 = 0.99, where y0 = -15.3132, a = 5.0430, b = 0.6468, and x = WAT.

CHAPTER 3 MINIMUM WEED-FREE PERIOD OF AMERICAN BLACK NIGHTSHADE IN TRIPLOID (SEEDLESS) WATERMELON

Introduction

Information on the minimum weed-free period can assist watermelon [Citrullus lanatus

(Thunb.) Matsumura and Nakai] producers in knowing when in the crop’s life cycle that

American black nightshade (Solanum americanum Mill. SOLAM) should be controlled in order to prevent unacceptable yield loss. The objective of this research was to determine the minimum weed-free period of SOLAM in triploid watermelon by means of a plant-back study.

Materials and Methods

The studies were conducted at the Plant Science Research and Education Unit, Citra, FL

(PSREU) and at the North Florida Research and Education Center, Live Oak, FL (NFREC). The studies were performed in the spring of 2007 and 2008 in both locations. The soil at the PSREU is a Hague series sand (loamy, siliceous, semiactive, hyperthermic Arenic Hapludalfs), cation exchange capacity of 6.1, 1.4% organic matter, and a pH of 5.8. The soil at the NFREC is

Blanton-Foxworth-Alpin Complex, cation exchange capacity of 4.9, 1% organic matter, and a pH of 6.7.

0.81 m wide. Beds were established on 2.13 m centers at the NFREC and on 2.44 m centers at

the PSREU. Triploid transplants were grown at an in-row spacing of one meter. Pollenizers were transplanted between every other triploid plant in the row.

SOLAM seeds were treated with a 50/50 mixture of bleach and water for 30 minutes.

In 2007, SOLAM was established into watermelon plots at 0, 1, 2, 3, 4, and 5 weeks after watermelon transplanting and remained until watermelon harvest. In 2008, an establishment period of 6 weeks after watermelon transplanting was added. The control plots were the removal at week 0 (weed-free) plots from the maximum period of competition study.

Measured Variables

In 2007, watermelons were harvested at the NFREC on 21 June [73 days after transplanting (DAT)] and 28 June (80 DAT) and at the PSREU on 19 June (69 DAT) and 26

June (76 DAT). In 2008, watermelons were harvested at the NFREC on 19 June (72 DAT) and

marketable fruit weight. All blemish-free fruits that weighed ≥ 3.63 kg were considered

marketable. Total fruit number and weight was calculated by adding together all fruit from both

harvests for each plot. Marketable fruit number and weight was calculated by adding together all

marketable fruit from both harvests for each plot. Data was also analyzed by weight per fruit which was calculated by dividing total fruit weight by total fruit number for each plot. The first harvest was also analyzed separately to determine if treatment had an effect on early yield.

Three SOLAM plants were cut from each plot at the soil line and placed in a plant dryer after the final harvest. Dry weights of the SOLAM samples were recorded. Measurement of total soluble solids (SS) was conducted on one watermelon from each plot. A small piece of watermelon flesh was cut from the center of each fruit. The flesh was juiced using a garlic press and SS were measured using a hand held brix refractometer.

Statistical Analysis

A randomized complete block design with four replications was used for all years and locations. An Analysis of Variance (ANOVA) was conducted to test for significant treatment effects and interactions (SAS Institute Inc., 2003). Regression analysis was then carried out on data expressed as percent of control for comparison purposes (Systat Software Inc., 2006).

Results

Statistical analysis of watermelon yield data revealed no significant interaction between the studies at the PSREU in 2007, NFREC in 2007, and PSREU in 2008 when examining plant- back treatments of 0 through 5 WAT. There was considerable variation in the data from the removal study treatments for 2008 at the NFREC. This was also apparent in a muskmelon study adjacent to this trial. Since the weed-free control treatment is located in the removal study, the plant-back study did not have an appropriate control to use for the calculation of percent control.

