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Critical Period of Interference between American Black Nightshade (Solanum americanum Mill.) and Triploid (Seedless) Wat...

Permanent Link: http://ufdc.ufl.edu/UFE0024147/00001

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Title: Critical Period of Interference between American Black Nightshade (Solanum americanum Mill.) and Triploid (Seedless) Watermelon Citrullus Lanatus (Thunb.) Matsumura and Nakai
Physical Description: 1 online resource (78 p.)
Language: english
Creator: Adkins, Joshua
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2009

Subjects

Subjects / Keywords: american, black, critical, nightshade, period, seedless, triploid, watermelon
Horticultural Science -- Dissertations, Academic -- UF
Genre: Horticultural Science thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: 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.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Joshua Adkins.
Thesis: Thesis (M.S.)--University of Florida, 2009.
Local: Adviser: Stall, William M.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2009
System ID: UFE0024147:00001

Permanent Link: http://ufdc.ufl.edu/UFE0024147/00001

Material Information

Title: Critical Period of Interference between American Black Nightshade (Solanum americanum Mill.) and Triploid (Seedless) Watermelon Citrullus Lanatus (Thunb.) Matsumura and Nakai
Physical Description: 1 online resource (78 p.)
Language: english
Creator: Adkins, Joshua
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2009

Subjects

Subjects / Keywords: american, black, critical, nightshade, period, seedless, triploid, watermelon
Horticultural Science -- Dissertations, Academic -- UF
Genre: Horticultural Science thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: 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.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Joshua Adkins.
Thesis: Thesis (M.S.)--University of Florida, 2009.
Local: Adviser: Stall, William M.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2009
System ID: UFE0024147:00001


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1 CRITICAL PERIOD OF INTERFERENCE BETWEEN AMERICAN BLACK NIGHTSHADE ( Solanum americanum Mill.) AND TRIPLOID (SEEDLESS) WATERMELON [ Citrullus lanatus (Thunb.) Matsumura and Nakai] By JOSHUA IRA ADKINS A THESIS PRESENTED TO TH E GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2009

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2 2009 Joshua Ira Adkins

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3 To my Parents

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4 ACKNOWL EDGMENTS Numerous individuals have helped me during the completion of my masters 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 expre ss 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, es pecially the efforts of John Morris. I am grateful for the as sistance 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 inspira tion of Kristen Hohenstein. Most importantly, I thank God for providing me with this wonderful opportunity.

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5 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 S ect. Solanum (S. nigrum C omplex) .......................................................................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) WATER MELON .....................................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 Fru it Weight .................................................................................................36 Weight per Fruit ..............................................................................................................37

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6 Early Yield .......................................................................................................................37 SOLAM Dry Weight .......................................................................................................37 Total Soluble Solids (SS) ................................................................................................38 Discussion ...............................................................................................................................38 3 MINIMUM WEED FR EE 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

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7 LIST OF TABLES Table page 21 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 31 Effect of SOLAM plant back treatments on weight per watermelon for the 2007 PSREU study, 2007 NFREC study, and 2008 PSREU study, combined. .........................59 32 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

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8 LIST OF FIGURES Figure page 21 Effect of SOLAM removal treatments on total fruit number of seedless watermelons from the 2007 PSREU study, 2007 NFREC study, and 2008 PSREU study, combi ned. ...........................................................................................................................40 22 Effect of SOLAM removal treatments on total fruit number of seedless watermelons fro m the 2008 PSREU study. .............................................................................................40 23 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 24 Effect of SOLAM remova l treatments on total fruit weight of seedless watermelons from the 2008 PSREU study. .............................................................................................41 25 Effect of SOLAM removal treatments on marketable fruit number of seedless watermelons from the 20 07 PSREU study, 2007 NFREC study, and 2008 PSREU s tudy, combined. ................................................................................................................42 26 Effect of SOLAM removal treatments on marketable fruit number of seedless watermelons fr om the 2008 PSREU study. .......................................................................42 27 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 28 Effect of SOLAM removal treatments on marketable fruit weight of seedless watermelons fr om the 2008 PSREU study. .......................................................................43 29 Effect of SOLAM remov al treatments on the average weight per SOLAM plant from the 2007 PSREU study and 2007 NFREC study, combined. ............................................44 210 Effect of SOLAM removal treatments on the average weight per SOLAM plant fro m the 2008 PSREU study. ......................................................................................................45 31 Effect of SOLAM plant back treatments on total fruit number of seedless watermelons from the 2007 PSREU study, 2007 NFREC study, and 2008 PSR EU study, combined. ................................................................................................................55 32 Effect of SOLAM plant back treatments on total fruit number of seedless watermelons from th e 2008 PSREU study. .......................................................................55 33 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

