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Spring 4-2017 Emergence, Competition, and Management of Glyphosate-Resistant Common Ragweed (Ambrosia artemisiifolia L.) in Nebraska Soybean Ethann R. Barnes University of Nebraska-Lincoln

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Barnes, Ethann R., "Emergence, Competition, and Management of Glyphosate-Resistant Common Ragweed (Ambrosia artemisiifolia L.) in Nebraska Soybean" (2017). Theses, Dissertations, and Student Research in Agronomy and Horticulture. 120. http://digitalcommons.unl.edu/agronhortdiss/120

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EMERGENCE, COMPETITION, AND MANAGEMENT OF GLYPHOSATE-

RESISTANT COMMON RAGWEED (Ambrosia artemisiifolia L.) IN NEBRASKA

SOYBEAN

Ethann Robert Barnes

A THESIS

Presented to the Faculty of

The Graduate College at the University of Nebraska

In Partial Fulfillment of Requirements

For the Degree of Master of Science

Major: Agronomy

Under the Supervision of Professor Amit J. Jhala

Lincoln, Nebraska

May, 2017

EMERGENCE, COMPETITION, AND MANAGEMENT OF GLYPHOSATE-

RESISTANT COMMON RAGWEED (Ambrosia artemisiifolia L.) IN NEBRASKA

SOYBEAN

Ethann R. Barnes, M.S.

University of Nebraska, 2017

Advisor: Amit J. Jhala

Common ragweed (Ambrosia artemisiifolia L.) is a competitive annual broadleaf weed in soybean (Glycine max) production fields throughout North America. The recent confirmation of glyphosate-resistant common ragweed in Nebraska justified the need to assess the emergence pattern and competitive ability of common ragweed in soybean and to evaluate alternative herbicide programs for effective management. The objectives of this research were to: 1) evaluate the effect of tillage and develop a predictive model for the emergence pattern of common ragweed in Nebraska; 2) model the competitive interaction between soybean and common ragweed as influenced by density and irrigation levels; 3) characterize the growth response of soybean and common ragweed in mixture and monoculture to varying irrigation levels and increasing common ragweed density; and 4) evaluate the efficacy of preplant herbicides followed by glufosinate applied alone or in tank-mixtures for control of glyphosate-resistant common ragweed in glufosinate-resistant soybean. A field study was conducted for three years to evaluate the effect of tillage timing and develop a predictive model for common ragweed emergence in Nebraska. The results of this study conclude that spring tillage does not stimulate

additional emergence; therefore, tillage could be used as a component of glyphosate- resistant common ragweed management programs in Nebraska. Additionally, thermal time calculations with a temperature base of 3 C can be used to predict emergence (%). A field study was conducted to model the competitive interaction and assess the growth response of soybean and common ragweed as influenced by density and irrigation level.

Soybean yield loss was not altered by irrigation amount and the leaf area ratio model at the soybean R6 growth stage best fit the data. Common ragweed densities of 1, 6, and 12 m─1 row resulted in yield losses of 61, 76, and 95% in 2015 and 25, 39, and 80% in 2016, respectively. Soybean growth was affected by common ragweed density however soybean demonstrated no plasticity. Common ragweed growth was affected by common ragweed density and irrigation. Common ragweed demonstrated plasticity by altering specific leaf area and biomass partitioning when in competition with soybean. A field study was conducted to evaluate the efficacy of glufosinate-based herbicide programs for season-long control of glyphosate-resistant common ragweed in glufosinate-resistant soybean. The results of this study conclude that glufosinate, paraquat, 2,4-D, dimethenamid-P, cloransulam-methyl, or plus chlorimuron ethyl applied preplant (PP) followed by glufosinate applied POST alone or in tank-mixture provided ≥ 84% control of glyphosate-resistant common ragweed, reduced density to ≤ 20 plants m─2, and secured ≥ 1819 kg ha─1 soybean yield. Preplant followed by POST resulted in the highest gross profit margins compared to PP alone or PRE followed by POST treatments. iv

DEDICATION

This thesis is dedicated to my wife and friend, Allison Barnes, for helping me realize my potential, for encouraging me to strive to be greater, and for her constant love and support. v

ACKNOWLEDGEMENTS

My sincere gratitude goes to my advisor, Dr. Amit Jhala, for his guidance, knowledge, and support. I would also like to acknowledge my committee members, Dr. John

Lindquist, Dr. Stevan Knezevic, and Dr. Peter Sikkema for their timely advice, direction, and input into my research and courses.

Thank you to Dr. Rodrigo Werle and Lowell Sandell for influencing me as an undergraduate and giving me exposure to weed science. Without the help of Jacob

Nikodym, Brad Meusch, Ian Rodgers, Mason Adams, Adam Leise, Michael Fitzgerald,

Murtaza Nalwala, Mitul Patel, Pedro Fiorese, and Francisco Júnior this research would have not been achievable. The additional assistance of Irvin Schleufer, Caleb Wilford,

Darren Binder, Daniel Robotham, Greg Teichmeier, Daniel Simon, and Logan

Loeffelholz was invaluable. I would like to recognize the friendship and guidance of my fellow lab members Dr. Debalin Sarangi, Dr. Zahoor Ganie, Parminder Chahal, Umme

Kulsoom, Jake Ziggafoos, Clint Beiermann, Maxwel Oliveira, Jeremie Kouame, Don

Treptow, and John Laborde. I also appreciate the support of the faculty and staff of the

University of Nebraska-Lincoln Agronomy and Horticulture department.

Finally, I would to thank my close friends, Jared and Molly Aden; my parents, Wyatt and

Kelli Barnes; my siblings and their families, Tyrell, Sheena, and Beccah; and my in-laws,

Darren and Leann Siekman, Grant Siekman, and Grace Lathrop for their moral support and encouragement throughout this program. vi

Table of Contents

List of Tables ...... ix List of Figures ...... xi chapter 1: INTRODUCTION AND OBJECTIVES ...... 1 Introduction ...... 1 Ambrosia...... 2 Common Ragweed Biology...... 2 Common Ragweed Competition...... 3 Objectives ...... 4 Literature Cited ...... 7 chapter 2: CHARACTERIZATION OF COMMON RAGWEED (Ambrosia artemisiifolia L.) EMERGENCE PATTERN INFLUENCED BY TILLAGE IN NEBRASKA ...... 12 Abstract ...... 12 Introduction ...... 13 Materials and Methods ...... 16 Field Experiments...... 16 Data Collection...... 16 Tillage Effects...... 17 Model Configuration...... 18 Fitting the Model...... 19 Model Goodness of Fit...... 20 Results and Discussion ...... 20 Spring Tillage...... 20 Model Selection and Fit...... 21 Practical Implications...... 22 Literature Cited ...... 24 chapter 3: MODELING THE EFFECT OF COMMON RAGWEED (Ambrosia artemisiifolia L.) ON SOYBEAN YIELD IN NEBRASKA ...... 35 Abstract ...... 35 Introduction ...... 36 Materials and Methods ...... 38 vii

Experimental Procedures...... 39 Data Collection...... 40 Model Configuration...... 41 Model Goodness of Fit...... 43 Results and Discussion ...... 44 Irrigation level...... 44 Year-to-Year Variation...... 44 Soybean Yield Loss...... 46 Literature Cited ...... 50 chapter 4: SOYBEAN AND COMMON RAGWEED GROWTH IN MONOCULTURE AND MIXTURE UNDER VARYING IRRIGATION LEVELS ..... 63 Abstract ...... 63 Materials and Methods ...... 66 Experimental Procedures...... 67 Data Collection...... 67 Statistical Analysis...... 70 Results and Discussion ...... 70 Soybean Biomass Partitioning...... 71 Soybean Leaves...... 73 Soybean Seed...... 74 Common Ragweed Biomass Partitioning...... 75 Common Ragweed Leaves...... 76 Common Ragweed Seed...... 78 Literature Cited ...... 80 chapter 5: CONTROL OF GLYPHOSATE-RESISTANT COMMON RAGWEED (Ambrosia artemisiifolia) IN GLUFOSINATE-RESISTANT SOYBEAN ...... 102 Abstract ...... 102 Introduction ...... 103 Materials and Methods ...... 106 Statistical Analysis...... 108 Results and Discussion ...... 108 Common Ragweed Control...... 108 viii

Common Ragweed Density and Biomass...... 111 Economics...... 113 Practical Implications...... 114 Literature Cited ...... 116

List of Tables

Table 1-1. Confirmed glyphosate-resistant weed species in Nebraska (Feb 2017)...... 11 Table 2-1. Influence of spring tillage timing on total common ragweed seedling emergence (total emergence) and time to 50% emergence (T50) in a field experiment conducted in 2014, 2015, and 2016 in Gage County, Nebraska...... 29 Table 2-2. Comparison of K, AICc, AICw, and LL for the 6 best models out of the 65 possible models to develop a predictive model for common ragweed emergence in Nebraska. Models are ordered from lowest to highest AICc with the lowest being the best fit (top model). The top model had a Tbase of 3 C with the next 5 best models also based on thermal time.a ...... 30 Table 3-1. Variation in yield loss model parameters across irrigation levels for multiple destructive samples in 2015.a ...... 55 Table 3-2. Variation in yield loss model parameters across irrigation levels (100%, 50%, and 0%) for multiple destructive samples in 2016.a ...... 56 Table 3-3. Variation in yield loss model parameters across years for multiple destructive samples in 2015 and 2016...... 57 Table 3-4. Comparison of AICc, k, AICw, ME, RMSE, I, and A for all models permutation tested where parameters varied between 2015 and 2016. Models are ordered from lowest to highest AICc with the lowest AICc considered the best fit and top model...... 58 Table 4-1. ANOVA table for soybean partitioning coefficients, aboveground biomass, specific leaf area, leaf area index, and 100 seed weight, in a field experiment conducted in 2015 and 2016 at the University of Nebraska-Lincoln ARDC...... 84 Table 4-2. ANOVA table for common ragweed partitioning coefficients, aboveground biomass, specific leaf area, leaf area index, 100 seed weight, and seed number in a field experiment conducted in 2015 and 2016 at the University of Nebraska-Lincoln ARDC. 85 Table 5-1. Herbicide treatments, application timing and rate, and products used in a field experiment conducted in Gage County, NE in 2015 and 2016...... 121 Table 5-2. Orthogonal contrasts for comparison of herbicide programs and control of glyphosate-resistant common ragweed in glufosinate-resistant soybean at 21 DAPP, 7 DAPRE, 14 DAEPOST, 14 DALPOST, and at harvest in a field experiment conducted in Gage County, NE in 2015 and 2016.a ...... 122 Table 5-3. Orthogonal contrasts for comparison of herbicide programs and effect of herbicide programs on glyphosate-resistant common ragweed density at 7 DAPRE, 14 DAEPOST, and 14 DALPOST, common ragweed biomass reduction, and soybean yield in a field experiment conducted in Gage County, NE in 2015 and 2016.a ...... 124 x

Table 5-4. Cost of herbicide programs for controlling glyphosate-resistant common ragweed in glufosinate-resistant soybean, income from soybean yield, and gross profit margin in a field experiment conducted in Gage County, NE in 2015 and 2016...... 126 xi

List of Figures

Figure 2-1. Daily soil temperature (T, C) and moisture potential (Ψ, kPa) at 2 cm depth estimated with STM2 (Soil temperature and moisture model software) during common ragweed emergence period in a field experiment conducted in Gage County, Nebraska in 2014, 2015, and 2016...... 31 Figure 2-2. Emergence pattern of common ragweed in Gage County, Nebraska in a field experiment conducted in 2014, 2015, and 2016. As no differences were detected between tillage treatments, data within experimental years were combined. Lines represent the fit of the Weibull function for each year...... 32 Figure 2-3. Corrected information-theoretic model comparison criterion (AICc) of predictive models for common ragweed emergence with a threshold soil temperature (Tbase) ranging from 0 to 15 C based on Ψbase values of -33 (wilting point), -750, -1500 (permanent wilting point), and -∞ [analogous to thermal time (TT)]...... 33 Figure 2-4. Weibull function fit to percent total emergence of common ragweed in 2014, 2015, and 2016 with cumulative thermal time (TT) calculated with a threshold soil temperature (Tbase) of 3 C. Model parameter asym (horizontal asymptote) was normalized to 100 while the model parameters lrc (natural logarithm for the rate of increase) and pwr (power to which TT is raised) were -17.3026 and 2.7249, respectively. The root mean squared error (RMSE) and modeling efficiency coefficient (ME) for this model were 20.27 and 0.63, respectively...... 34 Figure 3-1 A) cumulative soybean evapotranspiration (cm) and B) cumulative available soil water deficit (cm) obtained from SoyWater (http://www.hprcc3.unl.edu/soywater/) in a field experiment conducted at the University of Nebraska-Lincoln in 2015 and 2016. 59 Figure 3-2. A) Average daily air temperature (˚C) and B) total daily precipitation (cm) obtained from the nearest High Plain Regional Climate Center in a field experiment conducted at the University of Nebraska-Lincoln in 2015 and 2016...... 60 Figure 3-3. Leaf area ratio model and 95% prediction interval fitted to yield loss (%) of soybean and common ragweed relative leaf area (RLA) sampled at R6 soybean growth stage in a field experiment conducted at the University of Nebraska-Lincoln in 2015 and 2016. Model parameters A (asymptote and maximum yield loss) and q (damage coefficient) were 96 and 562, respectively. The root mean squared error (RMSE) and modeling efficiency coefficient (ME) for this model were 159 and 91, respectively...... 61 Figure 3-4. Hyperbolic yield loss model and 95% prediction interval fitted to soybean yield loss (%) and common ragweed leaf area index (LAI) sampled at R6 soybean growth stage in a field experiment conducted at the University of Nebraska-Lincoln in 2015 and 2016. Model parameters A (asymptote and maximum yield loss) and I (initial slope and damage coefficient) were 100 and 245, respectively. The root mean squared error (RMSE) and modeling efficiency coefficient (ME) for this model were 200 and 88, respectively...... 62 xii

Figure 4-1. A) Average daily air temperature (˚C) and B) total daily rainfall (cm) obtained from the nearest High Plain Regional Climate Center in a field experiment conducted at the University of Nebraska-Lincoln in 2015 and 2016...... 86 Figure 4-2. Volumetric water content (cm3 cm-3) collected throughout the growing season at a 30 cm depth in a field experiment conducted in 2015 and 2016 at the University of Nebraska-Lincoln ARDC...... 87 Figure 4-3. Soybean partitioning coefficient of reproductive, stems, and leaves throughout the growing season at V3, R1, R4, and R6 soybean growth stage in a field experiment conducted in 2015 and 2016 at the University of Nebraska-Lincoln ARDC. 88 Figure 4-4. Soybean aboveground biomass m-2 as affected by common ragweed density throughout the growing season at V3, R1, and R4 soybean growth stage in a field experiment conducted in 2015 at the University of Nebraska-Lincoln ARDC...... 89 Figure 4-5. Soybean aboveground biomass m-2 as affected by replacement ET level throughout the growing season at V3, R1, and R4 soybean growth stage in a field experiment conducted in 2015 at the University of Nebraska-Lincoln ARDC...... 90 Figure 4-6. Soybean aboveground biomass m-2 as affected by common ragweed density throughout the growing season at V3, R1, R4, R6 soybean growth stage in a field experiment conducted in 2016 at the University of Nebraska-Lincoln ARDC...... 91 Figure 4-7. Soybean specific leaf area throughout the growing season at V3, R1, R4, and R6 soybean growth stage in a field experiment conducted in 2015 and 2016 at the University of Nebraska-Lincoln ARDC...... 92 Figure 4-8. Soybean leaf area index as affected by common ragweed density throughout the growing season at V3, R1, R4, and R6 soybean growth stage in a field experiment conducted in 2015 at the University of Nebraska-Lincoln ARDC...... 93 Figure 4-9. Soybean leaf area index as affected by common ragweed density throughout the growing season at V3, R1, R4, and R6 soybean growth stage in a field experiment conducted in 2016 at the University of Nebraska-Lincoln ARDC...... 94 Figure 4-10. Soybean 100 seed weight as affected by common ragweed density in a field experiment conducted in 2015 and 2016 at the University of Nebraska-Lincoln ARDC. 95 Figure 4-11. Common ragweed partitioning coefficient of leaves and stems throughout the growing season in monoculture and in mixture with soybean in a field experiment conducted in 2015 and 2016 at the University of Nebraska-Lincoln ARDC...... 96 Figure 4-12. Common ragweed aboveground biomass as affected by replacement ET level throughout the growing season in a field experiment conducted 2015 at the University of Nebraska-Lincoln ARDC...... 97 Figure 4-13. Common ragweed aboveground biomass as affected by common ragweed density throughout the growing season in a field experiment conducted in 2015 at the University of Nebraska-Lincoln ARDC...... 98 xiii

Figure 4-14. Common ragweed aboveground biomass as affected by common ragweed density throughout the growing season in a field experiment conducted in 2016 at the University of Nebraska-Lincoln ARDC...... 99 Figure 4-15. Common ragweed specific leaf area throughout the growing season in monoculture and in mixture with soybean in a field experiment conducted in 2015 and 2016 at the University of Nebraska-Lincoln ARDC...... 100 Figure 4-16. Common ragweed leaf area index as affected by common ragweed density throughout the growing season in a field experiment conducted in 2015 and 2016 at the University of Nebraska-Lincoln ARDC...... 101

CHAPTER 1: INTRODUCTION AND OBJECTIVES

Introduction

Weeds have troubled crop producers since the beginning of agriculture, causing crop yield loss, lowered product quality, and increased management difficulties (Oerke 2006).

Tillage and inter-row cultivation were the first mechanical methods of weed control

(Gianessi and Reigner 2007), and the discovery of 2,4-D in the 1940s provided the opportunity for broadleaf weed control in cereals and non-crop areas (Peterson 1967).

The rapid adoption of herbicides in the United States began in the 1950s and has led their continued use on 80 to 90% of crop hectares since then (Gianessi and Reigner 2007). The discovery of glyphosate in 1970 and the development of glyphosate-resistant crops marked the beginning of a significant change in production agriculture (Duke et al.

2003). Glyphosate is a broad-spectrum, systemic herbicide (Duke and Powles 2008) that was first commercialized in 1974 (Franz et al. 1996). The glyphosate-resistant crop trait was first commercialized in soybean (Glycine max L.) in the United States and canola

(Brassica napus L.) in Canada in 1996 (Wiesbrook et al. 2001) and is currently used in six commercially available crops, including alfalfa (Medicago sativa L.), canola

(Brassica napus L.), corn (Zea mays L.), cotton (Gossypium hirsutum L.), soybean

(Glycine max L.), and sugarbeet (Beta vulgaris L.) (Duke and Powles 2009).

The rapid adoption of glyphosate-resistant crops and repeated application of glyphosate in corn-soybean cropping systems increased selection pressure and led to the evolution of glyphosate-resistant weeds (Dill 2005). Currently, 37 weed species have 2

evolved glyphosate resistance worldwide, including 17 species in the United States (Heap

2017). Currently in Nebraska there are 6 confirmed glyphosate-resistant species (Table 1;

Jhala 2016). One such species, common ragweed (Ambrosia artemisiifolia L.) was first confirmed resistant to glyphosate in Missouri in 2004 (Pollard 2007). Glyphosate- resistant common ragweed was recently confirmed in Nebraska (Ganie and Jhala 2016) and has been confirmed in a total of 15 states in the United States and in Ontario, Canada

(Heap 2017). In field dose-response studies, 15 and 40 times the labeled rate of glyphosate were needed to achieve 90% control and biomass reduction of glyphosate- resistant common ragweed, respectively (Ganie and Jhala 2016).