Therefore, the 2008 study at the NFREC was excluded. Yield data expressed as percent of

control was combined for the studies at the PSREU in 2007, NFREC in 2007, and PSREU in

2008. The means from the combined studies were subjected to regression analysis. The study

from the PSREU in 2008 was also subjected to regression analysis by itself to examine the 6

WAT plant-back treatment. The relationship between the yield categories and SOLAM

establishment time was regressed to best fit using an exponential rise to maximum, simple

exponent, three parameter equation.

Total Fruit Number

SOLAM establishment time significantly affected total fruit number for the combined

studies. The regression line fitted to the data had an r2 value of 0.92 (Figure 3-1). A 10% yield

loss was predicted to occur when SOLAM was established at 4.0 WAT. A 20% yield loss was

predicted to occur when SOLAM was established at 2.3 WAT. Therefore, a 10% and 20% yield

loss should not be exceeded as long as SOLAM establishment is delayed for 4.0 and 2.3 WAT,

respectively. SOLAM establishment time also significantly affected total fruit number when the

2008 study at the PSREU was examined by itself. The regression line fitted to the data had an r2

value of 0.85 (Figure 3-2). A 10% yield loss was predicted to occur when SOLAM was

established at 4.2 WAT. A 20% yield loss was predicted to occur when SOLAM was established

at 2.5 WAT. When SOLAM was established at 6 WAT, there was a predicted 6% yield loss.

Total Fruit Weight

SOLAM establishment time significantly affected total fruit weight for the combined

studies. The regression line fitted to the data had an r2 value of 0.94 (Figure 3-3). A 10% yield loss was predicted to occur when SOLAM was established at 4.0 WAT. A 20% yield loss was predicted to occur when SOLAM was established at 2.4 WAT. Therefore, a 10% and 20% yield loss should not be exceeded as long as SOLAM establishment is delayed for 4.0 and 2.4 WAT, respectively. SOLAM establishment time also significantly affected total fruit weight when the

2008 study at the PSREU was examined by itself. The regression line fitted to the data had an r2

value of 0.89 (Figure 3-4). A 10% yield loss was predicted to occur when SOLAM was

established at 4.7 WAT. A 20% yield loss was predicted to occur when SOLAM was established

at 2.4 WAT. When SOLAM was established at 6 WAT, there was a predicted 8% yield loss.

Marketable Fruit Number

SOLAM establishment time significantly affected marketable fruit number for the

combined studies. The regression line fitted to the data had an r2 value of 0.95 (Figure 3-5). A

10% yield loss was predicted to occur when SOLAM was established at 3.4 WAT. A 20% yield loss was predicted to occur when SOLAM was established at 2.2 WAT. Therefore, a 10% and

20% yield loss should not be exceeded as long as SOLAM establishment is delayed for 3.4 and

2.2 WAT, respectively. SOLAM establishment time also significantly affected marketable fruit number when the 2008 study at the PSREU was examined by itself. The regression line fitted to the data had an r2 value of 0.83 (Figure 3-6). A 10% yield loss was predicted to occur when

SOLAM was established at 3.7 WAT. A 20% yield loss was predicted to occur when SOLAM

was established at 2.2 WAT. When SOLAM was established at 6 WAT, there was a predicted

4% yield loss.

Marketable Fruit Weight

SOLAM establishment time significantly affected marketable fruit weight for the

combined studies. The regression line fitted to the data had an r2 value of 0.95 (Figure 3-7). A

10% yield loss was predicted to occur when SOLAM was established at 3.7 WAT. A 20% yield loss was predicted to occur when SOLAM was established at 2.3 WAT. Therefore, a 10% and

20% yield loss should not be exceeded as long as SOLAM establishment is delayed for 3.7 and

2.3 WAT, respectively. SOLAM establishment time also significantly affected marketable fruit weight when the 2008 study at the PSREU was examined by itself. The regression line fitted to

the data had an r2 value of 0.88 (Figure 3-8). A 10% yield loss was predicted to occur when

SOLAM was established at 4.4 WAT. A 20% yield loss was predicted to occur when SOLAM

was established at 2.3 WAT. When SOLAM was established at 6 WAT, there was a predicted

7% yield loss.