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9 34 Effect of SOLAM plant back treatments on total fruit weight of seedless watermelons from the 2008 PSREU study. .......................................................................56 35 Effect of SOLAM plant back treatments on marketable fruit number of seedless watermelon s from the 2007 PSREU study, 2007 NFREC study, and 2008 PSR EU study, combined. ................................................................................................................57 36 Effect of SOLAM plant back treatments on marketable fruit number of seedless watermelons from th e 2008 P SREU study. .......................................................................57 37 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, com bined. ................................................................................................................58 38 Effect of SOLAM plant back treatments on marketable fruit weight of seedless watermelons from th e 2008 PSREU study. .......................................................................58 39 Effect of SOLAM plant back treatments on total earl y fruit number for the 2007 PSREU study, 2007 NFREC study, and 2008 PSR EU study, combined. .........................59 310 Effect of SOLAM plant back treatments o n total ear ly fruit weight for the 2007 PSREU study, 2007 NFREC study, and 2008 PSREU study, combined. .........................60 311 Effect of SOLAM plant back treatments on marketable early fruit numb er for the 2007 PSREU study, 2007 NFREC study, and 2008 PSR EU study, combined. ................60 312 Effect of SOLAM plant back treatments on marketable ear ly fruit weight for the 2007 PSREU study, 2007 NFREC study, and 2008 PSR EU study, combined. ................61 313 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. .....61 314 Effect of SOLAM plant back treatments on average SOLAM dry weight per weed for the 2008 PSREU study. ................................................................................................62 41 Influe nce of time of weed emergence or weed removal on yield expressed as percent of check and magnitude of t he critical period. ...................................................................67 42 Three scenarios (relationships) that can exist in critical p erio d studies. ............................68 43 E ffect 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, combi ned. ...................................................................................................69 44 E ffect 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

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10 46 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 47 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

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11 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 (Th unb.) Matsumura and Nakai] By Joshua Ira Adkins May 2009 Chair: William M. Stall Major: Horticultural Science Florida has consistently been a major wa termelon [ Citrullus lanatus (Thunb.) Matsumura and Nakai] producing state. In 2007, Florida harvested 10,036 hectares with a producti on value of $152.5 million. In terms of production value, Florida has been the leading state since 2005. Seventy eight p ercent 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 yiel d 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 n ightshade 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

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12 black nightshade that was establish ed with the watermelon crop is removed by 3.9 weeks after transplanting and the crop is subsequently kept weedfree, resulting yield loss should not exceed 10% of a crop t hat was grown weedfree all season The minimum weed free period to prevent marketabl e 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, re sulting yield loss should not exceed 10% of a crop t hat 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 acc eptable yield loss we re set at a different level the period would vary accordingly.

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13 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 interferen ce, 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 spe cies. 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 inter ference as an all inclusive term that denotes all the direct effects that one plant may impose upon another, such as

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14 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 compet ition ( 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 po pulation 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 generall y consider a weed and crop species and, to less of an extent, two crops grown in an i ntercrop. Experimental Design of Competition Studies Experimental design selection is a critical element of weed competition studies (Rejmnek 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, syst ematic, 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) explaine d 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

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15 most common methods used to study weedcr op competition (Rejmnek 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 spe cies (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 part ial additive design has been criticized due to the simultaneous changes of total density and proportion (Rejmnek et al. 1989). Therefore, since two factors in the experiment vary, interpretation of the effects of either factor is difficult (Radosevich 19 87). 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,

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16 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 or der 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 othe r species frequencies from 0% to 100% (Goldberg and Werner 1983; Rejmnek et al. 1989). Traditionally, the substitutive design is used for two main reasons: to determine something about the manner in which t wo biotypes or species interact and/or to determ ine 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 (Rejmnek et al. 1989). Cousens (1991) states that this design has been criticized because its results rely on th e density chosen, it varies only plant ratio and not total density, it cannot be used in the prediction

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17 of population dynamics or to separate the intra and inter specific competition effects, and many of the indices are obscure in their interpretation. H owever, 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 specifi c 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 (Rejmnek 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 addi tion 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

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18 measured, resulting in difficulty obtaining the stand effect from neighbor relations. Additionally, the arcs may be chal lenging 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 (Nie to 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 weedfree 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 ecologic al and environmental degradation

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19 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 (De F el ice 2003). Ogg et al. (1981) suggest that t here are four main reasons that this is a taxonomically d ifficult group. First, there is an obvious similarity in the overall morphology among members. Second, the species exhibit considerable phenotypic plasti city. Hence, m any 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 w ere grouped together and referred to as black nightshade. A nother 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

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20 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 su ch 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 fl ower 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 consider ed 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 discernin g 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

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21 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 a mount 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 inflor escence is u sually unbelliform. The seeds of American black nightshade are smal l at 1.21.6 mm long (Schilling et al. 1992). Fifty to 110 light tannish seeds are found in each berry along with 05 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 m (Ogg e t 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 a nd 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 f ound 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 oc curs. Roos (1999) determined that one American black nightshade

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22 plant/m2 caused a 20% loss in early and total marketable yield of bell pepper ( Capsicum annuum L.) under seasonlong competition. Blackshaw (1991) found that two hairy nightshade ( S. sarrach oides ) 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. Bla ckshaw (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 arent 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 wee d control involves the use of phytotoxic chemicals. The phytotoxic chemicals used for weed control are

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23 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 sensit ive 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.) Matsum ura 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 Flori da, 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 Cucurbital es and the fa mily 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 be en 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

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24 Americas in the early 1600s, first being c ultivated in M assachusetts 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 fee d (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, highe r 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 syru p, 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 r unners (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 polli nators. Fruit maturation from pollination generally requires 3035 days for most commercial varieties.