Ambrosia. The genus Ambrosia of the Asteraceae family is comprised of more than 40 annuals and perennials native to North America (Béres et al. 2005). Ambrosia are commonly referred to as ragweed (Makra et al. 2015), with the Sonora desert in Arizona considered the Ambrosia center of origin (Bohár 1996). Ragweed species are best known for causing severe allergies commonly referred to as hay fever (Béres et al. 2003).

Common ragweed and giant ragweed (Ambrosia trifida L.) are two major agricultural weeds in North America. Common ragweed is an annual weed found throughout temperate North America (Dickerson and Sweet 1971), while giant ragweed is an annual weed found throughout the Midwest and East (Johnson et al. 2006).

Common Ragweed Biology. Common ragweed has a fibrous root system and an erect growth habit (Bassett and Crompton 1975). It can reach heights of up to 2 m (Clewis et al. 2001) and branches frequently in low population densities (Jordan et al. 2007). Its deeply lobbed leaves can be either glabrous or rough hairy (Bassett and Crompton 1975).

Common ragweed is also known as a major hay fever weed that produces prolific 3

amounts of pollen (Rodgers et al. 2006). Dickerson and Sweet (1971) reported that common ragweed planted on May 12 near Ithaca, NY produced an average of 32,485 seeds per plant and resulted in an average dry weight of 1,075 g, whereas common ragweed planted on July 8 produced an average of 3,835 seeds per plant with an average dry weight of 329 g. Additionally, one large common ragweed plant planted on May 12 had a fresh weight of 3,200 g and produced over 62,000 seeds. About 95% of common ragweed plants are monecious (Gebben 1965), and common ragweed is one of the most common weeds on disturbed sites throughout North America (Bazzaz 1970). It historically dominated early stages of old-field succession in the eastern and Midwestern

United States (Bazzaz 1968; Quartermann 1957), and typically emerges early in the season, from mid-April through May (Jordan et al. 2007). Common ragweed seed dormancy must be broken by cold stratification (Bazzaz 1970; Willemsen and Rice 1972) and seeds can remain viable for more than 39 years (Bassett and Crompton 1975). Since the introduction of no-till production systems, common ragweed has become more prevalent in production fields (Jordan et al. 2007).

Common Ragweed Competition. Common ragweed is a competitive broadleaf weed not only in soybean, but in several other crops. Four common ragweed plants 9.1 m─1 row resulted in 10% soybean yield loss in North Carolina (Coble et al. 1981), and

Weaver (2001) reported 11% soybean yield loss with 1.6 common ragweed plants m─1 row in Ontario. Similarly, Shurtleff and Coble (1985) reported 12% soybean yield loss with 1.6 common ragweed plants m─1 row in North Carolina. Common ragweed is a better competitor under conditions of high light, water, or nitrogen stress than under optimal conditions (Leskovšek et al. 2012). Common ragweed competition reduced 4

soybean leaf area to degrees similar to common lambsquarters (Chenopodium album) and redroot pigweed (Amaranthus retroflexus) (Shurtleff and Coble 1985).

Objectives

Due to the recent confirmation of glyphosate-resistant common ragweed in Nebraska and the widespread adoption of no-till corn and soybean production practices, it is necessary to address common ragweed’s potential as a major soybean competitor in Nebraska by understanding its biology from emergence to seed and evaluating management options.

Tillage could be implemented for the control of emerged glyphosate-resistant common ragweed before corn/soybean planting; however, the effect of tillage on the emergence pattern of common ragweed in Nebraska is unknown. Therefore, a field study was conducted to evaluate the effect of tillage and develop a predictive model for the emergence pattern of common ragweed in southeast Nebraska.

Common ragweed has been shown to be competitive in soybean; however, little information is available on different irrigation levels on the competitive interaction between soybean and common ragweed. A field study was conducted to model the competitive interaction between soybean and common ragweed as influenced by density and irrigation level. Though literature is available on the growth response of soybean and common ragweed in monoculture to light or water stress, the effect of limited irrigation on the growth response of soybean and common ragweed in competition has not been studied; thus, a field study was conducted for two years and a growth analysis was preformed to characterize the response of soybean and common ragweed to varying irrigation levels and increasing common ragweed density. Glyphosate is no longer a viable option for control of glyphosate-resistant common ragweed; therefore, a field 5

study was conducted to evaluate the efficacy of glufosinate-based herbicide programs for season-long control of glyphosate-resistant common ragweed in glufosinate-resistant soybean.

The objectives of this research were:

1. To evaluate the effect of tillage and develop a predictive model for the emergence

pattern of common ragweed in Nebraska.

2. To model the competitive interaction between soybean and common ragweed as

influenced by density and irrigation level. Specifically, soybean yield loss was fit

to common ragweed density, aboveground biomass, leaf area index, and leaf area

ratio to:

a. Determine the effect of available soil water and common ragweed density

on soybean yield.

b. Determine the most robust method for predicting soybean yield loss across

variable available soil water levels and year-to-year variation.

3. To characterize the growth response of soybean and common ragweed in mixture

and monoculture to varying irrigation levels and increasing common ragweed

density.

4. To evaluate the efficacy of preplant herbicides followed by glufosinate applied

alone or in tank-mixtures for control of glyphosate-resistant common ragweed in

glufosinate-resistant soybean, their effect on soybean injury and yield, and to

determine the economics of these herbicide programs.

The hypotheses of this research were:

1. Tillage will have no effect on the emergence pattern of common ragweed in

Nebraska.

2. Limited available soil water will benefit the competitive ability of common

ragweed over soybean.

3. Both soybean and common ragweed will respond to water and light stress by

altering their growth.

4. A preplant application of registered herbicides followed by a POST application of

glufosinate will provide effective control of glyphosate-resistant common

ragweed in glufosinate-resistant soybean. 7

Literature Cited

Bassett IJ, Crompton CW (1975) The biology of Canadian weeds. 11. Ambrosia

artemisiifolia L. and A. psilostachya DC. Can J Plant Sci 55:463─476

Bazzaz FA (1968) Succession on abandoned fields in Shawnee Hills, Southern Illinois.

Ecology 49:924─936

Bazzaz FA (1970) Secondary dormancy in the seeds of common ragweed Ambrosia

artemisiifolia. J Torrey Bot Soc 97:302─305

Béres I (2003) Distribution, importance and biology of common ragweed (Ambrosia

artemisiifolia L.). Növényvédelem 39:293─302

Béres I, Novák R, Hoffmanné Pathy ZS, Kazinczi G (2005) Distribution, morphology,

biology and importance of common ragweed (Ambrosia artemisiifolia L.) and

protection facilities. Gyomnövények, Gyomirtás 6:1─48

Bohár G (1996) Possibilities of biological control against common ragweed (Ambrosia

artemisiifolia) with phytopathogen fungi. Növényvédelem 32:489─492

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Coble HD, Williams FM, Ritter RL (1981) Common ragweed (Ambrosia artemisiifolia)

interference in soybeans (Glycine max). Weed Sci 29:339─342

Dickerson ET, Sweet RD (1971) Common ragweed ecotypes. Weed Sci 19:64─66

Dill GM (2005) Glyphosate-resistant crops: history, status and future. Pest Man Sci

61:219–224

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64:319─325 8

Duke SO, Baerson SR, Rimando AM (2003) Glyphosate. Encyclopedia of

Agrochemicals

Franz JE, Mao MK, Sikorski JA (1996) Glyphosate: A Unique Global Pesticide.

Washington, DC: American Chemical Society. Pp 653

Ganie Z, Jhala AJ (2016) Confirmation of glyphosate-resistant common ragweed

(Abrosia artemisiifolia) in Nebraska and response to POST corn and soybean

herbicides. Weed Technol (In press)

Gebben AI (1965) The ecology of common ragweed (Ambrosia artemisiifolia L.) in

southeastern Michigan. Univ Microfilms Inc, Ann Arbor, Michigan pp 234

Gianessi LP, Reigner NP (2007) The values of herbicides in U.S. crop production. Weed

Technol 21:559─566

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Wednesday, February 15, 2017. http://www.weedscience.org

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Jhala AJ, Klein RN, Kruger GR, Proctor CA, Shea PJ, Ogg CL, eds. 2016 guide

for weed, disease, and insect management in.NE: University of Nebraska–Lincoln

Extension

Johnson B, Loux MM, Nordby D, Sprague C, Nice G, Westhoven A, Stachler J (2006)

Biology and management of giant ragweed. West Lafayette, IN: Purdue Extension

Publication GWC-12

Jordan T, Nice G, Smeda R, Sprague C, Loux M (2007) Biology and management of

common ragweed. http://www.extension.purdue.edu/extmedia/BP/GWC-14.pdf.

Accessed 2, 2017 9

Leskovšek R, Eier K, Batič F, Simončič A (2012) The influence of nitrogen, water and

competition on the vegetative and reproductive growth of common ragweed

(Ambrosia artemisiifolia L.) Plant Ecol 213:769─781

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world. Applied Ecology and Environmental Research 13:489─512

Oerke EC (2006) Crop losses to pests. J Agr Sci 144:31─43

Quartermann E (1957) Early plant succession on abandoned cropland in the central basin

of Tennessee. Ecology 38:300─309

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41:243─254

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FA (2006) Interaction of the onset of spring and elevated atmospheric CO2 on

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Perspectives 114:865─869

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soybeans (Glycine max). Weed Sci 33(5):654─657

Weaver SE (2001) Impact of lamb’s-quarters, common ragweed and green foxtail on

yield of corn and soybean in Ontario. Can J Plant Sci 81: 821─828

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weed management systems in narrow-row, glyphosate- and glufosinate-resistant

soybean (Glycine max). Weed Technol 15:122─128

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artemisiifolia. Am J Bot 59:248─25 11

Table 1-1. Confirmed glyphosate-resistant weed species in Nebraska (Feb 2017).

Weed Scientific name Family Distribution in Nebraska species Common Ambrosia Asteraceae Isolated field in east ragweed artemisiifolia L. Giant Ambrosia trifida L. Asteraceae Isolated fields in east ragweed Kochia Kochia scoparia L. Chenopodiaceae West Marestail Conyza canadensis L. Asteraceae Southeast and south central Palmer Amaranthus palmeri Isolated fields in southwest and Amaranthaceae amaranth S. Wats. south central Common Amaranthus rudis Northeast, southeast, and south Amaranthaceae waterhemp Sauer central

CHAPTER 2: CHARACTERIZATION OF COMMON RAGWEED (AMBROSIA

ARTEMISIIFOLIA L.) EMERGENCE PATTERN INFLUENCED BY

TILLAGE IN NEBRASKA

Barnes ER, Werle R, Sandell LD, Lindquist JL, Knezevic SZ, Sikkema PH, Jhala AJ

(2017) Characterization of common ragweed (Ambrosia artemisiifolia L.)

emergence pattern influenced by tillage in Nebraska. Weed Technol (Accepted)

Abstract

Spring tillage is a component of an integrated weed management strategy for control of early emerging glyphosate-resistant weeds such as common ragweed; however, the effect of tillage on common ragweed emergence pattern is unknown. The objectives of this study were to evaluate the influence of spring tillage on the emergence pattern of common ragweed and to develop a predictive model for common ragweed emergence in

Nebraska. A field experiment was conducted for three years (2014 to 2016) in Gage

County, Nebraska in a grower’s field infested with glyphosate-resistant common ragweed. Treatments consisted of a no-tillage control and three spring tillage timings.

The Soil Temperature and Moisture Model (STM2) software was used to estimate soil temperature and moisture at a 2 cm depth. The Weibull function was fit to total common ragweed emergence (%) with day of year (DOY), thermal time, and hydrothermal time as independent variables. Tillage treatments and year of emergence had no effect on total common ragweed emergence (P = 0.875 and 0.349, respectively) and time to 50% emergence (P = 0.885); however, emergence was affected by year (P = <0.001) with 50%

total emergence reached on May 5 in 2014, April 20 in 2015, and April 08 in 2016.

According to the corrected information-theoretic model comparison criterion (AICc), the

Weibull function with thermal time and base temperature of 3 C best explained the emergence pattern over three years. A 50 and 90% total emergence can be estimated using a thermal time calculation with a base temperature of 3 C when thermal time reaches 501 and 778 degree days, respectively. This study concludes that spring tillage does not stimulate additional emergence; therefore, after the majority of the common ragweed has emerged and before the crop has been planted, tillage could be used as a component of an integrated glyphosate-resistant common ragweed management program in Nebraska.

Introduction

Common ragweed is a native herbaceous, annual, broadleaf weed found throughout temperate North America (Bazzaz 1970; Coble et al. 1981). Seeds can remain viable in the soil for 39 years or longer (Bassett and Crompton 1975) until broken by cold stratification (Bazzaz 1970; Willemsen and Rice 1972). Common ragweed typically emerges early in the season, from mid-April through May in the Midwest (Werle et al.

2014b). Under favorable conditions, common ragweed plants can reach heights over 2 m

(Bassett and Crompton 1975; Clewis et al. 2001). Common ragweed is one of the most prominent hay fever allergens, with the ability to produce more than one billion pollen grains per plant in late summer or early fall (Jordan et al. 2015).

Common ragweed’s early season emergence gives it a significant competitive advantage if not controlled before crop planting in many cropping systems, especially soybean (Coble et al. 1981) and peanut (Arachis hypogaea L.) (Clewis et al. 2001).

Common ragweed at a density of 4 plants 10 m─1 row has been shown to reduce soybean yield by 8% (Jordan et al. 2015). Similarly, Clewis et al. (2001) reported that a single common ragweed plant m─1 row reduced peanut yield by 40%. Dickerson and Sweet

(1971) reported that in the absence of competition, a small common ragweed plant (95 g fresh weight) produced 3,135 seeds plant─1 and a large plant (24,000 g fresh weight) produced 62,000 seeds.

The overreliance on glyphosate following commercialization of glyphosate- resistant corn (Zea mays L.) and soybean throughout the Midwestern United States has led to the evolution of glyphosate-resistant weeds, including common ragweed (Powles

2008; Shaner 2014). In the United States, glyphosate-resistant common ragweed was first documented in Missouri in 2004 (Heap 2016; Pollard 2007) and has been recently documented in Nebraska (Ganie and Jhala 2016). The failure of glyphosate to control glyphosate-resistant common ragweed has forced producers to adopt diversified weed management strategies including mechanical and cultural approaches, as well as the use of herbicides with alternate modes-of-action, both prior to and after crop establishment

(Beckie 2011; Van Wely et al. 2014; 2015).

Before extensive use of herbicides, tillage was an important tool for preplant weed control (Burnside 1996; Givens et al. 2009). Reduced or no-tillage production systems greatly increased in popularity after glyphosate-resistant crops were introduced in 1996 and the use of glyphosate for weed control widely replaced pre-plant tillage due to the affordability and effectiveness of glyphosate (Givens et al. 2009). Control of glyphosate-resistant giant ragweed (Ambrosia trifida L.), a closely related species of common ragweed, using spring tillage is being considered before soybean planting

(Ganie et al. 2016). A study to determine the effect of tillage on glyphosate-resistant giant ragweed revealed that tillage had no effect on emergence pattern (Kaur et al. 2016). This means preplant tillage can be exploited for giant ragweed management; however, the effect of tillage on the emergence pattern of common ragweed is unknown.

Understanding the emergence pattern of common ragweed, and its response to spring tillage, would be valuable in making management decisions. If spring tillage does not change common ragweed’s emergence pattern, the weed’s early season emergence can be capitalized on to control emerged seedlings with pre-plant tillage after most seedlings have emerged. To maximize the control of common ragweed using pre-plant tillage, a predictive model of emergence would be of great value to optimize timing of tillage for common ragweed control.

The two main environmental triggers of seedling emergence include soil temperature and water (Grundy 2003). Soil temperature can be used as a predictor of seedling emergence and can be expressed as thermal time (TT) with a growing-degree day calculation where TT is only accumulated above a threshold base temperature

(Forcella et al. 2000). Soil temperature and water can be combined as a predictor and expressed as hydrothermal time (HTT) where TT accumulates only when the soil water potential is above a base water potential (Gummerson 1986). Seedling emergence can also be modeled using day of year (DOY) where the emergence pattern of a species is described by DOY and the environment has no effect on the seedling emergence pattern.

The objectives of this study were to evaluate the influence of spring tillage on the emergence pattern of common ragweed in southeast Nebraska and to develop a predictive model for common ragweed emergence.

Materials and Methods

Field Experiments. Field experiments were conducted in Gage County, Nebraska

(40.4465, -96.6204) in 2014, 2015, and 2016 in a producer’s field with a confirmed glyphosate-resistant common ragweed biotype (Ganie and Jhala 2016). The level of glyphosate resistance in this biotype was 7-fold based on control estimates and 19-fold based on biomass reduction compared with a known glyphosate-susceptible common ragweed biotype (Ganie and Jhala 2016). The soil was a Wymore series silty clay loam

(37.6% silt, 37.6% clay, and 24.8% sand) with 2.5% organic matter and a pH of 6.0.

The experiment was arranged in a randomized complete block design with four treatments and four replications. Tillage treatments included three tillage timings and an untreated control (no tillage). The plot size was 1.5 m wide by 4.5 m long. Three 0.25 m2 quadrats that were established for common ragweed emergence counts and they were evenly spaced within each plot. The first tillage treatment was implemented one week after the first common ragweed seedlings emerged in the field, with the remaining tillage treatments implemented at two and five weeks after the first tillage treatment. Tillage was simulated using a 50 cm wide roto-tiller (Honda FRC800, American Honda, Alpharetta,

GA) operated at a depth of 10 cm. Quadrats/flags were removed before tillage and replaced at the same location immediately following treatment. In 2014, tillage treatments were implemented on May 7, May 21, and June 12; in 2015, the plots were tilled on April 16, April 30, and May 21; and on March 31, April 14, and May 5 in 2016.

The yearly variation in the timing of tillage treatments was due to variation in timing of common ragweed emergence as influenced by weather conditions (Figure 1).

Data Collection. Newly emerged common ragweed seedlings were counted and removed

from each quadrat on a weekly basis starting with the first week of emergence through the end of June when common ragweed emergence had ceased. Weekly emergence data were converted to a percent of total emergence based on the total number of seedlings emerged for each quadrat in each year (total emergence). Total emergence and percent total emergence were then averaged across quadrats in each plot, and total emergence was converted to plants m─2. To predict daily soil temperature (C) and moisture (kPa) at a

2 cm depth, the STM2 (Soil Temperature and Moisture Model Software) (Spokas and

Forcella 2009) was used (Figure 1). Daily precipitation and minimum and maximum air temperature were acquired from the nearest High Plains Regional Climate Center

(HPRCC) station located near Virginia, NE. The field soil texture properties and organic matter (%), along with the latitude, longitude, and elevation (436 m) of the research site were also included in the software.

Tillage Effects. The percent total emergence of each plot was separately modeled against the day of year (DOY) with the Weibull function:

푦 = 푎푠푦푚 × {1 − exp[−exp(푙푟푐) × (푥푝푤푟)]} [1] where y is the percent total emergence, x is the independent variable (DOY), and asym is the asymptote (normalized to 100%). Model parameters lrc and pwr are the natural logarithm for the rate of increase and the power to which x is raised, respectively

(Crawley 2007). The nls function in the STATS package in R version 3.3.1 (R

Foundation for Statistical Computing, Wien, Austria) was used to fit the Weibull function. Fifty percent total emergence (time to 50% emergence; T50) and related DOY were predicted from each model. Total emergence and T50 for each plot were subjected to

ANOVA in R version 3.3.1 (R Foundation) with treatments as fixed factors and

replications nested within years as random factors in the model. Fisher’s protected LSD was used to separate total emergence and T50 treatment means at less than α = 0.05. The

ANOVA assumptions of normality and homogeneity were tested prior to analysis and a

Box-Cox transformation was performed on T50 to meet the assumption of normality.