Weight per Fruit

Statistical analysis of the weight per fruit data revealed no significant interaction between

the studies at the PSREU in 2007, NFREC in 2007, and PSREU in 2008 when examining plant-

back treatments of 0 through 5 WAT. Therefore, the three studies were combined. SOLAM

establishment time significantly affected the weight per watermelon (Table 3-1). When

SOLAM was established at 5 WAT, actual fruit weight was an average of 11.4% larger than

when SOLAM was established at 0 WAT.

Early Yield

Early yield data was analyzed in the following categories: total early fruit number, total early fruit weight, marketable early fruit number, and marketable early fruit weight. With the exception of marketable early fruit number, there was no significant interaction concerning early yield between the studies at the PSREU in 2007, NFREC in 2007, and the PSREU in 2008 when examining plant-back treatments of 0 through 5 WAT. The three studies were combined for each early yield category. The relationship between the early yield categories and SOLAM establishment time was regressed to best fit using an exponential rise to maximum, simple exponent, three parameter equation.

Total Early Fruit Number. SOLAM establishment time significantly affected total early fruit number for the combined studies. The regression line fitted to the data had an r2 value of

0.94 (Figure 3-9). When SOLAM was established at 0 WAT, there was a predicted 3,540

watermelons per ha. When SOLAM was established at 5 WAT, there was a predicted 7,783

watermelons per ha. Therefore, the predicted number of watermelons per ha at the 5 WAT establishment time was 2.2 times greater than at the 0 WAT establishment time.

Total Early Fruit Weight. SOLAM establishment time significantly affected total early fruit weight for the combined studies. The regression line fitted to the data had an r2 value of

0.95 (Figure 3-10). When SOLAM was established at 0 WAT, there was a predicted 49,283 kg

of watermelons per ha. When SOLAM was established at 5 WAT, there was a predicted

113,413 kg of watermelons per ha. Therefore, the predicted total weight of watermelons per ha

at the 5 WAT establishment time was 2.3 times greater than at the 0 WAT establishment time.

Marketable Early Fruit Number. SOLAM establishment time significantly affected

marketable early fruit number for the combined studies. The regression line fitted to the data had

an r2 value of 0.92 (Figure 3-11). When SOLAM was established at 0 WAT, there was a

predicted 3,066 watermelons per ha. When SOLAM was established at 5 WAT, there was a

predicted 7,427 watermelons per ha. Therefore, the predicted number of watermelons per ha at

the 5 WAT establishment time was 2.4 times greater than at the 0 WAT establishment time.

Marketable Early Fruit Weight. SOLAM establishment time significantly affected

marketable early fruit weight for the combined studies. The regression line fitted to the data had

an r2 value of 0.94 (Figure 3-12). When SOLAM was established at 0 WAT, there was a

predicted 46,714 kg of watermelons per ha. When SOLAM was established at 5 WAT, there

was a predicted 111,281 kg of watermelons per ha. Therefore, the predicted total weight of

watermelons per ha at the 5 WAT establishment time was 2.4 times greater than at the 0 WAT establishment time.

SOLAM Dry Weight

Statistical analysis of average SOLAM dry weight per weed revealed no significant interaction between the studies at the PSREU in 2007, NFREC in 2007, and PSREU in 2008

when examining plant-back treatments of 0 through 5 WAT. Therefore, data was pooled for all

three studies. SOLAM establishment time significantly affected SOLAM dry weight per weed.