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25 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 r ind (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 appear ance (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 However, average temperatures as high as 35 watermelon crop takes 80 100 days to reach maturity (Olson et al. 2007). Using transplants, maturity may be reached within 6090 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 product ion, the pH of the so il should be 6.06.5 (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 blac k 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

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26 were approximately 10 cents less per kilogram (than seedless) during peak watermelon production (Freeman and Olson 2007). Polyethylene Mulch Polyet hylene 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 fr uit rot (Egel et al. 2008 and Olson 2007). H owever, 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 pri or 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 es tablishment 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 f our weeks after seedin g (Santos 2007). They are normally planted in trays with a one inch cell size at inch depth. Germination should take place in three days with an optimum germinat ion temperature of 90 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

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27 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 a nd Botts 2007). Consequently, methyl bromide was mandated by the Clean Air Act of 1 990 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. Floridas subtropical environment is conducive to the rapid accumulation of various e conomically 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 continu ally 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 specif ic to the cropping situation will likely be needed to resolve the situation. This would probably entail a system using fumigants and nonfumigants. One of the most commonly researched alternative tactic involves the combination of the

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28 fumigants 1,3dichl oropropene (1,3D) 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 var ieties 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 ha rvest 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 Sta tes 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 marke ting, 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 doublin g of the chromosome number ), a tetraploid plant with 44 chromos omes 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 waterme lon 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, 2033% 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

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29 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 c an 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 usa ge (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% y ield 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 e xceeding 20%, smooth amaranth must be controlled from 1.9 to 3.0 weeks after watermelon emergence.

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30 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 pe rcent. 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 treat ments where waterme lons 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 (seedle ss) watermelon. Although conducted in North Carolina, this study is important to Florida watermelon producers because much of Floridas watermelon production is that of seedless varieties. They determined that a critical weed free period between 0 and 6 weeks after transplant ing must be maintained to achieve the best quality and quantity of

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31 marketable fruit. No effect on marketable fruit weight or number of watermelons occurred from large crabgrass that emerged after 6 weeks. Marketable fruit yield decr eased 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 weed s. 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 herbi cides 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. Howeve r, terbacil is now available for preemergence, pretransplant, and row middle applications (Olson et al. 2007).

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32 CHAPTER 2 MAXIMUM PERIOD OF COMPETITION BETWEEN AM ERICAN BLACK NIGHTSHADE AND TRIPLOID (SEEDLE SS) W ATERMELON Introduction Information on the maximum period of competition can assist watermelon [ Citrullus lanatus (Thunb.) Matsumura and Nakai] producers in knowing when in the crops life cycle that American black nightshade ( Solanum americanum Mill. SOLA M ) 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 wa termelon by means of a removal study Materials and Methods The studies we re conducted at the Plant Science Research and Education Unit, Citra, FL (PSREU) and at the North Florida Research and Education Center, Li ve 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 BlantonFoxworthAlpin Complex, cation exchange capacity of 4.9, 1% organic matter, and a pH of 6.7. Super Crisp triploid watermelon seedl ings were transplanted into raised beds fumigated with methyl bromide and chloropicrin (50:50) at 449.3 kg/ha an d 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 t he mulch for all watermelon plants with a sharpe ned 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

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33 the PSREU. Triploid transplants were grown at an inrow spacing of one meter. Pollenizers were trans plante d 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 Hole s were made in the mulch for the weeds with a 6.7 cm diameter can T wo 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 a nd 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 D AT) 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 blemis hfree fruits that weighed were considered

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34 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 market able 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 de sign 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 expres sed 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 examin ing 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 adjac ent to this trial. Y ield 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. R egression analysis was carried out using the

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35 equation of best fit A linear equation was used to regress the total fruit number data for the combined s tudies. For all other regression analyses, data was best fit to a quadratic equation. Total Fruit Numbe r 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 predi cted 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 we ight 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.

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36 Marketable Fruit Number SOLAM removal time significantly affected marketable fruit number for the combined studies. The regression line fitted to t he 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 marke table 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.

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37 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 w as 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, a nd 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 dif ference 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 be tween both locations in 2007. T herefore, data was pooled from both locations in 2007. Data from the PSRE U 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 dr y weight from the 5 WAT removal time was 250 times larger than the mean SOLAM dry weight f rom 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

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38 WAT removal time was 938 time s 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 tr eatments 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 3.53.9 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 muc h greater when examining the 2008 PSREU study alone than when the combined studies were analyzed. Predicted y ield 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 los ses 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 SO LAM 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

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

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40 Weeks After Transplanting 0 1 2 3 4 5 Total Fruit Number (% of Control) 50 60 70 80 90 100 110 Figure 21. 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 r egression model is f = y0+a*x r2 = 0. 68, where y0 = 99.8825, a = 2.7941, and x = WAT Weeks After Transplanting 0 1 2 3 4 5 6 Total Fruit Number (% of Control) 60 80 100 120 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, a nd x = WAT

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41 Weeks After Transplanting 0 1 2 3 4 5 Total Fruit Weight (% of Control) 50 60 70 80 90 100 110 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, a nd x = WAT Weeks After Transplanting 0 1 2 3 4 5 6 Total Fruit Weight (% of Control) 60 80 100 120 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, a nd x = WAT