Presented T50 data have been back-transformed for presentation based on mean separation of transformed values.

Model Configuration. Accumulated soil hydrothermal time (HTT) was calculated starting from January 1 for each year and it was calculated using an equation

(Gummerson 1986):

푛 HTT = ∑푖=1[(T × Ψ) × (Tmean − Tbase)] [2] where T and Ψ represent the thermal and hydro portions of the equation, respectively; when Tmean ≥ Tbase then T=1, otherwise T =0; when Ψmean ≥ Ψbase then Ψ=1, otherwise

Ψ=0. Tmean and Ψmean are the average daily soil temperature (C) and the average daily matric potential (kPa) at the 2 cm depth, respectively. Tbase and Ψbase are the minimum threshold temperature for seed germination (C) and the matric potential required for seedling emergence (kPa), respectively. i is the starting date of accumulated HTT

(January 1) and n is the number of days after i. T and Ψ can only be 1 or 0; therefore,

Tmean – Tbase thermal units are accrued each day when both T and Ψ are sufficient for emergence (T and Ψ ≠ 0). Because of inconsistent Tbase values reported for common ragweed in the literature (3.6 C, Shrestha et al. 1999; 4.0 C, Willemsen 1975b; 13.0 C,

Werle et al. 2014b), 16 candidate threshold values ranging from 0 to 15 C were selected.

Similar to Werle et al. (2014b), Ψbase values of -33 (wilting point), -750, -1500

(permanent wilting point), and -∞ (analogous to thermal time [TT]) (kPa) were evaluated.

Fitting the Model. Percent total emergence data for treatments that had similar T50 (P- value > 0.05) were pooled over years. The Weibull function [Equation 1] was fit to the pooled data. The independent variables (x) were 48 combinations of HTT, 16 combinations of TT, and DOY. Data from quadrats for a specific year were only used if at least 3 seedlings emerged during the season to ensure a better fit of the model. The nls function in the STATS package in R version 3.3.1 (R Foundation) was used to estimate the Weibull model parameters (lrc and pwr). Model selection was based on the information-theoretic model comparison approach (AIC) (Anderson 2008). The corrected

AIC (AICc) and model probability (AICw) were obtained for the 65 models using the aictabCustom function in the AICcmodavg package in R version 3.3.1 (R Foundation).

AICc was calculated as (Anderson 2008):

퐴퐼퐶푐 = −2퐿퐿 + 2퐾(푛/[푛 − 퐾 − 1) [3]

where LL is the log-likelihood of the model parameters (calculated with logLik function in R version 3.3.1 [R foundation]), K is the number of model parameters, and n is the sample size. Models derived from HTT require an additional input (soil moisture) compared to TT (soil temperature) and DOY (Julian day); therefore, an additional parameter (K+1) was added to the HTT model’s AICc computations (Werle et al. 2014b).

AICw was calculated as (Anderson 2008):

1 1 퐴퐼퐶푤 = [exp (− )/ ∑푅 exp (− )] [4] 푖 2Δ푖 푟=1 2Δ푟 where Δ푖 is the difference between the model with the lowest AICc and the ith model and

R represents the total number of models (65). The AICw for each model represents the proportion of the total AICw for all models being compared. The model with the lowest

AICc and the highest AICw is considered the “top model” and the best explanation of the

results within the group of models being compared (Anderson 2008). The independent variable of the top model indicates the optimum emergence predictor (Tbase and Ψbase or

DOY) for this population of common ragweed.

Model Goodness of Fit. To assess goodness of fit, root mean square error (RMSE) and modeling efficiency coefficient (ME) were calculated for the top model. The RMSE was calculated as (Roman et al. 2000):

푛 2 1/2 푅푀푆퐸 = [1/푛 ∑푖=1(푃푖 − 푂푖) ] [5] where Pi and Oi are the predicted and observed values, respectively, and n is the total number of comparisons. The closer the predicted values are to the observed values, the lower the RMSE. The ME was calculated as (Mayer and Butler 1993):

푛 2 푛 ̅ 2 푀퐸 = 1 − [∑푖=1(푂푖 − 푃푖) / ∑푖=1(푂푖 − 푂푖) ] [6]

where 푂̅푖 is the mean observed value. The closer the value of ME is to 1, the more precise the prediction.

Results and Discussion

Spring Tillage. Treatment by year interactions for total common ragweed seedling

─2 emergence (plants m ) and T50 were not significant (P = 0.363 and 0.996, respectively;

Table 1); therefore, only main effects were evaluated. Tillage treatment had no effect on the total emergence in each plot (P = 0.875; Table 1). No difference was detected among years in total emergence between tillage treatments (P = 0.349; Table 1). T50 varied across years (P < 0.001; Table 1) with 2014, 2015, and 2016 reaching T50 emergence on

DOY 125, 110, and 92, respectively, with equivalent dates of May 5, 2014; April 20,

2015; and April 8, 2016 (Figure 2). However, tillage treatments had no effect on T50 within year (P = 0.885; Table 1). Willemsen (1975a) reported that soil temperature (due

to seeding depth) affected common ragweed emergence timing, but not the total emergence. In Nebraska, the usual soybean-planting season begins May 5 and ends June

8 (USDA 2010); thus, spring tillage could be used to control emerged common ragweed without changing the emergence pattern in the field. Because T50 varied between years, explanatory variables that rely on environmental factors such as temperature and/or soil moisture could better predict T50. Predicted soil temperature (T, C) and water potential

(Ψ, kPa) at 2 cm varied during the early emergence period (Figure 1), potentially explaining the differences in T50 across years.

Model Selection and Fit. For model selection, tillage treatments were pooled across years and timings because T50 did not vary between tillage treatments (P > 0.05). Based on the AIC criterion, a TT model best described common ragweed emergence pattern

(Figure 3). Werle et al. (2014b) reported that thirteen of the twenty-three summer annual weed species were better predicted with TT models than HTT or DOY models. Based on the results from this study, a Tbase of 3 C best predicted the emergence pattern of common ragweed (AICw = 65.82%; Table 2; Figure 4). This Tbase value is similar to Shrestha et al.

(1999) and Willemsen (1975b), but differs from Werle et al. (2014b), who reported a much higher Tbase (13 C) for common ragweed. The RMSE and ME for the top model were 20.27 and 0.63, respectively, within the range reported in the literature. For example, Werle et al. (2014b) reported RMSE and ME range for 23 annual species, including common ragweed, to be 3.7 to 14.9 and 0.82 to 0.99, respectively. Similarly, for the emergence pattern of several winter annual weeds, RMSE and ME range of 13.4 to 23.1 and 0.63 to 0.85, respectively, were reported by Werle et al. (2014a). For common

lambsquarters, RMSE values ranging from 6.5 to 37.1 were reported by Roman et al.

(2000).

Practical Implications. This is the first study that describes the emergence pattern of naturally occurring common ragweed in Nebraska as affected by tillage and to develop a predictive model. Soil temperature can be recorded or extrapolated from local weather stations and the STM2 software (Spokas and Forcella 2009). These data can be manipulated into thermal time (TT). The top model from this study predicts 10, 50, 75, and 90% total emergence at TT’s of 251, 501, 646, and 778, respectively. Once a preferred threshold of predicted emergence percentage is reached based on TT accumulation, tillage can be implemented to eliminate emerged seedlings before crop planting. Leblanc and Cloutier et al. (2002) report that predictive models of weed emergence could be used for cultivation scheduling. Control of giant ragweed increased with spring tillage prior to soybean planting compared with no-tillage due to its early season, monophasic emergence pattern in Nebraska (Ganie et al. 2016). In an adjacent study in 2016, tillage was performed at soybean planting (May 26, 2016) using the same method where emerged seedlings were not removed prior to tillage. There was 100% control of emerged seedlings with this tillage, suggesting that spring tillage effectively controls established common ragweed plants (Barnes; unpublished data). More research is needed to evaluate the efficacy of different types of tillage equipment and tillage depths for the control of common ragweed before crop planting and their effect on emergence pattern. Common ragweed had a short, concentrated emergence pattern in this study, with a TT accumulation between 10% and 90% predicted emergence of 527 degree

days. The early, monophasic emergence pattern of common ragweed in Nebraska, is a biological characteristic that can be exploited for control prior to crop planting.

Selection pressure through intensive management has led to an extended emergence pattern of giant ragweed in Ohio (Schutte et al. 2012), Illinois, Indiana, and

Wisconsin (Regnier et al. 2016). This differs from the short monophasic emergence pattern reported in Nebraska (Kaur et al. 2016) and Iowa (Werle et al. 2014b). Wortman et al. (2012) reported that giant ragweed demographic variation was attributed to local temperature, rainfall, and elevation differences rather than regional gradients; thus, it is important to note that these results should be used as a guide rather than an absolute predictor of common ragweed emergence due to possible biotype differences. Selection pressure can lead to herbicide resistance or shifts in emergence patterns and therefore integrated weed management programs should be implemented to ensure long-term success. Ganie et al. (2016a; 2016b) also reported the advantages of implementing preplant tillage into integrated glyphosate-resistant giant ragweed management programs in corn and soybean. The ability to predict the emergence pattern of common ragweed allows growers to properly time spring tillage and/or pre-plant herbicides in their weed management programs. Additionally, thermal time calculations with a temperature base of 3 C can be used to predict emergence (%) to be used for tillage scheduling before crops are planted and after most common ragweed seedlings have emerged.

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Table 2-1. Influence of spring tillage timing on total common ragweed seedling emergence (total emergence) and time to 50% emergence (T50) in a field experiment conducted in 2014, 2015, and 2016 in Gage County, Nebraska.

a Year Treatment Total emergence T50 (day of year) (seedlings m─2) 2014 No-till 433 126 (6 May)a 1st tillage (7 May) 215 126 (6 May)a 2nd tillage (21 May) 307 125 (5 May)a 3rd tillage (12 Jun.) 205 125 (5 May)a 2015 No-till 229 110 (20 Apr.)b 1st tillage (16 Apr.) 263 107 (17 Apr.)b 2nd tillage (30 Apr.) 392 109 (19 Apr.)b 3rd tillage (21 May) 365 112 (22 Apr.)b 2016 No-till 55 95 (4 Apr.)c 1st tillage (31 Mar.) 146 91 (31 Mar.)c 2nd tillage (14 Apr.) 74 88 (28 Mar.)c 3rd tillage (5 May) 241 95 (4 Apr.)c P-value Treatment 0.875 0.885 Year 0.349 <0.001 Treatment*Year 0.363 0.996 a Values with the same lowercased letters are not significantly different at P ≤ 0.05 according to Fisher’s protected LSD test.

Table 2-2. Comparison of K, AICc, AICw, and LL for the 6 best models out of the 65 possible models to develop a predictive model for common ragweed emergence in Nebraska. Models are ordered from lowest to highest AICc with the lowest being the best fit (top model). The top model had a Tbase of 3 C with the next 5 best models also based on thermal time.a

Tbase Ψbase K AICc AICW LL 3 C - 3 -403.85 0.66 204.93 4 C - 3 -400.91 0.15 203.47 2 C - 3 -400.82 0.15 203.42 1 C - 3 -396.64 0.02 201.33 5 C - 3 -396.56 0.02 201.29 0 C - 3 -395.18 0.01 200.60 a Abbreviations: AICc, corrected information-theoretic model comparison criterion; AICw, model probability; K, number of model parameters; LL, log-likelihood; Tbase, threshold soil temperature; Ψbase, threshold soil matric potential.

2014, 2015, and 2016.

Figure 2-2. Emergence pattern of common ragweed in Gage County, Nebraska in a field experiment conducted in 2014, 2015, and 2016. As no differences were detected between tillage treatments, data within experimental years were combined. Lines represent the fit of the Weibull function for each year.

Figure 2-3. Corrected information-theoretic model comparison criterion (AICc) of predictive models for common ragweed emergence with a threshold soil temperature

(Tbase) ranging from 0 to 15 C based on Ψbase values of -33 (wilting point), -750, -1500

(permanent wilting point), and -∞ [analogous to thermal time (TT)].

Figure 2-4. Weibull function fit to percent total emergence of common ragweed in 2014,

2015, and 2016 with cumulative thermal time (TT) calculated with a threshold soil temperature (Tbase) of 3 C. Model parameter asym (horizontal asymptote) was normalized to 100 while the model parameters lrc (natural logarithm for the rate of increase) and pwr

(power to which TT is raised) were -17.3026 and 2.7249, respectively. The root mean squared error (RMSE) and modeling efficiency coefficient (ME) for this model were

20.27 and 0.63, respectively.

CHAPTER 3: MODELING THE EFFECT OF COMMON RAGWEED

(AMBROSIA ARTEMISIIFOLIA L.) ON SOYBEAN YIELD IN NEBRASKA

Abstract

Common ragweed is an early emerging, competitive, annual broadleaf weed in soybean production fields in much of the north central United States and eastern Canada. The effect of available soil water on the competitiveness of common ragweed in soybean has not been determined. A field study was conducted in 2015 and 2016 at the University of

Nebraska─Lincoln to assess common ragweed interference in soybean as affected by variable available soil water and common ragweed density. The experiment was arranged in a split-plot design with irrigation level as the main plot and common ragweed density as the subplot. Periodic destructive sampling of crop and weed leaf area index (LAI), aboveground biomass, and yield were conducted. A model set was constructed using the rectangular hyperbolic yield loss and leaf area ratio models. Model parameters were compared using F-tests and model fits were compared using the information-theoretic criterion. No effect of irrigation level on soybean yield loss was detected in model parameters. Model parameters varied by year for most model permutations. The leaf area ratio model with relative leaf area at the R6 growth stage of soybean did not vary by year and best fit the data. This model was a good fit to the data with root mean squared error and modeling efficiency coefficients of 195 and 89, respectively. The leaf area ratio model includes both soybean and common ragweed leaf area and therefore accounts for more crop and weed variation and closer predictions among years. Common ragweed

densities of 1, 2, and 12 m─1 row resulted in yield loss of 61, 76, 95% in 2015 and 25, 39, and 80% in 2016, respectively. This study concludes that soybean-common ragweed interference is primarily influenced by competition for light and resulted in substantial soybean yield loss.

Introduction

Common ragweed is an herbaceous, annual weed, native to North America (Coble et al.

1981). Its seeds can remain viable in the soil for as long as 39 years, allowing it to survive in many environments (Bassett and Crompton 1975). Cold stratification is required for the seeds to germinate (Bazzaz 1970). Germination occurs at the soil surface in early spring (Bazzaz 1970). Common ragweed plants have a fibrous root system and can grow over 2 m in height (Bassett and Crompton 1975; Clewis et al. 2001). When grown in a non-competitive environment, a small (95 g fresh weight) and a large (2,400 g fresh weight) common ragweed plant produced 3,135 to 62,000 seeds, respectively

(Dickerson and Sweet 1971). Common ragweed is a competitive weed in soybean

(Glicine max) (Coble 1981). Common ragweed plants at densities of 4 plants 10 m─1 of row reduced soybean yield up to 8% (Coble 1981). Weaver (2001) and Shurtleff and

Coble (1985) reported 1.6 common ragweed m─1 row caused 11 and 12% soybean yield loss, respectively.

Two of the major resources that crop and weeds compete for are light and water

(Massinga et al. 2003). Light interception in competitive environments is determined by the leaf area index, height, and light absorption characteristics of the species (Kropff

1993). Light interception affects biomass accumulation and transpiration (Deen et al.

2003). Plant growth in response to an increase in light availability only occurs when the demand for carbon is greater than the supply in a plant (Craine and Dybzinski 2013).

Maximizing photosynthetic rates and limiting the photosynthetic rate and subsequent growth of a competing species are the direct and indirect benefits of producing leaves above those of a competitor (Falster and Westoby 2003). Plant competition for light has led to species maintaining higher than optimal leaf area than required to maximize carbon gain in the absence of competition (Anten 2005). This greater than optimal leaf area in the presence of competition for light provides a greater advantage than disadvantage

(Craine and Dybzinski 2003).

Under water stress, both crop yield and weed growth are reduced (Radosevich et al. 1997). Irrigation is a cultural practice that can directly affect interspecific competition between weeds and soybean, ultimately affecting soybean seed yield (Norsworthy and

Oliver 2002). Optimal irrigation early in the growing season may maximize crop growth allowing it to close the canopy earlier, reducing interspecific competition later in the growing season (Yelverton and Coble 1991). Redroot pigweed (Amaranthus retroflexus

L.), robust white foxtail (Setaria viridis var. robusta-alba Schreiber), and robust purple foxtail (Setaria viridis var. robusta-purpurea Schreiber) interference with soybean was more severe during a year with water stress (Orwick and Schreiber 1979). Soybean competed better with yellow foxtail (Setaria glauca L.) when growing season soil water was sufficient (Staniforth and Weber 1956). Weber and Staniforth (1957) reported

Pennsylvania smartweed (Polygonum pensylvanicum L.) and giant foxtail (Setaria faberi L.) reduced soybean yield more when moisture was limiting. Pitted morningglory

(Ipomoea lacunosa L.) was more competitive with soybean in a dry year, reducing soybean yield 17% over the wet year (Howe and Oliver 1987). Entireleaf morningglory

(Ipomoea hederacea var. integriuscula Gray) at a density of 3.3 plants m─2 reduced soybean yield 21% under dryland and 12% under irrigated conditions (Mosier and Oliver

1995). Mortensen and Coble (1989) reported cocklebur (Xanthium strumarium L.) caused less soybean yield loss in well-watered versus drought-stressed conditions.

Scientific literature is not available on the effect of density of common ragweed and available soil water on soybean yield. The hypothesis of this study was that limited available soil water would benefit the competitive ability of common ragweed over soybean. The objectives of this study were to model the competitive interaction between soybean and common ragweed as influenced by density and available soil water.

Specifically, soybean yield loss was fit to common ragweed density, aboveground biomass, leaf area index, and leaf area ratio to 1) determine the effect of available soil water and common ragweed density on soybean yield and 2) determine the most robust method for predicting soybean yield loss across variable available soil water levels and year to year variation.

Materials and Methods

Two field experiments were conducted over a two-year period (2015, 2016) at the

University of Nebraska-Lincoln’s Agriculture Research and Development Center

(ARDC) near Mead, Nebraska (41.16, -96.42). The soil type at the experimental site was a Filbert silt loam (fine, smectitic, mesic Vertic Argialbolls; 25.6% clay, 63.6% silt,

10.8% sand). The field contained 2.7% soil organic carbon and had a pH of 6.8. The

experiment was arranged in a split-plot design with four replications. The main plot treatments were non randomized irrigation levels established to achieve full, half, or zero replacement of available soil water of simulated evapotranspiration using SoyWater

(Specht et al. 2010). Irrigation levels were established as distance from a solid set sprinkler irrigation system, as the distribution of irrigation intensity has been shown to decline with distance (Specht et al. 1986; Specht et al. 2001). Full (100%), half (50%), and zero (0%) irrigation main plots were centered 2.3, 9.9, and 19 m from the solid set irrigation system, respectively. Within each irrigation main plot, there were 5 randomized subplots of common ragweed density, including 0 (weed-free control), 2, 6, and 12 common ragweed plants m─1 row length with soybean crop, and 2 common ragweed plants m─1 row length without crop. Subplots were 3 m wide (four soybean rows spaced

0.75 m apart) by 9 m long. The plots with 2 common ragweed plants m─1 row without soybean were excluded from this analysis.