Mean SOLAM weights were regressed to best fit using a hyperbola, hyperbolic decay, 3

parameter equation, r2 value of 0.99 (Figure 3-13). The mean SOLAM dry weight from the 0

WAT establishment time was 13 times larger than the mean SOLAM dry weight from the 5

WAT establishment time at 0.551 kg and 0.043 kg, respectively. SOLAM dry weight was also

independently analyzed for the 2008 study at the PSREU. Mean SOLAM weights were

regressed to the same hyperbolic decay equation, r2 value of 0.99 (Figure 3-14). The mean

SOLAM dry weight from the 0 WAT establishment time was 11 times larger than the mean

SOLAM dry weight from the 5 WAT establishment time at 0.525 kg and 0.05 kg, respectively.

Total Soluble Solids (SS)

Statistical analysis of SS data (expressed as degrees brix) revealed no significant

interaction between the studies at the PSREU in 2007, NFREC in 2007, and PSREU in 2008

when examining plant-back treatments of 0 through 5 WAT. Therefore, SS data was combined

for the three studies. SOLAM establishment time significantly affected SS for the combined

studies (Table 3-2). When SOLAM was established at 5 WAT, SS were an actual 9.0% higher

than when SOLAM was established at 0 WAT. The average values were 11.35 and 10.41

degrees brix, respectively.

Discussion

A 10% yield loss was predicted for the combined studies at approximately the same establishment time for the yield categories: total fruit number, total fruit weight, marketable fruit number, and marketable fruit weight. Since these categories experienced a predicted 10% yield loss at 4.0, 4.0, 3.4, and 3.7 WAT, respectively, the delay of SOLAM establishment to sometime between 3.4-4.0 WAT should be sufficient to prevent yield loss from exceeding 10% for any of

the categories. This case should hold true if weeds are kept out prior to SOLAM establishment.

When examining the 2008 study from the PSREU, the needed delay of SOLAM establishment was predicted to be within one week after the prediction from the combined studies.

100

50 Total Fruit Number (% of Control) of Number(% Fruit Total 40

0 1 2 3 4 5 Weeks After Transplanting Figure 3-1. Effect of SOLAM plant-back treatments on total fruit number of seedless watermelons from the 2007 PSREU study, 2007 NFREC study, and 2008 PSREU study, combined. The regression model is f = y0+a*(1-b^x), r2 = 0.92, where y0 = 45.5746, a = 52.8956, b = 0.6298, and x = WAT.

100

50 Total Fruit Number (% of Control) of Number(% Fruit Total 40

0 1 2 3 4 5 6 Weeks After Transplanting Figure 3-2. Effect of SOLAM plant-back treatments on total fruit number of seedless watermelons from the 2008 PSREU study. The regression model is f = y0+a*(1- b^x), r2 = 0.85, where y0 = 41.1336, a = 56.578, b = 0.6225, and x = WAT.

100

50 Total Fruit Weight (% of Control) of (% Weight Fruit Total

0 1 2 3 4 5 Weeks After Transplanting Figure 3-3. Effect of SOLAM plant-back treatments on total fruit weight of seedless watermelons from the 2007 PSREU study, 2007 NFREC study, and 2008 PSREU study, combined. The regression model is f = y0+a*(1-b^x), r2 = 0.94, where y0 = 42.0884, a = 56.7857, b = 0.6320, and x = WAT.

100

50 Total Fruit Weight (% of control) of (% Weight Fruit Total 40

0 1 2 3 4 5 6 Weeks After Transplanting Figure 3-4. Effect of SOLAM plant-back treatments on total fruit weight of seedless watermelons from the 2008 PSREU study. The regression model is f = y0+a*(1- b^x), r2 = 0.89, where y0 = 41.8692, a = 52.4677, b = 0.5857, and x = WAT.

100

40 Marketable Fruit Number (% of Control) of Number(% Fruit Marketable

0 1 2 3 4 5 Weeks After Transplanting Figure 3-5. Effect of SOLAM plant-back treatments on marketable fruit number of seedless watermelons from the 2007 PSREU study, 2007 NFREC study, and 2008 PSREU study, combined. The regression model is f = y0+a*(1-b^x), r2 = 0.95, where y0 = 40.0846, a = 62.8741, b = 0.6269, and x = WAT.