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42 Weeks After Transplanting 0 1 2 3 4 5 Marketable Fruit Number (% of Control) 50 60 70 80 90 100 110 Figure 2 5. Effect of SOLAM removal treatments on marketable fruit numbe r 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 Weeks After Transplanting 0 1 2 3 4 5 6 Marketable Fruit Number (% of Control) 60 80 100 120 Fig ure 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, a nd x = WAT

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43 Weeks After Transplanting 0 1 2 3 4 5 Marketable Fruit Weight (% of Control) 50 60 70 80 90 100 110 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 Weeks After Transplanting 0 1 2 3 4 5 6 Marketable Fruit Weight (% of Control) 40 60 80 100 120 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, a nd x = WAT

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44 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 fo determined by LSD. Means Expressed as Percent of Control Weeks After Transplanting Early Total Fruit Number Early Total Fruit Weight Early Marketable Fruit Number Early Marketable 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 Weeks After Transplanting 1 2 3 4 5 Average Weight (g) per SOLAM Plant 0 10 20 30 40 50 60 Figure 2 9. Effect of SOLAM removal treatments on the average weight per S OLAM plant from the 2007 PSREU study and 2007 NF REC 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, a nd x = WAT

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45 Weeks After Transplanting 1 2 3 4 5 6 Average Weight (g) per SOLAM Plant 0 50 100 150 200 250 Figure 2 10. Effect of SOLAM rem oval 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

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46 CHAPTER 3 MINIMUM WEED FREE PERIOD OF AMERI CAN BL ACK NIGHTSHADE IN TR IPLOID (SEEDLESS) WATERMELO N Introduction Information on the minimum weed free period can assist watermelon [ Citrullus lanatus (Thunb.) Matsumura and Nakai] producers in knowing whe n in the crops 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 watermelo n by means of a plant back study. Materials a nd 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 loca tions. 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 BlantonFoxworthAlpin Complex, cation exc hange capacity of 4.9, 1% organic matter, and a pH of 6.7. Super Crisp triploid watermelon seedl ings were transplanted into raised beds fumigated with methyl bromide and chloropicrin (50: 50) at 449.3 kg/ha and covered with black polyethylene mulch Tra nsplanting 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 al l watermelon plants with a sharpened 7.6 cm diameter pipe. In both locations, beds were 0.81 m wide. Beds were est ablished on 2.13 m centers at the NFREC and on 2.44 m centers at

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47 the PSREU. Triploid transplants were grown at an inrow spacing of one met er. Pollenizers were trans planted 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 dr ip tape t hat 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 treat ed 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 a pproximately 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 0, 1, 2, 3, 4, and 5 weeks after watermelon transplanting and remained until watermelon harve st. 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 D AT) and at the PSREU on 19 June (69 DAT) and 26 June (76 DAT). In 2008, wat ermelons were harvested at the NFREC on 19 June (72 DAT) and 26 June (79 D AT) 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

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48 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 plo t. Marketable fruit number and weight was calculated by adding together all marketable fruit fro m b oth harvest s 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 c omparison 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 stu dy, 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

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49 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 relationshi p 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 fo r the combined studies. The regression line fitted to the data had an r2 value of 0.92 (Figure 31). 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 exa mined 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 (Figur e 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 w hen SOLAM was established at 2.4 WAT. Therefore, a 10% and 20% yield loss should not be exceeded as long as SOLAM establis hme nt is delayed for 4.0 and 2.4 WAT, respectively. SOLAM establishment time also significa ntly affected total fruit weight when the

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50 2008 study at the PSREU was examined by itself. The regression line fitted to the data had an r2 value of 0.89 (Figur e 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 w hen SOLAM was established at 2.4 WAT. When SOLAM was established a t 6 WAT, there was a predicted 8% yield loss. Marketable Fruit Num ber 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 W AT. 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 a lso sign ificantly 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 36). A 10% yield loss was predicted to occur w hen 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 a t 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 significa ntly affected marketable fruit weight when the 2008 study at the PSREU was examined by itself. The regression line fitted to

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51 the data had an r2 value of 0.88 (Figure 38). A 10% yield loss was predicted to occur w hen SOLAM was established at 4.4 WAT. A 20% yield loss was predicted to occur w hen SOLAM was established at 2.3 WA T. When SOLAM was established a t 6 WAT, there was a predicted 7% yield loss. Weight p er 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 31). 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, t here 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 th ree 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 Ear ly 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

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52 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 a t 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 watermelo ns per ha at the 5 WAT establishment time was 2.3 times greater tha n 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 311). When SOLAM was established at 0 WAT, there w as 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 312). 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 tota l 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, NFR EC in 2007, and PSREU in 2008

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53 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 313). The mean SOLAM dry weight from the 0 WAT establishment time was 1 3 times larger than the mean SOLAM dry weight from the 5 WAT establishment time at 0.55 1 kg an d 0.043 kg, respectively. SOLAM dry weight was also independently analyzed for the 2 008 study at the PSREU Mean SOLAM weights were regressed to the same hyperbolic decay equation, r2 value of 0.99 (F igure 3 14). The mean SOLAM dry weight from the 0 WAT establishment time was 11 time s 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 in teraction 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 th e 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 3.44.0 WAT should be sufficient to prevent yield loss from exceeding 10% for any of

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54 the categories. This case should hold true if weeds are kept out prior to SOLA M 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.