Experimental Procedures. The experimental site was disked in early spring to prepare a uniform seedbed. Common ragweed seeds (Roundstone Native Seed LLC, Upton, KY) were broadcast by hand on April 30, 2015 and April 22, 2016 to ensure that there were enough plants to establish the required densities in both years. Fifty percent common ragweed emergence was observed on May 17, 2015 and May 27, 2016. From May 9 to

May 12, 2016 high rainfall (11 cm) caused some of the plots to become flooded for several days. Extended flooding and anoxic soil conditions killed germinating and emerged common ragweed seedlings; therefore, 2016 common ragweed emergence was delayed. Soybean was planted at 370,500 seeds ha─1 on May 13, 2015 (Pioneer 21T11)

and May 19, 2016 (Asgrow 2636). Buffers between main plots and the perimeter border of the experiment were planted uniformly to the same soybean population as the plots to eliminate border effects. Borders were maintained weed free by applying glyphosate (900 g a.e. ha-1) and hand hoeing as required. Uniform soybean emergence in 2015 and 2016 occurred on May 23 and May 27, respectively. Common ragweed was thinned to the required densities in a 15 cm band over the soybean row. Natural weed populations were removed by hand hoeing throughout the season. Irrigation was applied on August 3, 2015

(100%, 3.8 cm [± 0.12]; 50%, 1.6 cm [± 0.09]), July 25, 2016 (100%, 3.8 cm [± 0.18];

50%, 1.4 cm [± 0.16]), and August 09, 2016 (100%, 0.7 cm [± 0.05]; 50%, 0.4 cm [±

0.04]).

Data Collection. Daily precipitation and minimum and maximum air temperature were acquired from the nearest High Plains Regional Climate Center (HPRCC) station near

Ithaca, NE approximately 5.5 km from the experimental site. Destructive samples of soybean and common ragweed leaf area, and aboveground biomass were taken at soybean growth stages of V3, R1, R4, and R6. Only leaf area was measured at the R6 destructive sample in 2015 from the 0 and 100% irrigation main plots. Soybean and common ragweed plants within 1 m of row located 0.5 m from the plot edge were counted and clipped at the soil surface. Soybean and common ragweed leaves were removed at the point of attachment of the petiole and leaf area (m2) was measured using a leaf area meter (LAI 3100, LI-COR, Lincoln, NE). Soybean and common ragweed leaf area was measured from the entire 1 m sample for the V3 and R1 stage samples in 2015.

Leaf area was measured on 4 random soybean and 2 random common ragweed plants

from each 1 m sample at all other sampling events due to time and labor constraints.

Number of plants taken within each destructive sample was then converted to plants m─2.

Leaf area index (LAI) from soybean and common ragweed leaf area measurements were calculated as:

퐿퐴 푁0 LAI = × 2 [1] 푁푖 푚

2 where LA is the leaf area measured (m ), Ni is the number of plants sampled for LA, N0 is the number of plants m─2. Aboveground biomass of soybean and common ragweed was obtained from the entire 1 m row sample after drying to constant weight at 65˚C.

Soybean was harvested by hand and threshed using a plot combine from 3.05 m of the center two rows. Soybean grain was weighed and average grain moisture content obtained from 3 subsamples using a Dickey John GAC 2100 grain moisture tester

(Dickey-john, Auburn, IL). Soybean yield was adjusted to 13% moisture and converted to kg ha─1. Whole plot common ragweed density was determined from the soybean harvest area prior to harvest.

Model Configuration. Soybean yield loss (%) was calculated as:

YL = 1 - P/C [2] where YL is the yield loss in comparison to the weed-free control, P is the plot yield, and

C is the weed-free control yield from the associated main plots. Data were tested for normality before analysis. Yield loss was modeled using two equations with multiple parameterizations in R version 3.3.2 (R Foundation for Statistical Computing, Vienna,

Austria). The first was the rectangular hyperbolic yield loss model (Cousens et al. 1985):

YL = (I × N) / (1 + (I × N) / A) [3]

where YL is yield loss , I is the slope of the yield loss curve as density approaches zero

(sometimes referred to as the damage coefficient), A is the maximum yield loss bound between 0% and 100%, and N is the independent variable. Eight permutations were constructed with differing independent variables (N), including whole plot common ragweed density, common ragweed leaf area index (LAI) obtained at four sampling times

(V3, R1, R4, and R6), and common ragweed biomass (BM) at three sampling times (V3,

R1, and R4). The second model was the leaf area ratio model (Kropff et al. 1995):

YL = (q × Lw) / (1 + (q / A - 1) × Lw) [4] where YL is yield loss, q is the relative damage coefficient, A is the maximum yield loss bound between 0% and 100%, and Lw is the relative leaf area (RLA) of the weed calculated as:

Lw = WLAI / (CLAI + WLAI) [5] where WLAI is the LAI of the weed and CLAI is the LAI of the crop. Lw was calculated for each of the four sampling times (V3, R1, R4, and R6) and used for four permutations of equation 5. Irrigation levels were modeled separately within each year. Parameter differences between irrigation levels within year were assessed for each model permutation using F-tests (Knezevic et al. 1994). If both model parameters did not vary by irrigation level, the data were pooled across irrigation levels. Parameter differences between years were then assessed using F-tests. If both model parameters did not vary between years, the data were pooled across years and the model fitted to the pooled data.

Model permutations fitted to the same-pooled dataset were compared using the information-theoretic model criterion (AIC) (Anderson 2008). The use of the

information-theoretic criteria for comparing crop-weed competition models provides empirical support for multiple well-established models while reducing risk of misinformation or poor performance (Jasieniuk et al. 2008). The corrected AIC (AICc) and model probability (AICw) were obtained for the models using the aictabCustom function in the AICcmodavg package in R version 3.3.2 (R Foundation). The corrected

AIC (AICc) was calculated as (Anderson 2008):

AICc = −2LL + 2K(n/[n − K − 1]) [6]

where K is the number of model parameters, LL is the maximum log likelihood, and n is the sample size. AICw was calculated as (Anderson 2008):

1 1 AICw = [exp (− )/ ∑R exp (− )] [7] i 2Δi r=1 2Δr

where Δi is the difference between the model with the lowest AIC and the ith model, and

R represents the total number of models being compared. The model with the lowest

AICc and the highest AICw is considered the best predictor of the results within the model set (Anderson 2008).

Model Goodness of Fit. Root mean square error (RMSE) and modeling efficiency (ME) of the best model were calculated to evaluate goodness of fit. The RMSE was calculated as (Roman et al. 2000):

n 2 1/2 RMSE = [1/n ∑i=1(Pi − Oi) ] [8] where Pi and Oi are the predicted and observed values respectively and n is the total number of comparisons. The lower the RMSE, the closer the model predicted values are to the observed values. The ME was calculated as (Mayer and Butler 1993):

n 2 n ̅ 2 ME = 1 − [∑i=1(Oi − Pi) / ∑i=1(Oi − Oi) ] [9]

where O̅i is the mean observed value and all other parameters are the same as equation 6.

ME differs from R2 only in not having a lower limit. ME values closest to 1 indicate the most accurate predictions.

Results and Discussion

Irrigation level. Available soil water rarely reached the 50% depletion threshold necessary for irrigation due to frequent rain events in both years of this study (Figure 1).

Parameters did not vary between irrigation level for any model permutation in either year of this study (Table 1; Table 2), so all datasets were pooled across irrigation levels within a year. These results imply that either competition between soybean and common ragweed for soil water is not a prominent component of this relationship, or more likely, that soil water was not sufficiently depleted at any point in either year to influence competitive relationships. Munger et al. (1987) reported that soybean water status was unaffected by velvetleaf (Abutilon theophrasti Medik) competition for soil water and that soybean extracted soil water from greater depth than that of velvetleaf. Common cocklebur interference was reduced by increased drought stress (Mortensen and Coble

1989).

Year-to-Year Variation. Temperature and precipitation were similar throughout the two growing seasons (Figure 2). Parameter I and q did not vary among years for the R6 LAI and R6 RLA model permutations, respectively. Therefore, the 2015 and 2016 data were pooled for those models (Table 3). Parameter I and q did vary between years for all other model permutations, so analyses were conducted separately for 2015 and 2016 for those model permutations (Table 3). For example, the rectangular hyperbolic yield loss model

fitted to V3 aboveground biomass resulted in parameter I values of 872 and 23 in 2015 and 2016, respectively (Table 4). Parameter A did not vary between year for any model permutation or sampling time (Table 3). All rectangular hyperbolic (Equation 3) permutations predicted maximum yield loss (A) between 87 and 100% (Table 4). All relative leaf area (Equation 4) permutations predicted maximum yield loss between 93 and 100% (Table 4). Cowbrough et al. (2003) reported that I and A parameters in

Equation 3 fitted to common ragweed density and soybean yield differed between years.

Alternatively, Weaver et al. (2001) reported that I and A parameters in Equation 3 did not vary between years when soybean yield loss was fitted to common ragweed density. The hyperbolic yield loss model parameters (Equation 3) fitted to density has been reported to have considerable variation within a region and across years within a location in a regional study conducted on corn-velvetleaf interference (Lindquist et al. 1996).

All models fitted separately by year provided acceptable fit of the data with ME ranging from 89 to 99 and from 74 to 92 in 2015 and 2016, respectively (Table 4). All model permutations had higher damage coefficients (I or q) in 2015 than 2016 (Table 4).

In 2015, Equation 3 fitted to common ragweed aboveground biomass at the R4 growth stage provided the best fit to the data with the lowest AICc and 100% of the AICw (Table

4). This model fit the data well with an ME of 99 and RMSE of 22 (Table 4). Early- season destructive samples (V3 stage) in 2015 proved to be the worst predictors of soybean yield loss (Table 4). Equation 3 fitted to common ragweed density m─1 row was the best fit of the 2016 soybean yield loss data with the lowest AICc and carrying 100% of the model probability (Table 4). This model was well fit to the data with an ME of 92

and RMSE of 87 (Table 4). Again, common ragweed aboveground biomass and LAI at the V3 sampling were the worst predictors of soybean yield loss in 2016.

The pooled 2015 and 2016 soybean yield loss was tightly correlated to late destructive samples (R6) of LAI. Common ragweed relative leaf area sampled at soybean R6 growth stage in 2015 and 2016 and used to fit equation 4 resulted in q and A parameter estimates of 562 and 96, respectively (Figure 3). The goodness of fit tests resulted in an ME of 91 and RMSE of 159 (Figure 3). Common ragweed leaf area index sampled at soybean R6 growth stage in 2015 and 2016 and used to fit equation 3 resulted in I and A parameter estimates of 245 and 100, respectively (Figure 4). The ME was 88 and RMSE was 200, suggesting a good fit to the data (Figure 4). The RMSE and ME indicated that the RLA model (Equation 4) fitted to common ragweed RLA at the R6 stage better fit the pooled data than the rectangular hyperbolic yield loss model (Equation 3) fitted to common ragweed LAI at the R6 stag. Equation 4 accounts for year-to-year variation in weed and crop density, emergence timing, and leaf area (Chikoye and Swanton 1995; Kropff et al.

1995). Alternatively, Equation 3 lacks the ability to account for variation in the period between crop and weed emergence (Cousens et al. 1987; Kropff et al. 1995).

Soybean Yield Loss. In 2015 and 2016, the average soybean yield in the weed free control was 5,177 kg ha─1 (se ± 65) and 4,422 kg ha─1 (se ± 69), respectively. Equation

3 fitted to common ragweed density resulted in parameter A values of 100 in both years of this study. Cowbrough et al. (2003) reported parameter A values for common ragweed of 92 and 84 in 1999 and 2000, respectively. Weaver (2001) reported A values for common ragweed of 65 and 71 in 1991 and 1993, respectively. Equation 3 fitted to

common ragweed density resulted in I parameter values of 159 and 33 in 2015 and 2016, respectively (Table 4). Parameter I estimates in relevant literature cannot be compared to this study due to differing units of the independent variable (Equation 3). However, we can relate soybean yield losses at particular common ragweed densities (Weaver 2001).

Common ragweed densities of 1, 2, and 12 m─1 row resulted in 61, 76, and 95% predicted soybean yield loss in 2015, respectively. Similar common ragweed densities resulted in 34, 39, and 80% predicted soybean yield loss in 2016, respectively. All of the predicted yield losses in this study are greater than those reported in the literature.

Cowbrough et al. (2003) reported predicted soybean yield losses of 22, 35, and 72% in

1999; and 9, 16, and 49% in 2000 in drilled soybean with common ragweed densities of

1, 2, and 12 m─2, respectively using the rectangular hyperbolic yield loss model

(Equation 3). Weaver (2001) reported predicted soybean yield losses of 12, 20, 47% in

1991; and 5, 9, and 33% in 1993 in 60 cm soybean row spacing with equivalent common ragweed densities using the rectangular hyperbolic yield loss model (Equation 3). Similar to this study conducted in 2016 results, Coble et al. (1981) reported a three-year average yield loss of 20% with a density equivalent to 1 common ragweed m-1 row in 90 cm soybean row spacing using linear regression at low common ragweed densities.

The results of this study indicate that soybean-common ragweed competition is driven by light interference. Competition for soil water had no effect on the model parameters during the two years of this study. This suggests that soil water was not the most limiting factor in this study, primarily because soil water content never declined sufficiently to invoke direct competition for water between these species due to adequate

rainfall. All model permutations explained soybean yield loss well within a year.

However, late-season samplings (R6) of soybean and common ragweed leaf area provided the most robust predictor of soybean yield loss across years. The rectangular hyperbola yield loss model (Equation 3) fitted to common ragweed LAI at the R6 stage accounted for year-to-year variation in soybean-common ragweed interference effectively. The leaf area ratio model (Equation 4) fitted to the RLA of common ragweed at the R6 growth stage was the most robust predictor across years. The ability to efficiently assess leaf area or RLA does restrict the practical implementation of such models (Weaver 1991). Relative leaf cover has been reported to accurately estimate RLA and an efficient and precise method of estimating relative leaf cover could improve the applicability of such models (Lotz et al. 1993). Current imaging technologies using remote sensing by drones (Andújar et al. 2013; Zheng and Moskal 2009; Hosoi and

Omasa 2009; Tang and Shao 2015) may be a means of obtaining accurate estimates of crop yield loss from weed interference, which may be especially useful in circumstances where the crop has been overrun by weeds. Common ragweed was more competitive with soybean in this study than in other studies reported in the literature (Cowbough et al.

2003; Weaver 2001; Coble et al. 1981). This could be due in part to the methodology used in establishing the common ragweed in a narrow band within the crop row, promoting competition for light at an early growth stage. Certainly the difference in emergence dates of soybean and the period between common ragweed and soybean emergence would have affected the competitive relationship (Dieleman et al. 1995).

Cowborough et al. (2003) and Weaver (2001) reported lower soybean yield loss with 19

and 60 cm row spacing, respectively. Studies conducted with several other weed species report that narrow row spacing does reduce soybean yield loss due to weed interference

(Hock et al. 2006). Although studies have proposed potential effects of available soil water in wet versus dry years in crop-weed competition, more research is needed on the effects of variable soil water level on soybean-weed interaction. Better understanding of the relationship between soybean-weed competition and variable water would benefit future yield loss simulation models.

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Table 3-1. Variation in yield loss model parameters across irrigation levels for multiple destructive samples in 2015.a

Model Sample Parameter F-value P-value I 0.162 0.8509 NS Density A 0.609 0.5483 NS I 0.824 0.4452 NS V3 LAI A 0.353 0.7045 NS I 2.138 0.1297 NS R1 LAI A 0.144 0.8663 NS I 0.910 0.4098 NS R4 LAI Rectangular A 0.114 0.8925 NS hyperbola I 0.025 0.8754 NS R6 LAI A 0.617 0.5441 NS I 0.587 0.5602 NS V3 BM A 0.633 0.5357 NS I 3.102 0.0547 NS R1 BM A 0.095 0.9096 NS I 0.701 0.5014 NS R4 BM A 0.000 0.9999 NS

q 0.939 0.3985 NS V3 RLA A 0.585 0.5613 NS q 2.376 0.1045 NS R1 RLA A 0.495 0.6129 NS Leaf area ratio q 2.213 0.1211 NS R4 RLA A 0.484 0.6195 NS q 0.219 0.8042 NS R6 RLA A 0.990 0.3795 NS a Abbreviations: Density, common ragweed density at harvest; V3, R1, R4, R6, soybean stage at destructive sampling; LAI, leaf area index of common ragweed; BM, common ragweed aboveground biomass; RLA, common ragweed relative leaf area; NS, non-significant at α = 0.05

Table 3-2. Variation in yield loss model parameters across irrigation levels (100%, 50%, and 0%) for multiple destructive samples in 2016.a

Model Sample Parameter F-value P-value I 1.487 0.2369 NS Density A 0.000 0.9999 NS I 0.161 0.8518 NS V3 LAI A 0.414 0.6635 NS I 1.269 0.2910 NS R1 LAI A 1.000 0.3759 NS I 0.298 0.7438 NS R4 LAI Rectangular A 0.301 0.7416 NS hyperbola I 0.216 0.6455 NS R6 LAI A 0.266 0.7676 NS I 0.392 0.6780 NS V3 BM A 0.728 0.4885 NS I 0.767 0.4704 NS R1 BM A 0.000 0.9999 NS I 0.000 0.9999 NS R4 BM A 0.000 0.9999 NS

q 0.703 0.5005 NS V3 RLA A 0.980 0.3832 NS q 0.983 0.3821 NS R1 RLA A 0.119 0.8881 NS Leaf area ratio q 0.400 0.6727 NS R4 RLA A 0.000 0.9999 NS q 0.421 0.6589 NS R6 RLA A 0.537 0.5882 NS a Abbreviations: Density, common ragweed density at harvest; V3, R1, R4, R6, soybean growth stage at destructive sampling; LAI, leaf area index of common ragweed; BM, common ragweed aboveground biomass; RLA, common ragweed relative leaf area; NS, non-significant at α = 0.05

Table 3-3. Variation in yield loss model parameters across years for multiple destructive samples in 2015 and 2016.

Model Sample a Parameter F-value P-value b I 6.473 0.0127 * Density A 0.120 0.7298 NS I 10.288 0.0019 ** V3 LAI A 0.279 0.5987 NS I 15.680 0.0001 *** R1 LAI A 0.026 0.8723 NS I 7.390 0.0079 ** R4 LAI Rectangular A 0.164 0.6865 NS hyperbola I 0.555 0.4582 NS R6 LAI A 0.092 0.7623 NS I 8.638 0.0042 ** V3 BM A 0.199 0.6566 NS I 21.583 <0.0001 *** R1 BM A 0.039 0.8439 NS I 24.519 0.0000 *** R4 BM A 0.000 0.9999 NS

q 6.196 0.0146 * V3 RLA A 0.063 0.8024 NS q 13.310 0.0004 *** R1 RLA A 0.038 0.8459 NS Leaf area ratio q 18.703 <0.0001 *** R4 RLA A 0.022 0.8824 NS q 0.034 0.8541 NS R6 RLA A 0.090 0.7649 NS a Abbreviations: Density, common ragweed density at harvest; V3, R1, R4, R6, soybean stage at destructive sampling; LAI, leaf area index of common ragweed; BM, common ragweed aboveground biomass; RLA, common ragweed relative leaf area; NS, non-significant at α = 0.05

Table 3-4. Comparison of AICc, k, AICw, ME, RMSE, I, and A for all models permutation tested where parameters varied between 2015 and 2016. Models are ordered from lowest to highest AICc with the lowest AICc considered the best fit and top model.