100

40 Marketable Fruit Number (% of Control) of Number(% Fruit Marketable

0 1 2 3 4 5 6 Weeks After Transplanting Figure 3-6. Effect of SOLAM plant-back treatments on marketable fruit number of seedless watermelons from the 2008 PSREU study. The regression model is f = y0+a*(1- b^x), r2 = 0.83, where y0 = 39.2081, a = 59.3537, b = 0.5888, and x = WAT.

100

40 Marketable Fruit Weight (% of Control) of (% Weight Fruit Marketable

0 1 2 3 4 5 Weeks After Transplanting Figure 3-7. Effect of SOLAM plant-back treatments on marketable fruit weight of seedless watermelons from the 2007 PSREU study, 2007 NFREC study, and 2008 PSREU study, combined. The regression model is f = y0+a*(1-b^x), r2 = 0.95, where y0 = 39.8338, a = 62.2174, b = 0.6406, and x = WAT.

100

40 Marketable Fruit Weight (% of Control) of (% Weight Fruit Marketable

0 1 2 3 4 5 6 Weeks After Transplanting Figure 3-8. Effect of SOLAM plant-back treatments on marketable fruit weight of seedless watermelons from the 2008 PSREU study. The regression model is f = y0+a*(1- b^x), r2 = 0.88, where y0 = 40.8840, a = 53.8120, b = 0.5749, and x = WAT.

Table 3-1. Effect of SOLAM plant-back treatments on weight per watermelon for the 2007 PSREU study, 2007 NFREC study, and 2008 PSREU study, combined. Means within column followed by the same letter do not differ at the α = 0.05 as determined by LSD. Weeks After Transplanting Average Watermelon Weight (kg) 0 5.43 b 1 5.78 ab 2 5.95 ab 3 5.76 ab 4 6.08 a 5 6.05 ab

9000

8000

7000

6000

5000

4000

Total Early Fruit Num (Avg perha) (Avg Num Fruit Early Total 3000

0 1 2 3 4 5 Weeks After Transplanting Figure 3-9. Effect of SOLAM plant-back treatments on total early fruit number (average per ha) for the 2007 PSREU study, 2007 NFREC study, and 2008 PSREU study, combined. The regression model is f = y0+a*(1-b^x), r2 = 0.94, where y0 = 3540.3701, a = 4588.7732, b = 0.5965, and x = WAT.

140000

120000

100000

80000

60000 Total Early Fruit Wt (Avg kg/ha) (Avg Wt Fruit Early Total

40000 0 1 2 3 4 5 Weeks After Transplanting Figure 3-10. Effect of SOLAM plant-back treatments on total early fruit weight (average kg/ha) for the 2007 PSREU study, 2007 NFREC study, and 2008 PSREU study, combined. The regression model is f = y0+a*(1-b^x), r2 = 0.95, where y0 = 49283.0959, a = 69700.7782, b = 0.6033, and x = WAT.

9000

8000

7000

6000

5000

4000

3000 Marketable Early Fruit Num (Avg perha) (Avg Num Fruit Early Marketable

0 1 2 3 4 5 Weeks After Transplanting Figure 3-11. Effect of SOLAM plant-back treatments on marketable early fruit number (average per ha) for the 2007 PSREU study, 2007 NFREC study, and 2008 PSREU study, combined. The regression model is f = y0+a*(1-b^x), r2 = 0.92, where y0 = 3066.2760, a = 5024.4336, b = 0.6671, and x = WAT.