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55 Weeks After Transplanting 0 1 2 3 4 5 Total Fruit Number (% of Control) 40 50 60 70 80 90 100 Figure 3 1. Ef fect 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*(1b^x), r2 = 0.92, where y0 = 45.5746, a = 52.8956, b = 0.6298, a nd x = WAT Weeks After Transplanting 0 1 2 3 4 5 6 Total Fruit Number (% of Control) 40 50 60 70 80 90 100 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*(1b^x), r2 = 0.85, where y0 = 41.1336, a = 56.578, b = 0.6225, and x = WAT.

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56 Weeks After Transplanting 0 1 2 3 4 5 Total Fruit Weight (% of Control) 40 50 60 70 80 90 100 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 regress ion model is f = y0+a*(1b^x), r2 = 0.94, where y0 = 42.0884, a = 56.7857, b = 0.6320, and x = WAT. Weeks After Transplanting 0 1 2 3 4 5 6 Total Fruit Weight (% of control) 40 50 60 70 80 90 100 Figure 3 4. Effect of SOLAM plant back treatments on total fruit weight of seedless watermelons from the 2008 PSREU s tudy. The regression model is f = y0+a*(1b^x), r2 = 0.89, where y0 = 41.8692, a = 52.4677, b = 0.5857, and x = WAT.

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57 Weeks After Transplanting 0 1 2 3 4 5 Marketable Fruit Number (% of Control) 40 50 60 70 80 90 100 Figure 3 5. Effect of SOLAM plant back treatments on marketable fruit number of seedless watermelon s from the 2007 PSREU study, 2007 NFREC study, and 2008 PSREU study, combined. The regression model is f = y0+a*(1b^x), r2 = 0.95, where y0 = 40.0846, a = 62.8741, b = 0.6269, and x = WAT. Weeks After Transplanting 0 1 2 3 4 5 6 Marketable Fruit Number (% of Control) 40 60 80 100 Figure 3 6. Effect of SOLA M plant back treatments on marketable fruit number of seedless watermelons from the 2008 PSREU study. The regression model is f = y0+a*(1b^x) r2 = 0.83, where y0 = 39.2081, a = 59.3537, b = 0.5888, and x = WAT.

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58 Weeks After Transplanting 0 1 2 3 4 5 Marketable Fruit Weight (% of Control) 40 50 60 70 80 90 100 Figu re 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. Weeks After Transplanting 0 1 2 3 4 5 6 Marketable Fruit Weight (% of Control) 40 60 80 100 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*(1b^x), r2 = 0.88, wher e y0 = 40.8840, a = 53.8120, b = 0.5749, and x = WAT.

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59 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 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 Weeks After Transplanting 0 1 2 3 4 5 Total Early Fruit Num (Avg per ha) 3000 4000 5000 6000 7000 8000 9000 Figure 3 9. Effect of SOLAM plant back treatments on total earl y 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.

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60 Weeks After Transplanting 0 1 2 3 4 5 Total Early Fruit Wt (Avg kg/ha) 40000 60000 80000 100000 120000 140000 Figure 3 10. Effect of SOLAM plant back treatments on total early f ruit 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, whe re y0 = 49283.0959, a = 69700.7782, b = 0.6033, and x = WAT. Weeks After Transplanting 0 1 2 3 4 5 Marketable Early Fruit Num (Avg per ha) 3000 4000 5000 6000 7000 8000 9000 Figure 3 11. Effect of SOLAM plant back treatments on marketable earl y fruit number (average per ha ) for the 2007 PSREU study, 2007 NFREC study, and 2008 PS REU study, combined. The regression model is f = y0+a*(1b^x), r2 = 0.92, where y0 = 3066.2760, a = 5024.4336, b = 0.6671, and x = WAT.

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61 Weeks After Transplanting 0 1 2 3 4 5 Marketable Early Fruit Wt (Avg kg/ha) 40000 60000 80000 100000 120000 140000 Figure 3 12. Effect of SOLAM plant back treatments on marketable early f ruit wei ght (average kg/ha ) for the 2007 PSREU study, 2007 NFREC study, and 2008 PSREU study, combined. The regression model is f = y0+a*(1b^x), r2 = 0.94, where y0 = 46713.7218, a = 71669.1388, b = 0.6298, and x = WAT. Weeks After Transplanting 0 1 2 3 4 5 Avg SOLAM Dry Wt per Weed (kg) 0.0 0.1 0.2 0.3 0.4 0.5 0.6 Figu re 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.

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62 Weeks After Transplanting 0 1 2 3 4 5 6 Avg SOLAM Dry Wt per Weed (kg) 0.0 0.1 0.2 0.3 0.4 0.5 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 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

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63 CHAPTER 4 CRITICAL PERIOD OF I NTERFERENCE 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 41) between two separately determined elements: the maximum period of competition and the minimum weed free period (Knezevic et al. 2002; Oliver 1988). This time int erval 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 t he above scenario, the minimum weedfree 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, the re are two additional scenarios (relationships) that can exist in critical period studies (Roberts 1976; Martin et al. 2001). In one relationship, the minimum weedfree 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 com petition is longer than the minimum weed free period. In this situation, unacceptable y ield 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, th e accuracy of the estimate may b e reduced and the chances of substantial error may increase (Weaver 1984; Kne zevic et al. 2002). For example, the yield loss resulting from

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64 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 interfe rence between SOLAM and triploid watermelon can assist watermelon producers in knowing when in the crops 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 weedfree 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 stu dy 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 nu mber 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.