Year stage method AICc k AICw ME RMSE I A 2015 R4 BM 292 3 1 99 22 3.2 100 R1 BM 311 3 0 98 38 43 96 - D 312 3 0 98 34 159 100 R4 RLA 316 3 0 98 37 2198 97 R1 RLA 320 3 0 97 40 3930 96 R1 LAI 320 3 0 97 40 2477 96 R4 LAI 324 3 0 97 43 597 99 V3 RLA 327 3 0 97 46 18279 93 V3 BM 328 3 0 97 47 872 93 V3 LAI 391 3 0 89 175 25709 96

2016 - D 357 3 1 92 87 33 100 R1 BM 387 3 0 85 163 4.5 100 R1 LAI 390 3 0 85 171 319 100 R1 RLA 390 3 0 84 173 839 95 R4 RLA 393 3 0 83 184 469 100 R4 BM 395 3 0 83 190 0.89 100 V3 RLA 400 3 0 81 211 1248 100 R4 LAI 404 3 0 79 230 150 100 V3 BM 411 3 0 76 270 23 87 V3 LAI 415 3 0 74 289 2168 89 a Abbreviations: A, asymptotic model parameter and maximum predicted yield loss; AICc, corrected information-theoretic model comparison criterion; AICw, model probability; I, initial slope model parameter and damage coefficient; K, number of model parameters; ME, model efficiency; method, type of common ragweed measurement used as the independent variable; RMSE, root mean square error; stage, soybean growth stage at which the sample was taken.

Figure 3-1 A) cumulative soybean evapotranspiration (cm) and B) cumulative available soil water deficit (cm) obtained from SoyWater (http://www.hprcc3.unl.edu/soywater/) in a field experiment conducted at the University of Nebraska-Lincoln in 2015 and 2016.

Figure 3-2. A) Average daily air temperature (˚C) and B) total daily precipitation (cm) obtained from the nearest High Plain Regional Climate Center in a field experiment conducted at the University of Nebraska-Lincoln in 2015 and 2016.

Figure 3-3. Leaf area ratio model and 95% prediction interval fitted to yield loss (%) of soybean and common ragweed relative leaf area (RLA) sampled at R6 soybean growth stage in a field experiment conducted at the University of Nebraska-Lincoln in 2015 and

2016. Model parameters A (asymptote and maximum yield loss) and q (damage coefficient) were 96 and 562, respectively. The root mean squared error (RMSE) and modeling efficiency coefficient (ME) for this model were 159 and 91, respectively.

Figure 3-4. Hyperbolic yield loss model and 95% prediction interval fitted to soybean yield loss (%) and common ragweed leaf area index (LAI) sampled at R6 soybean growth stage in a field experiment conducted at the University of Nebraska-Lincoln in 2015 and

2016. Model parameters A (asymptote and maximum yield loss) and I (initial slope and damage coefficient) were 100 and 245, respectively. The root mean squared error

(RMSE) and modeling efficiency coefficient (ME) for this model were 200 and 88, respectively.

CHAPTER 4: SOYBEAN AND COMMON RAGWEED GROWTH IN

MONOCULTURE AND MIXTURE UNDER VARYING IRRIGATION

LEVELS

Abstract

Previous studies have characterized the effect of light or water stress on soybean yield or common ragweed growth in monoculture, but no study has considered the effect of stress on soybean-common ragweed interference. Field studies were conducted in 2015 and

2016 at University of Nebraska─Lincoln to characterize the growth response of soybean and common ragweed to different irrigation levels and intraspecific and interspecific interference. The experiment was arranged in a split-plot design with irrigation level as the main plot and common ragweed density as the subplot. A crop-free and weed-free control were included as subplots. Periodic destructive samples of leaf area and biomass of different organ groups were collected and LAI, aboveground biomass partitioning, and specific leaf area (SLA) were calculated. Additionally, soybean and common ragweed yield were harvested and 100 seed weight and seed counts were determined. Soybean partitioning was not affected by irrigation or common ragweed density. In mixture with soybean, common ragweed partitioned more to stems early in the season but less to stems late in the season compared to monoculture. Common ragweed biomass was greater in late season 100% irrigation treatments compared to 50 or 0% irrigation. Soybean LAI was reduced substantially throughout the season with increasing common ragweed density. Common ragweed LAI was reduced with increasing common ragweed density in

the presence of soybean. Soybean increased common ragweed SLA early and reduced it late in the growing season. Soybean 100 seed weight and common ragweed seed number per plant decreased in response to increased common ragweed density. The low plasticity soybean resulted in less biomass, LAI, and seed size whereas common ragweed demonstrated that it was able to modify its biomass allocation and SLA to respond to increased competition. The results of this study indicate the importance of assessing the effects of environmental stresses on crop and weed growth.

Introduction

Common ragweed (Ambrosia artemisiifolia L.) is a monoecious, annual weed in the Asteraceae family that is known for its early spring emergence and late season seed production (Bassett and Crompton 1975). Its greatest growth has been reported to occur in July and August in Ottawa, Canada (Bassett and Crompton 1975). Dickerson and

Sweet (1971) reported that non-competing plants emerging after July 8 produced an average of 3,135 seeds per plant, whereas one early emerging plant produced 62,000 seeds. In competitive environments, common ragweed can grow over 2 m tall (Clewis et al. 2001).

The outcome of crop-weed competition depends on the relative abilities of the crop and weed to obtain limited resources or tolerate a resource deficit (Patterson 1995).

The primary resource crops and weeds compete for is light because it is the only environmental resource for which there is no soil, atmosphere, or plant reservoir

(Patterson 1995). Many plants maintain a higher leaf area ratio than is optimal under no competition, which allows them to better compete for light when under competition

(Anten 2005). Plants respond to environmental variation by altering the partitioning of

new biomass among plant organs in order to maximize resource capture and growth

(Bloom et al. 1985). Increased organ growth can be obtained through increased size or number of its components such as leaves or branches (Bradshaw 1965). When light is the most limiting resource, plants often partition more biomass to aboveground tissues and when water or nutrients are the most limiting resources, plants partition more biomass to root growth (Bloom et al 1985). The competitive ability of a species depends on its growth response, such as differential biomass partitioning to organ groups, total leaf area, and vertical distribution of leaf area (Kropff and van Laar 1993).

Soybean specific leaf weight has been shown to increase with light intensity

(Bowes et al. 1972). Drought during soybean reproduction resulted in decreased leaf area index (LAI) and vegetative biomass, and increased seed size which compensated for less total reproductive structures per unit area (Andriani et al. 1991). Corn grown in season- long weed competition partitioned less biomass to reproductive organs and reduced maximum biomass and LAI by 52 and 66%, respectively compared to weed free (Evans et al. 2003). Several studies have reported the effect of common ragweed interference on soybean yield and have determined that it can result in substantial yield loss (Coble 1981;

Weaver 2001; Shurtleff and Coble 1985; Cowbrough et al. 2003). Other than yield loss, little is known about the growth response of soybean to common ragweed competition for light or soil water. Common ragweed has been shown to increase shoot biomass allocation with increasing intraspecific competition (Leskovšek et al. 2012a). Patracchini et al. (2011) determined that common ragweed in monoculture produced less LAI and biomass per plant but more per unit area with increasing densities. Common ragweed in monoculture produced less seeds per plant as intraspecific competition increased

(Leskovšek et al. 2012b). McConnaughay and Coleman (1999) reported no plastic response to extreme changes in water availability in Abulilon theophrasti, Chenopodium album, and Plygonum pensylvanicum.

To improve the understanding of the crop-weed competitive relationship, it is necessary to understand weed adaptive responses to unfavorable conditions (Brainard et al. 2005).

The effect of varying levels of available soil water on weed competition with soybean has not been determined. The hypothesis of this study was that soybean and common ragweed will respond to water and light stress by altering their growth.Therefore the objective of this study was to characterize the growth response of soybean and common ragweed in mixture and monoculture to varying irrigation levels and increasing common ragweed density.

Materials and Methods

A field study was conducted in the 2015 and 2016 growing seasons at the University of

Nebraska-Lincoln’s Agricultural Research and Development Center (ARDC) near Mead,

Nebraska (41.16, ─96.42). The soil was a Filbert silt loam (fine, smectitic, mesic Vertic

Argialbolls; 25.6% clay, 63.6% silt, 10.8% sand) with a pH of 6.8 and 2.7% soil organic carbon. A split-plot experimental design was used with four replications. Main plot irrigation treatments were established to achieve full, half, or zero replacement of available soil water using SoyWater (Specht et al. 2010). Plots were centered at 2.3, 9.9, and 19 m from a solid set irrigation system, respectively (Specht et al. 1986; 2001). Five subplot treatments, 3 m wide (four soybean row spaced 0.75 m apart) by 9 m long, were randomized within the main plots and consisted of common ragweed densities, including

0 (weed-free), 2, 6, and 12 common ragweed plants m─1 row with soybean, and 2 common ragweed plants m─1 row without soybean.

Experimental Procedures. The field was disked in early spring to prepare a seedbed.

Plots were hand-sown by broadcasting common ragweed seed (Roundstone Native Seed

LLC, Upton, KY) on April 30, 2015 and April 22, 2016. Fifty percent common ragweed emergence was observed May, 17, 2015 and May, 27, 2016. Eleven cm of rainfall between May 9 and May 12, 2016 resulted in flooding of a portion of the experimental area for several days. Although common ragweed was sown earlier than in 2015, anoxic soil conditions killed emerged and germinating seeds, ultimately delaying 2016 emergence. Soybeans were planted on 75 cm row spacing at 370,500 seeds ha─1 on May

13, 2015 (Pioneer 31T11) and May 19, 2016 (Asgrow 2636). Buffers between main plots and around the perimeter of the experiment also were planted in order to eliminate border effects. Buffers and borders were maintained weed-free with glyphosate applications and hand hoeing. Uniform soybean emergence occurred on May 23, 2015 and May 27, 2016.

Common ragweed was thinned by hand weeding to evenly spaced target densities restricted within a 15 cm band over the soybean row. Hand pulling and hoeing was used to maintain densities and remove naturally occurring weeds. Irrigation was applied on

August 3, 2015 (100%, 3.8 cm [± 0.12]; 50%, 1.6 cm [± 0.09]), July 25, 2016 (100%, 3.8 cm [± 0.18]; 50%, 1.4 cm [± 0.16]), and August 09, 2016 (100%, 0.7 cm [± 0.05]; 50%,

0.4 cm [± 0.04]). Two irrigation gauges were placed in each main plot replication. The

50% main plots received 39% the amount of water compared to the 100% plots.

Data Collection. Volumetric water content was measured weekly from each 0 and 6 subplot using a PR2 soil moisture probe (Dynamax Inc. Houston, TX). Destructive

samples of soybean and common ragweed were taken at the V3, R1, R4, and R6 soybean growth stages. Destructive samples were taken by counting and clipping plants at the soil surface from 1 m of row at least 0.5 m within each subplot. In 2015, soybean plants were separated into stems, petioles, leaves, and reproductive tissue groups and ragweed plants were separated into stems and leaf tissue groups for the entire 1 m sample. Soybean leaves were clipped at the base of the lamina separating each trifoliate leaflet. Soybean petioles were removed from soybean stem with the bud remaining with the petiole.

Common ragweed petioles remained with the stem and leaves were clipped at the base of the lamina. Soybean flowers and pods were removed to obtain reproductive tissue mass.

Leaf area was measured (LAI 3100; LI-COR, Lincoln, NE) on all plants in the V3 and R1 samples, but the R4 and R6 leaf area measurements were obtained from four randomly selected soybean and two ragweed plants within the 1 m sample. Only leaf area was measured at the R6 destructive sample in 2015 from the 0 and 100% irrigation main plots. In 2016, four random soybean plants from the 1 m sample were separated into stems, petioles, leaves, and reproductive tissue groups and two randomly selected ragweed plants from the 1 m sample were separated into stems and leaves tissue groups.

All aboveground biomass was dried in paper bags at 65 ˚C for 10 days and weighed. To obtain end of season biomass and seed production, two randomly selected common ragweed plants were harvested in the second week of October from the center two rows of each subplot, were clipped at the soil surface, dried to constant weight, number of seeds counted, and 100 seed weight determined from four random subsamples of 100 seeds. Soybean yield was obtained by harvesting 6.1 m of row using a plot combine and correcting grain mass (kg ha─1) to 13% moisture content. Soybean grain was dried at 65

˚C for 30 days and 100 seed weights were measured. Final common ragweed density was obtained by counting number of plants within the harvest area prior to soybean harvest.

Soybean and common ragweed partitioning coefficients (PCs; %) were calculated for the first three harvest intervals in 2015 and all four harvest intervals in 2016 using the following equation (Knezevic et al. 2001):

PC(stem, leaf, reproductive) = ∆W(stem, leaf, reproductive) /∆W(whole plant) [1] where PC(stem, leaf, reproductive) is the partitioning coefficient of stem, leaf, or reproductive tissue of soybean or common ragweed, ∆W(stem, leaf, reproductive) is the change in dry biomass of the stem, leaf, or reproductive tissue of soybean or common ragweed from the previous destructive sample, and ∆W(whole plant) is the change in dry biomass of total aboveground biomass of soybean or common ragweed from the previous destructive sample. Soybean and common ragweed specific leaf area (SLA; cm2 g─1) for the first three destructive samples in 2015 and all four destructive samples in 2016 were calculated by dividing the leaf area by the leaf dry weight (Brainard et al. 2005). Leaf area data from the four destructive samples were converted in to leaf area index (LAI; m2 m─2) based on the area of the sample and final soybean or common ragweed density.

Soybean PCleaf included the weight of the petiole; whereas Soybean SLA and LAI were calculated for just the lamina. Daily maximum and minimum temperatures were acquired from the nearest High Plains Regional Climate Center (HPRCC) station near Ithaca, NE

(41.14306, ─96.48083). Temperatures were converted to cumulative growing degree days (GDDs) after soybean and common ragweed emergence separately using the following equation (Gilmore and Rodgers 1958):

GDD = ∑([{Tmax + Tmin} / 2] − Tbase) [2]

Where Tmax and Tmin are the daily maximum and minimum air temperatures (˚C), respectively. Tbase is the base temperature. A Tbase of 10˚C was selected based on the minimum germination temperatures for common ragweed (Leskovšek et al. 2012a).

Statistical Analysis. Soybean and common ragweed PCs, total aboveground biomass m─2, SLA, and LAI, were subjected to ANOVA in R version 3.3.2 (R Core Team 2016) using the ssp.plot (split-split plot) function in the agricolae package (Mendiburu 2016).

Independent variables were replacement ET level (main plot), common ragweed density

(subplot), and sampling stage. The sampling stages were treated as pseudo-replications within subplots (sub-subplot). Soybean and common ragweed 100 seed weight, and common ragweed seeds plant─1 were subjected to ANOVA using the sp.plot (split plot) function in the agricolae package (Mendiburu 2016) where the independent variables were replacement ET level (main plot) and common ragweed density (subplot). For all

ANOVA tests, replication nested within year were treated as random effects. If differences between years were significant data were analysis separately. Tukey’s least significant difference was used to separate means less than α = 0.05. Monoculture versus mixture contrast analysis was preformed to better assess significant differences.

Results and Discussion

Temperature and rainfall in 2015 and 2016 were very similar (Figure 1). Most of the available soil water lost through evapotranspiration was replenished by rainfall. Due to ample rainfall and subsequent adequate soil water levels, irrigation was only triggered once in 2015 and twice in 2016. Volumetric water content at 30 cm remained between 20

and 35% throughout most of the reproductive stages in 2015 and 2016 (Figure 2) and was often greater at deeper soil depths (30 to 100 cm; data not shown).

Soybean Biomass Partitioning. Soybean biomass partitioning to the leaves, stems, and reproductive tissue was not affected by replacement ET level or common ragweed density but did change throughout the season (Table 1). Partitioning of new biomass to soybean leaves decreased at an increasing rate from 0.75 between emergence and the V3

(327 GDD) destructive sampling to 0.05 between the R4 (904 GDD) and R6 (1122 GDD) growth stage of soybean (Figure 3). Soybean biomass partitioning to stems increased linearly from 0.25 between emergence and V3 to 0.42 between the R1 (499 GDD) and

R4 stage. Soybean biomass partitioning to stems decreased to 0.10 between R4 and R6.

Gustafson et al. (2006) reported more biomass partitioning to stems and petioles in weedy plots during vegetative stages. However, no difference in partitioning to stems and petioles during reproductive stages. As expected, biomass partitioning to reproductive tissue increased at an increasing rate from 0.02 between V3 and R1 to 0.17 between R1 and R4 and 0.85 between R4 and R6. Gustafson et al. (2006) reported no difference in soybean biomass partitioning during the vegetative stages in weed-free versus weedy plots. Madhu and Hatfield (2016) reported higher biomass partitioning to reproductive tissue under high water stress. Soybean did not partition new biomass to stems or leaves differently with or without common ragweed present indicating the lack of plasticity in soybean.

Soybean total aboveground biomass g m─2 varied between years. There was an interaction between common ragweed density and destructive sampling stage (Table 1) and between replacement ET level and destructive sampling stage for aboveground

biomass in 2015 (Table 1). Soybean aboveground biomass increased as the season progressed and declined with increasing common ragweed density (Figure 4). These differences were seen as early as the V3 (309 GDD) development stage with weed free soybean achieving 24% greater biomass than the highest common ragweed density treatment, indicating that common ragweed interference already reduced soybean growth at this stage (Figure 4). These differences became greater as the season progressed, with as much as a 61% reduction in aboveground biomass in the 12 plants m─1 row treatment compared to the weed-free control. No differences in soybean aboveground biomass due to replacement ET level occurred early in the season. However, the 50% replacement ET treatment had 15 and 22% greater biomass at R4 (886 GDD; 451.4 g m─2) compared to

100 (385.2 g m─2) and 0% treatments, respectively (352.4 g m─2; Figure 5). Andriani et al. (1991) reported soybean dry matter accumulation decreased with drought stress during reproductive stages. Gustafson et al. (2006) reported season long weed interference resulted in 5-fold biomass reduction compared to weed-free.

Overall, soybean aboveground biomass was larger at all sampling times in 2016 compared to 2015. Significant interaction between common ragweed density and time of sampling occurred for aboveground soybean biomass in 2016 (Table 1). As in 2015, soybean aboveground biomass was greatest in the weed-free treatment and decreased as density increased. These differences were detected at V3 (345 GDD) and became greater with time during the season (Figure 6). At R6 (1180 GDD) soybean biomass in the 12 plants m─1 row treatment was 55% less than in the weed-free control. Hagood et al.

(1980) reported that velvetleaf interference reduced soybean biomass beginning early season and extending throughout the entire season.

Soybean Leaves. Soybean SLA (cm2 g─1) varied by destructive sampling stage, but was not affected by irrigation or common ragweed density treatment (Table 1). This indicates that soybean did not compensate for the common ragweed competition for light by adjusting its SLA. Specific leaf area at soybean V3 (327 GDD), R1 (499 GDD), R4 (904

GDD), and R6 (1122 GDD) were 264, 350, 285, and 254, respectively (Figure 7).