140000

120000

100000

80000

60000 Marketable Early Fruit Wt (Avg kg/ha) (Avg Wt Fruit Early Marketable 40000 0 1 2 3 4 5 Weeks After Transplanting Figure 3-12. Effect of SOLAM plant-back treatments on marketable early fruit weight (average kg/ha) for the 2007 PSREU study, 2007 NFREC study, and 2008 PSREU study, combined. The regression model is f = y0+a*(1-b^x), r2 = 0.94, where y0 = 46713.7218, a = 71669.1388, b = 0.6298, and x = WAT.

0.6

0.5

0.4

0.3

0.2

0.1 Avg SOLAM Dry Wt per Weed (kg) perWeed Wt Dry SOLAM Avg

0.0 0 1 2 3 4 5 Weeks After Transplanting Figure 3-13. Effect of SOLAM plant-back treatments on average SOLAM dry weight per weed (kg) for the 2007 PSREU study, 2007 NFREC study, and 2008 PSREU study, combined. The regression model is f = y0+(a*b)/(b+x), r2 = 0.99, where a = 1.0234, b = 5.1336, y0 = -0.4731, and x = WAT.

0.5

0.4

0.3

0.2

0.1 Avg SOLAM Dry Wt per Weed (kg) perWeed Wt Dry SOLAM Avg 0.0 0 1 2 3 4 5 6 Weeks After Transplanting Figure 3-14. Effect of SOLAM plant-back treatments on average SOLAM dry weight per weed (kg) for the 2008 PSREU study. The regression model is f = y0+(a*b)/(b+x), r2 = 0.99, where a = 2.1227, b = 17.9888, y0 = -1.5924, and x = WAT.

Table 3-2. Effect of SOLAM plant-back treatments on average total soluble solids expressed as degrees brix for the 2007 PSREU study, 2007 NFREC study, and 2008 PSREU study, combined. Means within column followed by the same letter do not differ at the α = 0.05 as determined by LSD. Weeks After Transplanting Total Soluble Solids (degrees brix) 0 10.41 b 1 10.84 ab 2 11.05 ab 3 10.67 ab 4 11.30 a 5 11.35 a

CHAPTER 4 CRITICAL PERIOD OF INTERFERENCE BETWEEN AMERICAN BLACK NIGHTSHADE AND TRIPLOID (SEEDLESS) WATERMELON

Introduction

The influence resulting from the length of time that weeds are present in a crop on the extent of crop yield losses has generally been analyzed in terms of the critical period of weed

competition (Weaver et al. 1992). This period represents the time interval (Figure 4-1) between

two separately determined elements: the maximum period of competition and the minimum

weed-free period (Knezevic et al. 2002; Oliver 1988). This time interval may be referred to as the critical period for weed control (Knezevic et al. 2002). Recently, this period has been described as a “window” during the growing season during which weeds must be controlled in order to avoid unacceptable yield loss.

In the above scenario, the minimum weed-free period is of longer duration than the

maximum period of competition and the crop must be kept free of weeds between these timings

to prevent yield loss from exceeding an acceptable level (Figure 4-2a). However, there are two

additional scenarios (relationships) that can exist in critical period studies (Roberts 1976; Martin

et al. 2001). In one relationship, the minimum weed-free period is equivalent to the maximum

period of competition. In this case, unacceptable yield loss should be avoided if control

measures are implemented at this one critical time (Figure 4-2b). The other relationship occurs

when the maximum period of competition is longer than the minimum weed-free period. In this

situation, unacceptable yield loss should not occur if weeds are controlled at any time between

these critical stages (Figure 4-2c).

Since the critical period for weed control is inferred via two separately measured

components, the accuracy of the estimate may be reduced and the chances of substantial error

may increase (Weaver 1984; Knezevic et al. 2002). For example, the yield loss resulting from

the delay in the beginning of weed control is not accounted for in determining the end of the

critical period (Knezevic et al. 2002). Therefore, a critical period based on an acceptable yield

loss of 5% may in reality result in a yield loss that is slightly greater than 5% of the weed-free control.