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65 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 com petition (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 weedfree all season. Marketable Fruit Number Marketable fruit number results (Figure 45) 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 3.43.9 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 weedfree all season. Discussion A weed control strategy could also be based upon making a decision to either control SOLAM only before the minimum weedfree period or a fter 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;

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66 Lin dquist 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. Fr om a practical standpoint, 3.44.0 WAT is an important time fr ame 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.

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67 Weeks After Emergence 5 10 15 2025 Yield (% of Check) 0 20 40 60 80 100 max period of competition min weed-free period CRITICAL PERIOD Figure 4 1. Influence of time of weed emerg ence 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.

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68 aTime Yield max period of competition min weed-free period bTime Yield max period of competition min weed-free period cTime Yield max period of competition min weed-free period Figure 4 2. Three scenarios (relationships) that can exist in critica l period studies (Roberts 1976; Martin et al. 2001). In scenario a, the minimum weedfree 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 weedfree period is equivalent to the maximum period of competition. Unacceptable yield loss s hould 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 bet ween these critical stages.

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69 Weeks After Transplanting 0 1 2 3 4 5 Total Fruit Number (% of Control) 40 60 80 100 max period of competition min weed-free period Figure 4 3. E ffect 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.

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70 Weeks After Transplanting 0 1 2 3 4 5 Total Fruit Weight (% of Control) 40 60 80 100 max period of competition min weed-free period Figure 4 4. E ffect 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 stud y, 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*(1b^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.

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71 Weeks After Transplanting 0 1 2 3 4 5 Marketable Fruit Number (% of Control) 40 60 80 100 max period of competition min weed-free period Figure 4 6. E ffect 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*(1b^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.

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72 Weeks After Transplanting 0 1 2 3 4 5 Marketable Fruit Weight (% of Control) 40 60 80 100 max period of competition min weed-free period Figure 4 7. E ffect of SOLAM removal and plant back treatments on marketable fruit weight of seedl ess 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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73 LITERATURE CITED Anderson, W. P. 1996. Weed Science: Principles and Applications. 3rd ed. St. P aul, MN: West Publishing Company. 388 p. Bewick, T. A., S. R. Kostewicz, W. M. Stall, D. G. Shilling, and K. Smith. 1990. Interaction of cupric hydroxide, paraquat, and biotype of American black nightshade ( Solanum americanum ). Weed Science. 38:634638. berry. Encyclopedia Britannica 2008. Encyclopedia Britannica Online. 21 Aug. 2008 < http://www.britannica.com/EBchecked/topic/62712/berry >. Blackshaw, R. E. 1991. Hairy nightshade ( Solanum sarrachoides ) interference in dry beans ( Phaseolus vulgaris ). W eed Science. 39:48 53. Buckelew, J. K., D. W. Monks, K. M. Jennings, G. D. Hoyt, and R. F. Walls, Jr. 2006. Eastern black nightshade ( Solanum ptycanthum ) reproduction and interference in transplanted plasticulture tomato. Weed Science. 54:490495. Buker, R S. III, 1999. The interference of yellow nutsedge with watermelon. MS thesis. Gainesville, FL: University of Florida. 120 pages. Buker, R. S. III, W. M. Stall, S. M. Olson, and D. G. Schilling. 2003. Seasonlong interference of yellow nutsedge ( Cyperus esculentus ) with direct seeded and transplanted watermelon ( Citrullus lanatus ). Weed Technol. 17:751754. Connell, J. H. 1990. Apparent versus real competition in plants. Pages 926 in J. B. Grace and D. Tilman, eds. Perspectives on Plant Competition. San Diego: Academic Press, Inc. Cousens, R. 1991. Aspects of the design and interpretation of competition (interference) experiments. Weed Technol. 5:664673. De F elice, M. S. 2003. The black nightshades, Solanum nigrum L. et al. poison, poultice, and pie. We ed Technol. 17:421427. Egel, D. S., R. M. Martyn, and C. Gunter. 2008. Planting method, plastic mulch, and fumigation influence growth, yield, and root structure of watermelon. HortScience 43(5): 14101414. Freeman, J. H. 2007. Use and effects of diploid polle nizers for triploid watermelon [ Citrullus lanatus (Thunberg) Matsumura and Nakai] production. PhD dissertation. Gainesville, FL : University of Florida. 84 pages. Freeman, J. H., G. A. Miller, S. M. Olson, and W. M. Stall. 2007b. Diploid watermelon poll enizer cultivars differ with respect to triploid watermelon yield. HortTechnology 17(4):518522.