Soybean SLA at R2 ranged from 200 to 457 and at R5 ranged from 116 to 204 in a 373 soybean line survey conducted 1975 to 1977 measured from 10 to 20 leaf punches (Lugg and Sinclair 1979). Lugg and Sinclair (1979) reported SLA increased until the R1 stage and then decreased steadily until mid-pod-fill (R5-R6), but actual SLA values also varied among soybean cultivars.

Soybean LAI varied between 2015 and 2016; therefore years were analyzed separately. In 2015, a significant interaction between common ragweed density and time of sampling occurred for soybean LAI (Table 1). Soybean LAI was always greatest in the weed-free control, and declined with increasing common ragweed density (Figure 8). At

V3 (GDD 309), soybean LAI was reduced 22% at 12 common ragweed plants m─1 row compared to the weed-free control. At R4 (886 GDD) soybean LAI was 5.27 and 2.08 m2 m─2 in the weed-free and 12 common ragweed density treatments, respectively (Figure

8). Soybean leaf senescence occurred between R4 and R6 (1064 GDD). Soybean LAI declined to 4.21, 1.19, 1.25, and 0.85 in common ragweed densities of 0, 2, 6, and 12 m─1 row, respectively, at R6 (Figure 8).

Similar to 2015, 2016 soybean LAI was greatest in the weed-free control, and declined with increasing common ragweed density (Figure 9). At V3 (345 GDD) soybean LAI was reduced 44% at the 12 common ragweed plants m─1 row compared to the weed-free

control (Figure 9). Soybean LAI was 4.53 and 2.74 m─2 in the weed-free and 12 common ragweed density treatments, respectively at R4 (920 GDD). At R6 (1180 GDD) soybean leaf senescence had begun and LAI declined to 4.53, 3.76, 3.10, and 2.74 in common ragweed densities of 0, 2, 6, and 12 m─1 row, respectively. Hagood et al. (1980) reported

10 and 20 velvetleaf m─2 reduced soybean LAI 38 and 47% at 76 days after emergence in

1978 and 38 and 41% at 86 days after emergence in 1979, respectively. Setiyono et al.

(2008) reported a similar trend in weed-free soybean LAI grown in near optimal conditions at Mead and Lincoln, NE with leaf senescence beginning before the R6 growth stage.

Soybean Seed. Soybean 100 seed weight was not affected by year or irrigation treatment, but declined asymptotically with increasing common ragweed density (Table 1).

Common ragweed densities of 0, 2, 6, and 12 m─1 row resulted in soybean 100 seed weight of 14.6, 12.2, 10.5, and 9.3 g 100 seed ─1, respectively, a 36% reduction in seed size (Figure 10). Eaton et al. (1973) reported soybean 100 seed weight was reduced 11% with 90 days of Venice mallow competition compared to weed-free soybean. Wyse et al.

(1986) reported 12% reduction in 100 seed weight by 4 Jerusalem artichoke (Helianthus tuberosus L.) m─1 row. Whereas total soybean aboveground biomass and LAI differed among irrigation treatments in 2015, 100 seed weight was not, suggesting that growth processes are more sensitive to brief periods of drought than soybean seed size.

Nevertheless, Specht et al. (1986) reported an average increase of 1.3 g per 100 seeds for each 10 cm of applied water, which may be a comparable reduction in seed size with increasing common ragweed density.

Common Ragweed Biomass Partitioning. Common ragweed destructive samples were separated into leaves and stems. Therefore, the sum of PCleaf and PCstem will always add to 1.0 and their errors are essentially identical. Irrigation treatment had no effect on ragweed partitioning coefficients; however common ragweed density did affect partitioning (Table 2). The fraction of new biomass partitioned to leaves declined while that to stems increased throughout the growing season, similar to the results of Leskovšek et al (2012a) (Figure 11). Contrast analysis indicated that common ragweed in monoculture and mixture partition biomass differently throughout the season (P =

0.0002). Common ragweed in monoculture partitioned 6 and 9% more biomass to leaves at 168 and 423 GDD, than common ragweed in mixture, respectively (Figure 11). Later in the growing season (1023 GDD) common ragweed in monoculture partitioned 9% less biomass to leaves than when in mixture.

Common ragweed aboveground biomass was varied among years. In 2015 there was an interaction between sampling date and common ragweed density and between sampling date and replacement ET level (Table 2). The 100% irrigation level produced

35 and 32% more biomass at harvest than 50 and 0% irrigation main plots, respectively

(Figure 12). Common ragweed in monoculture produced similar biomass to the 12 plants m─1 row treatment during the first three samplings, indicating that soybean reduces aboveground biomass of common ragweed. The 2 plants m─1 row in monoculture produced more than 2 plants m─1 row in crop but less than 6 plants m─1 row at the last sampling. With soybean interference, common ragweed biomass was smallest in the 2 plants m─1 row treatment and greatest in the 12 plants m─1 row treatment, regardless of time of sampling (Figure 13). Differences were small early in the season at 309 GDD

(soybean V3) with 2, 6, and 12 plants m─1 row producing 2.6, 4.9, and 11.0 g m─2, respectively; but became progressively greater as the season progressed with 2, 6, and 12 plants m─1 row producing 584, 1457, and 2234 g m─2 by the harvest sample. Common ragweed at the highest density at R6 was 383% larger than that in the lowest density.

Common ragweed aboveground biomass in 2016 resulted in an interaction common ragweed density and sampling date (Table 2). Common ragweed in monoculture again produced similar biomass to the 12 plants m─1 row treatment during the first three samplings (Figure 14). The 2 plants m─1 row in monoculture produced more than 2 plants m─1 row in crop but less than 6 plants m─1 row at the last sampling indicating that the maximum growth of 2 common ragweed m─1 row in ideal conditions could not produce as much biomass as 12 plants m─1 row. Differences in aboveground biomass were observed as early as the first sampling at 345 GDD (soybean V3) with 2, 6, and 12 plants m─1 row producing 8.3, 16.7, and 27.9 g m─2, respectively. These differences became greater throughout the season with 2, 6, and 12 plants m─1 row producing 584, 1189, and

1414 g m─2 at harvest, respectively. Patracchini et al. (2011) reported that higher common ragweed density resulted in greater aboveground biomass per unit area but less biomass per plant with 25 plants m─2 obtaining 1428 and 4377 g m─2 in 2006 and 2007, respectively. Leskovšek et al (2012b) reported greater biomass per plant in 1.3 plants m─2 density compared with 6.6 plants m─2 density suggesting competition for light.

Common Ragweed Leaves. Common ragweed specific leaf area did not vary among years, but an interaction between common ragweed density and destructive sampling date did influence ragweed SLA (Table 2). Common ragweed growing in mixture with soybean showed a similar trend in SLA to that of soybean, meaning that ragweed SLA

was low (152 cm2 g─1) during vegetative stages (337 GDD), was greatest at the beginning of soybean reproduction (234 cm2 g─1; 509 GDD), and then declined as the season progressed (Figure 15). Common ragweed growing in monoculture showed a similar initial trend in SLA early in the season through the beginning of soybean reproduction, except that the ragweed leaves were thicker (i.e. smaller SLA). However, later in the season (913 to 1132 GDD), common ragweed in monoculture produced thinner leaves

(i.e. larger SLA) than any of the plants grown in mixture. This may be due to a greater amount of late season leaf production in the absence of soybean interference that would not have occurred in mixture. Patracchini et al. (2011) similarly reported 252 cm2 g─1 maximum SLA 26 days after emergence in 2006 and 235 cm2 g─1 maximum SLA 116 days after emergence in 2007 for common ragweed and found no differences in SLA between monoculture densities throughout the growing season. Patracchini et al. (2011) reported no clear trend in seasonal SLA with periodic samples every two weeks throughout the growing season. Patracchini et al. (2011) report that SLA likely responded to differing available soil water levels between the two years and that no differences were detected between common ragweed densities.

Common ragweed LAI did not vary among years, but an interaction between sampling time and density affected ragweed LAI (Table 2). Common ragweed LAI increased nearly linearly until the soybean R4 stage of development, then declined due to leaf senescence (Figure 16). Common ragweed LAI in mixture with soybean increased with increasing ragweed density at all sampling times. In the absence of soybean interference, common ragweed LAI was similar to its equivalent density in mixture (2 m─1 row) at the V3 sampling time, but subsequently increased at a substantially greater

rate through the growing season, ultimately having a similar LAI to that in the 12 plants m─2 in mixture treatment (Figure 16). This suggests that the presence of soybean limited common ragweed leaf area growth, especially at lower densities. The greatest LAI (2.8) was achieved at 913 GDD in the monoculture 2 m─1 row density. Patracchini et al. (2011) reported 5.6 and 12.6 LAI at 25 plants m─2 compared to 2.2 and 5.2 at 4 plants m─2 in

2006 and 2007, respectively.

Common Ragweed Seed. Common ragweed 100 seed weight was not affected by any treatment with mean 100 seed weight of 0.31 g (Table 2). However, increasing common ragweed density reduced common ragweed seeds per plant (Table 2). The 2 m─1 row densities had the highest seed production of 6,013 seed plant─1. In soybean competition, the 6 and 12 m─1 row densities resulted in 3,182 and 2,586 seed plant─1 and had lower seed production than the 2 m─1 row density which resulted in 6,013 seed plant─1.

Leskovšek et al (2012b) reported decreasing common ragweed seed number per plant with increasing intraspecific competition.

Soybean did not respond to common ragweed density or slight differences in soil water by altering biomass partitioning. This suggests a lack of plasticity in the two cultivars tested. Soybean biomass was reduced with increasing common ragweed density.

Soybean LAI was limited by common ragweed density throughout the entire season indicating high competition for light. Soybean seed size decreased in response to increased common ragweed density. Common ragweed aboveground biomass partitioning throughout the season was affected the greatest by the presence of soybean by partitioning more to leaves early in the season and less late in the season once soybean had started senescence. Common ragweed aboveground biomass increased as density

increased. Additionally, soybean interference reduced common ragweed aboveground biomass throughout the growing season. Common ragweed SLA was influenced the most by the presence of soybean. Early season common ragweed leaves were the thickest without soybean and late season common ragweed leaves were the thinnest without soybean. Common ragweed LAI responded similarly to aboveground biomass with increasing common ragweed density increasing LAI and the presence of soybean reducing common ragweed LAI. The response of common ragweed to increasing density was to decrease seed number rather than seed size. This response demonstrates classic plant plasticity by sacrificing seed number to maintain the seed size and likely survivability of seeds. The growth responses of common ragweed in crop were different than common ragweed in a monoculture environment. Results of this study contribute to the understanding of the interaction between crop and weed growth throughout the season when grown together in a field environment. Additionally, this study examines the growth response of soybean and common ragweed when subjected to various stresses such as light and water.

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NS NS

NA NA NA NA

***

100 100

seed

weight

0.8151 0.4055

<0.0001

leaf area area leaf

----

NS NS NS NS

*** ***

LAI

2016 2016

0.9388 0.3146 0.8816 0.3312 0.0132

─

<0.0001 <0.0001

2 2

226

1753

NS NS NS NS

---- *** *** ***

LAI

2015 2015

0.9 0. 0.4591 0.8997

<0.0001 <0.0001 <0.0001

─

NS NS NS NS NS NS

***

SLA

0.7214 0.1324 0.6120 0.8453 0.8834 0.5726

<0.0001

----

ground biomass, specific leaf area, area, leaf specific biomass, ground

NS NS NS NS

*** *** ***

2016 2016

shoot

0.6084 0.2624 0.2920 0.2777

biomass

<0.0001 <0.0001 <0.0001

row row

─

g g m

NS NS

*** *** ***

2015 2015

shoot

0.4659 0.0266 0.2766 0.0013

----

biomass

<0.0001 <0.0001 <0.0001

repro

2105 8113

NS NS NS NS NS NS

***

C C

0.2733 0.0645 0.9140 0. 0.0780 0.

<0.0001

stem

artitioning coefficients, above coefficients, artitioning

1762 0677 8785 9193

NS NS NS NS NS NS

***

C C

0. 0. 0. 0. 0.0599 0.4159

<0.0001

leaf

555

9176 5923 0535

NS NS NS NS NS NS

% change change % in total biomass ***

0. 0.5969 0. 0.5 0. 0.5548

<0.0001

e for soybean p soybean for e

sample

100 seed weight, in a field experiment conducted in 2015 and 2016 at the University of Nebraska University the at 2016 and in 2015 conducted experiment field in a weight, seed 100

ANOVA tabl ANOVA

. .

1 sample

density

level*sample

level*density

density*sample

common ragweed

replacementET level

level*density*

Table andindex, ARDC. Lincoln

Table 4-2. ANOVA table for common ragweed partitioning coefficients, aboveground biomass, specific leaf area, leaf area index, 100 seed weight, and seed number in a field experiment conducted in 2015 and 2016 at the University of Nebraska-Lincoln ARDC. 2015 2016 100 Seed shoot shoot seed numbe PC leaf PC stem biomass biomass SLA LAI weight r % of total biomass ---- g m─1 row ---- cm2 g─1 m2 m─2 g change replacement ET 0.4677 0.4677 0.0685 0.1651 0.1192 0.1972 0.1285 0.0910 level NS NS NS NS NS NS NS NS

common 0.0508 0.0508 <0.0001 <0.0001 0.9903 <0.0001 0.6488 0.0118 ragweed density NS NS *** *** NS *** NS *

destructive <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 NA NA sample date *** *** *** *** *** ***

level*density 0.5692 0.5692 0.7142 0.6745 0.9665 0.9856 0.5991 0.3559 NS NS NS NS NS NS NS NS

level*sample 0.1510 0.1510 0.0055 0.9938 0.8165 0.1120 NA NA NS NS ** NS NS NS density*sample 0.0002 0.0002 <0.0001 <0.0001 0.0002 <0.0001 NA NA *** *** *** *** *** *** level*density* 0.4140 0.4140 0.5067 0.9852 0.8784 0.9999 NA NA sample NS NS NS NS NS NS

Figure 4-2. Volumetric water content (cm3 cm-3) collected throughout the growing season at a 30 cm depth in a field experiment conducted in 2015 and 2016 at the University of Nebraska-Lincoln ARDC.

Figure 4-3. Soybean partitioning coefficient of reproductive, stems, and leaves throughout the growing season at V3, R1, R4, and R6 soybean growth stage in a field experiment conducted in 2015 and 2016 at the University of Nebraska-Lincoln ARDC.

Figure 4-4. Soybean aboveground biomass m-2 as affected by common ragweed density throughout the growing season at V3, R1, and R4 soybean growth stage in a field experiment conducted in 2015 at the University of Nebraska-Lincoln ARDC.

Figure 4-5. Soybean aboveground biomass m-2 as affected by replacement ET level throughout the growing season at V3, R1, and R4 soybean growth stage in a field experiment conducted in 2015 at the University of Nebraska-Lincoln ARDC.

Figure 4-6. Soybean aboveground biomass m-2 as affected by common ragweed density throughout the growing season at V3, R1, R4, R6 soybean growth stage in a field experiment conducted in 2016 at the University of Nebraska-Lincoln ARDC.

Figure 4-7. Soybean specific leaf area throughout the growing season at V3, R1, R4, and R6 soybean growth stage in a field experiment conducted in 2015 and 2016 at the University of Nebraska-Lincoln ARDC.

Figure 4-8. Soybean leaf area index as affected by common ragweed density throughout the growing season at V3, R1, R4, and R6 soybean growth stage in a field experiment conducted in 2015 at the University of Nebraska-Lincoln ARDC.

Figure 4-9. Soybean leaf area index as affected by common ragweed density throughout the growing season at V3, R1, R4, and R6 soybean growth stage in a field experiment conducted in 2016 at the University of Nebraska-Lincoln ARDC.

Figure 4-10. Soybean 100 seed weight as affected by common ragweed density in a field experiment conducted in 2015 and 2016 at the University of Nebraska-Lincoln ARDC.

Figure 4-11. Common ragweed partitioning coefficient of leaves and stems throughout the growing season in monoculture and in mixture with soybean in a field experiment conducted in 2015 and 2016 at the University of Nebraska-Lincoln ARDC.

Figure 4-12. Common ragweed aboveground biomass as affected by replacement ET level throughout the growing season in a field experiment conducted 2015 at the University of Nebraska-Lincoln ARDC.

Figure 4-13. Common ragweed aboveground biomass as affected by common ragweed density throughout the growing season in a field experiment conducted in 2015 at the University of Nebraska-Lincoln ARDC.

Figure 4-14. Common ragweed aboveground biomass as affected by common ragweed density throughout the growing season in a field experiment conducted in 2016 at the University of Nebraska-Lincoln ARDC.

100

Figure 4-15. Common ragweed specific leaf area throughout the growing season in monoculture and in mixture with soybean in a field experiment conducted in 2015 and 2016 at the University of Nebraska-Lincoln ARDC.

101

Figure 4-16. Common ragweed leaf area index as affected by common ragweed density throughout the growing season in a field experiment conducted in 2015 and 2016 at the University of Nebraska-Lincoln ARDC.

102

CHAPTER 5: CONTROL OF GLYPHOSATE-RESISTANT COMMON

RAGWEED (AMBROSIA ARTEMISIIFOLIA) IN GLUFOSINATE-

RESISTANT SOYBEAN

Barnes EB, Knezevic SZ, Sikkema PH, Lindquist JL, Jhala AJ (2017) Control of

glyphosate-resistant common ragweed (Ambrosia artemisiifolia L.) in glufosinate-

resistant soybean. Weed Technol (under review)

Abstract

Common ragweed emerges early in the season in Nebraska and is competitive with soybean; therefore, herbicides applied prior to soybean emergence are important for effective control. Glyphosate has been used as a preplant burndown option; however, confirmation of glyphosate-resistant (GR) common ragweed in Nebraska necessitates evaluating other herbicide options. The objectives of this study were to evaluate the efficacy of preplant (PP) herbicides followed by glufosinate alone or in tank-mixture, applied postemergence (POST) for control of glyphosate-resistant common ragweed in glufosinate-resistant soybean; their effect on common ragweed density, biomass, and crop yield; and the partial economics of these herbicide programs. A field experiment was conducted in a grower’s field infested with GR common ragweed in Gage County,

NE in 2015 and 2016. Preplant herbicide programs containing glufosinate, paraquat, 2,4-

D, dimethenamid-P, cloransulam-methyl, or flumioxazin plus chlorimuron ethyl provided

90 to 99% control of common ragweed at 21 d after treatment (DAT). The aforementioned PP herbicides followed by a POST application of glufosinate, alone or in tank-mixture with imazethapyr, acetochlor, or S-metolachlor, controlled GR common

103 ragweed 84 to 98% at harvest, reduced density (≤ 20 plants m─2) and biomass by ≥ 93%, and secured soybean yield ≥ 1,819 kg ha─1. Preplant followed by POST herbicide programs resulted in the highest gross profit margins ($373 to $506) compared to preplant alone ($91) or preemergence (PRE) followed by POST programs ($158). The results of this study conclude that GR common ragweed control, soybean yield, and net returns are maximized with a two-pass program of an effective PP herbicide followed by glufosinate applied POST in glufosinate-resistant soybean.

Introduction

Common ragweed (Ambrosia artemisiifolia L.) is a native, herbaceous, annual weed that belongs to the Asteracea family and is commonly found throughout temperate North

America (Coble et al. 1981; Dickerson and Sweet 1971). It has historically dominated early stages of old-field succession in the eastern and Midwestern United States (Bazzaz

1968; Quarterman 1957). Common ragweed typically emerges early in the season in

Nebraska (Barnes et al. 2016; Werle et al. 2014) and is a competitive weed in several agronomic crops, including soybean. Coble et al. (1981) reported that 4 common ragweed plants 10 m─1 row reduced soybean yield 8%. Similarly, Shurtleff and Coble (1985) and

Weaver (2001) reported that 1.6 common ragweed plants m─1 row reduced soybean yield by 12 and 11%, respectively. Common ragweed is a monoecious species that has the potential to produce several thousand seeds per plant. A large (2.4 kg fresh weight) common ragweed plant can produce up to 62,000 seeds (Dickerson and Sweet 1971) and can grow up to 2 m in height (Bassett and Crompton 1975; Clewis et al 2001).