Information on the critical period of interference between SOLAM and triploid watermelon can assist watermelon producers in knowing when in the crop’s life cycle that

SOLAM should be controlled in order to prevent unacceptable yield loss. The objective of this research was to determine the critical period of weed interference by examining the maximum period of competition and the minimum weed-free period studies.

Materials and Methods

Figures that included results from both the maximum period of competition (removal) study and minimum weed-free period (plant-back) study were constructed using the same regression analyses as in Chapter 2 and Chapter 3. This was done in order to examine both periods on the same figure to visually depict the critical period.

Results

The combined data from the 2007 PSREU study, 2007 NFREC study, and 2008 PSREU study was examined to determine the critical period for the following categories: total fruit

number, total fruit weight, marketable fruit number, and marketable fruit weight. Acceptable

yield loss was set at 10% of control.

Total Fruit Number

Total fruit number results (Figure 4-3) followed the mentioned scenario where the

minimum weed-free period (4.0 WAT) is of longer duration than the maximum period of

competition (3.5 WAT). Therefore, if the crop is maintained weed-free from 3.5-4.0 WAT, yield

loss should not greatly exceed 10% of a crop kept weed-free all season.

Total Fruit Weight

Total fruit weight results (Figure 4-4) also followed the mentioned scenario where the

minimum weed-free period (4.0 WAT) is of longer duration than the maximum period of

competition (3.6 WAT). Therefore, if the crop is maintained weed-free from 3.6-4.0 WAT, yield

loss should not greatly exceed 10% of a crop kept weed-free all season.

Marketable Fruit Number

Marketable fruit number results (Figure 4-5) followed the mentioned scenario where the

maximum period of competition (3.9 WAT) is longer than the minimum weed-free period (3.4

WAT). Therefore, if the weeds are controlled at any time from 3.4-3.9 WAT, yield loss should

not greatly exceed 10% of a crop kept weed-free all season.

Marketable Fruit Weight

Marketable fruit weight results (Figure 4-6) also followed the mentioned scenario where

the maximum period of competition (3.9 WAT) is longer than the minimum weed-free period

(3.7 WAT). Therefore, if the weeds are controlled at any time from 3.7-3.9 WAT, yield loss should not greatly exceed 10% of a crop kept weed-free all season.

Discussion

A weed control strategy could also be based upon making a decision to either control

SOLAM only before the minimum weed-free period or after the maximum period of competition, respectively. For example, after examining both periods for marketable fruit number, a producer could decide to either allow SOLAM to compete until 3.9 WAT and then control it the remainder of the season or keep the crop SOLAM-free until 3.4 WAT and then allow it for the remainder of the season.

The competitive relationship between crops and weeds may considerably altered depending upon the environmental conditions present during the season (Knezevic et al. 2002;

Lindquist et al. 1999). Consequently, watermelon yield loss due to SOLAM may vary

depending upon location and growing season. The crop/weed response to other growing

conditions and cultural practices would provide further insight as to the critical period of

interference.

There was very little variation in the critical period when data was examined by total fruit

number, total fruit weight, marketable fruit number, and marketable fruit weight. From a practical standpoint, 3.4-4.0 WAT is an important time frame to control SOLAM in seedless watermelon. If acceptable yield loss is set at a level other than 10%, the time frame would need to be adjusted accordingly.

CRITICAL PERIOD 100

40 Yield (% of Check) of (% Yield max period of competition 20 min weed-free period

0 5 10 15 20 25

Weeks After Emergence Figure 4-1. Influence of time of weed emergence or weed removal on yield expressed as percent of check and magnitude of the critical period (Oliver, 1988). Critical period here is based on a 5% acceptable yield loss.

a Yield

max period of competition min weed-free period Time

b Yield

max period of competition min weed-free period

Time

c Yield

max period of competition min weed-free period Time Figure 4-2. Three scenarios (relationships) that can exist in critical period studies (Roberts 1976; Martin et al. 2001). In scenario a, the minimum weed-free period is of longer duration than the maximum period of competition and the crop must be kept free of weeds between these timings to prevent yield loss from exceeding an acceptable level. In scenario b, the minimum weed-free period is equivalent to the maximum period of competition. Unacceptable yield loss should be avoided if control measures are implemented at this one critical time. In scenario c, the maximum period of competition is longer than the minimum weed-free period. Unacceptable yield loss should not occur if weeds are controlled at any time between these critical stages.