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74 Freeman, J. H. and S. M. Olson. 2007. Using inrow pollenizers for seedless waterm elon production. University of Florida/IFAS Extension. Electronic Data Inform ation Source. Accessed: August 15, 2008. < http://edis.ifas.ufl.edu/HS333> Freeman, J. H., S. M. Olson, and W. M. Stall. 2007a. Com petitive effect of in row diploid watermelon pollenizers on triploid watermelon yield. HortScience 42(7):15751577. Gibson, L. R. and M. Liebman. 2003. A laboratory exercis e for teaching critical period for weed control concepts. Weed Technol. 17:403411. Gilbert, C. A. 2006. American black nightshade ( Solanum americanum MILL.) interference in watermelon ( Citrullus lanatus L.). M S thesis. Gainesville, FL : University of Florida. 70 pages. Gilbert, C. A., W. M. Stall, C. A. Chase, and R. Charudattan. 2008. Season long interference of American black nightshade with watermelon. Weed Technol. 22:186189. Goldberg, D. E. and P. A. Werne r. 1983. Equivalence of competitors in plant communities: a null hypothesis and a field experimental approach. Amer. J. Bot. 70(7): 10981104. Grace, J. and D. Tilman. 1990. Perspectives on plant competition: some introductory remarks. Pages 3 7 in J.B. Gra ce and D. Tilm an, eds. Perspectives on Plant Competition. San Diego: Academic Press, Inc. Knezevic, S. Z., S.P. Evans, E. E. Blankenship, R. C. V an Acker, and J. L. Lindquist. 2002. Critical period for weed control: the concept and data analysis. Weed Sci. 50:773786. Larson, B. C., M. A. Mossler, and O. N. Nesheim. 2004. Florida crop/pest manageme nt profile: watermelon. University of Florida/IFAS Extension. Electronic Data Information Source. Accessed: August 25, 2007. < http://edis.ifas.ufl.edu/PI031> Lin dquist, J. L., D. A. Mortensen, P. Westra, W. J. Lambert, T. T. Bauman, J. C. Fausey, J. J. Kells, S. J. Langton, R. G. Harvey, B. H. Bussler, K. Banken, S. Clay, and F. Forcella. Stability of corn ( Zea mays ) foxtail ( Setaria spp.) interference relationshi ps. Weed Sci. 47:195200. Martin, S. G., R. C. Van Acker, and L. F. Friesen. 2001. Critical period of weed control in spring canola. Weed Sci. 49:326333. Maynard, D. N. 1992. Seedless watermelon production. Pages 7 8 in D. N. Maynard, ed. Watermelon Production Guide for Florida. University of Florida. Institute of Food and Agricultural Sciences. Maynard, D. N. 2001. An introduction to the watermelon. Pages 920 in D. N. Maynard, ed. Watermelons Characteristics, Production, and Marketing. Alexandria, VA : ASHS Press. Mills, H. A. Watermelon Citrullus lanatus University of Georgia. Athens, GA. Accessed: August 15, 2008. < http://www.uga.edu/vegetable/watermelon.html>

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75 Monks, D. W. and J. R. Schultheis. 1998. Critical weedf ree period for large crabgrass ( Digi taria sanguinalis ) in transplanted watermelon ( Citrullus lanatus ). Weed Sci. 46:530532. Mossler, M. A. 2007. Watermelon pest management strategic plan (PMSP). University of Florida/IFAS Extension. Electronic Data Infor mation Source. Accessed August 25, 2007. < http://edis.ifas.ufl.edu/PI089 > Musmade, A. M. and U. T. Desai. 1998. Cucumber and Melon. Pages 245272 in D. K. Salunkhe and S. S. Kadam, eds. Handbook of Vegetable Science and Technology Production, Composition, Storage, and Processing. New York: Marcel Dekker, Inc. [NASS] National Agricu ltural Statistics Service 2008. United States Department of Agriculture. < http://www.usda.gov/ > Noling, J. W. and D. A. Botts. 2007. Alternatives to meth yl bromide soil fumigation for Florida vegetable production. Pages 127 133 in S. M. Olson and E. Simonne eds. Vegetable P roduction Handbook for Florida 20072008. University of Florida/IFAS Extension. Nonnecke, I. L. 1989. Vegetable Production. New York, NY : Van Nostrand Reinhold. 657 p. Ogg, A. G. and B. S. Rog ers. 1989. Taxonomy, distribution, biology, and control of black nightshade ( Solanum nigrum ) and related species in the United States and Canada. Rev. Weed. Sci. 4:2558. Ogg, A. G., Jr., B. S. Rogers, and E. E. Schilling. 1981. Characterization of black nightshade ( Solanum nigrum ) and related species in the United States. Weed Sci. 29:2732. Oliver, L. R. 1988. Principles of weed threshold research. Weed Technol. 2:398403. Olson, S. M. 2007. Mulching. Pages 2730 in S. M. Olson and E. Simonne eds. Veget able P roduction Handbook for Florida 20072008. University of Florida/IFAS Extension. Olson, S. M., E. H. Simonne, W. M. Stall, P. D. Roberts, S E. Webb, T. G. Taylor, S. A. Smith, and J. H. Freeman. Cucurbit production in Florida Pages 201 250 in S. M. Olson and E. Simonne, eds. Vegetable Produc tion Handbook for Florida 20072008. University of Florida/IFAS Extension. Park, S. E., L. R. Benjamin, and A. R. Watkinson. 2003. The theory and application of plant competition models: an agronomic perspective. Ann. Bot. 92:741748. Radosevich, S. R. 1987. Methods to study interactions among crops and weeds. Weed Technol. 1:190198. Radosevich, S., J. Holt, and C. Ghersa. 1997. Weed Ecology. 2nd ed. New York : John Wiley and Sons, Inc. 589 p. Rejmnek, M., G. R. R obinson, and E. Rejmnkov 1989. Weedcrop competition: experimental designs and models for data analysis. Weed Sci. 37:276284.