Glyphosate is a broad-spectrum, systemic, postemergence (POST) herbicide

(Duke and Powles 2008) first marketed in 1974 (Franz et al. 1997). In 1996, glyphosate-

104 resistant soybean was first commercialized in the United States (Wiesbrook et al. 2001), and as of 2016, commercially grown glyphosate-resistant crops include alfalfa (Medicago sativa L.), canola (Brassica napus L.), corn (Zea mays L.), cotton (Gossypium hirsutum

L.), soybean (Glycine max L.), and sugarbeet (Beta vulgaris L.) (Duke and Powles 2009).

With the commercialization of glyphosate-resistant crops, the POST in-crop application of glyphosate increased dramatically (Dill 2005), resulting in the evolution of glyphosate- resistant weeds. As of 2016, glyphosate resistance has been reported in 36 weed species globally, including 16 species in the United States (Heap 2016). Missouri was the first state to confirm glyphosate-resistant common ragweed in 2004 (Heap 2016; Pollard

2007), and since then, glyphosate-resistant common ragweed has been confirmed in 15 states in the United States and in Ontario, Canada (Heap 2016). Glyphosate-resistant common ragweed has been recently confirmed as the sixth glyphosate-resistant weed in

Nebraska (Ganie and Jhala 2017; Jhala 2016).

Glufosinate is a broad-spectrum, contact herbicide (Haas and Muller 1987).

Glufosinate-resistant soybean was first commercialized in 1999 (Wiesbrook et al. 2001).

Glufosinate blocks the glutamine synthetase enzyme, which leads to buildup of ammonium in plant tissue (Logusch et al. 1991). Glufosinate can be applied up to 1,329 g ai ha─1 per growing season in glufosinate-resistant soybean in either single or sequential

(> 5 d apart) application up to but not including the bloom soybean growth stage

(Anonymous 2016). Glufosinate has no plant-back interval for corn or soybean and can be applied in a range of 593 to 736 g ai ha─1 depending on weed pressure (Anonymous

2016). It is likely that the glufosinate application rate and cumulative rate per growing season may increase in the near future (K Watteyne, Bayer Crop Science; personal

105 communication). Glufosinate is an alternative herbicide option for control of glyphosate- resistant weeds in glufosinate-resistant soybean (Jhala et al. 2014; Kaur et al. 2014).

Management of glyphosate-resistant weeds is challenge for soybean producers in

Nebraska. Widespread occurrence of glyphosate-resistant weeds in several states in the

Midwestern United States, including Nebraska, requires alternate weed management programs. Planting of glufosinate-resistant soybean is increasing in several states, specifically for control of glyphosate-resistant weeds. A survey conducted in 2011 in

Arkansas reported that 12% of the soybean acreage was seeded to glufosinate-resistant cultivars (Riar et al. 2013), a number that had increased to 35% by 2016 (JK Norsworthy, personal communication). Similarly, the use of glufosinate-resistant soybean cultivars has increased in recent years in the Midwest (Jhala et al. 2017).

There have been no studies published in the scientific literature on the control of glyphosate-resistant common ragweed in glufosinate-resistant soybean. Preplant application of 2,4-D, flumioxazin, glufosinate, paraquat, saflufenacil, or sulfentrazone followed by a POST application of glufosinate, alone or in tank-mixtures, control glyphosate-resistant giant ragweed (Ambrosia trifida L.), a closely related species of common ragweed, in Nebraska (Kaur et al. 2014). Aulakh and Jhala (2015) reported that sulfentrazone plus metribuzin applied PRE followed by a POST application of glufosinate tank-mixed with acetochlor, pyroxasulfone, or S-metolachlor controlled common lambsquarters (Chenopodium album L.), common waterhemp (Ameranthus rudis Sauer), eastern black nightshade (Solanum ptychanthum Dunal), velvetleaf

(Abutilon theophrasti Medik.), large crabgrass (Digitaria sanguinalis L.), and green foxtail (Setaria viridis L.) ≥ 90% in glufosinate-resistant soybean. Van Wely et al. (2014;

106

2015) concluded that neither a single PP or a single POST herbicide application provided full season control of glyphosate-resistant common ragweed in Ontario and that two-pass programs would need to be considered.

The objectives of this study were to evaluate the efficacy of PP herbicides followed by glufosinate applied POST, alone or in tank-mixture with acetochlor, imazethapyr, or S-metolachlor, for control of glyphosate-resistant common ragweed in glufosinate-resistant soybean, their effect on soybean injury and yield, and the economics of these herbicide programs. The hypothesis for this study is that a PP application of an efficacious herbicide followed by glufosinate POST will provide effective control of glyphosate-resistant common ragweed in glufosinate-resistant soybean.

Materials and Methods

Field experiments were conducted in Gage County, Nebraska in 2015 and 2016 in a field with confirmed glyphosate-resistant common ragweed (Ganie and Jhala 2017).

The research site consisted of a Wymore silty clay loam (37.6% silt, 37.6% clay, and

24.8% sand) with 2.5% organic matter and a pH of 6.0. The experimental design was a randomized complete block with 14 treatments (Table 1) and four replications. The plot size was 3 m wide (4 soybean rows spaced 0.75 m apart) by 9 m in length. Glufosinate- resistant soybean (5290LL, NuPride Genetics Network P.O. Box 830911 Lincoln, NE

68583) was no-till planted on May 19, 2015 and May 26, 2016 at a population of 300,000 seeds ha─1 to a depth of 3 cm. The experiments included 13 herbicide programs comprised of 4 application timings: preplant (PP), pre-emergence (PRE), early POST

(EPOST), and late POST (LPOST) (Table 1). For comparison, a nontreated control was included. The labeled rate of each herbicide was used for all treatments.

107

Herbicides were applied with a CO2 pressurized backpack sprayer and a boom equipped with four TT 110015 flat-fan nozzles (TeeJet, Spraying Systems Co., P.O. Box

7900, Wheaton, IL 60189) spaced 60 cm apart. Treatments were applied as PP (May 1,

2015 and May 5, 2016), PRE (May 21, 2015 and May 26, 2016), EPOST (June 16, 2015 and June 16, 2016), and LPOST (July 17, 2015 and June 30, 2016). Common ragweed control was assessed visually on a scale of 0 to 100%, with 0% meaning no control and

100% meaning complete control, at 21 d after PP and PRE, 14 DAEPOST and LPOST, and at soybean harvest. Soybean injury was assessed on a scale of 0 to 100%, with 0% meaning no injury and 100% meaning plant death, at 21 DAPRE, and 14 DAEPOST and

LPOST. Common ragweed densities were assessed from two randomly placed 0.25 m2 quadrats in each plot at 7 DAPRE, 14 DAEPOST, and 14 DALPOST. Common ragweed aboveground biomass was assessed from a randomly placed 0.25 m2 quadrat in each plot at 70 DALPOST. Surviving common ragweed plants were cut near the soil surface, dried in paper bags at 50 C for 10 d, and their biomass was recorded. Percent biomass reduction compared with the nontreated control was calculated using the equation

(Wortman 2014):

% Biomass reduction = [(C-B)/C] × 100 [1] where C represents the common ragweed biomass from the nontreated control plot in the corresponding replication block and B represents the biomass of the treatment plots.

Soybean was harvested with a plot combine and the yields were adjusted to 13% moisture content. Gross profit margin was calculated as gross revenue minus herbicide and application costs (Norsworthy and Oliver 2001). Average herbicide prices from three independent commercial sources (Cargill, Country Partners Cooperative, Crop

108

Production Services) in Nebraska were used to calculate herbicide cost ha─1. Herbicide program cost was calculated by summing the herbicide cost ha─1 for each treatment and adding a custom application cost of $18.11 ha─1 application─1, the average of the three aforementioned independent sources in Nebraska. Gross revenue was calculated from the average yield for each treatment based on the average price received in Nebraska during harvest time in 2015 and 2016 ($0.33 kg─1; USDA 2016).

Statistical Analysis. Data were subjected to ANOVA using PROC GLIMMIX procedure in SAS version 9.3 (SAS Institute Inc., Cary, NC). Years and treatments were considered fixed effects and replications nested within year were considered random effects in the model. Data were tested for normality using PROC UNIVARIATE before analysis. An arcsine square-root transformation was performed on common ragweed control estimates and biomass reduction data before analysis; data were back-transformed for presentation of results. Treatment means were separated at P ≤ 0.05 using Fisher’s protected least significant difference test. Orthogonal contrasts were conducted to compare PP fb POST treatments vs. PP alone, PRE fb LPOST, or PP fb PRE fb LPOST treatments.

Results and Discussion

Year-by-treatment interactions for glyphosate-resistant common ragweed control, density, biomass, and soybean yield were not significant; therefore, data were combined.

Common Ragweed Control. Most of the PP herbicides controlled glyphosate-resistant common ragweed > 90% at 21 DAPP (Table 2). For example, herbicide programs containing glufosinate, paraquat, 2,4-D, imazethapyr, cloransulam-methyl, and flumioxazin provided 90 to 99% control of common ragweed at 21 DAPP (Table 2).

Similarly, Corbett et al. (2004) reported ≥ 99% control of 2 to 10 cm tall common

109 ragweed 14 to 20 DAT with glufosinate. Taylor et al. (2002) reported 83 to 85% common ragweed control at 14 DAT with cloransulam-methyl. Wilson and Worsham (1988) reported 83 and 64% common ragweed control at 28 DAT from paraquat and 2,4-D, respectively, with half the rates used in this study. A premix of flumioxazin and chlorimuron-ethyl provided 93 to 96% control at 14 DAPP in this study. Similarly,

Niekamp et al. (1999) reported 98% common ragweed control at 7 DAT with flumioxazin (90 g ai ha─1) and chlorimuron-ethyl (70 g ai ha─1). Saflufenacil controlled common ragweed 75% at 21 DAPP; however, tank-mixing with imazethapyr plus dimethenamid-P as well as with 2,4-D provided 97 and 99% control, respectively. Kaur et al. (2014) further reported 96% control of giant ragweed with saflufenacil applied alone and 99% control when tank-mixed with 2,4-D at 14 DATAmong PP herbicide programs, chlorimuron-ethyl plus flumioxazin plus thifensulfuron-methyl resulted in the lowest (52%) common ragweed control at 21 DAPP.

A PRE application of sulfentrazone plus metribuzin following a PP application of

2,4-D controlled glyphosate-resistant common ragweed 97% at 21 DAPRE, comparable with several other treatments with only PP application; however, when sulfentrazone plus metribuzin was applied PRE even at a higher rate (6.3 g ai ha─1) without a PP herbicide application, it resulted in 18% control of common ragweed at 21 DAPRE (Table 2). This indicates the importance of PP control of common ragweed due to its early emergence in

Nebraska. Similarly, Kaur et al. (2014) and Jhala et al. (2014) reported that PP application of herbicide is critical for control of glyphosate-resistant giant ragweed in

Nebraska. Ganie et al. (2016) reported that glyphosate-resistant giant ragweed control was reduced to < 83% at 21 DAPRE and ≤ 78% at harvest when PP herbicides were not

110 included in the program. Moreover, the contrast statement confirmed that PP applications controlled 80% of common ragweed compared to a PRE application that resulted in only

18% control at 21 DAPRE (Table 2).

The PP herbicides followed by glufosinate EPOST, alone or in tank-mixtures, controlled common ragweed 91 to 99% at 21 DAEPOST (Table 2). Tharp and Kells

(2002) reported that glufosinate POST following an effective PRE herbicide controlled common ragweed, redroot pigweed (Ameranthus retroflexus L.), and common lambsquarters ≥ 92% at 28 DAT. A LPOST application of glufosinate following a PP application of 2,4-D and a PRE application of sulfentrazone plus metribuzin controlled glyphosate-resistant common ragweed 99% at 14 DALPOST. Glufosinate, LPOST, following sulfentrazone plus metribuzin PRE controlled glyphosate-resistant common ragweed 92%, indicating the effectiveness of glufosinate for common ragweed control.

Glufosinate plus acetochlor applied LPOST following an EPOST application of glufosinate plus S-metolachlor and a PP application of flumioxazin plus chlorimuron- ethyl controlled glyphosate-resistant common ragweed 99% at 14 DALPOST, comparable with several PP followed by EPOST programs. This indicated that an effective PP herbicide followed by a single POST application of glufosinate controlled glyphosate-resistant common ragweed > 90% control and that a second POST application is not needed. Tharp and Kells (2002) also reported that glufosinate tank-mixed with S- metolachlor or acetochlor controlled common ragweed ≥ 99% at 28 DAT.

Most of the herbicide programs that included both a PP and POST herbicide application provided season-long control (≥ 87%) of common ragweed at harvest (Table

2). Herbicide programs including chlorimuron-ethyl plus flumioxazin plus

111 thifensulfuron-methyl or saflufenacil applied PP followed by glufosinate EPOST, alone or tank-mixed with acetochlor, controlled glyphosate-resistant common ragweed 62 to

64% at harvest. A single PP application of glufosinate controlled glyphosate-resistant common ragweed 0% at harvest, suggesting that an in-crop application is needed for full season control. Sulfentrazone plus metribuzin applied PRE followed by glufosinate applied LPOST controlled glyphosate-resistant common ragweed 88% at harvest; however, when a PP application of 2,4-D was added to the program, the control increased to 99%. Orthogonal contrasts conclude that PP and PP fb EPOST herbicide programs controlled glyphosate-resistant common ragweed 0 and 86% at harvest, respectively, on average, PP fb EPOST and PP fb PRE fb LPOST controlled glyphosate-resistant common ragweed 86 and 99% at harvest, respectively (Table 2).

Common Ragweed Density and Biomass. Common ragweed density for the nontreated control was 1,337 and 1,159 plants m─2 at 7 DAPRE and 14 DAEPOST, respectively, compared with the average of herbicide treatments (305 and 177 plants m─2), indicating that herbicides were effective for reducing common ragweed density (Table 3). Preplant herbicides resulted in common ragweed densities of 0 to 366 plants m─2, except saflufenacil (844 plants m─2), and chlorimuron-ethyl plus flumioxazin plus thifensulfuron-methyl (1,180 plants m─2; Table 3). The PP application of 2,4-D followed by sulfentrazone plus metribuzin applied PRE reduced common ragweed density to 8 plants m─2 at 7 DAPRE compared with sulfentrazone plus metribuzin applied PRE without PP herbicide application (908 plants m─2; Table 3).

According to orthogonal contrast analysis, PP herbicide programs on average resulted in 277 plants m─2 compared to the PRE application (908 plants m─2) with no PP

112 at 7 DAPRE (Table 3). Additionally, the three-pass application programs, PP fb PRE fb

LPOST, or PP fb EPOST fb LPOST, did not result in lower common ragweed densities compared with the PP fb EPOST treatments, suggesting that a two-pass program of PP fb

POST effectively reduces common ragweed density. Averaged across treatments, PP fb

EPOST treatments had lower common ragweed density (30 plants m─2) compared with

PP only (101 plants m─2) or PRE fb LPOST (93 plants m─2) at 14 DALPOST (Table 3).

Similarly, Aulakh and Jhala (2015) reported ≤ 4 plants m─2 for common lambsquarters, common waterhemp, eastern black nightshade, and velvetleaf; and ≤ 2 plants m─2 for green foxtail and large crabgrass at harvest with the use of PRE fb POST programs in glufosinate-resistant soybean. Most herbicide programs with PP application resulted in 81 to 100% common ragweed biomass reduction. Averaged across treatments, PP fb EPOST resulted in 92% biomass reduction compared with 14% biomass reduction with PP only and 83% reduction with PRE fb POST programs. Aulakh and Jhala (2015) reported the greatest biomass reduction of broadleaf and grass weeds in glufosinate-resistant soybean with PRE fb POST compared to single or sequential POST programs. Similarly, Kaur et al. (2014) reported that herbicide programs with PP applications of 2,4-D, flumioxazin plus chlorimuron ethyl, sulfentrazone plus cloransulam-methyl, or paraquat fb EPOST of glufosinate alone or in tank-mixture resulted in ≤ 14 plants m─2 and ≥ 88% biomass reduction of giant ragweed.

Soybean Yield. The lowest soybean yield (32 kg ha─1) was obtained in the nontreated control and with glufosinate PP (474 kg ha─1), indicating that common ragweed can be extremely competitive in soybean fields if not controlled, or if controlled with a non- residual herbicide applied PP. Kaur et al. (2014) reported 100% soybean yield reduction

113 when glyphosate-resistant giant ragweed was not controlled. Herbicide programs that included an effective herbicide PP followed by glufosinate POST, alone or in tank- mixture, resulted in soybean yields ≥ 1,819 kg ha─1 with no difference among them

(Table 3). Averaged across treatments, PP fb EPOST programs resulted in higher yields

(1,928 kg ha─1) compared with PP alone (474 kg ha─1) or PRE fb LPOST (1,014 kg ha─1) herbicide programs (Table 3). Similarly, Jhala et al. (2014) reported that a PP alone treatment resulted in 100% soybean yield reduction due to giant ragweed competition later in the season compared with PP fb POST programs. Furthermore, Aulakh and Jhala

(2015) reported that a single POST herbicide application was ineffective in protecting soybean yield potential. Averaged across treatments, PP fb EPOST programs resulted in similar yields (1,928 kg ha─1) compared with PP fb PRE fb EPOST (2,060 kg ha─1) or PP fb EPOST fb LPOST (2,003 kg ha─1); therefore, if common ragweed is the major weed in a soybean field, a PP fb EPOST program can provide full season control and three-pass herbicide programs are not needed to achieve optimum soybean yield (Table 3).

Economics. The cost of PP fb POST herbicide programs ranged from $131.30 to $257.87 ha─1 and provided maximum gross profit margins (Table 4). The PP application of saflufenacil plus imazethapyr plus dimethenamid-P fb glufosinate EPOST cost $197.37 ha─1 and resulted in the highest gross profit margin of $505.96 ha─1 (Table 4).

Glufosinate applied PP alone had the lowest cost of $63.25 ha─1; but resulted in a gross profit margin of only $91.23 ha─1 due to poor control of common ragweed that resulted in low soybean yield (Table 4). PRE fb LPOST resulted in a gross profit margin of $158.23 ha─1 (Table 4). Although the PP fb PRE fb POST program resulted in 99% common

114 ragweed control and 2060 kg ha─1 soybean yield, gross profit margin was $471.14 compared with $372.79 to $505.96 for PP fb POST programs.

Practical Implications. Six glyphosate-resistant weeds, including common ragweed, have been confirmed in Nebraska and their management is a challenge for crop producers. This is the first report describing control of glyphosate-resistant common ragweed in glufosinate-resistant soybean. Results of this study conclude that PP herbicide options are available for early season control of glyphosate-resistant common ragweed; however, a follow-up application of glufosinate, alone or in tank-mixture, is needed to achieve season-long control. Most of the PP herbicides tested in this study provided effective control (> 95%) of common ragweed during soybean emergence and establishment. Furthermore, glufosinate can be used as an effective POST herbicide for control of glyphosate-resistant common ragweed and can be tank-mixed with other herbicides such as S-metolachlor or acetochlor depending on the weed species present in the field. Preplant fb POST programs reduced common ragweed densities greater than when a PP application was not included in the program and provided 92% biomass reduction on average. Soybean yields were reduced when a PP application was not made compared to PP fb POST programs, primarily due to early season common ragweed competition. Three-pass programs did not decrease density, improve biomass reduction, or increase soybean yield compared to PP fb POST programs, suggesting that three-pass programs are not necessary for controlling glyphosate-resistant common ragweed or protecting soybean yield.