100

max period of competition

Total Fruit Number (% of Control) of Number(% Fruit Total min weed-free period 40 0 1 2 3 4 5

Weeks After Transplanting Figure 4-3. Effect of SOLAM removal and plant-back treatments on total fruit number of seedless watermelons from the 2007 PSREU study, 2007 NFREC study, and 2008 PSREU study, combined. The removal regression model is f = y0+a*x, r2 = 0.68, where y0 = 99.8825, a = -2.7941, and x = WAT. The plant-back regression model is f = y0+a*(1-b^x), r2 = 0.92, where y0 = 45.5746, a = 52.8956, b = 0.6298, and x = WAT. Dotted lines represent the point where a 10% yield loss is predicted.

100

max period of competition Total Fruit Weight (% of Control) of (% Weight Fruit Total min weed-free period 40

0 1 2 3 4 5

Weeks After Transplanting Figure 4-4. Effect of SOLAM removal and plant-back treatments on total fruit weight of seedless watermelons from the 2007 PSREU study, 2007 NFREC study, and 2008 PSREU study, combined. The removal regression model is f = y0+a*x+b*x2, r2 = 0.86, where y0 = 100.5682, a = -0.8437, b = -0.5887, and x = WAT. The plant-back regression model is f = y0+a*(1-b^x), r2 = 0.94, where y0 = 42.0884, a = 56.7857, b = 0.6320, and x = WAT. Dotted lines represent the point where a 10% yield loss is predicted.

100

max period of competition min weed-free period 40 Marketable Fruit Number (% of Control) of Number(% Fruit Marketable

0 1 2 3 4 5

Weeks After Transplanting Figure 4-6. Effect of SOLAM removal and plant-back treatments on marketable fruit number of seedless watermelons from the 2007 PSREU study, 2007 NFREC study, and 2008 PSREU study, combined. The removal regression model is f = y0+a*x+b*x2, r2 = 0.77, where y0 = 99.0579, a = 0.9199, b = -0.8397, and x = WAT. The plant-back regression model is f = y0+a*(1-b^x), r2 = 0.95, where y0 = 40.0846, a = 62.8741, b = 0.6269, and x = WAT. Dotted lines represent the point where a 10% yield loss is predicted.

100

max period of competition 40 min weed-free period Marketable Fruit Weight (% of Control) of (% Weight Fruit Marketable

0 1 2 3 4 5

Weeks After Transplanting Figure 4-7. Effect of SOLAM removal and plant-back treatments on marketable fruit weight of seedless watermelons from the 2007 PSREU study, 2007 NFREC study, and 2008 PSREU study, combined. The removal regression model is f = y0+a*x+b*x2, r2 = 0.88, where y0 = 100.2058, a = 1.4132, b = -1.0360, and x = WAT. The plant-back regression model is f = y0+a*(1-b^x), r2 = 0.95, where y0 = 39.8338, a = 62.2174, b = 0.6406, and x = WAT. Dotted lines represent the point where a 10% yield loss is predicted.

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BIOGRAPHICAL SKETCH

Joshua Adkins grew up in Homeland, Florida. He graduated from Bartow Senior High

School in 2002. That fall, he began coursework at Florida Southern College (Lakeland, Florida).

In the spring of 2006, he received a Bachelor of Science degree in horticultural science with a

minor in business administration. The next August, he began studies at the University of Florida

on a Master of Science degree in horticultural science.