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76 Rao, V. S. 2000. Principles of Weed Science. 2nd ed. Enfield, NH : Science Publishers, Inc. Roberts, H. A. 1976. Weed competiti on in vegetable crops. Ann. Appl. Biol. 83:321347. Robinson, R. W. and D. S. Decker Walters. 1997. Cucurbits. Cambridge, UK : CAB INTERNATIONAL. 226 p. Roos, D. L. 1999. American black nightshade ( Solanum americanum Mill.) interference in bell pepper ( Caps icum annuum L.). MS thesis. University of Florida Gainesville, FL. 90 pages. Santos, B. M. 2007. Transplant production. Pages 2325 in S. M. Olson and E. Simonne, eds. Vegetable Production Handbook for Florida 20072008. University of Florida/IFAS Extens ion. Santos, B. M. 2007. Life after methyl bromide: resear ch on 1,3 dichloropropene plus chloropicrin in Florida. University of Florida/IFAS Extension. Electronic Data Information Source. Accessed: August 25, 2008. < http://edis.ifas.ufl.edu/HS351> SAS Ins titute Inc. 2003. SAS 9.1. Cary, NC. Schilling, E. E. 1981. Systematics of Solanum sect. Solanum (Solanaceae) in N orth America. Syst. Bot. 6:172185. Schilling, E. E., Qi sheng Ma, and R. N. Andersen. 1992. Common names and species identification in black nightshades, Solanum sect. Solanum (Solanaceae). Economic Botany 46(2):223225. Spreen, T. H., J. J. VanSickle, A. E. Moseley, M. S. Dee pak, and L. Mathers. 1995. Use of methyl bromide and the economic impact of its proposed ban on the Florida fresh fruit and vegetable industry. University of Florida Agricultural Experiment Station, Institute of Food and Agricultural Sciences, Gainesville, Florida. Stall, W. M. 1992. Weed management. Pages 45 46 in D. N. Maynard, ed. Watermelon Production Guide for Florida University of Florida. Institute of Food and Agricultural Sciences. Stall, W. M. 2007. Weed control in cucurbit crops (muskmelon, cucumber, squash, and watermelon). University of Florida/IFAS Extension. Electronic Data Information Source. Accessed: Augus t 26, 2008. < http://edis.ifas.ufl.edu/PI089> Stephens, J. M. 1994. Watermelon, seedless Citrullus lanatus (Thunb.) Mansf.1 University of Florida/IFAS Extension. Elect ronic Data Information Source. Accessed: August 19, 2008. < http://edis.ifas.ufl.edu/MV152> Systat Software Inc. 2006. SigmaPlot 10.0. San Jose, CA.

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77 Terry, E. R. Jr., W. M. Stall, D. G. Shilling, T. A. Bewi ck, and S. R. Kostewicz. 1997. Smooth amaranth interference with waterm elon and muskmelon production. HortScience 32(4):630632. United Sta tes Department of Agriculture (USDA) Natural Resources Conservat ion Service. 2008. < http://plants.usda.gov> Vavrina, C. S. 1992. Watermelon transplants. Pages 1517 in D. N. Maynard, ed. Watermelon Production Guide for Florida. University of Florida Ins titute of Food and Agricultural Sciences. Wapshere, A. J. 1988. Prospects for the biological cont rol of silver leaf nightshade, Solanum elaeagnifolium in Australia. Aust. J. Agric. Res. 39:187197. Weaver, S. E. 1984. Critical period of weed competition i n three vegetable crops in relation to management practices. Weed Res. 24:317 325. Weaver, S. E., M. J. Kropff, and R.M.W. Groeneveld. 1992. Use of ecophysiological models for crop weed interference: the critical period of weed interference. Weed Sci. 40:3 02307. Weaver, S. E., N. Smits, and C. S. Tan. 1987. Estimating yield losses of tomatoes ( Lycopersicon esculentum ) caused by nightshade ( Solanum spp.) interference. Weed Science. 35:163168. Weaver, S. E. and C. S. Tan. 1983. Critical period of wee d inter ference in transplanted tomatoes ( Lycopersicon esculentum ): growth analysis. Weed Sci. 31:476481. Wehner, T. C. 2005. Watermelon Crop Information. Horticultural Science Department North Carolina State University. Raleigh, NC. < http://cuke.hort.ncsu.edu/cucurbit/wmelon/wmhndbk/wmcontents.html > Accessed: August 15, 2008. Wehner, T. C., N. V. Shetty, and G. W. Elmstrom. 2001. Breeding and seed production. Pages 2773 in D. N. Maynard, ed. Watermelons Characteristics, Production, and Marketing. Alexandria, V A: ASHS Press. Yoda, K., T. Kira, H. Ogawa, and K. Hozumi. 1963. Sel f thinning in overcrowded pure stands under cultivated and natural conditions (i ntraspecific competition among higher plants XI). Journal of Biology, Osaka City University 14:107 129.

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78 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 s cience.