The gross profit margins from this study suggest that PP fb POST programs increase gross revenues over the cost of the one-pass herbicide programs. The continued

115 use of herbicides with the same site of action can result in the evolution of herbicide- resistant weeds through increased selection pressure; for example, Italian ryegrass

(Lolium perenne L. ssp. multiflorum Lam. Husnot), perennial ryegrass (Lomium perenne

L.), and goosegrass (Galium indica L. Gaertn.) have been documented resistant to glufosinate due to continuous use (Avila-Garcia and Mallory-Smith 2011; Ghanizadeh et al. 2015; Jalaludin et al. 2015). Although results of this study reported that options exist for control of common ragweed with a glufosinate-based herbicide program, an integrated weed management approach should be adopted for control of herbicide- resistant weeds that can include rotation of herbicides, the use of herbicides with multiple effective sites of action, the reintegration of tillage, and crop rotation.

116

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121

15 and and 15

+ AMS

COC COC COC

AMS AMS AMS AMS AMS AMS AMS AMS AMS AMS AMS

Adjuvant

NIS + AMS NIS + AMS NIS + AMS NIS NIS

NIS + AMS NIS

COC + + AMS COC COC + AMS COC + AMS COC + AMS COC + AMS COC

MSO + AMS + MSO AMS + MSO AMS + MSO

PP, Preplant; EPOST, EPOST, Preplant; PP,

Bayer Bayer Bayer Bayer Bayer Bayer Bayer Bayer

FMC fb FMC fb FMC fb FMC

Bayer fb Bayer

BASF fb BASF

Valent fb Valent fb Valent

Monsanto

Dupont fb fb Dupont

9103; BASF Corporation, Research Triangle Triangle Research Corporation, BASF 9103;

Winfield fb Winfield fb Winfield

Syngenta fb fb Syngenta Syngenta fb fb Syngenta

Manufacturer

Bayer + BASF Bayer

BASF + BASF fb + BASF BASF

Bayer + Monsanto Bayer Bayer + Monsanto Bayer

BASF + Winfield fb + Winfield BASF

Bayer + Dupont + Monsanto + + Dupont Bayer

Helena Chemical Co., Collierville, TN); Collierville, Co., Chemical Helena

tant (Induce, Helena Chemical Co., Collierville, TN) Collierville, Co., Chemical Helena (Induce, tant

D Amine fb fb Amine D

D Amine fb Amine D fb Amine D

Warrant

- -

Envive fb Envive

Sharpen fb fb Sharpen

Liberty 280 Liberty 280 Liberty 280 Liberty 280 Liberty 280 Liberty 280 Liberty 280 Liberty 280 Liberty

Trade Name Trade

Boundary fb Boundary

Valor XLT fb XLT Valor fb XLT Valor

Liberty 280 fb Liberty

2,4 2,4

Authority First fb First Authority

Authority MTZ fb MTZ Authority fb MTZ Authority

Optill + Outlook fb fb + Outlook Optill

Liberty 280+ Pursuit Liberty fb Inteon Gramoxone

Liberty 280+ Warrant Liberty 280+ Warrant Liberty

Sharpen + 2,4 + Sharpen

Liberty 280 + Classic + Warrant + Classic 280+ Liberty

is, MO 63167; DuPont Crop Protection, P.O. Box 80705, Wilmington, DE 19880; Winfield Winfield 19880; DE Wilmington, 80705, Box P.O. Protection, Crop DuPont 63167; is, MO

2016.

1,260

2050 + 594 Rate ha aig 594 1,100 95 + 740 314 740 140 740 740 94 740 1,180 70 + 740 1,120 1,680 + 13.1 + 740 150 1,680 + 740 1,180 + 150 1,680 + 740 112 1,480 + 594 1,180 5.7 740 6.3 740

v/v) were mixed with herbicides. with mixed were v/v)

PP PP LPOST Timing PP PP EPOST PP EPOST PP EPOST EPOST PP EPOST PP EPOST PP EPOST PP EPOST PP EPOST PP EPOST PP PRE LPOST PRE LPOST

0589

P fb Glufosinate fb P

methyl fb methyl

metolachlor fb metolachlor

nce, Research Triangle Park, NC 27709; Valent U.S.A. Corporation, Walnut Creek, CA 94596; FMC Corporation, Philadelphia, PA 1 PA Philadelphia, Corporation, FMC 94596; CA Creek, Walnut Corporation, U.S.A. Valent 27709; Park, NC Triangle Research nce,

AMS, ammonium sulfate (DSM Chemicals North America Inc., Augusta, GA); COC, crop oil concentrate (Agridex, (Agridex, concentrate oil crop COC, GA); Augusta, Inc., America North Chemicals (DSM sulfate ammonium AMS,

Herbicide treatments, application timing and rate, and products used in a field experiment conducted in Gage County, NE in 20 NE County, Gage in conducted experiment field a in used products and rate, and timing application treatments, Herbicide

. .

Program

Glufosinate + Acetochlor Glufosinate Glufosinate Glufosinate Glufosinate Glufosinate + Imazethapyr Glufosinate + Acetochlor ethyl Chlorimuron + Glufosinate + Acetochlor Glufosinate + Acetochlor Glufosinate + S Glufosinate fb Metribuzin + Sulfentrazone Glufosinate Glufosinate

D fb D fb D

- -

metolachlor + Metribuzin fb fb + Metribuzin metolachlor

- Bayer CropScie Bayer

Abbreviations: Abbreviations:

AMS at 2% (wt/v), COC or MSO at 1% (v/v), and NIS at 0.25% ( at 0.25% NIS and 1% (v/v), at MSO COC or 2% at (wt/v), AMS

Table Table

S Herbicide Glufosinate +Dimethenamid + Imazethapyr Saflufenacil + Cloransulam Sulfentrazone fb ethyl Chlorimuron + Flumioxazin fb methyl +Thifensulfuron + Flumioxazin ethyl Chlorimuron 2,4 fb Paraquat fb Saflufenacil 2,4 + Saflufenacil ethyl Chlorimuron + Flumioxazin 2,4 fb Metribuzin + Sulfentrazone nonionic surfac NIS, GA); Suwanee, Inc., Ag (Southern oil seed MSO, methylated by; fb, followed POST; late POST; LPOST, early Lou St. Company, Monsanto NC 27419; Greensboro, Crop Protection, Syngenta 27709; Park, NC 55164 MN Paul, St. 64589, Box P.O. LLC, Solutions, a b c

122

At At

0 e

99 99 a 88 c 64 64 d

62 62 d

84 84 bc 98 ab 99 ab

97 97 abc 96 abc 87 abc 96 abc 94 abc

Harvest

<0.0001

7 DAPRE, 7

b,c

0 f

88 88 e 99 a 99 a 99 a 92 e

97 97 ab 97 ab 91 de 97 ab

96 96 abc 91 cde

14 DA 14 DA 95 bcd LPOST

<0.0001

ontrol ontrol

------

9 e

91 91 c 99 a 99 a

40 40 d

92 92 bc 98 ab 93 bc 93 bc

97 97 abc 97 abc 96 abc 96 abc

14 DA 14 DA

agweed agweed

EPOST

<0.0001 r

Common Common

18 18 f

45 45 e 98 a 97 a

PRE

81 81 bc 94 ab 91 ab 70 cd 80 bc 55 de 92 ab

84 84 abc 88 abc 21 DA 21 DA

<0.0001

soybean at 21 at DAPP, soybean

------

0 c

97 97 a 96 a 93 a 52 c 96 a 99 a 96 a 95 a

76 76 b 75 b

92 92 ab 90 ab

21 DA 21 DA

<0.0001

resistant

1,680

740 1,180 + 150 Rate ha aig 594 1,100 95 + 740 314 140 740 2,050 740 94 740 1,180 70 + 740 1,120 13.1 + 740 + 1,680 150 1,680 + 740 + 740 112 1,480 + 594 1,260 + 594 1,180 5.7 740 6.3 740

EPOST PP PP PP EPOST PP PP EPOST PP EPOST PP EPOST PP EPOST PP EPOST PP EPOST EPOST PP EPOST LPOST PP PRE LPOST PRE LPOST

Timing P fb fb P

methyl fb methyl -

metolachlor fb metolachlor

D fb D

resistant common ragweed in in common glufosinate resistant ragweed

Orthogonal contrasts for comparison of herbicide programs and control of of and control programs of herbicide comparison for contrasts Orthogonal

. .

Glufosinate Glufosinate Glufosinate Glufosinate fb methyl Thifensulfuron Glufosinate + Imazethapyr Glufosinate + ethyl Chlorimuron + Glufosinate Acetochlor + Acetochlor Glufosinate + Acetochlor Glufosinate + S Glufosinate + Acetochlor Glufosinate fb Metribuzin + Sulfentrazone Glufosinate Glufosinate

D fb D

metolachlor + Metribuzin fb fb + Metribuzin metolachlor Value

- -

Table Table glyphosate County, conducted experiment Gage in field in harvest a at and DALPOST, 14 DAEPOST, 14 2016. and 2015 in NE Herbicide Program Herbicide Glufosinate +Dimethenamid + Imazethapyr Saflufenacil + Cloransulam Sulfentrazone fb ethyl Chlorimuron + Flumioxazin S + + Flumioxazin ethyl Chlorimuron 2,4 fb Paraquat fb Saflufenacil fb ethyl Chlorimuron + Flumioxazin fb Metribuzin + Sulfentrazone P Saflufenacil + 2,4 + Saflufenacil 2,4

123

* *

vs 99 vs

---

At At

****

86 vs 0 86 vs

Harvest

86 vs 88 86 vs 86 99 86 vs

SD where α = α SD where

root transformed transformed root

b,c

--- ** **

****

14 DA 14 DA

95 vs 0 95 vs

LPOST

95 vs 92 95 vs 99 95 vs 99 95 vs

ontrol ontrol

ta.

sine sine square

------

**** ****

14 DA 14 DA

agweed agweed

EPOST

95 vs 9 95 vs

95 vs 40 95 vs 93 95 vs

Data were arc were Data

Common Common

------

****

PRE

21 DA 21 DA

80 vs 18 vs 80

PP, Preplant. PP,

------

21 DA 21 DA

LPOST, late POST; late LPOST,

; ;

Rate

Orthogonal Contrasts Orthogonal

Timing

T; fb, followed by fb, T; followed

values are presented based on the interpretation from the transformed da transformed the from interpretation the on based presented are values

EPOST, early P early EPOST,

significant; *, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001

back transformed

Herbicide Program Herbicide

; however,

------

Significance levels: ns, non levels: Significance

Year by treatment interaction was not significant; therefore, data from both years were combined. combined. were years both data from therefore, significant; not was interaction treatment by Year

Abbreviations: DA, days after; after; days DA, Abbreviations:

Means presented within the same column with no common letter(s) are significantly different according to Fisher’s Protected L Protected Fisher’s to according different significantly are letter(s) common no with column same the within presented Means

PP vs. PRE vs. PP only PP vs. fb EPOST PP LPOST fb PRE vs. fb EPOST PP LPOST fb PRE fb PP vs. fb EPOST PP fb LPOST EPOST fb PP vs. fb EPOST PP analysis before 0.05. c c a a b d

124

─

158 a 158 a 819 922 a 922 a 897 a 819 a 860 a 899 a 859 a 115 a 003 a 060

014 b 014

ield

32 32 c

, , , , , , , , , , ,

474 c 474

2 1 1 1 1 1 1 1 2 2 2

kg kg ha

<0.0001

Soybean Soybean

b, b,

------

0 e

14 14 e

82 82 d

81 cd 81 100 a 100 a 100 a 100

99 99 abc

100 ab 100 83 bcd

98 abcd 98 93 93 abcd 86 abcd 93 abcd

<0.0001

Biomass Biomass

eduction

------

0 d 0 0 d 0 d 0 d 0 d 0

8 cd 93 a

68 15 15 cd a 102

101 d 101

1145

56 56 abc

14 DA 14 DA

20 20 bcd

LPOST

<0.0001

------

ensity ensity

─

0c 6 c 3 c 0 c 0 c 7 c

159 a 159

62 c 62 17 17 c 18 c 57 c

120 c 120 c 108

744 b 744

14 DA 14 DA

agweed agweed

EPOST

<0.0001

Plantsm

Common Common

------

0 c 8 c

337 a 337

11 c 11 67 67 c 88 c 59 c 51 c

180 ab 180

159 c 159 c 221 c 366 0.0001

844 b 844

908 ab 908

7 DA PRE 7 DA

1,100

740 150 Rate ha aig ------594 95 + 314 740 140 740 2,050 740 94 740 1,180 70 + 740 1,120 13.1 + 740 + 1,680 1,680 + 740 1,180 + 150 1,680 + 740 112 1,480 + 594 1,260 + 594 1,180 5.7 740 6.3 740

EPOST PP Timing ------PP PP PP EPOST PP EPOST PP EPOST PP EPOST PP EPOST PP EPOST EPOST PP EPOST PP EPOST LPOST PP PRE LPOST PRE LPOST

in Gage County, 2016. and 2015 in County, NE Gage in

P fb fb P

resistant common ragweed density at 7 DAPRE, 7 at 14 and DAEPOST, 14 density ragweed common resistant

ragweed biomass reduction, and soybean yield in a a in conducted experiment field soybean reduction, ragweedbiomass and yield

methyl fb methyl

Orthogonal contrasts for comparison of herbicide programs and effect of herbicide herbicide of and programs comparison Orthogonalcontrasts of effect for herbicide

metolachlor fb metolachlor

. . -

D fb D

Table Table

Glufosinate + S Glufosinate

Glufosinate Glufosinate Glufosinate Glufosinate fb Glufosinate methyl Thifensulfuron + Imazethapyr Glufosinate + ethyl Chlorimuron + Glufosinate Acetochlor + Acetochlor Glufosinate + Acetochlor Glufosinate + Acetochlor Glufosinate fb Metribuzin + Sulfentrazone Glufosinate Glufosinate

programs on programs glyphosate

D fb D

metolachlor + Metribuzin fb fb + Metribuzin metolachlor Value

DALPOST, common DALPOST,

- -

Saflufenacil fb fb Saflufenacil Herbicide Control Nontreated Glufosinate +Dimethenamid + Imazethapyr Saflufenacil + Cloransulam Sulfentrazone fb ethyl Chlorimuron + Flumioxazin S + + Flumioxazin ethyl Chlorimuron 2,4 fb Paraquat fb Metribuzin + Sulfentrazone P

Flumioxazin + Chlorimuron ethyl fb ethyl Chlorimuron + Flumioxazin 2,4 Saflufenacil + 2,4 + Saflufenacil

125

060

014 003

---

, ,

ield

928 vs 928 vs 928 vs

**** ****

1 2

, , ,

1 1 1

Soybean Soybean

928 vs 474 474 928 vs

b, b,

ns ns ns

---

****

92 vs 14 92 vs 83 92 vs

Biomass Biomass

92 vs 100 100 92 vs 100 92 vs

LSD where = α 0.05. where LSD

eduction

root transformed before before transformed root

ns ns

--- **

***

sine sine square

14 DA 14 DA

30 vs 0 0 30 vs 0 30 vs

LPOST

30 vs 93 30 vs

30 vs 101 101 30 vs

ensity ensity

ns ns

------

****

Data were arc were Data

14 DA 14 DA

30 vs 7 7 30 vs

agweed agweed

EPOST

30 vs 120 120 30 vs 744 30 vs

preplant; vs., versus vs., preplant;

PP,

------

Common Common

------

rom the transformed data. transformed the rom

***

7 DA PRE 7 DA

277 vs 908 908 277 vs

tion f tion

LPOST, late POST; late LPOST,

; ;

Orthogonal Contrasts Orthogonal

Rate

Timing

; fb, followed by ; fb, followed

values are presented based on the interpreta the on based presented are values

; EPOST, early POST early ; EPOST,

significant; *, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001. < P ****, 0.001; < P ***, 0.01; < P **, 0.05; < P *, significant;

LPOST

however, back transformed back however,

------

;

Nontreated control was excluded from analysis as an outlier. analysis from excluded was control Nontreated

PP fb EPOST vs. PRE fb fb PRE vs. fb EPOST PP Herbicide PRE vs. PP only PP vs. fb EPOST PP LPOST fb PRE fb PP vs. fb EPOST PP fb LPOST EPOST fb PP vs. fb EPOST PP

Significance levels: ns, non levels: Significance Year by treatment interaction was not significant; therefore, data from both years were combined. combined. were years both data from therefore, significant; not was treatment interaction by Year

Means presented within the same column with no common letter(s) are significantly different according to Fisher’s Protected Protected to Fisher’s according different significantly are letter(s) common no with column the same within presented Means

Abbreviations: DA, days after days DA, Abbreviations:

analysis a a b c d e

126

Program Gross revenue from Gross profit Herbicide Timing Rate cost b soybean yield c margin d g ai ha-1 ------$ ha-1 ------Nontreated Control ------0.00 10.43 10.43 Glufosinate PP 594 63.25 154.48 91.23 Saflufenacil + Imazethapyr +Dimethenamid-P fb PP 95 + 1,100 Glufosinate EPOST 740 197.37 703.33 505.96 Sulfentrazone + Cloransulam-methyl fb PP 314 Glufosinate EPOST 740 180.21 626.41 446.20 Flumioxazin + Chlorimuron ethyl fb PP 140 Glufosinate EPOST 740 168.33 618.26 449.94 S-metolachlor + Metribuzin fb PP 2,050 Glufosinate EPOST 740 155.21 592.84 437.63 Chlorimuron ethyl + Flumioxazin + PP 94 Thifensulfuron methyl fb Glufosinate EPOST 740 131.30 606.20 474.91 2,4-D fb PP 1,180 Glufosinate + Imazethapyr EPOST 740 + 70 146.20 618.92 472.71 Paraquat fb 1,120 PP Glufosinate + Chlorimuron ethyl + 740 + 13.1 EPOST Acetochlor + 1,680 176.30 605.88 429.57 Saflufenacil fb PP 150 Glufosinate + Acetochlor EPOST 740 + 1,680 220.05 592.84 372.79 Saflufenacil + 2,4-D fb PP 150 + 1,180 Glufosinate + Acetochlor EPOST 740 + 1,680 230.01 689.31 459.30 Flumioxazin + Chlorimuron ethyl fb PP 112 Glufosinate + S-metolachlor fb EPOST 594 + 1,480 Glufosinate + Acetochlor LPOST 594 + 1,260 257.87 652.81 394.94 2,4-D fb PP 1,180 Sulfentrazone + Metribuzin fb PRE 5.7 Glufosinate LPOST 740 200.25 671.39 471.14 Sulfentrazone + Metribuzin fb PRE 6.3 Glufosinate LPOST 740 172.25 330.48 158.23 a Herbicide costs were averaged from three independent sources in Nebraska. b Program cost includes an average cost of application ($18.11 ha-1 application-1) from three independent sources in Nebraska. c Gross Revenue from soybean yield was based on an average price received in Nebraska on the harvest month. d Gross profit margins were calculated as gross revenue from soybean yield minus program cost.